diff --git a/.github/workflows/versioning.yaml b/.github/workflows/versioning.yaml
index 53709c4e..c4ccf09b 100644
--- a/.github/workflows/versioning.yaml
+++ b/.github/workflows/versioning.yaml
@@ -4,10 +4,11 @@ name: Versioning updates
on:
push:
branches:
- - main
+ - main
paths:
- - changelog_entry.yaml
+ - changelog_entry.yaml
+ - "!pyproject.toml"
jobs:
Versioning:
diff --git a/Makefile b/Makefile
index c1b795b7..49972175 100644
--- a/Makefile
+++ b/Makefile
@@ -3,6 +3,7 @@ all: build
documentation:
jb clean docs
jb build docs
+ python docs/add_plotly_to_book.py docs/
install:
pip install -e .[dev]
diff --git a/changelog_entry.yaml b/changelog_entry.yaml
index e69de29b..d1b35788 100644
--- a/changelog_entry.yaml
+++ b/changelog_entry.yaml
@@ -0,0 +1,4 @@
+- bump: minor
+ changes:
+ added:
+ - Chart generation for macro charts.
diff --git a/docs/_toc.yml b/docs/_toc.yml
index 6b3c3baa..e901296a 100644
--- a/docs/_toc.yml
+++ b/docs/_toc.yml
@@ -4,4 +4,9 @@ parts:
- caption: Reference
chapters:
- file: reference/simulation
+ - file: reference/calculate_single_household
+ - file: reference/calculate_household_comparison
+ - file: reference/calculate_single_economy
+ - file: reference/calculate_economy_comparison
+ - file: reference/create_charts
\ No newline at end of file
diff --git a/docs/add_plotly_to_book.py b/docs/add_plotly_to_book.py
new file mode 100644
index 00000000..822e77ab
--- /dev/null
+++ b/docs/add_plotly_to_book.py
@@ -0,0 +1,27 @@
+import argparse
+from pathlib import Path
+
+# This command-line tools enables Plotly charts to show in the HTML files for the Jupyter Book documentation.
+
+parser = argparse.ArgumentParser()
+parser.add_argument("book_path", help="Path to the Jupyter Book.")
+
+args = parser.parse_args()
+
+# Find every HTML file in the Jupyter Book. Then, add a script tag to the start of the tag in each file, with the contents:
+#
+
+book_folder = Path(args.book_path)
+
+for html_file in book_folder.glob("**/*.html"):
+ with open(html_file, "r") as f:
+ html = f.read()
+
+ # Add the script tag to the start of the tag.
+ html = html.replace(
+ "",
+ '',
+ )
+
+ with open(html_file, "w") as f:
+ f.write(html)
diff --git a/docs/reference/calculate_economy_comparison.ipynb b/docs/reference/calculate_economy_comparison.ipynb
new file mode 100644
index 00000000..2d6fcfd4
--- /dev/null
+++ b/docs/reference/calculate_economy_comparison.ipynb
@@ -0,0 +1,267 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Compare outcomes for large population scenarios\n",
+ "\n",
+ "Use `Simulation.calculate_economy_comparison()` to use PolicyEngine's tax-benefit model to compare how taxes, benefits and other household properties change under a reform scenario. This notebook demonstrates how to use this function."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "EconomyComparison(headlines=Headlines(budgetary_impact=-20688882917.062927, poverty_impact=-0.009522052793635733, winner_share=0.8345739005338316), fiscal=FiscalComparison(baseline=FiscalSummary(tax_revenue=658911285719.5891, federal_tax=658911285719.5891, federal_balance=309089098855.4849, state_tax=0.0, government_spending=349822186864.1042, tax_benefit_programs={'income_tax': 333376287037.05945, 'national_insurance': 52985626776.773834, 'ni_employer': 126330649370.35953, 'vat': 211671832822.39133, 'council_tax': 49007055050.00724, 'fuel_duty': 26506672341.204205, 'tax_credits': -34929879.49872104, 'universal_credit': -73459549194.97665, 'child_benefit': -14311471487.935827, 'state_pension': -132795868621.44594, 'pension_credit': -6252358021.417119}, household_net_income=1566028514855.0789), reform=FiscalSummary(tax_revenue=637686731286.3723, federal_tax=637686731286.3723, federal_balance=288400215938.422, state_tax=0.0, government_spending=349286515347.9503, tax_benefit_programs={'income_tax': 312151564998.6612, 'national_insurance': 52985626776.773834, 'ni_employer': 126330649370.35953, 'vat': 211671832822.39133, 'council_tax': 49007055050.00724, 'fuel_duty': 26506672341.204205, 'tax_credits': -34929879.49872104, 'universal_credit': -73099552696.20555, 'child_benefit': -14311471487.935827, 'state_pension': -132795868621.44594, 'pension_credit': -6181432287.167828}, household_net_income=1586717263122.5647), change=FiscalSummary(tax_revenue=-21224554433.216797, federal_tax=-21224554433.216797, federal_balance=-20688882917.062927, state_tax=0.0, government_spending=-535671516.1538696, tax_benefit_programs={'income_tax': -21224722038.398254, 'national_insurance': 0.0, 'ni_employer': 0.0, 'vat': 0.0, 'council_tax': 0.0, 'fuel_duty': 0.0, 'tax_credits': 0.0, 'universal_credit': 359996498.7711029, 'child_benefit': 0.0, 'state_pension': 0.0, 'pension_credit': 70925734.24929142}, household_net_income=20688748267.48584), relative_change=FiscalSummary(tax_revenue=-0.03221155092227281, federal_tax=-0.03221155092227281, federal_balance=-0.06693501321680725, state_tax=0.0, government_spending=-0.0015312679877619157, tax_benefit_programs={'income_tax': -0.06366596204858095, 'national_insurance': 0.0, 'ni_employer': 0.0, 'vat': 0.0, 'council_tax': 0.0, 'fuel_duty': 0.0, 'tax_credits': -0.0, 'universal_credit': -0.004900608603186478, 'child_benefit': -0.0, 'state_pension': -0.0, 'pension_credit': -0.01134383763795021}, household_net_income=0.013210965235457662)), inequality=InequalityComparison(baseline=InequalitySummary(gini=0.36255397405553097, top_10_share=0.3260927004295773, top_1_share=0.13145609415091833), reform=InequalitySummary(gini=0.36210731670764496, top_10_share=0.32470725674255146, top_1_share=0.1300325825945402), change=InequalitySummary(gini=-0.00044665734788601474, top_10_share=-0.0013854436870258113, top_1_share=-0.0014235115563781264), relative_change=InequalitySummary(gini=-0.0012319747674800056, top_10_share=-0.004248619135603775, top_1_share=-0.010828798509286775)), distributional=DecileImpacts(income=IncomeMeasureSpecificDecileImpacts(income_change=IncomeMeasureSpecificDecileIncomeChange(relative={1: 0.006888474767108438, 2: 0.012069475259054711, 3: 0.01362845736941666, 4: 0.016099117695117186, 5: 0.01374886913709005, 6: 0.016868831714948112, 7: 0.01753355279609095, 8: 0.016653943875645758, 9: 0.016768055951517695, 10: 0.007412182918826308}, average={1: 104.9760740138715, 2: 345.3564489476293, 3: 505.97466813661975, 4: 693.4608276094718, 5: 633.0583755079966, 6: 916.7936598562601, 7: 1160.9595929383368, 8: 1339.97187253835, 9: 1645.9437072259466, 10: 1555.8434910392261}), winners_and_losers=IncomeMeasureSpecificDecileWinnersLosers(deciles={1: IncomeMeasureSpecificDecileWinnersLosersGroupOutcomes(lose_more_than_5_percent_share=0.0, lose_less_than_5_percent_share=0.00016742770708814546, lose_share=0.00016742770708814546, no_change_share=0.7777537048412824, gain_share=0.22207886745162944, gain_less_than_5_percent_share=0.1144106783823729, gain_more_than_5_percent_share=0.10766818906925654), 2: IncomeMeasureSpecificDecileWinnersLosersGroupOutcomes(lose_more_than_5_percent_share=0.0, lose_less_than_5_percent_share=0.008307581771885529, lose_share=0.008307581771885529, no_change_share=0.32972785891469386, gain_share=0.6619645593134206, gain_less_than_5_percent_share=0.5622028657847857, gain_more_than_5_percent_share=0.09976169352863486), 3: IncomeMeasureSpecificDecileWinnersLosersGroupOutcomes(lose_more_than_5_percent_share=0.0, lose_less_than_5_percent_share=7.96157411760938e-05, lose_share=7.96157411760938e-05, no_change_share=0.24138511806270913, gain_share=0.7585352661961148, gain_less_than_5_percent_share=0.5985049210228661, gain_more_than_5_percent_share=0.16003034517324866), 4: IncomeMeasureSpecificDecileWinnersLosersGroupOutcomes(lose_more_than_5_percent_share=0.0, lose_less_than_5_percent_share=0.0, lose_share=0.0, no_change_share=0.1274989463786316, gain_share=0.8725010536213684, gain_less_than_5_percent_share=0.8390939448490379, gain_more_than_5_percent_share=0.033407108772330475), 5: IncomeMeasureSpecificDecileWinnersLosersGroupOutcomes(lose_more_than_5_percent_share=0.0, lose_less_than_5_percent_share=0.0, lose_share=0.0, no_change_share=0.039569164250678565, gain_share=0.9604308357493214, gain_less_than_5_percent_share=0.9576740433060911, gain_more_than_5_percent_share=0.002756792443230341), 6: IncomeMeasureSpecificDecileWinnersLosersGroupOutcomes(lose_more_than_5_percent_share=0.0, lose_less_than_5_percent_share=0.0, lose_share=0.0, no_change_share=0.047391872522306096, gain_share=0.9526081274776939, gain_less_than_5_percent_share=0.9027059122375464, gain_more_than_5_percent_share=0.049902215240147485), 7: 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IncomeMeasureSpecificDecileWinnersLosersGroupOutcomes(lose_more_than_5_percent_share=0.0, lose_less_than_5_percent_share=0.0, lose_share=0.0, no_change_share=0.09527409658848911, gain_share=0.9047259034115109, gain_less_than_5_percent_share=0.9047259034115109, gain_more_than_5_percent_share=0.0)}, all=IncomeMeasureSpecificDecileWinnersLosersGroupOutcomes(lose_more_than_5_percent_share=0.0, lose_less_than_5_percent_share=0.0008383266758003545, lose_share=0.0008383266758003545, no_change_share=0.16458777279036807, gain_share=0.8345739005338316, gain_less_than_5_percent_share=0.7829682328930914, gain_more_than_5_percent_share=0.051605667640740344))), wealth=IncomeMeasureSpecificDecileImpacts(income_change=IncomeMeasureSpecificDecileIncomeChange(relative={1: 