Add factors for eight benefits to growfactors.csv file

puf_stage1/benefit_growth_rates.csv

@@ -1,4 +1,4 @@
-,ssi_participation_growth,ssi_benefit_growth,ssi_average_benefit,snap_participation_growth,snap_benefit_growth,snap_average_benefit,oasdi_participation_growth,oasdi_benefit_growth,oasdi_average_benefit,mcare_participation_growth,mcare_benefit_growth,mcare_average_benefit,mcaid_participation_growth,mcaid_benefit_growth,mcaid_average_benefit,vet_participation_growth,vet_benefit_growth,vet_average_benefit,wic_participation_growth,wic_benefit_growth,wic_average_benefit,housing_participation_growth,housing_benefit_growth,,tanf_participation_growth,tanf_benefit_growth,tanf_average_benefit
+YEAR,ssi_participation_growth,ssi_benefit_growth,ssi_average_benefit,snap_participation_growth,snap_benefit_growth,snap_average_benefit,oasdi_participation_growth,oasdi_benefit_growth,oasdi_average_benefit,mcare_participation_growth,mcare_benefit_growth,mcare_average_benefit,mcaid_participation_growth,mcaid_benefit_growth,mcaid_average_benefit,vet_participation_growth,vet_benefit_growth,vet_average_benefit,wic_participation_growth,wic_benefit_growth,wic_average_benefit,housing_participation_growth,housing_benefit_growth,housing_average_benefit,tanf_participation_growth,tanf_benefit_growth,tanf_average_benefit
 2014,0,0,6663,0,0,1500,0,0,14848,0,0,11447,0,0,7224,0,0,7394,0,0,770,0,0,3776,0,0,8375
 2015,-0.002,0.0124,6792,-0.0192,-0.0051,1654,0.0223,0.0618,15422,0.022,0.0457,11714,0.0753,0.1199,7524,-0.0092,0.0372,7741,-0.028336159,0.008060041,776,0.0019,0.0287,3877,-0.293401457,-0.119066738,9486
 2016,-0.002,0.0151,6747,-0.0524,-0.0487,1637,0.0508,0.1016,15566,0.0561,0.0933,11851,0.1091,0.0421,7602,-0.0688,0.0775,8557,-0.040877368,0.003004556,778,-0.005,0.0383,3940,-0.135198968,-0.114219344,7856

puf_stage1/factors_finalprep.py

@@ -1,17 +1,46 @@
 """
-Transform Stage_I_factors.csv (written by stage1.py)
-into growfactors.csv (used by Tax-Calculator)
+Transform Stage_I_factors.csv (written by the stage1.py script) and
+benefit_growth_rates.csv into growfactors.csv (used by Tax-Calculator).
 """
-
+import numpy as np
 import pandas as pd
 
 # pylint: disable=invalid-name
+
+first_benefit_year = 2014
+inben_filename = 'benefit_growth_rates.csv'
 first_data_year = 2011
-input_filename = 'Stage_I_factors.csv'
+infac_filename = 'Stage_I_factors.csv'
 output_filename = 'growfactors.csv'
 
+# --------------------------------------------------------------------------
+# read in raw average benefit amounts by year and
+# convert into "one plus annual proportion change" factors
+bgr_all = pd.read_csv(inben_filename, index_col='YEAR')
+bnames = ['mcare', 'mcaid', 'ssi', 'snap', 'wic', 'housing', 'tanf', 'vet']
+keep_cols = ['{}_average_benefit'.format(bname) for bname in bnames]
+bgr_raw = bgr_all[keep_cols]
+gf_bnames = ['ABEN{}'.format(bname.upper()) for bname in bnames]
+bgr_raw.columns = gf_bnames
+bgf = 1.0 + bgr_raw.astype('float64').pct_change()
+
+# specify first row values because pct_change() leaves first year undefined
+for var in list(bgf):
+    bgf[var][first_benefit_year] = 1.0
+
+# add rows of ones for years from first_data_year thru first_benefit_year-1
+ones = [1.0] * len(bnames)
+for year in range(first_data_year, first_benefit_year):
+    row = pd.DataFrame(data=[ones], columns=gf_bnames, index=[year])
+    bgf = pd.concat([bgf, row], verify_integrity=True)
+bgf.sort_index(inplace=True)
+
+# round converted factors to six decimal digits of accuracy
+bgf = bgf.round(6)
+
+# --------------------------------------------------------------------------
 # read in blowup factors used internally in taxdata repository
-data = pd.read_csv(input_filename, index_col='YEAR')
+data = pd.read_csv(infac_filename, index_col='YEAR')
 
