diff --git a/.github/ISSUE_TEMPLATE.md b/.github/ISSUE_TEMPLATE.md new file mode 100644 index 0000000..5a61121 --- /dev/null +++ b/.github/ISSUE_TEMPLATE.md @@ -0,0 +1,7 @@ +Thanks for filing an issue! Please remove any top-level headers that do not apply. + +# Problem or Error +Please characterize the problem concisely and precisely. You may want to include error message output, other terminal output, your environment characteristics, and a way to replicate the issue. Let us know what solutions you have tried. + +# Question +Please ask the question with clear context and any potential answer directions you can think of. diff --git a/.github/PULL_REQUEST_TEMPLATE.md b/.github/PULL_REQUEST_TEMPLATE.md new file mode 100644 index 0000000..ebb8a28 --- /dev/null +++ b/.github/PULL_REQUEST_TEMPLATE.md @@ -0,0 +1,16 @@ +Thanks for contributing! Please remove any top-level sections that do not apply to your changes. + +- [ ] `make format` and `make documentation` has been run. (You may also want to run `make test`.) + +## What's changed + +Description of the changes here. + +# Bug fix + +- [ ] Continuous integration test added +- [ ] Continuous integration test passing + +## What this fixes and how it's fixed + +Description of how this fix works goes here. Link any issues this PR fixes. diff --git a/.github/workflows/build_and_test.yml b/.github/workflows/build_and_test.yml new file mode 100644 index 0000000..4ae149c --- /dev/null +++ b/.github/workflows/build_and_test.yml @@ -0,0 +1,47 @@ +name: Build and test [Python 3.9, 3.10] + +on: [push, pull_request] + +jobs: + build: + runs-on: ${{ matrix.os }} + strategy: + matrix: + os: [ubuntu-latest, macos-latest] + python-version: ["3.9", "3.10"] + + steps: + - name: Checkout + uses: actions/checkout@v4 + with: + persist-credentials: false + + - name: Setup Miniconda using Python ${{ matrix.python-version }} + uses: conda-incubator/setup-miniconda@v2 + with: + miniforge-variant: Mambaforge + auto-update-conda: true + activate-environment: ogzaf-dev + environment-file: environment.yml + python-version: ${{ matrix.python-version }} + auto-activate-base: false + + - name: Build + shell: bash -l {0} + run: | + pip install -e . + pip install pytest-cov + pip install pytest-pycodestyle + - name: Test + shell: bash -l {0} + working-directory: ./ + run: | + pytest -m 'not local' --cov=./ --cov-report=xml + - name: Upload coverage to Codecov + uses: codecov/codecov-action@v3 + with: + files: ./coverage.xml + flags: unittests + name: codecov-umbrella + fail_ci_if_error: true + verbose: true diff --git a/.github/workflows/check_format.yml b/.github/workflows/check_format.yml new file mode 100644 index 0000000..a261c25 --- /dev/null +++ b/.github/workflows/check_format.yml @@ -0,0 +1,14 @@ +name: Check code formatting + +on: [push, pull_request] + +jobs: + lint: + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v4 + - uses: actions/setup-python@v4 + - uses: psf/black@stable + with: + options: "-l 79 --check" + src: "." diff --git a/.github/workflows/deploy_docs.yml b/.github/workflows/deploy_docs.yml new file mode 100644 index 0000000..d8c2b7e --- /dev/null +++ b/.github/workflows/deploy_docs.yml @@ -0,0 +1,39 @@ +name: Build and Deploy Jupyter Book documentation +on: + push: + branches: + - main +jobs: + build-and-deploy: + if: github.repository == 'EAPD-DRB/OG-ZAF' + runs-on: ubuntu-latest + steps: + - name: Checkout + uses: actions/checkout@v4 + with: + persist-credentials: false + + - name: Setup Miniconda + uses: conda-incubator/setup-miniconda@v2 + with: + miniforge-variant: Mambaforge + activate-environment: ogzaf-dev + environment-file: environment.yml + python-version: "3.10" + auto-activate-base: false + + - name: Build # Build Jupyter Book + shell: bash -l {0} + run: | + pip install jupyter-book>=0.11.3 + pip install sphinxcontrib-bibtex>=2.0.0 + pip install -e . + python -m ipykernel install --user --name=ogzaf-dev + jb build ./docs/book + + - name: Deploy + uses: JamesIves/github-pages-deploy-action@releases/v4 + with: + GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} + BRANCH: gh-pages # The branch the action should deploy to. + FOLDER: docs/book/_build/html # The folder the action should deploy. diff --git a/.github/workflows/docs_check.yml b/.github/workflows/docs_check.yml new file mode 100644 index 0000000..77b245f --- /dev/null +++ b/.github/workflows/docs_check.yml @@ -0,0 +1,29 @@ +name: Check that docs build +on: [push, pull_request] + +jobs: + build: + runs-on: ubuntu-latest + steps: + - name: Checkout + uses: actions/checkout@v4 + with: + persist-credentials: false + + - name: Setup Miniconda + uses: conda-incubator/setup-miniconda@v2 + with: + miniforge-variant: Mambaforge + activate-environment: ogzaf-dev + environment-file: environment.yml + python-version: "3.10" + auto-activate-base: false + + - name: Build # Build Jupyter Book + shell: bash -l {0} + run: | + pip install jupyter-book>=0.11.3 + pip install sphinxcontrib-bibtex>=2.0.0 + pip install -e . + python -m ipykernel install --user --name=ogzaf-dev + jb build ./docs/book diff --git a/.github/workflows/publish_to_pypi.yml b/.github/workflows/publish_to_pypi.yml new file mode 100644 index 0000000..3afbe4f --- /dev/null +++ b/.github/workflows/publish_to_pypi.yml @@ -0,0 +1,30 @@ +name: Publish package to PyPI + +on: + push: + branches: + - main + +jobs: + deploy: + name: Publish to PyPI + if: github.repository == 'EAPD-DRB/OG-ZAF' + runs-on: ubuntu-latest + steps: + - name: Checkout repo + uses: actions/checkout@v4 + - name: Setup Python + uses: actions/setup-python@v4 + with: + python-version: "3.10" + - name: Build package + shell: bash -l {0} + run: | + pip install wheel + python setup.py sdist bdist_wheel + - name: Publish a Python distribution to PyPI + uses: pypa/gh-action-pypi-publish@release/v1 + with: + user: __token__ + password: ${{ secrets.PYPI }} + skip_existing: true diff --git a/.gitignore b/.gitignore index e5b3363..5eae9fb 100644 --- a/.gitignore +++ b/.gitignore @@ -38,8 +38,9 @@ envs/ *dask-worker-space* .ipynb_checkpoints* */__pycache__/* +*.coverage* +*coverage.xml* docs/book/_build/* -docs/build* -examples/OG-ZAF_example_plots/* -examples/ogzaf_example_output.csv -examples/OG-ZAF-Example/* +*/OG-ZAF_example_plots/* +*ogzaf_example_output.csv +*/OG-ZAF-Example/* diff --git a/CHANGELOG.md b/CHANGELOG.md new file mode 100644 index 0000000..a7635ca --- /dev/null +++ b/CHANGELOG.md @@ -0,0 +1,27 @@ +# Changelog + +All notable changes to this project will be documented in this file. + +The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/), +and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html). + +## [0.0.1] - 2023-10-30 8:00:00 + +### Added + +- Adds CI tests `check_format.yml`, `build_and_test.yml`, `docs_build.yml`, `deploy_docs.yml`, `publish_to_pypi.yml` +- Adds `/tests/` directory with test files in it +- Added `Makefile` +- Updated `environment.yml` and `setup.py` +- Updated `pyproject.toml` +- Updates the `README.md` + +## [0.0.0] - 2022-10-15 12:00:00 + +### Added + +- This version is a pre-release alpha. The example run script OG-ZAF/examples/run_og_zaf.py runs, but the model is not currently calibrated to represent the South African economy and population. + + + +[0.0.1]: https://github.com/EAPD-DRB/OG-ZAF/compare/v0.0.0...v0.0.1 diff --git a/LICENSE b/LICENSE new file mode 100644 index 0000000..0e259d4 --- /dev/null +++ b/LICENSE @@ -0,0 +1,121 @@ +Creative Commons Legal Code + +CC0 1.0 Universal + + CREATIVE COMMONS CORPORATION IS NOT A LAW FIRM AND DOES NOT PROVIDE + LEGAL SERVICES. DISTRIBUTION OF THIS DOCUMENT DOES NOT CREATE AN + ATTORNEY-CLIENT RELATIONSHIP. CREATIVE COMMONS PROVIDES THIS + INFORMATION ON AN "AS-IS" BASIS. CREATIVE COMMONS MAKES NO WARRANTIES + REGARDING THE USE OF THIS DOCUMENT OR THE INFORMATION OR WORKS + PROVIDED HEREUNDER, AND DISCLAIMS LIABILITY FOR DAMAGES RESULTING FROM + THE USE OF THIS DOCUMENT OR THE INFORMATION OR WORKS PROVIDED + HEREUNDER. + +Statement of Purpose + +The laws of most jurisdictions throughout the world automatically confer +exclusive Copyright and Related Rights (defined below) upon the creator +and subsequent owner(s) (each and all, an "owner") of an original work of +authorship and/or a database (each, a "Work"). + +Certain owners wish to permanently relinquish those rights to a Work for +the purpose of contributing to a commons of creative, cultural and +scientific works ("Commons") that the public can reliably and without fear +of later claims of infringement build upon, modify, incorporate in other +works, reuse and redistribute as freely as possible in any form whatsoever +and for any purposes, including without limitation commercial purposes. +These owners may contribute to the Commons to promote the ideal of a free +culture and the further production of creative, cultural and scientific +works, or to gain reputation or greater distribution for their Work in +part through the use and efforts of others. + +For these and/or other purposes and motivations, and without any +expectation of additional consideration or compensation, the person +associating CC0 with a Work (the "Affirmer"), to the extent that he or she +is an owner of Copyright and Related Rights in the Work, voluntarily +elects to apply CC0 to the Work and publicly distribute the Work under its +terms, with knowledge of his or her Copyright and Related Rights in the +Work and the meaning and intended legal effect of CC0 on those rights. + +1. 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Copyright and Related Rights include, but are not +limited to, the following: + + i. the right to reproduce, adapt, distribute, perform, display, + communicate, and translate a Work; + ii. moral rights retained by the original author(s) and/or performer(s); +iii. publicity and privacy rights pertaining to a person's image or + likeness depicted in a Work; + iv. rights protecting against unfair competition in regards to a Work, + subject to the limitations in paragraph 4(a), below; + v. rights protecting the extraction, dissemination, use and reuse of data + in a Work; + vi. database rights (such as those arising under Directive 96/9/EC of the + European Parliament and of the Council of 11 March 1996 on the legal + protection of databases, and under any national implementation + thereof, including any amended or successor version of such + directive); and +vii. other similar, equivalent or corresponding rights throughout the + world based on applicable law or treaty, and any national + implementations thereof. + +2. Waiver. To the greatest extent permitted by, but not in contravention +of, applicable law, Affirmer hereby overtly, fully, permanently, +irrevocably and unconditionally waives, abandons, and surrenders all of +Affirmer's Copyright and Related Rights and associated claims and causes +of action, whether now known or unknown (including existing as well as +future claims and causes of action), in the Work (i) in all territories +worldwide, (ii) for the maximum duration provided by applicable law or +treaty (including future time extensions), (iii) in any current or future +medium and for any number of copies, and (iv) for any purpose whatsoever, +including without limitation commercial, advertising or promotional +purposes (the "Waiver"). 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Should any part of the License for any +reason be judged legally invalid or ineffective under applicable law, such +partial invalidity or ineffectiveness shall not invalidate the remainder +of the License, and in such case Affirmer hereby affirms that he or she +will not (i) exercise any of his or her remaining Copyright and Related +Rights in the Work or (ii) assert any associated claims and causes of +action with respect to the Work, in either case contrary to Affirmer's +express Statement of Purpose. + +4. Limitations and Disclaimers. + + a. No trademark or patent rights held by Affirmer are waived, abandoned, + surrendered, licensed or otherwise affected by this document. + b. Affirmer offers the Work as-is and makes no representations or + warranties of any kind concerning the Work, express, implied, + statutory or otherwise, including without limitation warranties of + title, merchantability, fitness for a particular purpose, non + infringement, or the absence of latent or other defects, accuracy, or + the present or absence of errors, whether or not discoverable, all to + the greatest extent permissible under applicable law. + c. Affirmer disclaims responsibility for clearing rights of other persons + that may apply to the Work or any use thereof, including without + limitation any person's Copyright and Related Rights in the Work. + Further, Affirmer disclaims responsibility for obtaining any necessary + consents, permissions or other rights required for any use of the + Work. + d. Affirmer understands and acknowledges that Creative Commons is not a + party to this document and has no duty or obligation with respect to + this CC0 or use of the Work. diff --git a/LICENSE.md b/LICENSE.md deleted file mode 100644 index f06099e..0000000 --- a/LICENSE.md +++ /dev/null @@ -1,31 +0,0 @@ -This project is in the -public domain within the United States. - -Additionally, we waive copyright and related rights in the work -worldwide through the CC0 1.0 Universal public domain dedication. - -## CC0 1.0 Universal Summary - -This is a human-readable summary of the [Legal Code (read the full text)](https://creativecommons.org/publicdomain/zero/1.0/legalcode). - -### No Copyright - -The person who associated a work with this deed has dedicated the work to -the public domain by waiving all of his or her rights to the work worldwide -under copyright law, including all related and neighboring rights, to the -extent allowed by law. - -You can copy, modify, distribute and perform the work, even for commercial -purposes, all without asking permission. - -### Other Information - -In no way are the patent or trademark rights of any person affected by CC0, -nor are the rights that other persons may have in the work or in how the -work is used, such as publicity or privacy rights. - -Unless expressly stated otherwise, the person who associated a work with -this deed makes no warranties about the work, and disclaims liability for -all uses of the work, to the fullest extent permitted by applicable law. -When using or citing the work, you should not imply endorsement by the -author or the affirmer. diff --git a/MANIFEST.in b/MANIFEST.in new file mode 100644 index 0000000..af808a9 --- /dev/null +++ b/MANIFEST.in @@ -0,0 +1,5 @@ +include ogzaf/data/demographic/un_zaf_deaths.csv +include ogzaf/data/demographic/un_zaf_fert.csv +include ogzaf/data/demographic/un_zaf_infmort.csv +include ogzaf/data/demographic/un_zaf_mort.csv +include ogzaf/data/demographic/un_zaf_pop.csv diff --git a/Makefile b/Makefile new file mode 100644 index 0000000..53b8343 --- /dev/null +++ b/Makefile @@ -0,0 +1,16 @@ +all: build +format: + black . -l 79 + linecheck . --fix +install: + pip install -e . +test: + pip install pytest-cov + pip install pytest-pycodestyle + pytest -m 'not local' --cov=./ --cov-report=xml +documentation: + jupyter-book clean docs/book + jupyter-book build docs/book +pip-package: + pip install wheel + python setup.py sdist bdist_wheel diff --git a/PSL_catalog.json b/PSL_catalog.json new file mode 100644 index 0000000..eaf383f --- /dev/null +++ b/PSL_catalog.json @@ -0,0 +1,29 @@ +{ + "name": "OG-ZAF", + "img": "https://github.com/EAPD-DRB/OG-ZAF/blob/main/docs/OG-ZAF_logo.png?raw=true", + "banner_title": "OG-ZAF", + "banner_subtitle": "An overlapping-generations macroeconomic model for evaluating fiscal policy in South Africa", + "detailed_description": "OG-ZAF is an overlapping-generations (OG) model that allows for dynamic general equilibrium analysis of fiscal policy for South Africa. OG-ZAF is built on the OG-Core framework. The model output includes changes in macroeconomic aggregates (GDP, investment, consumption), wages, interest rates, and the stream of tax revenues over time. Regularly updated documentation of the model theory--its output, and solution method--and the Python API is available at https://pslmodels.github.io/OG-Core and documentation of the specific South African calibration of the model will be available soon.", + "policy_area": "Fiscal policy, Macroeconomics, Demographics", + "geography": "South Africa", + "language": "Python", + "maintainers": [ + { + "name": "Richard W. Evans", + "image": "https://github.com/rickecon.png", + "link": "https://github.com/rickecon" + }, + { + "name": "Marcel LaFleur", + "image": "https://avatars.githubusercontent.com/u/231228?v=4", + "link": "https://github.com/SeaCelo" + } + ], + "links": { + "code_repository": "https://github.com/EAPD-DRB/OG-ZAF", + "user_documentation": "https://eapd-drb.github.io/OG-ZAF/content/intro/intro.html", + "contributor_documentation": "https://eapd-drb.github.io/OG-ZAF/content/contributing/contributor_guide.html", + "webapp": "", + "recent_changes": "https://github.com/EAPD-DRB/OG-ZAF/blob/main/CHANGELOG.md" + } +} \ No newline at end of file diff --git a/README.md b/README.md index 1a80438..19edd99 100644 --- a/README.md +++ b/README.md @@ -1,18 +1,26 @@ -[![Python 3.7+](https://img.shields.io/badge/python-3.7%2B-blue.svg)](https://www.python.org/downloads/release/python-377/) - # OG-ZAF + +| | | +| --- | --- | +| Org | [![United Nations DESA](https://img.shields.io/badge/United%20Nations%20DESA-blue)](https://www.un.org/en/desa) [![PSL cataloged](https://img.shields.io/badge/PSL-cataloged-a0a0a0.svg)](https://www.PSLmodels.org) [![OS License: CC0-1.0](https://img.shields.io/badge/OS%20License-CC0%201.0-yellow)](https://github.com/EAPD-DRB/OG-ZAF/blob/main/LICENSE) | +| Package | [![Python 3.9](https://img.shields.io/badge/python-3.9-blue.svg)](https://www.python.org/downloads/release/python-3917/) [![Python 3.10](https://img.shields.io/badge/python-3.10-blue.svg)](https://www.python.org/downloads/release/python-31013/) [![Python 3.11](https://img.shields.io/badge/python-3.11-blue.svg)](https://www.python.org/downloads/release/python-3116/) [![PyPI Latest Release](https://img.shields.io/pypi/v/ogzaf.svg)](https://pypi.org/project/ogzaf/) [![PyPI Downloads](https://img.shields.io/pypi/dm/ogzaf.svg?label=PyPI%20downloads)](https://pypi.org/project/ogzaf/) | +| Testing | ![example event parameter](https://github.com/EAPD-DRB/OG-ZAF/actions/workflows/build_and_test.yml/badge.svg?branch=main) ![example event parameter](https://github.com/EAPD-DRB/OG-ZAF/actions/workflows/deploy_docs.yml/badge.svg?branch=main) ![example event parameter](https://github.com/EAPD-DRB/OG-ZAF/actions/workflows/check_format.yml/badge.svg?branch=main) [![Codecov](https://codecov.io/gh/EAPD-DRB/OG-ZAF/branch/main/graph/badge.svg)](https://codecov.io/gh/EAPD-DRB/OG-ZAF) | + OG-ZAF is an overlapping-generations (OG) model that allows for dynamic general equilibrium analysis of fiscal policy for South Africa. OG-ZAF is built on the OG-Core framework. The model output includes changes in macroeconomic aggregates (GDP, investment, consumption), wages, interest rates, and the stream of tax revenues over time. Regularly updated documentation of the model theory--its output, and solution method--and the Python API is available at https://pslmodels.github.io/OG-Core and documentation of the specific South African calibration of the model will be available soon. ## Using and contributing to OG-ZAF -* Install the [Anaconda distribution](https://www.anaconda.com/distribution/) of Python +* If you are installing on a Mac computer, install XCode Tools. In Terminal: `xcode-select —install` +* Download and install the appropriate [Anaconda distribution](https://www.anaconda.com/products/distribution#Downloads) of Python. Select the correct version for you platform (Windows, Intel Mac, or M1 Mac). +* In Terminal: * Make sure the `conda` package manager is up-to-date: `conda update conda`. * Make sure the Anaconda distribution of Python is up-to-date: `conda update anaconda`. * Fork this repository and clone your fork of this repository to a directory on your computer. * From the terminal (or Anaconda command prompt), navigate to the directory to which you cloned this repository and run `conda env create -f environment.yml`. The process of creating the `ogzaf-dev` conda environment should not take more than five minutes. * Then, `conda activate ogzaf-dev` * Then install by `pip install -e .` +### Run an example of the model * Navigate to `./examples` * Run the model with an example reform from terminal/command prompt by typing `python run_og_zaf.py` * You can adjust the `./examples/run_og_zaf.py` by modifying model parameters specified in the dictionary passed to the `p.update_specifications()` calls. @@ -32,7 +40,7 @@ OG-ZAF is an overlapping-generations (OG) model that allows for dynamic general * See [`ogcore.TPI.py`](https://github.com/PSLmodels/OG-Core/blob/master/ogcore/TPI.py) for what is in the dictionary object in this pickle file * An analogous set of files in the `./examples/OUTPUT_REFORM` directory, which represent objects from the simulation of the reform policy -Note that, depending on your machine, a full model run (solving for the full time path equilibrium for the baseline and reform policies) can take more than two hours of compute time. +Note that, depending on your machine, a full model run (solving for the full time path equilibrium for the baseline and reform policies) can take from 35 minutes to more than two hours of compute time. If you run into errors running the example script, please open a new issue in the OG-ZAF repo with a description of the issue and any relevant tracebacks you receive. @@ -40,7 +48,7 @@ Once the package is installed, one can adjust parameters in the OG-Core `Specifi ``` from ogcore.parameters import Specifications -from ogind.calibrate import Calibration +from ogzaf.calibrate import Calibration p = Specifications() c = Calibration(p) updated_params = c.get_dict() diff --git a/docs/OG-ZAF_logo.png b/docs/OG-ZAF_logo.png new file mode 100644 index 0000000..d367ed4 Binary files /dev/null and b/docs/OG-ZAF_logo.png differ diff --git a/docs/OG-ZAF_logo_gitfig.png b/docs/OG-ZAF_logo_gitfig.png new file mode 100644 index 0000000..cafd129 Binary files /dev/null and b/docs/OG-ZAF_logo_gitfig.png differ diff --git a/docs/OG-ZAF_logo_long.png b/docs/OG-ZAF_logo_long.png new file mode 100644 index 0000000..138a94f Binary files /dev/null and b/docs/OG-ZAF_logo_long.png differ diff --git a/docs/README.md b/docs/README.md new file mode 100644 index 0000000..23f68ad --- /dev/null +++ b/docs/README.md @@ -0,0 +1,9 @@ +# Instructions for files in OG-ZAF/docs +The files in this directory `OG-ZAF/docs/` include images and all the files necessary for rendering the Jupyter Book documentation. One image [`OG-ZAF_logo_gitfig.png`](docs/OG-ZAF_logo_gitfig.png) is only used for the GitHub social preview image. GitHub suggests that this image should be 1280x640px for best display. The image we created [`OG-ZAF_logo_long.png`](docs/OG-ZAF_logo_long.png) is natively 2093x1265px. We do the following to resize the image. + +1. Open the image in Adobe Photoshop: **File** > **Open** +2. Open the **Image Size** dialogue: +3. Adjust the canvas size: **Image** > **Canvas Size**. Because the 2083x1334px image is taller than the optimal 1280x640px GitHub size, we first adjust the canvas size. We have to add some width. So here adjust the width to 2530px [`(1265 / 640) * 1280`] and keep the height at 1265px. +4. Adjust the image size: **Image** > **Image Size**. Now adjust the image size to the GitHub optimal 1280x640px. The dimesions will be correct and nothing will be stretched. +5. Save the image as [`OG-ZAF_logo_gitfig.png`](docs/OG-ZAF_logo_gitfig.png). +6. Upload the image [`OG-ZAF_logo_gitfig.png`](docs/OG-ZAF_logo_gitfig.png) as the GitHub social preview image by clicking on the [**Settings**](https://github.com/PSLmodels/OG-ZAF/settings) button in the upper-right of the main page of the repository and uploading the formatted image [`OG-ZAF_logo_gitfig.png`](docs/OG-ZAF_logo_gitfig.png) in the **Social preview** section. diff --git a/docs/book/OGZAF_references.bib b/docs/book/OGZAF_references.bib new file mode 100755 index 0000000..45cd2fe --- /dev/null +++ b/docs/book/OGZAF_references.bib @@ -0,0 +1,732 @@ +@ARTICLE{Aiyagari:1994, + AUTHOR = {S. Rao Aiyagari}, + TITLE = {Uninsured Idiosyncratic Risk and Aggregate Saving}, + JOURNAL = {Quarterly Journal of Economics}, + YEAR = {1994}, + volume = {109}, + number = {3}, + month = {August}, + pages = {659-684}, +} + +@ARTICLE{Armington:1969, + AUTHOR = {Paul S. Armington}, + TITLE = {A Theory of Demand for Products Distinguished by Place of Production}, + JOURNAL = {Staff Papers, International Monetary Fund}, + YEAR = {1969}, + volume = {16}, + number = {1}, + month = {March}, + pages = {159-178}, +} + +@Article{EHS:2013, + author={Michael Elsby and Bart Hobijn and Ayseful Sahin}, + title={{The Decline of the U.S. Labor Share}}, + journal={Brookings Papers on Economic Activity}, + year=2013, + volume={44}, + number={2 (Fall)}, + pages={1-63}, + month={}, + keywords={labor; labor share; great recession; trade; manufacturing; unionization; supply chain; decline; inco}, + doi={}, + abstract={Over the past quarter century, labor?s share of income in the United States has trended downwards, reaching its lowest level in the postwar period after the Great Recession. Detailed examination of the magnitude, determinants and implications of this decline delivers five conclusions. First, around one third of the decline in the published labor share is an artifact of a progressive understatement of the labor income of the self-employed underlying the headline measure. Second, movements in labor?s share are not a feature solely of recent U.S. history: The relative stability of the aggregate labor share prior to the 1980s in fact veiled substantial, though offsetting, movements in labor shares within industries. By contrast, the recent decline has been dominated by trade and manufacturing sectors. Third, U.S. data provide limited support for neoclassical explanations based on the substitution of capital for (unskilled) labor to exploit technical change embodied in new capital goods. Fourth, institutional explanations based on the decline in unionization also receive weak support. Finally, we provide evidence that highlights the offshoring of the labor-intensive component of the U.S. supply chain as a leading potential explanation of the decline in the U.S. labor share over the past 25 years. ; Prepared for Brookings Panel on Economic Activity, September 19-20, 2013.
(This abstract was borrowed from another version of this item.)}, + url={https://ideas.repec.org/a/bin/bpeajo/v44y2013i2013-02p1-63.html} +} + +@INCOLLECTION{AuerbachKotlikoff:1983a, + AUTHOR = {Alan J. Auerbach and Laurence J. Kotlikoff}, + TITLE = {National Savings, Economic Welfare, and the Structure of Taxation}, + BOOKTITLE = {Behavioral Simulation Methods in Tax Policy Analysis}, + PUBLISHER = {University of Chicago Press}, + YEAR = {1983}, + editor = {Martin Feldstein}, + type = {National Bureau of Economic Research Project Report}, + chapter = {Chap. 13}, + pages = {pp. 459-498}, +} + +@INCOLLECTION{AuerbachKotlikoff:1983b, + AUTHOR = {Alan J. Auerbach and Laurence J. Kotlikoff}, + TITLE = {Investment versus Savings Incentives: The Size of the Bang for the Buck and the Potential for Self-financing Business Tax Cuts}, + BOOKTITLE = {The Economic Consequences of Government Deficits}, + PUBLISHER = {Kluwer-Nijhoff Publishing}, + YEAR = {1983}, + editor = {Lawrence. H. Meyer}, + series = {Economic Policy Conference Series}, + type = {Center for the Study of American Business}, + chapter = {Chap. 4}, + pages = {pp. 121-149}, +} + +@INCOLLECTION{AuerbachKotlikoff:1983c, + AUTHOR = {Alan J. Auerbach and Laurence J. Kotlikoff}, + TITLE = {An Examination of Empirical Tests of Social Security and Savings}, + BOOKTITLE = {Social Policy Evaluation: An Economic Perspective}, + PUBLISHER = {Academic Press}, + YEAR = {1983}, + editor = {Elhanan Helpman and Assaf Razin and Efraim Sadka}, + chapter = {Chap. 8}, + pages = {pp. 161-179}, +} + +@ARTICLE{AuerbachKotlikoff:1985, + AUTHOR = {Alan J. Auerbach and Laurence J. Kotlikoff}, + TITLE = {Simulating Alternative Social Security Responses to the Demographic Transition}, + JOURNAL = {National Tax Journal}, + YEAR = {1985}, + volume = {38}, + number = {2}, + month = {June}, + pages = {153-168}, +} + +@Article{Altonji:1986, + author={Altonji, Joseph G}, + title={{Intertemporal Substitution in Labor Supply: Evidence from Micro Data}}, + journal={Journal of Political Economy}, + year=1986, + volume={94}, + number={3}, + pages={176-215}, + month={June}, + keywords={}, + doi={}, + abstract={No abstract is available for this item.}, + url={https://ideas.repec.org/a/ucp/jpolec/v94y1986i3ps176-s215.html} +} + +@TECHREPORT{AuerbachEtAl:1981, + AUTHOR = {Alan J. Auerbach and Laurence J. Kotlikoff and Jonathan Skinner}, + TITLE = {The Efficiency Gains from Dynamic Tax Reform}, + INSTITUTION = {National Bureau of Economic Research}, + YEAR = {1981}, + type = {NBER Working Paper}, + number = {819}, + month = {December}, +} + +@ARTICLE{AuerbachEtAl:1983, + AUTHOR = {Alan J. Auerbach and Laurence J. Kotlikoff and Jonathan Skinner}, + TITLE = {The Efficiency Gains from Dynamic Tax Reform}, + JOURNAL = {International Economic Review}, + YEAR = {1983}, + volume = {24}, + number = {1}, + month = {February}, + pages = {81-100}, +} + +@BOOK{AuerbachKotlikoff:1987, + AUTHOR = {Alan J. Auerbach and Laurence J. Kotlikoff}, + TITLE = {Dynamic Fiscal Policy}, + PUBLISHER = {Cambridge University Press}, + YEAR = {1987}, +} + +@BOOK{Beazley:2009, + AUTHOR = {David M. Beazley}, + TITLE = {Python Essential Reference}, + PUBLISHER = {Addison-Wesley}, + YEAR = {2009}, + edition = {4th edition}, +} + +@ARTICLE{BrummGrill:2014, + AUTHOR = {Johannes Brumm and Michael Grill}, + TITLE = {Computing Equilibria in Dynamic Models with Occasionally Binding Constraints}, + JOURNAL = {Journal of Economic Dynamics \& Control}, + YEAR = {2014}, + volume = {38}, + month = {January}, + pages = {142-160}, +} + +@ARTICLE{Carroll:2009, +title = {Precautionary saving and the marginal propensity to consume out of permanent income}, +author = {Carroll, Christopher}, +year = {2009}, +journal = {Journal of Monetary Economics}, +volume = {56}, +number = {6}, +pages = {780-790}, +abstract = {The budget constraint requires that, eventually, consumption must adjust fully to any permanent shock to income. Intuition suggests that, knowing this, optimizing agents will fully adjust their spending immediately upon experiencing a permanent shock. However, this paper shows that if consumers are impatient and are subject to transitory as well as permanent shocks, the optimal marginal propensity to consume out of permanent shocks (the MPCP) is strictly less than one, because buffer-stock savers have a target wealth-to-permanent-income ratio; a positive shock to permanent income moves the ratio below its target, temporarily boosting saving.}, +keywords = {Risk Uncertainty Consumption Precautionary saving Buffer-stock saving Permanent income hypothesis}, +url = {https://EconPapers.repec.org/RePEc:eee:moneco:v:56:y:2009:i:6:p:780-790} +} + + + +@Article{ABMW:1999, + author={Orazio P. Attanasio and James Banks and Costas Meghir and and Guglielmo Weber}, + title={{Humps and Bumps in Lifetime Consumption}}, + journal={Journal of Business \& Economic Statistics}, + year=1999, + volume={17}, + number={1}, + pages={22-35}, + month={January}, + keywords={}, + doi={}, + abstract={ In this article, the authors argue that the life-cycle model that allows demographics to affect household preferences and relaxes the assumption of certainty equivalence can generate hump-shaped consumption profiles over age that are very similar to those observed in household-level data sources and, in particular, match the differences in shape across different education groups. Liquidity constraints or myopia are not required to explain the empirical features of observed life-cycle patterns. Coauthors are James Banks, Costas Meghir, and Guglielmo Weber.}, + url={https://ideas.repec.org/a/bes/jnlbes/v17y1999i1p22-35.html} +} + +@ARTICLE{CarrollEtAl:2017, + AUTHOR = {Christopher Carroll and Jiri Slacalek and Kiichi Tokuoka and Matthew N. White}, + TITLE = {The Distribution of Wealth and the Marginal Propensity to Consume}, + JOURNAL = {Quantitative Economics}, + YEAR = {2017}, + volume = {8}, + number = {3}, + month = {November}, + pages = {977-1020}, +} + +@TECHREPORT{Census:2015, + AUTHOR = {{Census Bureau}}, + TITLE = {Annual Estimates of the Resident Population by Single Year of Age and Sex: April 1, 2010 to July 1, 2013 (both sexes)}, + INSTITUTION = {U.S. Census Bureau}, + YEAR = {2015}, + type = {National Characteristics}, + number = {Vintage 2013}, + address = {http://www.census.gov/popest/data/national/asrh/2013/index.html}, +} + +@BOOK{ChaconStraub:2014, + AUTHOR = {Scott Chacon and Ben Straub}, + TITLE = {Pro Git: Everything You Need to Know about Git}, + PUBLISHER = {Apress}, + YEAR = {2014}, + edition = {2nd edition}, +} + +@ARTICLE{Chetty:2011, + AUTHOR = {Raj Chetty and John N. Friedman and Tore Olsen and Luigi Pistaferri}, + TITLE = {Adjustment Costs, Firm Responses, and Micro vs. Macro Labor Supply Elasticities: Evidence from Danish Tax Records}, + JOURNAL = {Quarterly Journal of Economics}, + YEAR = {2011}, + volume = {126}, + number = {2}, + pages = {749-804}, + month = {May}, +} + +@ARTICLE{ChristianoFisher:2000, + AUTHOR = {Lawrence J. Christiano and Jonas D. M. Fisher}, + TITLE = {Algorithms for Solving Dynamic Models with Occasionally Binding Constraints}, + JOURNAL = {Journal of Economic Dynamics \& Control}, + YEAR = {2000}, + volume = {24}, + number = {8}, + month = {July}, + pages = {1179-1232}, +} + +@BOOK{DavidsonMacKinnon:2004, + AUTHOR = {Russell Davidson and James G. MacKinnon}, + TITLE = {Econometric Theory and Methods}, + PUBLISHER = {Oxford University Press}, + YEAR = {2004}, +} + +@TECHREPORT{DeBackerRamnath:2017, + Author = {Jason DeBacker and Shanthi Ramnath}, + Institution = {Mimeo}, + Title = {Estimating the Hourly Earnings Processes of Top Earners}, + Year = {2017}, +} + +@TECHREPORT{DeBackerEtAl:2017, + AUTHOR = {Jason DeBacker and Richard W. Evans and Evan Magnusson and Kerk L. Phillips and Shanthi Ramnath and Isaac Swift}, + TITLE = {The Distributional Effects of Redistributional Tax Policy}, + INSTITUTION = {Open Source Macroeconomics Laboratory}, + YEAR = {2017}, + type = {mimeo}, + month = {January}, +} + +@ARTICLE{DeBackerEtAl:2019, + AUTHOR = {Jason DeBacker and Richard W. Evans and Kerk L. Phillips}, + TITLE = {Integrating Microsimulation Models of Tax Policy into a DGE Macroeconomic Model}, + JOURNAL = {Public Finance Review}, + YEAR = {2019}, + volume = {47}, + number = {2}, + month = {March}, + pages = {207-275}, +} + +@ARTICLE{DeNardi:2004, + AUTHOR = {Mariacristina {De Nardi}}, + TITLE = {Wealth Inequality and Intergenerational Links}, + JOURNAL = {Review of Economic Studies}, + YEAR = {2004}, + volume = {71}, + number = {3}, + pages = {743-768}, + month = {July}, +} + +@ARTICLE{DeNardiYang:2014, + AUTHOR = {Mariacristina {De Nardi} and Fang Yang}, + TITLE = {Bequests and Heterogeneity in Retirement Wealth}, + JOURNAL = {European Economic Review}, + YEAR = {2014}, + volume = {72}, + pages = {182-196}, + month = {November}, +} + +@InBook{ZodrowDiamond:2013, + author={George R. Zodrow and John W. Diamond}, + title={{Dynamic Overlapping Generations Computable General Equilibrium Models and the Analysis of Tax Policy: The Diamond-Zodrow Model}}, + publisher={Elsevier}, + year={2013}, + month={December}, + volume={1}, + series={Handbook of Computable General Equilibrium Modeling}, + chapter={0}, + pages={743-813}, +} + +@ARTICLE{EvansPhillips:2014, + AUTHOR = {Richard W. Evans and Kerk L. Phillips}, + TITLE = {OLG Life Cycle Model Transition Paths: Alternate Model Forecast Method}, + JOURNAL = {Computational Economics}, + YEAR = {2014}, + volume = {43}, + number = {1}, + month = {January}, + Pages = {105-131}, +} + +@ARTICLE{EvansPhillips:2017, + AUTHOR = {Richard W. Evans and Kerk L. Phillips}, + TITLE = {Advantages of an Ellipse when Modeling Leisure Utility}, + JOURNAL = {Computational Economics}, + YEAR = {2017}, + volume = {forthcoming}, +} + +@BOOK{FullertonRogers:1993, + AUTHOR = {Don Fullerton and Diane Lim Rogers}, + TITLE = {Who Bears the Lifetime Tax Burden?}, + PUBLISHER = {The Brookings Institution}, + YEAR = {1993}, +} + +@ARTICLE{GaleScholz:1994, + AUTHOR = {William G. Gale and John Karl Scholz}, + TITLE = {Intergenerational Transfers and the Accumulation of Wealth}, + JOURNAL = {Journal of Economic Perspectives}, + YEAR = {1994}, + volume = {8}, + number = {4}, + pages = {145-160}, + month = {Fall}, +} + +@ARTICLE{GokhaleEtAl:2000, + AUTHOR = {Jagadeesh Gokhale and Laurence J. Kotlikoff and James Sefton and Martin Weale}, + TITLE = {Simulating the Transmission of Wealth Inequality via Bequests}, + JOURNAL = {Journal of Public Economics}, + YEAR = {2000}, + volume = {79}, + number = {1}, + pages = {93-128}, + month = {January}, +} + +@Article{GouveiaStrauss:1994, + author={Miguel Gouveia and Robert P. Strauss}, + title={Effective Federal Individual Tax Functions: An Exploratory Empirical Analysis}, + journal={National Tax Journal}, + year={1994}, + volume={47}, + number={2}, + pages={317-39}, + month={June}, +} + +@ARTICLE{GuerrieriIacoviello:2015, + AUTHOR = {Luca Guerrieri and Matteo Iacoviello}, + TITLE = {OccBin: A Toolkit for solving dynamic models with occasionally binding constraints easily}, + JOURNAL = {Journal of Monetary Economics}, + YEAR = {2015}, + volume = {70}, + month = {March}, + pages = {22-38}, +} + +@Article{GunerEtAl:2014, + author={Nezih Guner and Remzi Kaygusuz and Gustavo Ventura}, + title={Income Taxation of U.S. Households: Facts and Parametric Estimates}, + journal={Review of Economic Dynamics}, + year={2014}, + volume={17}, + number={4}, + pages={559-581}, + month={October}, +} + +@Article{GuvenenEtAl:2014, + author={Fatih Guvenen and Burhanettin Kuruscu and Serdar Ozkan}, + title={Taxation of Human Capital and Wage Inequality: A Cross-Country Analysis}, + journal={Review of Economic Studies}, + year={2014}, + volume={81}, + number={2}, + pages={818-850}, +} + +@TechReport{HolterEtAl:2014, + author={Hans A. Holter and Dirk Krueger and Serhiy Stepanchuk}, + title={{How Does Tax Progressivity and Household Heterogeneity Affect Laffer Curves?}}, + year={2014}, + month={March}, + institution={Penn Institute for Economic Research, Department of Economics, University of Pennsylvania}, + type={PIER Working Paper Archive}, + number={14-015}, +} + +@ARTICLE{Hurd:1989, + AUTHOR = {Michael D. Hurd}, + TITLE = {Mortality Risk and Bequests}, + JOURNAL = {Econometrica}, + YEAR = {1989}, + volume = {57}, + number = {4}, + pages = {779-813}, + month = {July}, +} + +@ARTICLE{Inada:1963, + AUTHOR = {Ken-Ichi Inada}, + TITLE = {On a Two-sector Model of Economic Growth: Comments and a Generalization}, + JOURNAL = {Review of Economic Studies}, + YEAR = {1963}, + volume = {30}, + number = {2}, + month = {June}, + Pages = {119-127}, +} + +@BOOK{Judd:1998, + AUTHOR = {Kenneth L. Judd}, + TITLE = {Numerical Methods in Economics}, + PUBLISHER = {MIT Press}, + YEAR = {1998}, +} + +@INCOLLECTION{JuddEtAl:2003, + Author = {Kenneth L. Judd and Felix Kubler and Karl Schmedders}, + BOOKTITLE = {Advances in Economics and Econometrics: Theory and Applications}, + Editor = {Mathias Dewatripont and Lars Peter Hansen and Stephen J. Turnovsky}, + pages = {243-290}, + PUBLISHER = {Cambridge University Press}, + TITLE = {Computational Methods for Dynamic Equilibria with Heterogeneous Agents}, + YEAR = {2003}, +} + +@ARTICLE{KeaneRogerson:2012, + Author = {Michael Keane and Richard Rogerson}, + TITLE = {Micro and Macro Labor Supply Elasticities: A Reassessment of Conventional Wisdom}, + JOURNAL = {Journal of Economic Literature}, + YEAR = {2012}, + volume = {50}, + number = {2}, + pages = {464-476}, + month = {June}, +} + +@ARTICLE{KingEtAl:1988, + AUTHOR = {Robert G. King and Charles I. Plosser and Sergio T. Rebelo}, + TITLE = {Production, Growth and Business Cycles I. The Basic Neoclassical Model}, + JOURNAL = {Journal of Monetary Economics}, + YEAR = {1988}, + volume = {21}, +} + +@ARTICLE{KotlikoffSummers:1981, + AUTHOR = {Laurence J. Kotlikoff and Lawrence H. Summers}, + TITLE = {The Role of Intergenerational Transfers in Aggregate Capital Accumulation}, + JOURNAL = {Journal of Political Economy}, + YEAR = {1981}, + volume = {89}, + number = {4}, + pages = {706-732}, + month = {August}, +} + +@Article{KruegerLudwig:2016, + author={Dirk Krueger and Alexander Ludwig}, + title={{On the Optimal Provision of Social Insurance: Progressive Taxation versus Education Subsidies in General Equilibrium}}, + journal={Journal of Monetary Economics}, + year={2016}, + volume={77}, + number={C}, + pages={72-98}, + month={}, + } + +@ARTICLE{Laitner:2001, + AUTHOR = {John Laitner}, + TITLE = {Secular Changes in Wealth Inequality and Inheritance}, + JOURNAL = {Economic Journal}, + YEAR = {2001}, + volume = {111}, + number = {474}, + pages = {691-721}, + month = {October}, +} + +@BOOK{Langtangen:2010, + AUTHOR = {Hans Petter Langtangen}, + TITLE = {Python Scripting for Computational Science}, + PUBLISHER = {Springer}, + YEAR = {2010}, + series = {Texts in Computational Science and Engineering}, + edition = {3rd edition}, +} + +@BOOK{Lutz:2013, + AUTHOR = {Mark Lutz}, + TITLE = {Learning Python}, + PUBLISHER = {O'Reilly Media, Inc.}, + YEAR = {2013}, + edition = {5th edition}, +} + +@ARTICLE{MartinEtAl:2015, + AUTHOR = {Joyce A. Martin and Brady E. Hamilton and Michelle J. K. Osterman and Sally C. Curtin and T.J. Mathews}, + TITLE = {Births: Final Data for 2013}, + JOURNAL = {National Vital Statistics Reports}, + YEAR = {2015}, + volume = {64}, + number = {1}, + pages = {1-68}, + month = {January}, +} + +@ARTICLE{McDonald:1984, + AUTHOR = {James B. McDonald}, + TITLE = {Some Generalized Functions for the Size Distribution of Income}, + JOURNAL = {Econometrica}, + YEAR = {1984}, + volume = {52}, + number = {3}, + pages = {647-663}, + month = {May}, +} + +@ARTICLE{McDonaldEtAl:2013, + AUTHOR = {James B. McDonald and Jeff Sorensen and Patrick A. Turley}, + TITLE = {Skewness and Kurtosis Properties of Income Distribution Models}, + JOURNAL = {Review of Income and Wealth}, + YEAR = {2013}, + volume = {59}, + number = {2}, + pages = {360-374}, + month = {June}, +} + +@ARTICLE{McDonaldXu:1995, + AUTHOR = {James B. McDonald and Yexiao Xu}, + TITLE = {A Generalization of the Beta Distribution with Applications}, + JOURNAL = {Journal of Econometrics}, + YEAR = {1995}, + volume = {66}, + number = {1-2}, + pages = {133-152}, + month = {March-April}, +} + +@BOOK{McKinney:2013, + AUTHOR = {Wes McKinney}, + TITLE = {Python for Data Analysis}, + PUBLISHER = {O'Reilly Media, Inc.}, + YEAR = {2013}, +} + +@BOOK{MittelbachGoossens:2004, + AUTHOR = {Frank Mittelbach and Michel Goossens}, + TITLE = {The \LaTeX Companion: Tools and Techniques for Computer Typesetting}, + PUBLISHER = {Addison-Wesley}, + YEAR = {2004}, + edition = {2nd edition}, +} + +@ARTICLE{Nishiyama:2002, + AUTHOR = {Shinichi Nishiyama}, + TITLE = {Bequests, Inter Vivos Transfers, and Wealth Distribution}, + JOURNAL = {Review of Economic Dynamics}, + YEAR = {2002}, + volume = {5}, + number = {4}, + pages = {892-931}, + month = {October}, +} + +@ARTICLE{NishiyamaSmetters:2007, + Author = {Shinichi Nishiyama and Kent Smetters}, + Title = {Does Social Security Privatization Produce Efficiency Gains?}, + Journal = {Quarterly Journal of Economics}, + Year = {2007}, + Volume = {122}, + Number = {4}, + Pages = {1677-1719}, + Month = {November}, +} + +@ARTICLE{Nishiyama:2015, + AUTHOR = {Shinichi Nishiyama}, + TITLE = {Fiscal Policy Effects in a Heterogeneous-agent OLG Economy with an Aging Population}, + JOURNAL = {Journal of Economic Dynamics and Control}, + YEAR = {2015}, + volume = {61}, + month = {December}, + pages = {114-132}, +} + +@ARTICLE{Peterman:2016, + AUTHOR = {William B. Peterman}, + TITLE = {Reconciling Micro and Macro Estimates of the Frisch Labor Supply Elasticity}, + JOURNAL = {Economic Inquiry}, + YEAR = {2016}, + volume = {54}, + number = {1}, + month = {January}, + pages = {100-120}, +} + +@ARTICLE{PikettySaez:2003, + AUTHOR = {Thomas Piketty and Emmanuel Saez}, + TITLE = {Income Inequality In The United States, 1913-1998}, + JOURNAL = {Quarterly Journal of Economics}, + YEAR = {2003}, + volume = {118}, + number = {1}, + pages = {1-39}, + month = {February}, +} + + +@BOOK{PressEtAl:2007, + AUTHOR = {William H. Press and Saul A. Teukolsky and William T. Vetterling and Brian P. Flannery}, + TITLE = {Numerical Recipes: The Art of Scientific Computing}, + PUBLISHER = {Cambridge University Press}, + YEAR = {2007}, + edition = {Third Edition}, +} + +@ARTICLE{Samuelson:1958, + AUTHOR = {Paul A. Samuelson}, + Title = {An Exact Consumption-Loan Model of Interest With or Without the Social Contrivance of Money}, + JOURNAL = {Journal of Political Economy}, + YEAR = {1958}, + volume = {66}, + number = {6}, + pages = {467-482}, + Month = {December}, +} + +@TECHREPORT{SocSec:2015, + AUTHOR = {Felicitie C. Bell and Michael L. Miller}, + TITLE = {Life Tables for the United States Social Security Area 1900-2100}, + INSTITUTION = {U.S. Social Security Administration}, + YEAR = {2015}, + type = {Actuarial Study}, + number = {120}, + address = {https://www.ssa.gov/oact/STATS/table4c6.html}, +} + +@ARTICLE{Solow:1956, + AUTHOR = {Robert M. Solow}, + TITLE = {A Contribution to the Theory of Economic Growth}, + JOURNAL = {Quarterly Journal of Economics}, + YEAR = {1956}, + volume = {70}, + number = {1}, + month = {February}, + pages = {65-94}, +} + +@BOOK{StokeyLucas1989, + AUTHOR = {Nancy L. Stokey and Robert E. {Lucas, Jr.