diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..e5b3363 --- /dev/null +++ b/.gitignore @@ -0,0 +1,45 @@ +*.pyc +MANIFEST +build/* +dist/* +*.cache/ +.coverage +.coveragerc +htmlcov/* +*.aux +*.bbl +*.blg +*.fdb_latexmk +*.idx +*.ilg +*.ind +*.lof +*.log +*.lot +*.out +*.pdfsync +*.synctex.gz +*.toc +*.swp +*.asv +*.nav +*.snm +*.gz +*.bib.bak +*.fls +*.m~ +*.sublime* +*.DS_Store* +*puf.csv +*tax_func_loop_inputs_large.pkl +*/OUTPUT/* +envs/ +*.egg-info/ +*dask-worker-space* +.ipynb_checkpoints* +*/__pycache__/* +docs/book/_build/* +docs/build* +examples/OG-ZAF_example_plots/* +examples/ogzaf_example_output.csv +examples/OG-ZAF-Example/* diff --git a/LICENSE.md b/LICENSE.md new file mode 100644 index 0000000..f06099e --- /dev/null +++ b/LICENSE.md @@ -0,0 +1,31 @@ +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/README.md b/README.md index c568157..1a80438 100644 --- a/README.md +++ b/README.md @@ -1,2 +1,62 @@ +[![Python 3.7+](https://img.shields.io/badge/python-3.7%2B-blue.svg)](https://www.python.org/downloads/release/python-377/) + # OG-ZAF -Overlapping-generations macroeconomic model for evaluating fiscal policy in South Africa +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 + * 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 .` +* 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. +* Model outputs will be saved in the following files: + * `./examples/OG-ZAF_example_plots` + * This folder will contain a number of plots generated from OG-Core to help you visualize the output from your run + * `./examples/ogzaf_example_output.csv` + * This is a summary of the percentage changes in macro variables over the first ten years and in the steady-state. + * `./examples/OG-ZAF-Example/OUTPUT_BASELINE/model_params.pkl` + * Model parameters used in the baseline run + * See [`ogcore.execute.py`](https://github.com/PSLmodels/OG-Core/blob/master/ogcore/execute.py) for items in the dictionary object in this pickle file + * `./examples/OG-ZAF-Example/OUTPUT_BASELINE/SS/SS_vars.pkl` + * Outputs from the model steady state solution under the baseline policy + * See [`ogcore.SS.py`](https://github.com/PSLmodels/OG-Core/blob/master/ogcore/SS.py) for what is in the dictionary object in this pickle file + * `./examples/OG-ZAF-Example/OUTPUT_BASELINE/TPI/TPI_vars.pkl` + * Outputs from the model timepath solution under the baseline policy + * 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. + +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. + +Once the package is installed, one can adjust parameters in the OG-Core `Specifications` object using the `Calibration` class as follows: + +``` +from ogcore.parameters import Specifications +from ogind.calibrate import Calibration +p = Specifications() +c = Calibration(p) +updated_params = c.get_dict() +p.update_specifications({'initial_debt_ratio': updated_params['initial_debt_ratio']}) +``` + +## Disclaimer +The organization of this repository will be changing rapidly, but the `OG-ZAF/examples/run_og_zaf.py` script will be kept up to date to run with the master branch of this repo. + +## Core Maintainers + +The core maintainers of the OG-ZAF repository are: + +* Marcelo LaFleur (GitHub handle: [@SeaCelo](https://github.com/SeaCelo)), Senior Economist, Department of Economic and Social Affairs (DESA), United Nations +* [Richard W. Evans](https://sites.google.com/site/rickecon/) (GitHub handle: [@rickecon](https://github.com/rickecon)), Senior Research Fellow and Director of Open Policy, Center for Growth and Opportunity at Utah State University; President, Open Research Group, Inc. + +## Citing OG-ZAF + +OG-ZAF (Version #.#.