 # convert some aggregate factors into per-capita factors
 elderly_pop = data['APOPSNR']
@@ -29,30 +58,23 @@
 data['ACGNS'] = data['ACGNS'] / pop
 data['ABOOK'] = data['ABOOK'] / pop
 data['ABENEFITS'] = data['ABENEFITS'] / pop
+data.rename(columns={'ABENEFITS': 'ABENOTHER'}, inplace=True)
 
 # convert factors into "one plus annual proportion change" format
 data = 1.0 + data.pct_change()
 
 # specify first row values because pct_change() leaves first year undefined
-# (these values have been transferred from Tax-Calculator records.py)
 for var in list(data):
     data[var][first_data_year] = 1.0
-"""
-Double check that these are still needed
-data['ACGNS'][first_data_year] = 1.1781
-data['ADIVS'][first_data_year] = 1.0606
-data['AINTS'][first_data_year] = 1.0357
-data['ASCHCI'][first_data_year] = 1.0041
-data['ASCHCL'][first_data_year] = 1.1629
-data['ASCHEI'][first_data_year] = 1.1089
-data['ASCHEL'][first_data_year] = 1.2953
-data['AUCOMP'][first_data_year] = 1.0034
-data['AWAGE'][first_data_year] = 1.0053
-"""
 
 # round converted factors to six decimal digits of accuracy
 data = data.round(6)
 
+# --------------------------------------------------------------------------
+# combine data and bgf DataFrames
+gfdf = pd.concat([data, bgf], axis='columns', verify_integrity=True)
+
+# --------------------------------------------------------------------------
 # delete from data the variables not used by Tax-Calculator (TC)
 TC_USED_VARS = set(['ABOOK',
                     'ACGNS',
@@ -70,10 +92,10 @@
                     'ATXPY',
                     'AUCOMP',
                     'AWAGE',
-                    'ABENEFITS'])
-ALL_VARS = set(list(data))
+                    'ABENOTHER'] + gf_bnames)
+ALL_VARS = set(list(gfdf))
 TC_UNUSED_VARS = ALL_VARS - TC_USED_VARS
-data = data.drop(TC_UNUSED_VARS, axis=1)
+gfdf = gfdf.drop(TC_UNUSED_VARS, axis=1)
 
-# write out blowup factors used in Tax-Calculator repository
-data.to_csv(output_filename, index='YEAR')
+# write out grow factors used in blowup logic in Tax-Calculator repository
+gfdf.to_csv(output_filename, index='YEAR')