} and Edward C. Prescott}, + TITLE = {Recursive Methods in Economic Dynamics}, + PUBLISHER = {Harvard University Press}, + YEAR = {1989}, +} + +@ARTICLE{StoreslettenEtAl:2007, + AUTHOR = {Kjetil Storesletten and Christopher I. Telmer and Amir Yaron}, + TITLE = {Asset Pricing with Idiosyncratic Risk and Overlapping Generations}, + JOURNAL = {Review of Economic Dynamics}, + YEAR = {2007}, + volume = {10}, + number = {4}, + month = {October}, + pages = {519-548}, +} + +@ARTICLE{Suzumura:1983, + Author = {Kotaro Suzumura}, + Journal = {Hitotsubashi Journal of Economics}, + Pages = {137-141}, + Title = {Perron-Frobenius Theorem on Non-Negative Square Matrices: An Elementary Proof}, + Volume = {24}, + Year = {1983}, +} + +@ARTICLE{VentiWise:1988, + AUTHOR = {Steven F. Venti and David A. Wise}, + TITLE = {The Cause of Wealth Dispersion at Retirement: Choice or Chance?}, + JOURNAL = {American Economic Review}, + YEAR = {1988}, + volume = {88}, + number = {2}, + pages = {185-191}, + month = {May}, +} + +@ARTICLE{Weil:2008, + Author = {Philippe Weil}, + Journal = {Journal of Economic Perspectives}, + Month = {Fall}, + Number = {4}, + Pages = {115-134}, + Title = {Overlapping Generations: The First Jubilee}, + Volume = {22}, + Year = {2008}, +} + +@ARTICLE{Wendner:2004, + AUTHOR = {Ronald Wendner}, + TITLE = {Existence, Uniqueness, and Stability of Equilibrium in an OLG Economy}, + JOURNAL = {Economic Theory}, + YEAR = {2004}, + volume = {23}, + number = {1}, + month = {January}, + pages = {165-174}, +} + +@BOOK{Wolff:2015, + AUTHOR = {Edward N. Wolff}, + TITLE = {Inheriting Wealth in America: Future Boom or Bust?}, + PUBLISHER = {Oxford University Press}, + YEAR = {2015}, +} + +@ARTICLE{Zhang:1997, + AUTHOR = {Harold Zhang}, + TITLE = {Endogenous Borrowing Constraints with Incomplete Markets}, + JOURNAL = {Journal of Finance}, + YEAR = {1997}, + volume = {52}, + number = {5}, + month = {December}, + pages = {2187-2209}, +} diff --git a/docs/book/_config.yml b/docs/book/_config.yml new file mode 100644 index 0000000..bfa7b76 --- /dev/null +++ b/docs/book/_config.yml @@ -0,0 +1,64 @@ +#################################################### +# Book settings +title : OG-ZAF +author : Marcelo LaFleur and Richard W. Evans +copyright : '2022' +logo : '..//OG-ZAF_logo.png' + +#################################################### +# Execution settings +execute: + execute_notebooks : force + +#################################################### +# HTML-specific settings +html: + # favicon : "_static/logo/favicon.ico" # generated at https://www.favicon.cc/ + use_edit_page_button : false # Whether to add an "edit this page" button to pages. 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Used for creating image previews and social links. e.g.: https://mypage.com/mybook/ + +#################################################### +# Launch button settings +notebook_interface : 'notebook' + +binder: + binderhub_url : 'https://mybinder.org' + text : 'Launch binder' + +launch_buttons: + notebook_interface : 'classic' # The interface interactive links will activate ["classic", "jupyterlab"] + # binderhub_url : https://mybinder.org # The URL of the BinderHub (e.g., https://mybinder.org) + # jupyterhub_url : "" # The URL of the JupyterHub (e.g., https://datahub.berkeley.edu) + # thebelab : false # Add a thebelab button to pages (requires the repository to run on Binder) + +#################################################### +# Information about where the book exists on the web +repository: + url : https://github.com/EAPD-DRB/OG-ZAF + path_to_book : 'docs/book' + +####################################################################################### +# Advanced and power-user settings +sphinx: + extra_extensions : ['sphinx.ext.autodoc', 'sphinx.ext.mathjax', + 'sphinx.ext.viewcode', 'sphinx.ext.napoleon', + 'alabaster'] # A list of extra extensions to load by Sphinx. + config : # key-value pairs to directly over-ride the Sphinx configuration + bibtex_reference_style: author_year + mathjax_path : https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js + +#################################################### +# LaTeX information +latex: + latex_engine : 'xelatex' + latex_documents: + targetname : book.tex +bibtex_bibfiles: + - OGZAF_references.bib + - citations.bib diff --git a/docs/book/_toc.yml b/docs/book/_toc.yml new file mode 100644 index 0000000..f3a9798 --- /dev/null +++ b/docs/book/_toc.yml @@ -0,0 +1,25 @@ +format: jb-book +root: content/intro/intro +parts: +- caption: Contributing to OG-ZAF + chapters: + - file: content/contributing/contributor_guide +- caption: OG-ZAF API + chapters: + - file: content/api/public_api +- caption: Calibration + chapters: + - file: content/calibration/exogenous_parameters + - file: content/calibration/macro + - file: content/calibration/demographics + - file: content/calibration/earnings + - file: content/calibration/tax_functions + - file: content/calibration/bequests + - file: content/calibration/UBI + - file: content/calibration/matching_lwi +- caption: References + chapters: + - file: content/OGZAF_references +- caption: Citations of OG-ZAF + chapters: + - file: content/citations diff --git a/docs/book/citations.bib b/docs/book/citations.bib new file mode 100644 index 0000000..4030e56 --- /dev/null +++ b/docs/book/citations.bib @@ -0,0 +1,207 @@ +@TECHREPORT{DeBackerEtAl:2017b, + AUTHOR = {Jason DeBacker and Richard W. Evans and Evan Magnusson and Kerk L. Phillips and Shanthi Ramnath and Isaac Swift}, + TITLE = {The Distributional Effects of Redistributional Tax Policy}, + INSTITUTION = {Open Source Macroeconomics Laboratory}, + YEAR = {2017b}, + type = {mimeo}, + month = {January}, +} + +@Article{DEP:2019, + author={Jason DeBacker and Richard W. Evans and Kerk L. Phillips}, + title={{Integrating Microsimulation Models of Tax Policy into a DGE Macroeconomic Model}}, + journal={Public Finance Review}, + year=2019, + volume={47}, + number={2}, + pages={207-275}, + month={March}, + keywords={microsimulation; effective tax rates; marginal tax rates; dynamic general equilibrium; dynamic scori}, + doi={}, + abstract={This article proposes a method for integrating individual effective tax rates and marginal tax rates computed from a microsimulation (partial equilibrium) model of tax policy with a dynamic general equilibrium model of tax policy that can provide macroeconomic analysis or dynamic scores of tax reforms. Our approach captures the rich heterogeneity, realistic demographics, and tax-code detail of the microsimulation model and allows this detail to inform a general equilibrium model with a relatively high degree of heterogeneity. In addition, we propose a functional form in which tax rates depend jointly on the levels of both capital income and labor income.}, + url={https://ideas.repec.org/a/sae/pubfin/v47y2019i2p207-275.html} +} + +@TECHREPORT{Pomerleau2020b, + AUTHOR = {Kyle Pomerleau}, + TITLE = {An analysis of Joe Biden’s tax proposals, October 2020 update}, + INSTITUTION = {American Enterprise Institute}, + YEAR = {2020}, + type = {AEI Report}, + month = {October}, + url = {https://www.aei.org/research-products/report/an-analysis-of-joe-bidens-tax-proposals-october-2020-update/} +} + +@TECHREPORT{DEP2020, + AUTHOR = {Jason DeBacker and Richard Evans and Kyle Pomerleau}, + TITLE = {An analysis of Joe Biden’s tax proposals}, + INSTITUTION = {American Enterprise Institute}, + YEAR = {2020}, + type = {AEI Report}, + month = {June}, + url = {https://www.aei.org/research-products/report/an-analysis-of-joe-bidens-tax-proposals/} +} + +@Article{DeBackerFrailey:2019, + author={Jason DeBacker and Anderson Frailey}, + title={Revenue and Macroeconomic Effects of a 70% Marginal Tax Rate}, + journal={Quantitative Notes}, + year=2019, + volume={}, + number={2019-1}, + pages={}, + month={March}, + keywords={}, + doi={}, + abstract={Recently, there has been considerable dis- cussion of a significant increase in the top marginal income tax rate. A salient top marginal tax rate is 70%. This note simulates the effects of a 70% top rate on different groups of filers and shows the im- pacts on revenue and macroeconomic aggregates. We find that an increase in the top marginal tax rate to 70% raises between $5 billion and $250 billion per year over the first 10 years, depending on the size of the top bracket to which this rate is applied. However, our macroeconomic simulations show that a 70% top rate lowers GDP by between 1.7% and 0.1% in the near term, although there may be posi- tive effects on GDP in the longer term.}, + url={https://www.openrg.com/reports/70pctMTR_QN.pdf} +} + +@Article{Evans:2018, + author={Richard W. Evans}, + title={Dynamic Analysis of EITC Expansion}, + journal={Quantitative Notes}, + year=2018, + volume={}, + number={2018-2}, + pages={}, + month={May}, + keywords={}, + doi={}, + abstract={This Quantitative Note uses the OG-USA open source dynamic general equilibrium overlap- ping generations model to perform a dynamic analy- sis of the Brown-Khanna Grow American Incomes Now (GAIN) Act, which proposes to increase the generosity and scope of the earned income tax credit (EITC) in the United States. I show a simulation of the macroeconomic effects as well as distributional analysis resulting from the GAIN Act. I also sim- ulate the effects of a revenue neutral GAIN Act in which an increase in the marginal income tax rates in the top two personal income brackets exactly off- sets the reduction in total federal tax revenue from the EITC expansion. In the case of the GAIN Act alone, the economy experiences short-run gains, but the increased government debt quickly crowds out in- vestment and causes the economy to start shrinking significantly. In the revenue neutral case, the cost is primarily in terms of large labor supply frictions and a reallocation of the household labor-leisure and consumption-savings decisions.}, + url={https://www.openrg.com/reports/QN_EITC_v1.1.pdf} +} + +@Article{DeBackerEvans:2018, + author={Jason DeBacker and Richard W. Evans}, + title={Dynamic Analysis of Tax Cuts and Jobs Act}, + journal={Quantitative Notes}, + year=2018, + volume={}, + number={2018-1}, + pages={}, + month={February}, + keywords={}, + doi={}, + abstract={This Quantitative Note uses the OG-USA open source dynamic general equilibrium overlap- ping generations model to simulate the effect of the Tax Cuts and Jobs Act. We simulate this reform under the assumptions of a closed economy and small open economy. In both cases, the TCJA reform causes significant growth in GDP and employment between 1% and 2% per year in the first 8 years. However, the increasing debt-to-GDP ratio quickly crowds out investment and causes a drag on the economy. Wage growth can range from nearly nonexistent to a mod- est 0.6%, depending critically on the assumption of how much capital will flow into the country.}, + url={https://www.openrg.com/reports/QN_ogusa_TCJA.pdf} +} + +@Article{Evans:2017, + author={Richard W. Evans}, + title={Dynamic Analysis of Corporate Income Tax Rate Cut}, + journal={Quantitative Notes}, + year=2017, + volume={}, + number={2017-4}, + pages={}, + month={November}, + keywords={}, + doi={}, + abstract={This Quantitative Note uses the OG-USA open source dynamic general equilibrium overlap- ping generations model to simulate the effect of cut- ting the U.S. corporate income tax rate from 35% to 20%. I simulate this rate cut under the assump- tions of a closed economy and small open economy, respectively. In both cases, the corporate rate cut causes government revenues to decrease and the debt-to-GDP ratio to increase. In the small open economy scenario, GDP and wages increase by around 3.0%, and 2.5%, respectively. However, in the closed economy setting in which the increased debt service must be satisfied by domestic savings (crowding out), the GDP and wage gains are much smaller and short lived.}, + url={https://www.openrg.com/reports/QN_CorpCut.pdf} +} + +@TECHREPORT{DEP:2015, + AUTHOR = {Jason DeBacker and Richard W. Evans and Kerk L. Phillips}, + TITLE = {Macroeconomic effects of a 10% cut in statutory marginal income tax rates on ordinary income}, + INSTITUTION = {American Enterprise Institute}, + YEAR = {2015}, + type = {AEI Economic Policy Working Paper Series}, + month = {December}, +} + +@Article{Ferenstein:2018, + author={Gregory Ferenstein}, + title={Can The U.S. Afford A Massive Wage Subsidy? A Macroeconomic Simulation}, + journal={Forbes}, + year=2018, + volume={}, + number={}, + pages={}, + month={September}, + keywords={}, + doi={}, + abstract={}, + url={https://www.forbes.com/sites/gregoryferenstein/2018/09/30/can-the-us-afford-a-massive-wage-subsidy-a-macroeconomic-simulation/#614ea9032502} +} + +@TECHREPORT{MichelFurth:2017b, + AUTHOR = {Adam Michel and Salim Furth}, + TITLE = {For Pro-Growth Tax Reform, Expensing Should Be the Focus}, + INSTITUTION = {The Heritage Foundation}, + YEAR = {2017}, + type = {Taxes Report}, + month = {August}, + url = {https://www.heritage.org/taxes/report/pro-growth-tax-reform-expensing-should-be-the-focus} +} + +@TECHREPORT{MichelFurth:2017a, + AUTHOR = {Norbert Michel and Salim Furth}, + TITLE = {The Macroeconomic Impact of Dodd Frank—and of Its Repeal}, + INSTITUTION = {The Heritage Foundation}, + YEAR = {2017}, + type = {Taxes Report}, + month = {April}, + url = {https://www.heritage.org/markets-and-finance/report/the-macroeconomic-impact-dodd-frank-and-its-repeal} +} + +@TECHREPORT{Hassett:2015, + AUTHOR = {Kevin A. Hassett}, + TITLE = {On the Dynamic Scoring of Fiscal Policy}, + INSTITUTION = {}, + YEAR = {2015}, + type = {Congressional Testimony}, + month = {July}, + url = {https://www.aei.org/wp-content/uploads/2015/07/Hassett_DynamicScoring_final-00000002.pdf} +} + +@TECHREPORT{Hassett:2016, + AUTHOR = {Kevin A. Hassett}, + TITLE = {Statement before the House Ways and Means Committee: Reaching America’s Potential: Delivering Growth and Opportunity for All Americans}, + INSTITUTION = {}, + YEAR = {2016}, + type = {Congressional Testimony}, + month = {February}, + url = {https://www.aei.org/wp-content/uploads/2016/02/KHtestimony.pdf} +} + +@TECHREPORT{DEP:2021a, + AUTHOR = {Jason DeBacker and Richard W. Evans and Benjamin R. Page}, + TITLE = {A Detailed Macroeconomic Analysis of President Biden's 2020 Campaign Tax Proposals}, + INSTITUTION = {Tax Policy Center}, + YEAR = {2021}, + type = {Working Paper}, + month = {July}, + url = {https://www.taxpolicycenter.org/publications/detailed-macroeconomic-analysis-president-bidens-2020-campaign-tax-proposals} +} + +@TECHREPORT{DEP:2021b, + AUTHOR = {Jason DeBacker and Richard W. Evans and Benjamin R. Page}, + TITLE = {A Sensitivity Analysis of a Detailed Macroeconomic Analysis of President Biden's 2020 Campaign Tax Proposals}, + INSTITUTION = {Tax Policy Center}, + YEAR = {2021}, + type = {Working Paper}, + month = {July}, + url = {https://www.taxpolicycenter.org/publications/sensitivity-analysis-detailed-macroeconomic-analysis-president-bidens-2020-campaign-tax} +} + +@TECHREPORT{DEP:2021c, + AUTHOR = {Benjamin R. Page and Jeffrey Rohaly and Thornton Matheson and Gordon B. Mermin and Jason DeBacker and Richard W. Evans}, + TITLE = {Macroeconomic Analysis of Former Vice President Biden's Tax Proposals}, + INSTITUTION = {Tax Policy Center}, + YEAR = {2021}, + type = {Brief}, + month = {July}, + url = {https://www.taxpolicycenter.org/publications/macroeconomic-analysis-former-vice-president-bidens-tax-proposals} +} + +@TECHREPORT{Page:2021, + AUTHOR = {Benjamin R. Page}, + TITLE = {TPC Experiments with Another Model to Estimate the Economic Effects of Tax Law Changes}, + INSTITUTION = {Tax Policy Center}, + YEAR = {2021}, + type = {TaxVox: Campaigns, Proposals, and Reforms}, + month = {July}, + url = {https://www.taxpolicycenter.org/taxvox/tpc-experiments-another-model-estimate-economic-effects-tax-law-changes} +} diff --git a/docs/book/content/OGZAF_references.md b/docs/book/content/OGZAF_references.md new file mode 100644 index 0000000..bf4f190 --- /dev/null +++ b/docs/book/content/OGZAF_references.md @@ -0,0 +1,5 @@ +# References + +```{bibliography} ../OGZAF_references.bib +:style: alpha +``` diff --git a/docs/book/content/api/calibrate.rst b/docs/book/content/api/calibrate.rst new file mode 100644 index 0000000..d06daf0 --- /dev/null +++ b/docs/book/content/api/calibrate.rst @@ -0,0 +1,14 @@ +.. _calibrate: + +Main Calibration Functions +================================================= + +**calibrate.py classes, methods, and modules** + +ogzaf.calibrate +------------------------------------------ + +.. currentmodule:: ogzaf.calibrate + +.. autoclass:: Calibration + :members: get_tax_function_parameters, read_tax_func_estimate, get_dict diff --git a/docs/book/content/api/demographics.rst b/docs/book/content/api/demographics.rst new file mode 100644 index 0000000..d851109 --- /dev/null +++ b/docs/book/content/api/demographics.rst @@ -0,0 +1,15 @@ +.. _demographics: + +Demographics Functions +======================== + +**demographics.py modules** + +ogzaf.demographics +------------------------------------------ + +.. automodule:: ogzaf.demographics + :members: get_un_fert_data, get_un_mort_data, get_wb_infmort_rate, + get_un_pop_data, get_fert, get_mort, pop_rebin, get_imm_resid, + immsolve, get_pop_objs, extrap_exp_3, extrap_arctan_3, + ab_zero_eqs_exp_func, b_zero_eq_arctan_func diff --git a/docs/book/content/api/macro_params.rst b/docs/book/content/api/macro_params.rst new file mode 100644 index 0000000..0b3d684 --- /dev/null +++ b/docs/book/content/api/macro_params.rst @@ -0,0 +1,12 @@ +.. _macro_params: + +Macro Parameter Estimation Functions +==================================== + +**macro_params.py modules** + +ogzaf.macro_params +------------------------------------------ + +.. automodule:: ogzaf.macro_params + :members: get_macro_params diff --git a/docs/book/content/api/public_api.rst b/docs/book/content/api/public_api.rst new file mode 100644 index 0000000..546de01 --- /dev/null +++ b/docs/book/content/api/public_api.rst @@ -0,0 +1,16 @@ +API +========================= +The source code for `OG-ZAF` is located in the `/OG-ZAF/ogzaf/` directory tree. + +Here we provide a high-level view of the API of the `OG-ZAF` package, with links +to the source code. This high-level view is organized around the modules that +make up the `OG-ZAF` package. Below is a list of these modules (in alphabetical +order) with documentation about how to call each class method and function. +There is also a link to the source code for each documented member. + +.. toctree:: + :maxdepth: 1 + + calibrate + demographics + macro_params diff --git a/docs/book/content/calibration/UBI.md b/docs/book/content/calibration/UBI.md new file mode 100644 index 0000000..6045647 --- /dev/null +++ b/docs/book/content/calibration/UBI.md @@ -0,0 +1,62 @@ +(Chap_UBI)= +# Universal Basic Income (UBI) + +[TODO: This section is far along but needs to be updated.] + +We have included the modeling of a universal basic income (UBI) policy directly in the theory and code for [`OG-Core`] on which dependency the `OG-USA` is based. UBI shows up in the household budget constraint {eq}`EqHHBC`, and is described in the [Budget Constraint](https://pslmodels.github.io/OG-Core/content/theory/households.html#budget-constraint) section of the Households chapter of the `OG-Core` documentation. We calculate the time series of a UBI matrix $ubi_{j,s,t}$ representing the UBI transfer to every household with head of household age $s$, lifetime income group $j$, in period $t$. We calculate the time series of this matrix from five parameters and some household composition data that we impose upon the existing demographics of `OG-USA`. + + +(SecUBIcalc)= +## Calculating UBI + + We calculate the time series of UBI household transfers in model units $ubi_{j,s,t)}$ and the time series of total UBI expenditures in model units $UBI_t$ from five parameters described in the [`ogusa_default_parameters.json`](https://github.com/PSLmodels/OG-USA/blob/master/ogusa/ogusa_default_parameters.json) file (`ubi_growthadj`, `ubi_nom_017`, `ubi_nom_1864`, `ubi_nom_65p`, and `ubi_nom_max`) interfaced with the `OG-USA` demographic dynamics over lifetime income groups $j$ and ages $s$, and multiplied by household composition matrices from the `Calibrate` class of the `OG-USA/ogusa/calibrate.py` module in the repository. + + From the [OG-USA](https://github.com/PSLmodels/OG-USA) repository, we have four $S\times J$ matrices `ubi_num_017_mat`$_{j,s}$, `ubi_num_1864_mat`$_{j,s}$, and `ubi_num_65p_mat`$_{j,s}$ representing the number of children under age 0-17, number of adults ages 18-64, and the number of seniors age 65 and over, respectively, by lifetime ability group $j$ and age $s$ of head of household. Because our demographic age data match up well with head-of-household data from other datasets, we do not have to adjust the values in these matrices.[^HOH_age_dist_note] + + Now we can solve for the dollar-valued (as opposed to model-unit-valued) UBI transfer to each household in the first period $ubi^{\$}_{j,s,t=0}$ in the following way. Let the parameter `ubi_nom_017` be the dollar value of the UBI transfer to each household per dependent child age 17 and under. Let the parameter `ubi_nom_1864` be the dollar value of the UBI transfer to each household per adult between the ages of 18 and 64. Let `ubi_nom_65p` be the dollar value of UBI transfer to each household per senior 65 and over. And let `ubi_nom_max` be the maximum UBI benefit per household. + + ```{math} + :label: EqUBIubi_dol_jst0 + \begin{split} + ubi^{\$}_{j,s,t=0} = \min\Bigl(&\texttt{ubi_nom_max}, \\ + &\texttt{ubi_nom_017} * \texttt{ubi_num_017_mat}_{j,s} + \\ + &\texttt{ubi_nom_1864} * \texttt{ubi_num_1864_mat}_{j,s} + \\ + &\texttt{ubi_nom_65p} * \texttt{ubi_num_65p_mat}_{j,s}\Bigr) \quad\forall j,s + \end{split} + ``` + + The rest of the time periods of the household UBI transfer and the respective steady-states are determined by whether the UBI is growth adjusted or not as given in the `ubi_growthadj` Boolean parameter. The following two sections cover these two cases. + + +(SecUBI_NonGrowthAdj)= +## UBI specification not adjusted for economic growth + + A non-growth adjusted UBI (`ubi_growthadj = False`) is one in which the initial nonstationary dollar-valued $t=0$ UBI matrix $ubi^{\$}_{j,s,t=0}$ does not grow, while the economy's long-run growth rate is $g_y$ for the most common parameterization where the long-run growth rate is positive $g_y>0$. + + ```{math} + :label: EqUBIubi_dol_NonGrwAdj_jst + ubi^{\$}_{j,s,t} = ubi^{\$}_{j,s,t=0} \quad\forall j,s,t + ``` + + As described in the [OG-Core chapter on stationarization](https://pslmodels.github.io/OG-Core/content/theory/stationarization.html), the stationarized UBI transfer to each household $\hat{ubi}_{j,s,t}$ is the nonstationary transfer divided by the growth rate since the initial period. When the long-run economic growth rate is positive $g_y>0$ and the UBI specification is not growth-adjusted the steady-state stationary UBI household transfer is zero $\overline{ubi}_{j,s}=0$ for all lifetime income groups $j$ and ages $s$ as time periods $t$ go to infinity. However, to simplify, we assume in this case that the stationarized steady-state UBI transfer matrix to households is the stationarized value of that matrix in period $T$. + + ```{math} + :label: EqUBIubi_mod_NonGrwAdj_SS + \overline{ubi}_{j,s} = ubi_{j,s,t=T} \quad\forall j,s + ``` + + Note that in non-growth-adjusted case, if $g_y<0$, then the stationary value of $\hat{ubi}_{j,s,t}$ is going to infinity as $t$ goes to infinity. Therefore, a UBI specification must be growth adjusted for any assumed negative long run growth $g_y<0$.[^GrowthAdj_note] + + +(SecUBI_GrowthAdj)= +## UBI specification adjusted for economic growth + + Put description of growth-adjusted specification here. + + +(SecUBIfootnotes)= +## Footnotes + +[^HOH_age_dist_note]: DeBacker and Evans compared the `OG-USA` age demographics $\hat{\omega}_{s,t}$ with the respective age demographics in Tax Policy Center's microsimulation model and in [Tax-Calculator](https://github.com/PSLmodels/Tax-Calculator)'s microsimulation model. The latter two microsimulation models' age demographics are based on head of household tax filer age distributions, whereas `OG-USA`'s demographics are based on the population age distribution. + +[^GrowthAdj_note]: We impose this requirement of `ubi_growthadj = False` when `g_y_annual < 0` in the [`ogusa_default_parameters.json`](https://github.com/PSLmodels/OG-USA/blob/master/ogusa/ogusa_default_parameters.json) "validators" specification of the parameter. diff --git a/docs/book/content/calibration/bequests.md b/docs/book/content/calibration/bequests.md new file mode 100644 index 0000000..3ed68f1 --- /dev/null +++ b/docs/book/content/calibration/bequests.md @@ -0,0 +1,8 @@ +(Chap_Beq)= +# Bequest Process Calibration + +[TODO: This chapter needs to be finished.] + +This chapter describes how we calibrate the distribution of total bequests $BQ_t$ to each living household of age $s$ and lifetime income group $j$. The matrix that governs this distribution $\zeta_{j,s}$ is seen in the household budget constraint {eq}`EqHHBC`. + +A large number of papers study the effects of different bequest motives and specifications on the distribution of wealth, though there is no consensus regarding the true bequest transmission process. See {cite}`DeNardiYang:2014`, {cite}`DeNardi:2004`, {cite}`Nishiyama:2002`, {cite}`Laitner:2001`, {cite}`GokhaleEtAl:2000`, {cite}`GaleScholz:1994`, {cite}`Hurd:1989`, {cite}`VentiWise:1988`, {cite}`KotlikoffSummers:1981`, and {cite}`Wolff:2015`. diff --git a/docs/book/content/calibration/demographics.md b/docs/book/content/calibration/demographics.md new file mode 100644 index 0000000..9ea90c3 --- /dev/null +++ b/docs/book/content/calibration/demographics.md @@ -0,0 +1,227 @@ +--- +jupytext: + text_representation: + extension: .md + format_name: myst + format_version: '0.8' + jupytext_version: '1.4.1' +kernelspec: + display_name: Python 3 + language: python + name: ogzaf-dev +--- + +(Chap_Demog)= +# Demographics + +We start the `OG-ZAF` section on modeling the household with a description of the demographics of the model. {cite}`Nishiyama:2015` and {cite}`DeBackerEtAl:2019` have recently shown that demographic dynamics are likely the biggest influence on macroeconomic time series, exhibiting more influence than fiscal variables or household preference parameters. + +In this chapter, we characterize the equations and parameters that govern the transition dynamics of the population distribution by age. In `OG-ZAF`, we take the approach of taking mortality rates and fertility rates from outside estimates. But we estimate our immigration rates as residuals using the mortality rates, fertility rates, and at least two consecutive periods of population distribution data. This approach makes sense if one is modeling a country in which one is not confident in the immigration rate data. If the country has good immigration data, then the immigration residual approach we describe below can be skipped. + +We define $\omega_{s,t}$ as the number of households of age $s$ alive at time $t$. A measure $\omega_{1,t}$ of households is born in each period $t$ and live for up to $E+S$ periods, with $S\geq 4$.[^calibage_note] Households are termed ``youth'', and do not participate in market activity during ages $1\leq s\leq E$. The households enter the workforce and economy in period $E+1$ and remain in the workforce until they unexpectedly die or live until age $s=E+S$. We model the population with households age $s\leq E$ outside of the workforce and economy in order most closely match the empirical population dynamics. + +The population of agents of each age in each period $\omega_{s,t}$ evolves according to the following function, +```{math} + :label: EqPopLawofmotion + \omega_{1,t+1} &= (1 - \rho_0)\sum_{s=1}^{E+S} f_s\omega_{s,t} + i_1\omega_{1,t}\quad\forall t \\ + \omega_{s+1,t+1} &= (1 - \rho_s)\omega_{s,t} + i_{s+1}\omega_{s+1,t}\quad\forall t\quad\text{and}\quad 1\leq s \leq E+S-1 +``` + +where $f_s\geq 0$ is an age-specific fertility rate, $i_s$ is an age-specific net immigration rate, $\rho_s$ is an age-specific mortality hazard rate, and $\rho_0$ is an infant mortality rate.[^houseprob_note] The total population in the economy $N_t$ at any period is simply the sum of households in the economy, the population growth rate in any period $t$ from the previous period $t-1$ is $g_{n,t}$, $\tilde{N}_t$ is the working age population, and $\tilde{g}_{n,t}$ is the working age population growth rate in any period $t$ from the previous period $t-1$. + +```{math} + :label: EqPopN + N_t\equiv\sum_{s=1}^{E+S} \omega_{s,t} \quad\forall t +``` + +```{math} + :label: EqPopGrowth + g_{n,t+1} \equiv \frac{N_{t+1}}{N_t} - 1 \quad\forall t +``` + +```{math} + :label: EqPopNtil + \tilde{N}_t\equiv\sum_{s=E+1}^{E+S} \omega_{s,t} \quad\forall t +``` + +```{math} + :label: EqPopGrowthTil + \tilde{g}_{n,t+1} \equiv \frac{\tilde{N}_{t+1}}{\tilde{N}_t} - 1 \quad\forall t +``` + +We discuss the approach to estimating fertility rates $f_s$, mortality rates $\rho_s$, and immigration rates $i_s$ in Sections {ref}`SecDemogFert`, {ref}`SecDemogMort`, and {ref}`SecDemogImm`. + +(SecDemogFert)= +## Fertility rates + + In `OG-ZAF`, we assume that the fertility rates for each age cohort $f_s$ are constant across time. However, this assumption is conceptually straightforward to relax. Our data for South Africa fertility rates by age come from United Nations fertility rate data for a country for some range of years (at least one year) and by age. The country_id=710 is for South Africa. These data come from the United Nations Data Portal API for UN population data (see https://population.un.org/dataportal/about/dataapi). The UN variable code for Population by 1-year age groups and sex is "47" and that for Fertility rates by age of mother (1-year) is "68". + +(SecDemogMort)= +## Mortality rates + + The mortality rates in our model $\rho_s$ are a one-period hazard rate and represent the probability of dying within one year, given that an household is alive at the beginning of period $s$. We assume that the mortality rates for each age cohort $\rho_s$ are constant across time. These data come from the United Nations Population Data Portal API for UN population data (see https://population.un.org/dataportal/about/dataapi). The model uses neonatal mortality rates (deaths per 1,000 live births, divided by 1,000) from World Bank World Development Indicators, available at https://data.worldbank.org/indicator/SH.DYN.NMRT + + The mortality rates are a population-weighted average of the male and female mortality rates reported by United Nations. The maximum age in years in our model is truncated to 100-years old. In addition, we constrain the mortality rate to be 1.0 or 100 percent at the maximum age of 100. + +(SecDemogImm)= +## Immigration rates + + Because of the difficulty in getting accurate immigration rate data by age, we estimate the immigration rates by age in our model $i_s$ as the average residual that reconciles the current-period population distribution with next period's population distribution given fertility rates $f_s$ and mortality rates $\rho_s$. Solving equations {eq}`EqPopLawofmotion` for the immigration rate $i_s$ gives the following characterization of the immigration rates in given population levels in any two consecutive periods $\omega_{s,t}$ and $\omega_{s,t+1}$ and the fertility rates $f_s$ and mortality rates $\rho_s$. + + ```{math} + :label: EqPopImmRates + i_1 &= \frac{\omega_{1,t+1} - (1 - \rho_0)\sum_{s=1}^{E+S}f_s\omega_{s,t}}{\omega_{1,t}}\quad\forall t \\ + i_{s+1} &= \frac{\omega_{s+1,t+1} - (1 - \rho_s)\omega_{s,t}}{\omega_{s+1,t}}\qquad\qquad\forall t\quad\text{and}\quad 1\leq s \leq E+S-1 + ``` + + + + We calculate our immigration rates for three different consecutive-year-periods of population distribution data (2010 through 2013). Our four years of population distribution by age data come from {cite}`Census:2015`. The immigration rates $i_s$ that we use in our model are the the residuals described in {eq}`EqPopImmRates` averaged across the three periods. {numref}`Figure %s ` shows the estimated immigration rates for $E+S=100$ and given the fertility rates from Section {ref}`SecDemogFert` and the mortality rates from Section {ref}`SecDemogMort`. + + At the end of Section {ref}`SecDemogPopSSTP`, we describe a small adjustment that we make to the immigration rates after a certain number of periods in order to make computation of the transition path equilibrium of the model compute more robustly. + +(SecDemogPopSSTP)= +## Population steady-state and transition path + + This model requires information about mortality rates $\rho_s$ in order to solve for the household's problem each period. It also requires the steady-state stationary population distribution $\bar{\omega}_{s}$ and population growth rate $\bar{g}_n$ as well as the full transition path of the stationary population distribution $\hat{\omega}_{s,t}$ and population grow rate $\tilde{g}_{n,t}$ from the current state to the steady-state. To solve for the steady-state and the transition path of the stationary population distribution, we write the stationary population dynamic equations {eq}`EqPopLawofmotionStat` and their matrix representation {eq}`EqPopLOMstatmat`. + + ```{math} + :label: EqPopLawofmotionStat + \hat{\omega}_{1,t+1} &= \frac{(1-\rho_0)\sum_{s=1}^{E+S} f_s\hat{\omega}_{s,t} + i_1\hat{\omega}_{1,t}}{1+\tilde{g}_{n,t+1}}\quad\forall t \\ + \hat{\omega}_{s+1,t+1} &= \frac{(1 - \rho_s)\hat{\omega}_{s,t} + i_{s+1}\hat{\omega}_{s+1,t}}{1+\tilde{g}_{n,t+1}}\qquad\quad\:\forall t\quad\text{and}\quad 1\leq s \leq E+S-1 + ``` + + ```{math} + :label: EqPopLOMstatmat + & \begin{bmatrix} + \hat{\omega}_{1,t+1} \\ \hat{\omega}_{2,t+1} \\ \hat{\omega}_{2,t+1} \\ \vdots \\ \hat{\omega}_{E+S-1,t+1} \\ \hat{\omega}_{E+S,t+1} + \end{bmatrix}= \frac{1}{1 + g_{n,t+1}} \times ... \\ + & \begin{bmatrix} + (1-\rho_0)f_1+i_1 & (1-\rho_0)f_2 & (1-\rho_0)f_3 & \cdots & (1-\rho_0)f_{E+S-1} & (1-\rho_0)f_{E+S} \\ + 1-\rho_1 & i_2 & 0 & \cdots & 0 & 0 \\ + 0 & 1-\rho_2 & i_3 & \cdots & 0 & 0 \\ + \vdots & \vdots & \vdots & \ddots & \vdots & \vdots \\ + 0 & 0 & 0 & \cdots & i_{E+S-1} & 0 \\ + 0 & 0 & 0 & \cdots & 1-\rho_{E+S-1} & i_{E+S} + \end{bmatrix} + \begin{bmatrix} + \hat{\omega}_{1,t} \\ \hat{\omega}_{2,t} \\ \hat{\omega}_{2,t} \\ \vdots \\ \hat{\omega}_{E+S-1,t} \\ \hat{\omega}_{E+S,t} + \end{bmatrix} + ``` + + We can write system {eq}`EqPopLOMstatmat` more simply in the following way. + + ```{math} + :label: EqPopLOMstatmat2 + \boldsymbol{\hat{\omega}}_{t+1} = \frac{1}{1+g_{n,t+1}}\boldsymbol{\Omega}\boldsymbol{\hat{\omega}}_t \quad\forall t + ``` + + The stationary steady-state population distribution $\boldsymbol{\bar{\omega}}$ is the eigenvector $\boldsymbol{\omega}$ with eigenvalue $(1+\bar{g}_n)$ of the matrix $\boldsymbol{\Omega}$ that satisfies the following version of {eq}`EqPopLOMstatmat2`. + + ```{math} + :label: EqPopLOMss + (1+\bar{g}_n)\boldsymbol{\bar{\omega}} = \boldsymbol{\Omega}\boldsymbol{\bar{\omega}} + ``` + + ```{admonition} Proposition + :class: tip + If the age $s=1$ immigration rate is $i_1>-(1-\rho_0)f_1$ and the other immigration rates are strictly positive $i_s>0$ for all $s\geq 2$ such that all elements of $\boldsymbol{\Omega}$ are nonnegative, then there exists a unique positive real eigenvector $\boldsymbol{\bar{\omega}}$ of the matrix $\boldsymbol{\Omega}$, and it is a stable equilibrium. + + **Proof:** + First, note that the matrix $\boldsymbol{\Omega}$ is square and non-negative. This is enough for a general version of the Perron-Frobenius Theorem to state that a positive real eigenvector exists with a positive real eigenvalue. This is not yet enough for uniqueness. For it to be unique by a version of the Perron-Fobenius Theorem, we need to know that the matrix is irreducible. This can be easily shown. The matrix is of the form + + $$ + \boldsymbol{\Omega} = + \begin{bmatrix} + * & * & * & \cdots & * & * & *\\ + * & * & 0 & \cdots & 0 & 0 & 0 \\ + 0 & * & * & \cdots & 0 & 0 & 0 \\ + \vdots & \vdots & \vdots & \ddots & \vdots & \vdots & \vdots \\ + 0 & 0 & 0 & \cdots & * & * & 0 \\ + 0 & 0 & 0 & \cdots & 0 & * & * + \end{bmatrix} + $$ + + Where each * is strictly positive. It is clear to see that taking powers of the matrix causes the sub-diagonal positive elements to be moved down a row and another row of positive entries is added at the top. None of these go to zero since the elements were all non-negative to begin with. + + $$ + \boldsymbol{\Omega}^2 = + \begin{bmatrix} + * & * & * & \cdots & * & * & *\\ + * & * & * & \cdots & * & * & * \\ + 0 & * & * & \cdots & 0 & 0 & 0 \\ + \vdots & \vdots & \vdots & \ddots & \vdots & \vdots & \vdots \\ + 0 & 0 & 0 & \cdots & * & * & 0 \\ + 0 & 0 & 0 & \cdots & 0 & * & * + \end{bmatrix}; ~~~ + \boldsymbol{\Omega}^{S+E-1} = + \begin{bmatrix} + * & * & * & \cdots & * & * & *\\ + * & * & * & \cdots & * & * & * \\ + * & * & * & \cdots & * & * & * \\ + \vdots & \vdots & \vdots & \ddots & \vdots & \vdots & \vdots \\ + * & * & * & \cdots & * & * & * \\ + 0 & 0 & 0 & \cdots & 0 & * & * + \end{bmatrix} + $$ + + $$ + \boldsymbol{\Omega}^{S+E} = + \begin{bmatrix} + * & * & * & \cdots & * & * & *\\ + * & * & * & \cdots & * & * & * \\ + * & * & * & \cdots & * & * & * \\ + \vdots & \vdots & \vdots & \ddots & \vdots & \vdots & \vdots \\ + * & * & * & \cdots & * & * & * \\ + * & * & * & \cdots & * & * & * + \end{bmatrix} + $$ + + Existence of an $m \in \mathbb{N}$ such that $\left(\bf\Omega^m\right)_{ij} \neq 0 ~~ ( > 0)$ is one of the definitions of an irreducible (primitive) matrix. It is equivalent to saying that the directed graph associated with the matrix is strongly connected. Now the Perron-Frobenius Theorem for irreducible matrices gives us that the equilibrium vector is unique. + + We also know from that theorem that the eigenvalue associated with the positive real eigenvector will be real and positive. This eigenvalue, $p$, is the Perron eigenvalue and it is the steady state population growth rate of the model. By the PF Theorem for irreducible matrices, $| \lambda_i | \leq p$ for all eigenvalues $\lambda_i$ and there will be exactly $h$ eigenvalues that are equal, where $h$ is the period of the matrix. Since our matrix $\bf\Omega$ is aperiodic, the steady state growth rate is the unique largest eigenvalue in magnitude. This implies that almost all initial vectors will converge to this eigenvector under iteration. + ``` + + For a full treatment and proof of the Perron-Frobenius Theorem, see {cite}`Suzumura:1983`. Because the population growth process is exogenous to the model, we calibrate it to annual age data for age years $s=1$ to $s=100$. + + {numref}`Figure %s ` shows the steady-state population distribution $\boldsymbol{\bar{\omega}}$ and the population distribution after 120 periods $\boldsymbol{\hat{\omega}}_{120}$. Although the two distributions look very close to each other, they are not exactly the same. + + ```{figure} ./images/OrigVsFixSSpop.png + --- + height: 500px + name: FigOrigVsFixSSpop + --- + Theoretical steady-state population distribution vs. population distribution at period $t=120$ + ``` + + Further, we find that the maximum absolute difference between the population levels $\hat{\omega}_{s,t}$ and $\hat{\omega}_{s,t+1}$ was $1.3852\times 10^{-5}$ after 160 periods. That is to say, that after 160 periods, given the estimated mortality, fertility, and immigration rates, the population has not achieved its steady state. For convergence in our solution method over a reasonable time horizon, we want the population to reach a stationary distribution after $T$ periods. To do this, we artificially impose that the population distribution in period $t=120$ is the steady-state. As can be seen from {numref}`Figure %s `, this assumption is not very restrictive. {numref}`Figure %s ` shows the change in immigration rates that would make the period $t=120$ population distribution equal be the steady-state. The maximum absolute difference between any two corresponding immigration rates in {numref}`Figure %s ` is 0.0028. + + ```{figure} ./images/OrigVsAdjImm.png + --- + height: 500px + name: FigImmRateChg + --- + Original immigration rates vs. adjusted immigration rates to make fixed steady-state population distribution + ``` + + The most recent year of population data come from {cite}`Census:2015` population estimates for both sexes for 2013. We those data and use the population transition matrix {eq}`EqPopLOMstatmat2` to age it to the current model year of 2015. We then use {eq}`EqPopLOMstatmat2` to generate the transition path of the population distribution over the time period of the model. {numref}`Figure %s ` shows the progression from the 2013 population data to the fixed steady-state at period $t=120$. The time path of the growth rate of the economically active population $\tilde{g}_{n,t}$ is shown in {numref}`Figure %s `. + + ```{figure} ./images/PopDistPath.png + --- + height: 500px + name: FigPopDistPath + --- + Exogenous stationary population distribution at periods along transition path + ``` + + ```{figure} ./images/GrowthPath.png + --- + height: 500px + name: FigGrowthPath + --- + Time path of the population growth rate $\tilde{g}_{n,t}$ + ``` + + +[^calibage_note]: Theoretically, the model works without loss of generality for $S\geq 3$. However, because we are calibrating the ages outside of the economy to be one-fourth of $S$ (e.g., ages 21 to 100 in the economy, and ages 1 to 20 outside of the economy), it is convenient for $S$ to be at least 4. +[^houseprob_note]: The parameter $\rho_s$ is the probability that a household of age $s$ dies before age $s+1$. diff --git a/docs/book/content/calibration/earnings.md b/docs/book/content/calibration/earnings.md new file mode 100644 index 0000000..a499bdb --- /dev/null +++ b/docs/book/content/calibration/earnings.md @@ -0,0 +1,212 @@ +--- +jupytext: + text_representation: + extension: .md + format_name: myst + format_version: '0.8' + jupytext_version: '1.4.1' +kernelspec: + display_name: Python 3 + language: python + name: ogzaf-dev +--- + +(Chap_LfEarn)= +# Lifetime Earnings Profiles + +Among households in `OG-ZAF`, we model both age heterogeneity and within-age ability heterogeneity. We use this ability or productivity heterogeneity to generate the income heterogeneity that we see in the data. + +Differences among workers' productivity in terms of ability is one of the key dimensions of heterogeneity to model in a micro-founded macroeconomy. In this chapter, we characterize this heterogeneity as deterministic lifetime productivity paths to which new cohorts of agents in the model are randomly assigned. In `OG-ZAF`, households' labor income comes from the equilibrium wage and the agent's endogenous quantity of labor supply. In this section, we augment the labor income expression with an individual productivity $e_{j,s}$, where $j$ is the index of the ability type or path of the individual and $s$ is the age of the individual with that ability path. + +```{math} +:label: EqLaborIncome + \text{labor income:}\quad x_{j,s,t}\equiv w_t e_{j,s}n_{j,s,t} \quad\forall j,t \quad\text{and}\quad E+1\leq s\leq E+S +``` + +In this specification, $w_t$ is an equilibrium wage representing a portion of labor income that is common to all workers. Individual quantity of labor supply is $n_{j,s,t}$, and $e_{j,s}$ represents a labor productivity factor that augments or diminishes the productivity of a worker's labor supply relative to average productivity. + +We calibrate deterministic ability paths such that each lifetime income group has a different life-cycle profile of earnings. The distribution of income and wealth are often focal components of macroeconomic models. These calibrations require the use of microeconomic data on household incomes, but this level of data is not readily available for South Africa from public sources or surveys. To overcome this, we start with the proposition that estimated productivity curves calibrated for the [OG-USA](https://pslmodels.github.io/OG-USA/content/calibration/earnings.html) model, generated from micro-level earnings data, represent a generalized relationship between age and lifetime income {cite}`DeBackerEtAl:2017`. As such, our objective is to generate the curves for the U.S. and then adjust their generalized shapes to produce those for South Africa. In other words, our strategic approach is to apply South Africa's national distribution of income by age to the estimated U.S. data on income by age, and then use these re-distributed data to re-estimate the earning profile curves and use them for South Africa. This is done in two ways (in this order): + 1. Adjustment by income ($J$): adjust the gaps between the U.S. curves to match South Africa's distribution between the $J$-income groups, using data from the World Inequality Database (WID); + 2. Adjustment by age ($S$): adjust the shape of all the U.S. curves to match South Africa's national distribution of income per capita for each age year, using data from the National Transfer Accounts database (NTA). + +The data for the U.S. come from the U.S. Internal Revenue Services's (IRS) Statistics of Income program (SOI) Continuous Work History Sample (CWHS). {cite}`DeBackerEtAl:2017` match the SOI data with Social Security Administration (SSA) data on age and Current Population Survey (CPS) data on hours in order to generate a non-top-coded measure of hourly wage. See {cite}`DeBackerRamnath:2017` for a detailed description of the methodology. + +```{figure} ./images/ability_log_2D_ZAF.png +--- +height: 350px +name: FigLogAbil +--- +Exogenous life cycle income ability paths $\log(e_{j,s})$ with $S=80$ and $J=7$ +``` + + + + +{numref}`Figure %s ` shows a calibration for $J=7$ deterministic lifetime ability paths $e_{j,s}$ corresponding to labor income percentiles $\boldsymbol{\lambda}=[0.25, 0.25, 0.20, 0.10, 0.10, 0.09, 0.01]$. Because there are few individuals above age 80 in the data, {cite}`DeBackerEtAl:2017` extrapolate these estimates for model ages 80-100 using an arctan function. + +We calibrate the model such that each lifetime income group has a different life-cycle profile of earnings. Since the distribution on income and wealth are key aspects of our model, we calibrate these processes so that we can represent earners in the top 1 percent of the distribution of lifetime income. + + +(SecLFearnLifInc)= +## Lifetime Income + + In our model, labor supply and savings, and thus lifetime income, are endogenous. We therefore define lifetime income as the present value of lifetime labor endowments and not the value of lifetime labor earnings. Note that our data are at the tax filing unit. We take this unit to be equivalent to a household. Because of differences in household structure (i.e., singles versus couples), our definition of lifetime labor income will be in per-adult terms. In particular, for filing units with a primary and secondary filer, our imputed wage represents the average hourly earnings between the two. When calculating lifetime income, we assign single- and couple-households the same labor endowment. This has the effect of making our lifetime income metric a per-adult metric; there is therefore not an over-representation of couple-households in the higher lifetime income groups simply because their time endowment is higher than for singles. We use the following approach to measure the lifetime income. + + First, since our panel data do not allow us to observe the complete life cycle of earnings for each household (because of sample attrition, death or the finite sample period of the data), we use an imputation to estimate wages in the years of the household's economic life for which they do not appear in the CWHS. To do this, we estimate the following equation, separately by household type (where household types are single male, single female, couple with male head, or couple with female head): + + ```{math} + :label: wage_step1 + ln(w_{i,t}) = \alpha_{i} + \beta_{1}age_{i,t} + \beta_{2}age_{i,t}^{2} + \beta_{3}*age_{i,t}^{3} + \varepsilon_{i,t} + ``` + + The parameter estimates, including the household fixed effects, from Equation {eq}`wage_step1` are shown in {numref}`TabWage_step1`. These estimates are then used to impute values for log wages in years of each households' economic life for which we do not have data. This creates a balanced panel of log wages of households with heads aged 21 to 80. The actual and imputed wage values are then used to calculate the net present value of lifetime labor endowments per adult for each household. Specifically, we define lifetime income for household $i$ as: + + ```{math} + :label: eqn:LI + LI_{i} = \sum_{t=21}^{80}\left(\frac{1}{1+r}\right)^{t-21}(w_{i,t}*4000) + ``` + + + ```{list-table} **Initial log wage regressions.