0.008709379861137914, 2: 0.009055801198624003, 3: 0.012492298883059996, 4: 0.011526120801509328, 5: 0.013211703237973082, 6: 0.015499285448067033, 7: 0.01507564337455649, 8: 0.013872555873086258, 9: 0.01486308861202786, 10: 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relative_change=-0.00011828715007259712), PovertyRateMetricComparison(age_group='child', gender='all', racial_group='all', relative=True, poverty_rate='regular', baseline=0.0866740345954895, reform=0.08660321682691574, change=-7.081776857376099e-05, relative_change=-0.0008170586370447596), PovertyRateMetricComparison(age_group='child', gender='all', racial_group='all', relative=True, poverty_rate='deep', baseline=0.005887233652174473, reform=0.005886665545403957, change=-5.681067705154419e-07, relative_change=-9.649808451302242e-05), PovertyRateMetricComparison(age_group='child', gender='all', racial_group='all', relative=False, poverty_rate='regular', baseline=1636588.5, reform=1635251.25, change=-1337.25, relative_change=-0.0008170960507176972), PovertyRateMetricComparison(age_group='child', gender='all', racial_group='all', relative=False, poverty_rate='deep', baseline=111163.3828125, reform=111152.65625, change=-10.7265625, relative_change=-9.649366750643566e-05), PovertyRateMetricComparison(age_group='working_age', gender='male', racial_group='all', relative=True, poverty_rate='regular', baseline=0.09058031439781189, reform=0.08946093916893005, change=-0.001119375228881836, relative_change=-0.012357820088432772), PovertyRateMetricComparison(age_group='working_age', gender='male', racial_group='all', relative=True, poverty_rate='deep', baseline=0.0320294052362442, reform=0.03202878311276436, change=-6.221234798431396e-07, relative_change=-1.9423510216766375e-05), PovertyRateMetricComparison(age_group='working_age', gender='male', racial_group='all', relative=False, poverty_rate='regular', baseline=2207394.25, reform=2180115.75, change=-27278.5, relative_change=-0.012357783390982377), PovertyRateMetricComparison(age_group='working_age', gender='male', racial_group='all', relative=False, poverty_rate='deep', baseline=780539.625, reform=780524.5, change=-15.125, relative_change=-1.9377619682024472e-05), PovertyRateMetricComparison(age_group='working_age', gender='female', racial_group='all', relative=True, poverty_rate='regular', baseline=0.06885619461536407, reform=0.06755512952804565, change=-0.0013010650873184204, relative_change=-0.018895396334146386), PovertyRateMetricComparison(age_group='working_age', gender='female', racial_group='all', relative=True, poverty_rate='deep', baseline=0.006544755306094885, reform=0.006544151809066534, change=-6.034970283508301e-07, relative_change=-9.221078560245269e-05), PovertyRateMetricComparison(age_group='working_age', gender='female', racial_group='all', relative=False, poverty_rate='regular', baseline=1406585.625, reform=1380007.625, change=-26578.0, relative_change=-0.01889540140864158), PovertyRateMetricComparison(age_group='working_age', gender='female', racial_group='all', relative=False, poverty_rate='deep', baseline=133695.4375, reform=133683.109375, change=-12.328125, relative_change=-9.221051391525608e-05), PovertyRateMetricComparison(age_group='working_age', gender='all', racial_group='all', relative=True, poverty_rate='regular', baseline=0.08067396283149719, reform=0.07947173714637756, change=-0.001202225685119629, relative_change=-0.014902276309776728), PovertyRateMetricComparison(age_group='working_age', gender='all', racial_group='all', relative=True, poverty_rate='deep', baseline=0.02040824294090271, reform=0.020407630130648613, change=-6.128102540969849e-07, relative_change=-3.0027585219929702e-05), PovertyRateMetricComparison(age_group='working_age', gender='all', racial_group='all', relative=False, poverty_rate='regular', baseline=3613979.5, reform=3560123.0, change=-53856.5, relative_change=-0.014902270474970874), PovertyRateMetricComparison(age_group='working_age', gender='all', racial_group='all', relative=False, poverty_rate='deep', baseline=914235.1875, reform=914207.75, change=-27.4375, relative_change=-3.001142416649764e-05), PovertyRateMetricComparison(age_group='senior', gender='male', racial_group='all', relative=True, poverty_rate='regular', baseline=0.019906071946024895, reform=0.01964147388935089, change=-0.0002645980566740036, relative_change=-0.013292328963316242), PovertyRateMetricComparison(age_group='senior', gender='male', racial_group='all', relative=True, poverty_rate='deep', baseline=0.0001492029696237296, reform=0.00014823427773080766, change=-9.686918929219246e-07, relative_change=-0.0064924437855683375), PovertyRateMetricComparison(age_group='senior', gender='male', racial_group='all', relative=False, poverty_rate='regular', baseline=120630.84375, reform=119027.3828125, change=-1603.4609375, relative_change=-0.013292296461285425), PovertyRateMetricComparison(age_group='senior', gender='male', racial_group='all', relative=False, poverty_rate='deep', baseline=904.1703491210938, reform=898.3001098632812, change=-5.8702392578125, relative_change=-0.006492404073545117), PovertyRateMetricComparison(age_group='senior', gender='female', racial_group='all', relative=True, poverty_rate='regular', baseline=0.04476610943675041, reform=0.04424646124243736, change=-0.0005196481943130493, relative_change=-0.011608071392651511), PovertyRateMetricComparison(age_group='senior', gender='female', racial_group='all', relative=True, poverty_rate='deep', baseline=0.0012297447538003325, reform=0.0012286300770938396, change=-1.1146767064929008e-06, relative_change=-0.0009064293244985742), PovertyRateMetricComparison(age_group='senior', gender='female', racial_group='all', relative=False, poverty_rate='regular', baseline=315600.8125, reform=311937.28125, change=-3663.53125, relative_change=-0.011608117295325404), PovertyRateMetricComparison(age_group='senior', gender='female', racial_group='all', relative=False, poverty_rate='deep', baseline=8669.693359375, reform=8661.8349609375, change=-7.8583984375, relative_change=-0.000906421728169001), PovertyRateMetricComparison(age_group='senior', gender='all', racial_group='all', relative=True, poverty_rate='regular', baseline=0.03327473625540733, reform=0.03287297859787941, change=-0.0004017576575279236, relative_change=-0.012073954679735011), PovertyRateMetricComparison(age_group='senior', gender='all', racial_group='all', relative=True, poverty_rate='deep', baseline=0.0007302720914594829, reform=0.0007292248192243278, change=-1.0472722351551056e-06, relative_change=-0.0014340849765490601), PovertyRateMetricComparison(age_group='senior', gender='all', racial_group='all', relative=False, poverty_rate='regular', baseline=436231.65625, reform=430964.59375, change=-5267.0625, relative_change=-0.01207400339828043), PovertyRateMetricComparison(age_group='senior', gender='all', racial_group='all', relative=False, poverty_rate='deep', baseline=9573.8642578125, reform=9560.134765625, change=-13.7294921875, relative_change=-0.0014340596250146758), PovertyRateMetricComparison(age_group='all', gender='male', racial_group='all', relative=True, poverty_rate='regular', baseline=0.08017819374799728, reform=0.0794147327542305, change=-0.0007634609937667847, relative_change=-0.009522052793635733), PovertyRateMetricComparison(age_group='all', gender='male', racial_group='all', relative=True, poverty_rate='deep', baseline=0.021698515862226486, reform=0.021697862073779106, change=-6.537884473800659e-07, relative_change=-3.013056061212938e-05), PovertyRateMetricComparison(age_group='all', gender='male', racial_group='all', relative=False, poverty_rate='regular', baseline=3098396.25, reform=3068893.0, change=-29503.25, relative_change=-0.009522103572130259), PovertyRateMetricComparison(age_group='all', gender='male', racial_group='all', relative=False, poverty_rate='deep', baseline=838514.75, reform=838489.5, change=-25.25, relative_change=-3.0112767843380216e-05), PovertyRateMetricComparison(age_group='all', gender='female', racial_group='all', relative=True, poverty_rate='regular', baseline=0.06785593926906586, reform=0.06704439222812653, change=-0.0008115470409393311, relative_change=-0.011959852736270336), PovertyRateMetricComparison(age_group='all', gender='female', racial_group='all', relative=True, poverty_rate='deep', baseline=0.005150204990059137, reform=0.005149507895112038, change=-6.970949470996857e-07, relative_change=-0.0001353528545844699), PovertyRateMetricComparison(age_group='all', gender='female', racial_group='all', relative=False, poverty_rate='regular', baseline=2588403.75, reform=2557447.0, change=-30956.75, relative_change=-0.011959784094734062), PovertyRateMetricComparison(age_group='all', gender='female', racial_group='all', relative=False, poverty_rate='deep', baseline=196457.53125, reform=196430.9375, change=-26.59375, relative_change=-0.000135366406320959), PovertyRateMetricComparison(age_group='all', gender='all', racial_group='all', relative=True, poverty_rate='regular', baseline=0.07405701279640198, reform=0.07326967269182205, change=-0.0007873401045799255, relative_change=-0.01063154014521874), PovertyRateMetricComparison(age_group='all', gender='all', racial_group='all', relative=True, poverty_rate='deep', baseline=0.013478054665029049, reform=0.013477379456162453, change=-6.752088665962219e-07, relative_change=-5.0096908150117427e-05), PovertyRateMetricComparison(age_group='all', gender='all', racial_group='all', relative=False, poverty_rate='regular', baseline=5686797.5, reform=5626338.5, change=-60459.0, relative_change=-0.010631467007573243), PovertyRateMetricComparison(age_group='all', gender='all', racial_group='all', relative=False, poverty_rate='deep', baseline=1034972.5, reform=1034920.625, change=-51.875, relative_change=-5.012210469360297e-05)], labor_supply=[])"
