Skip to content

Commit

Permalink
Merge pull request #72 from martinholmer/tmd-gf-file
Browse files Browse the repository at this point in the history 
Create Tax-Calculator-style growth factors file for 2021-2034
  • Loading branch information
@martinholmer
martinholmer authored May 5, 2024
2 parents 2a0a1a5 + 35d3461 commit 78304c2
Show file tree
Hide file tree
Showing 10 changed files with 178 additions and 46 deletions.
59 changes: 57 additions & 2 deletions tax_microdata_benchmarking/cbo_population_forecast.yaml
View file
Original file line number Diff line number Diff line change
@@ -1,6 +1,21 @@
# Source: CBO Jan 2024 publication "The Demographic Outlook: 2024 to 2054"
# Sources:
# CBO Jan 2024 publication "The Demographic Outlook: 2024 to 2054"
# URL: https://www.cbo.gov/publication/59697
# Spreadsheet: Data Underlying Figures, Figure 2, Total Population (millions)
# (a) Data Underlying Figures, Figure 2 spreadsheet tab,
# Total Population (millions) [for years from 2021 through 2054]
# (b) Demographic Projections, Population and growth spreadsheet tab,
# Total Social Security area population (millions) [for years after 2054]
2010: 313.985
2011: 316.096
2012: 318.030
2013: 319.857
2014: 321.766
2015: 324.530
2016: 327.042
2017: 328.899
2018: 330.808
2019: 332.199
2020: 333.263
2021: 334.181
2022: 335.458
2023: 338.442
Expand All @@ -15,3 +30,43 @@
2032: 360.285
2033: 361.832
2034: 363.330
2035: 364.774
2036: 366.164
2037: 367.499
2038: 368.777
2039: 369.998
2040: 371.161
2041: 372.266
2042: 373.315
2043: 374.308
2044: 375.250
2045: 376.144
2046: 376.993
2047: 377.801
2048: 378.573
2049: 379.313
2050: 380.026
2051: 380.718
2052: 381.394
2053: 382.062
2054: 382.726
2055: 383.393
2056: 384.067
2057: 384.752
2058: 385.451
2059: 386.167
2060: 386.898
2061: 387.644
2062: 388.404
2063: 389.175
2064: 389.955
2065: 390.739
2066: 391.523
2067: 392.302
2068: 393.073
2069: 393.831
2070: 394.572
2071: 395.291
2072: 395.984
2073: 396.650
2074: 397.286
39 changes: 39 additions & 0 deletions tax_microdata_benchmarking/create_taxcalc_growth_factors.py
View file
Original file line number Diff line number Diff line change
@@ -0,0 +1,39 @@
"""
Construct growfactors.csv, a Tax-Calculator-style GrowFactors file that
extends through LAST_YEAR from the puf_growfactors.csv file, which is a
copy of the most recent growfactors.csv file in the Tax-Calculator repository.
"""

import pandas as pd

LAST_YEAR = 2034
PGFFILE = "puf_growfactors.csv"
TGFFILE = "growfactors.csv"


def create_factors_file():
"""
Create Tax-Calculator-style factors file for FIRST_YEAR through LAST_YEAR.
"""
# read PUF-factors from PGFFILE
gfdf = pd.read_csv(PGFFILE)
first_puf_year = gfdf.YEAR.iat[0]
last_puf_year = gfdf.YEAR.iat[-1]

# add rows thru LAST_YEAR by copying values for last year in PUF file
if LAST_YEAR > last_puf_year:
idx = last_puf_year - FIRST_YEAR
last_row = gfdf.iloc[-1, :].copy()
num_rows = LAST_YEAR - last_puf_year
added = pd.DataFrame([last_row] * num_rows, columns=gfdf.columns)
for idx in range(0, num_rows):
added.YEAR.iat[idx] = last_puf_year + idx + 1
gfdf = pd.concat([gfdf, added], ignore_index=True)

