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Merge pull request #83 from jdebacker/example_scripts
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‎examples/run_og_zaf_education.py‎

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# Need to fix references to Calculator, reform json, and substitute new tax
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# function call
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import multiprocessing
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from distributed import Client
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import os
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import json
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import time
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import copy
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import numpy as np
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import matplotlib.pyplot as plt
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from ogzaf.calibrate import Calibration
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from ogcore.parameters import Specifications
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from ogcore import output_tables as ot
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from ogcore import output_plots as op
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import ogcore
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from ogcore.execute import runner
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from ogcore.utils import safe_read_pickle
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# Use a custom matplotlib style file for plots
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plt.style.use("ogcore.OGcorePlots")
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def main():
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# Define parameters to use for multiprocessing
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num_workers = min(multiprocessing.cpu_count(), 7)
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client = Client(n_workers=num_workers, threads_per_worker=1)
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print("Number of workers = ", num_workers)
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# Directories to save data
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CUR_DIR = os.path.dirname(os.path.realpath(__file__))
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save_dir = os.path.join(CUR_DIR, "OG-ZAF-Education")
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base_dir = os.path.join(save_dir, "OUTPUT_BASELINE")
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reform_dir = os.path.join(save_dir, "OUTPUT_EDUC")
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reform_dir2 = os.path.join(save_dir, "OUTPUT_EDUC_SPEND")
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"""
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---------------------------------------------------------------------------
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Run baseline policy
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---------------------------------------------------------------------------
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"""
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# Set up baseline parameterization
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p = Specifications(
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baseline=True,
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num_workers=num_workers,
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baseline_dir=base_dir,
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output_base=base_dir,
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)
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# Update parameters for baseline from default json file
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p.update_specifications(
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json.load(
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open(
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os.path.join(
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CUR_DIR, "..", "ogzaf", "ogzaf_default_parameters.json"
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)
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)
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)
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)
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# move closure rule out to 50 years since educaation phases in over 20 years
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p.tG1 = 50
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# Run model
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start_time = time.time()
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runner(p, time_path=True, client=client)
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print("run time = ", time.time() - start_time)
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"""
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---------------------------------------------------------------------------
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Run counterfactual simulation
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---------------------------------------------------------------------------
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"""
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# create new Specifications object for reform simulation
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p2 = copy.deepcopy(p)
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p2.baseline = False
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p2.output_base = reform_dir
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# adjust labor productivity to account for education investment
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# Public spending may be able to obtain universal enrollment in Africa: https://www.inderscienceonline.com/doi/abs/10.1504/IJEED.2015.075794
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# no time for paper, but let's assume this increases productivity of those bottom 70% by 20% for all ages 20+
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# Let's assume this phases in linearly over 20 years
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num_years = 20 # 20 years to phase in
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total_benefit = 0.2 # total effect on productivity when fully phased in
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benefits = np.linspace(0, total_benefit, num_years)
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for t, benefit in enumerate(benefits):
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p2.e[t, :, :3] = p.e[t, :, :3] * (
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1 + benefit
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) # just apply to bottom 70%
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p2.e[num_years:, :, :3] = p.e[num_years:, :, :3] * (1 + total_benefit)
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# Run sim with just the benefits of education
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start_time = time.time()
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runner(p2, time_path=True, client=client)
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print("run time = ", time.time() - start_time)
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# Now increase spending and solve model again
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p3 = copy.deepcopy(p2)
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p3.baseline = False
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p3.output_base = reform_dir2
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# increase gov't spending to account for costs of education
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# Spending currently about 6.6% of GDP (source: https://data.worldbank.org/indicator/SE.XPD.TOTL.GD.ZS?locations=ZA)
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# Let's assume this increases to 10% of GDP
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p3.alpha_G = (
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p3.alpha_G + 0.034
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) # counterfactual 10% of GDP - current 6.6% of GDP
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start_time = time.time()
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runner(p3, time_path=True, client=client)
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print("run time = ", time.time() - start_time)
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client.close()
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"""
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---------------------------------------------------------------------------
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Save some results of simulations
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---------------------------------------------------------------------------
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"""
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base_tpi = safe_read_pickle(os.path.join(base_dir, "TPI", "TPI_vars.pkl"))
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base_params = safe_read_pickle(os.path.join(base_dir, "model_params.pkl"))
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reform_tpi = safe_read_pickle(
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os.path.join(reform_dir, "TPI", "TPI_vars.pkl")
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)
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reform_params = safe_read_pickle(
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os.path.join(reform_dir, "model_params.pkl")
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)
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ans = ot.macro_table(
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base_tpi,
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base_params,
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reform_tpi=reform_tpi,
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reform_params=reform_params,
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var_list=["Y", "C", "K", "L", "r", "w"],
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output_type="pct_diff",
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num_years=10,
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start_year=base_params.start_year,
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)
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# create plots of output
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op.plot_all(
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base_dir, reform_dir, os.path.join(save_dir, "OG-ZAF_example_plots")
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)
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print("Percentage changes in aggregates:", ans)
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# save percentage change output to csv file
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ans.to_csv(os.path.join(save_dir, "ogzaf_example_output.csv"))
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if __name__ == "__main__":
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# execute only if run as a script
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main()

