Recipe 1: Directly Comparing Two Reforms#

This is an advanced recipe that should be followed only after mastering the basic recipe. This recipe shows how to compare two reforms (instead of comparing a reform to current-law policy) and also shows how to use the reform files available on the Tax-Calculator website (instead of reform files on your computer’s disk).

import pandas as pd
import taxcalc as tc

# read an "old" reform file
# ("old" means the reform file is defined relative to pre-TCJA policy)
# specify reform dictionary for pre-TCJA policy
reform1 = tc.Policy.read_json_reform('github://PSLmodels:[email protected]/psl_examples/taxcalc/2017_law.json')

# specify reform dictionary for TCJA as passed by Congress in late 2017
reform2 = tc.Policy.read_json_reform('github://PSLmodels:[email protected]/psl_examples/taxcalc/TCJA.json')

# specify Policy object for pre-TCJA policy
bpolicy = tc.Policy()
bpolicy.implement_reform(reform1, print_warnings=False, raise_errors=False)
assert not bpolicy.parameter_errors

# specify Policy object for TCJA reform relative to pre-TCJA policy
rpolicy = tc.Policy()
rpolicy.implement_reform(reform1, print_warnings=False, raise_errors=False)
assert not rpolicy.parameter_errors
rpolicy.implement_reform(reform2, print_warnings=False, raise_errors=False)
assert not rpolicy.parameter_errors

# specify Calculator objects using bpolicy and rpolicy
recs = tc.Records.cps_constructor()
calc1 = tc.Calculator(policy=bpolicy, records=recs)
calc2 = tc.Calculator(policy=rpolicy, records=recs)

CYR = 2018

# calculate for specified CYR
calc1.advance_to_year(CYR)
calc1.calc_all()
calc2.advance_to_year(CYR)
calc2.calc_all()

# compare aggregate individual income tax revenue in cyr
iitax_rev1 = calc1.weighted_total('iitax')
iitax_rev2 = calc2.weighted_total('iitax')

# construct reform-vs-baseline difference table with results for income deciles
diff_table = calc1.difference_table(calc2, 'weighted_deciles', 'iitax')
assert isinstance(diff_table, pd.DataFrame)
diff_extract = pd.DataFrame()
dif_colnames = ['count', 'tax_cut', 'tax_inc',
                'tot_change', 'mean', 'pc_aftertaxinc']
ext_colnames = ['funits(#m)', 'taxfall(#m)', 'taxrise(#m)',
                'agg_diff($b)', 'mean_diff($)', 'aftertax_income_diff(%)']
for dname, ename in zip(dif_colnames, ext_colnames):
    diff_extract[ename] = diff_table[dname]

# print total revenue estimates for cyr
# (estimates in billons of dollars)
print('{}_REFORM1_iitax_rev($B)= {:.3f}'.format(CYR, iitax_rev1 * 1e-9))
print('{}_REFORM2_iitax_rev($B)= {:.3f}'.format(CYR, iitax_rev2 * 1e-9))
print('')
2018_REFORM1_iitax_rev($B)= 1357.983
2018_REFORM2_iitax_rev($B)= 1191.571

Print reform2-vs-reform1 difference table

title = 'Extract of {} income-tax difference table by expanded-income decile'
print(title.format(CYR))
print('(taxfall is count of funits with cut in income tax in reform 2 vs 1)')
print('(taxrise is count of funits with rise in income tax in reform 2 vs 1)')
print(diff_extract.to_string())
Extract of 2018 income-tax difference table by expanded-income decile
(taxfall is count of funits with cut in income tax in reform 2 vs 1)
(taxrise is count of funits with rise in income tax in reform 2 vs 1)
        funits(#m)  taxfall(#m)  taxrise(#m)  agg_diff($b)  mean_diff($)  aftertax_income_diff(%)
0-10n     0.099275     0.000000     0.000000      0.000000      0.000000                 0.000000
0-10z     8.084575     0.000000     0.000000      0.000000      0.000000                      NaN
0-10p    11.736716     0.169109     0.011897     -0.011921     -1.015679                 0.031167
10-20    19.920648     6.244056     2.883407     -0.773509    -38.829518                 0.322414
20-30    19.920369    10.151692     2.378508     -1.896750    -95.216622                 0.419343
30-40    19.920510     9.246090     1.994306     -3.503089   -175.853380                 0.571074
40-50    19.920961    11.036375     2.113940     -5.831999   -292.756920                 0.767197
50-60    19.919733    13.189452     2.028448     -8.722770   -437.895906                 0.920883
60-70    19.920039    14.175127     1.732710    -11.956366   -600.218012                 1.000142
70-80    19.922180    15.556599     1.507744    -17.063260   -856.495621                 1.126855
80-90    19.920490    16.985303     1.623547    -26.169107  -1313.677861                 1.300507
90-100   19.920987    18.176452     1.484443    -90.484066  -4542.147681                 2.145438
ALL     199.206484   114.930254    17.758950   -166.412837   -835.378618                 1.388565
90-95     9.960346     8.974041     0.753146    -22.527613  -2261.729963                 1.659858
95-99     7.967575     7.400193     0.540449    -37.482913  -4704.431831                 2.289373
Top 1%    1.993066     1.802218     0.190848    -30.473540 -15289.777108                 2.491596