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app_reform_breakdown1.py
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app_reform_breakdown1.py
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import pandas as pd
import matplotlib.pyplot as plt
# import seaborn as sns
import numpy as np
import json
from taxcalc import *
from babel.numbers import format_currency
from collections import defaultdict
import copy
def remove_decimal(S):
S = str(S)
S = S[:-3]
return S
def ind_currency(curr):
curr_str = format_currency(curr, 'INR', locale='en_IN').replace(u'\xa0', u' ')
return(remove_decimal(curr_str))
def run_calculator(reform, ref_num, reform_desc):
# create Records object containing pit.csv and pit_weights.csv input data
recs = Records(data='pit.csv', weights='pit_weights.csv')
grecs = GSTRecords()
crecs = CorpRecords()
# create Policy object containing current-law policy
pol = Policy()
# specify Calculator object for current-law policy
calc1 = Calculator(policy=pol, records=recs, gstrecords=grecs, corprecords=crecs, verbose=False)
# specify Calculator object for reform in JSON file
#print(reform['policy'])
pol.implement_reform(reform['policy'])
calc2 = Calculator(policy=pol, records=recs, gstrecords=grecs, corprecords=crecs, verbose=False)
# loop through years 2017, 2018, 2019, and 2020 and print out pitax
wtd_tax_clp={}
wtd_tax_ref={}
wtd_tax_diff={}
wtd_tot={}
for year in range(START_YEAR, END_YEAR+1):
calc1.advance_to_year(year)
calc2.advance_to_year(year)
calc1.calc_all()
calc2.calc_all()
wtd_tax_clp[year] = calc1.weighted_total('pitax')
wtd_tax_ref[year] = calc2.weighted_total('pitax')
wtd_tax_diff[year] = wtd_tax_ref[year] - wtd_tax_clp[year]
if (year>=BASE_YEAR):
print('******')
print(f'Current Law: Tax Collection in Rs. Cr. for {year}:', end=' ')
print(f'{ind_currency(wtd_tax_clp[year] * 1e-7)}')
print(f' Reform-> \n{reform_desc}Reform: Tax Collection in Rs. Cr. for {year}:', end=' ')
print(f'{ind_currency(wtd_tax_ref[year] * 1e-7)}')
print(' Difference in Tax Collection:', end=' ')
print(f'{ind_currency(wtd_tax_diff[year] * 1e-7)} Cr.')
return(wtd_tax_clp, wtd_tax_ref, wtd_tax_diff, wtd_tot)
def gen_reform(list_tdict):
param_key_dict = dict(list_tdict)
year_param = dict()
for pkey, sdict in param_key_dict.items():
#print(f'pkey: {pkey}')
#print(f'sdict: {sdict}')
rdict = dict()
for skey, val in sdict.items():
year = int(skey)
rdict[year] = val
year_param[pkey] = rdict
year_key_dict = dict()
years = set()
for param, sdict in year_param.items():
for year, val in sdict.items():
if year not in years:
years.add(year)
year_key_dict[year] = dict()
year_key_dict[year][param] = val
rpol_dict = year_key_dict
cons_dict = dict()
behv_dict = dict()
gdiff_base_dict = dict()
gdiff_resp_dict = dict()
growmodel_dict = dict()
param_dict = dict()
param_dict['policy'] = rpol_dict
param_dict['consumption'] = cons_dict
param_dict['behavior'] = behv_dict
param_dict['growdiff_baseline'] = gdiff_base_dict
param_dict['growdiff_response'] = gdiff_resp_dict
param_dict['growmodel'] = growmodel_dict
#print(f'param_dict: {param_dict}')
return param_dict
def get_reform_desc(list_tdict):
ref_num = len(list_tdict)
reform_desc = ""
for i in range(ref_num):
part1 = list_tdict[i][0][1:].upper()
part2 = list(list_tdict[i][1].keys())[0]
part3 = str(list(list_tdict[i][1].values())[0][0])
ref = (part1 + " in " + part2 + "->" + part3)
reform_desc = reform_desc + ref + '\n'
return(reform_desc)
START_YEAR = 2017
END_YEAR = 2023
BASE_YEAR = 2019
# check if Calculator object for reform in JSON file is valid
# using all the checks in the Calculator object
reform_file = 'Budget2019_reform.json'
txt = open(reform_file, 'r').read()
json_str = re.sub('//.*', ' ', txt)
raw_dict = json.loads(json_str)
tdict = Policy.translate_json_reform_suffixes(raw_dict['policy'])
list_tdict=list(tdict.items())
keys_tdict=list(tdict.keys())
ref_num = len(tdict)
reform_desc=[]
for i in range(ref_num):
part1 = list_tdict[i][0][1:].upper()
part2 = list(list_tdict[i][1].keys())[0]
part3 = str(list(list_tdict[i][1].values())[0][0])
ref = (part1 + " in " + part2 + "->" + part3)
reform_desc.append(ref)
wtd_tax_clp={}
wtd_tax_ref={}
wtd_tax_diff={}
wtd_tot={}
reform={}
#reform_diff={}
# Calculating impact for full reform
"""
reform_overall = gen_reform(list_tdict)
run_calculator(reform_overall, ref_num=99, reform_desc=reform_desc_combined)
wtd_tax_clp['ref_99'], wtd_tax_ref['ref_99'], wtd_tax_diff['ref_99'], wtd_tot['ref_99'] = run_calculator(reform_overall, ref_num=99, reform_desc=reform_desc_combined)
d = dict((k, v) for k, v in wtd_tax_diff['ref_99'].items() if k >= BASE_YEAR)
reform_base = list(d.values())
reform_base = np.array(list(d.values()))
reform_base = np.round(reform_base/10**7)
reform_base = reform_base.astype(int)
"""
# Using stacking process, we first use one reform and then add subsequent reforms
#print('**********************************************')
list_tdict_orig = list_tdict[:]
for i in range(ref_num):
list_tdict = list_tdict_orig[0:i+1]
reform_dict = gen_reform(list_tdict)
ref='ref_'+str(i)
wtd_tax_clp[ref], wtd_tax_ref[ref], wtd_tax_diff[ref], wtd_tot[ref] = run_calculator(reform_dict, i, get_reform_desc(list_tdict))
for i in range(ref_num):
ref_tag='ref_'+str(i)
d = dict((k, v) for k, v in wtd_tax_diff[ref_tag].items() if k >= BASE_YEAR)
reform[i] = np.array(list(d.values()))
reform[i] = np.round(reform[i]/10**7)
reform[i] = reform[i].astype(int)
for i in range(ref_num-1):
reform[i+1] = reform[i+1] - reform[i]
years = list(range(BASE_YEAR,END_YEAR+1))
N = len(years)
width = 0.35
fig, ax = plt.subplots(figsize=(9, 5))
p={}
bottom_num=np.zeros(N)
for i in range(ref_num):
p[i] = plt.bar(years, reform[i], width, bottom=bottom_num, label="")
bottom_num = np.array(bottom_num) + np.array(reform[i])
ax.invert_yaxis()
ax.set_ylabel('Rupees in Crores.', fontweight="bold")
ax.set_xlabel('Year', fontweight="bold")
plt.legend(reform_desc, loc=2)
plt.suptitle('Composition of the Changes to Total Tax Collection', fontsize=16, fontweight="bold")
plt.savefig('Composition of Tax Changes due to Reforms.png')
plt.show()