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Fix payroll tax revenue double-counted in aggregates.revenue (Issue #1199) #1204

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Merged
jdebacker merged 1 commit into from
Aug 17, 2026
Merged

Fix payroll tax revenue double-counted in aggregates.revenue (Issue #1199) #1204

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16 changes: 16 additions & 0 deletions CHANGELOG.md
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Original file line number Diff line number Diff line change
Expand Up @@ -5,6 +5,22 @@ All notable changes to this project will be documented in this file.
The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/),
and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html).

## [Unreleased]

### Bug Fixes

- Fixes Issue [#1199](https://github.com/PSLmodels/OG-Core/issues/1199):
`aggregates.revenue` counted payroll tax revenue twice. Each household's
`income_payroll_tax_liab` already includes `T_P = tau_payroll *
labor_income`, so payroll revenue is inside `iit_payroll_tax_revenue`;
PR #1184 then added `get_payroll_tax_revenue` (the same take, re-derived
from the aggregate wage bill) into that total again when `tau_payroll`
was nonzero. Revenue was overstated, the government over-spent, and any
model with a nonzero `tau_payroll` failed the steady-state resource
constraint (OG-USA was unaffected only because its `tau_payroll` is 0).
The erroneous addition is removed; `get_payroll_tax_revenue` still
provides the income vs payroll reporting split, which is unchanged.

## [0.19.1] - 2026-08-10 12:00:00

### Added
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12 changes: 6 additions & 6 deletions ogcore/aggregates.py
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Expand Up @@ -415,15 +415,15 @@ def revenue(
cons_tax_revenue = (
(tax.cons_tax_liab(c, p_i, p, method) * pop_weights).sum(1).sum(1)
)
# Payroll tax revenue is already inside iit_payroll_tax_revenue: each
# household's income_payroll_tax_liab includes T_P = tau_payroll *
# labor_income (see tax.income_tax_liab). get_payroll_tax_revenue just
# re-derives that same amount from the aggregate wage bill so the
# income vs payroll split can be reported; it must NOT be added into
# the total again (that double-counted payroll revenue -- Issue #1199).
payroll_tax_revenue = get_payroll_tax_revenue(
w, L, iit_payroll_tax_revenue, p, method
)
# When payroll taxes are modeled explicitly via tau_payroll, they are
# excluded from the income and payroll tax functions, so payroll tax
# revenue must be added into iit_payroll_tax_revenue (which enters
# total revenue) before the income tax portion is separated out.
if np.any(p.tau_payroll != 0):
iit_payroll_tax_revenue += payroll_tax_revenue
business_tax_revenue = tax.get_biz_tax(w, Y, L, K, p_m, p, m, method).sum(
-1
)
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184 changes: 92 additions & 92 deletions tests/test_aggregates.py
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Expand Up @@ -1474,111 +1474,111 @@ def test_get_C(c, p, method, expected):
# vector of output prices
p_m = np.ones((p.T, p.M))

expected1 = 0.7598207140204407
expected1 = 0.5688319028341413
expected2 = np.array(
[
0.78077777,
0.72068128,
0.77564557,
0.75220586,
0.78498646,
0.82226742,
0.78953295,
0.75499567,
0.75017449,
0.79555133,
0.77677308,
0.78748408,
0.73859649,
0.75895348,
0.75059024,
0.85599023,
0.77630208,
0.84016811,
0.90505241,
0.82363203,
0.80717282,
0.77141901,
0.73973490,
0.72241447,
0.74309837,
0.84927868,
0.78312174,
0.83015615,
0.77981290,
0.74258374,
0.58978896,
0.5318829,
0.58291302,
0.56616446,
0.60152253,
0.63685373,
0.60718972,
0.56236328,
0.56929121,
0.60536959,
0.58763365,
0.59627562,
0.55409009,
0.56782614,
0.56400569,
0.6636463,
0.59160813,
0.64735391,
0.72066489,
0.64096484,
0.61899218,
0.58806093,
0.54783766,
0.5393597,
0.55685316,
0.65395071,
0.58946501,
0.64129696,
0.58759922,
0.5580478,
]
)
expected3 = (
np.array(
[
0.78077777,
0.72068128,
0.77564557,
0.75220586,
0.78498646,
0.82226742,
0.78953295,
0.75499567,
0.75017449,
0.79555133,
0.77677308,
0.78748408,
0.73859649,
0.75895348,
0.75059024,
0.85599023,
0.77630208,
0.84016811,
0.90505241,
0.82363203,
0.80717282,
0.77141901,
0.73973490,
0.72241447,
0.74309837,
0.84927868,
0.78312174,
0.83015615,
0.77981290,
0.74258374,
0.58978896,
0.5318829,
0.58291302,
0.56616446,
0.60152253,
0.63685373,
0.60718972,
0.56236328,
0.56929121,
0.60536959,
0.58763365,
0.59627562,
0.55409009,
0.56782614,
0.56400569,
0.6636463,
0.59160813,
0.64735391,
0.72066489,
0.64096484,
0.61899218,
0.58806093,
0.54783766,
0.5393597,
0.55685316,
0.65395071,
0.58946501,
0.64129696,
0.58759922,
0.5580478,
]
)
- inv_tax_cred_rev3
)
expected4 = 0.7598207140204407
expected4 = 0.5688319028341413
expected5 = np.array(
[
0.78077777,
0.72068128,
0.77564557,
0.75220586,
0.78498646,
0.82226742,
0.78953295,
0.75499567,
0.75017449,
0.79555133,
0.77677308,
0.78748408,
0.73859649,
0.75895348,
0.75059024,
0.85599023,
0.77630208,
0.84016811,
0.90505241,
0.82363203,
0.80717282,
0.77141901,
0.73973490,
0.72241447,
0.74309837,
0.84927868,
0.78312174,
0.83015615,
0.77981290,
0.74258374,
0.58978896,
0.5318829,
0.58291302,
0.56616446,
0.60152253,
0.63685373,
0.60718972,
0.56236328,
0.56929121,
0.60536959,
0.58763365,
0.59627562,
0.55409009,
0.56782614,
0.56400569,
0.6636463,
0.59160813,
0.64735391,
0.72066489,
0.64096484,
0.61899218,
0.58806093,
0.54783766,
0.5393597,
0.55685316,
0.65395071,
0.58946501,
0.64129696,
0.58759922,
0.5580478,
]
)
test_data = [
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