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app_gen_etr_marginal_rates.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(year, data_file, weights_file):
# create Records object containing pit.csv and pit_weights.csv input data
recs = Records(data=data_file, weights=weights_file)
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, corprecords=crecs,
gstrecords=grecs, verbose=False)
calc1.advance_to_year(year)
calc1.calc_all()
pit_df = calc1.dataframe(['FILING_SEQ_NO', 'AGE', 'weight', 'GTI',
'pitax'])
return(pit_df)
START_YEAR = 2017
END_YEAR = 2023
BASE_YEAR = 2019
year = 2017
data_file = 'taxcalc/pitSmallData.csv'
data_file_1 = 'taxcalc/pitSmallData_1.csv'
# data_file ='taxcalc/pit.csv'
# data_file_1 ='taxcalc/pit_1.csv'
weights_file = 'pit_weightsSD.csv'
# weights_file ='pit_weights.csv'
delta = 1
pit_orig_df = pd.read_csv(data_file)
pit_orig_1_df = pit_orig_df.copy()
pit_orig_1_df['SALARIES'] = pit_orig_1_df['SALARIES'] + delta
pit_orig_1_df.to_csv(data_file_1)
data_file = 'pitSmallData.csv'
data_file_1 = 'pitSmallData_1.csv'
# data_file ='pit.csv'
# data_file_1 ='pit_1.csv'
pit_df = run_calculator(year, data_file, weights_file)
pit_1_df = run_calculator(year, data_file_1, weights_file)
pit_1_df = pit_1_df.rename(columns={'weight': 'weight_1', 'GTI': 'GTI_1',
'pitax': 'pitax_1'})
pit_1_df.drop('AGE', axis=1, inplace=True)
pit_df = pd.merge(pit_df, pit_1_df, how="inner", on='FILING_SEQ_NO')
pit_1_df = pit_df[['FILING_SEQ_NO', 'pitax']]
pit_1_df = pd.merge(pit_1_df, pit_orig_1_df, how="inner", on='FILING_SEQ_NO')
pit_1_df.to_csv('pitSmallData_1_1.csv', index=False)
pit_df['MTR_Labor'] = (pit_df['pitax_1'] - pit_df['pitax'])/delta
pit_df['ETR'] = pit_df['pitax']/pit_df['GTI']
pit_1_df = pit_df.copy()
sns.distplot(tuple(pit_df['MTR_Labor']), hist=False, kde=True,
kde_kws={'linewidth': 3}, label="MTR")
pit_df['ETR'] = pit_df['pitax']/pit_df['GTI']
pit_df = pit_df[['FILING_SEQ_NO', 'MTR_Labor', 'ETR', 'pitax']]
pit_df = pd.merge(pit_df, pit_orig_df, how="inner", on='FILING_SEQ_NO')
pit_df.to_csv('pitSmallData_MTR.csv', index=False)
pit_clean_df = pit_df.copy()
pit_df = pit_df[pit_df["MTR_Labor"] > -10]
pit_df = pit_df[pit_df["MTR_Labor"] < 50]
pit_df = pit_df[pit_df['GROSS_TOTAL_INCOME'] < 10000000]
pit_df = pit_df[pit_df["ETR"] < 1]
pit_df.plot.scatter(x='GROSS_TOTAL_INCOME', y="MTR_Labor")
pit_df.plot.scatter(x='GROSS_TOTAL_INCOME', y="ETR")
plt.show()