Tax_microsimulation_VAT_Indo/app0.py at main · adeputra2477/Tax_microsimulation_VAT_Indo · GitHub

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
"""
app0.py illustrates use of pitaxcalc-demo release 2.0.0 (India version).
USAGE: python app0.py > app0.res
CHECK: Use your favorite Windows diff utility to confirm that app0.res is
       the same as the app0.out file that is in the repository.
"""

import json
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

# Initialize the variables

vars = {}

vars['pit'] = 1
vars['cit'] = 0
vars['vat'] = 0

tax_type = 'pit'
vars['DEFAULTS_FILENAME'] = "current_law_policy_pit_training.json"
vars['GROWFACTORS_FILENAME'] = "growfactors_pit_training.csv"
vars['pit_data_filename'] = "pit_data_training.csv"
vars['pit_weights_filename'] = "pit_weights_training.csv"
vars['pit_records_variables_filename'] = "records_variables_pit_training.json"
vars['pit_benchmark_filename'] = "tax_incentives_benchmark_pit_training.json"
vars['pit_elasticity_filename'] = "elasticity_pit_training.json"
vars['pit_functions_filename'] = "functions_pit_training.py"
vars['pit_function_names_filename'] = "function_names_pit_training.json"
vars['pit_distribution_json_filename'] = 'pit_distribution_training.json'

vars['vat_data_filename'] = "gst.csv"
vars['vat_weights_filename'] = "gst_weights.csv"
vars['vat_records_variables_filename'] = "gstrecords_variables.json"

vars['cit_data_filename'] = "cit_cross.csv"
vars['cit_weights_filename'] = "cit_cross_wgts1.csv"
vars['cit_records_variables_filename'] = "corprecords_variables.json"

vars['gdp_filename'] = 'gdp_nominal_training.csv'
vars["start_year"] = 2022
vars["end_year"] = 2027
vars["SALARY_VARIABLE"] = "gross_i_w"
vars['elasticity_filename'] = "elasticity_pit_training.json"
vars['DIST_VARIABLES'] = ['weight', 'total_gross_income', 'pitax']
vars['DIST_TABLE_COLUMNS'] = ['weight', 'total_gross_income', 'pitax']
vars['DIST_TABLE_LABELS'] = ['Returns',
                     'Gross Total Income',
                     'PITax']
vars['DECILE_ROW_NAMES'] = ['0-10n', '0-10z', '0-10p',
                    '10-20', '20-30', '30-40', '40-50',
                    '50-60', '60-70', '70-80', '80-90', '90-100',
                    'ALL',
                    '90-95', '95-99', 'Top 1%']
vars['STANDARD_ROW_NAMES'] = [ "<0", "=0", "0-0.5 m", "0.5-1m", "1-1.5m", "1.5-2m",
                      "2-3m", "3-4m", "4-5m", "5-10m", ">10m", "ALL"]
vars['STANDARD_INCOME_BINS'] = [-9e99, -1e-9, 1e-9, 5e5, 10e5, 15e5, 20e5, 30e5,
                        40e5, 50e5, 100e5, 9e99]
vars['income_measure'] = "total_gross_income"
vars['show_error_log'] = 0
vars['verbose'] = 0
vars['data_start_year'] = 2018

f = open('taxcalc/'+vars['pit_distribution_json_filename'])
distribution_vardict_dict = json.load(f)
f.close()
#print(distribution_vardict_dict)

with open('global_vars.json', 'w') as f:
    f.write(json.dumps(vars, indent=2))
f.close()

from taxcalc import *

# create Records object containing pit.csv and pit_weights.csv input data
recs = Records()

#policy_filename = "current_law_policy_pit_training"
# create Policy object containing current-law policy
#pol = Policy(DEFAULTS_FILENAME=policy_filename)

pol = Policy()

# specify Calculator object for current-law policy
#calc1 = Calculator(policy=pol, records=recs, gstrecords=grecs,
#                   corprecords=crecs, verbose=False)

calc1 = Calculator(policy=pol, records=recs, verbose=False)

# NOTE: calc1 now contains a PRIVATE COPY of pol and a PRIVATE COPY of recs,
#       so we can continue to use pol and recs in this script without any
#       concern about side effects from Calculator method calls on calc1.

calc1.calc_all()

dump_vars = ['id_n', 'Year', 'income_wage_l', 'income_dividends_c',
             'income_interest_c', 'total_gross_income', 'pitax_w', 'pitax_c', 'pitax']
dumpdf = calc1.dataframe(dump_vars)
column_order = dumpdf.columns

assert len(dumpdf.index) == calc1.array_len

dumpdf.to_csv('app0-dump.csv', columns=column_order,
              index=False, float_format='%.0f')