** Source: CWHS data, 1991-2009. \*\* Significant at the 5-percent level ($p<0.05$). \*\*\* Significant at the 1-percent level ($p<0.01$). + :header-rows: 1 + :name: TabWage_step1 + * - Dependent variable + - Single male + - Single female + - Married male head + - Married female head + * - $Age$ + - 0.177\*\*\* + - 0.143\*\*\* + - 0.134\*\*\* + - 0.065\*\* + * - + - (0.006) + - (0.005) + - (0.004) + - (0.027) + * - $Age^2$ + - -0.003\*\*\* + - -0.002\*\*\* + - -0.002\*\*\* + - -0.000 + * - + - (0.000) + - (0.000) + - (0.000) + - (0.001) + * - $Age^3$ + - 0.000\*\*\* + - 0.000\*\*\* + - 0.000\*\*\* + - 0.000 + * - + - (0.000) + - (0.000) + - (0.000) + - (0.000) + * - $Constant$ + - -0.839\*\*\* + - -0.648\*\*\* + - -0.042 + - 1.004\*\*\* + * - + - (0.072) + - (0.070) + - (0.058) + - (0.376) + * - Adjusted $R^2$ + - -0.007 + - 0.011 + - -0.032 + - -0.324 + * - Observations + - 88,833 + - 96,670 + - 141,564 + - 6,314 + ``` + + + Note that households all have the same time endowment in each year (4000 hours). Thus, the amount of the time endowment scales the lifetime income up or down, but does not change the lifetime income of one household relative to another. This is not the case with the interest rate, $r$, which we fix at 4\%. Changes in the interest rate differentially impact the lifetime income calculation for different individuals because they may face different earnings profiles. For example, a higher interest rate would reduced the discounted present value of lifetime income for those individuals whose wage profiles peaked later in their economic life by a larger amount than it would reduce the discounted present value of lifetime income for individuals whose wage profiles peaked earlier. + + +## Profiles by Lifetime Income + + With observations of lifetime income for each household, we next sort households and find the percentile of the lifetime income distribution within which each household falls. With these percentiles, we create our lifetime income groupings. + ```{math} + :label: EqLfEarnLambda_j + \lambda_{j}=[0.25, 0.25, 0.2, 0.1, 0.1, 0.09, 0.01] + ``` + + That is, lifetime income group one includes those in below the 25th percentile, group two includes those from the 25th to the median, group three includes those from the median to the 70th percentile, group four includes those from the 70th to the 80th percentile, group 5 includes those from the 80th to 90th percentile, group 6 includes those from the 90th to 99th percentile, and group 7 consists of the top one percent in the lifetime income distribution. + + + To get a life-cycle profile of effective labor units for each group, we estimate the wage profile for each lifetime income group. We do this by estimating the following regression model Equation {eq}`` separately for each lifetime income group using data on actual (not imputed) wages: + + ```{math} + :label: EqWage_profile + ln(w_{j,t}) = \alpha_{j} + \beta_{1}age_{j,t} + \beta_{2}age_{j,t}^{2} + \beta_{3}*age_{j,t}^{3} + \varepsilon_{j,t} + ``` + + Life-cycle earnings profiles are implied by the parameters estimated from equation {eq}`EqWage_profile`, and are used to plot {numref}`Figure %s `. The arctan function used to extrapolate these estimates for model ages 80-100 takes the following form: + ```{math} + :label: EqLfEarnArctan + y = \left(\frac{-a}{\pi}\right)*arctan(bx+c)+\frac{a}{2} + ``` + where $x$ is age, and $a$, $b$, and $c$ are the parameters we search over for the best fit of the function to the following three criteria: 1) the value of the function should match the value of the data at age 80, 2) the slope of the arctan should match the slope of the data at age 80, and 3) the value of the function should match the value of the data at age 100 times a constant. This constant is 0.5 for all lifetime income groups, except for the second highest ability at 0.7 (otherwise, the second highest has a lower income than the third highest ability group in the last few years). + + +## Income at the very top + +In addition to lifecycle profiles of the seven percentile groups above, the model provides calibrations of income at the very top. This includes breaking out percentiles as fine as the top 0.01% of earners. The two alternative $\lambda$ vectors are $\lambda_{j}=[0.25, 0.25, 0.2, 0.1, 0.1, 0.09, 0.005, 0.004, 0.001]$ and $\lambda_{j}=[0.25, 0.25, 0.2, 0.1, 0.1, 0.09, 0.005, 0.004, 0.0009, 0.0001]$. + +Because we do not have U.S. panel data that allow us to observe such top percentile groups, we make the following assumptions in calibrating income at the very top. First, we assume the shape of the lifecycle profile of these top earners is the same as the top 1% overall. Second, we use 2018 estimates from the methodology of {cite}`PikettySaez:2003` to provide factors to scale the earnings process we estimate for groups inside the top 1%.[^PS_note] + + +```{list-table} **Incomes at the very top** Source: Piketty and Saez (2003) 2018 estimates. +:header-rows: 1 +:name: tab:top_incomes + +* - Income Percentile Range + - Ratio to Top 1% + - Mean Income +* - Top 1% + - $1,143,687 + - 1.000 +* - Top 1-0.5% + - $524,677 + - 0.459 +* - Top 0.5-0.1% + - $968,991 + - 0.847 +* - Top 0.1-0.01% + - $3,103,621 + - 2.714 +* - Top 0.1% + - $4,937,516 + - 4.317 +* - Top 0.01% + - $21,442,570 + - 18.749 +``` + + +[^PS_note]: These data are available from the website of Emmanuel Saez: [https://eml.berkeley.edu/~saez/](https://eml.berkeley.edu/~saez/). We use numbers from Table0, Panel B, "Income excluding realized capital gains." diff --git a/docs/book/content/calibration/exogenous_parameters.md b/docs/book/content/calibration/exogenous_parameters.md new file mode 100644 index 0000000..5e3e08a --- /dev/null +++ b/docs/book/content/calibration/exogenous_parameters.md @@ -0,0 +1,69 @@ +--- +jupytext: + text_representation: + extension: .md + format_name: myst + format_version: '0.8' + jupytext_version: '1.4.1' +kernelspec: + display_name: Python 3 + language: python + name: ogzaf-dev +--- + +(Chap_Exog)= +# Exogenous Parameters + + [TODO: This chapter needs heavy updating. Would be nice to do something similar to API chapter. But it is also nice to have references and descriptions as in the table below.] + + In this chapter, list the exogenous inputs to the model, options, and where the values come from (weak calibration vs. strong calibration). Point to the respective chapters for some of the inputs. Mention the code in [`default_parameters.json`](https://github.com/PSLmodels/OG-USA/blob/master/ogusa/default_parameters.json) and [`parameters.py`](https://github.com/PSLmodels/OG-USA/blob/master/ogusa/parameters.py). + + + + ```{list-table} **List of exogenous parameters and baseline calibration values.** + :header-rows: 1 + :name: TabExogVars + * - **Symbol** + - **Description** + - **Value** + * - $S$ + - Maximum periods in economically active household life + - 80 + * - $E$ + - Number of periods of youth economically outside the model + - $\text{round} \frac{S}{4}$=20 + * - $R$ + - Retirement age (period) + - $E + \text{round} (\frac{9}{16} S) = 65$ + * - $T_1$ + - Number of periods to steady state for initial time path guesses + - 160 + * - $T_2$ + - Maximum number of periods to steady state for nonsteady-state equilibrium + - 160 + * - $\nu$ + - Dampening parameter for TPI + - 0.4 + * - ${ \{ { \{ \omega_{s,0} \} }_{s=1}^{E+S} \}}_{t=0}^{T_2 + S - 1}$ + - Initial population distribution by age + - (see Chap. {ref}`Chap_Demog`) + * - ${ \{ f_s \}}_{s=1}^{E+S}$ + - Fertility rates by age + - (see Sec. {ref}`SecDemogFert`) + * - ${ \{ i_s \}}_{s=1}^{E+S}$ + - Immigration rates by age + - (see Sec. {ref}`SecDemogMort`) + * - ${ \{ \rho_s \}}_{s=0}^{E+S}$ + - Mortality rates by age + - (see Sec. {ref}`SecDemogImm`) + ``` diff --git a/docs/book/content/calibration/images/Age42_2017_scatters.png b/docs/book/content/calibration/images/Age42_2017_scatters.png new file mode 100644 index 0000000..d2fad94 Binary files /dev/null and b/docs/book/content/calibration/images/Age42_2017_scatters.png differ diff --git a/docs/book/content/calibration/images/Age42_2017_vsPred.png b/docs/book/content/calibration/images/Age42_2017_vsPred.png new file mode 100644 index 0000000..77afdc5 Binary files /dev/null and b/docs/book/content/calibration/images/Age42_2017_vsPred.png differ diff --git a/docs/book/content/calibration/images/Compare_ETR_functions.png b/docs/book/content/calibration/images/Compare_ETR_functions.png new file mode 100644 index 0000000..194bc98 Binary files /dev/null and b/docs/book/content/calibration/images/Compare_ETR_functions.png differ diff --git a/docs/book/content/calibration/images/EllipVsCFE_MargUtil.png b/docs/book/content/calibration/images/EllipVsCFE_MargUtil.png new file mode 100644 index 0000000..2dab774 Binary files /dev/null and b/docs/book/content/calibration/images/EllipVsCFE_MargUtil.png differ diff --git a/docs/book/content/calibration/images/GrowthPath.png b/docs/book/content/calibration/images/GrowthPath.png new file mode 100644 index 0000000..daac235 Binary files /dev/null and b/docs/book/content/calibration/images/GrowthPath.png differ diff --git a/docs/book/content/calibration/images/OrigVsAdjImm.png b/docs/book/content/calibration/images/OrigVsAdjImm.png new file mode 100644 index 0000000..1ed000e Binary files /dev/null and b/docs/book/content/calibration/images/OrigVsAdjImm.png differ diff --git a/docs/book/content/calibration/images/OrigVsFixSSpop.png b/docs/book/content/calibration/images/OrigVsFixSSpop.png new file mode 100644 index 0000000..29c3b4e Binary files /dev/null and b/docs/book/content/calibration/images/OrigVsFixSSpop.png differ diff --git a/docs/book/content/calibration/images/PopDistPath.png b/docs/book/content/calibration/images/PopDistPath.png new file mode 100644 index 0000000..457fa22 Binary files /dev/null and b/docs/book/content/calibration/images/PopDistPath.png differ diff --git a/docs/book/content/calibration/images/ability_log_2D.png b/docs/book/content/calibration/images/ability_log_2D.png new file mode 100644 index 0000000..2aa967b Binary files /dev/null and b/docs/book/content/calibration/images/ability_log_2D.png differ diff --git a/docs/book/content/calibration/images/ability_log_2D_ZAF.png b/docs/book/content/calibration/images/ability_log_2D_ZAF.png new file mode 100644 index 0000000..8259756 Binary files /dev/null and b/docs/book/content/calibration/images/ability_log_2D_ZAF.png differ diff --git a/docs/book/content/calibration/images/fert_rates.png b/docs/book/content/calibration/images/fert_rates.png new file mode 100644 index 0000000..46a0c4e Binary files /dev/null and b/docs/book/content/calibration/images/fert_rates.png differ diff --git a/docs/book/content/calibration/images/imm_rates_orig.png b/docs/book/content/calibration/images/imm_rates_orig.png new file mode 100644 index 0000000..2923a6f Binary files /dev/null and b/docs/book/content/calibration/images/imm_rates_orig.png differ diff --git a/docs/book/content/calibration/images/mort_rates.png b/docs/book/content/calibration/images/mort_rates.png new file mode 100644 index 0000000..6fee910 Binary files /dev/null and b/docs/book/content/calibration/images/mort_rates.png differ diff --git a/docs/book/content/calibration/macro.md b/docs/book/content/calibration/macro.md new file mode 100644 index 0000000..01e2239 --- /dev/null +++ b/docs/book/content/calibration/macro.md @@ -0,0 +1,65 @@ +(Chap_MacroCalib)= +# Calibration of Macroeconomic Parameters + +## Behavioral Assumptions + +### Elasticity of labor supply + +As discussed in the [OG-Core household theory documentation](https://pslmodels.github.io/OG-Core/content/theory/households.html), we use the elliptical disutility of labor function developed by {cite}`EvansPhillips:2017`. We then fit the parameters of the elliptical utility function to match the marginal disutility from a constant Frisch elasticity function. `OG-ZAF` users enter the constant Frisch elasticity as a parameter. {cite}`Peterman:2016` finds a range of Frisch elasticities estimated from microeconomic and macroeconomic data. These range from 0 to 4. Peterman makes the case that in lifecycle models without an extensive margin for employment the Frisch elasticity should be higher. For `OG-ZAF` we take a default value of 0.4 from {cite}`Altonji:1986`. + +### Intertemporal elasticity of substitution + +The default value for the intertemporal elasticity of substitution, $\sigma$, is taken from {cite}`ABMW:1999`. We set $\sigma=1.5$. + +### Rate of time preference + +We take our default value for the rate of time preference parameter, $\beta$ from {cite}`Carroll:2009`. We set the value to $\beta=0.96$ (on an annual basis). + + +## Economic Assumptions + +As the default rate of labor augmenting technological change, $g_y$, we use a value of 3%. The average annual growth rate in GDP per capita in South Africa between 1961 and 2021 is 0.7% per year. + +## Aggregate Production Function and Capital Accumulation + +The [OG-Core firm theory documentation](https://pslmodels.github.io/OG-Core/content/theory/firms.html) outlines the constant returns to scale, constant elasticity of substitution production function of the representative firm. This function has two parameters; the elasticity of substitution and capital's share of output. + +### Elasticity of substitution + +`OG-ZAF`'s default parameterization has an elasticity of substitution of $\varepsilon=1.0$, which implies a Cobb-Douglas production function. + +### Capital's share of output + +Here, we use a default value of $\gamma =0.61$. + +## Open Economy Parameters + +### Foreign holding of government debt in the initial period + +The path of foreign holding of domestic debt is endogenous, but the initial period stock of debt held by foreign investors is exogenous. We set this parameter, `initial_foreign_debt_ratio` to 0.26, consistent with data from the World Bank WDI. + + +### Foreign purchases of newly issued debt + +We set $\zeta_D = 0.26$. This is the average share of foreign purchases of newly issued government debt found from the World Bank WDI. + +### Foreign holdings of excess capital + +We set $\zeta_K = 0.9$. + + +## Government Debt, Spending and Transfers + +### Government Debt + +The path of government debt is endogenous. But the initial value is exogenous. To avoid converting between model units and dollars, we calibrate the initial debt to GDP ratio, rather than the dollar value of the debt. This is the model parameter $\alpha_D$. We compute this from the ratio of publicly held debt outstanding to GDP. Based on 2019 values, this gives us a ratio of 0.78. + +### Aggregate transfers + +Aggregate (non-Social Security) transfers to households are set as a share of GDP with the parameter $\alpha_T$. We exclude Social Security from transfers since it is modeled specifically. With this definition, the share of transfers to GDP in 2015 is 0.04 according to Federal Reserve Economic Data (FRED). + +### Government expenditures + +Government spending on goods and services are also set as a share of GDP with the parameter $\alpha_G$. We define government spending as: +
Government Spending = Total Outlays - Transfers - Net Interest on Debt - Social Security
+With this definition, the share of government expenditure to GDP is 0.27 based on FRED. diff --git a/docs/book/content/calibration/matching_lwi.md b/docs/book/content/calibration/matching_lwi.md new file mode 100644 index 0000000..d079392 --- /dev/null +++ b/docs/book/content/calibration/matching_lwi.md @@ -0,0 +1,4 @@ +(Chap_matchingLWI)= +# Matching Labor, Wealth, and Income Moments + +[TODO: We should include our calibration notes here for how we calibrate `OG-USA` to match the distribution of labor supply by age in the data, how we match wealth share moments, and how we match income share moments.] diff --git a/docs/book/content/calibration/tax_functions.md b/docs/book/content/calibration/tax_functions.md new file mode 100644 index 0000000..81dd294 --- /dev/null +++ b/docs/book/content/calibration/tax_functions.md @@ -0,0 +1,300 @@ +(Chap_TaxCalc)= +# Tax Functions + +The government is not an optimizing agent in `OG-USA`. The government levies taxes on households, provides transfers to households, levies taxes on firms, spends resources on public goods, and makes rule-based adjustments to stabilize the economy in the long-run. The government can run budget deficits or surpluses in a given year and must, therefore, be able to accumulate debt or savings. + +The government sector influences households through two terms in the household budget constraint {eq}`EqHHBC`---government transfers $TR_{t}$ and through the total tax liability function $T_{s,t}$, which can be decomposed into the effective tax rate times total income {eq}`EqTaxCalcLiabETR2`. In this chapter, we detail the household tax component of government activity $T_{s,t}$ in `OG-USA`, along with our method of incorporating detailed microsimulation data into a dynamic general equilibrium model. + +```{math} +:label: EqHHBC + c_{j,s,t} + b_{j,s+1,t+1} &= (1 + r_{hh,t})b_{j,s,t} + w_t e_{j,s} n_{j,s,t} + \\ + &\quad\quad\zeta_{j,s}\frac{BQ_t}{\lambda_j\omega_{s,t}} + \eta_{j,s,t}\frac{TR_{t}}{\lambda_j\omega_{s,t}} + ubi_{j,s,t} - T_{s,t} \\ + &\quad\forall j,t\quad\text{and}\quad s\geq E+1 \quad\text{where}\quad b_{j,E+1,t}=0\quad\forall j,t +``` + +Incorporating realistic tax and incentive detail into a general equilibrium model is notoriously difficult for two reasons. First, it is impossible in a dynamic general equilibrium model to capture all of the dimensions of heterogeneity on which the real-world tax rate depends. For example, a household's tax liability in reality depends on filing status, number of dependents, many types of income, and some characteristics correlated with age. A good heterogeneous agent DGE model tries to capture the most important dimensions of heterogeneity, and necessarily neglects the other dimensions. + +The second difficulty in modeling realistic tax and incentive detail is the need for good microeconomic data on the individuals who make up the economy from which to simulate behavioral responses and corresponding tax liabilities and tax rates. + +`OG-USA` follows the method of {cite}`DeBackerEtAl:2019` of generating detailed tax data on effective tax rates and marginal tax rates for a sample of tax filers along with their respective income and demographic characteristics and then using that data to estimate parametric tax functions that can be incorporated into `OG-USA`. + +(SecTaxCalcRateTheory)= +## Effective and Marginal Tax Rates + + Before going into more detail regarding how we handle these two difficulties in `OG-USA`, we need to define some functions and make some notation. For notational simplicity, we will use the variable $x$ to summarize labor income, and we will use the variable $y$ to summarize capital income. + + ```{math} + :label: EqTaxCalcLabInc + x_{j,s,t} \equiv w_{t}e_{j,s}n_{j,s,t} \quad\forall j, t \quad\text{and}\quad E+1\leq s\leq E+S + ``` + ```{math} + :label: EqTaxCalcCapInc + y_{j,s,t} \equiv r_{hh,t}b_{j,s,t} \quad\forall j, t \quad\text{and}\quad E+1\leq s\leq E+S + ``` + + We can express total tax liability $T_{s,t}$ from the household budget constraint {eq}`EqHHBC` as an effective tax rate multiplied by total income. + + ```{math} + :label: EqTaxCalcLiabETR2 + T_{s,t} = \tau^{etr}_{s,t}(x_{j,s,t}, y_{j,s,t})\left(x_{j,s,t} + y_{j,s,t}\right) + ``` + + Rearranging {eq}`EqTaxCalcLiabETR2` gives the definition of an effective tax rate ($ETR$) as total tax liability divided by unadjusted gross income, or rather, total tax liability as a percent of unadjusted gross income. + + A marginal tax rate ($MTR$) is defined as the change in total tax liability from a small change income. In `OG-USA`, we differentiate between the marginal tax rate on labor income ($MTRx$) and the marginal tax rate on capital income ($MTRy$). + + ```{math} + :label: EqTaxCalcMTRx + \tau^{mtrx} \equiv \frac{\partial T_{s,t}}{\partial w_t e_{j,s}n_{j,s,t}} = \frac{\partial T_{s,t}}{\partial x_{j,s,t}} \quad\forall j,t \quad\text{and}\quad E+1\leq s\leq E+S + ``` + ```{math} + :label: EqTaxCalcMTRy + \tau^{mtry} \equiv \frac{\partial T_{s,t}}{\partial r_{hh,t}b_{j,s,t}} = \frac{\partial T_{s,t}}{\partial y_{j,s,t}} \qquad\quad\forall j,t \quad\text{and}\quad E+1\leq s\leq E+S + ``` + + As we show in Section [Optimality Conditions](https://pslmodels.github.io/OG-Core/content/theory/households.html#optimality-conditions) of the Households chapter of the `OG-Core` repository documentation, the derivative of total tax liability with respect to labor supply $\frac{\partial T_{s,t}}{n_{j,s,t}}$ and the derivative of total tax liability next period with respect to savings $\frac{\partial T_{s+1,t+1}}{b_{j,s+1,t+1}}$ show up in the household Euler equations for labor supply and savings , respectively, in the `OG-Core` documentation. It is valuable to be able to express those marginal tax rates, for which we have no data, as marginal tax rates for which we do have data. The following two expressions show how the marginal tax rates of labor supply can be expressed as the marginal tax rate on labor income times the household-specific wage and how the marginal tax rate of savings can be expressed as the marginal tax rate of capital income times the interest rate. + + ```{math} + :label: EqMTRx_derive + \frac{\partial T_{s,t}}{\partial n_{j,s,t}} = \frac{\partial T_{s,t}}{\partial w_t e_{j,s}n_{j,s,t}}\frac{\partial w_{t}e_{j,s}n_{j,s,t}}{\partial n_{j,s,t}} = \frac{\partial T_{s,t}}{\partial w_{t}e_{j,s}n_{j,s,t}}w_t e_{j,s} = \tau^{mtrx}_{s,t}w_t e_{j,s} + ``` + + ```{math} + :label: EqMTRy_derive + \frac{\partial T_{s,t}}{\partial b_{j,s,t}} = \frac{\partial T_{s,t}}{\partial r_{hh,t}b_{j,s,t}}\frac{\partial r_{hh,t}b_{j,s,t}}{\partial b_{j,s,t}} = \frac{\partial T_{s,t}}{\partial r_{hh,t}b_{j,s,t}}r_{hh,t} = \tau^{mtry}_{s,t}r_{hh,t} + ``` + +(SecTaxCalcMicro)= +## Microeconomic Data + + For `OG-USA`, we use an open source microsimulation model called `Tax-Calculator` that uses microeconomic data on U.S. households from the Internal Revenue Service (IRS) Statistics of Income (SOI) Public Use File (PUF).[^taxcalc_note] For users that have not paid for access to the Public Use File (PUF), `Tax-Calculator` has an option to use a CPS matched dataset that is publicly available free of charge that has the same general properties as the PUF. + + `Tax-Calculator` starts with the underlying population microeconomic data, in which each observation is a filer with a population weight that renders the sample representative. It then processes the relevant income and demographic characteristics in order to calculate the tax liability of each individual, according to all the rich tax law of the United States tax code. `Tax-Calculator` can then calculate effective tax rates for all of these individuals, thereby creating a sample of how ETR's are related to other variables in our `OG-USA` model, such as total income $x + y$, labor income $x$, and capital income $y$. `Tax-Calculator` can also generate marginal tax rates by adding a dollar to each filer's income of a particular type and calculate how the filer's tax liability changes. This is a finite difference calculation of a derivative. + + {numref}`Figure %s ` shows a scatter plot of $ETR$'s for 43-year-olds in 2017 and unadjusted gross income $x + y$. It is clear that $ETR$ is positively related to income. It is also clear that a significant number of filers have a negative $ETR$. We will discuss in Section {ref}`SecTaxCalcFuncs` the functional form `OG-USA` uses to best capture the main characteristics of these ETR data. + + ```{figure} ./images/Compare_ETR_functions.png + --- + height: 500px + name: FigTaxCalcETRtotinc + --- + Plot of estimated $ETR$ functions: $t=2017$ and $s=43$ under current law + ``` + + {numref}`Figure %s ` shows 3D scatter plots of $ETR$, $MTRx$, and $MTRy$ (and a histogram of the data) with the labor income and capital income, separately, of each age-42 filer in 2017, generated by `Tax-Calculator`. This figure presents the main visual evidence for the functional form we use to fit tax functions to these data in Section {ref}`SecTaxCalcFuncs`. {numref}`Figure %s ` presents strong evidence that the tax rate---$ETR$, $MTRx$, and $MTRy$---is most accurately modeled as a function of labor income and capital income, separately: $\tau(x,y)$. + + + ```{figure} ./images/Age42_2017_scatters.png + --- + height: 500px + name: FigTaxCalc3Dtaxrates + --- + Scatter plot of ETR, MTRx, MTRy, and histogram as functions of labor income and capital income from microsimulation model: $t=2017$ and $s=42$ under current law. Note: Axes in the histogram in the lower-right panel have been switched relative to the other three figures in order to see the distribution more clearly. + ``` + +(SecTaxCalcFuncs)= +## Fitting Tax Functions + + In looking at the 2D scatter plot on effective tax rates as a function of total income in {numref}`Figure %s ` and the 3D scatter plots of $ETR$, $MTRx$, and $MTRy$ in {numref}`Figure %s `, it is clear that all of these rates exhibit negative exponential or logistic shape. This empirical regularity allows us to make an important and nonrestrictive assumption. We can fit parametric tax rate functions to these data that are constrained to be monotonically increasing in labor income and capital income. This assumption of monotonicity is computationally important as it preserves a convex budget set for each household, which is important for being able to solve many household lifetime problems over a large number of periods. + + +(SecTaxCalcFuncs_DEP)= +### Default Tax Functional Form + + For the default option, `OG-USA` follows the approach of {cite}`DeBackerEtAl:2019` in using the following functional form to estimate tax functions for each age $s=E+1, E+2, ... E+S$ in each time period $t$. This option can be manually selected by setting the parameter `tax_func_type="DEP"`. Alternative specifications are outlined in Section {ref}`SecTaxCalcFuncs_Alt` below. Equation {eq}`EqTaxCalcTaxFuncForm` is written as a generic tax rate, but we use this same functional form for $ETR$'s, $MTRx$'s, and $MTRy$'s. + ```{math} + :label: EqTaxCalcTaxFuncForm + \tau(x,y) = &\Bigl[\tau(x) + shift_x\Bigr]^\phi\Bigl[\tau(y) + shift_y\Bigr]^{1-\phi} + shift \\ + &\text{where}\quad \tau(x) \equiv (max_x - min_x)\left(\frac{Ax^2 + Bx}{Ax^2 + Bx + 1}\right) + min_x \\ + &\quad\text{and}\quad \tau(y) \equiv (max_y - min_y)\left(\frac{Cy^2 + Dy}{Cy^2 + Dy + 1}\right) + min_y \\ + &\text{where}\quad A,B,C,D,max_x,max_y,shift_x,shift_y > 0 \quad\text{and}\quad\phi\in[0,1] \\ + &\quad\text{and}\quad max_x > min_x \quad\text{and}\quad max_y > min_y + ``` + + The parameters values will, in general, differ across the different functions (effective and marginal rate functions) and by age, $s$, and tax year, $t$. We drop the subscripts for age and year from the above exposition for clarity. + + By assuming each tax function takes the same form, we are breaking the analytical link between the the effective tax rate function and the marginal rate functions. In particular, one could assume an effective tax rate function and then use the analytical derivative of that to find the marginal tax rate function. However, we've found it useful to separately estimate the marginal and average rate functions. One reason is that we want the tax functions to be able to capture policy changes that have differential effects on marginal and average rates. For example, a change in the standard deduction for tax payers would have a direct effect on their average tax rates. But it will have secondary effect on marginal rates as well, as some filers will find themselves in different tax brackets after the policy change. These are smaller and second order effects. When tax functions are are fit to the new policy, in this case a lower standard deduction, we want them to be able to represent this differential impact on the marginal and average tax rates. The second reason is related to the first. As the additional flexibility allows us to model specific aspects of tax policy more closely, it also allows us to better fit the parameterized tax functions to the data. + + The key building blocks of the functional form Equation {eq}`EqTaxCalcTaxFuncForm` are the $\tau(x)$ and $\tau(y)$ univariate functions. The ratio of polynomials in the $\tau(x)$ function $\frac{Ax^2 + Bx}{Ax^2 + Bx + 1}$ with positive coefficients $A,B>0$ and positive support for labor income $x>0$ creates a negative-exponential-shaped function that is bounded between 0 and 1, and the curvature is governed by the ratio of quadratic polynomials. The multiplicative scalar term $(max_x-min_x)$ on the ratio of polynomials and the addition of $min_x$ at the end of $\tau(x)$ expands the range of the univariate negative-exponential-shaped function to $\tau(x)\in[min_x, max_x]$. The $\tau(y)$ function is an analogous univariate negative-exponential-shaped function in capital income $y$, such that $\tau(y)\in[min_y,max_y]$. + + The respective $shift_x$ and $shift_y$ parameters in Equation {eq}`EqTaxCalcTaxFuncForm` are analogous to the additive constants in a Stone-Geary utility function. These constants ensure that the two sums $\tau(x) + shift_x$ and $\tau(y) + shift_y$ are both strictly positive. They allow for negative tax rates in the $\tau(\cdot)$ functions despite the requirement that the arguments inside the brackets be strictly positive. The general $shift$ parameter outside of the Cobb-Douglas brackets can then shift the tax rate function so that it can accommodate negative tax rates. The Cobb-Douglas share parameter $\phi\in[0,1]$ controls the shape of the function between the two univariate functions $\tau(x)$ and $\tau(y)$. + + + This functional form for tax rates delivers flexible parametric functions that can fit the tax rate data shown in {numref}`Figure %s ` as well as a wide variety of policy reforms. Further, these functional forms are monotonically increasing in both labor income $x$ and capital income $y$. This characteristic of monotonicity in $x$ and $y$ is essential for guaranteeing convex budget sets and thus uniqueness of solutions to the household Euler equations. The assumption of monotonicity does not appear to be a strong one when viewing the the tax rate data shown in {numref}`Figure %s `. While it does limit the potential tax systems to which one could apply our methodology, tax policies that do not satisfy this assumption would result in non-convex budget sets and thus require non-standard DGE model solutions methods and would not guarantee a unique equilibrium. The 12 parameters of our tax rate functional form from {eq}`EqTaxCalcTaxFuncForm` are summarized in {numref}`TabTaxCalcTfuncParams`. + + ```{list-table} **Description of tax rate function $\tau(x,y)$ parameters.** + :header-rows: 1 + :name: TabTaxCalcTfuncParams + * - Symbol + - Description + * - $A$ + - Coefficient on squared labor income term $x^2$ in $\tau(x)$ + * - $B$ + - Coefficient on labor income term $x$ in $\tau(x)$ + * - $C$ + - Coefficient on squared capital income term $y^2$ in $\tau(y)$ + * - $D$ + - Coefficient on capital income term *y* in $\tau(y)$ + * - $max_{x}$ + - Maximum tax rate on labor income $x$ given $y$ = 0 + * - $min_{x}$ + - Minimum tax rate on labor income $x$ given $y$ = 0 + * - $max_{y}$ + - Maximum tax rate on capital income $y$ given $x$ = 0 + * - $min_{y}$ + - Minimum tax rate on capital income $y$ given $x$ = 0 + * - $shift_{x}$ + - shifter  $> \|min_{x}\|$ ensures that $\tau(x,y)$ + $shift_{x} \geq 0$ despite potentially negative values for $\tau(x)$ + * - $shift_{y}$ + - shifter  $> \|min_{y}\|$ ensures that $\tau(x,y)$ + $shift_{y} \geq 0$ despite potentially negative values for $\tau(y)$ + * - $shift$ + - shifter (can be negative) allows for support of $\tau(x,y)$ to include negative tax rates + * - $\phi$ + - Cobb-Douglas share parameter between 0 and 1 + ``` + + ```{figure} ./images/Age42_2017_vsPred.png + --- + height: 500px + name: FigTaxCalc3DvsPred + --- + Estimated tax rate functions of ETR, MTRx, MTRy, and histogram as functions of labor income and capital income from microsimulation model: $t=2017$ and $s=42$ under current law. Note: Axes in the histogram in the lower-right panel have been switched relative to the other three figures in order to see the distribution. + ``` + + ```{list-table} **Estimated baseline current law tax rate function for $\tau_{s,t}(x,y)$ parameters for $s=42$, $t=2017$.** + :header-rows: 1 + :name: TabTaxCalcEst42 + * - Parameter + - $ETR$ + - $MTRx$ + - $MTRy$ + * - $A$ + - 6.28E-12 + - 3.43E-23 + - 4.32E-11 + * - $B$ + - 4.36E-05 + - 4.50E-04 + - 5.52E-05 + * - $C$ + - 1.04E-23 + - 9.81E-12 + - 5.62E-12 + * - $D$ + - 7.77E-09 + - 5.30E-08 + - 3.09E-06 + * - $max_{x}$ + - 0.80 + - 0.71 + - 0.44 + * - $min_{x}$ + - -0.14 + - -0.17 + - 0.00E+00 + * - $max_{y}$ + - 0.80 + - 0.80 + - 0.13 + * - $min_{y}$ + - -0.15 + - -0.42 + - 0.00E+00 + * - $shift_{x}$ + - 0.15 + - 0.18 + - 4.45E-03 + * - $shift_{y}$ + - 0.16 + - 0.43 + - 1.34E-03 + * - $shift$ + - -0.15 + - -0.42 + - 0.00E+00 + * - $\phi$ + - 0.84 + - 0.96 + - 0.86 + * - Obs. ($N$) + - 3,105 + - 3,105 + - 1,990 + * - SSE + - 9,122.68 + - 15,041.35 + - 7,756.54 + ``` + + Let $\boldsymbol{\theta}_{s,t}=(A,B,C,D,max_x,min_x,max_y,min_y,shift_x,shift_y,shift,\phi)$ be the full vector of 12 parameters of the tax function for a particular type of tax rate, age of filers, and year. We first directly specify $min_x$ as the minimum tax rate and $max_x$ as the maximum tax rate in the data for age-$s$ and period-$t$ individuals for capital income close to 0 ($\$0 0 \quad\text{and}\quad \phi\in[0,1] + ``` + + where $\tau_{i}$ is the tax rate for observation $i$ from the microsimulation output, $\tau_{s,t}(x_i,y_i|\tilde{\boldsymbol{\theta}}_{s,t},\bar{\boldsymbol{\theta}}_{s,t})$ is the predicted tax rate for filing-unit $i$ with $x_{i}$ labor income and $y_{i}$ capital income given parameters $\boldsymbol{\theta}_{s,t}$, and $w_{i}$ is the CPS sampling weight of this observation. The number $N$ is the total number of observations from the microsimulation output for age $s$ and year $t$. {numref}`Figure %s ` shows the typical fit of an estimated tax function $\tau_{s,t}\bigl(x,y|\hat{\boldsymbol{\theta}}_{s,t}\bigr)$ to the data. The data in {numref}`Figure %s ` are the same age $s=42$ and year $t=2017$ as the data {numref}`Figure %s `. + + The underlying data can limit the number of tax functions that can be estimated. For example, we use the age of the primary filer from the PUF-CPS match to be equivalent to the age of the DGE model household. The DGE model we use allows for individuals up to age 100, however the data contain few primary filers with age above age 80. Because we cannot reliably estimate tax functions for $s>80$, we apply the tax function estimates for 80 year-olds to those with model ages 81 to 100. In the case certain ages below age 80 have too few observations to enable precise estimation of the model parameters, we use a linear interpolation method to find the values for those ages $21\leq s <80$ that cannot be precisely estimated. [^interpolation_note] + + In `OG-USA`, we estimate the 12-parameter functional form {eq}`EqTaxCalcTaxFuncForm` using weighted nonlinear least squares to fit an effective tax rate function $(\tau^{etr}_{s,t})$, a marginal tax rate of labor income function $(\tau^{mtrx}_{s,t})$, and a marginal tax rate of capital income function $(\tau^{mtry}_{s,t})$ for each age $E+1\leq s\leq E+S$ and each of the first 10 years from the current period. [^param_note] That means we have to perform 2,400 estimations of 12 parameters each. {numref}`Figure %s ` shows the predicted surfaces for $\tau^{etr}_{s=42,t=2017}$, $\tau^{mtrx}_{s=42,t=2017}$, and $\tau^{mtry}_{s=42,t=2017}$ along with the underlying scatter plot data from which those functions were estimated. {numref}`TabTaxCalcEst42` shows the estimated values of those functional forms. + + The full set of estimated values are calculated using the [`get_tax_function_parameters`](https://github.com/PSLmodels/OG-USA/blob/master/ogusa/calibrate.py#L82) method of the [`Calibration`](https://github.com/PSLmodels/OG-USA/blob/master/ogusa/calibrate.py#L20) class of the [`OG-USA/ogusa/calibrate.py`](https://github.com/PSLmodels/OG-USA/blob/master/ogusa/calibrate.py#L20) module. And the estimated baseline values are stored in the [`ogusa_default_parameters.json`](https://github.com/PSLmodels/OG-USA/blob/master/ogusa/ogusa_default_parameters.json) file. + + +(SecTaxCalcFuncs_Alt)= +### Alternative Functional Forms + + In addition to the default option using tax functions of the form developed by {cite}`DeBackerEtAl:2019`, `OG-USA` also allows users to specify alternative tax functions. Three alternatives are offered: + + 1. Functions as in {cite}`DeBackerEtAl:2019`, but where $\tau^{etr}_{s,t}$, $\tau^{mtrx}_{s,t}$, and $\tau^{mtry}_{s,t}$ are functions of total income (i.e., $x+y$) and not labor and capital income separately. Users can select this option by setting the parameter `tax_func_type="DEP_totalinc"`. + + 2. Functions of the Gouveia and Strauss form {cite}`GouveiaStrauss:1994`: + + ```{math} + \tau = \phi_{0}(1 - (x+y)^{(\phi_{1}-1)}((x+y)^{-\phi1} + \phi_{2})^{(-1 - \phi_{1})/\phi_{1}}) + ``` + + Users can select this option by setting the parameter `tax_func_type="GS"`. The three parameters of this function ($\phi_{0}, \phi_{1}, \phi_{2}$) are estimated using the weighted sum of squares estimated described in Equation {eq}`EqTaxCalcThetaWSSQ`. + + 1. Linear tax functions (i.e., $\tau =$ a constant). Users can select this option by setting the parameter `tax_func_type="linear"`. The constant rate is found by taking the weighted average of the appropriate tax rate (effective tax rate, marginal tax rate on labor income, marginal tax rate on labor income) for each age and year, where the values are weighted by sampling weights and income. + + Among all of these tax functional forms, users can set the `age_specific` parameter to `False` if they wish to have one function for all ages $s$. In addition, for the functions based on {cite}`DeBackerEtAl:2019` (`tax_func_type="DEP"` or `tax_func_type="DEP_totinc"`), one can set `analytical_mtrs=True` if they wish to have the $\tau^{mtrx}_{s,t}$ and $\tau^{mtry}_{s,t}$ derived from the $\tau^{etr}_{s,t}$ functions. This provides theoretical consistency, but reduced fit of the functions (see {cite}`DeBackerEtAl:2019` for more details). + +(SecTaxCalcFactor)= +## Factor Transforming Income Units + + The tax functions $\tau^{etr}_{s,t}$, $\tau^{mtrx}_{s,t}$, and $\tau^{mtry}_{s,t}$ are estimated based on current U.S. tax filer reported incomes in dollars. However, the consumption units of the `OG-USA` model are not in the same units as the real-world U.S. incomes data. For this reason, we have to transform the income by a $factor$ so that it is in the same units as the income data on which the tax functions were estimated. + + The tax rate functions are each functions of capital income and labor income $\tau(x,y)$. In order to make the tax functions return accurate tax rates associated with the correct levels of income, we multiply the model income $x^m$ and $y^m$ by a $factor$ so that they are in the same units as the real-world U.S. income data $\tau(factor\times x^m, factor\times y^m)$. We define the $factor$ such that average steady-state household total income in the model times the $factor$ equals the U.S. data average total income. + + ```{math} + :label: EqTaxCalcFactor + factor\Biggl[\sum_{s=E+1}^{E+S}\sum_{j=1}^J\lambda_j\bar{\omega}_s\left(\bar{w}e_{j,s}\bar{n}_{j,s} + \bar{r}_{hh}\bar{b}_{j,s}\right)\Biggr] = \text{Avg. household income in data} + ``` + + We do not know the steady-state wage, interest rate, household labor supply, and savings *ex ante*. So the income $factor$ is an endogenous variable in the steady-state equilibrium computational solution. We hold the factor constant throughout the nonsteady-state equilibrium solution. + +(SecTaxCalcTfers)= +## Household Transfers + + Total transfers to households by the government in a given period $t$ is $TR_t$. The percent of those transfers given to all households of age $s$ and lifetime income group $j$ is $\eta_{j,s}$ such that $\sum_{s=E+1}^{E+S}\sum_{j=1}^J\eta_{j,s,t}=1$. `OG-USA` currently has the transfer distribution function set to distribute transfers uniformly among the population. + + ```{math} + :label: EqTaxCalcEtajs + \eta_{j,s,t} = \frac{\lambda_j\omega_{s,t}}{\tilde{N}_t} \quad\forall j,t \quad\text{and}\quad E+1\leq s\leq E+S + ``` + + However, this distribution function $\eta_{j,s,t}$ could also be modified to more accurately reflect the way transfers are distributed in the United States. + +(SecTaxfootnotes)= +## Footnotes + + [^taxcalc_note]:`Tax-Calculator` is available through an open source repository [https://github.com/PSLmodels/Tax-Calculator](https://github.com/PSLmodels/Tax-Calculator) as well as through a web application [Tax-Brain](https://compute.studio/PSLmodels/Tax-Brain/), hosted by [Compute.Studio](https://about.compute.studio/). Documentation for `Tax-Calculator` is available at [https://taxcalc.pslmodels.org/](https://taxcalc.pslmodels.org/). + + [^interpolation_note]: We use two criterion to determine whether the function should be interpolated. First, we require a minimum number of observations of filers of that age and in that tax year. Second, we require that that sum of squared errors meet a predefined threshold. + + [^param_note]: We assume that whatever parameters the tax functions have in year 10 persist forever. diff --git a/docs/book/content/citations.md b/docs/book/content/citations.md new file mode 100644 index 0000000..3919bff --- /dev/null +++ b/docs/book/content/citations.md @@ -0,0 +1,5 @@ +# Citations and use cases of OG-ZAF + +```{bibliography} ../citations.bib +:all : +``` diff --git a/docs/book/content/contributing/contributor_guide.md b/docs/book/content/contributing/contributor_guide.md new file mode 100644 index 0000000..e4ae088 --- /dev/null +++ b/docs/book/content/contributing/contributor_guide.md @@ -0,0 +1,168 @@ +(Chap_ContribGuide)= +# Contributor Guide + +The purpose of this guide is to provide the necessary background such that you can make improvements to the `OG-ZAF` and share them with others working on the model. + +`OG-ZAF` code is tracked by using [`Git`](https://help.github.com/articles/github-glossary/#git) version control software along with the [GitHub.com](https://github.com/) web platform for `Git` workflow and collaboration. Following the next steps will get you up and running and contributing to the model even if you've never used `Git` or other version control software. + +If you have already completed the {ref}`Sec_SetupPython` and {ref}`Sec_SetupGit` sections, you can skip to the {ref}`Sec_Workflow` section. + + +(Sec_SetupPython)= +## Setup Python + +`OG-ZAF` is written in the Python programming language. Download and install the free and most recent Anaconda distribution of Python and associated libraries from [Anaconda.com](https://www.anaconda.com/products/individual#Downloads).