This document tells you how to use Tax-Calculator, an open-source federal income and payroll tax simulation model, in Python scripts that you can run on your own computer. Note that these recipes -require Tax-Calculator release 0.14.0 or higher. For other ways of +require Tax-Calculator release 0.20.0 or higher. For other ways of using Tax-Calculator, see the user documentation, which this Cookbook assumes you have read.
@@ -47,11 +47,13 @@Advanced Recipes
-Directly Comparing Two Reforms
+(1) Directly Comparing Two Reforms
-Estimating Behavioral Response to Tax Reform
+(2) Estimating Behavioral Response to Tax Reform
- + + +(4) Estimating Reform Response by Earnings Group
This is an advanced recipe that should be followed only after mastering the basic recipe. This recipe @@ -223,12 +225,12 @@
Expected text results from executing python recipe01.py > recipe01.out at -the command prompt as shown above.
+the command prompt as illustrated above. -This is an advanced recipe that should be followed only after @@ -252,12 +254,12 @@
Expected text results from executing python recipe02.py > recipe02.out at -the command prompt as shown above.
+the command prompt as illustrated above. -This is an advanced recipe that should be followed only after mastering the basic recipe. This recipe @@ -277,7 +279,41 @@
Expected text results from executing python recipe03.py > recipe03.out at -the command prompt as shown above.
+the command prompt as illustrated above. + + + + +This is an advanced recipe that should be followed only after +mastering the basic recipe. This recipe shows +how to estimate the reform response in charitable giving when the +response elasticities vary by earnings group. It employs the groupby +technique used in the Creating a Custom Table +recipe, so you might want to read that recipe first.
+ +Ingredients
+ +Policy reform in +the ingredients/reformA.json file. Note that this reform +increases the personal exemption amount (from zero where it is under +current-law policy) and decreases the standard deduction amounts (so +they are approximately where they were before the TCJA reform that is +now current-law policy). The lowering of the standard deduction +amounts generates several warning messages per year that can safely be +ignored.
+ +Instructions
+ +Step-by-step +instructions in the recipe04.py file.
+ +Results
+ +Expected text +results from executing python recipe04.py > recipe04.out at +the command prompt as illustrated above.
diff --git a/docs/cookbook/ingredients/reformB.json b/docs/cookbook/ingredients/reformB.json new file mode 100644 index 000000000..028df3da5 --- /dev/null +++ b/docs/cookbook/ingredients/reformB.json @@ -0,0 +1,7 @@ +// Raise personal exemption and lower standard deduction beginning in 2020. +{ + "policy": { + "_II_em": {"2020": [2000]}, + "_STD": {"2020": [[6000, 12000, 6000, 9000, 12000]]} + } +} diff --git a/docs/cookbook/recipe03.py b/docs/cookbook/recipe03.py index b3bb75a62..8d1efe6aa 100644 --- a/docs/cookbook/recipe03.py +++ b/docs/cookbook/recipe03.py @@ -21,10 +21,10 @@ gbydf = vardf.groupby('table_row', as_index=False) # print AGI table with ALL row at bottom -print('Filing Units Receiving EITC and Average Positive EITC by AGI category') +print('Filing Units Receiving EITC and Average Positive EITC by AGI Category') results = '{:23s}\t{:8.3f}\t{:8.3f}' colhead = '{:23s}\t{:>8s}\t{:>8s}' -print(colhead.format('AGI category', 'Num(#M)', 'Avg($K)')) +print(colhead.format('AGI Category', 'Num(#M)', 'Avg($K)')) tot_recips = 0. tot_amount = 0. idx = 0 diff --git a/docs/cookbook/recipe04.py b/docs/cookbook/recipe04.py new file mode 100644 index 000000000..9d87a8f2f --- /dev/null +++ b/docs/cookbook/recipe04.py @@ -0,0 +1,82 @@ +from __future__ import print_function # necessary only if using Python 2.7 +from taxcalc import * + +# use publicly-available CPS input file without including benefits +recs = Records.cps_constructor(no_benefits=True) + +# specify Calculator object for static analysis of current-law policy +pol = Policy() +calc1 = Calculator(policy=pol, records=recs) + +cyr = 2020 + +# calculate current-law tax liabilities for cyr +calc1.advance_to_year(cyr) +calc1.calc_all() + +# calculate marginal tax rate wrt cash charitable giving +(_, _, mtr1) = calc1.mtr('e19800', calc_all_already_called=True, + wrt_full_compensation=False) + +# read JSON reform file and use (the default) static analysis assumptions +reform_filename = './ingredients/reformB.