+ ]
+ },
+ "execution_count": 1,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "from policyengine import Simulation\n",
+ "\n",
+ "sim = Simulation({\n",
+ " \"scope\": \"macro\", # Required for this\n",
+ " \"country\": \"uk\", # or \"us\"\n",
+ " \"time_period\": 2025,\n",
+ " \"reform\": {\n",
+ " \"gov.hmrc.income_tax.allowances.personal_allowance.amount\": 15_000,\n",
+ " }\n",
+ "})\n",
+ "\n",
+ "sim.calculate()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Output schema\n",
+ "\n",
+ "`calculate_economy_comparison` or `calculate` (when `scope=household` and `reform is not None`) return the following schema."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "{'$defs': {'DecileImpacts': {'properties': {'income': {'$ref': '#/$defs/IncomeMeasureSpecificDecileImpacts'},\n",
+ " 'wealth': {'anyOf': [{'$ref': '#/$defs/IncomeMeasureSpecificDecileImpacts'},\n",
+ " {'type': 'null'}]}},\n",
+ " 'required': ['income', 'wealth'],\n",
+ " 'title': 'DecileImpacts',\n",
+ " 'type': 'object'},\n",
+ " 'FiscalComparison': {'properties': {'baseline': {'$ref': '#/$defs/FiscalSummary'},\n",
+ " 'reform': {'$ref': '#/$defs/FiscalSummary'},\n",
+ " 'change': {'$ref': '#/$defs/FiscalSummary'},\n",
+ " 'relative_change': {'$ref': '#/$defs/FiscalSummary'}},\n",
+ " 'required': ['baseline', 'reform', 'change', 'relative_change'],\n",
+ " 'title': 'FiscalComparison',\n",
+ " 'type': 'object'},\n",
+ " 'FiscalSummary': {'properties': {'tax_revenue': {'title': 'Tax Revenue',\n",
+ " 'type': 'number'},\n",
+ " 'federal_tax': {'title': 'Federal Tax', 'type': 'number'},\n",
+ " 'federal_balance': {'title': 'Federal Balance', 'type': 'number'},\n",
+ " 'state_tax': {'title': 'State Tax', 'type': 'number'},\n",
+ " 'government_spending': {'title': 'Government Spending', 'type': 'number'},\n",
+ " 'tax_benefit_programs': {'additionalProperties': {'type': 'number'},\n",
+ " 'title': 'Tax Benefit Programs',\n",
+ " 'type': 'object'},\n",
+ " 'household_net_income': {'title': 'Household Net Income',\n",
+ " 'type': 'number'}},\n",
+ " 'required': ['tax_revenue',\n",
+ " 'federal_tax',\n",
+ " 'federal_balance',\n",
+ " 'state_tax',\n",
+ " 'government_spending',\n",
+ " 'tax_benefit_programs',\n",
+ " 'household_net_income'],\n",
+ " 'title': 'FiscalSummary',\n",
+ " 'type': 'object'},\n",
+ " 'Headlines': {'properties': {'budgetary_impact': {'title': 'Budgetary Impact',\n",
+ " 'type': 'number'},\n",
+ " 'poverty_impact': {'title': 'Poverty Impact', 'type': 'number'},\n",
+ " 'winner_share': {'title': 'Winner Share', 'type': 'number'}},\n",
+ " 'required': ['budgetary_impact', 'poverty_impact', 'winner_share'],\n",
+ " 'title': 'Headlines',\n",
+ " 'type': 'object'},\n",
+ " 'IncomeMeasureSpecificDecileImpacts': {'properties': {'income_change': {'$ref': '#/$defs/IncomeMeasureSpecificDecileIncomeChange'},\n",
+ " 'winners_and_losers': {'$ref': '#/$defs/IncomeMeasureSpecificDecileWinnersLosers'}},\n",
+ " 'required': ['income_change', 'winners_and_losers'],\n",
+ " 'title': 'IncomeMeasureSpecificDecileImpacts',\n",
+ " 'type': 'object'},\n",
+ " 'IncomeMeasureSpecificDecileIncomeChange': {'properties': {'relative': {'additionalProperties': {'type': 'number'},\n",
+ " 'title': 'Relative',\n",
+ " 'type': 'object'},\n",
+ " 'average': {'additionalProperties': {'type': 'number'},\n",
+ " 'title': 'Average',\n",
+ " 'type': 'object'}},\n",
+ " 'required': ['relative', 'average'],\n",
+ " 'title': 'IncomeMeasureSpecificDecileIncomeChange',\n",
+ " 'type': 'object'},\n",
+ " 'IncomeMeasureSpecificDecileWinnersLosers': {'properties': {'deciles': {'additionalProperties': {'$ref': '#/$defs/IncomeMeasureSpecificDecileWinnersLosersGroupOutcomes'},\n",
+ " 'title': 'Deciles',\n",
+ " 'type': 'object'},\n",
+ " 'all': {'$ref': '#/$defs/IncomeMeasureSpecificDecileWinnersLosersGroupOutcomes'}},\n",
+ " 'required': ['deciles', 'all'],\n",
+ " 'title': 'IncomeMeasureSpecificDecileWinnersLosers',\n",
+ " 'type': 'object'},\n",
+ " 'IncomeMeasureSpecificDecileWinnersLosersGroupOutcomes': {'properties': {'lose_more_than_5_percent_share': {'title': 'Lose More Than 5 Percent Share',\n",
+ " 'type': 'number'},\n",
+ " 'lose_less_than_5_percent_share': {'title': 'Lose Less Than 5 Percent Share',\n",
+ " 'type': 'number'},\n",
+ " 'lose_share': {'title': 'Lose Share', 'type': 'number'},\n",
+ " 'no_change_share': {'title': 'No Change Share', 'type': 'number'},\n",
+ " 'gain_share': {'title': 'Gain Share', 'type': 'number'},\n",
+ " 'gain_less_than_5_percent_share': {'title': 'Gain Less Than 5 Percent Share',\n",
+ " 'type': 'number'},\n",
+ " 'gain_more_than_5_percent_share': {'title': 'Gain More Than 5 Percent Share',\n",
+ " 'type': 'number'}},\n",
+ " 'required': ['lose_more_than_5_percent_share',\n",
+ " 'lose_less_than_5_percent_share',\n",
+ " 'lose_share',\n",
+ " 'no_change_share',\n",
+ " 'gain_share',\n",
+ " 'gain_less_than_5_percent_share',\n",
+ " 'gain_more_than_5_percent_share'],\n",
+ " 'title': 'IncomeMeasureSpecificDecileWinnersLosersGroupOutcomes',\n",
+ " 'type': 'object'},\n",
+ " 'InequalityComparison': {'properties': {'baseline': {'$ref': '#/$defs/InequalitySummary'},\n",
+ " 'reform': {'$ref': '#/$defs/InequalitySummary'},\n",
+ " 'change': {'$ref': '#/$defs/InequalitySummary'},\n",
+ " 'relative_change': {'$ref': '#/$defs/InequalitySummary'}},\n",
+ " 'required': ['baseline', 'reform', 'change', 'relative_change'],\n",
+ " 'title': 'InequalityComparison',\n",
+ " 'type': 'object'},\n",
+ " 'InequalitySummary': {'properties': {'gini': {'title': 'Gini',\n",
+ " 'type': 'number'},\n",
+ " 'top_10_share': {'title': 'Top 10 Share', 'type': 'number'},\n",
+ " 'top_1_share': {'title': 'Top 1 Share', 'type': 'number'}},\n",
+ " 'required': ['gini', 'top_10_share', 'top_1_share'],\n",
+ " 'title': 'InequalitySummary',\n",
+ " 'type': 'object'},\n",
+ " 'LaborSupplyMetricImpact': {'properties': {'elasticity': {'enum': ['income',\n",
+ " 'substitution',\n",
+ " 'all'],\n",
+ " 'title': 'Elasticity',\n",
+ " 'type': 'string'},\n",
+ " 'decile': {'enum': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 'all'],\n",
+ " 'title': 'Decile'},\n",
+ " 'unit': {'enum': ['earnings', 'hours'], 'title': 'Unit', 'type': 'string'},\n",
+ " 'baseline': {'title': 'Baseline', 'type': 'number'},\n",
+ " 'reform': {'title': 'Reform', 'type': 'number'},\n",
+ " 'change': {'title': 'Change', 'type': 'number'},\n",
+ " 'relative_change': {'title': 'Relative Change', 'type': 'number'},\n",
+ " 'average_change': {'title': 'Average Change', 'type': 'number'}},\n",
+ " 'required': ['elasticity',\n",
+ " 'decile',\n",
+ " 'unit',\n",
+ " 'baseline',\n",
+ " 'reform',\n",
+ " 'change',\n",
+ " 'relative_change',\n",
+ " 'average_change'],\n",
+ " 'title': 'LaborSupplyMetricImpact',\n",
+ " 'type': 'object'},\n",
+ " 'PovertyRateMetricComparison': {'properties': {'age_group': {'enum': ['child',\n",
+ " 'working_age',\n",
+ " 'senior',\n",
+ " 'all'],\n",
+ " 'title': 'Age Group',\n",
+ " 'type': 'string'},\n",
+ " 'gender': {'enum': ['male', 'female', 'all'],\n",
+ " 'title': 'Gender',\n",
+ " 'type': 'string'},\n",
+ " 'racial_group': {'enum': ['white', 'black', 'hispanic', 'other', 'all'],\n",
+ " 'title': 'Racial Group',\n",
+ " 'type': 'string'},\n",
+ " 'relative': {'title': 'Relative', 'type': 'boolean'},\n",
+ " 'poverty_rate': {'enum': ['regular',\n",
+ " 'deep',\n",
+ " 'uk_hbai_bhc',\n",
+ " 'uk_hbai_bhc_half',\n",
+ " 'us_spm',\n",
+ " 'us_spm_half'],\n",
+ " 'title': 'Poverty Rate',\n",
+ " 'type': 'string'},\n",
+ " 'baseline': {'title': 'Baseline', 'type': 'number'},\n",
+ " 'reform': {'title': 'Reform', 'type': 'number'},\n",
+ " 'change': {'title': 'Change', 'type': 'number'},\n",
+ " 'relative_change': {'title': 'Relative Change', 'type': 'number'}},\n",
+ " 'required': ['age_group',\n",
+ " 'gender',\n",
+ " 'racial_group',\n",
+ " 'relative',\n",
+ " 'poverty_rate',\n",
+ " 'baseline',\n",
+ " 'reform',\n",
+ " 'change',\n",
+ " 'relative_change'],\n",
+ " 'title': 'PovertyRateMetricComparison',\n",
+ " 'type': 'object'}},\n",
+ " 'properties': {'headlines': {'$ref': '#/$defs/Headlines'},\n",
+ " 'fiscal': {'$ref': '#/$defs/FiscalComparison'},\n",
+ " 'inequality': {'$ref': '#/$defs/InequalityComparison'},\n",
+ " 'distributional': {'$ref': '#/$defs/DecileImpacts'},\n",
+ " 'poverty': {'items': {'$ref': '#/$defs/PovertyRateMetricComparison'},\n",
+ " 'title': 'Poverty',\n",
+ " 'type': 'array'},\n",
+ " 'labor_supply': {'items': {'$ref': '#/$defs/LaborSupplyMetricImpact'},\n",
+ " 'title': 'Labor Supply',\n",
+ " 'type': 'array'}},\n",
+ " 'required': ['headlines',\n",
+ " 'fiscal',\n",
+ " 'inequality',\n",
+ " 'distributional',\n",
+ " 'poverty',\n",
+ " 'labor_supply'],\n",
+ " 'title': 'EconomyComparison',\n",
+ " 'type': 'object'}"