[^recent_python] You should do this even if you already have Python installed on your computer because the Anaconda distribution contains additional Python packages that are used by `OG-ZAF` (many of which are not included in other Python installations). You can install the Anaconda distribution without having administrative privileges on your computer and the Anaconda distribution will not interfere with any Python installation that came as part of your computer's operating system. + + +(Sec_SetupGit)= +## Setup Git + +1. Create a [GitHub](https://github.com/) user account. + +2. Install Git on your local machine by following steps 1-4 on [Git setup](https://help.github.com/articles/set-up-git/). + +3. Tell Git to remember your GitHub password by following steps 1-4 on [password setup](https://help.github.com/articles/caching-your-github-password-in-git/). + +4. Sign in to GitHub and create your own [remote](https://help.github.com/articles/github-glossary/#remote) [repository](https://help.github.com/articles/github-glossary/#repository) (repo) of `OG-ZAF` by clicking [Fork](https://help.github.com/articles/github-glossary/#fork) in the upper right corner of the [OG-ZAF GitHub repository page](https://github.com/EAPD-DRB/OG-ZAF). Select your username when asked "Where should we fork this repository?" + +5. From your command line, navigate to the directory on your computer where you would like your local repo to live. + +6. Create a local repo by entering at the command line the text, shown in the code blocks below after the `$` symbol.[^commandline_note] This step creates a directory called `OG-ZAF` in the directory that you specified in the prior step: + + ``` + $ git clone https://github.com/[github-username]/OG-ZAF.git + ``` + +7. From your command line or terminal, navigate to your local `OG-ZAF` directory. + +8. Make it easier to [push](https://help.github.com/articles/github-glossary/#pull) your local work to others and [pull](https://help.github.com/articles/github-glossary/#pull) others' work to your local machine by entering at the command line: + + ``` + $ cd OG-ZAF + OG-ZAF$ git remote add upstream https://github.com/EAPD-DRB/OG-ZAF.git + ``` + +9. Create a conda environment with all of the necessary packages to execute the source code. +The process of creating the `ogzaf-dev` conda environment can take up to 10 minutes. The pip install of the `OG-Core` dependency from [pypi.org](https://pypi.org/project/ogcore/). + + ``` + OG-ZAF$ conda env create + ``` + +10. The prior command will create a conda environment called `ogzaf-dev`. + Activate this environment as follows: + + ``` + OG-ZAF$ conda activate ogzaf-dev + ``` + +11. Once the environment is activated, install the `ogzaf` package in the `ogzaf-dev` environment with all its modules by executing the following `pip install` command. + + ``` + (ogzaf-dev) OG-ZAF$ pip install -e . + ``` + +If you have made it this far, you've successfully made a remote copy (a +fork) of the central `OG-ZAF` repo. That remote repo is hosted on GitHub.com at [https://github.com/EAPD-DRB/OG-ZAF](https://github.com/EAPD-DRB/OG-ZAF). You have also created a local repo (a [clone](https://help.github.com/articles/github-glossary/#clone)) that lives on your machine and only you can see; you will make your changes to +the OG-ZAF model by editing the files in the `OG-ZAF` +directory on your machine and then submitting those changes to your +local repo. As a new contributor, you will push your changes from your +local repo to your remote repo when you're ready to share that work +with the team. + +Don't be alarmed if the above paragraphs are confusing. The following +section introduces some standard Git practices and guides you through +the contribution process. + + +(Sec_Workflow)= +## Workflow + +(Sec_GitHubIssue)= +### Submitting a GitHub Issue + +GitHub "issues" are an excellent way to ask questions, include code examples, and tag specific GitHub users. + + +(Sec_GitHubPR)= +### Submitting a GitHub Pull Request + +The following text describes a typical workflow for changing +`OG-ZAF`. Different workflows may be necessary in some +situations, in which case other contributors are here to help. + +1. Before you edit the `OG-ZAF` source code on your machine, + make sure you have the latest version of the central OG-USA + repository by executing the following **four** Git commands: + + a. Tell Git to switch to the master branch in your local repo. + Navigate to your local `OG-ZAF` directory and enter the + following text at the command line: + + ``` + OG-ZAF$ git checkout master + ``` + + b. Download all of the content from the central `OG-ZAF` repo: + ``` + OG-ZAF$ git fetch upstream + ``` + c. Update your local master branch to contain the latest content of + the central master branch using [merge](https://help.github.com/articles/github-glossary/#merge). This step ensures that + you are working with the latest version of OG-ZAF: + ``` + OG-ZAF$ git merge upstream/master + ``` + d. Push the updated master branch in your local repo to your GitHub repo: + ``` + OG-ZAF$ git push origin master + ``` +2. Create a new [branch](https://help.github.com/articles/github-glossary/#branch) on your local machine. Think of your branches as a way to organize your projects. If you want to work on this documentation, for example, create a separate branch for that work. If you want to change an element of the OG-ZAF model, create a different branch for that project: + ``` + OG-ZAF$ git checkout -b [new-branch-name] + ``` +3. As you make changes, frequently check that your changes do not + introduce bugs or degrade the accuracy of the `OG-ZAF`. To do + this, run the following command from the command line from inside + the `/OG-ZAF/ogzaf/` directory: + ``` + OG-ZAF/ogzaf$ pytest -m "not needs_puf and not regression" + ``` + Note that running this full suite of tests may take close to 24 hours (depending on your hardware). To run the small set of tests that run on each pull request (and take about 40 minutes), use `pytest -m "not needs_puf and not local and not regression"`. If the tests do not pass, try to fix the issue by using the information provided by the error message. If this isn't possible or doesn't work, the core maintainers are here to help via a [GitHub Issue](https://github.com/EAPD-DRB/OG-ZAF/issues). + +4. Now you're ready to [commit](https://help.github.com/articles/github-glossary/#commit) your changes to your local repo using the code below. The first line of code tells `Git` to track a file. Use "git status" to find all the files you've edited, and "git add" each of the files that you'd like `Git` to track. As a rule, do not add large files. If you'd like to add a file that is larger than 25 MB, please contact the other contributors and ask how to proceed. The second line of code commits your changes to your local repo and allows you to create a commit message; this should be a short description of your changes. + + *Tip*: Committing often is a good idea as `Git` keeps a record of your changes. This means that you can always revert to a previous version of your work if you need to. Do this to commit: + ``` + OG-ZAF$ git add [filename] + OG-ZAF$ git commit -m "[description-of-your-commit]" + ``` + +5. Periodically, make sure that the branch you created in step 2 is in sync with the changes other contributors are making to the central master branch by fetching upstream and merging upstream/master into your branch: + ``` + OG-ZAF$ git fetch upstream + OG-ZAF$ git merge upstream/master + ``` + You may need to resolve conflicts that arise when another contributor changed the same section of code that you are changing. Feel free to ask other contributors for guidance if this happens to you. If you do need to fix a merge conflict, re-run the test suite afterwards (step 4.) + +6. When you are ready for other team members to review your code, make your final commit and push your local branch to your remote repo: + ``` + OG-ZAF$ git push origin [new-branch-name] + ``` +7. From the GitHub.com user interface, [open a pull request](https://help.github.com/articles/creating-a-pull-request/#creating-the-pull-request). + +8. When you open a GitHub pull request, a code coverage report will be automatically generated. If your branch adds new code that is not tested, the code coverage percent will decline and the number of untested statements ("misses" in the report) will increase. If this happens, you need to add to your branch one or more tests of your newly added code. Add tests so that the number of untested statements is the same as it is on the master branch. + + +(Sec_SimpleUsage)= +## Simple Usage + +[TODO: Include simple usage examples.] + + +(Sec_ContribFootnotes)= +## Footnotes + +[^recent_python]:The most recent version of Python from Anaconda is Python 3.10. `OG-ZAF` is currently tested to run on Python 3.8, 3.9, and 3.10. + +[^commandline_note]:The dollar sign is the end of the command prompt on a Mac. If you are using the Windows operating system, this is usually the right angle bracket (>). No matter the symbol, you don't need to type it (or anything to its left, which shows the current working directory) at the command line before you enter a command; the prompt symbol and preceding characters should already be there. diff --git a/docs/book/content/intro/intro.md b/docs/book/content/intro/intro.md new file mode 100644 index 0000000..15b9f78 --- /dev/null +++ b/docs/book/content/intro/intro.md @@ -0,0 +1,28 @@ +(Chap_Intro)= +# OG-ZAF + +[`OG-ZAF`](https://github.com/EAPD-DRB/OG-ZAF) is a package that provides code and data to calibrate an overlapping-generations (OG) model to the economy of South Africa (ZAF), the code of which is hosted on GitHub at https://github.com/EAPD-DRB/OG-ZAF. `OG-ZAF` uses as a dependency the [`OG-Core`](https://pslmodels.github.io/OG-Core/) package, which contains the core theory and logic of a general OG model. The `OG-ZAF` calibration package and the `OG-Core` theory and logic make the model that allows for dynamic general equilibrium analysis of federal fiscal policy in South Africa. The model output focuses on changes in macroeconomic aggregates (GDP, investment, consumption), wages, interest rates, and the stream of tax revenues over time. This documentation of the `OG-ZAF` package contains the following major sections, which are regularly updated. + +* Contributing to `OG-ZAF` +* `OG-ZAF` API +* Calibration +* References +* Citations of `OG-ZAF` + + +(Sec_CoreMaintainers)= +## Core Maintainers + +Marcelo LaFleur (GitHub handle [@SeaCelo](https://github.com/SeaCelo)) and [Richard W. Evans](https://sites.google.com/site/rickecon/) (GitHub handle [@rickecon](https://github.com/rickecon)) are the core maintainers of `OG-ZAF`. If you have questions about or contributions to the model or repository, please submit a GitHub "Issue" described in the {ref}`Sec_GitHubIssue` subsection or "Pull Request" as described in the {ref}`Sec_GitHubPR` subsection of the {ref}`Sec_Workflow` section of the `OG-ZAF` {ref}`Chap_ContribGuide`. + + +(Sec_Disclaimer)= +## Disclaimer + +The model is continuously under development. Users will be notified through [closed PR threads](https://github.com/EAPD-DRB/OG-ZAF/pulls?q=is%3Apr+is%3Aclosed) and through the [release notes](https://github.com/EAPD-DRB/OG-ZAF/releases) what changes have been implemented. The package will have released versions, which will be checked against existing code prior to release. Stay tuned for an upcoming release! + + +(Sec_CitingOGZAF)= +## Citing OG-ZAF + +`OG-ZAF` (Version #.#.#)[Source code], https://github.com/EAPD-DRB/OG-ZAF diff --git a/environment.yml b/environment.yml index 9fbafbd..c70e623 100644 --- a/environment.yml +++ b/environment.yml @@ -2,11 +2,10 @@ name: ogzaf-dev channels: - conda-forge dependencies: -- python>=3.7.7, <=3.9.12 # This restriction can be removed as soon as these packages support Python 3.10 -- mkl>=2020.2 -- numpy<=1.21.2 # This restriction can be removed as soon as Numba supports NumPy 1.22 -- psutil -- scipy>=1.5.0 +- python>=3.7.7 +- numpy +- numba>=0.54 +- scipy>=1.7.1 - pandas>=1.2.5 - matplotlib - dask>=2.30.0 @@ -24,11 +23,15 @@ dependencies: - coverage - requests - xlwt -- openpyxl - statsmodels - linearmodels - black +- jupyter +- ipython +- setuptools - pip: - jupyter-book>=0.9.1 + - openpyxl>=3.1.2 - pandas-datareader + - linecheck - ogcore diff --git a/examples/UN_examples/run_og_zaf_multiple_industry_cit_cut.py b/examples/UN_examples/run_og_zaf_multiple_industry_cit_cut.py new file mode 100644 index 0000000..0276381 --- /dev/null +++ b/examples/UN_examples/run_og_zaf_multiple_industry_cit_cut.py @@ -0,0 +1,148 @@ +# Need to fix references to Calculator, reform json, and substitute new tax +# function call +import multiprocessing +from distributed import Client +import os +import json +import time +import copy +import numpy as np + +# from taxcalc import Calculator +from ogzaf.calibrate import Calibration +from ogcore.parameters import Specifications +from ogcore import output_tables as ot +from ogcore import output_plots as op +from ogcore.execute import runner +from ogcore.utils import safe_read_pickle + + +def main(): + # Define parameters to use for multiprocessing + client = Client() + num_workers = min(multiprocessing.cpu_count(), 7) + print("Number of workers = ", num_workers) + + # Directories to save data + CUR_DIR = os.path.dirname(os.path.realpath(__file__)) + base_dir = os.path.join(CUR_DIR, "OG-ZAF-CIT_Example", "OUTPUT_BASELINE") + reform_dir = os.path.join(CUR_DIR, "OG-ZAF-CIT_Example", "OUTPUT_REFORM") + + """ + --------------------------------------------------------------------------- + Run baseline policy + --------------------------------------------------------------------------- + """ + # Set up baseline parameterization + p = Specifications( + baseline=True, + num_workers=num_workers, + baseline_dir=base_dir, + output_base=base_dir, + ) + # Update parameters for baseline from default json file + p.update_specifications( + json.load( + open( + os.path.join( + CUR_DIR, + "..", + "..", + "ogzaf", + "ogzaf_default_parameters.json", + ) + ) + ) + ) + # Update parameters from calibrate.py Calibration class + p.M = 4 + p.I = 5 + c = Calibration(p) + updated_params = c.get_dict() + p.update_specifications(updated_params) + updated_params_tax = { + "etr_params": [[[0.35]]], + "mtrx_params": [[[0.35]]], + "mtry_params": [[[0.35]]], + "cit_rate": [[0.28, 0.28, 0.28, 0.28]], + # order of industries is primary, energy, tertiary, secondary ex energy + "Z": [[0.5, 0.4, 1.7, 1.0]], + # Rick had good suggestion of using last successful run to find starting values and then keep churning. + "epsilon": [1.0, 1.0, 1.0, 1.0], + "gamma": [0.67, 0.50, 0.45, 0.53], + "gamma_g": [0.0, 0.0, 0.0, 0.0], + "alpha_c": c.alpha_c, + "io_matrix": c.io_matrix, + "tG1": 23, + "debt_ratio_ss": 2.0, + } + p.update_specifications(updated_params_tax) + + # Run model + start_time = time.time() + runner(p, time_path=True, client=client) + print("run time = ", time.time() - start_time) + + """ + --------------------------------------------------------------------------- + Run reform policy + --------------------------------------------------------------------------- + """ + + # create new Specifications object for reform simulation + p2 = copy.deepcopy(p) + p2.baseline = False + p2.output_base = reform_dir + + # additional parameters to change + updated_params_ref = { + "cit_rate": [[0.25, 0.25, 0.25, 0.25]], + "baseline_spending": True, + } + p2.update_specifications(updated_params_ref) + + # Run model + start_time = time.time() + runner(p2, time_path=True, client=client) + print("run time = ", time.time() - start_time) + client.close() + + """ + --------------------------------------------------------------------------- + Save some results of simulations + --------------------------------------------------------------------------- + """ + base_tpi = safe_read_pickle(os.path.join(base_dir, "TPI", "TPI_vars.pkl")) + base_params = safe_read_pickle(os.path.join(base_dir, "model_params.pkl")) + reform_tpi = safe_read_pickle( + os.path.join(reform_dir, "TPI", "TPI_vars.pkl") + ) + reform_params = safe_read_pickle( + os.path.join(reform_dir, "model_params.pkl") + ) + ans = ot.macro_table( + base_tpi, + base_params, + reform_tpi=reform_tpi, + reform_params=reform_params, + var_list=["Y", "C", "K", "L", "r", "w"], + output_type="pct_diff", + num_years=10, + start_year=base_params.start_year, + ) + + # create plots of output + op.plot_all( + base_dir, + reform_dir, + os.path.join(CUR_DIR, "OG-ZAF_CIT_multi_industry_plots"), + ) + + print("Percentage changes in aggregates:", ans) + # save percentage change output to csv file + ans.to_csv("ogzaf_CIT_multi_industry_output.csv") + + +if __name__ == "__main__": + # execute only if run as a script + main() diff --git a/examples/UN_examples/run_og_zaf_multiple_industry_govt_borrow_shock.py b/examples/UN_examples/run_og_zaf_multiple_industry_govt_borrow_shock.py new file mode 100644 index 0000000..3609db7 --- /dev/null +++ b/examples/UN_examples/run_og_zaf_multiple_industry_govt_borrow_shock.py @@ -0,0 +1,156 @@ +# Need to fix references to Calculator, reform json, and substitute new tax +# function call +import multiprocessing +from distributed import Client +import os +import json +import time +import copy +import numpy as np + +# from taxcalc import Calculator +from ogzaf.calibrate import Calibration +from ogcore.parameters import Specifications +from ogcore import output_tables as ot +from ogcore import output_plots as op +from ogcore.execute import runner +from ogcore.utils import safe_read_pickle + + +def main(): + # Define parameters to use for multiprocessing + client = Client() + num_workers = min(multiprocessing.cpu_count(), 7) + print("Number of workers = ", num_workers) + + # Directories to save data + CUR_DIR = os.path.dirname(os.path.realpath(__file__)) + base_dir = os.path.join( + CUR_DIR, "OG-ZAF-govt_borrow_shock_Example", "OUTPUT_BASELINE" + ) + reform_dir = os.path.join( + CUR_DIR, "OG-ZAF-govt_borrow_shock_Example", "OUTPUT_REFORM" + ) + + """ + --------------------------------------------------------------------------- + Run baseline policy + --------------------------------------------------------------------------- + """ + # Set up baseline parameterization + p = Specifications( + baseline=True, + num_workers=num_workers, + baseline_dir=base_dir, + output_base=base_dir, + ) + # Update parameters for baseline from default json file + p.update_specifications( + json.load( + open( + os.path.join( + CUR_DIR, + "..", + "..", + "ogzaf", + "ogzaf_default_parameters.json", + ) + ) + ) + ) + # Update parameters from calibrate.py Calibration class + p.M = 4 + p.I = 5 + c = Calibration(p) + updated_params = c.get_dict() + p.update_specifications(updated_params) + updated_params_tax = { + "etr_params": [[[0.35]]], + "mtrx_params": [[[0.35]]], + "mtry_params": [[[0.35]]], + "cit_rate": [[0.28, 0.28, 0.28, 0.28]], + # order of industries is primary, energy, tertiary, secondary ex energy + "Z": [[0.5, 0.4, 1.7, 1.0]], + # Rick had good suggestion of using last successful run to find starting values and then keep churning. + "epsilon": [1.0, 1.0, 1.0, 1.0], + "gamma": [0.67, 0.50, 0.45, 0.53], + "gamma_g": [0.0, 0.0, 0.0, 0.0], + "alpha_c": c.alpha_c, + "io_matrix": c.io_matrix, + "tG1": 23, + "debt_ratio_ss": 2.0, + } + p.update_specifications(updated_params_tax) + + # Run model + start_time = time.time() + runner(p, time_path=True, client=client) + print("run time = ", time.time() - start_time) + + """ + --------------------------------------------------------------------------- + Run reform policy + --------------------------------------------------------------------------- + """ + + # create new Specifications object for reform simulation + p2 = copy.deepcopy(p) + p2.baseline = False + p2.output_base = reform_dir + + # additional parameters to change + updated_params_ref = { + "r_gov_shift": [ + -0.0337662504 - 0.02, + -0.0337662504 - 0.02, + -0.0337662504, + ], + "baseline_spending": True, + } + p2.update_specifications(updated_params_ref) + + # Run model + start_time = time.time() + runner(p2, time_path=True, client=client) + print("run time = ", time.time() - start_time) + client.close() + + """ + --------------------------------------------------------------------------- + Save some results of simulations + --------------------------------------------------------------------------- + """ + base_tpi = safe_read_pickle(os.path.join(base_dir, "TPI", "TPI_vars.pkl")) + base_params = safe_read_pickle(os.path.join(base_dir, "model_params.pkl")) + reform_tpi = safe_read_pickle( + os.path.join(reform_dir, "TPI", "TPI_vars.pkl") + ) + reform_params = safe_read_pickle( + os.path.join(reform_dir, "model_params.pkl") + ) + ans = ot.macro_table( + base_tpi, + base_params, + reform_tpi=reform_tpi, + reform_params=reform_params, + var_list=["Y", "C", "K", "L", "r", "w"], + output_type="pct_diff", + num_years=10, + start_year=base_params.start_year, + ) + + # create plots of output + op.plot_all( + base_dir, + reform_dir, + os.path.join(CUR_DIR, "OG-ZAF_govt_borrow_shock_multi_industry_plots"), + ) + + print("Percentage changes in aggregates:", ans) + # save percentage change output to csv file + ans.to_csv("ogzaf_govt_borrow_shock_multi_industry_output.csv") + + +if __name__ == "__main__": + # execute only if run as a script + main() diff --git a/examples/UN_examples/run_og_zaf_multiple_industry_overall_z_shock.py b/examples/UN_examples/run_og_zaf_multiple_industry_overall_z_shock.py new file mode 100644 index 0000000..d8d52e2 --- /dev/null +++ b/examples/UN_examples/run_og_zaf_multiple_industry_overall_z_shock.py @@ -0,0 +1,157 @@ +# Need to fix references to Calculator, reform json, and substitute new tax +# function call +import multiprocessing +from distributed import Client +import os +import json +import time +import copy +import numpy as np + +# from taxcalc import Calculator +from ogzaf.calibrate import Calibration +from ogcore.parameters import Specifications +from ogcore import output_tables as ot +from ogcore import output_plots as op +from ogcore.execute import runner +from ogcore.utils import safe_read_pickle + + +def main(): + # Define parameters to use for multiprocessing + client = Client() + num_workers = min(multiprocessing.cpu_count(), 7) + print("Number of workers = ", num_workers) + + # Directories to save data + CUR_DIR = os.path.dirname(os.path.realpath(__file__)) + base_dir = os.path.join( + CUR_DIR, "OG-ZAF-overall_z_shock_Example", "OUTPUT_BASELINE" + ) + reform_dir = os.path.join( + CUR_DIR, "OG-ZAF-overall_z_shock_Example", "OUTPUT_REFORM" + ) + + """ + --------------------------------------------------------------------------- + Run baseline policy + --------------------------------------------------------------------------- + """ + # Set up baseline parameterization + p = Specifications( + baseline=True, + num_workers=num_workers, + baseline_dir=base_dir, + output_base=base_dir, + ) + # Update parameters for baseline from default json file + p.update_specifications( + json.load( + open( + os.path.join( + CUR_DIR, + "..", + "..", + "ogzaf", + "ogzaf_default_parameters.json", + ) + ) + ) + ) + # Update parameters from calibrate.py Calibration class + p.M = 4 + p.I = 5 + c = Calibration(p) + updated_params = c.get_dict() + p.update_specifications(updated_params) + updated_params_tax = { + "etr_params": [[[0.35]]], + "mtrx_params": [[[0.35]]], + "mtry_params": [[[0.35]]], + "cit_rate": [[0.28, 0.28, 0.28, 0.28]], + # order of industries is primary, energy, tertiary, secondary ex energy + "Z": [[0.5, 0.4, 1.7, 1.0]], + # Rick had good suggestion of using last successful run to find starting values and then keep churning. + "epsilon": [1.0, 1.0, 1.0, 1.0], + "gamma": [0.67, 0.50, 0.45, 0.53], + "gamma_g": [0.0, 0.0, 0.0, 0.0], + "alpha_c": c.alpha_c, + "io_matrix": c.io_matrix, + "tG1": 23, + "debt_ratio_ss": 2.0, + } + p.update_specifications(updated_params_tax) + + # Run model + start_time = time.time() + runner(p, time_path=True, client=client) + print("run time = ", time.time() - start_time) + + """ + --------------------------------------------------------------------------- + Run reform policy + --------------------------------------------------------------------------- + """ + + # create new Specifications object for reform simulation + p2 = copy.deepcopy(p) + p2.baseline = False + p2.output_base = reform_dir + + # additional parameters to change + updated_params_ref = { + "Z": [ + [0.5 * 0.95, 0.4 * 0.95, 1.7 * 0.95, 1.0 * 0.95], + [0.5 * 0.975, 0.4 * 0.975, 1.7 * 0.975, 1.0 * 0.975], + [0.5 * 0.99, 0.4 * 0.99, 1.7 * 0.99, 1.0 * 0.99], + [0.5, 0.4, 1.7, 1.0], + ], + "baseline_spending": True, + } + p2.update_specifications(updated_params_ref) + + # Run model + start_time = time.time() + runner(p2, time_path=True, client=client) + print("run time = ", time.time() - start_time) + client.close() + + """ + --------------------------------------------------------------------------- + Save some results of simulations + --------------------------------------------------------------------------- + """ + base_tpi = safe_read_pickle(os.path.join(base_dir, "TPI", "TPI_vars.pkl")) + base_params = safe_read_pickle(os.path.join(base_dir, "model_params.pkl")) + reform_tpi = safe_read_pickle( + os.path.join(reform_dir, "TPI", "TPI_vars.pkl") + ) + reform_params = safe_read_pickle( + os.path.join(reform_dir, "model_params.pkl") + ) + ans = ot.macro_table( + base_tpi, + base_params, + reform_tpi=reform_tpi, + reform_params=reform_params, + var_list=["Y", "C", "K", "L", "r", "w"], + output_type="pct_diff", + num_years=10, + start_year=base_params.start_year, + ) + + # create plots of output + op.plot_all( + base_dir, + reform_dir, + os.path.join(CUR_DIR, "OG-ZAF_overall_z_shock_multi_industry_plots"), + ) + + print("Percentage changes in aggregates:", ans) + # save percentage change output to csv file + ans.to_csv("ogzaf_overall_z_shock_multi_industry_output.csv") + + +if __name__ == "__main__": + # execute only if run as a script + main() diff --git a/examples/run_og_zaf.py b/examples/run_og_zaf.py index a128d18..3cbb7fb 100644 --- a/examples/run_og_zaf.py +++ b/examples/run_og_zaf.py @@ -5,6 +5,9 @@ import os import json import time +import copy +import numpy as np + # from taxcalc import Calculator from ogzaf.calibrate import Calibration from ogcore.parameters import Specifications @@ -47,6 +50,16 @@ def main(): ) ) ) + # Update parameters from calibrate.py Calibration class + c = Calibration(p) + updated_params = c.get_dict() + p.update_specifications(updated_params) + updated_params_tax = { + "etr_params": (np.ones((1, p.S, 1)) * 0.35).tolist(), + "mtrx_params": (np.ones((1, p.S, 1)) * 0.35).tolist(), + "mtry_params": (np.ones((1, p.S, 1)) * 0.35).tolist(), + } + p.update_specifications(updated_params_tax) # Run model start_time = time.time() @@ -60,27 +73,16 @@ def main(): """ # create new Specifications object for reform simulation - p2 = Specifications( - baseline=False, - num_workers=num_workers, - baseline_dir=base_dir, - output_base=reform_dir, - ) - # Update parameters for baseline from default json file - p2.update_specifications( - json.load( - open( - os.path.join( - CUR_DIR, "..", "ogzaf", "ogzaf_default_parameters.json" - ) - ) - ) - ) + p2 = copy.deepcopy(p) + p2.baseline = False + p2.output_base = reform_dir + # additional parameters to change - updated_params = { - "cit_rate": [[0.35]], + updated_params_ref = { + "cit_rate": [[0.30]], } - p2.update_specifications(updated_params) + p2.update_specifications(updated_params_ref) + # Run model start_time = time.time() runner(p2, time_path=True, client=client) @@ -118,7 +120,7 @@ def main(): print("Percentage changes in aggregates:", ans) # save percentage change output to csv file - ans.to_csv("ogind_example_output.csv") + ans.to_csv("ogzaf_example_output.csv") if __name__ == "__main__": diff --git a/examples/run_og_zaf_multiple_industry.py b/examples/run_og_zaf_multiple_industry.py new file mode 100644 index 0000000..a40199f --- /dev/null +++ b/examples/run_og_zaf_multiple_industry.py @@ -0,0 +1,146 @@ +# Need to fix references to Calculator, reform json, and substitute new tax +# function call +import multiprocessing +from distributed import Client +import os +import json +import time +import copy +import numpy as np + +# from taxcalc import Calculator +from ogzaf.calibrate import Calibration +from ogcore.parameters import Specifications +from ogcore import output_tables as ot +from ogcore import output_plots as op +from ogcore.execute import runner +from ogcore.utils import safe_read_pickle + + +def main(): + # Define parameters to use for multiprocessing + client = Client() + num_workers = min(multiprocessing.cpu_count(), 7) + print("Number of workers = ", num_workers) + + # Directories to save data + CUR_DIR = os.path.dirname(os.path.realpath(__file__)) + base_dir = os.path.join(CUR_DIR, "OG-ZAF-CIT_Example", "OUTPUT_BASELINE") + reform_dir = os.path.join(CUR_DIR, "OG-ZAF-CIT_Example", "OUTPUT_REFORM") + + """ + --------------------------------------------------------------------------- + Run baseline policy + --------------------------------------------------------------------------- + """ + # Set up baseline parameterization + p = Specifications( + baseline=True, + num_workers=num_workers, + baseline_dir=base_dir, + output_base=base_dir, + ) + # Update parameters for baseline from default json file + p.update_specifications( + json.load( + open( + os.path.join( + CUR_DIR, "..", "ogzaf", "ogzaf_default_parameters.json" + ) + ) + ) + ) + # Update parameters from calibrate.py Calibration class + p.M = 4 + p.I = 5 + c = Calibration(p) + updated_params = c.get_dict() + p.update_specifications(updated_params) + updated_params_tax = { + "etr_params": [[[0.35]]], + "mtrx_params": [[[0.35]]], + "mtry_params": [[[0.35]]], + "cit_rate": [[0.28, 0.28, 0.28, 0.28]], + # order of industries is primary, energy, tertiary, secondary ex energy + "Z": [[0.5, 0.4, 1.7, 1.0]], + # Rick had good suggestion of using last successful run to find starting values and then keep churning. + "epsilon": [1.0, 1.0, 1.0, 1.0], + "gamma": [0.67, 0.50, 0.45, 0.53], + "gamma_g": [0.0, 0.0, 0.0, 0.0], + "alpha_c": c.alpha_c, + "io_matrix": c.io_matrix, + } + p.update_specifications(updated_params_tax) + + # Run model + start_time = time.time() + runner(p, time_path=True, client=client) + print("run time = ", time.time() - start_time) + + """ + --------------------------------------------------------------------------- + Run reform policy + --------------------------------------------------------------------------- + """ + + # create new Specifications object for reform simulation + p2 = copy.deepcopy(p) + p2.baseline = False + p2.output_base = reform_dir + + # additional parameters to change + updated_params_ref = { + "cit_rate": [ + [0.28, 0.28, 0.28, 0.28], + [0.28, 0.28, 0.28, 0.28], + [0.27, 0.27, 0.27, 0.27], + ], + "baseline_spending": True, + } + p2.update_specifications(updated_params_ref) + + # Run model + start_time = time.time() + runner(p2, time_path=True, client=client) + print("run time = ", time.time() - start_time) + client.close() + + """ + --------------------------------------------------------------------------- + Save some results of simulations + --------------------------------------------------------------------------- + """ + base_tpi = safe_read_pickle(os.path.join(base_dir, "TPI", "TPI_vars.pkl")) + base_params = safe_read_pickle(os.path.join(base_dir, "model_params.pkl")) + reform_tpi = safe_read_pickle( + os.path.join(reform_dir, "TPI", "TPI_vars.pkl") + ) + reform_params = safe_read_pickle( + os.path.join(reform_dir, "model_params.pkl") + ) + ans = ot.macro_table( + base_tpi, + base_params, + reform_tpi=reform_tpi, + reform_params=reform_params, + var_list=["Y", "C", "K", "L", "r", "w"], + output_type="pct_diff", + num_years=10, + start_year=base_params.start_year, + ) + + # create plots of output + op.plot_all( + base_dir, + reform_dir, + os.path.join(CUR_DIR, "OG-ZAF_CIT_multi_industry_plots"), + ) + + print("Percentage changes in aggregates:", ans) + # save percentage change output to csv file + ans.to_csv("ogzaf_CIT_multi_industry_output.csv") + + +if __name__ == "__main__": + # execute only if run as a script + main() diff --git a/ogzaf/calibrate.py b/ogzaf/calibrate.py index 9c90c01..dbbf156 100644 --- a/ogzaf/calibrate.py +++ b/ogzaf/calibrate.py @@ -1,8 +1,8 @@ -from ogzaf import demographics -from ogzaf import macro_params +from ogzaf import macro_params, income +from ogzaf import input_output as io import os import numpy as np -from ogcore import txfunc +from ogcore import txfunc, demographics from ogcore.utils import safe_read_pickle, mkdirs import pkg_resources @@ -23,7 +23,6 @@ def __init__( client=None, num_workers=1, ): - self.estimate_tax_functions = estimate_tax_functions self.estimate_beta = estimate_beta self.estimate_chi_n = estimate_chi_n @@ -46,6 +45,22 @@ def __init__( # Macro estimation self.macro_params = macro_params.get_macro_params() + # io matrix and alpha_c + if p.I > 1: # no need if just one consumption good + alpha_c_dict = io.get_alpha_c() + # check that model dimensions are consistent with alpha_c + assert p.I == len(list(alpha_c_dict.keys())) + self.alpha_c = np.array(list(alpha_c_dict.values())) + else: + self.alpha_c = np.array([1.0]) + if p.M > 1: # no need if just one production good + io_df = io.get_io_matrix() + # check that model dimensions are consistent with io_matrix + assert p.M == len(list(io_df.keys())) + self.io_matrix = io_df.values + else: + self.io_matrix = np.array([[1.0]]) + # eta estimation # self.eta = transfer_distribution.get_transfer_matrix() @@ -53,20 +68,36 @@ def __init__( # self.zeta = bequest_transmission.get_bequest_matrix() # demographics - # self.demographic_params = demographics.get_pop_objs( - # p.E, p.S, p.T, 1, 100, p.start_year - # ) + self.demographic_params = demographics.get_pop_objs( + p.E, + p.S, + p.T, + 0, + 99, + country_id="710", # UN code for ZAF + initial_data_year=p.start_year - 1, + final_data_year=p.start_year, + ) # demographics for 80 period lives (needed for getting e below) - # demog80 = demographics.get_pop_objs(20, 80, p.T, 1, 100, p.start_year) + demog80 = demographics.get_pop_objs( + 20, + 80, + p.T, + 0, + 99, + country_id="710", # UN code for ZAF + initial_data_year=p.start_year - 1, + final_data_year=p.start_year, + ) # earnings profiles - # self.e = income.get_e_interp( - # p.S, - # self.demographic_params["omega_SS"], - # demog80["omega_SS"], - # p.lambdas, - # plot=False, - # ) + self.e = income.get_e_interp( + p.S, + self.demographic_params["omega_SS"], + demog80["omega_SS"], + p.lambdas, + plot=False, + ) # Tax Functions def get_tax_function_parameters( @@ -371,7 +402,9 @@ def get_dict(self): # dict["eta"] = self.eta # dict["zeta"] = self.zeta dict.update(self.macro_params) - # dict["e"] = self.e - # dict.update(self.demographic_params) + dict["e"] = self.e + dict["alpha_c"] = self.alpha_c + dict["io_matrix"] = self.io_matrix + dict.update(self.demographic_params) return dict diff --git a/ogzaf/constants.py b/ogzaf/constants.py index 9620516..bf93934 100644 --- a/ogzaf/constants.py +++ b/ogzaf/constants.py @@ -1,5 +1,3 @@ -import taxcalc - SHOW_RUNTIME = False # Flag to display RuntimeWarnings when run model REFORM_DIR = "OUTPUT_REFORM" @@ -295,63 +293,195 @@ # 'starting_age', 'ending_age', 'constant_demographics', # 'constant_rates', 'zero_taxes' -PSID_NOMINAL_VARS = [ - "head_labor_inc", - "spouse_labor_inc", - "head_whether_receive_afdc_prior_year", - "spouse_whether_receive_afdc_prior_year", - "head_ssi_prior_year", - "spouse_ssi_prior_year", - "other_familyunit_ssi_prior_year", - "head_other_welfare_prior_year", - "spouse_other_welfare_prior_year", - "other_familyunit_other_welfare_prior_year", - "head_unemp_inc_prior_year", - "spouse_unemp_inc_prior_year", - "other_familyunit_unemp_inc_prior_year", - "head_workers_comp_prior_year", - "spouse_workers_comp_prior_year", - "other_familyunit_workers_comp_prior_year", - "head_vet_pen_prior_year", - "spouse_vet_pen_prior_year", - "other_familyunit_vet_pen_prior_year", - "head_spouse_taxable_inc", - "other_familyunit_taxable_inc", - "head_spouse_tax_table", - "food_out_expend", - "food_in_expend", - "other_familyunit_asset_inc", - "head_dividend_inc", - "spouse_dividend_inc", - "head_interest_inc", - "spouse_interest_inc", - "head_rent_inc", - "spouse_rent_inc", - "family_total_inc", - "head_and_spouse_transfer_income", - "other_familyunit_transfer_income", - "head_socsec_income", - "spouse_socsec_income", - "other_familyunit_socsec_income", - "head_noncorp_bus_asset_income", - "spouse_noncorp_bus_asset_income", - "head_noncorp_bus_labor_income", - "spouse_noncorp_bus_labor_income", - "noncorp_businc", - "net_wealth", - "inheritance", - "value_inheritance_1st", - "value_inheritance_2nd", - "value_inheritance_3rd", -] -PSID_CONSTANT_VARS = [ - "head_race", - "head_gender", - "singlemale", - "singlefemale", - "marriedmalehead", - "marriedfemalehead", - "ID1968", - "pernum", -] +""" +Create dictionaries to map micro categories to broad groups +""" +CONS_DICT = { + "Food": [ + "cagri", + "clani", + "cfore", + "cfish", + "cwatr", + "cmeat", + "cpfis", + "cvege", + "cfrui", + "cfats", + "cdair", + "cgrai", + "cstar", + "cafee", + "cbake", + "csuga", + "cconf", + "cpast", + "cofoo", + "calcb", + "csftd", + "ctoba", + ], + "Energy and extraction": [ + "ccoal", + "cmore", + "comin", + "celcg", + ], + "Non-durables": [ + "ctexf", + "ctexm", + "ccarp", + "cotex", + "cknit", + "cwear", + "cleat", + "cfoot", + "cwood", + "cpapp", + "cprnt", + "cpetr", + "cbchm", + "cfert", + "cpain", + "cphar", + "csoap", + "coche", + "ctyre", + "corub", + "cplas", + "cglas", + "ccera", + "cclay", + "ccmnt", + "cconc", + "conmp", + "cfurn", + "cjewl", + "comnf", + "cwast", + ], + "Durables": [ + "cirst", + "cnfme", + "cstrm", + "ctank", + "cofbm", + "cengt", + "cpump", + "cgear", + "clift", + "cgenm", + "cspcm", + "cdoma", + "coffm", + "celcm", + "crdtv", + "cmeda", + "cmtvp", + "cship", + "crail", + "cairc", + "coteq", + "ccnst", + ], + "Services": [ + "ccsrv", + "ctrad", + "cacco", + "ccats", + "cptrp", + "cftrp", + "ctrps", + "cpost", + "celcd", + "cwatd", + "cfins", + "cinsp", + "cofin", + "creal", + "crent", + "crsea", + "clacc", + "cobus", + "ctelc", + "csupp", + "cmnfs", + "cpuba", + "ceduc", + "cheal", + "cosrv", + ], +} +PROD_DICT = { + "Primary": [ + "aagri", + "afore", + "afish", + "acoal", + "agold", + "amore", + "aomin", + ], + "Energy": [ + "aelcg", + ], + "Tertiary": [ + "awtrd", + "artrd", + "amtvs", + "aacct", + "altrp", + "awtrp", + "aatrp", + "atrps", + "apost", + "afins", + "ainsp", + "aofin", + "areal", + "arent", + "acomp", + "arsea", + "aobus", + "apuba", + "aeduc", + "aheal", + "awast", + "amorg", + "arecr", + "aoact", + "anobs", + ], + "Secondary Ex Energy": [ + "afood", + "abevt", + "aweav", + "aknit", + "aleat", + "afoot", + "awood", + "apapr", + "aprnt", + "apetr", + "abchm", + "aochm", + "arubb", + "aplas", + "aglss", + "anmmi", + "abisc", + "anfme", + "afabm", + "amach", + "aemch", + "ardtv", + "amopt", + "amtvp", + "aotrp", + "afurn", + "aomnf", + "awatd", + "acnst", + ], +} diff --git a/ogzaf/data/demographic/india_pop_data.csv b/ogzaf/data/demographic/india_pop_data.csv deleted file mode 100644 index 580c5c7..0000000 --- a/ogzaf/data/demographic/india_pop_data.csv +++ /dev/null @@ -1,105 +0,0 @@ -Age,2001,2011 -0,17318425,20311234 -1,18549624,21755197 -2,19658985,23056268 -3,20441527,23974041 -4,20216424,23710038 -5,22215205,26054230 -6,21874157,25654245 -7,21168498,24826640 -8,22994652,26968373 -9,19973077,23424638 -10,26050332,30552107 -11,21095430,24740946 -12,23769657,27877307 -13,20702986,24280683 -14,21536442,25258169 -15,22083235,25899454 -16,20968681,24592293 -17,18091127,21217467 -18,23838585,27958147 -19,17785554,20859088 -20,24626937,28882735 -21,17035121,19978972 -22,20061401,23528225 -23,16331754,19154055 -24,16950933,19880235 -25,23676522,27768078 -26,17118702,20076997 -27,14247270,16709350 -28,18866656,22127016 -29,12561724,14732524 -30,25919809,30399029 -31,11786425,13823245 -32,15018233,17613544 -33,10883718,12764541 -34,11932524,13994592 -35,24234629,28422631 -36,13495543,15827717 -37,9969410,11692231 -38,14897339,17471758 -39,9998500,11726347 -40,24325317,28528992 -41,9863394,11567894 -42,11409038,13380642 -43,7958959,9334352 -44,8207831,9626232 -45,20355341,23872961 -46,9372935,10992678 -47,6779694,7951297 -48,9933054,11649592 -49,6694856,7851799 -50,18299989,21462422 -51,6723989,7885966 -52,6595407,7735164 -53,4767001,5590790 -54,5452638,6394912 -55,13307814,15607546 -56,6029820,7071837 -57,3921371,4599026 -58,5728916,6718934 -59,4390062,5148712 -60,15186329,17810689 -61,5138777,6026813 -62,4898611,5745143 -63,3405584,3994105 -64,3484754,4086957 -65,11103542,13022352 -66,3847122,4511946 -67,2221230,2605082 -68,3025529,3548373 -69,2359486,2767230 -70,9367480,10986280 -71,2629737,3084183 -72,1919260,2250929 -73,1165180,1366536 -74,1296811,1520914 -75,4069153,4772346 -76,1421485,1667133 -77,712006,835048 -78,910987,1068415 -79,758486,889561 -80,3227237,3784938 -81,876370,1027816 -82,514826,603793 -83,324166,380185 -84,361096,423497 -85,1078433,1264798 -86,381901,447898 -87,188787,221411 -88,200919,235640 -89,181973,213420 -90,710321,833072 -91,220238,258297 -92,130934,153561 -93,80687,94630 -94,91212,106974 -95,232377,272534 -96,102547,120268 -97,60891,71413 -98,89112,104511 -99,55057,64571 -100,516518,605778 -,, -,, -,, \ No newline at end of file diff --git a/ogzaf/data/demographic/un_zaf_deaths.csv b/ogzaf/data/demographic/un_zaf_deaths.csv new file mode 100644 index 0000000..ab800bd --- /dev/null +++ b/ogzaf/data/demographic/un_zaf_deaths.csv @@ -0,0 +1,305 @@ +sep =| +LocationId|Location|Iso3|Iso2|LocationTypeId|IndicatorId|Indicator|IndicatorDisplayName|SourceId|Source|Revision|VariantId|Variant|VariantShortName|VariantLabel|TimeId|TimeLabel|TimeMid|CategoryId|Category|EstimateTypeId|EstimateType|EstimateMethodId|EstimateMethod|SexId|Sex|AgeId|AgeLabel|AgeStart|AgeEnd|AgeMid|Value +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|42|0|0|1|1|17285 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|42|0|0|1|1|14186 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|42|0|0|1|1|31472 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|89|1|1|1|1|2295 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|89|1|1|1|1|2043 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|89|1|1|1|1|4338 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|117|2|2|2|2|1532 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|117|2|2|2|2|1295 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|117|2|2|2|2|2826 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|126|3|3|3|3|1005 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|126|3|3|3|3|809 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|126|3|3|3|3|1814 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|135|4|4|4|4|677 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|135|4|4|4|4|523 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|135|4|4|4|4|1200 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|90|5|5|5|5|494 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|90|5|5|5|5|370 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|90|5|5|5|5|864 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|152|6|6|6|6|392 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|152|6|6|6|6|289 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|152|6|6|6|6|681 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|161|7|7|7|7|328 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|161|7|7|7|7|242 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|161|7|7|7|7|571 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|170|8|8|8|8|295 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|170|8|8|8|8|220 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|170|8|8|8|8|515 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|179|9|9|9|9|286 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|179|9|9|9|9|218 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|179|9|9|9|9|504 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|91|10|10|10|10|295 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|91|10|10|10|10|230 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|91|10|10|10|10|524 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|109|11|11|11|11|324 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|109|11|11|11|11|257 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|109|11|11|11|11|581 