# write gfdf to CSV-formatted file
gfdf.YEAR = gfdf.YEAR.astype(int)
gfdf.to_csv(TGFFILE, index=False)


if __name__ == "__main__":
create_factors_file()
27 changes: 8 additions & 19 deletions tax_microdata_benchmarking/create_taxcalc_input_variables.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -2,7 +2,6 @@
Construct tmd.csv.gz, a Tax-Calculator-style input variable file for 2021.
"""

import pandas as pd
import taxcalc as tc
from tax_microdata_benchmarking.create_flat_file import (
create_stacked_flat_file,
Expand Down Expand Up @@ -32,7 +31,7 @@ def create_variable_file():
print(f"WARNING: FINAL vs INITIAL scale diff = {abs_diff:.6f}")
print(f" INITIAL pt_w2_wages_scale = {INITIAL_PT_W2_WAGES_SCALE:.6f}")
print(f" FINAL pt_w2_wages_scale = {pt_w2_wages_scale:.6f}")
# streamline variables dataframe
# streamline dataframe so that it includes only input variables
rec = tc.Records(
data=vdf,
start_year=TAXYEAR,
Expand All @@ -41,27 +40,17 @@ def create_variable_file():
adjust_ratios=None,
)
vdf.drop(columns=rec.IGNORED_VARS, inplace=True)
vdf.e00200p = vdf.e00200p.to_numpy().round()
vdf.e00200s = vdf.e00200s.to_numpy().round()
vdf.e00200 = vdf.e00200p + vdf.e00200s
vdf.e00900p = vdf.e00900p.to_numpy().round()
vdf.e00900s = vdf.e00900s.to_numpy().round()
vdf.e00900 = vdf.e00900p + vdf.e00900s
vdf.e02100p = vdf.e02100p.to_numpy().round()
vdf.e02100s = vdf.e02100s.to_numpy().round()
vdf.e02100 = vdf.e02100p + vdf.e02100s
# round all float variables to nearest integer except for weights
weights = vdf.s006.copy()
vdf = vdf.astype(int)
vdf.s006 = weights
for var in ["e00200", "e00900", "e02100"]:
vdf[var] = vdf[f"{var}p"] + vdf[f"{var}s"]
# write streamlined variables dataframe to CSV-formatted file
vdf.to_csv(
"tmd.csv.gz",
index=False,
float_format="%.0f",
compression="gzip",
)
# write exact TAXYEAR weights to CSV-formatted file
wdf = pd.DataFrame.from_dict({"exact_weight": vdf.s006})
wdf.to_csv(
f"tmd_exact_{TAXYEAR}_weights.csv.gz",
index=False,
float_format="%.2f",
compression="gzip",
)

Expand Down
13 changes: 6 additions & 7 deletions tax_microdata_benchmarking/create_taxcalc_sampling_weights.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -9,7 +9,7 @@

FIRST_YEAR = 2021
LAST_YEAR = 2034
EXWFILE = "tmd_exact_2021_weights.csv.gz"
VARFILE = "tmd.csv.gz"
POPFILE = "cbo_population_forecast.yaml"
WGTFILE = "tmd_weights.csv.gz"

Expand All @@ -22,12 +22,11 @@ def create_weights_file():
with open(POPFILE, "r", encoding="utf-8") as pfile:
pop = yaml.safe_load(pfile.read())

# get exact FIRST_YEAR weights from EXWFILE
edf = pd.read_csv(EXWFILE)
weights = edf.exact_weight
weights *= 100 # scale up weights by 100 for Tax-Calculator
# get FIRST_YEAR weights from VARFILE
vdf = pd.read_csv(VARFILE)
weights = vdf.s006 * 100 # scale up weights by 100 for Tax-Calculator

# construct dictionary of weights by year
# construct dictionary of scaled-up weights by year
wdict = {f"WT{FIRST_YEAR}": weights}
cum_pop_growth = 1.0
for year in range(FIRST_YEAR + 1, LAST_YEAR + 1):
Expand All @@ -36,7 +35,7 @@ def create_weights_file():
wght = weights.copy() * cum_pop_growth
wdict[f"WT{year}"] = wght

# write rounded integer weights to CSV-formatted file
# write rounded integer scaled-up weights to CSV-formatted file
wdf = pd.DataFrame.from_dict(wdict)
wdf.to_csv(WGTFILE, index=False, float_format="%.0f", compression="gzip")