+ ]
+ },
+ "execution_count": 2,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "from policyengine.outputs.macro.comparison.calculate_economy_comparison import EconomyComparison\n",
+ "\n",
+ "EconomyComparison.model_json_schema()"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "base",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.10.14"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/docs/reference/calculate_household_comparison.ipynb b/docs/reference/calculate_household_comparison.ipynb
new file mode 100644
index 00000000..f86d3f12
--- /dev/null
+++ b/docs/reference/calculate_household_comparison.ipynb
@@ -0,0 +1,155 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Compare outcomes for specific household scenarios\n",
+ "\n",
+ "Use `Simulation.calculate_household_comparison()` to use PolicyEngine's tax-benefit model to compare how taxes, benefits and other household properties change under a change in tax or benefit rules. This notebook demonstrates how to use this function to compare outcomes for specific households."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "HouseholdComparison(full_household_baseline={'people': {'person': {'age': {'2025': 30.0}, 'employment_income': {'2025': 30000.0}, 'employment_income_before_lsr': {'2025': 30000.0}, 'private_pension_income': {'2025': 0.0}, 'pension_income': {'2025': 0.0}, 'state_pension': {'2025': 0.0}, 'self_employment_income': {'2025': 0.0}, 'property_income': {'2025': 0.0}, 'savings_interest_income': {'2025': 0.0}, 'dividend_income': {'2025': 0.0}, 'sublet_income': {'2025': 0.0}, 'miscellaneous_income': {'2025': 0.0}, 'private_transfer_income': {'2025': 0.0}, 'lump_sum_income': {'2025': 0.0}, 'maintenance_income': {'2025': 0.0}, 'other_investment_income': {'2025': 0.0}, 'dla_sc_category': {'2025': 'NONE'}, 'dla_m_category': {'2025': 'NONE'}, 'pip_m_category': {'2025': 'NONE'}, 'pip_dl_category': {'2025': 'NONE'}, 'receives_carers_allowance': {'2025': False}, 'childcare_expenses': {'2025': 0.0}, 'employer_pension_contributions': {'2025': 0.0}, 'employee_pension_contributions': {'2025': 0.0}, 'personal_pension_contributions': {'2025': 0.0}, 'maintenance_expenses': {'2025': 0.0}, 'bi_individual_phaseout': {'2025': 0.0}, 'bi_household_phaseout': {'2025': 0.0}, 'bi_phaseout': {'2025': 0.0}, 'basic_income': {'2025': 0.0}, 'bi_maximum': {'2025': 0.0}, 'attends_private_school': {'2025': False}, 'employer_cost': {'2025': 32884.2}, 'baseline_employer_cost': {'2025': 32884.2}, 'adjusted_employer_cost': {'2025': 32884.2}, 'employer_ni_response_consumer_incidence': {'2025': 0.0}, 'employer_ni_response_capital_incidence': {'2025': 0.0}, 'employer_ni_fixed_employer_cost_change': {'2025': 0.0}, 'marginal_tax_rate': {'2025': 0.27999997}, 'cliff_evaluated': {'2025': True}, 'cliff_gap': {'2025': 0.0}, 'is_on_cliff': {'2025': False}, 'person_id': {'2025': 0.0}, 'people': {'2025': 1.0}, 'raw_person_weight': {'2025': 1.0}, 'person_weight': {'2025': 1.0}, 'adult_index': {'2025': 1.0}, 'birth_year': {'2025': 1995.0}, 'over_16': {'2025': True}, 'is_adult': {'2025': True}, 'is_child': {'2025': False}, 'child_index': {'2025': -1.0}, 'is_eldest_child': {'2025': True}, 'is_benunit_eldest_child': {'2025': False}, 'marital_status': {'2025': 'SINGLE'}, 'current_education': {'2025': 'NOT_IN_EDUCATION'}, 'highest_education': {'2025': 'UPPER_SECONDARY'}, 'in_FE': {'2025': False}, 'in_HE': {'2025': False}, 'gender': {'2025': 'MALE'}, 'is_male': {'2025': True}, 'is_female': {'2025': False}, 'is_household_head': {'2025': True}, 'is_benunit_head': {'2025': True}, 'in_social_housing': {'2025': False}, 'is_WA_adult': {'2025': True}, 'is_young_child': {'2025': False}, 'age_under_18': {'2025': False}, 'age_18_64': {'2025': True}, 'age_over_64': {'2025': False}, 'is_older_child': {'2025': False}, 'is_higher_earner': {'2025': True}, 'person_benunit_id': {'2025': 0.0}, 'person_household_id': {'2025': 0.0}, 'role': {'2025': ''}, 'person_benunit_role': {'2025': ''}, 'person_household_role': {'2025': ''}, 'is_disabled_for_benefits': {'2025': False}, 'is_enhanced_disabled_for_benefits': {'2025': 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'relative_capital_gains_mtr_change': {'2025': 0.0}, 'capital_gains_elasticity': {'2025': 0.0}, 'capital_gains_behavioural_response': {'2025': 0.0}, 'capital_gains_before_response': {'2025': 0.0}, 'adult_index_cg': {'2025': 1.0}, 'marginal_tax_rate_on_capital_gains': {'2025': 0.0}, 'capital_gains_tax': {'2025': 0.0}, 'ni_employee': {'2025': 1394.4033}, 'national_insurance': {'2025': 1394.4033}, 'ni_employer': {'2025': 2884.2}, 'ni_self_employed': {'2025': 0.0}, 'total_national_insurance': {'2025': 4278.6035}, 'ni_class_4_maximum': {'2025': 917.73663}, 'ni_class_4': {'2025': 0.0}, 'ni_class_4_main': {'2025': 0.0}, 'ni_class_3': {'2025': 0.0}, 'ni_class_2': {'2025': 0.0}, 'ni_class_1_employee_primary': {'2025': 1394.4033}, 'ni_class_1_income': {'2025': 30000.0}, 'ni_class_1_employee': {'2025': 1394.4033}, 'ni_class_1_employee_additional': {'2025': 0.0}, 'ni_liable': {'2025': True}, 'ni_class_1_employer': {'2025': 2884.2}, 'other_tax_credits': {'2025': 0.0}, 'earned_income_tax': {'2025': 3000.0}, 'total_income': {'2025': 30000.0}, 'income_tax': {'2025': 3000.0}, 'taxed_income': {'2025': 15000.0}, 'adjusted_net_income': {'2025': 30000.0}, 'total_pension_income': {'2025': 0.0}, 'social_security_income': {'2025': 0.0}, 'income_tax_pre_charges': {'2025': 3000.0}, 'personal_allowance': {'2025': 15000.0}, 'blind_persons_allowance': {'2025': 0.0}, 'married_couples_allowance': {'2025': 0.0}, 'married_couples_allowance_deduction': {'2025': 0.0}, 'capped_mcad': {'2025': 0.0}, 'pension_annual_allowance': {'2025': 40000.0}, 'trading_allowance': {'2025': 1000.0}, 'trading_allowance_deduction': {'2025': 0.0}, 'property_allowance': {'2025': 1000.0}, 'property_allowance_deduction': {'2025': 0.0}, 'savings_allowance': {'2025': 1000.0}, 'dividend_allowance': {'2025': 500.0}, 'gift_aid': {'2025': 0.0}, 'covenanted_payments': {'2025': 0.0}, 'charitable_investment_gifts': {'2025': 0.0}, 'other_deductions': {'2025': 0.0}, 'allowances': {'2025': 15000.0}, 'unused_personal_allowance': {'2025': 0.0}, 'meets_marriage_allowance_income_conditions': {'2025': True}, 'partners_unused_personal_allowance': {'2025': 0.0}, 'marriage_allowance': {'2025': 0.0}, 'received_allowances': {'2025': 15000.0}, 'received_allowances_savings_income': {'2025': 0.0}, 'received_allowances_dividend_income': {'2025': 0.0}, 'received_allowances_earned_income': {'2025': 15000.0}, 'savings_income_tax': {'2025': 0.0}, 'dividend_income_tax': {'2025': 0.0}, 'loss_relief': {'2025': 0.0}, 'capital_allowances': {'2025': 0.0}, 'deficiency_relief': {'2025': 0.0}, 'employment_deductions': {'2025': 0.0}, 'employment_expenses': {'2025': 0.0}, 'CB_HITC': {'2025': 0.0}, 'higher_rate_earned_income': {'2025': 0.0}, 'add_rate_earned_income': {'2025': 0.0}, 'earned_taxable_income': {'2025': 15000.0}, 'basic_rate_earned_income': {'2025': 15000.0}, 'taxable_pension_income': {'2025': 0.0}, 'taxable_miscellaneous_income': {'2025': 0.0}, 'taxed_dividend_income': {'2025': 0.0}, 'taxable_dividend_income': {'2025': 0.0}, 'taxable_social_security_income': {'2025': 0.0}, 'taxable_employment_income': {'2025': 30000.0}, 'trading_loss': {'2025': 0.0}, 'taxable_self_employment_income': {'2025': 0.0}, 'taxable_property_income': {'2025': 0.0}, 'individual_savings_account_interest_income': {'2025': 0.0}, 'taxable_savings_interest_income': {'2025': 0.0}, 'tax_free_savings_income': {'2025': 0.0}, 'higher_rate_earned_income_tax': {'2025': 0.0}, 'add_rate_earned_income_tax': {'2025': 0.0}, 'tax_band': {'2025': 'BASIC'}, 'basic_rate_earned_income_tax': {'2025': 3000.0}, 'basic_rate_savings_income_pre_starter': {'2025': 0.0}, 'taxed_savings_income': {'2025': 0.0}, 'higher_rate_savings_income': {'2025': 0.0}, 'savings_starter_rate_income': {'2025': 0.0}, 'add_rate_savings_income': {'2025': 0.0}, 'basic_rate_savings_income': {'2025': 0.0}, 'personal_pension_contributions_tax': {'2025': 0.0}, 'pension_contributions_relief': {'2025': 0.0}, 'pension_contributions': {'2025': 0.0}, 'statutory_maternity_pay': {'2025': 0.0}, 'statutory_sick_pay': {'2025': 0.0}, 'pays_scottish_income_tax': {'2025': False}}}}, change={'people': {'person': {'age': {'2025': 0.0}, 'employment_income': {'2025': 0.0}, 'employment_income_before_lsr': {'2025': 0.0}, 'private_pension_income': {'2025': 0.0}, 'pension_income': {'2025': 0.0}, 'state_pension': {'2025': 0.0}, 'self_employment_income': {'2025': 0.0}, 'property_income': {'2025': 0.0}, 'savings_interest_income': {'2025': 0.0}, 'dividend_income': {'2025': 0.0}, 'sublet_income': {'2025': 0.0}, 'miscellaneous_income': {'2025': 0.0}, 'private_transfer_income': {'2025': 0.0}, 'lump_sum_income': {'2025': 0.0}, 'maintenance_income': {'2025': 0.0}, 'other_investment_income': {'2025': 0.0}, 'dla_sc_category': {'2025': 0.0}, 'dla_m_category': {'2025': 0.0}, 'pip_m_category': {'2025': 0.0}, 'pip_dl_category': {'2025': 0.0}, 'receives_carers_allowance': {'2025': 0.0}, 'childcare_expenses': {'2025': 0.0}, 'employer_pension_contributions': {'2025': 0.0}, 'employee_pension_contributions': {'2025': 0.0}, 'personal_pension_contributions': {'2025': 0.0}, 'maintenance_expenses': {'2025': 0.0}, 'bi_individual_phaseout': {'2025': 0.0}, 'bi_household_phaseout': {'2025': 