#)[Source code], https://github.com/EAPD-DRB/OG-ZAF diff --git a/environment.yml b/environment.yml new file mode 100644 index 0000000..9fbafbd --- /dev/null +++ b/environment.yml @@ -0,0 +1,34 @@ +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 +- pandas>=1.2.5 +- matplotlib +- dask>=2.30.0 +- dask-core>=2.30.0 +- distributed>=2.30.1 +- paramtools>=0.15.0 +- sphinx>=3.5.4 +- sphinx-book-theme>=0.1.3 +- pip +- pytest>=6.0 +- pytest-pep8 +- pytest-xdist +- pycodestyle +- pylint +- coverage +- requests +- xlwt +- openpyxl +- statsmodels +- linearmodels +- black +- pip: + - jupyter-book>=0.9.1 + - pandas-datareader + - ogcore diff --git a/examples/run_og_zaf.py b/examples/run_og_zaf.py new file mode 100644 index 0000000..a128d18 --- /dev/null +++ b/examples/run_og_zaf.py @@ -0,0 +1,126 @@ +# 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 +# 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-Example", "OUTPUT_BASELINE") + reform_dir = os.path.join(CUR_DIR, "OG-ZAF-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" + ) + ) + ) + ) + + # 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 = 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" + ) + ) + ) + ) + # additional parameters to change + updated_params = { + "cit_rate": [[0.35]], + } + p2.update_specifications(updated_params) + # 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_example_plots") + ) + + print("Percentage changes in aggregates:", ans) + # save percentage change output to csv file + ans.to_csv("ogind_example_output.csv") + + +if __name__ == "__main__": + # execute only if run as a script + main() diff --git a/ogzaf/calibrate.py b/ogzaf/calibrate.py new file mode 100644 index 0000000..9c90c01 --- /dev/null +++ b/ogzaf/calibrate.py @@ -0,0 +1,377 @@ +from ogzaf import demographics +from ogzaf import macro_params +import os +import numpy as np +from ogcore import txfunc +from ogcore.utils import safe_read_pickle, mkdirs +import pkg_resources + + +class Calibration: + """OG-ZAF calibration class""" + + def __init__( + self, + p, + estimate_tax_functions=False, + estimate_beta=False, + estimate_chi_n=False, + tax_func_path=None, + iit_reform={}, + guid="", + data="cps", + client=None, + num_workers=1, + ): + + self.estimate_tax_functions = estimate_tax_functions + self.estimate_beta = estimate_beta + self.estimate_chi_n = estimate_chi_n + if estimate_tax_functions: + self.tax_function_params = self.get_tax_function_parameters( + p, + iit_reform, + guid, + data, + client, + num_workers, + run_micro=True, + tax_func_path=tax_func_path, + ) + # if estimate_beta: + # self.beta_j = estimate_beta_j.beta_estimate(self) + # if estimate_chi_n: + # chi_n = self.get_chi_n() + + # Macro estimation + self.macro_params = macro_params.get_macro_params() + + # eta estimation + # self.eta = transfer_distribution.get_transfer_matrix() + + # zeta estimation + # 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 + # ) + # demographics for 80 period lives (needed for getting e below) + # demog80 = demographics.get_pop_objs(20, 80, p.T, 1, 100, 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, + # ) + + # Tax Functions + def get_tax_function_parameters( + self, + p, + iit_reform={}, + guid="", + data="", + client=None, + num_workers=1, + run_micro=False, + tax_func_path=None, + ): + """ + Reads pickle file of tax function parameters or estimates the + parameters from microsimulation model output. + + Args: + client (Dask client object): client + run_micro (bool): whether to estimate parameters from + microsimulation model + tax_func_path (string): path where find or save tax + function parameter estimates + + Returns: + None + + """ + # set paths if none given + if tax_func_path is None: + if p.baseline: + pckl = "TxFuncEst_baseline{}.pkl".format(guid) + tax_func_path = os.path.join(p.output_base, pckl) + print("Using baseline tax parameters from ", tax_func_path) + else: + pckl = "TxFuncEst_policy{}.pkl".format(guid) + tax_func_path = os.path.join(p.output_base, pckl) + print( + "Using reform policy tax parameters from ", tax_func_path + ) + # create directory for tax function pickles to be saved to + mkdirs(os.path.split(tax_func_path)[0]) + # If run_micro is false, check to see if parameters file exists + # and if it is consistent with Specifications instance + if not run_micro: + dict_params, run_micro = self.read_tax_func_estimate( + p, tax_func_path + ) + if run_micro: + micro_data, _ = get_micro_data.get_data( + baseline=p.baseline, + start_year=p.start_year, + reform=iit_reform, + data=data, + path=p.output_base, + client=client, + num_workers=num_workers, + ) + p.BW = len(micro_data) + dict_params = txfunc.tax_func_estimate( # pragma: no cover + micro_data, + p.BW, + p.S, + p.starting_age, + p.ending_age, + start_year=p.start_year, + baseline=p.baseline, + analytical_mtrs=p.analytical_mtrs, + tax_func_type=p.tax_func_type, + age_specific=p.age_specific, + reform=iit_reform, + data=data, + client=client, + num_workers=num_workers, + tax_func_path=tax_func_path, + ) + mean_income_data = dict_params["tfunc_avginc"][0] + frac_tax_payroll = np.append( + dict_params["tfunc_frac_tax_payroll"], + np.ones(p.T + p.S - p.BW) + * dict_params["tfunc_frac_tax_payroll"][-1], + ) + + # Reorder indices of tax function and tile for all years after + # budget window ends + num_etr_params = dict_params["tfunc_etr_params_S"].shape[2] + num_mtrx_params = dict_params["tfunc_mtrx_params_S"].shape[2] + num_mtry_params = dict_params["tfunc_mtry_params_S"].shape[2] + # First check to see if tax parameters that are used were + # estimated with a budget window and ages that are as long as + # the those implied based on the start year and model age. + # N.B. the tax parameters dictionary does not save the years + # that correspond to the parameter estimates, so the start year + # used there may name match what is used in a run that reads in + # some cached tax function parameters. Likewise for age. + params_list = ["etr", "mtrx", "mtry"] + BW_in_tax_params = dict_params["tfunc_etr_params_S"].shape[1] + S_in_tax_params = dict_params["tfunc_etr_params_S"].shape[0] + if p.BW != BW_in_tax_params: + print( + "Warning: There is a discrepency between the start" + + " year of the model and that of the tax functions!!" + ) + # After printing warning, make it work by tiling + if p.BW > BW_in_tax_params: + for item in params_list: + dict_params["tfunc_" + item + "_params_S"] = np.concatenate( + ( + dict_params["tfunc_" + item + "_params_S"], + np.tile( + dict_params["tfunc_" + item + "_params_S"][ + :, -1, : + ].reshape(S_in_tax_params, 1, num_etr_params), + (1, p.BW - BW_in_tax_params, 1), + ), + ), + axis=1, + ) + dict_params["tfunc_avg_" + item] = np.append( + dict_params["tfunc_avg_" + item], + np.tile( + dict_params["tfunc_avg_" + item][-1], + (p.BW - BW_in_tax_params), + ), + ) + if p.S != S_in_tax_params: + print( + "Warning: There is a discrepency between the ages" + + " used in the model and those in the tax functions!!" + ) + # After printing warning, make it work by tiling + if p.S > S_in_tax_params: + for item in params_list: + dict_params["tfunc_" + item + "_params_S"] = np.concatenate( + ( + dict_params["tfunc_" + item + "_params_S"], + np.tile( + dict_params["tfunc_" + item + "_params_S"][ + -1, :, : + ].reshape(1, p.BW, num_etr_params), + (p.S - S_in_tax_params, 1, 1), + ), + ), + axis=0, + ) + etr_params = np.empty((p.T, p.S, num_etr_params)) + mtrx_params = np.empty((p.T, p.S, num_mtrx_params)) + mtry_params = np.empty((p.T, p.S, num_mtry_params)) + etr_params[: p.BW, :, :] = np.transpose( + dict_params["tfunc_etr_params_S"][: p.S, : p.BW, :], axes=[1, 0, 2] + ) + etr_params[p.BW :, :, :] = np.tile( + np.transpose( + dict_params["tfunc_etr_params_S"][: p.S, -1, :].reshape( + p.S, 1, num_etr_params + ), + axes=[1, 0, 2], + ), + (p.T - p.BW, 1, 1), + ) + mtrx_params[: p.BW, :, :] = np.transpose( + dict_params["tfunc_mtrx_params_S"][: p.S, : p.BW, :], + axes=[1, 0, 2], + ) + mtrx_params[p.BW :, :, :] = np.transpose( + dict_params["tfunc_mtrx_params_S"][: p.S, -1, :].reshape( + p.S, 1, num_mtrx_params + ), + axes=[1, 0, 2], + ) + mtry_params[: p.BW, :, :] = np.transpose( + dict_params["tfunc_mtry_params_S"][: p.S, : p.BW, :], + axes=[1, 0, 2], + ) + mtry_params[p.BW :, :, :] = np.transpose( + dict_params["tfunc_mtry_params_S"][: p.S, -1, :].reshape( + p.S, 1, num_mtry_params + ), + axes=[1, 0, 2], + ) + + if p.constant_rates: + print("Using constant rates!") + # Make all ETRs equal the average + etr_params = np.zeros(etr_params.shape) + # set shift to average rate + etr_params[: p.BW, :, 10] = np.tile( + dict_params["tfunc_avg_etr"].reshape(p.BW, 1), (1, p.S) + ) + etr_params[p.BW :, :, 10] = dict_params["tfunc_avg_etr"][-1] + + # # Make all MTRx equal the average + mtrx_params = np.zeros(mtrx_params.shape) + # set shift to average rate + mtrx_params[: p.BW, :, 10] = np.tile( + dict_params["tfunc_avg_mtrx"].reshape(p.BW, 1), (1, p.S) + ) + mtrx_params[p.BW :, :, 10] = dict_params["tfunc_avg_mtrx"][-1] + + # # Make all MTRy equal the average + mtry_params = np.zeros(mtry_params.shape) + # set shift to average rate + mtry_params[: p.BW, :, 10] = np.tile( + dict_params["tfunc_avg_mtry"].reshape(p.BW, 1), (1, p.S) + ) + mtry_params[p.BW :, :, 10] = dict_params["tfunc_avg_mtry"][-1] + if p.zero_taxes: + print("Zero taxes!") + etr_params = np.zeros(etr_params.shape) + mtrx_params = np.zeros(mtrx_params.shape) + mtry_params = np.zeros(mtry_params.shape) + tax_param_dict = { + "etr_params": etr_params, + "mtrx_params": mtrx_params, + "mtry_params": mtry_params, + "mean_income_data": mean_income_data, + "frac_tax_payroll": frac_tax_payroll, + } + + return tax_param_dict + + def read_tax_func_estimate(self, p, tax_func_path): + """ + This function reads in tax function parameters from pickle + files. + + Args: + tax_func_path (str): path to pickle with tax function + parameter estimates + + Returns: + dict_params (dict): dictionary containing arrays of tax + function parameters + run_micro (bool): whether to estimate tax function parameters + + """ + flag = 0 + if os.path.exists(tax_func_path): + print("Tax Function Path Exists") + dict_params = safe_read_pickle(tax_func_path) + # check to see if tax_functions compatible + try: + if p.start_year != dict_params["start_year"]: + print( + "Model start year not consistent with tax " + + "function parameter estimates" + ) + flag = 1 + except KeyError: + pass + try: + if p.BW != dict_params["BW"]: + print( + "Model budget window length is " + + str(p.BW) + + " but the tax function parameter " + + "estimates have a budget window length of " + + str(dict_params["BW"]) + ) + flag = 1 + except KeyError: + pass + try: + if p.tax_func_type != dict_params["tax_func_type"]: + print( + "Model tax function type is not " + + "consistent with tax function parameter " + + "estimates" + ) + flag = 1 + except KeyError: + pass + if flag >= 1: + raise RuntimeError( + "Tax function parameter estimates at given path" + + " are not consistent with model parameters" + + " specified." + ) + else: + flag = 1 + print( + "Tax function parameter estimates do not exist at" + + " given path. Running new estimation." + ) + if flag >= 1: + dict_params = None + run_micro = True + else: + run_micro = False + + return dict_params, run_micro + + # method to return all newly calibrated parameters in a dictionary + def get_dict(self): + dict = {} + if self.estimate_tax_functions: + dict.update(self.tax_function_params) + # if self.estimate_beta: + # dict["beta_annual"] = self.beta + # if self.estimate_chi_n: + # dict["chi_n"] = self.chi_n + # dict["eta"] = self.eta + # dict["zeta"] = self.zeta + dict.update(self.macro_params) + # dict["e"] = self.e + # dict.update(self.demographic_params) + + return dict diff --git a/ogzaf/constants.py b/ogzaf/constants.py new file mode 100644 index 0000000..9620516 --- /dev/null +++ b/ogzaf/constants.py @@ -0,0 +1,357 @@ +import taxcalc + +SHOW_RUNTIME = False # Flag to display RuntimeWarnings when run model + +REFORM_DIR = "OUTPUT_REFORM" +BASELINE_DIR = "OUTPUT_BASELINE" + +# Default year for model runs +DEFAULT_START_YEAR = 2022 + + +VAR_LABELS = { + "Y": "GDP ($Y_t$)", + "C": "Consumption ($C_t$)", + "L": "Labor ($L_t$)", + "G": "Government Expenditures ($G_t$)", + "TR": "Lump sum transfers ($TR_t$)", + "B": "Wealth ($B_t$)", + "I_total": "Investment ($I_t$)", + "K": "Capital Stock ($K_t$)", + "K_d": "Domestically-owned Capital Stock ($K^d_t$)", + "K_f": "Foreign-owned Capital Stock ($K^f_t$)", + "D": "Government Debt ($D_t$)", + "D_d": "Domestically-owned Gov Debt ($D^d_t$)", + "D_f": "Foreign-owned Gov Debt ($D^f_t$)", + "r": "Real interest rate ($r_t$)", + "r_gov": "Real interest rate on gov debt ($r_{gov,t}$)", + "r_hh": "Real interest rate on HH portfolio ($r_{hh,t}$)", + "w": "Wage rate", + "BQ": "Aggregate bequests ($BQ_{j,t}$)", + "total_tax_revenue": "Total tax revenue ($REV_t$)", + "business_tax_revenue": "Business tax revenue", + "iit_revenue": "Individual income tax revenue", + "payroll_tax_revenue": "Payroll tax revenue", + "iit_payroll_tax_revenue": "IIT and payroll tax revenue", + "n_mat": "Labor Supply ($n_{j,s,t}$)", + "c_path": "Consumption ($c_{j,s,t}$)", + "bmat_splus1": "Savings ($b_{j,s+1,t+1}$)", + "bq_path": "Bequests ($bq_{j,s,t}$)", + "bmat_s": "Savings ($b_{j,s,t}$)", + "y_before_tax_mat": "Before tax income", + "etr_path": "Effective Tax Rate ($ETR_{j,s,t}$)", + "mtrx_path": "Marginal Tax Rate, Labor Income ($MTRx_{j,s,t}$)", + "mtry_path": "Marginal Tax Rate, Capital Income ($MTRy_{j,s,t}$)", + "tax_path": "Total Taxes", + "nssmat": "Labor Supply ($\\bar{n}_{j,s}$)", + "bssmat_s": "Savings ($\\bar{b}_{j,s}$)", + "bssmat_splus1": "Savings ($\\bar{b}_{j,s+1}$)", + "cssmat": "Consumption ($\\bar{c}_{j,s}$)", + "yss_before_tax_mat": "Before-tax Income", + "etr_ss": "Effective Tax Rate ($\\bar{ETR}_{j,s}$)", + "mtrx_ss": "Marginal Tax Rate, Labor Income ($\\bar{MTRx}_{j,s}$)", + "mtry_ss": "Marginal Tax Rate, Capital Income ($\\bar{MTRy}_{j,s}$)", + "ETR": "Effective Tax Rates", + "MTRx": "Marginal Tax Rates on Labor Income", + "MTRy": "Marginal Tax Rates on Capital Income", + "etr": "Effective Tax Rates", + "mtrx": "Marginal Tax Rates on Labor Income", + "mtry": "Marginal Tax Rates on Capital Income", + "Yss": "GDP ($\\bar{Y}$)", + "Css": "Consumption ($\\bar{C}$)", + "Lss": "Labor ($\\bar{L}$)", + "Gss": "Government Expenditures ($\\bar{G}$)", + "TR_ss": "Lump sum transfers, ($\\bar{TR}$)", + "Bss": "Wealth ($\\bar{B}$)", + "Iss_total": "Investment ($\\bar{I}$)", + "Kss": "Capital Stock ($\\bar{K}$)", + "K_d_ss": "Domestically-owned Capital Stock ($\\bar{K}^d$)", + "K_f_ss": "Foreign-owned Capital Stock ($\\bar{K}^f$)", + "Dss": "Government Debt ($\\bar{D}$)", + "D_d_ss": "Domestically-owned Gov Debt ($\\bar{D}^d$)", + "D_f_ss": "Foreign-owned Gov Debt ($\\bar{D}^f$)", + "rss": "Real interest rate ($\\bar{r}$)", + "r_gov_ss": "Real interest rate on gov debt ($\\bar{r}_{gov}$)", + "r_hh_ss": "Real interest rate on HH portfolio ($\\bar{r}_{hh}$)", + "wss": "Wage rate ($\\bar{w}$)", + "BQss": "Aggregate bequests ($\\bar{BQ}_{j}$)", + "debt_service_ss": "Debt service cost ($\\bar{r}_{gov}\\bar{D}$)", + "D/Y": "Debt to GDP ratio", + "T_Pss": "Government Pensions", +} + +ToGDP_LABELS = { + "D": "Debt-to-GDP ($D_{t}/Y_t$)", + "D_d": "Domestically-owned Debt-to-GDP ($D^d_{t}/Y_t$)", + "D_f": "Foreign-owned Debt-to-GDP ($D^f_{t}/Y_t$)", + "G": "Govt Spending-to-GDP ($G_{t}/Y_t$)", + "K": "Capital-Output Ratio ($K_{t}/Y_t$)", + "K_d": "Domestically-owned Capital-Output Ratio ($K^d_{t}/Y_t$)", + "K_f": "Foreign-owned Capital-Output Ratio ($K^f_{t}/Y_t$)", + "C": "Consumption-Output Ratio ($C_{t}/Y_t$)", + "I": "Investment-Output Ratio ($I_{t}/Y_t$)", + "total_tax_revenue": "Tax Revenue-to-GDP ($REV_{t}/Y_t$)", +} + +GROUP_LABELS = { + 7: { + 0: "0-25%", + 1: "25-50%", + 2: "50-70%", + 3: "70-80%", + 4: "80-90%", + 5: "90-99%", + 6: "Top 1%", + }, + 9: { + 0: "0-25%", + 1: "25-50%", + 2: "50-70%", + 3: "70-80%", + 4: "80-90%", + 5: "90-99%", + 6: "99-99.5%", + 7: "99.5-99.9%", + 8: "Top 0.1%", + }, + 10: { + 0: "0-25%", + 1: "25-50%", + 2: "50-70%", + 3: "70-80%", + 4: "80-90%", + 5: "90-99%", + 6: "99-99.5%", + 7: "99.5-99.9%", + 8: "99.9-99.99%", + 9: "Top 0.01%", + }, +} + +CBO_UNITS = { + "Y": r"Billions of ₹", + "r": "Percent", + "w_growth": "Percent", + "L_growth": "Percent", + "I_total": r"Billions of ₹", + "L": "2012=100", + "C": r"Billions of ₹", + "agg_pension_outlays": r"Billions of ₹", + "G": r"Billions of ₹", + "iit_revenue": r"Billions of ₹", + "payroll_tax_revenue": r"Billions of ₹", + "business_tax_revenue": r"Billions of ₹", + "wL": r"Billions of ₹", + "D": r"Billions of ₹", +} + +PARAM_LABELS = { + "start_year": ["Initial year", r"$\texttt{start_year}$"], + # 'Gamma': ['Initial distribution of savings', r'\hat{\Gamma}_{0}'], + # 'N': ['Initial population', 'N_{0}'], + "omega": ["Population by age over time", r"${{\omega_{s,t}}}_{s=1}^{S}$"], + # 'fert_rates': ['Fertility rates by age', + # r'\left{f_{s}\right}_{s=1}^{S}'], + "imm_rates": ["Immigration rates by age", r"${{i_{s}}}_{s=1}^{S}$"], + "rho": ["Mortality rates by age", r"${{\rho_{s}}}_{s=1}^{S}$"], + "e": ["Deterministic ability process", r"${{e_{j,s}}}_{j,s=1}^{J,S}$"], + "lambdas": [ + "Lifetime income group percentages", + r"${{\lambda_{j}}}_{j=1}^{J}$", + ], + "J": ["Number of lifetime income groups", "$J$"], + "S": ["Maximum periods in economically active individual life", "$S$"], + "E": ["Number of periods of youth economically outside the model", "$E$"], + "T": ["Number of periods to steady-state", "$T$"], + "retirement_age": ["Retirement age", "$R$"], + "ltilde": ["Maximum hours of labor supply", r"$\tilde{l}$"], + "beta": ["Discount factor", r"$\beta$"], + "sigma": ["Coefficient of constant relative risk aversion", r"$\sigma$"], + "frisch": ["Frisch elasticity of labor supply", r"$\nu$"], + "b_ellipse": ["Scale parameter in utility of leisure", "$b$"], + "upsilon": ["Shape parameter in utility of leisure", r"$\upsilon$"], + # 'k': ['Constant parameter in utility of leisure', 'k'], + "chi_n": [ + "Disutility of labor level parameters", + r"$\left{\chi^{n}_{s}\right}_{s=1}^{S}$", + ], + "chi_b": [ + "Utility of bequests level parameters", + r"$\left{\chi^{b}_{j}\right}_{j=1}^{J}$", + ], + "use_zeta": [ + "Whether to distribute bequests between lifetime income groups", + r"$\texttt{use_zeta}$", + ], + "zeta": ["Distribution of bequests", r"$\zeta$"], + "Z": ["Total factor productivity", "$Z_{t}$"], + "gamma": ["Capital share of income", r"$\gamma$"], + "epsilon": [ + "Elasticity of substitution between capital and labor", + r"\varepsilon", + ], + "delta": ["Capital depreciation rate", r"$\delta$"], + "g_y": [ + "Growth rate of labor augmenting technological progress", + r"$g_{y}$", + ], + "tax_func_type": [ + "Functional form used for income tax functions", + r"$\texttt{tax_func_type}$", + ], + "analytical_mtrs": [ + "Whether use analytical MTRs or estimate MTRs", + r"$\texttt{analytical_mtrs}$", + ], + "age_specific": [ + "Whether use age-specific tax