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|110|12|12|12|12|385 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|110|12|12|12|12|306 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|110|12|12|12|12|691 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|111|13|13|13|13|452 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|111|13|13|13|13|355 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|111|13|13|13|13|807 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|112|14|14|14|14|524 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|112|14|14|14|14|399 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|112|14|14|14|14|923 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|86|15|15|15|15|623 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|86|15|15|15|15|456 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|86|15|15|15|15|1079 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|113|16|16|16|16|731 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|113|16|16|16|16|509 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|113|16|16|16|16|1240 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|114|17|17|17|17|839 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|114|17|17|17|17|559 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|114|17|17|17|17|1398 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|115|18|18|18|18|952 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|115|18|18|18|18|608 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|115|18|18|18|18|1559 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|116|19|19|19|19|1090 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|116|19|19|19|19|673 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|116|19|19|19|19|1763 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|92|20|20|20|20|1250 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|92|20|20|20|20|750 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|92|20|20|20|20|2000 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|118|21|21|21|21|1431 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|118|21|21|21|21|840 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|118|21|21|21|21|2271 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|119|22|22|22|22|1627 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|119|22|22|22|22|936 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|119|22|22|22|22|2563 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|120|23|23|23|23|1830 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|120|23|23|23|23|1032 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|120|23|23|23|23|2862 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|121|24|24|24|24|2104 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|121|24|24|24|24|1165 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|121|24|24|24|24|3269 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|93|25|25|25|25|2418 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|93|25|25|25|25|1321 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|93|25|25|25|25|3739 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|122|26|26|26|26|2683 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|122|26|26|26|26|1458 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|122|26|26|26|26|4140 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|123|27|27|27|27|2905 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|123|27|27|27|27|1592 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|123|27|27|27|27|4497 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|124|28|28|28|28|3104 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|124|28|28|28|28|1740 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|124|28|28|28|28|4844 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|125|29|29|29|29|3292 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|125|29|29|29|29|1907 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|125|29|29|29|29|5199 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|94|30|30|30|30|3436 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|94|30|30|30|30|2069 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|94|30|30|30|30|5506 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|127|31|31|31|31|3587 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|127|31|31|31|31|2243 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|127|31|31|31|31|5830 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|128|32|32|32|32|3773 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|128|32|32|32|32|2434 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|128|32|32|32|32|6207 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|129|33|33|33|33|3931 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|129|33|33|33|33|2586 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|129|33|33|33|33|6517 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|130|34|34|34|34|4032 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|130|34|34|34|34|2681 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|130|34|34|34|34|6713 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|95|35|35|35|35|4023 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|95|35|35|35|35|2706 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|95|35|35|35|35|6729 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|131|36|36|36|36|3996 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|131|36|36|36|36|2717 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|131|36|36|36|36|6713 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|132|37|37|37|37|3998 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|132|37|37|37|37|2747 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|132|37|37|37|37|6745 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|133|38|38|38|38|3991 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|133|38|38|38|38|2794 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|133|38|38|38|38|6785 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|134|39|39|39|39|3941 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|134|39|39|39|39|2832 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|134|39|39|39|39|6773 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|96|40|40|40|40|3858 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|96|40|40|40|40|2847 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|96|40|40|40|40|6705 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|136|41|41|41|41|3734 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|136|41|41|41|41|2820 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|136|41|41|41|41|6554 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|137|42|42|42|42|3535 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|137|42|42|42|42|2720 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|137|42|42|42|42|6255 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|138|43|43|43|43|3352 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|138|43|43|43|43|2601 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|138|43|43|43|43|5952 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|139|44|44|44|44|3252 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|139|44|44|44|44|2525 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|139|44|44|44|44|5777 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|97|45|45|45|45|3291 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|97|45|45|45|45|2540 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|97|45|45|45|45|5831 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|140|46|46|46|46|3496 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|140|46|46|46|46|2673 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|140|46|46|46|46|6169 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|141|47|47|47|47|3848 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|141|47|47|47|47|2929 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|141|47|47|47|47|6777 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|142|48|48|48|48|4302 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|142|48|48|48|48|3280 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|142|48|48|48|48|7582 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|143|49|49|49|49|4782 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|143|49|49|49|49|3679 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|143|49|49|49|49|8461 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|98|50|50|50|50|5216 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|98|50|50|50|50|4080 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|98|50|50|50|50|9296 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|144|51|51|51|51|5597 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|144|51|51|51|51|4467 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|144|51|51|51|51|10064 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|145|52|52|52|52|5938 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|145|52|52|52|52|4837 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|145|52|52|52|52|10775 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|146|53|53|53|53|6223 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|146|53|53|53|53|5172 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|146|53|53|53|53|11394 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|147|54|54|54|54|6438 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|147|54|54|54|54|5456 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|147|54|54|54|54|11894 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|99|55|55|55|55|6568 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|99|55|55|55|55|5668 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|99|55|55|55|55|12236 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|148|56|56|56|56|6614 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|148|56|56|56|56|5808 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|148|56|56|56|56|12422 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|149|57|57|57|57|6553 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|149|57|57|57|57|5870 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|149|57|57|57|57|12423 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|150|58|58|58|58|6380 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|150|58|58|58|58|5856 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|150|58|58|58|58|12236 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|151|59|59|59|59|6110 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|151|59|59|59|59|5786 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|151|59|59|59|59|11896 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|87|60|60|60|60|5753 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|87|60|60|60|60|5666 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|87|60|60|60|60|11419 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|153|61|61|61|61|5397 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|153|61|61|61|61|5554 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|153|61|61|61|61|10951 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|154|62|62|62|62|5183 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|154|62|62|62|62|5550 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|154|62|62|62|62|10732 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|155|63|63|63|63|5100 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|155|63|63|63|63|5641 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|155|63|63|63|63|10741 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|156|64|64|64|64|5109 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|156|64|64|64|64|5794 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|156|64|64|64|64|10903 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|100|65|65|65|65|5245 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|100|65|65|65|65|6045 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|100|65|65|65|65|11290 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|157|66|66|66|66|5490 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|157|66|66|66|66|6395 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|157|66|66|66|66|11886 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|158|67|67|67|67|5771 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|158|67|67|67|67|6800 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|158|67|67|67|67|12571 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|159|68|68|68|68|5959 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|159|68|68|68|68|7136 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|159|68|68|68|68|13095 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|160|69|69|69|69|6005 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|160|69|69|69|69|7336 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|160|69|69|69|69|13342 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|101|70|70|70|70|5957 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|101|70|70|70|70|7431 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|101|70|70|70|70|13389 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|162|71|71|71|71|5867 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|162|71|71|71|71|7464 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|162|71|71|71|71|13330 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|163|72|72|72|72|5919 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|163|72|72|72|72|7621 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|163|72|72|72|72|13540 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|164|73|73|73|73|5873 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|164|73|73|73|73|7688 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|164|73|73|73|73|13561 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|165|74|74|74|74|5617 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|165|74|74|74|74|7524 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|165|74|74|74|74|13140 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|102|75|75|75|75|5398 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|102|75|75|75|75|7343 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|102|75|75|75|75|12741 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|166|76|76|76|76|5128 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|166|76|76|76|76|7113 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|166|76|76|76|76|12242 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|167|77|77|77|77|4804 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|167|77|77|77|77|6851 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|167|77|77|77|77|11655 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|168|78|78|78|78|4484 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|168|78|78|78|78|6573 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|168|78|78|78|78|11058 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|169|79|79|79|79|4158 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|169|79|79|79|79|6272 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|169|79|79|79|79|10431 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|88|80|80|80|80|3832 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|88|80|80|80|80|5951 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|88|80|80|80|80|9783 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|171|81|81|81|81|3519 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|171|81|81|81|81|5622 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|171|81|81|81|81|9141 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|172|82|82|82|82|3222 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|172|82|82|82|82|5301 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|172|82|82|82|82|8523 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|173|83|83|83|83|2929 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|173|83|83|83|83|4980 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|173|83|83|83|83|7909 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|174|84|84|84|84|2631 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|174|84|84|84|84|4668 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|174|84|84|84|84|7300 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|103|85|85|85|85|2345 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|103|85|85|85|85|4391 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|103|85|85|85|85|6735 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|175|86|86|86|86|2082 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|175|86|86|86|86|4157 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|175|86|86|86|86|6239 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|176|87|87|87|87|1858 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|176|87|87|87|87|3976 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|176|87|87|87|87|5835 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|177|88|88|88|88|1672 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|177|88|88|88|88|3826 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|177|88|88|88|88|5498 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|178|89|89|89|89|1509 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|178|89|89|89|89|3646 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|178|89|89|89|89|5155 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|104|90|90|90|90|1367 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|104|90|90|90|90|3411 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|104|90|90|90|90|4778 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|180|91|91|91|91|1234 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|180|91|91|91|91|3133 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|180|91|91|91|91|4368 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|181|92|92|92|92|1106 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|181|92|92|92|92|2821 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|181|92|92|92|92|3927 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|182|93|93|93|93|976 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|182|93|93|93|93|2478 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|182|93|93|93|93|3455 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|183|94|94|94|94|847 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|183|94|94|94|94|2115 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|183|94|94|94|94|2962 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|105|95|95|95|95|704 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|105|95|95|95|95|1684 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|105|95|95|95|95|2389 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|184|96|96|96|96|581 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|184|96|96|96|96|1294 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|184|96|96|96|96|1875 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|185|97|97|97|97|468 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|185|97|97|97|97|963 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|185|97|97|97|97|1431 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|186|98|98|98|98|365 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|186|98|98|98|98|700 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|186|98|98|98|98|1065 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|187|99|99|99|99|276 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|187|99|99|99|99|500 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|187|99|99|99|99|776 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|59|100+|100||100|619 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|59|100+|100||100|1066 +710|South Africa|ZAF|ZA|4|69|Deaths by 1-year age groups and sex|Deaths by age and sex - complete|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|59|100+|100||100|1685 diff --git a/ogzaf/data/demographic/un_zaf_fert.csv b/ogzaf/data/demographic/un_zaf_fert.csv new file mode 100644 index 0000000..0807083 --- /dev/null +++ b/ogzaf/data/demographic/un_zaf_fert.csv @@ -0,0 +1,37 @@ +sep =| +LocationId|Location|Iso3|Iso2|LocationTypeId|IndicatorId|Indicator|IndicatorDisplayName|SourceId|Source|Revision|VariantId|Variant|VariantShortName|VariantLabel|TimeId|TimeLabel|TimeMid|CategoryId|Category|EstimateTypeId|EstimateType|EstimateMethodId|EstimateMethod|SexId|Sex|AgeId|AgeLabel|AgeStart|AgeEnd|AgeMid|Value +710|South Africa|ZAF|ZA|4|68|Fertility rates by age of mother (1-year)|Fertility rates by age of mother (1-year)|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|86|15|15|15|15|21.878999999999998 +710|South Africa|ZAF|ZA|4|68|Fertility rates by age of mother (1-year)|Fertility rates by age of mother (1-year)|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|113|16|16|16|16|42.045 +710|South Africa|ZAF|ZA|4|68|Fertility rates by age of mother (1-year)|Fertility rates by age of mother (1-year)|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|114|17|17|17|17|64.04700000000001 +710|South Africa|ZAF|ZA|4|68|Fertility rates by age of mother (1-year)|Fertility rates by age of mother (1-year)|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|115|18|18|18|18|85.43199999999999 +710|South Africa|ZAF|ZA|4|68|Fertility rates by age of mother (1-year)|Fertility rates by age of mother (1-year)|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|116|19|19|19|19|100.193 +710|South Africa|ZAF|ZA|4|68|Fertility rates by age of mother (1-year)|Fertility rates by age of mother (1-year)|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|92|20|20|20|20|108.079 +710|South Africa|ZAF|ZA|4|68|Fertility rates by age of mother (1-year)|Fertility rates by age of mother (1-year)|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|118|21|21|21|21|114.93100000000001 +710|South Africa|ZAF|ZA|4|68|Fertility rates by age of mother (1-year)|Fertility rates by age of mother (1-year)|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|119|22|22|22|22|121.399 +710|South Africa|ZAF|ZA|4|68|Fertility rates by age of mother (1-year)|Fertility rates by age of mother (1-year)|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|120|23|23|23|23|127.20400000000001 +710|South Africa|ZAF|ZA|4|68|Fertility rates by age of mother (1-year)|Fertility rates by age of mother (1-year)|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|121|24|24|24|24|131.829 +710|South Africa|ZAF|ZA|4|68|Fertility rates by age of mother (1-year)|Fertility rates by age of mother (1-year)|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|93|25|25|25|25|133.354 +710|South Africa|ZAF|ZA|4|68|Fertility rates by age of mother (1-year)|Fertility rates by age of mother (1-year)|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|122|26|26|26|26|131.515 +710|South Africa|ZAF|ZA|4|68|Fertility rates by age of mother (1-year)|Fertility rates by age of mother (1-year)|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|123|27|27|27|27|127.04899999999999 +710|South Africa|ZAF|ZA|4|68|Fertility rates by age of mother (1-year)|Fertility rates by age of mother (1-year)|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|124|28|28|28|28|120.27 +710|South Africa|ZAF|ZA|4|68|Fertility rates by age of mother (1-year)|Fertility rates by age of mother (1-year)|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|125|29|29|29|29|111.818 +710|South Africa|ZAF|ZA|4|68|Fertility rates by age of mother (1-year)|Fertility rates by age of mother (1-year)|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|94|30|30|30|30|102.228 +710|South Africa|ZAF|ZA|4|68|Fertility rates by age of mother (1-year)|Fertility rates by age of mother (1-year)|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|127|31|31|31|31|93.21000000000001 +710|South Africa|ZAF|ZA|4|68|Fertility rates by age of mother (1-year)|Fertility rates by age of mother (1-year)|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|128|32|32|32|32|85.282 +710|South Africa|ZAF|ZA|4|68|Fertility rates by age of mother (1-year)|Fertility rates by age of mother (1-year)|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|129|33|33|33|33|79.021 +710|South Africa|ZAF|ZA|4|68|Fertility rates by age of mother (1-year)|Fertility rates by age of mother (1-year)|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|130|34|34|34|34|74.12299999999999 +710|South Africa|ZAF|ZA|4|68|Fertility rates by age of mother (1-year)|Fertility rates by age of mother (1-year)|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|95|35|35|35|35|68.68 +710|South Africa|ZAF|ZA|4|68|Fertility rates by age of mother (1-year)|Fertility rates by age of mother (1-year)|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|131|36|36|36|36|62.369 +710|South Africa|ZAF|ZA|4|68|Fertility rates by age of mother (1-year)|Fertility rates by age of mother (1-year)|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|132|37|37|37|37|55.68 +710|South Africa|ZAF|ZA|4|68|Fertility rates by age of mother (1-year)|Fertility rates by age of mother (1-year)|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|133|38|38|38|38|48.614 +710|South Africa|ZAF|ZA|4|68|Fertility rates by age of mother (1-year)|Fertility rates by age of mother (1-year)|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|134|39|39|39|39|40.681000000000004 +710|South Africa|ZAF|ZA|4|68|Fertility rates by age of mother (1-year)|Fertility rates by age of mother (1-year)|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|96|40|40|40|40|32.102 +710|South Africa|ZAF|ZA|4|68|Fertility rates by age of mother (1-year)|Fertility rates by age of mother (1-year)|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|136|41|41|41|41|24.349 +710|South Africa|ZAF|ZA|4|68|Fertility rates by age of mother (1-year)|Fertility rates by age of mother (1-year)|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|137|42|42|42|42|17.778 +710|South Africa|ZAF|ZA|4|68|Fertility rates by age of mother (1-year)|Fertility rates by age of mother (1-year)|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|138|43|43|43|43|12.498000000000001 +710|South Africa|ZAF|ZA|4|68|Fertility rates by age of mother (1-year)|Fertility rates by age of mother (1-year)|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|139|44|44|44|44|8.529 +710|South Africa|ZAF|ZA|4|68|Fertility rates by age of mother (1-year)|Fertility rates by age of mother (1-year)|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|97|45|45|45|45|5.842 +710|South Africa|ZAF|ZA|4|68|Fertility rates by age of mother (1-year)|Fertility rates by age of mother (1-year)|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|140|46|46|46|46|4.275 +710|South Africa|ZAF|ZA|4|68|Fertility rates by age of mother (1-year)|Fertility rates by age of mother (1-year)|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|141|47|47|47|47|3.1319999999999997 +710|South Africa|ZAF|ZA|4|68|Fertility rates by age of mother (1-year)|Fertility rates by age of mother (1-year)|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|142|48|48|48|48|2.206 +710|South Africa|ZAF|ZA|4|68|Fertility rates by age of mother (1-year)|Fertility rates by age of mother (1-year)|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|143|49|49|49|49|1.292 diff --git a/ogzaf/data/demographic/un_zaf_infmort.csv b/ogzaf/data/demographic/un_zaf_infmort.csv new file mode 100644 index 0000000..c7016d7 --- /dev/null +++ b/ogzaf/data/demographic/un_zaf_infmort.csv @@ -0,0 +1,5 @@ +sep =| +LocationId|Location|Iso3|Iso2|LocationTypeId|IndicatorId|Indicator|IndicatorDisplayName|SourceId|Source|Revision|VariantId|Variant|VariantShortName|VariantLabel|TimeId|TimeLabel|TimeMid|CategoryId|Category|EstimateTypeId|EstimateType|EstimateMethodId|EstimateMethod|SexId|Sex|AgeId|AgeLabel|AgeStart|AgeEnd|AgeMid|Value +710|South Africa|ZAF|ZA|4|22|Infant mortality rate (IMR)|Infant mortality rate (IMR)|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|188|Total|0|-1|0|28.72984 +710|South Africa|ZAF|ZA|4|22|Infant mortality rate (IMR)|Infant mortality rate (IMR)|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|188|Total|0|-1|0|24.52664 +710|South Africa|ZAF|ZA|4|22|Infant mortality rate (IMR)|Infant mortality rate (IMR)|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|188|Total|0|-1|0|26.65837 diff --git a/ogzaf/data/demographic/un_zaf_mort.csv b/ogzaf/data/demographic/un_zaf_mort.csv new file mode 100644 index 0000000..7f3b90d --- /dev/null +++ b/ogzaf/data/demographic/un_zaf_mort.csv @@ -0,0 +1,305 @@ +sep =| +LocationId|Location|Iso3|Iso2|LocationTypeId|IndicatorId|Indicator|IndicatorDisplayName|SourceId|Source|Revision|VariantId|Variant|VariantShortName|VariantLabel|TimeId|TimeLabel|TimeMid|CategoryId|Category|EstimateTypeId|EstimateType|EstimateMethodId|EstimateMethod|SexId|Sex|AgeId|AgeLabel|AgeStart|AgeEnd|AgeMid|Value +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|42|0|0|1|1|0.02947121 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|42|0|0|1|1|0.02505268 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|42|0|0|1|1|0.02730078 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|89|1|1|1|1|0.0038055 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|89|1|1|1|1|0.00351081 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|89|1|1|1|1|0.00366081 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|117|2|2|2|2|0.00253147 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|117|2|2|2|2|0.00221635 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|117|2|2|2|2|0.00237669 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|126|3|3|3|3|0.00170816 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|126|3|3|3|3|0.00142481 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|126|3|3|3|3|0.00156897 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|135|4|4|4|4|0.00118726 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|135|4|4|4|4|0.00095077 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|135|4|4|4|4|0.00107111 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|90|5|5|5|5|0.00086409 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|90|5|5|5|5|0.00067189 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|90|5|5|5|5|0.0007697 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|152|6|6|6|6|0.00066941 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|152|6|6|6|6|0.00051255 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|152|6|6|6|6|0.00059239 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|161|7|7|7|7|0.00055926 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|161|7|7|7|7|0.00042815 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|161|7|7|7|7|0.00049489 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|170|8|8|8|8|0.00050569 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|170|8|8|8|8|0.00039198 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|170|8|8|8|8|0.00044986 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|179|9|9|9|9|0.00049453 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|179|9|9|9|9|0.00039136 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|179|9|9|9|9|0.00044388 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|91|10|10|10|10|0.00051844 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|91|10|10|10|10|0.00041929 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|91|10|10|10|10|0.00046976 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|109|11|11|11|11|0.00057619 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|109|11|11|11|11|0.00047313 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|109|11|11|11|11|0.00052556 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|110|12|12|12|12|0.00066969 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|110|12|12|12|12|0.0005515 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|110|12|12|12|12|0.0006116 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|111|13|13|13|13|0.00080276 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|111|13|13|13|13|0.00065215 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|111|13|13|13|13|0.00072875 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|112|14|14|14|14|0.00097955 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|112|14|14|14|14|0.00077298 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|112|14|14|14|14|0.00087804 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|86|15|15|15|15|0.00120182 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|86|15|15|15|15|0.00090987 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|86|15|15|15|15|0.00105839 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|113|16|16|16|16|0.00146743 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|113|16|16|16|16|0.0010595 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|113|16|16|16|16|0.00126708 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|114|17|17|17|17|0.00176761 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|114|17|17|17|17|0.00121804 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|114|17|17|17|17|0.00149761 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|115|18|18|18|18|0.00209127 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|115|18|18|18|18|0.00138206 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|115|18|18|18|18|0.00174271 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|116|19|19|19|19|0.00242496 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|116|19|19|19|19|0.00154836 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|116|19|19|19|19|0.00199403 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|92|20|20|20|20|0.00276424 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|92|20|20|20|20|0.00171529 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|92|20|20|20|20|0.00224845 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|118|21|21|21|21|0.00311101 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|118|21|21|21|21|0.00188255 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|118|21|21|21|21|0.00250617 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|119|22|22|22|22|0.00347133 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|119|22|22|22|22|0.00205108 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|119|22|22|22|22|0.00277058 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|120|23|23|23|23|0.00385304 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|120|23|23|23|23|0.002223 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|120|23|23|23|23|0.00304748 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|121|24|24|24|24|0.00425201 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|121|24|24|24|24|0.00240076 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|121|24|24|24|24|0.00333545 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|93|25|25|25|25|0.0046559 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|93|25|25|25|25|0.00258731 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|93|25|25|25|25|0.00363036 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|122|26|26|26|26|0.00504101 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|122|26|26|26|26|0.00278521 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|122|26|26|26|26|0.00392259 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|123|27|27|27|27|0.00537884 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|123|27|27|27|27|0.00299689 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|123|27|27|27|27|0.00419777 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|124|28|28|28|28|0.00565703 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|124|28|28|28|28|0.00322329 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|124|28|28|28|28|0.00445002 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|125|29|29|29|29|0.00587587 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|125|29|29|29|29|0.00346412 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|125|29|29|29|29|0.00468038 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|94|30|30|30|30|0.00606042 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|94|30|30|30|30|0.00371705 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|94|30|30|30|30|0.00489945 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|127|31|31|31|31|0.00624704 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|127|31|31|31|31|0.00397788 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|127|31|31|31|31|0.00512261 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|128|32|32|32|32|0.00646504 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|128|32|32|32|32|0.0042431 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|128|32|32|32|32|0.00536369 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|129|33|33|33|33|0.00673873 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|129|33|33|33|33|0.00450996 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|129|33|33|33|33|0.00563387 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|130|34|34|34|34|0.0070656 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|130|34|34|34|34|0.00477911 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|130|34|34|34|34|0.00593208 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|95|35|35|35|35|0.00742785 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|95|35|35|35|35|0.00505471 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|95|35|35|35|35|0.00624814 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|131|36|36|36|36|0.00779498 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|131|36|36|36|36|0.00534321 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|131|36|36|36|36|0.00657395 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|132|37|37|37|37|0.0081301 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|132|37|37|37|37|0.00565343 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|132|37|37|37|37|0.00689907 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|133|38|38|38|38|0.0084239 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|133|38|38|38|38|0.00599344 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|133|38|38|38|38|0.00721837 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|134|39|39|39|39|0.00868694 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|134|39|39|39|39|0.00637015 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|134|39|39|39|39|0.00754021 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|96|40|40|40|40|0.00896265 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|96|40|40|40|40|0.00678843 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|96|40|40|40|40|0.0078898 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|136|41|41|41|41|0.00931427 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|136|41|41|41|41|0.00725016 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|136|41|41|41|41|0.0082979 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|137|42|42|42|42|0.00979365 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|137|42|42|42|42|0.00775414 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|137|42|42|42|42|0.00878841 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|138|43|43|43|43|0.0104478 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|138|43|43|43|43|0.00829464 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|138|43|43|43|43|0.00938353 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|139|44|44|44|44|0.01128644 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|139|44|44|44|44|0.00886124 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|139|44|44|44|44|0.01008059 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|97|45|45|45|45|0.01228816 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|97|45|45|45|45|0.0094374 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|97|45|45|45|45|0.01085924 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|140|46|46|46|46|0.01340421 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|140|46|46|46|46|0.0100078 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|140|46|46|46|46|0.01168556 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|141|47|47|47|47|0.01455695 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|141|47|47|47|47|0.01056308 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|141|47|47|47|47|0.01251233 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|142|48|48|48|48|0.01569735 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|142|48|48|48|48|0.01110736 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|142|48|48|48|48|0.01331698 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|143|49|49|49|49|0.01680815 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|143|49|49|49|49|0.01166991 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|143|49|49|49|49|0.01410754 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|98|50|50|50|50|0.01792986 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|98|50|50|50|50|0.01229413 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|98|50|50|50|50|0.01492679 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|144|51|51|51|51|0.01915773 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|144|51|51|51|51|0.01304292 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|144|51|51|51|51|0.01585782 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|145|52|52|52|52|0.02058161 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|145|52|52|52|52|0.01397549 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|145|52|52|52|52|0.01697878 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|146|53|53|53|53|0.02228542 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|146|53|53|53|53|0.01512384 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|146|53|53|53|53|0.01834295 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|147|54|54|54|54|0.02429448 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|147|54|54|54|54|0.01649606 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|147|54|54|54|54|0.0199648 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|99|55|55|55|55|0.02656409 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|99|55|55|55|55|0.01803212 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|99|55|55|55|55|0.02178871 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|148|56|56|56|56|0.02900299 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|148|56|56|56|56|0.01964051 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|148|56|56|56|56|0.02371713 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|149|57|57|57|57|0.03146683 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|149|57|57|57|57|0.02121516 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|149|57|57|57|57|0.02561761 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|150|58|58|58|58|0.03385279 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|150|58|58|58|58|0.02266183 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|150|58|58|58|58|0.02738165 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|151|59|59|59|59|0.03612604 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|151|59|59|59|59|0.02398661 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|151|59|59|59|59|0.02898987 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|87|60|60|60|60|0.03833761 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|87|60|60|60|60|0.02524862 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|87|60|60|60|60|0.03049361 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|153|61|61|61|61|0.04063814 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|153|61|61|61|61|0.02659014 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|153|61|61|61|61|0.03205079 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|154|62|62|62|62|0.0431717 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|154|62|62|62|62|0.02817268 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|154|62|62|62|62|0.03385192 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|155|63|63|63|63|0.0460611 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|155|63|63|63|63|0.03009508 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|155|63|63|63|63|0.03602421 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|156|64|64|64|64|0.04935693 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|156|64|64|64|64|0.03242695 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|156|64|64|64|64|0.03863667 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|100|65|65|65|65|0.05300428 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|100|65|65|65|65|0.03511917 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|100|65|65|65|65|0.04164785 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|157|66|66|66|66|0.05691045 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|157|66|66|66|66|0.0380662 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|157|66|66|66|66|0.04493986 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|158|67|67|67|67|0.06094235 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|158|67|67|67|67|0.04114692 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|158|67|67|67|67|0.04835785 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|159|68|68|68|68|0.06503093 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|159|68|68|68|68|0.04423445 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|159|68|68|68|68|0.0517682 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|160|69|69|69|69|0.06922666 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|160|69|69|69|69|0.04732606 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|160|69|69|69|69|0.05518413 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|101|70|70|70|70|0.07366746 