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Addition totals calculated for dropq calculation #1060

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Nov 15, 2016
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Addition totals calculated for dropq calculation #1060

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Addition totals calculated for dropq calculation
Nov 14, 2016
48c83c2
remove backslash line continuation
Nov 15, 2016
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64 changes: 53 additions & 11 deletions taxcalc/dropq/dropq.py
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Original file line number Diff line number Diff line change
Expand Up @@ -32,6 +32,7 @@

total_row_names = ["ind_tax", "payroll_tax", "combined_tax"]


GDP_elast_row_names = ["gdp_elasticity"]

ogusa_row_names = ["GDP", "Consumption", "Investment", "Hours Worked", "Wages",
Expand Down Expand Up @@ -241,13 +242,26 @@ def groupby_means_and_comparisons(df1, df2, mask, debug=False):

df1, df2 = drop_records(df1, df2, mask)

# Totals for diff between baseline and reform
dec_sum = (df2['tax_diff_dec'] * df2['s006']).sum()
bin_sum = (df2['tax_diff_bin'] * df2['s006']).sum()
pr_dec_sum = (df2['payrolltax_diff_dec'] * df2['s006']).sum()
pr_bin_sum = (df2['payrolltax_diff_bin'] * df2['s006']).sum()
combined_dec_sum = (df2['combined_diff_dec'] * df2['s006']).sum()
combined_bin_sum = (df2['combined_diff_bin'] * df2['s006']).sum()

# Totals for baseline
sum_baseline = (df1['_iitax'] * df1['s006']).sum()
pr_sum_baseline = (df1['_payrolltax'] * df1['s006']).sum()
combined_sum_baseline = (df1['_combined'] * df1['s006']).sum()

# Totals for reform
sum_reform = (df2['_iitax_dec'] * df2['s006']).sum()
pr_sum_reform = (df2['_payrolltax_dec'] * df2['s006']).sum()
combined_sum_reform = (df2['_combined_dec'] * df2['s006']).sum()

# Totals for reform

# Create Difference tables, grouped by deciles and bins
diffs_dec = dropq_diff_table(df1, df2,
groupby="weighted_deciles",
Expand Down Expand Up @@ -299,7 +313,9 @@ def groupby_means_and_comparisons(df1, df2, mask, debug=False):

return (mY_dec, mX_dec, diffs_dec, pr_diffs_dec, comb_diffs_dec,
mY_bin, mX_bin, diffs_bin, pr_diffs_bin, comb_diffs_bin,
dec_sum, pr_dec_sum, combined_dec_sum)
dec_sum, pr_dec_sum, combined_dec_sum, sum_baseline,
pr_sum_baseline, combined_sum_baseline, sum_reform,
pr_sum_reform, combined_sum_reform)


def results(c):
Expand Down Expand Up @@ -502,16 +518,27 @@ def run_nth_year(year_n, start_year, is_strict, tax_dta="", user_mods="",
# diffs of plan Y by decile
# Means of plan Y by income bin
# diffs of plan Y by income bin
mY_dec, mX_dec, df_dec, pdf_dec, cdf_dec, mY_bin, mX_bin, df_bin, \
pdf_bin, cdf_bin, diff_sum, payrolltax_diff_sum, combined_diff_sum = \
groupby_means_and_comparisons(soit_baseline, soit_reform, mask)
(mY_dec, mX_dec, df_dec, pdf_dec, cdf_dec, mY_bin, mX_bin, df_bin,
pdf_bin, cdf_bin, diff_sum, payrolltax_diff_sum, combined_diff_sum,
sum_baseline, pr_sum_baseline, combined_sum_baseline, sum_reform,
pr_sum_reform,
combined_sum_reform) = groupby_means_and_comparisons(soit_baseline,
soit_reform, mask)

elapsed_time = time.time() - start_time
print("elapsed time for this run: ", elapsed_time)
start_year += 1

tots = [diff_sum, payrolltax_diff_sum, combined_diff_sum]
fiscal_tots = pd.DataFrame(data=tots, index=total_row_names)
fiscal_tots_diff = pd.DataFrame(data=tots, index=total_row_names)

tots_baseline = [sum_baseline, pr_sum_baseline, combined_sum_baseline]
fiscal_tots_baseline = pd.DataFrame(data=tots_baseline,
index=total_row_names)

tots_reform = [sum_reform, pr_sum_reform, combined_sum_reform]
fiscal_tots_reform = pd.DataFrame(data=tots_reform,
index=total_row_names)

