<title>recipe00.py</title><pre>

from __future__ import print_function # necessary only if using Python 2.7

from taxcalc import *

# use publicly-available CPS input file

recs = Records.cps_constructor()

# NOTE: if you have access to the restricted-use IRS-SOI PUF-based input file

# and you have that file (named 'puf.csv') located in the directory

# where this script is located, then you can substitute the following

# statement for the prior statement:

# recs = Records()

# specify Calculator object for static analysis of current-law policy

pol = Policy()

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

# 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.

# calculate aggregate current-law income tax liabilities for 2018

calc1.advance_to_year(2018)

calc1.calc_all()

itax_rev1 = calc1.weighted_total('iitax')

# read JSON reform file and use (the default) static analysis assumptions

reform_filename = './ingredients/reformA.json'

params = Calculator.read_json_param_objects(reform=reform_filename,

assump=None)

# specify Calculator object for static analysis of reform policy

pol.implement_reform(params['policy'])

if pol.reform_errors: # check for reform error messages

print(pol.reform_errors)

exit(1)

calc2 = Calculator(policy=pol, records=recs)

# calculate reform income tax liabilities for 2018

calc2.advance_to_year(2018)

calc2.calc_all()

itax_rev2 = calc2.weighted_total('iitax')

# print reform documentation

print('')

print(Calculator.reform_documentation(params))

# print total revenue estimates for 2018

# (estimates in billons of dollars rounded to nearest hundredth of a billion)

print('2018_CLP_itax_rev($B)= {:.2f}'.format(itax_rev1 * 1e-9))

print('2018_REF_itax_rev($B)= {:.2f}'.format(itax_rev2 * 1e-9))

print('')

# generate several other standard results tables:

# aggregate diagnostic tables for 2018

clp_diagnostic_table = calc1.diagnostic_table(1)

ref_diagnostic_table = calc2.diagnostic_table(1)

# income-tax distribution for 2018 with CLP and REF results side-by-side

dist_table1, dist_table2 = calc1.distribution_tables(calc2)

assert isinstance(dist_table1, pd.DataFrame)

assert isinstance(dist_table2, pd.DataFrame)

dist_extract = pd.DataFrame()

dist_extract['funits(#m)'] = dist_table1['s006'] * 1e-6

dist_extract['itax1($b)'] = dist_table1['iitax'] * 1e-9

dist_extract['itax2($b)'] = dist_table2['iitax'] * 1e-9

dist_extract['aftertax_inc1($b)'] = dist_table1['aftertax_income'] * 1e-9

dist_extract['aftertax_inc2($b)'] = dist_table2['aftertax_income'] * 1e-9

# income-tax difference table by expanded-income decile for 2018

diff_table = calc1.difference_table(calc2, tax_to_diff='iitax')

assert isinstance(diff_table, pd.DataFrame)

diff_extract = pd.DataFrame()

dif_colnames = ['count', 'tot_change', 'mean',

'pc_aftertaxinc']

ext_colnames = ['funits(#m)', 'agg_diff($b)', 'mean_diff($)',

'aftertaxinc_diff(%)']

scaling_factors = [1e-6, 1e-9, 1e0, 1e0, 1e0]

for dname, ename, sfactor in zip(dif_colnames, ext_colnames, scaling_factors):

diff_extract[ename] = diff_table[dname] * sfactor

# generate a graph and save in an HTML file

fig = calc1.decile_graph(calc2)

write_graph_file(fig, 'recipe00.graph.html', 'recipe00.graph')

print('CLP diagnostic table for 2018:')

print(clp_diagnostic_table)

print('')

print('REF diagnostic table for 2018:')

print(ref_diagnostic_table)

print('')

print('Extract of 2018 distribution tables by baseline expanded-income decile:')

print(dist_extract)

print('Note: deciles are numbered 0-9 with top decile divided into bottom 5%,')

print(' next 4%, and top 1%, in the lines numbered 11-13, respectively')

print('')

print('Extract of 2018 income-tax difference table by expanded-income decile:')

print(diff_extract)

print('Note: deciles are numbered 0-9 with top decile divided into bottom 5%,')

print(' next 4%, and top 1%, in the lines numbered 11-13, respectively')