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']) +calc2 = Calculator(policy=pol, records=recs) + +# calculate reform tax liabilities for cyr +calc2.advance_to_year(cyr) +calc2.calc_all() + +# calculate marginal tax rate wrt cash charitable giving +(_, _, mtr2) = calc2.mtr('e19800', calc_all_already_called=True, + wrt_full_compensation=False) + +# extract variables needed for quantity_response utility function +# (note the aftertax price is 1+mtr because mtr wrt charity is non-positive) +vdf = calc1.dataframe(['s006', 'e19800', 'e00200']) +vdf['price1'] = 1.0 + mtr1 +vdf['price2'] = 1.0 + mtr2 +vdf['atinc1'] = calc1.array('aftertax_income') +vdf['atinc2'] = calc2.array('aftertax_income') + +# group filing units into earnings groups with different response elasticities +# (note earnings groups are just an example based on no empirical results) +earnings_bins = [-9e99, 50e3, 9e99] # two groups: below and above $50,000 +vdf = add_income_table_row_variable(vdf, 'e00200', earnings_bins) +gbydf = vdf.groupby('table_row', as_index=False) + +# compute percentage response in charitable giving +# (note elasticity values are just an example based on no empirical results) +price_elasticity = [-0.1, -0.4] +income_elasticity = [0.1, 0.1] +print('\nResponse in Charitable Giving by Earnings Group') +results = '{:18s}\t{:8.3f}\t{:8.3f}\t{:8.2f}' +colhead = '{:18s}\t{:>8s}\t{:>8s}\t{:>8s}' +print(colhead.format('Earnings Group', 'Num(#M)', 'Resp($B)', 'Resp(%)')) +tot_funits = 0. +tot_response = 0. +tot_baseline = 0. +idx = 0 +for gname, grp in gbydf: + funits = grp['s006'].sum() * 1e-6 + tot_funits += funits + response = quantity_response(grp['e19800'], + price_elasticity[idx], + grp['price1'], + grp['price2'], + income_elasticity[idx], + grp['atinc1'], + grp['atinc2']) + grp_response = (response * grp['s006']).sum() * 1e-9 + tot_response += grp_response + grp_baseline = (grp['e19800'] * grp['s006']).sum() * 1e-9 + tot_baseline += grp_baseline + pct_response = 100. * grp_response / grp_baseline + print(results.format(gname, funits, grp_response, pct_response)) + idx += 1 +pct_response = 100. * tot_response / tot_baseline +print(results.format('ALL', tot_funits, tot_response, pct_response)) diff --git a/docs/cookbook/recipe04.res b/docs/cookbook/recipe04.res new file mode 100644 index 000000000..552338795 --- /dev/null +++ b/docs/cookbook/recipe04.res @@ -0,0 +1,59 @@ +You loaded data for 2014. +Tax-Calculator startup automatically extrapolated your data to 2014. +WARNING: Tax-Calculator packages for Python 2.7 will + no longer be provided beginning in 2019 + because Pandas is stopping development for 2.7 +SOLUTION: upgrade to Python 3.6 now +WARNING: 2020 _STD_0 value 6000.0 < min value 12390.92 +WARNING: 2020 _STD_1 value 12000.0 < min value 24881.84 +WARNING: 2020 _STD_2 value 6000.0 < min value 12390.92 +WARNING: 2020 _STD_3 value 9000.0 < min value 18636.38 +WARNING: 2020 _STD_4 value 12000.0 < min value 24881.84 +WARNING: 2021 _STD_0 value 6127.8 < min value 12656.98 +WARNING: 2021 _STD_1 value 12255.6 < min value 25413.95 +WARNING: 2021 _STD_2 value 6127.8 < min value 12656.98 +WARNING: 2021 _STD_3 value 9191.7 < min value 19035.46 +WARNING: 2021 _STD_4 value 12255.6 < min value 25413.95 +WARNING: 2022 _STD_0 value 6261.39 < min value 12935.08 +WARNING: 2022 _STD_1 value 12522.77 < min value 25970.15 +WARNING: 2022 _STD_2 value 6261.39 < min value 12935.08 +WARNING: 2022 _STD_3 value 9392.08 < min value 19452.61 +WARNING: 2022 _STD_4 value 12522.77 < min value 25970.15 +WARNING: 2023 _STD_0 value 6394.13 < min value 13211.42 +WARNING: 2023 _STD_1 value 12788.25 < min value 26522.84 +WARNING: 2023 _STD_2 value 6394.13 < min value 13211.42 +WARNING: 2023 _STD_3 value 9591.19 < min value 19867.13 +WARNING: 2023 _STD_4 value 12788.25 < min value 26522.84 +WARNING: 2024 _STD_0 value 6531.6 < min value 13497.62 +WARNING: 2024 _STD_1 value 13063.2 < min value 27095.23 +WARNING: 2024 _STD_2 value 6531.6 < min value 13497.62 +WARNING: 2024 _STD_3 value 9797.4 < min value 20296.42 +WARNING: 2024 _STD_4 value 13063.2 < min value 27095.23 +WARNING: 2025 _STD_0 value 6670.72 < min value 13787.25 +WARNING: 2025 _STD_1 value 13341.45 < min value 27674.49 +WARNING: 2025 _STD_2 value 6670.72 < min value 13787.25 +WARNING: 2025 _STD_3 value 10006.08 < min value 20730.86 +WARNING: 2025 _STD_4 value 13341.45 < min value 27674.49 +WARNING: 2026 _STD_0 value 6812.81 < min value 7555.0 +WARNING: 2026 _STD_1 value 13625.62 < min value 15211.0 +WARNING: 2026 _STD_2 value 6812.81 < min value 7555.0 +WARNING: 2026 _STD_3 value 10219.21 < min value 11172.0 +WARNING: 2026 _STD_4 value 13625.62 < min value 15211.0 +WARNING: 2027 _STD_0 value 6959.29 < min value 7719.58 +WARNING: 2027 _STD_1 value 13918.57 < min value 15540.19 +WARNING: 2027 _STD_2 value 6959.29 < min value 7719.58 +WARNING: 2027 _STD_3 value 10438.92 < min value 11414.35 +WARNING: 2027 _STD_4 value 13918.57 < min value 15540.19 + +You loaded data for 2014. +Tax-Calculator startup automatically extrapolated your data to 2014. +WARNING: Tax-Calculator packages for Python 2.7 will + no longer be provided beginning in 2019 + because Pandas is stopping development for 2.7 +SOLUTION: upgrade to Python 3.6 now + +Response in Charitable Giving by Earnings Group +Earnings Group Num(#M) Resp($B) Resp(%) +[-9e+99, 50000.0) 115.196 0.641 0.59 +[50000.0, 9e+99) 59.317 3.478 2.05 +ALL 174.513 4.118 1.48