puf_stage1/growfactors.csv

@@ -1,18 +1,18 @@
-YEAR,ATXPY,ASCHF,ABOOK,ACPIU,ACPIM,AWAGE,ASCHCI,ASCHCL,ASCHEI,ASCHEL,AINTS,ADIVS,ACGNS,ASOCSEC,AUCOMP,AIPD,ABENEFITS
-2011,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0
-2012,1.043862,0.950283,1.104992,1.0209,1.0365,1.032649,1.049023,0.956138,1.165922,0.926962,0.923588,1.327776,1.58966,1.02827,0.7711,0.9231,0.992359
-2013,1.012518,1.142179,1.033784,1.014791,1.024602,1.019984,0.99505,1.050098,0.997245,1.013128,0.893658,0.819381,0.776217,1.014786,0.728829,0.896219,0.992515
-2014,1.029476,0.931683,0.976566,1.015927,0.964218,1.039999,1.040616,1.030349,1.075978,0.991321,0.925886,1.17606,1.387522,1.004801,0.641103,0.970506,0.99257
-2015,1.043858,0.508206,0.999544,1.001235,1.010449,1.042272,1.045643,1.045687,0.999528,0.999533,0.992874,1.157209,1.268161,1.015868,0.910908,1.052061,1.053858
-2016,1.027314,0.689296,0.990701,1.012621,1.01353,1.03404,1.031041,1.031132,0.990745,0.990685,1.000698,0.989028,0.889463,1.005343,0.782145,1.035752,1.097065
-2017,1.033861,0.813631,0.995785,1.022585,1.025269,1.037738,1.041126,1.041095,0.995835,0.995814,1.031828,1.012056,1.101495,1.005072,0.996884,1.042176,1.011911
-2018,1.037313,1.48793,0.993846,1.022361,1.012742,1.036076,1.025419,1.025377,0.993871,0.993912,1.036421,1.043694,1.025287,1.024288,0.977335,1.045734,1.103035
-2019,1.035569,1.296752,1.001475,1.023127,1.013316,1.030217,1.023213,1.023267,1.001456,1.001478,1.063017,1.034493,0.993445,1.037255,1.082367,1.043838,1.054052
-2020,1.034169,1.205152,1.006471,1.023743,1.013866,1.026805,1.021165,1.021158,1.006439,1.006348,1.081241,1.024377,1.017719,1.035877,1.18212,1.042299,0.996727
-2021,1.035865,1.16783,1.012885,1.02439,1.014213,1.028412,1.025171,1.025174,1.012827,1.01294,1.090913,1.019573,1.023735,1.033631,1.113125,1.04386,1.03007
-2022,1.035881,1.107286,1.019598,1.023726,1.014102,1.030728,1.02925,1.02929,1.019656,1.019575,1.072062,1.019234,1.027913,1.034139,1.064851,1.043771,1.030159
-2023,1.035254,1.063374,1.028614,1.023992,1.01434,1.031473,1.031689,1.031633,1.028548,1.028594,1.058859,1.019495,1.029178,1.035356,1.051938,1.043174,1.030193
-2024,1.035337,1.048499,1.030505,1.023749,1.014395,1.031564,1.032954,1.032966,1.030531,1.030529,1.048357,1.023404,1.030403,1.036368,1.043358,1.043084,1.030334
-2025,1.034629,1.044463,1.030914,1.023898,1.014528,1.031933,1.033635,1.033618,1.030967,1.030889,1.040376,1.026049,1.031554,1.034024,1.043478,1.042117,1.030635
-2026,1.036122,1.016269,1.033285,1.023949,1.014736,1.032043,1.033896,1.033848,1.03325,1.033314,1.040659,1.028251,1.032329,1.035647,1.043508,1.043536,1.030633
-2027,1.0358,0.99068,1.032812,1.024131,1.015015,1.032291,1.034216,1.034262,1.03286,1.032865,1.039756,1.030999,1.032011,1.03812,1.034414,1.04314,1.030788
+YEAR,ATXPY,ASCHF,ABOOK,ACPIU,ACPIM,AWAGE,ASCHCI,ASCHCL,ASCHEI,ASCHEL,AINTS,ADIVS,ACGNS,ASOCSEC,AUCOMP,AIPD,ABENOTHER,ABENMCARE,ABENMCAID,ABENSSI,ABENSNAP,ABENWIC,ABENHOUSING,ABENTANF,ABENVET
+2011,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0
+2012,1.043862,0.950283,1.104992,1.0209,1.0365,1.032649,1.049023,0.956138,1.165922,0.926962,0.923588,1.327776,1.58966,1.02827,0.7711,0.9231,0.992359,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0