Expand Down
18 changes: 9 additions & 9 deletions tax_microdata_benchmarking/examination/taxcalculator/tmd-23.res-expect
View file
Original file line number Diff line number Diff line change
@@ -1,27 +1,27 @@
Weighted Tax Reform Totals by Baseline Expanded-Income Decile
Returns ExpInc IncTax PayTax LSTax AllTax
A 222.51 20246.5 2317.1 1618.1 0.0 3935.3
A 222.51 20245.8 2317.0 1618.1 0.0 3935.1

==> tmd-23-#-cgqd-#-tab.text <==
A 222.51 20246.5 273.0 0.0 0.0 273.0
A 222.51 20245.8 272.9 0.0 0.0 272.9

==> tmd-23-#-clp-#-tab.text <==
A 222.51 20246.5 0.0 0.0 0.0 0.0
A 222.51 20245.8 0.0 0.0 0.0 0.0

==> tmd-23-#-ctc-#-tab.text <==
A 222.51 20246.5 124.6 0.0 0.0 124.6
A 222.51 20245.8 124.6 0.0 0.0 124.6

==> tmd-23-#-eitc-#-tab.text <==
A 222.51 20246.5 70.0 0.0 0.0 70.0
A 222.51 20245.8 70.0 0.0 0.0 70.0

==> tmd-23-#-niit-#-tab.text <==
A 222.51 20246.5 -68.1 0.0 0.0 -68.1
A 222.51 20245.8 -68.1 0.0 0.0 -68.1

==> tmd-23-#-qbid-#-tab.text <==
A 222.51 20246.5 63.1 0.0 0.0 63.1
A 222.51 20245.8 63.1 0.0 0.0 63.1

==> tmd-23-#-salt-#-tab.text <==
A 222.51 20246.5 15.8 0.0 0.0 15.8
A 222.51 20245.8 15.8 0.0 0.0 15.8

==> tmd-23-#-ssben-#-tab.text <==
A 222.51 20246.5 81.9 0.0 0.0 81.9
A 222.51 20245.8 81.9 0.0 0.0 81.9
18 changes: 9 additions & 9 deletions tax_microdata_benchmarking/examination/taxcalculator/tmd-26.res-expect
View file
Original file line number Diff line number Diff line change
@@ -1,27 +1,27 @@
Weighted Tax Reform Totals by Baseline Expanded-Income Decile
Returns ExpInc IncTax PayTax LSTax AllTax
A 229.77 23030.6 2934.4 1875.5 0.0 4809.9
A 229.77 23030.4 2934.4 1875.5 0.0 4809.9

==> tmd-26-#-cgqd-#-tab.text <==
A 229.77 23030.6 271.8 0.0 0.0 271.8
A 229.77 23030.4 271.8 0.0 0.0 271.8

==> tmd-26-#-clp-#-tab.text <==
A 229.77 23030.6 0.0 0.0 0.0 0.0
A 229.77 23030.4 0.0 0.0 0.0 0.0

==> tmd-26-#-ctc-#-tab.text <==
A 229.77 23030.6 44.7 0.0 0.0 44.7
A 229.77 23030.4 44.7 0.0 0.0 44.7

==> tmd-26-#-eitc-#-tab.text <==
A 229.77 23030.6 78.7 0.0 0.0 78.7
A 229.77 23030.4 78.7 0.0 0.0 78.7

==> tmd-26-#-niit-#-tab.text <==
A 229.77 23030.6 -66.4 0.0 0.0 -66.4
A 229.77 23030.4 -66.4 0.0 0.0 -66.4

==> tmd-26-#-qbid-#-tab.text <==
A 229.77 23030.6 0.0 0.0 0.0 0.0
A 229.77 23030.4 0.0 0.0 0.0 0.0

==> tmd-26-#-salt-#-tab.text <==
A 229.77 23030.6 195.6 0.0 0.0 195.6
A 229.77 23030.4 195.6 0.0 0.0 195.6