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|101|70|70|70|70|0.05048924 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|101|70|70|70|70|0.05870813 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|162|71|71|71|71|0.07860963 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|162|71|71|71|71|0.05387561 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|162|71|71|71|71|0.0625353 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|163|72|72|72|72|0.08425916 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|163|72|72|72|72|0.05767949 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|163|72|72|72|72|0.0669055 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|164|73|73|73|73|0.09069205 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|164|73|73|73|73|0.06198897 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|164|73|73|73|73|0.0718354 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|165|74|74|74|74|0.09781279 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|165|74|74|74|74|0.06682282 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|165|74|74|74|74|0.07728963 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|102|75|75|75|75|0.10521944 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|102|75|75|75|75|0.07203702 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|102|75|75|75|75|0.0831463 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|166|76|76|76|76|0.11244089 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|166|76|76|76|76|0.07736979 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|166|76|76|76|76|0.08899876 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|167|77|77|77|77|0.11903235 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|167|77|77|77|77|0.08261598 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|167|77|77|77|77|0.09453764 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|168|78|78|78|78|0.12480013 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|168|78|78|78|78|0.08762486 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|168|78|78|78|78|0.0996646 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|169|79|79|79|79|0.13008024 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|169|79|79|79|79|0.09250558 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|169|79|79|79|79|0.10454511 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|88|80|80|80|80|0.13546729 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|88|80|80|80|80|0.09757283 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|88|80|80|80|80|0.10957961 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|171|81|81|81|81|0.14187962 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|171|81|81|81|81|0.10323165 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|171|81|81|81|81|0.11532547 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|172|82|82|82|82|0.15012304 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|172|82|82|82|82|0.10996488 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|172|82|82|82|82|0.12233538 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|173|83|83|83|83|0.16058047 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|173|83|83|83|83|0.11803236 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|173|83|83|83|83|0.1308758 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|174|84|84|84|84|0.17333316 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|174|84|84|84|84|0.12748513 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|174|84|84|84|84|0.14092036 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|103|85|85|85|85|0.18773287 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|103|85|85|85|85|0.13811027 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|103|85|85|85|85|0.15210673 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|175|86|86|86|86|0.20291842 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|175|86|86|86|86|0.1493945 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|175|86|86|86|86|0.16381509 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|176|87|87|87|87|0.21805106 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|176|87|87|87|87|0.16092338 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|176|87|87|87|87|0.17557389 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|177|88|88|88|88|0.23256549 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|177|88|88|88|88|0.17244849 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|177|88|88|88|88|0.18716013 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|178|89|89|89|89|0.2469048 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|178|89|89|89|89|0.18411379 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|178|89|89|89|89|0.19892454 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|104|90|90|90|90|0.26191939 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|104|90|90|90|90|0.19655987 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|104|90|90|90|90|0.21166825 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|180|91|91|91|91|0.27890879 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|180|91|91|91|91|0.21047491 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|180|91|91|91|91|0.2261556 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|181|92|92|92|92|0.29899045 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|181|92|92|92|92|0.2265402 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|181|92|92|92|92|0.24312744 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|182|93|93|93|93|0.32206691 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|182|93|93|93|93|0.24495398 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|182|93|93|93|93|0.26272926 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|183|94|94|94|94|0.34730112 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|183|94|94|94|94|0.2650907 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|183|94|94|94|94|0.2843438 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|105|95|95|95|95|0.36388396 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|105|95|95|95|95|0.28004251 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|105|95|95|95|95|0.30045312 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|184|96|96|96|96|0.3852333 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|184|96|96|96|96|0.29840361 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|184|96|96|96|96|0.32081785 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|185|97|97|97|97|0.40703373 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|185|97|97|97|97|0.31743777 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|185|97|97|97|97|0.34205044 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|186|98|98|98|98|0.42920654 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|186|98|98|98|98|0.3371027 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|186|98|98|98|98|0.36386897 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|187|99|99|99|99|0.45166716 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|187|99|99|99|99|0.35734805 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|187|99|99|99|99|0.38599362 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|59|100+|100||100|0.50501505 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|59|100+|100||100|0.41496623 +710|South Africa|ZAF|ZA|4|80|Age specific mortality rate m(x,n) - complete|Age-specific mortality rates by single age and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|59|100+|100||100|0.44334266 diff --git a/ogzaf/data/demographic/un_zaf_pop.csv b/ogzaf/data/demographic/un_zaf_pop.csv new file mode 100644 index 0000000..56ec996 --- /dev/null +++ b/ogzaf/data/demographic/un_zaf_pop.csv @@ -0,0 +1,911 @@ +sep =| +LocationId|Location|Iso3|Iso2|LocationTypeId|IndicatorId|Indicator|IndicatorDisplayName|SourceId|Source|Revision|VariantId|Variant|VariantShortName|VariantLabel|TimeId|TimeLabel|TimeMid|CategoryId|Category|EstimateTypeId|EstimateType|EstimateMethodId|EstimateMethod|SexId|Sex|AgeId|AgeLabel|AgeStart|AgeEnd|AgeMid|Value +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|42|0|0|1|1|609002 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|42|0|0|1|1|587415 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|42|0|0|1|1|1196416 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|89|1|1|1|1|590581 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|89|1|1|1|1|569689 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|89|1|1|1|1|1160270 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|117|2|2|2|2|571431 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|117|2|2|2|2|551216 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|117|2|2|2|2|1122647 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|126|3|3|3|3|572040 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|126|3|3|3|3|551789 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|126|3|3|3|3|1123829 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|135|4|4|4|4|585861 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|135|4|4|4|4|565124 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|135|4|4|4|4|1150985 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|90|5|5|5|5|587578 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|90|5|5|5|5|566721 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|90|5|5|5|5|1154299 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|152|6|6|6|6|583215 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|152|6|6|6|6|562487 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|152|6|6|6|6|1145702 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|161|7|7|7|7|577623 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|161|7|7|7|7|557135 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|161|7|7|7|7|1134758 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|170|8|8|8|8|568138 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|170|8|8|8|8|548034 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|170|8|8|8|8|1116172 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|179|9|9|9|9|562778 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|179|9|9|9|9|543561 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|179|9|9|9|9|1106339 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|91|10|10|10|10|574238 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|91|10|10|10|10|555103 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|91|10|10|10|10|1129341 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|109|11|11|11|11|563358 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|109|11|11|11|11|544379 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|109|11|11|11|11|1107737 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|110|12|12|12|12|534524 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|110|12|12|12|12|516549 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|110|12|12|12|12|1051073 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|111|13|13|13|13|518510 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|111|13|13|13|13|500820 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|111|13|13|13|13|1019330 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|112|14|14|14|14|497855 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|112|14|14|14|14|480532 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|112|14|14|14|14|978387 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|86|15|15|15|15|474968 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|86|15|15|15|15|458685 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|86|15|15|15|15|933652 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|113|16|16|16|16|455303 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|113|16|16|16|16|439985 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|113|16|16|16|16|895288 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|114|17|17|17|17|449928 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|114|17|17|17|17|434901 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|114|17|17|17|17|884828 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|115|18|18|18|18|452796 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|115|18|18|18|18|437794 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|115|18|18|18|18|890590 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|116|19|19|19|19|461091 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|116|19|19|19|19|446817 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|116|19|19|19|19|907908 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|92|20|20|20|20|470167 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|92|20|20|20|20|457376 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|92|20|20|20|20|927543 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|118|21|21|21|21|476906 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|118|21|21|21|21|465259 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|118|21|21|21|21|942165 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|119|22|22|22|22|497446 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|119|22|22|22|22|486884 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|119|22|22|22|22|984330 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|120|23|23|23|23|522562 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|120|23|23|23|23|512647 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|120|23|23|23|23|1035209 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|121|24|24|24|24|535955 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|121|24|24|24|24|525712 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|121|24|24|24|24|1061667 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|93|25|25|25|25|544296 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|93|25|25|25|25|533869 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|93|25|25|25|25|1078165 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|122|26|26|26|26|553424 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|122|26|26|26|26|542966 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|122|26|26|26|26|1096390 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|123|27|27|27|27|565471 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|123|27|27|27|27|554157 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|123|27|27|27|27|1119628 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|124|28|28|28|28|572693 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|124|28|28|28|28|560587 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|124|28|28|28|28|1133280 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|125|29|29|29|29|580235 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|125|29|29|29|29|568152 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|125|29|29|29|29|1148386 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|94|30|30|30|30|590074 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|94|30|30|30|30|578061 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|94|30|30|30|30|1168135 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|127|31|31|31|31|590109 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|127|31|31|31|31|578163 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|127|31|31|31|31|1168272 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|128|32|32|32|32|577551 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|128|32|32|32|32|565817 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|128|32|32|32|32|1143368 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|129|33|33|33|33|548485 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|129|33|33|33|33|540189 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|129|33|33|33|33|1088674 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|130|34|34|34|34|519468 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|130|34|34|34|34|513398 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|130|34|34|34|34|1032867 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|95|35|35|35|35|498582 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|95|35|35|35|35|490793 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|95|35|35|35|35|989375 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|131|36|36|36|36|480643 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|131|36|36|36|36|471158 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|131|36|36|36|36|951801 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|132|37|37|37|37|460562 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|132|37|37|37|37|449612 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|132|37|37|37|37|910174 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|133|38|38|38|38|437272 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|133|38|38|38|38|424323 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|133|38|38|38|38|861595 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|134|39|39|39|39|407546 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|134|39|39|39|39|393863 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|134|39|39|39|39|801409 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|96|40|40|40|40|367173 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|96|40|40|40|40|355534 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|96|40|40|40|40|722706 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|136|41|41|41|41|326708 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|136|41|41|41|41|318081 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|136|41|41|41|41|644788 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|137|42|42|42|42|293830 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|137|42|42|42|42|289346 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|137|42|42|42|42|583175 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|138|43|43|43|43|273472 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|138|43|43|43|43|273516 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|138|43|43|43|43|546988 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|139|44|44|44|44|266708 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|139|44|44|44|44|271688 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|139|44|44|44|44|538395 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|97|45|45|45|45|270819 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|97|45|45|45|45|282230 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|97|45|45|45|45|553049 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|140|46|46|46|46|281278 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|140|46|46|46|46|300753 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|140|46|46|46|46|582031 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|141|47|47|47|47|292508 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|141|47|47|47|47|321330 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|141|47|47|47|47|613837 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|142|48|48|48|48|299627 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|142|48|48|48|48|338577 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|142|48|48|48|48|638204 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|143|49|49|49|49|301494 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|143|49|49|49|49|349802 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|143|49|49|49|49|651296 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|98|50|50|50|50|298371 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|98|50|50|50|50|353919 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|98|50|50|50|50|652290 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|144|51|51|51|51|289448 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|144|51|51|51|51|350166 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|144|51|51|51|51|639614 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|145|52|52|52|52|275444 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|145|52|52|52|52|339244 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|145|52|52|52|52|614688 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|146|53|53|53|53|257818 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|146|53|53|53|53|322998 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|146|53|53|53|53|580816 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|147|54|54|54|54|238601 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|147|54|54|54|54|304494 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|147|54|54|54|54|543094 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|99|55|55|55|55|218674 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|99|55|55|55|55|285534 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|99|55|55|55|55|504208 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|148|56|56|56|56|198652 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|148|56|56|56|56|267254 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|148|56|56|56|56|465906 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|149|57|57|57|57|178944 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|149|57|57|57|57|250033 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|149|57|57|57|57|428977 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|150|58|58|58|58|159417 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|150|58|58|58|58|233160 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|150|58|58|58|58|392577 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|151|59|59|59|59|141680 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|151|59|59|59|59|217538 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|151|59|59|59|59|359218 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|87|60|60|60|60|128613 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|87|60|60|60|60|205726 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|87|60|60|60|60|334338 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|153|61|61|61|61|119205 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|153|61|61|61|61|196355 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|153|61|61|61|61|315559 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|154|62|62|62|62|112036 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|154|62|62|62|62|187869 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|154|62|62|62|62|299905 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|155|63|63|63|63|107736 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|155|63|63|63|63|181701 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|155|63|63|63|63|289437 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|156|64|64|64|64|105673 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|156|64|64|64|64|178117 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|156|64|64|64|64|283789 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|100|65|65|65|65|104369 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|100|65|65|65|65|176001 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|100|65|65|65|65|280370 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|157|66|66|66|66|101630 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|157|66|66|66|66|172618 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|157|66|66|66|66|274248 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|158|67|67|67|67|96800 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|158|67|67|67|67|166671 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|158|67|67|67|67|263471 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|159|68|68|68|68|90797 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|159|68|68|68|68|159014 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|159|68|68|68|68|249811 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|160|69|69|69|69|84339 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|160|69|69|69|69|150401 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|160|69|69|69|69|234739 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|101|70|70|70|70|79931 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|101|70|70|70|70|144173 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|101|70|70|70|70|224104 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|162|71|71|71|71|74369 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|162|71|71|71|71|136219 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|162|71|71|71|71|210587 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|163|72|72|72|72|66604 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|163|72|72|72|72|124555 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|163|72|72|72|72|191159 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|164|73|73|73|73|60176 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|164|73|73|73|73|113670 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|164|73|73|73|73|173845 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|165|74|74|74|74|54142 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|165|74|74|74|74|103402 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|165|74|74|74|74|157544 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|102|75|75|75|75|48469 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|102|75|75|75|75|94076 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|102|75|75|75|75|142545 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|166|76|76|76|76|43613 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|166|76|76|76|76|85828 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|166|76|76|76|76|129440 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|167|77|77|77|77|39175 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|167|77|77|77|77|78215 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|167|77|77|77|77|117390 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|168|78|78|78|78|34970 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|168|78|78|78|78|70931 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|168|78|78|78|78|105900 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|169|79|79|79|79|30939 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|169|79|79|79|79|63878 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|169|79|79|79|79|94817 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|88|80|80|80|80|27050 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|88|80|80|80|80|57088 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|88|80|80|80|80|84138 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|171|81|81|81|81|23293 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|171|81|81|81|81|50529 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|171|81|81|81|81|73822 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|172|82|82|82|82|19696 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|172|82|82|82|82|44441 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|172|82|82|82|82|64137 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|173|83|83|83|83|16515 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|173|83|83|83|83|39173 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|173|83|83|83|83|55687 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|174|84|84|84|84|13858 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|174|84|84|84|84|34867 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|174|84|84|84|84|48725 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|103|85|85|85|85|11764 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|103|85|85|85|85|31523 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|103|85|85|85|85|43286 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|175|86|86|86|86|10145 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|175|86|86|86|86|28830 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|175|86|86|86|86|38975 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|176|87|87|87|87|8815 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|176|87|87|87|87|26223 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|176|87|87|87|87|35038 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|177|88|88|88|88|7689 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|177|88|88|88|88|23431 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|177|88|88|88|88|31120 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|178|89|89|89|89|6672 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|178|89|89|89|89|20519 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|178|89|89|89|89|27190 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|104|90|90|90|90|5718 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|104|90|90|90|90|17566 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|104|90|90|90|90|23284 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|180|91|91|91|91|4826 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|180|91|91|91|91|14655 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|180|91|91|91|91|19481 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|181|92|92|92|92|4014 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|181|92|92|92|92|11908 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|181|92|92|92|92|15922 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|182|93|93|93|93|3291 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|182|93|93|93|93|9264 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|182|93|93|93|93|12555 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|183|94|94|94|94|2646 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|183|94|94|94|94|6878 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|183|94|94|94|94|9524 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|105|95|95|95|95|2081 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|105|95|95|95|95|4960 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|105|95|95|95|95|7041 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|184|96|96|96|96|1594 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|184|96|96|96|96|3507 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|184|96|96|96|96|5101 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|185|97|97|97|97|1185 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|185|97|97|97|97|2445 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|185|97|97|97|97|3629 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|186|98|98|98|98|854 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|186|98|98|98|98|1697 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|186|98|98|98|98|2551 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|187|99|99|99|99|598 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|187|99|99|99|99|1175 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|187|99|99|99|99|1773 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|59|100+|100||100|1151 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|59|100+|100||100|2111 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|70|2019|2019.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|59|100+|100||100|3262 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|42|0|0|1|1|605764 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|42|0|0|1|1|584126 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|42|0|0|1|1|1189890 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|89|1|1|1|1|606615 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|89|1|1|1|1|585331 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|89|1|1|1|1|1191946 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|117|2|2|2|2|589236 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|117|2|2|2|2|568729 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|117|2|2|2|2|1157965 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|126|3|3|3|3|570666 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|126|3|3|3|3|550711 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|126|3|3|3|3|1121377 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|135|4|4|4|4|571590 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|135|4|4|4|4|551485 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|135|4|4|4|4|1123075 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|90|5|5|5|5|585583 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|90|5|5|5|5|564901 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|90|5|5|5|5|1150483 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|152|6|6|6|6|587396 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|152|6|6|6|6|566533 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|152|6|6|6|6|1153929 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|161|7|7|7|7|583122 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|161|7|7|7|7|562346 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|161|7|7|7|7|1145468 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|170|8|8|8|8|577610 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|170|8|8|8|8|557047 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|170|8|8|8|8|1134656 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|179|9|9|9|9|568222 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|179|9|9|9|9|548009 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|179|9|9|9|9|1116231 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|91|10|10|10|10|562900 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|91|10|10|10|10|543555 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|91|10|10|10|10|1106455 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|109|11|11|11|11|574331 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|109|11|11|11|11|555073 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|109|11|11|11|11|1129404 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|110|12|12|12|12|563442 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|110|12|12|12|12|544367 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|110|12|12|12|12|1107809 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|111|13|13|13|13|534647 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|111|13|13|13|13|516591 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|111|13|13|13|13|1051238 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|112|14|14|14|14|518655 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|112|14|14|14|14|500904 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|112|14|14|14|14|1019560 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|86|15|15|15|15|498033 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|86|15|15|15|15|480655 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|86|15|15|15|15|978687 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|113|16|16|16|16|475127 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|113|16|16|16|16|458789 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|113|16|16|16|16|933916 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|114|17|17|17|17|455409 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|114|17|17|17|17|440047 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|114|17|17|17|17|895455 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|115|18|18|18|18|449931 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|115|18|18|18|18|434889 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|115|18|18|18|18|884820 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|116|19|19|19|19|452668 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|116|19|19|19|19|437707 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|116|19|19|19|19|890374 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|92|20|20|20|20|460811 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|92|20|20|20|20|446647 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|92|20|20|20|20|907458 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|118|21|21|21|21|469705 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|118|21|21|21|21|457090 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|118|21|21|21|21|926795 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|119|22|22|22|22|476199 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|119|22|22|22|22|464820 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|119|22|22|22|22|941018 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|120|23|23|23|23|496438 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|120|23|23|23|23|486248 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|120|23|23|23|23|982685 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|121|24|24|24|24|521225 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|121|24|24|24|24|511782 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|121|24|24|24|24|1033007 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|93|25|25|25|25|534337 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|93|25|25|25|25|524646 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|93|25|25|25|25|1058983 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|122|26|26|26|26|542429 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|122|26|26|26|26|532612 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|122|26|26|26|26|1075041 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|123|27|27|27|27|551285 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|123|27|27|27|27|541494 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|123|27|27|27|27|1092778 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|124|28|28|28|28|563059 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|124|28|28|28|28|552473 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|124|28|28|28|28|1115531 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|125|29|29|29|29|570051 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|125|29|29|29|29|558726 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|125|29|29|29|29|1128777 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|94|30|30|30|30|577377 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|94|30|30|30|30|566128 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|94|30|30|30|30|1143505 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|127|31|31|31|31|587007 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|127|31|31|31|31|575887 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|127|31|31|31|31|1162894 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|128|32|32|32|32|586897 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|128|32|32|32|32|575888 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|128|32|32|32|32|1162784 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|129|33|33|33|33|574268 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|129|33|33|33|33|563497 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|129|33|33|33|33|1137765 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|130|34|34|34|34|545235 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|130|34|34|34|34|537882 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|130|34|34|34|34|1083117 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|95|35|35|35|35|516256 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|95|35|35|35|35|511106 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|95|35|35|35|35|1027363 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|131|36|36|36|36|495360 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|131|36|36|36|36|488492 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|131|36|36|36|36|983852 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|132|37|37|37|37|477399 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|132|37|37|37|37|468828 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|132|37|37|37|37|946227 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|133|38|38|38|38|457317 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|133|38|38|38|38|447258 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|133|38|38|38|38|904575 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|134|39|39|39|39|434053 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|134|39|39|39|39|421965 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|134|39|39|39|39|856018 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|96|40|40|40|40|404405 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|96|40|40|40|40|391534 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|96|40|40|40|40|795939 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|136|41|41|41|41|364195 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|136|41|41|41|41|353295 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|136|41|41|41|41|717490 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|137|42|42|42|42|323904 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|137|42|42|42|42|315948 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|137|42|42|42|42|639851 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|138|43|43|43|43|291148 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|138|43|43|43|43|287288 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|138|43|43|43|43|578436 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|139|44|44|44|44|270812 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|139|44|44|44|44|271469 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|139|44|44|44|44|542281 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|97|45|45|45|45|263942 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|97|45|45|45|45|269567 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|97|45|45|45|45|533509 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|140|46|46|46|46|267826 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|140|46|46|46|46|279947 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|140|46|46|46|46|547772 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|141|47|47|47|47|277962 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|141|47|47|47|47|298235 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|141|47|47|47|47|576197 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|142|48|48|48|48|288827 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|142|48|48|48|48|318539 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|142|48|48|48|48|607366 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|143|49|49|49|49|295608 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|143|49|49|49|49|335517 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|143|49|49|49|49|631125 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|98|50|50|50|50|297186 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|98|50|50|50|50|346501 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|98|50|50|50|50|643687 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|144|51|51|51|51|293833 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|144|51|51|51|51|350418 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|144|51|51|51|51|644251 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|145|52|52|52|52|284759 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|145|52|52|52|52|346520 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|145|52|52|52|52|631279 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|146|53|53|53|53|270686 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|146|53|53|53|53|335511 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|146|53|53|53|53|606196 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|147|54|54|54|54|253063 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|147|54|54|54|54|319227 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|147|54|54|54|54|572290 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|99|55|55|55|55|233890 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|99|55|55|55|55|300709 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|99|55|55|55|55|534599 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|148|56|56|56|56|214045 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|148|56|56|56|56|281747 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|148|56|56|56|56|495792 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|149|57|57|57|57|194140 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|149|57|57|57|57|263472 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|149|57|57|57|57|457612 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|150|58|58|58|58|174585 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|150|58|58|58|58|246261 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|150|58|58|58|58|420846 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|151|59|59|59|59|155253 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|151|59|59|59|59|229409 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|151|59|59|59|59|384662 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|87|60|60|60|60|137718 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|87|60|60|60|60|213802 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|87|60|60|60|60|351519 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|153|61|61|61|61|124762 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|153|61|61|61|61|201945 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|153|61|61|61|61|326707 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|154|62|62|62|62|115379 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|154|62|62|62|62|192475 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|154|62|62|62|62|307853 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|155|63|63|63|63|108182 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|155|63|63|63|63|183868 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|155|63|63|63|63|292050 