0.0}, 'bi_phaseout': {'2025': 0.0}, 'basic_income': {'2025': 0.0}, 'bi_maximum': {'2025': 0.0}, 'attends_private_school': {'2025': 0.0}, 'employer_cost': {'2025': 0.0}, 'baseline_employer_cost': {'2025': 0.0}, 'adjusted_employer_cost': {'2025': 0.0}, 'employer_ni_response_consumer_incidence': {'2025': 0.0}, 'employer_ni_response_capital_incidence': {'2025': 0.0}, 'employer_ni_fixed_employer_cost_change': {'2025': 0.0}, 'marginal_tax_rate': {'2025': 0.0}, 'cliff_evaluated': {'2025': 0.0}, 'cliff_gap': {'2025': 0.0}, 'is_on_cliff': {'2025': 0.0}, 'person_id': {'2025': 0.0}, 'people': {'2025': 0.0}, 'raw_person_weight': {'2025': 0.0}, 'person_weight': {'2025': 0.0}, 'adult_index': {'2025': 0.0}, 'birth_year': {'2025': 0.0}, 'over_16': {'2025': 0.0}, 'is_adult': {'2025': 0.0}, 'is_child': {'2025': 0.0}, 'child_index': {'2025': 0.0}, 'is_eldest_child': {'2025': 0.0}, 'is_benunit_eldest_child': {'2025': 0.0}, 'marital_status': {'2025': 0.0}, 'current_education': {'2025': 0.0}, 'highest_education': {'2025': 0.0}, 'in_FE': {'2025': 0.0}, 'in_HE': {'2025': 0.0}, 'gender': {'2025': 0.0}, 'is_male': {'2025': 0.0}, 'is_female': {'2025': 0.0}, 'is_household_head': {'2025': 0.0}, 'is_benunit_head': {'2025': 0.0}, 'in_social_housing': {'2025': 0.0}, 'is_WA_adult': {'2025': 0.0}, 'is_young_child': {'2025': 0.0}, 'age_under_18': {'2025': 0.0}, 'age_18_64': {'2025': 0.0}, 'age_over_64': {'2025': 0.0}, 'is_older_child': {'2025': 0.0}, 'is_higher_earner': {'2025': 0.0}, 'person_benunit_id': {'2025': 0.0}, 'person_household_id': {'2025': 0.0}, 'role': {'2025': 0.0}, 'person_benunit_role': {'2025': 0.0}, 'person_household_role': {'2025': 0.0}, 'is_disabled_for_benefits': {'2025': 0.0}, 'is_enhanced_disabled_for_benefits': {'2025': 0.0}, 'is_severely_disabled_for_benefits': {'2025': 0.0}, 'person_state_id': {'2025': 0.0}, 'person_state_role': {'2025': 0.0}, 'is_blind': {'2025': 0.0}, 'is_carer_for_benefits': {'2025': 0.0}, 'personal_rent': {'2025': 0.0}, 'weekly_childcare_expenses': {'2025': 0.0}, 'earned_income': {'2025': 0.0}, 'market_income': {'2025': 0.0}, 'hours_worked': {'2025': 0.0}, 'in_work': {'2025': 0.0}, 'weekly_hours': {'2025': 0.0}, 'employment_status': {'2025': 0.0}, 'capital_income': {'2025': 0.0}, 'base_net_income': {'2025': 0.0}, 'is_apprentice': {'2025': 0.0}, 'minimum_wage_category': {'2025': 0.0}, 'minimum_wage': {'2025': 0.0}, 'income_decile': {'2025': 0.0}, 'household_statutory_maternity_pay': {'2025': 0.0}, 'household_statutory_paternity_pay': {'2025': 0.0}, 'household_statutory_sick_pay': {'2025': 0.0}, 'capital_gains': {'2025': 0.0}, 'is_QYP': {'2025': 0.0}, 'is_child_or_QYP': {'2025': 0.0}, 'employment_benefits': {'2025': 0.0}, 'relative_income_change': {'2025': 0.019470373}, 'relative_wage_change': {'2025': 0.0}, 'income_elasticity_lsr': {'2025': 0.0}, 'substitution_elasticity_lsr': {'2025': 0.0}, 'employment_income_behavioral_response': {'2025': 0.0}, 'income_support_reported': {'2025': 0.0}, 'attendance_allowance': {'2025': 0.0}, 'attendance_allowance_reported': {'2025': 0.0}, 'aa_category': {'2025': 0.0}, 'esa_income_reported': {'2025': 0.0}, 'iidb': {'2025': 0.0}, 'iidb_reported': {'2025': 0.0}, 'jsa_contrib': {'2025': 0.0}, 'jsa_contrib_reported': {'2025': 0.0}, 'maternity_allowance_reported': {'2025': 0.0}, 'maternity_allowance': {'2025': 0.0}, 'ssmg_reported': {'2025': 0.0}, 'ssmg': {'2025': 0.0}, 'council_tax_benefit_reported': {'2025': 0.0}, 'working_tax_credit_reported': {'2025': 0.0}, 'child_tax_credit_reported': {'2025': 0.0}, 'is_CTC_child_limit_exempt': {'2025': 0.0}, 'is_child_for_CTC': {'2025': 0.0}, 'jsa_income_reported': {'2025': 0.0}, 'bsp': {'2025': 0.0}, 'bsp_reported': {'2025': 0.0}, 'incapacity_benefit': {'2025': 0.0}, 'incapacity_benefit_reported': {'2025': 0.0}, 'sda': {'2025': 0.0}, 'sda_reported': {'2025': 0.0}, 'carers_allowance': {'2025': 0.0}, 'care_hours': {'2025': 0.0}, 'carers_allowance_reported': {'2025': 0.0}, 'armed_forces_independence_payment': {'2025': 0.0}, 'esa_contrib': {'2025': 0.0}, 'esa_contrib_reported': {'2025': 0.0}, 'esa': {'2025': 0.0}, 'afcs': {'2025': 0.0}, 'afcs_reported': {'2025': 0.0}, 'student_loans': {'2025': 0.0}, 'adult_ema': {'2025': 0.0}, 'child_ema': {'2025': 0.0}, 'access_fund': {'2025': 0.0}, 'education_grants': {'2025': 0.0}, 'student_payments': {'2025': 0.0}, 'state_pension_age': {'2025': 0.0}, 'is_SP_age': {'2025': 0.0}, 'state_pension_type': {'2025': 0.0}, 'basic_state_pension': {'2025': 0.0}, 'additional_state_pension': {'2025': 0.0}, 'new_state_pension': {'2025': 0.0}, 'state_pension_reported': {'2025': 0.0}, 'winter_fuel_allowance_reported': {'2025': 0.0}, 'dla_m_reported': {'2025': 0.0}, 'dla_m': {'2025': 0.0}, 'dla_sc_reported': {'2025': 0.0}, 'dla_sc': {'2025': 0.0}, 'dla_sc_middle_plus': {'2025': 0.0}, 'receives_highest_dla_sc': {'2025': 0.0}, 'dla': {'2025': 0.0}, 'housing_benefit_reported': {'2025': 0.0}, 'household_benefits_individual_non_dep_deduction': {'2025': 0.0}, 'housing_benefit_individual_non_dep_deduction_eligible': {'2025': 0.0}, 'universal_credit_reported': {'2025': 0.0}, 'uc_is_child_born_before_child_limit': {'2025': 0.0}, 'uc_individual_child_element': {'2025': 0.0}, 'uc_individual_disabled_child_element': {'2025': 0.0}, 'uc_individual_severely_disabled_child_element': {'2025': 0.0}, 'uc_is_in_startup_period': {'2025': 0.0}, 'uc_minimum_income_floor': {'2025': 0.0}, 'uc_mif_applies': {'2025': 0.0}, 'uc_mif_capped_earned_income': {'2025': 0.0}, 'uc_limited_capability_for_WRA': {'2025': 0.0}, 'uc_individual_non_dep_deduction_eligible': {'2025': 0.0}, 'uc_individual_non_dep_deduction': {'2025': 0.0}, 'uc_non_dep_deduction_exempt': {'2025': 0.0}, 'pip_m_reported': {'2025': 0.0}, 'pip_m': {'2025': 0.0}, 'pip_dl_reported': {'2025': 0.0}, 'pip_dl': {'2025': 0.0}, 'receives_enhanced_pip_dl': {'2025': 0.0}, 'pip': {'2025': 0.0}, 'pension_credit_reported': {'2025': 0.0}, 'tax': {'2025': -486.0}, 'tax_reported': {'2025': 0.0}, 'tax_modelling': {'2025': -486.0}, 'child_benefit_reported': {'2025': 0.0}, 'child_benefit_respective_amount': {'2025': 0.0}, 'relative_capital_gains_mtr_change': {'2025': 0.0}, 'capital_gains_elasticity': {'2025': 0.0}, 'capital_gains_behavioural_response': {'2025': 0.0}, 'capital_gains_before_response': {'2025': 0.0}, 'adult_index_cg': {'2025': 0.0}, 'marginal_tax_rate_on_capital_gains': {'2025': 0.0}, 'capital_gains_tax': {'2025': 0.0}, 'ni_employee': {'2025': 0.0}, 'national_insurance': {'2025': 0.0}, 'ni_employer': {'2025': 0.0}, 'ni_self_employed': {'2025': 0.0}, 'total_national_insurance': {'2025': 0.0}, 'ni_class_4_maximum': {'2025': 0.0}, 'ni_class_4': {'2025': 0.0}, 'ni_class_4_main': {'2025': 0.0}, 'ni_class_3': {'2025': 0.0}, 'ni_class_2': {'2025': 0.0}, 'ni_class_1_employee_primary': {'2025': 0.0}, 'ni_class_1_income': {'2025': 0.0}, 'ni_class_1_employee': {'2025': 0.0}, 'ni_class_1_employee_additional': {'2025': 0.0}, 'ni_liable': {'2025': 0.0}, 'ni_class_1_employer': {'2025': 0.0}, 'other_tax_credits': {'2025': 0.0}, 'earned_income_tax': {'2025': -486.0}, 'total_income': {'2025': 0.0}, 'income_tax': {'2025': -486.0}, 'taxed_income': {'2025': -2430.0}, 'adjusted_net_income': {'2025': 0.0}, 'total_pension_income': {'2025': 0.0}, 'social_security_income': {'2025': 0.0}, 'income_tax_pre_charges': {'2025': -486.0}, 'personal_allowance': {'2025': 2430.0}, 'blind_persons_allowance': {'2025': 0.0}, 'married_couples_allowance': {'2025': 0.0}, 'married_couples_allowance_deduction': {'2025': 0.0}, 'capped_mcad': {'2025': 0.0}, 'pension_annual_allowance': {'2025': 0.0}, 'trading_allowance': {'2025': 0.0}, 'trading_allowance_deduction': {'2025': 0.0}, 'property_allowance': {'2025': 0.0}, 'property_allowance_deduction': {'2025': 0.0}, 'savings_allowance': {'2025': 0.0}, 'dividend_allowance': {'2025': 0.0}, 'gift_aid': {'2025': 0.0}, 'covenanted_payments': {'2025': 0.0}, 'charitable_investment_gifts': {'2025': 0.0}, 'other_deductions': {'2025': 0.0}, 'allowances': {'2025': 2430.0}, 'unused_personal_allowance': {'2025': 0.0}, 'meets_marriage_allowance_income_conditions': {'2025': 0.0}, 'partners_unused_personal_allowance': {'2025': 0.0}, 'marriage_allowance': {'2025': 0.0}, 'received_allowances': {'2025': 2430.0}, 'received_allowances_savings_income': {'2025': 0.0}, 'received_allowances_dividend_income': {'2025': 0.0}, 'received_allowances_earned_income': {'2025': 2430.0}, 'savings_income_tax': {'2025': 0.0}, 'dividend_income_tax': {'2025': 0.0}, 'loss_relief': {'2025': 0.0}, 'capital_allowances': {'2025': 0.0}, 'deficiency_relief': {'2025': 0.0}, 'employment_deductions': {'2025': 0.0}, 'employment_expenses': {'2025': 0.0}, 'CB_HITC': {'2025': 0.0}, 'higher_rate_earned_income': {'2025': 0.0}, 'add_rate_earned_income': {'2025': 0.0}, 'earned_taxable_income': {'2025': -2430.0}, 'basic_rate_earned_income': {'2025': -2430.0}, 'taxable_pension_income': {'2025': 0.0}, 'taxable_miscellaneous_income': {'2025': 0.0}, 'taxed_dividend_income': {'2025': 0.0}, 'taxable_dividend_income': {'2025': 0.0}, 'taxable_social_security_income': {'2025': 0.0}, 'taxable_employment_income': {'2025': 0.0}, 'trading_loss': {'2025': 0.0}, 'taxable_self_employment_income': {'2025': 0.0}, 'taxable_property_income': {'2025': 0.0}, 'individual_savings_account_interest_income': {'2025': 0.0}, 'taxable_savings_interest_income': {'2025': 0.0}, 'tax_free_savings_income': {'2025': 0.0}, 'higher_rate_earned_income_tax': {'2025': 0.0}, 'add_rate_earned_income_tax': {'2025': 0.0}, 'tax_band': {'2025': 0.0}, 'basic_rate_earned_income_tax': {'2025': -486.0}, 'basic_rate_savings_income_pre_starter': {'2025': 0.0}, 'taxed_savings_income': {'2025': 0.0}, 'higher_rate_savings_income': {'2025': 0.0}, 'savings_starter_rate_income': {'2025': 0.0}, 'add_rate_savings_income': {'2025': 0.0}, 'basic_rate_savings_income': {'2025': 0.0}, 'personal_pension_contributions_tax': {'2025': 0.0}, 'pension_contributions_relief': {'2025': 0.0}, 'pension_contributions': {'2025': 0.0}, 'statutory_maternity_pay': {'2025': 0.0}, 'statutory_sick_pay': {'2025': 0.0}, 'pays_scottish_income_tax': {'2025': 0.0}}}})"