functions", + r"$\texttt{age_specific}$", + ], + "tau_payroll": ["Payroll tax rate", r"$\tau^{p}_{t}$"], + # 'theta': ['Replacement rate by average income', + # r'\left{\theta_{j}\right}_{j=1}^{J}'], + "tau_bq": ["Bequest (estate) tax rate", r"$\tau^{BQ}_{t}}$"], + "tau_b": ["Entity-level business income tax rate", r"$\tau^{b}_{t}$"], + "delta_tau": [ + "Rate of depreciation for tax purposes", + r"$\delta^{\tau}_{t}$", + ], + "tau_c": ["Consumption tax rates", r"$\tau^{c}_{t,s,j}$"], + "h_wealth": ["Coefficient on linear term in wealth tax function", "$H$"], + "m_wealth": ["Constant in wealth tax function", "$M$"], + "p_wealth": ["Coefficient on level term in wealth tax function", "$P$"], + "budget_balance": [ + "Whether have a balanced budget in each period", + r"$\texttt{budget_balance}$", + ], + "baseline_spending": [ + "Whether level of spending constant between " + + "the baseline and reform runs", + r"$\texttt{baseline_spending}$", + ], + "alpha_T": ["Transfers as a share of GDP", r"$\alpha^{T}_{t}$"], + "eta": ["Distribution of transfers", r"$\eta_{j,s,t}$"], + "alpha_G": ["Government spending as a share of GDP", r"$\alpha^{G}_{t}$"], + "tG1": ["Model period in which budget closure rule starts", r"$t_{G1}$"], + "tG2": ["Model period in which budget closure rule ends", r"$t_{G2}$"], + "rho_G": ["Budget closure rule smoothing parameter", r"$\rho_{G}$"], + "debt_ratio_ss": ["Steady-state Debt-to-GDP ratio", r"$\bar{\alpha}_{D}$"], + "initial_debt_ratio": [ + "Initial period Debt-to-GDP ratio", + r"$\alpha_{D,0}$", + ], + "r_gov_scale": [ + "Scale parameter in government interest rate wedge", + r"$\tau_{d,t}$", + ], + "r_gov_shift": [ + "Shift parameter in government interest rate wedge", + r"$\mu_{d,t}$", + ], + "AIME_num_years": [ + "Number of years over which compute AIME", + r"$\texttt{AIME_num_years}$", + ], + "AIME_bkt_1": ["First AIME bracket threshold", r"$\texttt{AIME_bkt_1}$"], + "AIME_bkt_2": ["Second AIME bracket threshold", r"$\texttt{AIME_bkt_2}$"], + "PIA_rate_bkt_1": [ + "First AIME bracket PIA rate", + r"$\texttt{PIA_rate_bkt_1}$", + ], + "PIA_rate_bkt_2": [ + "Second AIME bracket PIA rate", + r"\texttt{PIA_rate_bkt_2}", + ], + "PIA_rate_bkt_3": [ + "Third AIME bracket PIA rate", + r"$\texttt{PIA_rate_bkt_3}$", + ], + "PIA_maxpayment": ["Maximum PIA payment", r"$\texttt{PIA_maxpayment}$"], + "PIA_minpayment": ["Minimum PIA payment", r"$\texttt{PIA_maxpayment}$"], + "replacement_rate_adjust": [ + "Adjustment to replacement rate", + r"$theta_{adj,t}$", + ], + "world_int_rate": ["World interest rate", r"$r^{*}_{t}$"], + "initial_foreign_debt_ratio": [ + "Share of government debt held by foreigners in initial period", + r"$D_{f,0}$", + ], + "zeta_D": [ + "Share of new debt issues purchased by foreigners", + r"$\zeta_{D, t}$", + ], + "zeta_K": [ + "Share of excess capital demand satisfied by foreigners", + r"$\zeta_{K, t}$", + ], + "nu": ["Dampening parameter for TPI", r"$\xi$"], + "maxiter": ["Maximum number of iterations for TPI", r"$\texttt{maxiter}$"], + "mindist_SS": ["SS solution tolerance", r"$\texttt{mindist_SS}$"], + "mindist_TPI": ["TPI solution tolerance", r"$\texttt{mindist_TPI}$"], +} + +# Ignoring the following: +# '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", +] diff --git a/ogzaf/data/demographic/india_pop_data.csv b/ogzaf/data/demographic/india_pop_data.csv new file mode 100644 index 0000000..580c5c7 --- /dev/null +++ b/ogzaf/data/demographic/india_pop_data.csv @@ -0,0 +1,105 @@ +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/demographics.py b/ogzaf/demographics.py new file mode 100755 index 0000000..f305e50 --- /dev/null +++ b/ogzaf/demographics.py @@ -0,0 +1,662 @@ +""" +------------------------------------------------------------------------ +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/macro_params.py