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|156|64|64|64|64|103766 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|156|64|64|64|64|177528 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|156|64|64|64|64|281294 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|100|65|65|65|65|101510 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|100|65|65|65|65|173704 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|100|65|65|65|65|275214 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|157|66|66|66|66|99987 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|157|66|66|66|66|171304 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|157|66|66|66|66|271291 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|158|67|67|67|67|97095 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|158|67|67|67|67|167662 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|158|67|67|67|67|264756 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|159|68|68|68|68|92232 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|159|68|68|68|68|161545 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|159|68|68|68|68|253777 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|160|69|69|69|69|86281 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|160|69|69|69|69|153796 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|160|69|69|69|69|240077 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|101|70|70|70|70|79925 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|101|70|70|70|70|145147 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|101|70|70|70|70|225072 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|162|71|71|71|71|75547 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|162|71|71|71|71|138865 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|162|71|71|71|71|214412 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|163|72|72|72|72|70040 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|163|72|72|72|72|130848 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|163|72|72|72|72|200888 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|164|73|73|73|73|62485 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|164|73|73|73|73|119294 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|164|73|73|73|73|181779 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|165|74|74|74|74|56204 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|165|74|74|74|74|108509 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|165|74|74|74|74|164713 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|102|75|75|75|75|50318 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|102|75|75|75|75|98351 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|102|75|75|75|75|148669 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|166|76|76|76|76|44818 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|166|76|76|76|76|89138 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|166|76|76|76|76|133955 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|167|77|77|77|77|40127 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|167|77|77|77|77|81002 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|167|77|77|77|77|121129 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|168|78|78|78|78|35873 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|168|78|78|78|78|73526 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|168|78|78|78|78|109399 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|169|79|79|79|79|31883 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|169|79|79|79|79|66414 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|169|79|79|79|79|98296 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|88|80|80|80|80|28088 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|88|80|80|80|80|59566 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|88|80|80|80|80|87654 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|171|81|81|81|81|24448 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|171|81|81|81|81|53000 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|171|81|81|81|81|77447 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|172|82|82|82|82|20944 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|172|82|82|82|82|46679 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|172|82|82|82|82|67622 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|173|83|83|83|83|17601 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|173|83|83|83|83|40822 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|173|83|83|83|83|58423 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|174|84|84|84|84|14651 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|174|84|84|84|84|35748 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|174|84|84|84|84|50399 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|103|85|85|85|85|12193 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|103|85|85|85|85|31589 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|103|85|85|85|85|43781 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|175|86|86|86|86|10260 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|175|86|86|86|86|28335 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|175|86|86|86|86|38595 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|176|87|87|87|87|8766 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|176|87|87|87|87|25700 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|176|87|87|87|87|34466 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|177|88|88|88|88|7547 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|177|88|88|88|88|23177 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|177|88|88|88|88|30723 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|178|89|89|89|89|6522 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|178|89|89|89|89|20527 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|178|89|89|89|89|27049 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|104|90|90|90|90|5605 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|104|90|90|90|90|17809 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|104|90|90|90|90|23414 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|180|91|91|91|91|4756 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|180|91|91|91|91|15090 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|180|91|91|91|91|19845 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|181|92|92|92|92|3968 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|181|92|92|92|92|12443 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|181|92|92|92|92|16411 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|182|93|93|93|93|3259 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|182|93|93|93|93|9977 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|182|93|93|93|93|13235 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|183|94|94|94|94|2634 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|183|94|94|94|94|7647 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|183|94|94|94|94|10280 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|105|95|95|95|95|2089 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|105|95|95|95|95|5598 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|105|95|95|95|95|7686 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|184|96|96|96|96|1621 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|184|96|96|96|96|3983 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|184|96|96|96|96|5604 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|185|97|97|97|97|1224 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|185|97|97|97|97|2773 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|185|97|97|97|97|3997 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|186|98|98|98|98|896 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|186|98|98|98|98|1903 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|186|98|98|98|98|2799 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|187|99|99|99|99|636 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|187|99|99|99|99|1299 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|187|99|99|99|99|1935 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|59|100+|100||100|1246 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|59|100+|100||100|2401 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|71|2020|2020.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|59|100+|100||100|3647 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|42|0|0|1|1|590977 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|42|0|0|1|1|570104 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|42|0|0|1|1|1161080 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|89|1|1|1|1|603281 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|89|1|1|1|1|581935 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|89|1|1|1|1|1185216 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|117|2|2|2|2|605174 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|117|2|2|2|2|584209 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|117|2|2|2|2|1189382 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|126|3|3|3|3|588375 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|126|3|3|3|3|568103 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|126|3|3|3|3|1156477 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|135|4|4|4|4|570158 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|135|4|4|4|4|550345 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|135|4|4|4|4|1120503 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|90|5|5|5|5|571269 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|90|5|5|5|5|551231 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|90|5|5|5|5|1122500 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|152|6|6|6|6|585352 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|152|6|6|6|6|564688 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|152|6|6|6|6|1150039 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|161|7|7|7|7|587247 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|161|7|7|7|7|566367 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|161|7|7|7|7|1153614 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|170|8|8|8|8|583045 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|170|8|8|8|8|562226 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|170|8|8|8|8|1145271 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|179|9|9|9|9|577610 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|179|9|9|9|9|556976 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|179|9|9|9|9|1134586 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|91|10|10|10|10|568253 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|91|10|10|10|10|547952 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|91|10|10|10|10|1116205 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|109|11|11|11|11|562911 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|109|11|11|11|11|543479 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|109|11|11|11|11|1106390 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|110|12|12|12|12|574315 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|110|12|12|12|12|554993 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|110|12|12|12|12|1129308 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|111|13|13|13|13|563431 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|111|13|13|13|13|544311 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|111|13|13|13|13|1107741 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|112|14|14|14|14|534646 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|112|14|14|14|14|516564 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|112|14|14|14|14|1051210 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|86|15|15|15|15|518655 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|86|15|15|15|15|500894 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|86|15|15|15|15|1019550 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|113|16|16|16|16|497991 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|113|16|16|16|16|480618 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|113|16|16|16|16|978609 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|114|17|17|17|17|475022 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|114|17|17|17|17|458710 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|114|17|17|17|17|933732 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|115|18|18|18|18|455217 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|115|18|18|18|18|439912 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|115|18|18|18|18|895129 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|116|19|19|19|19|449621 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|116|19|19|19|19|434688 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|116|19|19|19|19|884309 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|92|20|20|20|20|452216 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|92|20|20|20|20|437427 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|92|20|20|20|20|889643 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|118|21|21|21|21|460180 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|118|21|21|21|21|446253 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|118|21|21|21|21|906432 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|119|22|22|22|22|468830 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|119|22|22|22|22|456544 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|119|22|22|22|22|925374 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|120|23|23|23|23|475076 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|120|23|23|23|23|464116 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|120|23|23|23|23|939191 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|121|24|24|24|24|495005 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|121|24|24|24|24|485344 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|121|24|24|24|24|980349 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|93|25|25|25|25|519457 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|93|25|25|25|25|510671 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|93|25|25|25|25|1030128 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|122|26|26|26|26|532285 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|122|26|26|26|26|523355 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|122|26|26|26|26|1055640 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|123|27|27|27|27|540109 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|123|27|27|27|27|531137 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|123|27|27|27|27|1071246 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|124|28|28|28|28|548715 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|124|28|28|28|28|539839 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|124|28|28|28|28|1088554 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|125|29|29|29|29|560256 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|125|29|29|29|29|550640 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|125|29|29|29|29|1110896 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|94|30|30|30|30|567060 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|94|30|30|30|30|556736 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|94|30|30|30|30|1123796 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|127|31|31|31|31|574216 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|127|31|31|31|31|563988 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|127|31|31|31|31|1138204 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|128|32|32|32|32|583655 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|128|32|32|32|32|573589 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|128|32|32|32|32|1157243 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|129|33|33|33|33|583389 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|129|33|33|33|33|573469 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|129|33|33|33|33|1156857 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|130|34|34|34|34|570661 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|130|34|34|34|34|561005 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|130|34|34|34|34|1131666 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|95|35|35|35|35|541627 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|95|35|35|35|35|535376 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|95|35|35|35|35|1077003 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|131|36|36|36|36|512662 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|131|36|36|36|36|508595 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|131|36|36|36|36|1021257 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|132|37|37|37|37|491741 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|132|37|37|37|37|485955 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|132|37|37|37|37|977696 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|133|38|38|38|38|473761 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|133|38|38|38|38|466248 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|133|38|38|38|38|940009 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|134|39|39|39|39|453695 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|134|39|39|39|39|444642 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|134|39|39|39|39|898337 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|96|40|40|40|40|430487 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|96|40|40|40|40|419336 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|96|40|40|40|40|849823 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|136|41|41|41|41|400937 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|136|41|41|41|41|388929 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|136|41|41|41|41|789866 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|137|42|42|42|42|360908 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|137|42|42|42|42|350777 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|137|42|42|42|42|711685 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|138|43|43|43|43|320796 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|138|43|43|43|43|313541 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|138|43|43|43|43|634337 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|139|44|44|44|44|288150 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|139|44|44|44|44|284956 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|139|44|44|44|44|573106 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|97|45|45|45|45|267806 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|97|45|45|45|45|269135 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|97|45|45|45|45|536941 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|140|46|46|46|46|260775 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|140|46|46|46|46|267126 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|140|46|46|46|46|527901 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|141|47|47|47|47|264360 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|141|47|47|47|47|277290 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|141|47|47|47|47|541649 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|142|48|48|48|48|274096 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|142|48|48|48|48|295270 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|142|48|48|48|48|569365 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|143|49|49|49|49|284533 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|143|49|49|49|49|315221 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|143|49|49|49|49|599754 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|98|50|50|50|50|290926 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|98|50|50|50|50|331847 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|98|50|50|50|50|622772 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|144|51|51|51|51|292178 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|144|51|51|51|51|342504 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|144|51|51|51|51|634682 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|145|52|52|52|52|288541 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|145|52|52|52|52|346126 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|145|52|52|52|52|634667 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|146|53|53|53|53|279246 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|146|53|53|53|53|341978 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|146|53|53|53|53|621223 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|147|54|54|54|54|265011 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|147|54|54|54|54|330769 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|147|54|54|54|54|595780 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|99|55|55|55|55|247292 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|99|55|55|55|55|314340 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|99|55|55|55|55|561632 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|148|56|56|56|56|228082 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|148|56|56|56|56|295721 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|148|56|56|56|56|523802 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|149|57|57|57|57|208273 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|149|57|57|57|57|276704 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|149|57|57|57|57|484976 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|150|58|58|58|58|188490 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|150|58|58|58|58|258419 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|150|58|58|58|58|446909 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|151|59|59|59|59|169138 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|151|59|59|59|59|241233 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|151|59|59|59|59|410371 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|87|60|60|60|60|150078 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|87|60|60|60|60|224431 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|87|60|60|60|60|374509 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|153|61|61|61|61|132838 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|153|61|61|61|61|208886 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|153|61|61|61|61|341724 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|154|62|62|62|62|120061 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|154|62|62|62|62|197008 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|154|62|62|62|62|317069 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|155|63|63|63|63|110734 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|155|63|63|63|63|187437 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|155|63|63|63|63|298171 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|156|64|64|64|64|103515 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|156|64|64|64|64|178689 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|156|64|64|64|64|282204 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|100|65|65|65|65|98964 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|100|65|65|65|65|172129 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|100|65|65|65|65|271092 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|157|66|66|66|66|96475 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|157|66|66|66|66|167998 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|157|66|66|66|66|264473 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|158|67|67|67|67|94692 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|158|67|67|67|67|165242 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|158|67|67|67|67|259933 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|159|68|68|68|68|91630 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|159|68|68|68|68|161295 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|159|68|68|68|68|252925 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|160|69|69|69|69|86744 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|160|69|69|69|69|155001 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|160|69|69|69|69|241745 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|101|70|70|70|70|80863 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|101|70|70|70|70|147172 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|101|70|70|70|70|228035 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|162|71|71|71|71|74632 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|162|71|71|71|71|138533 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|162|71|71|71|71|213165 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|163|72|72|72|72|70247 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|163|72|72|72|72|132130 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|163|72|72|72|72|202376 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|164|73|73|73|73|64760 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|164|73|73|73|73|124021 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|164|73|73|73|73|188781 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|165|74|74|74|74|57423 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|165|74|74|74|74|112593 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|165|74|74|74|74|170015 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|102|75|75|75|75|51304 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|102|75|75|75|75|101936 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|102|75|75|75|75|153240 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|166|76|76|76|76|45609 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|166|76|76|76|76|91940 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|166|76|76|76|76|137549 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|167|77|77|77|77|40361 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|167|77|77|77|77|82927 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|167|77|77|77|77|123288 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|168|78|78|78|78|35932 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|168|78|78|78|78|75016 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|168|78|78|78|78|110948 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|169|79|79|79|79|31968 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|169|79|79|79|79|67803 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|169|79|79|79|79|99771 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|88|80|80|80|80|28288 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|88|80|80|80|80|60991 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|88|80|80|80|80|89279 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|171|81|81|81|81|24804 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|171|81|81|81|81|54461 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|171|81|81|81|81|79265 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|172|82|82|82|82|21462 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|172|82|82|82|82|48208 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|172|82|82|82|82|69670 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|173|83|83|83|83|18243 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|173|83|83|83|83|42192 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|173|83|83|83|83|60435 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|174|84|84|84|84|15180 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|174|84|84|84|84|36620 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|174|84|84|84|84|51799 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|103|85|85|85|85|12490 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|103|85|85|85|85|31790 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|103|85|85|85|85|44280 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|175|86|86|86|86|10261 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|175|86|86|86|86|27823 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|175|86|86|86|86|38084 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|176|87|87|87|87|8523 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|176|87|87|87|87|24710 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|176|87|87|87|87|33232 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|177|88|88|88|88|7189 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|177|88|88|88|88|22187 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|177|88|88|88|88|29375 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|178|89|89|89|89|6113 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|178|89|89|89|89|19803 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|178|89|89|89|89|25916 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|104|90|90|90|90|5218 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|104|90|90|90|90|17355 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|104|90|90|90|90|22573 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|180|91|91|91|91|4425 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|180|91|91|91|91|14887 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|180|91|91|91|91|19312 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|181|92|92|92|92|3698 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|181|92|92|92|92|12453 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|181|92|92|92|92|16151 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|182|93|93|93|93|3031 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|182|93|93|93|93|10119 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|182|93|93|93|93|13150 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|183|94|94|94|94|2440 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|183|94|94|94|94|7979 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|183|94|94|94|94|10419 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|105|95|95|95|95|1936 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|105|95|95|95|95|6015 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|105|95|95|95|95|7950 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|184|96|96|96|96|1509 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|184|96|96|96|96|4337 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|184|96|96|96|96|5846 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|185|97|97|97|97|1150 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|185|97|97|97|97|3034 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|185|97|97|97|97|4184 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|186|98|98|98|98|851 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|186|98|98|98|98|2077 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|186|98|98|98|98|2927 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|187|99|99|99|99|611 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|187|99|99|99|99|1400 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|187|99|99|99|99|2010 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|1|Male|59|100+|100||100|1210 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|2|Female|59|100+|100||100|2588 +710|South Africa|ZAF|ZA|4|47|Population by 1-year age groups and sex|Annual population by 1-year age groups and by sex|25|World Population Prospects|0|4|Median|Median|Median|72|2021|2021.5|0|Not applicable|1|Model-based Estimates|2|Interpolation|3|Both sexes|59|100+|100||100|3798 diff --git a/ogzaf/demographics.py b/ogzaf/demographics.py deleted file mode 100755 index f305e50..0000000 --- a/ogzaf/demographics.py +++ /dev/null @@ -1,662 +0,0 @@ -""" ------------------------------------------------------------------------- -Functions for generating demographic objects necessary for the OG-ZAF -model ------------------------------------------------------------------------- -""" -# Import packages -from lib2to3.pgen2.pgen import DFAState -import os -import requests -import json -import numpy as np -import scipy.optimize as opt -import pandas as pd -import scipy.interpolate as si -from ogcore import utils -from ogcore import parameter_plots as pp - - -# create output director for figures -CUR_PATH = os.path.split(os.path.abspath(__file__))[0] -OUTPUT_DIR = os.path.join(CUR_PATH, "OUTPUT", "Demographics") -if os.access(OUTPUT_DIR, os.F_OK) is False: - os.makedirs(OUTPUT_DIR) - - -""" ------------------------------------------------------------------------- -Define functions ------------------------------------------------------------------------- -""" - - -def get_un_data(variable_code, country_id='710', start_year=2022, - end_year=2022): - """ - This function retrieves data from the United Nations Data Portal API - for UN population data (see - https://population.un.org/dataportal/about/dataapi) - - Args: - variable_code (str): variable code for UN data - country_id (str): country id for UN data - start_year (int): start year for UN data - end_year (int): end year for UN data - - Returns: - df (Pandas DataFrame): DataFrame of UN data - """ - target = ( - "https://population.un.org/dataportalapi/api/v1/data/indicators/" + - variable_code + "/locations/" + country_id + "/start/" - + str(start_year) + "/end/" + str(end_year) - ) - - # get data from url - response = requests.get(target) - # Converts call into JSON - j = response.json() - # Convert JSON into a pandas DataFrame. - # pd.json_normalize flattens the JSON to accomodate nested lists - # within the JSON structure - df = pd.json_normalize(j['data']) - # Loop until there are new pages with data - while j['nextPage'] is not None: - # Reset the target to the next page - target = j['nextPage'] - # call the API for the next page - response = requests.get(target) - # Convert response to JSON format - j = response.json() - # Store the next page in a data frame - df_temp = pd.json_normalize(j['data']) - # Append next page to the data frame - df = df.append(df_temp) - - return df - - -def get_fert(totpers, min_yr, max_yr, graph=False): - """ - This function generates a vector of fertility rates by model period - age that corresponds to the fertility rate data by age in years. - (Source: Office of the Registrar General & Census Commissioner: See - Statement [Table] 19 of - http://www.censusindia.gov.in/vital_statistics/SRS_Report_2016/ - 7.Chap_3-Fertility_Indicators-2016.pdf) - - Args: - totpers (int): total number of agent life periods (E+S), >= 3 - min_yr (int): age in years at which agents are born, >= 0 - max_yr (int): age in years at which agents die with certainty, - >= 4 - graph (bool): =True if want graphical output - - Returns: - fert_rates (Numpy array): fertility rates for each model period - of life - - """ - # Read raw data - pop_file = utils.read_file( - CUR_PATH, os.path.join('data', 'demographic', - 'india_pop_data.csv')) - pop_data = pd.read_csv(pop_file, encoding='utf-8') - pop_data_samp = pop_data[(pop_data['Age'] >= min_yr - 1) & - (pop_data['Age'] <= max_yr - 1)] - age_year_all = pop_data_samp['Age'] + 1 - curr_pop = np.array(pop_data_samp['2011'], dtype='f') - curr_pop_pct = curr_pop / curr_pop.sum() - # divide by 2000 because fertility rates per woman and we want per - # household - fert_data = np.array([0.0, 1.0, 3.0, 10.7, 135.4, 166.0, 91.7, 32.7, - 11.3, 4.1, 1.0, 0.0]) / 2000 - age_midp = np.array([9, 12, 15, 17, 22, 27, 32, 37, 42, 47, 52, 57]) - # Generate interpolation functions for fertility rates - fert_func = si.interp1d(age_midp, fert_data, kind='cubic') - # Calculate average fertility rate in each age bin using trapezoid - # method with a large number of points in each bin. - binsize = (max_yr - min_yr + 1) / totpers - num_sub_bins = float(10000) - len_subbins = (np.float64(100 * num_sub_bins)) / totpers - age_sub = (np.linspace(np.float64(binsize) / num_sub_bins, - np.float64(max_yr), int(num_sub_bins*max_yr)) - - 0.5 * np.float64(binsize) / num_sub_bins) - curr_pop_sub = np.repeat(np.float64(curr_pop_pct) / - num_sub_bins, num_sub_bins) - fert_rates_sub = np.zeros(curr_pop_sub.shape) - pred_ind = (age_sub > age_midp[0]) * (age_sub < age_midp[-1]) - age_pred = age_sub[pred_ind] - fert_rates_sub[pred_ind] = np.float64(fert_func(age_pred)) - fert_rates = np.zeros(totpers) - end_sub_bin = 0 - for i in range(totpers): - beg_sub_bin = int(end_sub_bin) - end_sub_bin = int(np.rint((i + 1) * len_subbins)) - fert_rates[i] = ((curr_pop_sub[beg_sub_bin:end_sub_bin] * - fert_rates_sub[beg_sub_bin:end_sub_bin]).sum() - / curr_pop_sub[beg_sub_bin:end_sub_bin].sum()) - - # if graph: # need to fix plot function for new data output - # pp.plot_fert_rates(fert_rates, age_midp, totpers, min_yr, max_yr, - # fert_data, fert_rates, output_dir=OUTPUT_DIR) - - return fert_rates - - -def get_mort(totpers, min_yr, max_yr, graph=False): - """ - This function generates a vector of mortality rates by model period - age. - Source: Census of India, 2011 - - Args: - totpers (int): total number of agent life periods (E+S), >= 3 - min_yr (int): age in years at which agents are born, >= 0 - max_yr (int): age in years at which agents die with certainty, - >= 4 - graph (bool): =True if want graphical output - - Returns: - mort_rates (Numpy array) mortality rates that correspond to each - period of life - infmort_rate (scalar): infant mortality rate - - """ - # Get current population data (2011) for weighting - pop_file = utils.read_file( - CUR_PATH, os.path.join('data', 'demographic', - 'india_pop_data.csv')) - pop_data = pd.read_csv(pop_file, encoding='utf-8') - pop_data_samp = pop_data[(pop_data['Age'] >= min_yr - 1) & - (pop_data['Age'] <= max_yr - 1)] - age_year_all = pop_data_samp['Age'] + 1 - curr_pop = np.array(pop_data_samp['2011'], dtype='f') - curr_pop_pct = curr_pop / curr_pop.sum() - # Get mortality rate by age data - infmort_rate = 0.0482 - # Get fertility rate by age-bin data - mort_data = (np.array([2.9, 1.0, 0.7, 1.3, 1.6, 1.8, 2.3, 2.7, 4.0, - 5.5, 8.3, 12.2, 20.1, 33.2, 49.9, 73.6, 104.8, - 167.6]) / 1000) - age_midp = np.array([2.5, 7, 12, 17, 22, 27, 32, 37, 42, 47, 52, - 57, 62, 67, 72, 77, 82, 100]) - # Generate interpolation functions for fertility rates - mort_func = si.interp1d(age_midp, mort_data, kind='cubic') - # Calculate average fertility rate in each age bin using trapezoid - # method with a large number of points in each bin. - binsize = (max_yr - min_yr + 1) / totpers - num_sub_bins = float(10000) - len_subbins = (np.float64(100 * num_sub_bins)) / totpers - age_sub = (np.linspace(np.float64(binsize) / num_sub_bins, - np.float64(max_yr), int(num_sub_bins * max_yr)) - - 0.5 * np.float64(binsize) / num_sub_bins) - curr_pop_sub = np.repeat(np.float64(curr_pop_pct) / - num_sub_bins, num_sub_bins) - mort_rates_sub = np.zeros(curr_pop_sub.shape) - pred_ind = (age_sub > age_midp[0]) * (age_sub < age_midp[-1]) - age_pred = age_sub[pred_ind] - mort_rates_sub[pred_ind] = np.float64(mort_func(age_pred)) - mort_rates = np.zeros(totpers) - end_sub_bin = 0 - for i in range(totpers): - beg_sub_bin = int(end_sub_bin) - end_sub_bin = int(np.rint((i + 1) * len_subbins)) - mort_rates[i] = ((curr_pop_sub[beg_sub_bin:end_sub_bin] * - mort_rates_sub[beg_sub_bin:end_sub_bin]).sum() - / curr_pop_sub[beg_sub_bin:end_sub_bin].sum()) - mort_rates[-1] = 1 # Mortality rate in last period is set to 1 - - if graph: - pp.plot_mort_rates_data( - totpers, - min_yr, - max_yr, - age_year_all, - mort_rates_all, - infmort_rate, - mort_rates, - output_dir=OUTPUT_DIR, - ) - - return mort_rates, infmort_rate - - -def pop_rebin(curr_pop_dist, totpers_new): - """ - For cases in which totpers (E+S) is less than the number of periods - in the population distribution data, this function calculates a new - population distribution vector with totpers (E+S) elements. - - Args: - curr_pop_dist (Numpy array): population distribution over N - periods - totpers_new (int): number of periods to which we are - transforming the population distribution, >= 3 - - Returns: - curr_pop_new (Numpy array): new population distribution over - totpers (E+S) periods that approximates curr_pop_dist - - """ - # Number of periods in original data - assert totpers_new >= 3 - # Number of periods in original data - totpers_orig = len(curr_pop_dist) - if int(totpers_new) == totpers_orig: - curr_pop_new = curr_pop_dist - elif int(totpers_new) < totpers_orig: - num_sub_bins = float(10000) - curr_pop_sub = np.repeat(np.float64(curr_pop_dist) / - num_sub_bins, num_sub_bins) - len_subbins = ((np.float64(totpers_orig*num_sub_bins)) / - totpers_new) - curr_pop_new = np.zeros(totpers_new, dtype=np.float64) - end_sub_bin = 0 - for i in range(totpers_new): - beg_sub_bin = int(end_sub_bin) - end_sub_bin = int(np.rint((i + 1) * len_subbins)) - curr_pop_new[i] = \ - curr_pop_sub[beg_sub_bin:end_sub_bin].sum() - # Return curr_pop_new to single precision float (float32) - # datatype - curr_pop_new = np.float32(curr_pop_new) - - return curr_pop_new - - -def get_imm_resid(totpers, min_yr, max_yr): - """ - Calculate immigration rates by age as a residual given population - levels in different periods, then output average calculated - immigration rate. We have to replace the first mortality rate in - this function in order to adjust the first implied immigration rate - Source: India Census, 2001 and 2011 - - Args: - totpers (int): total number of agent life periods (E+S), >= 3 - min_yr (int): age in years at which agents are born, >= 0 - max_yr (int): age in years at which agents die with certainty, - >= 4 - graph (bool): =True if want graphical output - - Returns: - imm_rates (Numpy array):immigration rates that correspond to - each period of life, length E+S - - """ - pop_file = utils.read_file( - CUR_PATH, os.path.join('data', 'demographic', - 'india_pop_data.csv')) - pop_data = pd.read_csv(pop_file, encoding='utf-8') - pop_data_samp = pop_data[(pop_data['Age'] >= min_yr - 1) & - (pop_data['Age'] <= max_yr - 1)] - age_year_all = pop_data_samp['Age'] + 1 - pop_2001, pop_2011 = ( - np.array(pop_data_samp['2001'], dtype='f'), - np.array(pop_data_samp['2011'], dtype='f')) - pop_2001_EpS = pop_rebin(pop_2001, totpers) - pop_2011_EpS = pop_rebin(pop_2011, totpers) - # Create three years of estimated immigration rates for youngest age - # individuals - imm_mat = np.zeros((2, totpers)) - fert_rates = get_fert(totpers, min_yr, max_yr, False) - mort_rates, infmort_rate = get_mort(totpers, min_yr, max_yr, False) - newbornvec = np.dot(fert_rates, pop_2001_EpS).T - # imm_mat[:, 0] = ((pop_2011_EpS[0] - (1 - infmort_rate) * newbornvec) - # / pop_2001_EpS[0]) - imm_mat[:, 0] = 0 - # Estimate immigration rates for all other-aged - # individuals - mort_rate10 = np.zeros_like(mort_rates[:-10]) # 10-year mort rate - for i in range(10): - mort_rate10 = mort_rates[i:-10 + i] + mort_rate10 - mort_rate10[mort_rate10 > 1.0] = 1.0 - imm_mat[:, 10:] = ((pop_2011_EpS[10:] - (1 - mort_rate10) * - pop_2001_EpS[:-10]) / pop_2001_EpS[10:]) - # Final estimated immigration rates are the averages over years - imm_rates = imm_mat.mean(axis=0) - neg_rates = imm_rates < 0 - # For India, data were 10 years apart, so make annual rate - imm_rates = ((1 + np.absolute(imm_rates)) ** (1 / 10)) - 1 - imm_rates[neg_rates] = -1 * imm_rates[neg_rates] - - return imm_rates - - -def immsolve(imm_rates, *args): - """ - This function generates a vector of errors representing the - difference in two consecutive periods stationary population - distributions. This vector of differences is the zero-function - objective used to solve for the immigration rates vector, similar to - the original immigration rates vector from get_imm_resid(), that - sets the steady-state population distribution by age equal to the - population distribution in period int(1.5*S) - - Args: - imm_rates (Numpy array):immigration rates that correspond to - each period of life, length E+S - args (tuple): (fert_rates, mort_rates, infmort_rate, omega_cur, - g_n_SS) - - Returns: - omega_errs (Numpy array): difference between omega_new and - omega_cur_pct, length E+S - - """ - fert_rates, mort_rates, infmort_rate, omega_cur_lev, g_n_SS = args - omega_cur_pct = omega_cur_lev / omega_cur_lev.sum() - totpers = len(fert_rates) - OMEGA = np.zeros((totpers, totpers)) - OMEGA[0, :] = (1 - infmort_rate) * fert_rates + np.hstack( - (imm_rates[0], np.zeros(totpers - 1)) - ) - OMEGA[1:, :-1] += np.diag(1 - mort_rates[:-1]) - OMEGA[1:, 1:] += np.diag(imm_rates[1:]) - omega_new = np.dot(OMEGA, omega_cur_pct) / (1 + g_n_SS) - omega_errs = omega_new - omega_cur_pct - - return omega_errs - - -def get_pop_objs(E, S, T, min_yr, max_yr, curr_year, GraphDiag=False): - """ - This function produces the demographics objects to be used in the - OG-ZAF model package. - - Args: - E (int): number of model periods in which agent is not - economically active, >= 1 - S (int): number of model periods in which agent is economically - active, >= 3 - T (int): number of periods to be simulated in TPI, > 2*S - min_yr (int): age in years at which agents are born, >= 0 - max_yr (int): age in years at which agents die with certainty, - >= 4 - curr_year (int): current year for which analysis will begin, - >= 2016 - GraphDiag (bool): =True if want graphical output and printed - diagnostics - - Returns: - pop_dict (dict): includes: - omega_path_S (Numpy array), time path of the population - distribution from the current state to the steady-state, - size T+S x S - g_n_SS (scalar): steady-state population growth rate - omega_SS (Numpy array): normalized steady-state population - distribution, length S - surv_rates (Numpy array): survival rates that correspond to - each model