==> tmd-26-#-ssben-#-tab.text <==
A 229.77 23030.6 109.8 0.0 0.0 109.8
A 229.77 23030.4 109.8 0.0 0.0 109.8
25 changes: 25 additions & 0 deletions tax_microdata_benchmarking/growfactors.csv
View file
Original file line number Diff line number Diff line change
@@ -0,0 +1,25 @@
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
2014,1.029476,0.931683,0.976566,1.015927,1.023917,1.039999,1.040616,1.030349,1.075978,0.991321,0.925886,1.17606,1.387522,1.004801,0.641103,0.970506,0.99257,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0
2015,1.043858,0.508206,0.999544,1.001235,1.026485,1.024119,1.038052,1.040061,1.04481,1.057257,1.013311,1.013846,1.004308,1.017188,0.81793,0.988666,1.053858,1.023325,1.041528,1.019361,1.102667,1.007792,1.026748,1.132657,1.04693
2016,1.021978,1.071198,0.984833,1.012621,1.037807,1.006659,0.984484,1.005593,0.982695,0.983807,0.999649,0.968237,0.881651,0.991403,0.933831,1.001764,1.097065,1.011695,1.010367,0.993375,0.989722,1.002577,1.01625,0.828168,1.105413
2017,1.049373,0.907035,1.018491,1.01677,1.025035,1.040577,1.060677,1.132498,1.093477,1.160123,1.088642,1.102389,1.344721,1.00723,0.94186,1.029301,1.011911,1.030968,1.014601,0.981621,1.0,0.998715,1.063959,1.0,1.0
2018,1.042394,0.976786,1.074059,1.02,1.019707,1.041821,1.042769,1.042713,1.074048,1.074033,1.031272,1.076804,1.074755,1.018778,0.92284,1.050825,1.103035,1.045097,1.045897,1.005738,1.0,1.002574,1.034828,1.0,1.0
2019,1.032351,0.979401,1.019085,1.01341,1.028328,1.039292,1.004274,1.004387,1.019131,1.019122,1.014806,1.041751,0.925878,1.031182,0.940635,1.040577,1.054052,1.052158,1.045866,1.000751,1.0,1.002567,1.034809,1.0,1.0
2020,1.067957,1.167938,0.947032,1.00799,1.041121,1.006381,1.016054,1.015991,0.947016,0.94708,0.989319,1.091767,1.292516,1.023383,1.034222,1.076313,0.996727,1.050763,1.046106,1.00255,1.0,1.003841,1.034974,1.0,1.0
2021,1.065517,1.126248,1.216697,1.04269,1.012343,1.079707,1.057058,1.05708,1.216716,1.216644,0.999214,1.057982,1.792117,1.011965,8.619252,1.073809,1.03007,1.047248,1.047927,1.001796,1.0,1.002551,1.034869,1.0,1.0
2022,1.014347,1.742914,1.047552,1.07229,1.040311,1.076282,1.022527,1.022546,1.047553,1.047566,1.049118,1.042358,0.631565,1.04749,0.152665,1.022138,1.030159,1.048769,1.047573,0.999851,1.0,1.002545,1.034942,1.0,1.0
2023,1.050108,0.653145,1.091056,1.05402,1.004761,1.050035,1.013156,1.013121,1.091037,1.091047,1.026196,1.126711,1.0525,1.085497,0.748857,1.058072,1.030193,1.050822,1.048715,1.000448,1.0,1.003807,1.034968,1.0,1.0
2024,1.046242,0.895528,1.007166,1.0255,1.01407,1.040377,1.0397,1.03963,1.007187,1.007157,1.156028,1.023049,0.932271,1.052921,1.337549,1.054081,1.030334,1.048426,1.051767,0.99776,1.0,1.002528,1.034951,1.0,1.0
2025,1.040442,0.963117,1.020457,1.02198,0.958663,1.038977,1.037682,1.037745,1.020415,1.020444,1.091746,1.02538,0.97747,1.031721,1.154874,1.047914,1.030635,1.046248,1.052213,1.002245,1.0,1.003783,1.034897,1.0,1.0
2026,1.039294,0.987094,1.014705,1.02074,1.014023,1.035978,1.037783,1.037762,1.014711,1.014716,1.098184,1.019802,0.970235,1.030992,1.035291,1.046856,1.030633,1.072236,1.0,0.999552,1.0,1.002513,1.034808,1.0,1.0
2027,1.037119,0.998822,1.017535,1.01946,1.013312,1.033569,1.03414,1.034138,1.017568,1.017583,1.066606,1.013266,0.993714,1.031791,1.045541,1.044372,1.030788,1.0,1.0,1.0,1.0,1.002506,1.034863,1.0,1.0