b/ogzaf/macro_params.py new file mode 100644 index 0000000..9c745d9 --- /dev/null +++ b/ogzaf/macro_params.py @@ -0,0 +1,118 @@ +""" +This module uses data from 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 +import pandas as pd +import numpy as np +import datetime +import statsmodels.api as sm + + +def get_macro_params(): + """ + Compute values of parameters that are derived from macro data + """ + + # set beginning and end dates for data + # format is year (1940),month (1),day (1) + start = datetime.datetime(1947, 1, 1) + end = datetime.date.today() # go through today + baseline_date = datetime.datetime(2019, 3, 31) + + 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", + } + + # 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 + ) + + # 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() + ) + fred_data_a = fred_data[["Labor share"]] + 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] + + # 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] + + # 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] + + # find gamma + macro_parameters["gamma"] = 1 - fred_data_a["Labor share"].mean() + + # find g_y + macro_parameters["g_y"] = ( + fred_data_q["GDP Per Capita"].pct_change(periods=4, freq="Q").mean() + ) + + # # 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']]) + mod = sm.OLS( + rate_data["10 year govt bond rate"], + rate_data[["constant", "BAA Corp Bond Rates"]], + ) + res = mod.fit() + macro_parameters["r_gov_shift"] = res.params["BAA Corp Bond Rates"] + macro_parameters["r_gov_scale"] = res.params["constant"] + + return macro_parameters diff --git a/ogzaf/ogzaf_default_parameters.json b/ogzaf/ogzaf_default_parameters.json new file mode 100644 index 0000000..5512fda --- /dev/null +++ b/ogzaf/ogzaf_default_parameters.json @@ -0,0 +1,1744 @@ +{ + "frisch": 0.4, + "g_y_annual": 0.03, + "S": 80, + "J": 7, + "T": 320, + "lambdas": [ + 0.25, + 0.25, + 0.2, + 0.1, + 0.1, + 0.09, + 0.01 + ], + "e": [[ + 0.40547084393622496, + 0.2716226561254501, + 0.2899049902450634, + 0.3408270212293285, + 0.4240941105541254, + 0.6918316180259083, + 1.7928884714308537 + ], + [ + 0.3989292087449047, + 0.28582607585554826, + 0.3164066582534865, + 0.37660018951787877, + 0.4688091703774397, + 0.7383993769886736, + 1.9453769063354296 + ], + [0.3934839697658682, 0.3005408694211688, 0.34414451450348615, 0.41437038327356684, 0.5159963384642161, 0.7880580380211653, 2.108123995775691], + [0.38904985581571017, 0.31576027411207386, 0.37305123126821094, 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Multiline strings are treated as +# verbose regular expressions by Black. Use [ ] to denote a significant space +# character. + +[tool.black] +line-length = 79 +target-version = ['py36', 'py37', 'py38'] +include = '\.pyi?$' diff --git a/pytest.ini b/pytest.ini new file mode 100644 index 0000000..4330851 --- /dev/null +++ b/pytest.ini @@ -0,0 +1,7 @@ +# pytest.ini +[pytest] +minversion = 6.0 +testpaths = + ./tests +markers = + local: marks tests that are run locally only and not on GH Actions diff --git a/setup.py b/setup.py new file mode 100644 index 0000000..d834f1a --- /dev/null +++ b/setup.py @@ -0,0 +1,40 @@ +try: + from setuptools import setup +except ImportError: + from distutils.core import setup + +with open("README.md") as f: + longdesc = f.read() + +version = "0.0.0" + +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", + "Intended Audience :: Developers", + "Natural Language :: English", + "License :: OSI Approved :: CC0 1.0 Universal public domain dedication", + "Operating System :: OS Independent", + "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", + "Topic :: Software Development :: Libraries :: Python Modules", + ], + "tests_require": ["pytest"], +} + +setup(**config)