+ ]
+ },
+ "execution_count": 1,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "from policyengine import Simulation\n",
+ "\n",
+ "sim = Simulation({\n",
+ " \"scope\": \"household\", # Required for this\n",
+ " \"country\": \"uk\", # or \"us\"\n",
+ " \"time_period\": 2025,\n",
+ " \"data\": { # Required for this\n",
+ " \"people\": {\n",
+ " \"person\": {\n",
+ " \"age\": {\n",
+ " \"2025\": 30,\n",
+ " },\n",
+ " \"employment_income\": {\n",
+ " \"2025\": 30_000,\n",
+ " },\n",
+ " }\n",
+ " }\n",
+ " },\n",
+ " \"reform\": {\n",
+ " \"gov.hmrc.income_tax.allowances.personal_allowance.amount\": 15_000,\n",
+ " }\n",
+ "})\n",
+ "\n",
+ "sim.calculate()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Output schema\n",
+ "\n",
+ "`calculate_household_comparison` or `calculate` (when `scope=household` and `reform is not None`) return the following schema."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "{'properties': {'full_household_baseline': {'additionalProperties': {'anyOf': [{'additionalProperties': {'additionalProperties': {'additionalProperties': {'anyOf': [{'type': 'number'},\n",
+ " {'type': 'string'},\n",
+ " {'type': 'boolean'},\n",
+ " {'items': {}, 'type': 'array'},\n",
+ " {'type': 'null'}]},\n",
+ " 'type': 'object'},\n",
+ " 'type': 'object'},\n",
+ " 'type': 'object'},\n",
+ " {'items': {'items': {'additionalProperties': {'anyOf': [{'type': 'string'},\n",
+ " {'type': 'integer'}]},\n",
+ " 'type': 'object'},\n",
+ " 'type': 'array'},\n",
+ " 'type': 'array'}]},\n",
+ " 'title': 'Full Household Baseline',\n",
+ " 'type': 'object'},\n",
+ " 'full_household_reform': {'additionalProperties': {'anyOf': [{'additionalProperties': {'additionalProperties': {'additionalProperties': {'anyOf': [{'type': 'number'},\n",
+ " {'type': 'string'},\n",
+ " {'type': 'boolean'},\n",
+ " {'items': {}, 'type': 'array'},\n",
+ " {'type': 'null'}]},\n",
+ " 'type': 'object'},\n",
+ " 'type': 'object'},\n",
+ " 'type': 'object'},\n",
+ " {'items': {'items': {'additionalProperties': {'anyOf': [{'type': 'string'},\n",
+ " {'type': 'integer'}]},\n",
+ " 'type': 'object'},\n",
+ " 'type': 'array'},\n",
+ " 'type': 'array'}]},\n",
+ " 'title': 'Full Household Reform',\n",
+ " 'type': 'object'},\n",
+ " 'change': {'additionalProperties': {'anyOf': [{'additionalProperties': {'additionalProperties': {'additionalProperties': {'anyOf': [{'type': 'number'},\n",
+ " {'type': 'string'},\n",
+ " {'type': 'boolean'},\n",
+ " {'items': {}, 'type': 'array'},\n",
+ " {'type': 'null'}]},\n",
+ " 'type': 'object'},\n",
+ " 'type': 'object'},\n",
+ " 'type': 'object'},\n",
+ " {'items': {'items': {'additionalProperties': {'anyOf': [{'type': 'string'},\n",
+ " {'type': 'integer'}]},\n",
+ " 'type': 'object'},\n",
+ " 'type': 'array'},\n",
+ " 'type': 'array'}]},\n",
+ " 'title': 'Change',\n",
+ " 'type': 'object'}},\n",
+ " 'required': ['full_household_baseline', 'full_household_reform', 'change'],\n",
+ " 'title': 'HouseholdComparison',\n",
+ " 'type': 'object'}"
+ ]
+ },
+ "execution_count": 2,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "from policyengine.outputs.household.comparison.calculate_household_comparison import HouseholdComparison\n",
+ "\n",
+ "HouseholdComparison.model_json_schema()"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "base",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.10.14"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/docs/reference/calculate_single_economy.ipynb b/docs/reference/calculate_single_economy.ipynb
new file mode 100644
index 00000000..0966e8ee
--- /dev/null
+++ b/docs/reference/calculate_single_economy.ipynb
@@ -0,0 +1,157 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Simulate outcomes for a large population\n",
+ "\n",
+ "Use `Simulation.calculate_single_economy()` to use PolicyEngine's tax-benefit model to compute taxes, benefits and other household properties for a large dataset (usually representing a country). This notebook demonstrates how to use this function."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "SingleEconomy(fiscal=FiscalSummary(tax_revenue=658911285719.5891, federal_tax=658911285719.5891, federal_balance=309089098855.4849, state_tax=0.0, government_spending=349822186864.1042, tax_benefit_programs={'income_tax': 333376287037.05945, 'national_insurance': 52985626776.773834, 'ni_employer': 126330649370.35953, 'vat': 211671832822.39133, 'council_tax': 49007055050.00724, 'fuel_duty': 26506672341.204205, 'tax_credits': -34929879.49872104, 'universal_credit': -73459549194.97665, 'child_benefit': -14311471487.935827, 'state_pension': -132795868621.44594, 'pension_credit': -6252358021.417119}, household_net_income=1566028514855.0789), inequality=InequalitySummary(gini=0.36255397405553097, top_10_share=0.3260927004295773, top_1_share=0.13145609415091833), poverty=[PovertyRateMetric(age_group='child', racial_group='all', gender='male', relative=True, poverty_rate='regular', value=0.0937829241156578), PovertyRateMetric(age_group='child', racial_group='all', gender='male', relative=True, poverty_rate='deep', value=0.006947669200599194), PovertyRateMetric(age_group='child', racial_group='all', gender='male', relative=False, poverty_rate='regular', value=770371.125), PovertyRateMetric(age_group='child', racial_group='all', gender='male', relative=False, poverty_rate='deep', value=57070.984375), PovertyRateMetric(age_group='child', racial_group='all', gender='female', relative=True, poverty_rate='regular', value=0.08119998127222061), PovertyRateMetric(age_group='child', racial_group='all', gender='female', relative=True, poverty_rate='deep', value=0.005070670507848263), PovertyRateMetric(age_group='child', racial_group='all', gender='female', relative=False, poverty_rate='regular', value=866217.375), PovertyRateMetric(age_group='child', racial_group='all', gender='female', relative=False, poverty_rate='deep', value=54092.4140625), PovertyRateMetric(age_group='child', racial_group='all', gender='all', relative=True, poverty_rate='regular', value=0.0866740345954895), PovertyRateMetric(age_group='child', racial_group='all', gender='all', relative=True, poverty_rate='deep', value=0.005887233652174473), PovertyRateMetric(age_group='child', racial_group='all', gender='all', relative=False, poverty_rate='regular', value=1636588.5), PovertyRateMetric(age_group='child', racial_group='all', gender='all', relative=False, poverty_rate='deep', value=111163.3828125), PovertyRateMetric(age_group='working_age', racial_group='all', gender='male', relative=True, poverty_rate='regular', value=0.09058031439781189), PovertyRateMetric(age_group='working_age', racial_group='all', gender='male', relative=True, poverty_rate='deep', value=0.0320294052362442), PovertyRateMetric(age_group='working_age', racial_group='all', gender='male', relative=False, poverty_rate='regular', value=2207394.25), PovertyRateMetric(age_group='working_age', racial_group='all', gender='male', relative=False, poverty_rate='deep', value=780539.625), PovertyRateMetric(age_group='working_age', racial_group='all', gender='female', relative=True, poverty_rate='regular', value=0.06885619461536407), PovertyRateMetric(age_group='working_age', racial_group='all', gender='female', relative=True, poverty_rate='deep', value=0.006544755306094885), PovertyRateMetric(age_group='working_age', racial_group='all', gender='female', relative=False, poverty_rate='regular', value=1406585.625), PovertyRateMetric(age_group='working_age', racial_group='all', gender='female', relative=False, poverty_rate='deep', value=133695.4375), PovertyRateMetric(age_group='working_age', racial_group='all', gender='all', relative=True, poverty_rate='regular', value=0.08067396283149719), PovertyRateMetric(age_group='working_age', racial_group='all', gender='all', relative=True, poverty_rate='deep', value=0.02040824294090271), PovertyRateMetric(age_group='working_age', racial_group='all', gender='all', relative=False, poverty_rate='regular', value=3613979.5), PovertyRateMetric(age_group='working_age', racial_group='all', gender='all', relative=False, poverty_rate='deep', value=914235.1875), PovertyRateMetric(age_group='senior', racial_group='all', gender='male', relative=True, poverty_rate='regular', value=0.019906071946024895), PovertyRateMetric(age_group='senior', racial_group='all', gender='male', relative=True, poverty_rate='deep', value=0.0001492029696237296), PovertyRateMetric(age_group='senior', racial_group='all', gender='male', relative=False, poverty_rate='regular', value=120630.84375), PovertyRateMetric(age_group='senior', racial_group='all', gender='male', relative=False, poverty_rate='deep', value=904.1703491210938), PovertyRateMetric(age_group='senior', racial_group='all', gender='female', relative=True, poverty_rate='regular', value=0.04476610943675041), PovertyRateMetric(age_group='senior', racial_group='all', gender='female', relative=True, poverty_rate='deep', value=0.0012297447538003325), PovertyRateMetric(age_group='senior', racial_group='all', gender='female', relative=False, poverty_rate='regular', value=315600.8125), PovertyRateMetric(age_group='senior', racial_group='all', gender='female', relative=False, poverty_rate='deep', value=8669.693359375), PovertyRateMetric(age_group='senior', racial_group='all', gender='all', relative=True, poverty_rate='regular', value=0.03327473625540733), PovertyRateMetric(age_group='senior', racial_group='all', gender='all', relative=True, poverty_rate='deep', value=0.0007302720914594829), PovertyRateMetric(age_group='senior', racial_group='all', gender='all', relative=False, poverty_rate='regular', value=436231.65625), PovertyRateMetric(age_group='senior', racial_group='all', gender='all', relative=False, poverty_rate='deep', value=9573.8642578125), PovertyRateMetric(age_group='all', racial_group='all', gender='male', relative=True, poverty_rate='regular', value=0.08017819374799728), PovertyRateMetric(age_group='all', racial_group='all', gender='male', relative=True, poverty_rate='deep', value=0.021698515862226486), PovertyRateMetric(age_group='all', racial_group='all', gender='male', relative=False, poverty_rate='regular', value=3098396.25), PovertyRateMetric(age_group='all', racial_group='all', gender='male', relative=False, poverty_rate='deep', value=838514.75), PovertyRateMetric(age_group='all', racial_group='all', gender='female', relative=True, poverty_rate='regular', value=0.06785593926906586), PovertyRateMetric(age_group='all', racial_group='all', gender='female', relative=True, poverty_rate='deep', value=0.005150204990059137), PovertyRateMetric(age_group='all', racial_group='all', gender='female', relative=False, poverty_rate='regular', value=2588403.75), PovertyRateMetric(age_group='all', racial_group='all', gender='female', relative=False, poverty_rate='deep', value=196457.53125), PovertyRateMetric(age_group='all', racial_group='all', gender='all', relative=True, poverty_rate='regular', value=0.07405701279640198), PovertyRateMetric(age_group='all', racial_group='all', gender='all', relative=True, poverty_rate='deep', value=0.013478054665029049), PovertyRateMetric(age_group='all', racial_group='all', gender='all', relative=False, poverty_rate='regular', value=5686797.5), PovertyRateMetric(age_group='all', racial_group='all', gender='all', relative=False, poverty_rate='deep', value=1034972.5)])"
+ ]
+ },
+ "execution_count": 1,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "from policyengine import Simulation\n",
+ "\n",
+ "sim = Simulation({\n",
+ " \"scope\": \"macro\", # Required for this\n",
+ " \"country\": \"uk\", # or \"us\"\n",
+ " \"time_period\": 2025,\n",
+ "})\n",
+ "\n",
+ "sim.calculate()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Output schema\n",
+ "\n",
+ "`calculate_single_economy` or `calculate` (when `scope=macro` and `reform=None`) return the following schema."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "{'$defs': {'FiscalSummary': {'properties': {'tax_revenue': {'title': 'Tax Revenue',\n",
+ " 'type': 'number'},\n",
+ " 'federal_tax': {'title': 'Federal Tax', 'type': 'number'},\n",
+ " 'federal_balance': {'title': 'Federal Balance', 'type': 'number'},\n",
+ " 'state_tax': {'title': 'State Tax', 'type': 'number'},\n",
+ " 'government_spending': {'title': 'Government Spending', 'type': 'number'},\n",
+ " 'tax_benefit_programs': {'additionalProperties': {'type': 'number'},\n",
+ " 'title': 'Tax Benefit Programs',\n",
+ " 'type': 'object'},\n",
+ " 'household_net_income': {'title': 'Household Net Income',\n",
+ " 'type': 'number'}},\n",
+ " 'required': ['tax_revenue',\n",
+ " 'federal_tax',\n",
+ " 'federal_balance',\n",
+ " 'state_tax',\n",
+ " 'government_spending',\n",
+ " 'tax_benefit_programs',\n",
+ " 'household_net_income'],\n",
+ " 'title': 'FiscalSummary',\n",
+ " 'type': 'object'},\n",
+ " 'InequalitySummary': {'properties': {'gini': {'title': 'Gini',\n",
+ " 'type': 'number'},\n",
+ " 'top_10_share': {'title': 'Top 10 Share', 'type': 'number'},\n",
+ " 'top_1_share': {'title': 'Top 1 Share', 'type': 'number'}},\n",
+ " 'required': ['gini', 'top_10_share', 'top_1_share'],\n",
+ " 'title': 'InequalitySummary',\n",
+ " 'type': 'object'},\n",
+ " 'PovertyRateMetric': {'properties': {'age_group': {'enum': ['child',\n",
+ " 'working_age',\n",
+ " 'senior',\n",
+ " 'all'],\n",
+ " 'title': 'Age Group',\n",
+ " 'type': 'string'},\n",
+ " 'racial_group': {'enum': ['white', 'black', 'hispanic', 'other', 'all'],\n",
+ " 'title': 'Racial Group',\n",
+ " 'type': 'string'},\n",
+ " 'gender': {'enum': ['male', 'female', 'all'],\n",
+ " 'title': 'Gender',\n",
+ " 'type': 'string'},\n",
+ " 'relative': {'title': 'Relative', 'type': 'boolean'},\n",
+ " 'poverty_rate': {'enum': ['regular',\n",
+ " 'deep',\n",
+ " 'uk_hbai_bhc',\n",
+ " 'uk_hbai_bhc_half',\n",
+ " 'us_spm',\n",
+ " 'us_spm_half'],\n",
+ " 'title': 'Poverty Rate',\n",
+ " 'type': 'string'},\n",
+ " 'value': {'title': 'Value', 'type': 'number'}},\n",
+ " 'required': ['age_group',\n",
+ " 'racial_group',\n",
+ " 'gender',\n",
+ " 'relative',\n",
+ " 'poverty_rate',\n",
+ " 'value'],\n",
+ " 'title': 'PovertyRateMetric',\n",
+ " 'type': 'object'}},\n",
+ " 'properties': {'fiscal': {'$ref': '#/$defs/FiscalSummary'},\n",
+ " 'inequality': {'$ref': '#/$defs/InequalitySummary'},\n",
+ " 'poverty': {'items': {'$ref': '#/$defs/PovertyRateMetric'},\n",
+ " 'title': 'Poverty',\n",
+ " 'type': 'array'}},\n",
+ " 'required': ['fiscal', 'inequality', 'poverty'],\n",
+ " 'title': 'SingleEconomy',\n",
+ " 'type': 'object'}"
+ ]
+ },
+ "execution_count": 2,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "from policyengine.outputs.macro.single.calculate_single_economy import SingleEconomy\n",
+ "\n",
+ "SingleEconomy.model_json_schema()"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "base",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.10.14"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/docs/reference/calculate_single_household.ipynb b/docs/reference/calculate_single_household.ipynb
new file mode 100644
index 00000000..7801a627
--- /dev/null
+++ b/docs/reference/calculate_single_household.ipynb
@@ -0,0 +1,123 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Simulate outcomes for specific households\n",
+ "\n",
+ "Use `Simulation.calculate_single_household()` to use PolicyEngine's tax-benefit model to compute taxes, benefits and other downstream properties of individual households that you specify. This notebook demonstrates how to use this function to simulate outcomes for specific households."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "SingleHousehold(full_household={'people': {'person': {'age': {'2025': 30.0}, 'employment_income': {'2025': 30000.0}, 'employment_income_before_lsr': {'2025': 30000.0}, 'private_pension_income': {'2025': 0.0}, 'pension_income': {'2025': 0.0}, 'state_pension': {'2025': 0.0}, 'self_employment_income': {'2025': 0.0}, 'property_income': {'2025': 0.0}, 'savings_interest_income': {'2025': 0.0}, 'dividend_income': {'2025': 0.0}, 'sublet_income': {'2025': 0.0}, 'miscellaneous_income': {'2025': 0.0}, 'private_transfer_income': {'2025': 0.0}, 'lump_sum_income': {'2025': 0.0}, 'maintenance_income': {'2025': 0.0}, 'other_investment_income': {'2025': 0.0}, 'dla_sc_category': {'2025': 'NONE'}, 'dla_m_category': {'2025': 'NONE'}, 'pip_m_category': {'2025': 'NONE'}, 'pip_dl_category': {'2025': 'NONE'}, 'receives_carers_allowance': {'2025': False}, 'childcare_expenses': {'2025': 0.0}, 'employer_pension_contributions': {'2025': 0.0}, 'employee_pension_contributions': {'2025': 0.0}, 'personal_pension_contributions': {'2025': 0.0}, 'maintenance_expenses': {'2025': 0.0}, 'bi_individual_phaseout': {'2025': 0.0}, 'bi_household_phaseout': {'2025': 0.0}, 'bi_phaseout': {'2025': 0.0}, 'basic_income': {'2025': 0.0}, 'bi_maximum': {'2025': 0.0}, 'attends_private_school': {'2025': False}, 'employer_cost': {'2025': 32884.2}, 'baseline_employer_cost': {'2025': 32884.2}, 'adjusted_employer_cost': {'2025': 32884.2}, 'employer_ni_response_consumer_incidence': {'2025': 0.0}, 'employer_ni_response_capital_incidence': {'2025': 0.0}, 'employer_ni_fixed_employer_cost_change': {'2025': 0.0}, 'marginal_tax_rate': {'2025': 0.27999997}, 'cliff_evaluated': {'2025': True}, 'cliff_gap': {'2025': 0.0}, 'is_on_cliff': {'2025': False}, 'person_id': {'2025': 0.0}, 'people': {'2025': 1.0}, 'raw_person_weight': {'2025': 1.0}, 'person_weight': {'2025': 1.0}, 'adult_index': {'2025': 1.0}, 'birth_year': {'2025': 1995.0}, 'over_16': {'2025': True}, 'is_adult': {'2025': True}, 'is_child': {'2025': False}, 'child_index': {'2025': -1.0}, 'is_eldest_child': {'2025': True}, 'is_benunit_eldest_child': {'2025': False}, 'marital_status': {'2025': 'SINGLE'}, 'current_education': {'2025': 'NOT_IN_EDUCATION'}, 'highest_education': {'2025': 'UPPER_SECONDARY'}, 'in_FE': {'2025': False}, 'in_HE': {'2025': False}, 'gender': {'2025': 'MALE'}, 'is_male': {'2025': True}, 