2028,1.036799,1.006582,1.023966,1.01942,1.013356,1.033042,1.031594,1.03158,1.023985,1.02393,1.050716,1.021542,1.009158,1.03344,1.043558,1.043967,1.030942,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0
2029,1.035913,1.010333,1.028149,1.01966,1.013612,1.033365,1.030869,1.030888,1.028085,1.028143,1.03013,1.032091,1.018962,1.033664,1.045739,1.042825,1.031131,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0
2030,1.036423,1.01018,1.024121,1.01977,1.013855,1.03321,1.030563,1.030595,1.02417,1.024128,1.036979,1.032934,1.024538,1.034401,1.043738,1.043174,1.03133,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0
2031,1.036362,1.010259,1.024733,1.01991,1.014016,1.032812,1.031233,1.03124,1.024699,1.024734,1.039197,1.032793,1.027842,1.036645,1.038241,1.042951,1.03151,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0
2032,1.036409,1.009979,1.028,1.01999,1.014306,1.032126,1.032334,1.032295,1.028004,1.027983,1.04014,1.03261,1.029719,1.036435,1.031319,1.042807,1.031644,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0
2033,1.035793,1.008195,1.02813,1.02002,1.014309,1.031481,1.033961,1.033991,1.028128,1.02811,1.031669,1.03246,1.030798,1.037554,1.028443,1.042009,1.031857,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0
2034,1.035385,1.008203,1.02971,1.02,1.014443,1.030912,1.033294,1.033253,1.029705,1.029722,1.027096,1.032231,1.031308,1.03748,1.029528,1.041408,1.032059,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0
25 changes: 25 additions & 0 deletions tax_microdata_benchmarking/puf_growfactors.csv
View file
Original file line number Diff line number Diff line change
@@ -0,0 +1,25 @@
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
2011,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
2012,1.043862,0.950283,1.104992,1.0209,1.0365,1.032649,1.049023,0.956138,1.165922,0.926962,0.923588,1.327776,1.58966,1.02827,0.7711,0.9231,0.992359,1,1,1,1,1,1,1,1
2013,1.012518,1.142179,1.033784,1.014791,1.024602,1.019984,0.99505,1.050098,0.997245,1.013128,0.893658,0.819381,0.776217,1.014786,0.728829,0.896219,0.992515,1,1,1,1,1,1,1,1
2014,1.029476,0.931683,0.976566,1.015927,1.023917,1.039999,1.040616,1.030349,1.075978,0.991321,0.925886,1.17606,1.387522,1.004801,0.641103,0.970506,0.99257,1,1,1,1,1,1,1,1
2015,1.043858,0.508206,0.999544,1.001235,1.026485,1.024119,1.038052,1.040061,1.04481,1.057257,1.013311,1.013846,1.004308,1.017188,0.81793,0.988666,1.053858,1.023325,1.041528,1.019361,1.102667,1.007792,1.026748,1.132657,1.04693
2016,1.021978,1.071198,0.984833,1.012621,1.037807,1.006659,0.984484,1.005593,0.982695,0.983807,0.999649,0.968237,0.881651,0.991403,0.933831,1.001764,1.097065,1.011695,1.010367,0.993375,0.989722,1.002577,1.01625,0.828168,1.105413
2017,1.049373,0.907035,1.018491,1.01677,1.025035,1.040577,1.060677,1.132498,1.093477,1.160123,1.088642,1.102389,1.344721,1.00723,0.94186,1.029301,1.011911,1.030968,1.014601,0.981621,1,0.998715,1.063959,1,1
2018,1.042394,0.976786,1.074059,1.02,1.019707,1.041821,1.042769,1.042713,1.074048,1.074033,1.031272,1.076804,1.074755,1.018778,0.92284,1.050825,1.103035,1.045097,1.045897,1.005738,1,1.002574,1.034828,1,1