# Get rid of negative incomes
df_bin.drop(df_bin.index[0], inplace=True)
Expand All @@ -529,7 +556,9 @@ def append_year(x):
append_year(pdf_dec), append_year(cdf_dec),
append_year(mY_bin), append_year(mX_bin), append_year(df_bin),
append_year(pdf_bin), append_year(cdf_bin),
append_year(fiscal_tots))
append_year(fiscal_tots_diff),
append_year(fiscal_tots_baseline),
append_year(fiscal_tots_reform))

decile_row_names_i = [x + '_' + str(year_n) for x in decile_row_names]

Expand Down Expand Up @@ -577,14 +606,24 @@ def append_year(x):
row_names=bin_row_names_i,
column_types=diff_column_types)

fiscal_yr_total = create_json_table(fiscal_tots,
row_names=total_row_names_i)
fiscal_yr_total_df = create_json_table(fiscal_tots_diff,
row_names=total_row_names_i)

fiscal_yr_total_bl = create_json_table(fiscal_tots_baseline,
row_names=total_row_names_i)

fiscal_yr_total_rf = create_json_table(fiscal_tots_reform,
row_names=total_row_names_i)

# Make the one-item lists of strings just strings
fiscal_yr_total = dict((k, v[0]) for k, v in fiscal_yr_total.items())
fiscal_yr_total_df = dict((k, v[0]) for k, v in fiscal_yr_total_df.items())
fiscal_yr_total_bl = dict((k, v[0]) for k, v in fiscal_yr_total_bl.items())
fiscal_yr_total_rf = dict((k, v[0]) for k, v in fiscal_yr_total_rf.items())

return (mY_dec_table_i, mX_dec_table_i, df_dec_table_i, pdf_dec_table_i,
cdf_dec_table_i, mY_bin_table_i, mX_bin_table_i, df_bin_table_i,
pdf_bin_table_i, cdf_bin_table_i, fiscal_yr_total)
pdf_bin_table_i, cdf_bin_table_i, fiscal_yr_total_df,
fiscal_yr_total_bl, fiscal_yr_total_rf)


def run_models(tax_dta, start_year, is_strict=False, user_mods="",
Expand Down Expand Up @@ -614,9 +653,12 @@ def run_models(tax_dta, start_year, is_strict=False, user_mods="",

(mY_dec_table_i, mX_dec_table_i, df_dec_table_i, pdf_dec_table_i,
cdf_dec_table_i, mY_bin_table_i, mX_bin_table_i, df_bin_table_i,
pdf_bin_table_i, cdf_bin_table_i, num_fiscal_year_total) = json_tables
pdf_bin_table_i, cdf_bin_table_i, num_fiscal_year_total,
num_fiscal_year_total_bl, num_fiscal_year_total_rf) = json_tables

num_fiscal_year_totals.append(num_fiscal_year_total)
num_fiscal_year_totals.append(num_fiscal_year_total_bl)
num_fiscal_year_totals.append(num_fiscal_year_total_rf)
mY_dec_table.update(mY_dec_table_i)
mX_dec_table.update(mX_dec_table_i)
df_dec_table.update(df_dec_table_i)
Expand Down
10 changes: 10 additions & 0 deletions taxcalc/tests/test_dropq.py
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Original file line number Diff line number Diff line change
Expand Up @@ -2,6 +2,7 @@
import numpy as np
import pandas as pd
import pytest
import numpy.testing as npt
from pandas import DataFrame, Series

from taxcalc.dropq.dropq_utils import *
Expand Down Expand Up @@ -103,6 +104,15 @@ def test_full_dropq_puf(puf_path):
# Assert that dropq revenue is similar to the "pure" calculation
assert diff / dropq_reform_revenue < 0.02

# Assert that Reform - Baseline = Reported Delta
delta_yr0 = fiscal_tots[0]
baseline_yr0 = fiscal_tots[1]
reform_yr0 = fiscal_tots[2]
diff_yr0 = (reform_yr0.loc['combined_tax'] -
baseline_yr0.loc['combined_tax']).values
delta_yr0 = delta_yr0.loc['combined_tax'].values
npt.assert_array_almost_equal(diff_yr0, delta_yr0, decimal=3)


@pytest.mark.parametrize("is_strict, rjson, growth_params, no_elast",
[(True, True, False, False), (True, True, True, True),
Expand Down