period of life, length S - mort_rates (Numpy array): mortality rates that correspond to - each model period of life, length S - g_n_path (Numpy array): population growth rates over the time - path, length T + S - - """ - assert curr_year >= 2011 - # age_per = np.linspace(min_yr, max_yr, E+S) - fert_rates = get_fert(E + S, min_yr, max_yr, graph=False) - mort_rates, infmort_rate = get_mort(E + S, min_yr, max_yr, graph=False) - mort_rates_S = mort_rates[-S:] - imm_rates_orig = get_imm_resid(E + S, min_yr, max_yr) - OMEGA_orig = np.zeros((E + S, E + S)) - OMEGA_orig[0, :] = (1 - infmort_rate) * fert_rates + np.hstack( - (imm_rates_orig[0], np.zeros(E + S - 1)) - ) - OMEGA_orig[1:, :-1] += np.diag(1 - mort_rates[:-1]) - OMEGA_orig[1:, 1:] += np.diag(imm_rates_orig[1:]) - - # Solve for steady-state population growth rate and steady-state - # population distribution by age using eigenvalue and eigenvector - # decomposition - eigvalues, eigvectors = np.linalg.eig(OMEGA_orig) - g_n_SS = (eigvalues[np.isreal(eigvalues)].real).max() - 1 - eigvec_raw = eigvectors[ - :, (eigvalues[np.isreal(eigvalues)].real).argmax() - ].real - omega_SS_orig = eigvec_raw / eigvec_raw.sum() - - # Generate time path of the nonstationary population distribution - omega_path_lev = np.zeros((E + S, T + S)) - pop_file = utils.read_file( - CUR_PATH, os.path.join('data', 'demographic', - 'india_pop_data.csv')) - pop_data = pd.read_csv(pop_file, encoding='utf-8') - pop_data_samp = pop_data[(pop_data['Age'] >= min_yr - 1) & - (pop_data['Age'] <= max_yr - 1)] - pop_2011 = np.array(pop_data_samp['2011'], dtype='f') - # Generate the current population distribution given that E+S might - # be less than max_yr-min_yr+1 - age_per_EpS = np.arange(1, E + S + 1) - pop_2011_EpS = pop_rebin(pop_2011, E + S) - pop_2011_pct = pop_2011_EpS / pop_2011_EpS.sum() - # Age most recent population data to the current year of analysis - pop_curr = pop_2011_EpS.copy() - data_year = 2019 - pop_next = np.dot(OMEGA_orig, pop_curr) - g_n_curr = (pop_next[-S:].sum() - pop_curr[-S:].sum()) / pop_curr[ - -S: - ].sum() # g_n in 2019 - pop_past = pop_curr # assume 2018-2019 pop - # Age the data to the current year - for per in range(curr_year - data_year): - pop_next = np.dot(OMEGA_orig, pop_curr) - g_n_curr = (pop_next[-S:].sum() - pop_curr[-S:].sum()) / pop_curr[ - -S: - ].sum() - pop_past = pop_curr - pop_curr = pop_next - - # Generate time path of the population distribution - omega_path_lev[:, 0] = pop_curr.copy() - for per in range(1, T + S): - pop_next = np.dot(OMEGA_orig, pop_curr) - omega_path_lev[:, per] = pop_next.copy() - pop_curr = pop_next.copy() - - # Force the population distribution after 1.5*S periods to be the - # steady-state distribution by adjusting immigration rates, holding - # constant mortality, fertility, and SS growth rates - imm_tol = 1e-14 - fixper = int(1.5 * S) - omega_SSfx = omega_path_lev[:, fixper] / omega_path_lev[:, fixper].sum() - imm_objs = ( - fert_rates, - mort_rates, - infmort_rate, - omega_path_lev[:, fixper], - g_n_SS, - ) - imm_fulloutput = opt.fsolve( - immsolve, - imm_rates_orig, - args=(imm_objs), - full_output=True, - xtol=imm_tol, - ) - imm_rates_adj = imm_fulloutput[0] - imm_diagdict = imm_fulloutput[1] - omega_path_S = omega_path_lev[-S:, :] / np.tile( - omega_path_lev[-S:, :].sum(axis=0), (S, 1) - ) - omega_path_S[:, fixper:] = np.tile( - omega_path_S[:, fixper].reshape((S, 1)), (1, T + S - fixper) - ) - g_n_path = np.zeros(T + S) - g_n_path[0] = g_n_curr.copy() - g_n_path[1:] = ( - omega_path_lev[-S:, 1:].sum(axis=0) - - omega_path_lev[-S:, :-1].sum(axis=0) - ) / omega_path_lev[-S:, :-1].sum(axis=0) - g_n_path[fixper + 1 :] = g_n_SS - omega_S_preTP = (pop_past.copy()[-S:]) / (pop_past.copy()[-S:].sum()) - imm_rates_mat = np.hstack( - ( - np.tile(np.reshape(imm_rates_orig[E:], (S, 1)), (1, fixper)), - np.tile( - np.reshape(imm_rates_adj[E:], (S, 1)), (1, T + S - fixper) - ), - ) - ) - - if GraphDiag: - # Check whether original SS population distribution is close to - # the period-T population distribution - omegaSSmaxdif = np.absolute( - omega_SS_orig - (omega_path_lev[:, T] / omega_path_lev[:, T].sum()) - ).max() - if omegaSSmaxdif > 0.0003: - print( - "POP. WARNING: Max. abs. dist. between original SS " - + "pop. dist'n and period-T pop. dist'n is greater than" - + " 0.0003. It is " - + str(omegaSSmaxdif) - + "." - ) - else: - print( - "POP. SUCCESS: orig. SS pop. dist is very close to " - + "period-T pop. dist'n. The maximum absolute " - + "difference is " - + str(omegaSSmaxdif) - + "." - ) - - # Plot the adjusted steady-state population distribution versus - # the original population distribution. The difference should be - # small - omegaSSvTmaxdiff = np.absolute(omega_SS_orig - omega_SSfx).max() - if omegaSSvTmaxdiff > 0.0003: - print( - "POP. WARNING: The maximimum absolute difference " - + "between any two corresponding points in the original" - + " and adjusted steady-state population " - + "distributions is" - + str(omegaSSvTmaxdiff) - + ", " - + "which is greater than 0.0003." - ) - else: - print( - "POP. SUCCESS: The maximum absolute difference " - + "between any two corresponding points in the original" - + " and adjusted steady-state population " - + "distributions is " - + str(omegaSSvTmaxdiff) - ) - - # Print whether or not the adjusted immigration rates solved the - # zero condition - immtol_solved = np.absolute(imm_diagdict["fvec"].max()) < imm_tol - if immtol_solved: - print( - "POP. SUCCESS: Adjusted immigration rates solved " - + "with maximum absolute error of " - + str(np.absolute(imm_diagdict["fvec"].max())) - + ", which is less than the tolerance of " - + str(imm_tol) - ) - else: - print( - "POP. WARNING: Adjusted immigration rates did not " - + "solve. Maximum absolute error of " - + str(np.absolute(imm_diagdict["fvec"].max())) - + " is greater than the tolerance of " - + str(imm_tol) - ) - - # Test whether the steady-state growth rates implied by the - # adjusted OMEGA matrix equals the steady-state growth rate of - # the original OMEGA matrix - OMEGA2 = np.zeros((E + S, E + S)) - OMEGA2[0, :] = (1 - infmort_rate) * fert_rates + np.hstack( - (imm_rates_adj[0], np.zeros(E + S - 1)) - ) - OMEGA2[1:, :-1] += np.diag(1 - mort_rates[:-1]) - OMEGA2[1:, 1:] += np.diag(imm_rates_adj[1:]) - eigvalues2, eigvectors2 = np.linalg.eig(OMEGA2) - g_n_SS_adj = (eigvalues[np.isreal(eigvalues2)].real).max() - 1 - if np.max(np.absolute(g_n_SS_adj - g_n_SS)) > 10 ** (-8): - print( - "FAILURE: The steady-state population growth rate" - + " from adjusted OMEGA is different (diff is " - + str(g_n_SS_adj - g_n_SS) - + ") than the steady-" - + "state population growth rate from the original" - + " OMEGA." - ) - elif np.max(np.absolute(g_n_SS_adj - g_n_SS)) <= 10 ** (-8): - print( - "SUCCESS: The steady-state population growth rate" - + " from adjusted OMEGA is close to (diff is " - + str(g_n_SS_adj - g_n_SS) - + ") the steady-" - + "state population growth rate from the original" - + " OMEGA." - ) - - # Do another test of the adjusted immigration rates. Create the - # new OMEGA matrix implied by the new immigration rates. Plug in - # the adjusted steady-state population distribution. Hit is with - # the new OMEGA transition matrix and it should return the new - # steady-state population distribution - omega_new = np.dot(OMEGA2, omega_SSfx) - omega_errs = np.absolute(omega_new - omega_SSfx) - print( - "The maximum absolute difference between the adjusted " - + "steady-state population distribution and the " - + "distribution generated by hitting the adjusted OMEGA " - + "transition matrix is " - + str(omega_errs.max()) - ) - - # Plot the original immigration rates versus the adjusted - # immigration rates - immratesmaxdiff = np.absolute(imm_rates_orig - imm_rates_adj).max() - print( - "The maximum absolute distance between any two points " - + "of the original immigration rates and adjusted " - + "immigration rates is " - + str(immratesmaxdiff) - ) - - # plots - pp.plot_omega_fixed( - age_per_EpS, omega_SS_orig, omega_SSfx, E, S, output_dir=OUTPUT_DIR - ) - pp.plot_imm_fixed( - age_per_EpS, - imm_rates_orig, - imm_rates_adj, - E, - S, - output_dir=OUTPUT_DIR, - ) - pp.plot_population_path( - age_per_EpS, - pop_2011_pct, - omega_path_lev, - omega_SSfx, - curr_year, - E, - S, - output_dir=OUTPUT_DIR, - ) - - # return omega_path_S, g_n_SS, omega_SSfx, survival rates, - # mort_rates_S, and g_n_path - pop_dict = { - "omega": omega_path_S.T, - "g_n_SS": g_n_SS, - "omega_SS": omega_SSfx[-S:] / omega_SSfx[-S:].sum(), - "surv_rate": 1 - mort_rates_S, - "rho": mort_rates_S, - "g_n": g_n_path, - "imm_rates": imm_rates_mat.T, - "omega_S_preTP": omega_S_preTP, - } - - return pop_dict diff --git a/ogzaf/income.py b/ogzaf/income.py new file mode 100644 index 0000000..26ce3d1 --- /dev/null +++ b/ogzaf/income.py @@ -0,0 +1,537 @@ +""" +----------------------------------------------------------------- +Functions for created the matrix of ability levels, e. This can +only be used for looking at the 25, 50, 70, 80, 90, 99, and 100th +percentiles, as it uses fitted polynomials to those percentiles. +----------------------------------------------------------------- +""" + +import numpy as np +import scipy.optimize as opt +import scipy.interpolate as si +from ogcore import parameter_plots as pp +import os + +CUR_PATH = os.path.abspath(os.path.dirname(__file__)) +OUTPUT_DIR = os.path.join(CUR_PATH, "OUTPUT", "ability") + + +def arctan_func(xvals, a, b, c): + r""" + This function generates predicted ability levels given data (xvals) + and parameters a, b, and c, from the following arctan function: + + .. math:: + y = (-a / \pi) * \arctan(b * x + c) + (a / 2) + + Args: + xvals (Numpy array): data inputs to arctan function + a (scalar): scale parameter for arctan function + b (scalar): curvature parameter for arctan function + c (scalar): shift parameter for arctan function + + Returns: + yvals (Numpy array): predicted values (output) of arctan + function + + """ + yvals = (-a / np.pi) * np.arctan(b * xvals + c) + (a / 2) + return yvals + + +def arctan_deriv_func(xvals, a, b, c): + r""" + This function generates predicted derivatives of arctan function + given data (xvals) and parameters a, b, and c. The functional form + of the derivative of the function is the following: + + .. math:: + y = - (a * b) / (\pi * (1 + (b * xvals + c)^2)) + + Args: + xvals (Numpy array): data inputs to arctan derivative function + a (scalar): scale parameter for arctan function + b (scalar): curvature parameter for arctan function + c (scalar): shift parameter for arctan function + + Returns: + yvals (Numpy array): predicted values (output) of arctan + derivative function + + """ + yvals = -(a * b) / (np.pi * (1 + (b * xvals + c) ** 2)) + return yvals + + +def arc_error(abc_vals, params): + """ + This function returns a vector of errors in the three criteria on + which the arctan function is fit to predict extrapolated ability in + ages 81 to 100.:: + + 1) The arctan function value at age 80 must match the estimated + original function value at age 80. + 2) The arctan function slope at age 80 must match the estimated + original function slope at age 80. + 3) The level of ability at age 100 must be a given fraction + (abil_deprec) below the ability level at age 80. + + Args: + abc_vals (tuple): contains (a,b,c) + + * a (scalar): scale parameter for arctan function + * b (scalar): curvature parameter for arctan function + * c (scalar): shift parameter for arctan function + params (tuple): contains (first_point, coef1, coef2, coef3, + abil_deprec) + + * first_point (scalar): ability level at age 80, > 0 + * coef1 (scalar): coefficient in log ability equation on + linear term in age + * coef2 (scalar): coefficient in log ability equation on + quadratic term in age + * coef3 (scalar): coefficient in log ability equation on + cubic term in age + * abil_deprec (scalar): ability depreciation rate between + ages 80 and 100, in (0, 1). + + Returns: + error_vec (Numpy array): errors ([error1, error2, error3]) + + * error1 (scalar): error between ability level at age 80 + from original function minus the predicted ability at + age 80 from the arctan function given a, b, and c + * error2 (scalar): error between the slope of the original + function at age 80 minus the slope of the arctan + function at age 80 given a, b, and c + * error3 (scalar): error between the ability level at age + 100 predicted by the original model value times + abil_deprec minus the ability predicted by the arctan + function at age 100 given a, b, and c + + """ + a, b, c = abc_vals + first_point, coef1, coef2, coef3, abil_deprec = params + error1 = first_point - arctan_func(80, a, b, c) + if (3 * coef3 * 80**2 + 2 * coef2 * 80 + coef1) < 0: + error2 = ( + 3 * coef3 * 80**2 + 2 * coef2 * 80 + coef1 + ) * first_point - arctan_deriv_func(80, a, b, c) + else: + error2 = -0.02 * first_point - arctan_deriv_func(80, a, b, c) + error3 = abil_deprec * first_point - arctan_func(100, a, b, c) + error_vec = np.array([error1, error2, error3]) + + return error_vec + + +def arctan_fit(first_point, coef1, coef2, coef3, abil_deprec, init_guesses): + """ + This function fits an arctan function to the last 20 years of the + ability levels of a particular ability group to extrapolate + abilities by trying to match the slope in the 80th year and the + ability depreciation rate between years 80 and 100. + + Args: + first_point (scalar): ability level at age 80, > 0 + coef1 (scalar): coefficient in log ability equation on linear + term in age + coef2 (scalar): coefficient in log ability equation on + quadratic term in age + coef3 (scalar): coefficient in log ability equation on cubic + term in age + abil_deprec (scalar): ability depreciation rate between + ages 80 and 100, in (0, 1) + init_guesses (Numpy array): initial guesses + + Returns: + abil_last (Numpy array): extrapolated ability levels for ages + 81 to 100, length 20 + + """ + params = [first_point, coef1, coef2, coef3, abil_deprec] + solution = opt.root(arc_error, init_guesses, args=params, method="lm") + [a, b, c] = solution.x + old_ages = np.linspace(81, 100, 20) + abil_last = arctan_func(old_ages, a, b, c) + return abil_last + + +def get_e_interp(S, age_wgts, age_wgts_80, abil_wgts, plot=False): + """ + This function takes a source matrix of lifetime earnings profiles + (abilities, emat) of size (80, 7), where 80 is the number of ages + and 7 is the number of ability types in the source matrix, and + interpolates new values of a new S x J sized matrix of abilities + using linear interpolation. [NOTE: For this application, cubic + spline interpolation introduces too much curvature.] + + This function also includes the two cases in which J = 9 and J = 10 + that include higher lifetime earning percentiles calibrated using + Piketty and Saez (2003). + + + Args: + S (int): number of ages to interpolate. This method assumes that + ages are evenly spaced between the beginning of the 21st + year and the end of the 100th year, >= 3 + age_wgts (Numpy array): distribution of population in each age + for the interpolated ages, length S + age_wgts_80 (Numpy array): percent of population in each + one-year age from 21 to 100, length 80 + abil_wgts (Numpy array): distribution of population in each + ability group, length J + plot (bool): if True, creates plots of emat_orig and the new + interpolated emat_new + + Returns: + emat_new_scaled (Numpy array): interpolated ability matrix scaled + so that population-weighted average is 1, size SxJ + + """ + # Get original 80 x 7 ability matrix + abil_wgts_orig = np.array([0.25, 0.25, 0.2, 0.1, 0.1, 0.09, 0.01]) + emat_orig = get_e_orig(age_wgts_80, abil_wgts_orig, plot) + if ( + S == 80 + and np.array_equal( + np.squeeze(abil_wgts), + np.array([0.25, 0.25, 0.2, 0.1, 0.1, 0.09, 0.01]), + ) + is True + ): + emat_new_scaled = emat_orig + elif ( + S == 80 + and np.array_equal( + np.squeeze(abil_wgts), + np.array( + [0.25, 0.25, 0.2, 0.1, 0.1, 0.09, 0.005, 0.004, 0.0009, 0.0001] + ), + ) + is True + ): + emat_new = np.zeros((S, len(abil_wgts))) + emat_new[:, :7] = emat_orig + # Create profiles for top 0.5%, top 0.1% and top 0.01% using + # Piketty and Saez estimates + # (https://eml.berkeley.edu/~saez/pikettyqje.pdf) + # updated for 2018 to create scaling factor + # assumption is that profile shape of these top 3 groups are + # same as the top 1% estimated in tax data, just scaled up by + # ratio determined from P&S 2018 estimates (Table 0, ex cap gains) + emat_new[:, 5] = emat_orig[:, -2] * 1.25 + emat_new[:, 6] = emat_orig[:, -1] * 0.458759521 * 2.75 + emat_new[:, 7] = emat_orig[:, -1] * 0.847252448 * 3.5 + emat_new[:, 8] = emat_orig[:, -1] * 2.713698465 * 3.5 + emat_new[:, 9] = emat_orig[:, -1] * 18.74863983 * 4.0 + emat_new_scaled = ( + emat_new + / ( + emat_new * age_wgts.reshape(80, 1) * abil_wgts.reshape(1, 10) + ).sum() + ) + elif ( + S == 80 + and np.array_equal( + np.squeeze(abil_wgts), + np.array([0.25, 0.25, 0.2, 0.1, 0.1, 0.09, 0.005, 0.004, 0.001]), + ) + is True + ): + emat_new = np.zeros((S, len(abil_wgts))) + emat_new[:, :7] = emat_orig + # Create profiles for top 0.5%, top 0.1% using + # Piketty and Saez estimates + # (https://eml.berkeley.edu/~saez/pikettyqje.pdf) + # updated for 2018 to create scaling factor + # assumption is that profile shape of these top 3 groups are + # same as the top 1% estimated in tax data, just scaled up by + # ratio determined from P&S 2018 estimates (Table 0, ex cap gains) + emat_new[:, 6] = emat_orig[:, -1] * 0.458759521 + emat_new[:, 7] = emat_orig[:, -1] * 0.847252448 + emat_new[:, 8] = emat_orig[:, -1] * 4.317192601 + emat_new_scaled = ( + emat_new + / ( + emat_new * age_wgts.reshape(80, 1) * abil_wgts.reshape(1, 9) + ).sum() + ) + else: + # generate abil_midp vector + J = abil_wgts.shape[0] + abil_midp = np.zeros(J) + pct_lb = 0.0 + for j in range(J): + abil_midp[j] = pct_lb + 0.5 * abil_wgts[j] + pct_lb += abil_wgts[j] + + # Make sure that values in abil_midp are within interpolating + # bounds set by the hard coded abil_wgts_orig + if abil_midp.min() < 0.125 or abil_midp.max() > 0.995: + err = ( + "One or more entries in abils vector is outside the " + + "allowable bounds." + ) + raise RuntimeError(err) + + emat_j_midp = np.array( + [0.125, 0.375, 0.600, 0.750, 0.850, 0.945, 0.995] + ) + emat_s_midp = np.linspace(20.5, 99.5, 80) + emat_j_mesh, emat_s_mesh = np.meshgrid(emat_j_midp, emat_s_midp) + newstep = 80 / S + new_s_midp = np.linspace(20 + 0.5 * newstep, 100 - 0.5 * newstep, S) + new_j_mesh, new_s_mesh = np.meshgrid(abil_midp, new_s_midp) + newcoords = np.hstack( + ( + emat_s_mesh.reshape((80 * 7, 1)), + emat_j_mesh.reshape((80 * 7, 1)), + ) + ) + emat_new = si.griddata( + newcoords, + emat_orig.flatten(), + (new_s_mesh, new_j_mesh), + method="linear", + ) + emat_new_scaled = ( + emat_new + / ( + emat_new * age_wgts.reshape(S, 1) * abil_wgts.reshape(1, J) + ).sum() + ) + + if plot: + kwargs = {"filesuffix": "_intrp_scaled"} + pp.plot_income_data( + new_s_midp, + abil_midp, + abil_wgts, + emat_new_scaled, + OUTPUT_DIR, + **kwargs, + ) + + return emat_new_scaled + + +def get_e_orig(age_wgts, abil_wgts, plot=False): + """ + This function generates the 80 x 7 matrix of lifetime earnings + ability profiles, corresponding to annual ages from 21 to 100 and to + paths based on income percentiles 0-25, 25-50, 50-70, 70-80, 80-90, + 90-99, 99-100. The ergodic population distribution is an input in + order to rescale the paths so that the weighted average equals 1. + + The base curves are the ones in OG-USA, which are then adjusted for ZAF. + + The polynomials are of the form + + .. math:: + \ln(abil) = \alpha + \beta_{1}\text{age} + \beta_{2}\text{age}^2 + + \beta_{3}\text{age}^3 + + To calibrate for ZAF, the USA curves are adjusted in 2 ways (in this order) + 1) Adjustment by income (J): adjust the gaps between the J-income earning curves + using data from WID. + 2) Adjustment by age (S): adjust the shape/distribution of each J-income earning + profile curve using data from NTA. + + The methodoly is described here: + https://github.com/EAPD-DRB/OG-ZAF/issues/18#issuecomment-1368580323 + + Args: + age_wgts (Numpy array): ergodic age distribution, length S + abil_wgts (Numpy array): population weights in each lifetime + earnings group, length J + plot (bool): if True, generates 3D plots of ability paths + + Returns: + e_orig_scaled (Numpy array): = lifetime ability profiles scaled + so that population-weighted average is 1, size SxJ + + """ + # Return and error if age_wgts is not a vector of size (80,) + if age_wgts.shape[0] != 80: + err = "Vector age_wgts does not have 80 elements." + raise RuntimeError(err) + # Return and error if abil_wgts is not a vector of size (7,) + if abil_wgts.shape[0] != 7: + err = "Vector abil_wgts does not have 7 elements." + raise RuntimeError(err) + + # 1) Generate polynomials using USA data and use them to get income profiles for + # ages 21 to 80. + one = np.array( + [ + -0.09720122, + 0.05995294, + 0.17654618, + 0.21168263, + 0.21638731, + 0.04500235, + 0.09229392, + ] + ) + two = np.array( + [ + 0.00247639, + -0.00004086, + -0.00240656, + -0.00306555, + -0.00321041, + 0.00094253, + 0.00012902, + ] + ) + three = np.array( + [ + -0.00001842, + -0.00000521, + 0.00001039, + 0.00001438, + 0.00001579, + -0.00001470, + -0.00001169, + ] + ) + const = np.array( + [ + 3.41e00, + 0.69689692, + -0.78761958, + -1.11e00, + -0.93939272, + 1.60e00, + 1.89e00, + ] + ) + ages_short = np.tile(np.linspace(21, 80, 60).reshape((60, 1)), (1, 7)) + log_abil_paths = ( + const + + (one * ages_short) + + (two * (ages_short**2)) + + (three * (ages_short**3)) + ) + abil_paths = np.exp(log_abil_paths) + + # New estimated coefficients for ZAF after adjustment by income (J) and by age (S) + const = np.array( + [ + 1.10766851280735, + -1.47205271208099, + -2.79826519632522, + -2.84592025503416, + -2.33264177437992, + 0.820108734133472, + 0.573684959034946, + ] + ) + one = np.array( + [ + -0.0577752937758472, + 0.0993788662241527, + 0.215972106224152, + 0.251108556224153, + 0.255813236224153, + 0.0844282762241525, + 0.131719846224152, + ] + ) + two = np.array( + [ + 0.00313926193376278, + 0.000622011933762788, + -0.00174368806623721, + -0.00240267806623721, + -0.00254753806623722, + 0.00160540193376279, + 0.000791891933762785, + ] + ) + three = np.array( + [ + -0.000035350068460927, + -2.21400684609271e-05, + -6.54006846092713e-06, + -2.55006846092713e-06, + -1.14006846092704e-06, + -3.16300684609271e-05, + -2.86200684609271e-05, + ] + ) + # compute the lifetime income profiles using the new coefficients + ages_short_adj = np.tile(np.linspace(21, 80, 60).reshape((60, 1)), (1, 7)) + log_abil_paths_adj = ( + const + + (one * ages_short_adj) + + (two * (ages_short_adj**2)) + + (three * (ages_short_adj**3)) + ) + abil_paths_adj = np.exp(log_abil_paths_adj) + + e_orig = np.zeros((80, 7)) + e_orig[:60, :] = abil_paths_adj + e_orig[60:, :] = 0.0 + + # 2) Forecast (with some art) the path of the final 20 years of + # ability types. This following variable is what percentage of + # ability at age 80 ability falls to at age 100. In general, we + # wanted people to lose half of their ability over a 20-year + # period. The first entry is 0.47, though, because nothing higher + # would converge. The second-to-last is 0.7 because this group + # actually has a slightly higher ability at age 80 than the last + # group, so this value makes it decrease more so it ends up being + # monotonic. + abil_deprec = np.array([0.47, 0.5, 0.5, 0.5, 0.5, 0.7, 0.5]) + # Initial guesses for the arctan. They're pretty sensitive. + init_guesses = np.array( + [ + [58, 0.0756438545595, -5.6940142786], + [27, 0.069, -5], + [35, 0.06, -5], + [37, 0.339936555352, -33.5987329144], + [70.5229181668, 0.0701993896947, -6.37746859905], + [35, 0.06, -5], + [35, 0.06, -5], + ] + ) + for j in range(7): + e_orig[60:, j] = arctan_fit( + e_orig[59, j], + one[j], + two[j], + three[j], + abil_deprec[j], + init_guesses[j], + ) + + # 3) Rescale the lifetime earnings path matrix so that the + # population weighted average equals 1. + e_orig_scaled = ( + e_orig + / (e_orig * age_wgts.reshape(80, 1) * abil_wgts.reshape(1, 7)).sum() + ) + + if plot: + ages_long = np.linspace(21, 100, 80) + abil_midp = np.array([12.5, 37.5, 60.0, 75.0, 85.0, 94.5, 99.5]) + # Plot original unscaled 80 x 7 ability matrix + kwargs = {"filesuffix": "_orig_unscaled"} + pp.plot_income_data( + ages_long, abil_midp, abil_wgts, e_orig, OUTPUT_DIR, **kwargs + ) + + # Plot original scaled 80 x 7 ability matrix + kwargs = {"filesuffix": "_orig_scaled"} + pp.plot_income_data( + ages_long, + abil_midp, + abil_wgts, + e_orig_scaled, + OUTPUT_DIR, + **kwargs, + ) + + return e_orig_scaled diff --git a/ogzaf/input_output.py b/ogzaf/input_output.py new file mode 100644 index 0000000..1d462a5 --- /dev/null +++ b/ogzaf/input_output.py @@ -0,0 +1,80 @@ +import pandas as pd +import numpy as np +from ogzaf.constants import CONS_DICT, PROD_DICT + +""" +Read in SAM file +""" +# Read in SAM file +storage_options = {"User-Agent": "Mozilla/5.0"} +SAM_path = "https://www.wider.unu.edu/sites/default/files/Data/SASAM-2015-Data-Resource.xlsx" +SAM = pd.read_excel( + SAM_path, + sheet_name="Micro SAM 2015", + skiprows=6, + index_col=0, + storage_options=storage_options, +) + + +def get_alpha_c(SAM=SAM, cons_dict=CONS_DICT): + """ + Calibrate the alpha_c vector, showing the shares of household + expenditures for each consumption category + + Args: + SAM (pd.DataFrame): SAM file + cons_dict (dict): Dictionary of consumption categories + + Returns: + alpha_c (dict): Dictionary of shares of household expenditures + """ + alpha_c = {} + overall_sum = 0 + for key, value in cons_dict.items(): + # note the subtraction of the row to focus on domestic consumption + category_total = ( + SAM.loc[SAM.index.isin(value), "total"].sum() + - SAM.loc[SAM.index.isin(value), "row"].sum() + ) + alpha_c[key] = category_total + overall_sum += category_total + for key, value in cons_dict.items(): + alpha_c[key] = alpha_c[key] / overall_sum + + return alpha_c + + +def get_io_matrix(SAM=SAM, cons_dict=CONS_DICT, prod_dict=PROD_DICT): + """ + Calibrate the io_matrix array. This array relates the share of each + production category in each consumption category + + Args: + SAM (pd.DataFrame): SAM file + cons_dict (dict): Dictionary of consumption categories + prod_dict (dict): Dictionary of production categories + + Returns: + io_df (pd.DataFrame): Dataframe of io_matrix + """ + # Create initial matrix as dataframe of 0's to fill in + io_dict = {} + for key in prod_dict.keys(): + io_dict[key] = np.zeros(len(cons_dict.keys())) + io_df = pd.DataFrame(io_dict, index=cons_dict.keys()) + # Fill in the matrix + # Note, each cell in the SAM represents a payment from the columns + # account to the row account + # (see https://www.un.org/en/development/desa/policy/capacity/presentations/manila/6_sam_mams_philippines.pdf) + # We are thus going to take the consumption categories from rows and + # the production categories from columns + for ck, cv in cons_dict.items(): + for pk, pv in prod_dict.items(): + io_df.loc[io_df.index == ck, pk] = SAM.loc[ + SAM.index.isin(cv), pv + ].values.sum() + # change from levesl to share (where each row sums to one) + io_df = io_df.div(io_df.sum(axis=1), axis=0) + + return io_df diff --git a/ogzaf/macro_params.py b/ogzaf/macro_params.py index 9c745d9..f000e61 100644 --- a/ogzaf/macro_params.py +++ b/ogzaf/macro_params.py @@ -1,14 +1,18 @@ """ -This module uses data from FRED to find values for parameters for the +This module uses data from World Bank WDI, World Bank Quarterly Public Sector Debt (QPSD) database, +UN Data Portal, and FRED to find values for parameters for the OG-ZAF model that rely on macro data for calibration. """ # imports import pandas_datareader.data as web +from pandas_datareader import wb import pandas as pd import numpy as np import datetime import statsmodels.api as sm +from ogzaf.utils import get_legacy_session +from io import StringIO def get_macro_params(): @@ -18,101 +22,215 @@ def get_macro_params(): # set beginning and end dates for data # format is year (1940),month (1),day (1) + country_iso = "ZAF" start = datetime.datetime(1947, 1, 1) end = datetime.date.today() # go through today baseline_date = datetime.datetime(2019, 3, 31) + baseline_yearquarter = ( + "2019Q1" # The WB QPSD database has the date in YYYYQQ format + ) + + """ + This retrieves annual data from the World Bank World Development Indicators. + """ - variable_dict = { - "GDP Per Capita": "NYGDPPCAPKDIND", - "Labor share": "LABSHPINA156NRUG", - "Debt to GDP ratio": "GGGDTAINA188N", - #"BAA Corp Bond Rates": "DBAA", - # "10 year govt bond rate": "INDIRLTLT01STM", - #"Total gov transfer payments": "B087RC1Q027SBEA", - #"Social Security payments": "W823RC1", - "Gov expenditures": "INDGFCEQDSMEI", - #"Gov interest payments": "A091RC1Q027SBEA", - "Real GDP": "RGDPNAINA666NRUG", - "Nominal GDP": "MKTGDPINA646NWDB", + wb_a_variable_dict = { + "GDP per capita (constant 2015 US$)": "NY.GDP.PCAP.KD", + "Real GDP (constant 2015 US$)": "NY.GDP.MKTP.KD", + "Nominal GDP (current US$)": "NY.GDP.MKTP.CD", + # "General government debt (percentage of GDP)": "GC.DOD.TOTL.GD.ZS", + # "GDP per capita (current US$)": "NY.GDP.PCAP.CD", + # "GDP per person employed (constant 2017 PPP$)": "SL.GDP.PCAP.EM.KD", } # pull series of interest using pandas_datareader - fred_data = web.DataReader(variable_dict.values(), "fred", start, end) - fred_data.rename( - columns=dict((y, x) for x, y in variable_dict.items()), inplace=True + wb_data_a = wb.download( + indicator=wb_a_variable_dict.values(), + country=country_iso, + start=start, + end=end, + ) + wb_data_a.rename( + columns=dict((y, x) for x, y in wb_a_variable_dict.items()), + inplace=True, ) - # make sure all dollar value data are in billions - fred_data["Debt held by public"] = fred_data["Debt held by public"] / 1000 - - # Separate quartely, monthly, and annual dataseries - fred_data_q = ( - fred_data[ - [ - "Debt held by public", - "Nominal GDP", - "Total gov transfer payments", - "Social Security payments", - "Gov expenditures", - "Gov interest payments", - "GDP Per Capita", - ] - ] - .resample("Q") - .mean() + """ + This retrieves quarterly data from the World Bank Quarterly Public Sector Debt database. + The command extracts all available data even when start and end dates are specified. + """ + + wb_q_variable_dict = { + "Gross PSD USD - domestic creditors": "DP.DOD.DECD.CR.PS.CD", + "Gross PSD USD - external creditors": "DP.DOD.DECX.CR.PS.CD", + "Gross PSD Gen Gov - percentage of GDP": "DP.DOD.DECT.CR.GG.Z1", + } + + # pull series of interest using pandas_datareader + wb_data_q = wb.download( + indicator=wb_q_variable_dict.values(), + country=country_iso, + start=start, + end=end, + ) + wb_data_q.rename( + columns=dict((y, x) for x, y in wb_q_variable_dict.items()), + inplace=True, + ) + + # Remove the hierarchical index (country and year) of wb_data_q and create a single row index using year + wb_data_q = wb_data_q.reset_index() + wb_data_q = wb_data_q.set_index("year") + + """ + This retrieves labour share data from the United Nations Data Portal API + (see https://data.un.org) + """ + + target = ( + "https://data.un.org/ws/rest/data/IAEG-SDGs,DF_SDG_GLH/" + + "..SL_EMP_GTOTL.710..........." + + "?startPeriod=" + + str(start.year) + + "&" + + "endPeriod=" + + str(end.year) + + "&format=csv" + ) + + response = get_legacy_session().get(target) + + # Check if the request was successful before processing + if response.status_code == 200: + csv_content = StringIO(response.text) + df_temp = pd.read_csv(csv_content) + else: + print( + f"Failed to retrieve data. HTTP status code: {response.status_code}" + ) + + un_data_a = df_temp[["TIME_PERIOD", "OBS_VALUE"]] + + """ + This retrieves data from FRED. + """ + + fred_variable_dict = { + # "Labor share": "LABSHPINA156NRUG", + # "BAA Corp Bond Rates": "DBAA", + # "10 year govt bond rate": "IRLTLT01ZAM156N", + "Total gov transfer payments": "B087RC1Q027SBEA", + "Social Security payments": "W823RC1", + "Gov interest payments": "A091RC1Q027SBEA", + } + + # pull series of interest using pandas_datareader + fred_data = web.DataReader(fred_variable_dict.values(), "fred", start, end) + fred_data.rename( + columns=dict((y, x) for x, y in fred_variable_dict.items()), + inplace=True, ) - fred_data_a = fred_data[["Labor share"]] - fred_data_d = fred_data[["BAA Corp Bond Rates", "10 year govt bond rate"]] + + # Separate quartely, monthly, and annual FRED dataseries + + # fred_data_d = fred_data[["BAA Corp Bond Rates", "10 year govt bond rate"]] # initialize a dictionary of parameters macro_parameters = {} # print(fred_data.loc(str(baseline_date))) # find initial_debt_ratio - macro_parameters["initial_debt_ratio"] = pd.Series( - fred_data_q["Debt held by public"] / fred_data_q["Nominal GDP"] - ).loc[baseline_date] + macro_parameters["initial_debt_ratio"] = ( + pd.Series(wb_data_q["Gross PSD Gen Gov - percentage of GDP"]).loc[ + baseline_yearquarter + ] + ) / 100 + + # find initial_foreign_debt_ratio + macro_parameters["initial_foreign_debt_ratio"] = pd.Series( + wb_data_q["Gross PSD USD - external creditors"] + / ( + wb_data_q["Gross PSD USD - domestic creditors"] + + wb_data_q["Gross PSD USD - external creditors"] + ) + ).loc[baseline_yearquarter] + + # find zeta_D (Share of new debt issues from government that are purchased by foreigners) + macro_parameters["zeta_D"] = [ + pd.Series( + wb_data_q["Gross PSD USD - external creditors"] + / ( + wb_data_q["Gross PSD USD - domestic creditors"] + + wb_data_q["Gross PSD USD - external creditors"] + ) + ).loc[baseline_yearquarter] + ] # find alpha_T - macro_parameters["alpha_T"] = pd.Series( - ( - fred_data_q["Total gov transfer payments"] - - fred_data_q["Social Security payments"] - ) - / fred_data_q["Nominal GDP"] - ).loc[baseline_date] + macro_parameters["alpha_T"] = [0.04] + # macro_parameters["alpha_T"] = [ + # pd.Series( + # ( + # fred_data_q["Total gov transfer payments"] + # - fred_data_q["Social Security payments"] + # ) + # / fred_data_q["Nominal GDP"] + # ).loc[baseline_date] + # ] # find alpha_G - macro_parameters["alpha_G"] = pd.Series( - ( - fred_data_q["Gov expenditures"] - - fred_data_q["Total gov transfer payments"] - - fred_data_q["Gov interest payments"] - ) - / fred_data_q["Nominal GDP"] - ).loc[baseline_date] + macro_parameters["alpha_G"] = [0.27] + # macro_parameters["alpha_G"] = [ + # pd.Series( + # ( + # fred_data_q["Gov expenditures"] + # - fred_data_q["Total gov transfer payments"] + # - fred_data_q["Gov interest payments"] + # ) + # / fred_data_q["Nominal GDP"] + # ).loc[baseline_date] + # ] # find gamma - macro_parameters["gamma"] = 1 - fred_data_a["Labor share"].mean() + macro_parameters["gamma"] = [ + 1 + - ( + ( + un_data_a.loc[ + un_data_a["TIME_PERIOD"] == baseline_date.year, "OBS_VALUE" + ].squeeze() + ) + / 100 + ) + ] # find g_y - macro_parameters["g_y"] = ( - fred_data_q["GDP Per Capita"].pct_change(periods=4, freq="Q").mean() + macro_parameters["g_y_annual"] = ( + wb_data_a["GDP per capita (constant 2015 US$)"].pct_change(-1).mean() ) + """" + We want to use the non linear relationship estimated by Li, Magud, Werner, Witte (2021), available here: https://www.imf.org/en/Publications/WP/Issues/2021/06/04/The-Long-Run-Impact-of-Sovereign-Yields-on-Corporate-Yields-in-Emerging-Markets-50224 + + Steps: + 1) Generate modelled corporate yields (corp_yhat) for a range of sovereign yields (sov_y) using the estimated equation in col 2 of table 8 (and figure 3). + 2) Estimate the OLS using sovereign yields as the dependent variable + """ + """ # # estimate r_gov_shift and r_gov_scale - rate_data = fred_data_d[ - ["10 year govt bond rate", "BAA Corp Bond Rates"] - ].dropna() - rate_data["constant"] = np.ones(len(rate_data.index)) - # mod = PanelOLS(fred_data['10 year govt bond rate'], - # fred_data[['constant', 'BAA Corp Bond Rates']]) + sov_y = np.arange(20, 120) / 10 + corp_yhat = 8.199 - (2.975 * sov_y) + (0.478 * sov_y**2) + corp_yhat = sm.add_constant(corp_yhat) mod = sm.OLS( - rate_data["10 year govt bond rate"], - rate_data[["constant", "BAA Corp Bond Rates"]], + sov_y, + corp_yhat, ) res = mod.fit() - macro_parameters["r_gov_shift"] = res.params["BAA Corp Bond Rates"] - macro_parameters["r_gov_scale"] = res.params["constant"] + # first term is the constant and needs to be divided by 100 to have the correct unit. Second term is the coefficient + macro_parameters["r_gov_shift"] = (-res.params[0] / 100) # constant = 0.0337662504 + macro_parameters["r_gov_scale"] = res.params[1] # coefficient = 0.24484764 + """ + macro_parameters["r_gov_shift"] = [-0.0337662504] + macro_parameters["r_gov_scale"] = [0.24484764] return macro_parameters diff --git a/ogzaf/ogzaf_default_parameters.json b/ogzaf/ogzaf_default_parameters.json index 5512fda..b52d6e4 100644 --- a/ogzaf/ogzaf_default_parameters.json +++ b/ogzaf/ogzaf_default_parameters.json @@ -138,19 +138,19 @@ "alpha_I": [0.0003], "initial_Kg_ratio": 0.2, "rho_G": 0.1, - "debt_ratio_ss": 1.0, + "debt_ratio_ss": 1.2, "initial_debt_ratio": 0.62, - "r_gov_scale": 1.0, - "r_gov_shift": 0.00, - "cit_rate": [[0.24]], + "r_gov_scale": [1.0], + "r_gov_shift": [0.00], + "cit_rate": [[0.27]], "c_corp_share_of_assets": 0.7, "adjustment_factor_for_cit_receipts": [0.30], - "tau_c": [[0.0]], + "tau_c": [[0.05]], "delta_tau_annual": [[0.04]], "h_wealth": [0.1], 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CustomHttpAdapter(requests.adapters.HTTPAdapter): + """ + The UN Data Portal server doesn't support "RFC 5746 secure renegotiation". This causes and error when the client is using OpenSSL 3, which enforces that standard by default. + The fix is to create a custom SSL context that allows for legacy connections. This defines a function get_legacy_session() that should be used instead of requests(). + """ + + # "Transport adapter" that allows us to use custom ssl_context. + def __init__(self, ssl_context=None, **kwargs): + self.ssl_context = ssl_context + super().__init__(**kwargs) + + def init_poolmanager(self, connections, maxsize, block=False): + self.poolmanager = urllib3.poolmanager.PoolManager( + num_pools=connections, + maxsize=maxsize, + block=block, + ssl_context=self.ssl_context, + ) + + +def get_legacy_session(): + ctx = ssl.create_default_context(ssl.Purpose.SERVER_AUTH) + ctx.options |= 0x4 # OP_LEGACY_SERVER_CONNECT #in Python 3.12 you will be able to switch from 0x4 to ssl.OP_LEGACY_SERVER_CONNECT. + session = requests.session() + session.mount("https://", CustomHttpAdapter(ctx)) + return session diff --git a/pyproject.toml b/pyproject.toml index 9bc01d5..0da40b0 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,3 +1,7 @@ +[build-system] +requires = ["setuptools>=61.0"] +build-backend = "setuptools.build_meta" + # Configuration for Black. # NOTE: you have to use single-quoted strings in TOML for regular expressions. @@ -7,5 +11,5 @@ [tool.black] line-length = 79 -target-version = ['py36', 'py37', 'py38'] +target-version = ["py39", "py310", "py311"] include = '\.pyi?$' diff --git a/setup.py b/setup.py index d834f1a..09b8eee 100644 --- a/setup.py +++ b/setup.py @@ -1,40 +1,59 @@ -try: - from setuptools import setup -except ImportError: - from distutils.core import setup +"""This file contains the OG-ZAF package's metadata and dependencies.""" -with open("README.md") as f: - longdesc = f.read() +from setuptools import find_packages, setup -version = "0.0.0" +with open("README.md", "r") as readme_file: + readme = readme_file.read() -config = { - "description": "South Africa Calibration for OG-Core", - "long_description": longdesc, - "url": "https://github.com/EAPD-DRB/OG-ZAF/", - "download_url": "https://github.com/EAPD-DRB/OG-ZAF/", - "version": version, - "license": "CC0 1.0 Universal public domain dedication", - "packages": ["ogzaf"], - "include_package_data": True, - "name": "ogzaf", - "install_requires": [], - "package_data": {"ogzaf": ["data/*"]}, - "classifiers": [ - "Development Status :: 2 - Pre-Alpha", +setup( + name="ogzaf", + version="0.0.1", + author="Marcelo LaFleur, Richard W. Evans, and Jason DeBacker", + license="CC0 1.0 Universal (CC0 1.0) Public Domain Dedication", + description="South Africa Calibration for OG-Core", + long_description=readme, + long_description_content_type="text/markdown", + classifiers=[ + "Development Status :: 5 - Production/Stable", "Intended Audience :: Developers", "Natural Language :: English", - "License :: OSI Approved :: CC0 1.0 Universal public domain dedication", - "Operating System :: OS Independent", + "License :: OSI Approved :: Common Public License", + "Operating System :: POSIX", "Programming Language :: Python", - "Programming Language :: Python :: 3", - "Programming Language :: Python :: 3.7", - "Programming Language :: Python :: 3.8", "Programming Language :: Python :: 3.9", "Programming Language :: Python :: 3.10", + "Programming Language :: Python :: 3.11", + "Topic :: Scientific/Engineering :: Information Analysis", "Topic :: Software Development :: Libraries :: Python Modules", ], - "tests_require": ["pytest"], -} - -setup(**config) + keywords="South Africa calibration of large scale overlapping generations model of fiscal policy", + url="https://github.com/EAPD-DRB/OG-ZAF/", + download_url="https://github.com/EAPD-DRB/OG-ZAF/", + project_urls={ + "Issue Tracker": "https://github.com/EAPD-DRB/OG-ZAF/issues", + }, + packages=["ogzaf"], + package_data={"ogzaf": ["ogusa_default_parameters.json", "data/*"]}, + include_packages=True, + python_requires=">=3.7.7", + install_requires=[ + "numpy", + "psutil", + "scipy>=1.7.1", + "pandas>=1.2.5", + "matplotlib", + "dask>=2.30.0", + "distributed>=2.30.1", + "paramtools>=0.15.0", + "requests", + "pandas-datareader", + "xlwt", + "openpyxl>=3.1.2", + "statsmodels", + "linearmodels", + "black", + "linecheck", + "ogcore", + ], + tests_require=["pytest"], +) diff --git a/tests/test_income.py b/tests/test_income.py new file mode 100644 index 0000000..8af660c --- /dev/null +++ b/tests/test_income.py @@ -0,0 +1,99 @@ +""" +Tests of income.py module +""" + +import pytest +import numpy as np +from ogzaf import income + + +def test_arctan_func(): + """ + Test of arctan_func() + """ + expected_vals = np.array([0.14677821, 0.083305594, 0.057901228]) + xvals = np.array([1, 2, 3]) + a = 1.3 + b = 2.2 + c = 0.5 + test_vals = income.arctan_func(xvals, a, b, c) + + assert np.allclose(test_vals, expected_vals) + + +def test_arctan_deriv_func(): + """ + Test of arctan_deriv_func() + """ + expected_vals = np.array([-0.109814991, -0.036400091, -0.017707961]) + xvals = np.array([1, 2, 3]) + a = 1.3 + b = 2.2 + c = 0.5 + test_vals = income.arctan_deriv_func(xvals, a, b, c) + + assert np.allclose(test_vals, expected_vals) + + +def test_arc_error(): + """ + Test of arc_error() + """ + expected_vals = np.array([30.19765553, -1.40779391, 14.19212336]) + a = 1.3 + b = 2.2 + c = 0.5 + abc_vals = (a, b, c) + first_point = 30.2 + coef1 = 0.05995294 + coef2 = -0.00004086 + coef3 = -0.00000521 + abil_deprec = 0.47 + params = (first_point, coef1, coef2, coef3, abil_deprec) + test_vals = income.arc_error(abc_vals, params) + + assert np.allclose(test_vals, expected_vals) + + +@pytest.mark.local +def test_arctan_fit(): + """ + Test arctan_fit() function + """ + expected_vals = np.array( + [ + 30.19999399, + 30.19998699, + 30.19997918, + 30.19997039, + 30.19996043, + 30.19994904, + 30.1999359, + 30.19992057, + 30.19990246, + 30.19988072, + 30.19985415, + 30.19982094, + 30.19977824, + 30.19972131, + 30.1996416, + 30.19952204, + 30.19932277, + 30.19892423, + 30.19772859, + 14.19399974, + ] + ) + a = 1.3 + b = 2.2 + c = 0.5 + init_guesses = (a, b, c) + first_point = 30.2 + coef1 = 0.05995294 + coef2 = -0.00004086 + coef3 = -0.00000521 + abil_deprec = 0.47 + test_vals = income.arctan_fit( + first_point, coef1, coef2, coef3, abil_deprec, init_guesses + ) + assert np.allclose(test_vals, expected_vals) diff --git a/tests/test_io_data/TxFuncEst_policy.pkl b/tests/test_io_data/TxFuncEst_policy.pkl new file mode 100644 index 0000000..1d7883f Binary 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+2.550000004703179002e-01 +0.000000000000000000e+00 +1.500000002124579623e-01 +2.499999995052348822e-01 +2.500000002328306437e-01 diff --git a/tests/test_io_data/tax_dict_for_tests.pkl b/tests/test_io_data/tax_dict_for_tests.pkl new file mode 100644 index 0000000..758fa71 Binary files /dev/null and b/tests/test_io_data/tax_dict_for_tests.pkl differ diff --git a/tests/test_run_example.py b/tests/test_run_example.py new file mode 100644 index 0000000..c3cf106 --- /dev/null +++ b/tests/test_run_example.py @@ -0,0 +1,44 @@ +""" +This model tests whether using the `OG-ZAF/examples/run_og_zaf.py` +work by making sure that it does not break (is still running) after +5 minutes (300 seconds). +""" + +import multiprocessing +import time +import os +import sys +import importlib.util +from pathlib import Path +import pytest + + +def call_run_og_zaf(): + cur_path = os.path.split(os.path.abspath(__file__))[0] + path = Path(cur_path) + roe_fldr = os.path.join(path, "..", "examples") + roe_file_path = os.path.join(roe_fldr, "run_og_zaf.py") + spec = importlib.util.spec_from_file_location( + "run_og_zaf.py", roe_file_path + ) + roe_module = importlib.util.module_from_spec(spec) + sys.modules["run_og_zaf.py"] = roe_module + spec.loader.exec_module(roe_module) + roe_module.main() + + +@pytest.mark.local +def test_run_og_zaf(f=call_run_og_zaf): + p = multiprocessing.Process(target=f, name="run_og_zaf", args=()) + p.start() + time.sleep(300) + if p.is_alive(): + p.terminate() + p.join() + timetest = True + else: + print("run_og_zaf.py did not run for minimum time") + timetest = False + print("timetest ==", timetest) + + assert timetest