2019,1.032351,0.979401,1.019085,1.01341,1.028328,1.039292,1.004274,1.004387,1.019131,1.019122,1.014806,1.041751,0.925878,1.031182,0.940635,1.040577,1.054052,1.052158,1.045866,1.000751,1,1.002567,1.034809,1,1
2020,1.067957,1.167938,0.947032,1.00799,1.041121,1.006381,1.016054,1.015991,0.947016,0.94708,0.989319,1.091767,1.292516,1.023383,1.034222,1.076313,0.996727,1.050763,1.046106,1.00255,1,1.003841,1.034974,1,1
2021,1.065517,1.126248,1.216697,1.04269,1.012343,1.079707,1.057058,1.05708,1.216716,1.216644,0.999214,1.057982,1.792117,1.011965,8.619252,1.073809,1.03007,1.047248,1.047927,1.001796,1,1.002551,1.034869,1,1
2022,1.014347,1.742914,1.047552,1.07229,1.040311,1.076282,1.022527,1.022546,1.047553,1.047566,1.049118,1.042358,0.631565,1.04749,0.152665,1.022138,1.030159,1.048769,1.047573,0.999851,1,1.002545,1.034942,1,1
2023,1.050108,0.653145,1.091056,1.05402,1.004761,1.050035,1.013156,1.013121,1.091037,1.091047,1.026196,1.126711,1.0525,1.085497,0.748857,1.058072,1.030193,1.050822,1.048715,1.000448,1,1.003807,1.034968,1,1
2024,1.046242,0.895528,1.007166,1.0255,1.01407,1.040377,1.0397,1.03963,1.007187,1.007157,1.156028,1.023049,0.932271,1.052921,1.337549,1.054081,1.030334,1.048426,1.051767,0.99776,1,1.002528,1.034951,1,1
2025,1.040442,0.963117,1.020457,1.02198,0.958663,1.038977,1.037682,1.037745,1.020415,1.020444,1.091746,1.02538,0.97747,1.031721,1.154874,1.047914,1.030635,1.046248,1.052213,1.002245,1,1.003783,1.034897,1,1
2026,1.039294,0.987094,1.014705,1.02074,1.014023,1.035978,1.037783,1.037762,1.014711,1.014716,1.098184,1.019802,0.970235,1.030992,1.035291,1.046856,1.030633,1.072236,1,0.999552,1,1.002513,1.034808,1,1
2027,1.037119,0.998822,1.017535,1.01946,1.013312,1.033569,1.03414,1.034138,1.017568,1.017583,1.066606,1.013266,0.993714,1.031791,1.045541,1.044372,1.030788,1,1,1,1,1.002506,1.034863,1,1
2028,1.036799,1.006582,1.023966,1.01942,1.013356,1.033042,1.031594,1.03158,1.023985,1.02393,1.050716,1.021542,1.009158,1.03344,1.043558,1.043967,1.030942,1,1,1,1,1,1,1,1
2029,1.035913,1.010333,1.028149,1.01966,1.013612,1.033365,1.030869,1.030888,1.028085,1.028143,1.03013,1.032091,1.018962,1.033664,1.045739,1.042825,1.031131,1,1,1,1,1,1,1,1
2030,1.036423,1.01018,1.024121,1.01977,1.013855,1.03321,1.030563,1.030595,1.02417,1.024128,1.036979,1.032934,1.024538,1.034401,1.043738,1.043174,1.03133,1,1,1,1,1,1,1,1
2031,1.036362,1.010259,1.024733,1.01991,1.014016,1.032812,1.031233,1.03124,1.024699,1.024734,1.039197,1.032793,1.027842,1.036645,1.038241,1.042951,1.03151,1,1,1,1,1,1,1,1
2032,1.036409,1.009979,1.028,1.01999,1.014306,1.032126,1.032334,1.032295,1.028004,1.027983,1.04014,1.03261,1.029719,1.036435,1.031319,1.042807,1.031644,1,1,1,1,1,1,1,1
2033,1.035793,1.008195,1.02813,1.02002,1.014309,1.031481,1.033961,1.033991,1.028128,1.02811,1.031669,1.03246,1.030798,1.037554,1.028443,1.042009,1.031857,1,1,1,1,1,1,1,1
2034,1.035385,1.008203,1.02971,1.02,1.014443,1.030912,1.033294,1.033253,1.029705,1.029722,1.027096,1.032231,1.031308,1.03748,1.029528,1.041408,1.032059,1,1,1,1,1,1,1,1
Binary file removed tax_microdata_benchmarking/tmd_exact_2021_weights.csv.gz
View file
Binary file not shown.
Binary file modified tax_microdata_benchmarking/tmd_weights.csv.gz
View file
Binary file not shown.

0 comments on commit 78304c2

Please sign in to comment.