+2013,1.012518,1.142179,1.033784,1.014791,1.024602,1.019984,0.99505,1.050098,0.997245,1.013128,0.893658,0.819381,0.776217,1.014786,0.728829,0.896219,0.992515,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0
+2014,1.029476,0.931683,0.976566,1.015927,0.964218,1.039999,1.040616,1.030349,1.075978,0.991321,0.925886,1.17606,1.387522,1.004801,0.641103,0.970506,0.99257,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0
+2015,1.043858,0.508206,0.999544,1.001235,1.010449,1.042272,1.045643,1.045687,0.999528,0.999533,0.992874,1.157209,1.268161,1.015868,0.910908,1.052061,1.053858,1.023325,1.041528,1.019361,1.102667,1.007792,1.026748,1.132657,1.04693
+2016,1.027314,0.689296,0.990701,1.012621,1.01353,1.03404,1.031041,1.031132,0.990745,0.990685,1.000698,0.989028,0.889463,1.005343,0.782145,1.035752,1.097065,1.011695,1.010367,0.993375,0.989722,1.002577,1.01625,0.828168,1.105413
+2017,1.033861,0.813631,0.995785,1.022585,1.025269,1.037738,1.041126,1.041095,0.995835,0.995814,1.031828,1.012056,1.101495,1.005072,0.996884,1.042176,1.011911,1.030968,1.014601,0.981621,1.0,0.998715,1.063959,1.0,1.0
+2018,1.037313,1.48793,0.993846,1.022361,1.012742,1.036076,1.025419,1.025377,0.993871,0.993912,1.036421,1.043694,1.025287,1.024288,0.977335,1.045734,1.103035,1.045097,1.045897,1.005738,1.0,1.002574,1.034828,1.0,1.0
+2019,1.035569,1.296752,1.001475,1.023127,1.013316,1.030217,1.023213,1.023267,1.001456,1.001478,1.063017,1.034493,0.993445,1.037255,1.082367,1.043838,1.054052,1.052158,1.045866,1.000751,1.0,1.002567,1.034809,1.0,1.0
+2020,1.034169,1.205152,1.006471,1.023743,1.013866,1.026805,1.021165,1.021158,1.006439,1.006348,1.081241,1.024377,1.017719,1.035877,1.18212,1.042299,0.996727,1.050763,1.046106,1.00255,1.0,1.003841,1.034974,1.0,1.0
+2021,1.035865,1.16783,1.012885,1.02439,1.014213,1.028412,1.025171,1.025174,1.012827,1.01294,1.090913,1.019573,1.023735,1.033631,1.113125,1.04386,1.03007,1.047248,1.047927,1.001796,1.0,1.002551,1.034869,1.0,1.0
+2022,1.035881,1.107286,1.019598,1.023726,1.014102,1.030728,1.02925,1.02929,1.019656,1.019575,1.072062,1.019234,1.027913,1.034139,1.064851,1.043771,1.030159,1.048769,1.047573,0.999851,1.0,1.002545,1.034942,1.0,1.0
+2023,1.035254,1.063374,1.028614,1.023992,1.01434,1.031473,1.031689,1.031633,1.028548,1.028594,1.058859,1.019495,1.029178,1.035356,1.051938,1.043174,1.030193,1.050822,1.048715,1.000448,1.0,1.003807,1.034968,1.0,1.0
+2024,1.035337,1.048499,1.030505,1.023749,1.014395,1.031564,1.032954,1.032966,1.030531,1.030529,1.048357,1.023404,1.030403,1.036368,1.043358,1.043084,1.030334,1.048426,1.051767,0.99776,1.0,1.002528,1.034951,1.0,1.0
+2025,1.034629,1.044463,1.030914,1.023898,1.014528,1.031933,1.033635,1.033618,1.030967,1.030889,1.040376,1.026049,1.031554,1.034024,1.043478,1.042117,1.030635,1.046248,1.052213,1.002245,1.0,1.003783,1.034897,1.0,1.0
+2026,1.036122,1.016269,1.033285,1.023949,1.014736,1.032043,1.033896,1.033848,1.03325,1.033314,1.040659,1.028251,1.032329,1.035647,1.043508,1.043536,1.030633,1.072236,1.0,0.999552,1.0,1.002513,1.034808,1.0,1.0
+2027,1.0358,0.99068,1.032812,1.024131,1.015015,1.032291,1.034216,1.034262,1.03286,1.032865,1.039756,1.030999,1.032011,1.03812,1.034414,1.04314,1.030788,1.0,1.0,1.0,1.0,1.002506,1.034863,1.0,1.0