‎examples/run_og_zaf_multiple_industry_cit_cut.py‎

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# Need to fix references to Calculator, reform json, and substitute new tax
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# function call
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import multiprocessing
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from distributed import Client
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import importlib.resources
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import os
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import json
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import time
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import copy
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import numpy as np
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# from taxcalc import Calculator
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from ogzaf.calibrate import Calibration
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from ogcore.parameters import Specifications
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from ogcore import output_tables as ot
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from ogcore import output_plots as op
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from ogcore.execute import runner
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from ogcore.utils import safe_read_pickle
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def main():
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# Define parameters to use for multiprocessing
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client = Client()
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num_workers = min(multiprocessing.cpu_count(), 7)
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print("Number of workers = ", num_workers)
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# Directories to save data
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CUR_DIR = os.path.dirname(os.path.realpath(__file__))
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base_dir = os.path.join(CUR_DIR, "OG-ZAF-CIT_Example", "OUTPUT_BASELINE")
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reform_dir = os.path.join(CUR_DIR, "OG-ZAF-CIT_Example", "OUTPUT_REFORM")
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"""
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---------------------------------------------------------------------------
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Run baseline policy
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---------------------------------------------------------------------------
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"""
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# Set up baseline parameterization
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p = Specifications(
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baseline=True,
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num_workers=num_workers,
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baseline_dir=base_dir,
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output_base=base_dir,
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)
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# Update parameters for baseline from default json file
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with importlib.resources.open_text(
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"ogzaf", "ogzaf_default_parameters.json"
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) as file:
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defaults = json.load(file)
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p.update_specifications(defaults)
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# Update parameters from calibrate.py Calibration class for multiple industries
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p.M = 4
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p.I = 5
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c = Calibration(p)
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updated_params = c.get_dict()
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p.update_specifications(updated_params)
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updated_params_tax = {
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"cit_rate": [[0.27, 0.27, 0.27, 0.27]],
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# order of industries is primary, energy, tertiary, secondary ex energy
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"Z": [[0.5, 0.4, 1.7, 1.0]],
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"epsilon": [1.0, 1.0, 1.0, 1.0],
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"gamma": [0.67, 0.50, 0.45, 0.53],
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"gamma_g": [0.0, 0.0, 0.0, 0.0],
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"alpha_c": c.alpha_c,
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"io_matrix": c.io_matrix,
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"tG1": 23,
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"debt_ratio_ss": 1.8,
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}
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p.update_specifications(updated_params_tax)
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# Run model
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start_time = time.time()
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runner(p, time_path=True, client=client)
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print("run time = ", time.time() - start_time)
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"""
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---------------------------------------------------------------------------
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Run reform policy
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---------------------------------------------------------------------------
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"""
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# create new Specifications object for reform simulation
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p2 = copy.deepcopy(p)
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p2.baseline = False
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p2.output_base = reform_dir
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# additional parameters to change
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updated_params_ref = {
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"cit_rate": [[0.25, 0.25, 0.25, 0.25]],
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"baseline_spending": True,
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}
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p2.update_specifications(updated_params_ref)
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# Run model
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start_time = time.time()
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runner(p2, time_path=True, client=client)
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print("run time = ", time.time() - start_time)
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client.close()
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"""
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---------------------------------------------------------------------------
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Save some results of simulations
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---------------------------------------------------------------------------
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"""
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base_tpi = safe_read_pickle(os.path.join(base_dir, "TPI", "TPI_vars.pkl"))
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base_params = safe_read_pickle(os.path.join(base_dir, "model_params.pkl"))
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reform_tpi = safe_read_pickle(
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os.path.join(reform_dir, "TPI", "TPI_vars.pkl")
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)
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reform_params = safe_read_pickle(
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os.path.join(reform_dir, "model_params.pkl")
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)
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ans = ot.macro_table(
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base_tpi,
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base_params,
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reform_tpi=reform_tpi,
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reform_params=reform_params,
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var_list=["Y", "C", "K", "L", "r", "w"],
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output_type="pct_diff",
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num_years=10,
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start_year=base_params.start_year,
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)
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# create plots of output
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op.plot_all(
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base_dir,
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reform_dir,
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os.path.join(CUR_DIR, "OG-ZAF_CIT_multi_industry_plots"),
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)
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print("Percentage changes in aggregates:", ans)
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# save percentage change output to csv file
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ans.to_csv("ogzaf_CIT_multi_industry_output.csv")
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if __name__ == "__main__":
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# execute only if run as a script
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main()

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