'is_female': {'2025': False}, 'is_household_head': {'2025': True}, 'is_benunit_head': {'2025': True}, 'in_social_housing': {'2025': False}, 'is_WA_adult': {'2025': True}, 'is_young_child': {'2025': False}, 'age_under_18': {'2025': False}, 'age_18_64': {'2025': True}, 'age_over_64': {'2025': False}, 'is_older_child': {'2025': False}, 'is_higher_earner': {'2025': True}, 'person_benunit_id': {'2025': 0.0}, 'person_household_id': {'2025': 0.0}, 'role': {'2025': ''}, 'person_benunit_role': {'2025': ''}, 'person_household_role': {'2025': ''}, 'is_disabled_for_benefits': {'2025': False}, 'is_enhanced_disabled_for_benefits': {'2025': False}, 'is_severely_disabled_for_benefits': {'2025': False}, 'person_state_id': {'2025': 0.0}, 'person_state_role': {'2025': ''}, 'is_blind': {'2025': False}, 'is_carer_for_benefits': {'2025': False}, 'personal_rent': {'2025': 0.0}, 'weekly_childcare_expenses': {'2025': 0.0}, 'earned_income': {'2025': 30000.0}, 'market_income': {'2025': 30000.0}, 'hours_worked': {'2025': 0.0}, 'in_work': {'2025': True}, 'weekly_hours': {'2025': 0.0}, 'employment_status': {'2025': 'UNEMPLOYED'}, 'capital_income': {'2025': 0.0}, 'base_net_income': {'2025': 0.0}, 'is_apprentice': {'2025': False}, 'minimum_wage_category': {'2025': 'OVER_24'}, 'minimum_wage': {'2025': 9.5}, 'income_decile': {'2025': 10.0}, 'household_statutory_maternity_pay': {'2025': 0.0}, 'household_statutory_paternity_pay': {'2025': 0.0}, 'household_statutory_sick_pay': {'2025': 0.0}, 'capital_gains': {'2025': 0.0}, 'is_QYP': {'2025': False}, 'is_child_or_QYP': {'2025': False}, 'employment_benefits': {'2025': 0.0}, 'relative_income_change': {'2025': 0.0}, 'relative_wage_change': {'2025': 0.0}, 'income_elasticity_lsr': {'2025': 0.0}, 'substitution_elasticity_lsr': {'2025': 0.0}, 'employment_income_behavioral_response': {'2025': 0.0}, 'income_support_reported': {'2025': 0.0}, 'attendance_allowance': {'2025': 0.0}, 'attendance_allowance_reported': {'2025': 0.0}, 'aa_category': {'2025': 'NONE'}, 'esa_income_reported': {'2025': 0.0}, 'iidb': {'2025': 0.0}, 'iidb_reported': {'2025': 0.0}, 'jsa_contrib': {'2025': 0.0}, 'jsa_contrib_reported': {'2025': 0.0}, 'maternity_allowance_reported': {'2025': 0.0}, 'maternity_allowance': {'2025': 0.0}, 'ssmg_reported': {'2025': 0.0}, 'ssmg': {'2025': 0.0}, 'council_tax_benefit_reported': {'2025': 0.0}, 'working_tax_credit_reported': {'2025': 0.0}, 'child_tax_credit_reported': {'2025': 0.0}, 'is_CTC_child_limit_exempt': {'2025': True}, 'is_child_for_CTC': {'2025': False}, 'jsa_income_reported': {'2025': 0.0}, 'bsp': {'2025': 0.0}, 'bsp_reported': {'2025': 0.0}, 'incapacity_benefit': {'2025': 0.0}, 'incapacity_benefit_reported': {'2025': 0.0}, 'sda': {'2025': 0.0}, 'sda_reported': {'2025': 0.0}, 'carers_allowance': {'2025': 0.0}, 'care_hours': {'2025': 0.0}, 'carers_allowance_reported': {'2025': 0.0}, 'armed_forces_independence_payment': {'2025': 0.0}, 'esa_contrib': {'2025': 0.0}, 'esa_contrib_reported': {'2025': 0.0}, 'esa': {'2025': 0.0}, 'afcs': {'2025': 0.0}, 'afcs_reported': {'2025': 0.0}, 'student_loans': {'2025': 0.0}, 'adult_ema': {'2025': 0.0}, 'child_ema': {'2025': 0.0}, 'access_fund': {'2025': 0.0}, 'education_grants': {'2025': 0.0}, 'student_payments': {'2025': 0.0}, 'state_pension_age': {'2025': 66.0}, 'is_SP_age': {'2025': False}, 'state_pension_type': {'2025': 'NONE'}, 'basic_state_pension': {'2025': 0.0}, 'additional_state_pension': {'2025': 0.0}, 'new_state_pension': {'2025': 0.0}, 'state_pension_reported': {'2025': 0.0}, 'winter_fuel_allowance_reported': {'2025': 0.0}, 'dla_m_reported': {'2025': 0.0}, 'dla_m': {'2025': 0.0}, 'dla_sc_reported': {'2025': 0.0}, 'dla_sc': {'2025': 0.0}, 'dla_sc_middle_plus': {'2025': False}, 'receives_highest_dla_sc': {'2025': False}, 'dla': {'2025': 0.0}, 'housing_benefit_reported': {'2025': 0.0}, 'household_benefits_individual_non_dep_deduction': {'2025': 6476.6}, 'housing_benefit_individual_non_dep_deduction_eligible': {'2025': True}, 'universal_credit_reported': {'2025': 0.0}, 'uc_is_child_born_before_child_limit': {'2025': False}, 'uc_individual_child_element': {'2025': 0.0}, 'uc_individual_disabled_child_element': {'2025': 0.0}, 'uc_individual_severely_disabled_child_element': {'2025': 0.0}, 'uc_is_in_startup_period': {'2025': False}, 'uc_minimum_income_floor': {'2025': 17290.0}, 'uc_mif_applies': {'2025': False}, 'uc_mif_capped_earned_income': {'2025': 30000.0}, 'uc_limited_capability_for_WRA': {'2025': False}, 'uc_individual_non_dep_deduction_eligible': {'2025': True}, 'uc_individual_non_dep_deduction': {'2025': 1125.3458}, 'uc_non_dep_deduction_exempt': {'2025': False}, 'pip_m_reported': {'2025': 0.0}, 'pip_m': {'2025': 0.0}, 'pip_dl_reported': {'2025': 0.0}, 'pip_dl': {'2025': 0.0}, 'receives_enhanced_pip_dl': {'2025': False}, 'pip': {'2025': 0.0}, 'pension_credit_reported': {'2025': 0.0}, 'tax': {'2025': 4880.4033}, 'tax_reported': {'2025': 0.0}, 'tax_modelling': {'2025': 4880.4033}, 'child_benefit_reported': {'2025': 0.0}, 'child_benefit_respective_amount': {'2025': 0.0}, 'relative_capital_gains_mtr_change': {'2025': 0.0}, 'capital_gains_elasticity': {'2025': 0.0}, 'capital_gains_behavioural_response': {'2025': 0.0}, 'capital_gains_before_response': {'2025': 0.0}, 'adult_index_cg': {'2025': 1.0}, 'marginal_tax_rate_on_capital_gains': {'2025': 0.0}, 'capital_gains_tax': {'2025': 0.0}, 'ni_employee': {'2025': 1394.4033}, 'national_insurance': {'2025': 1394.4033}, 'ni_employer': {'2025': 2884.2}, 'ni_self_employed': {'2025': 0.0}, 'total_national_insurance': {'2025': 4278.6035}, 'ni_class_4_maximum': {'2025': 917.73663}, 'ni_class_4': {'2025': 0.0}, 'ni_class_4_main': {'2025': 0.0}, 'ni_class_3': {'2025': 0.0}, 'ni_class_2': {'2025': 0.0}, 'ni_class_1_employee_primary': {'2025': 1394.4033}, 'ni_class_1_income': {'2025': 30000.0}, 'ni_class_1_employee': {'2025': 1394.4033}, 'ni_class_1_employee_additional': {'2025': 0.0}, 'ni_liable': {'2025': True}, 'ni_class_1_employer': {'2025': 2884.2}, 'other_tax_credits': {'2025': 0.0}, 'earned_income_tax': {'2025': 3486.0}, 'total_income': {'2025': 30000.0}, 'income_tax': {'2025': 3486.0}, 'taxed_income': {'2025': 17430.0}, 'adjusted_net_income': {'2025': 30000.0}, 'total_pension_income': {'2025': 0.0}, 'social_security_income': {'2025': 0.0}, 'income_tax_pre_charges': {'2025': 3486.0}, 'personal_allowance': {'2025': 12570.0}, 'blind_persons_allowance': {'2025': 0.0}, 'married_couples_allowance': {'2025': 0.0}, 'married_couples_allowance_deduction': {'2025': 0.0}, 'capped_mcad': {'2025': 0.0}, 'pension_annual_allowance': {'2025': 40000.0}, 'trading_allowance': {'2025': 1000.0}, 'trading_allowance_deduction': {'2025': 0.0}, 'property_allowance': {'2025': 1000.0}, 'property_allowance_deduction': {'2025': 0.0}, 'savings_allowance': {'2025': 1000.0}, 'dividend_allowance': {'2025': 500.0}, 'gift_aid': {'2025': 0.0}, 'covenanted_payments': {'2025': 0.0}, 'charitable_investment_gifts': {'2025': 0.0}, 'other_deductions': {'2025': 0.0}, 'allowances': {'2025': 12570.0}, 'unused_personal_allowance': {'2025': 0.0}, 'meets_marriage_allowance_income_conditions': {'2025': True}, 'partners_unused_personal_allowance': {'2025': 0.0}, 'marriage_allowance': {'2025': 0.0}, 'received_allowances': {'2025': 12570.0}, 'received_allowances_savings_income': {'2025': 0.0}, 'received_allowances_dividend_income': {'2025': 0.0}, 'received_allowances_earned_income': {'2025': 12570.0}, 'savings_income_tax': {'2025': 0.0}, 'dividend_income_tax': {'2025': 0.0}, 'loss_relief': {'2025': 0.0}, 'capital_allowances': {'2025': 0.0}, 'deficiency_relief': {'2025': 0.0}, 'employment_deductions': {'2025': 0.0}, 'employment_expenses': {'2025': 0.0}, 'CB_HITC': {'2025': 0.0}, 'higher_rate_earned_income': {'2025': 0.0}, 'add_rate_earned_income': {'2025': 0.0}, 'earned_taxable_income': {'2025': 17430.0}, 'basic_rate_earned_income': {'2025': 17430.0}, 'taxable_pension_income': {'2025': 0.0}, 'taxable_miscellaneous_income': {'2025': 0.0}, 'taxed_dividend_income': {'2025': 0.0}, 'taxable_dividend_income': {'2025': 0.0}, 'taxable_social_security_income': {'2025': 0.0}, 'taxable_employment_income': {'2025': 30000.0}, 'trading_loss': {'2025': 0.0}, 'taxable_self_employment_income': {'2025': 0.0}, 'taxable_property_income': {'2025': 0.0}, 'individual_savings_account_interest_income': {'2025': 0.0}, 'taxable_savings_interest_income': {'2025': 0.0}, 'tax_free_savings_income': {'2025': 0.0}, 'higher_rate_earned_income_tax': {'2025': 0.0}, 'add_rate_earned_income_tax': {'2025': 0.0}, 'tax_band': {'2025': 'BASIC'}, 'basic_rate_earned_income_tax': {'2025': 3486.0}, 'basic_rate_savings_income_pre_starter': {'2025': 0.0}, 'taxed_savings_income': {'2025': 0.0}, 'higher_rate_savings_income': {'2025': 0.0}, 'savings_starter_rate_income': {'2025': 0.0}, 'add_rate_savings_income': {'2025': 0.0}, 'basic_rate_savings_income': {'2025': 0.0}, 'personal_pension_contributions_tax': {'2025': 0.0}, 'pension_contributions_relief': {'2025': 0.0}, 'pension_contributions': {'2025': 0.0}, 'statutory_maternity_pay': {'2025': 0.0}, 'statutory_sick_pay': {'2025': 0.0}, 'pays_scottish_income_tax': {'2025': False}}}})"
+ ]
+ },
+ "execution_count": 1,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "from policyengine import Simulation\n",
+ "\n",
+ "sim = Simulation({\n",
+ " \"scope\": \"household\", # Required for this\n",
+ " \"country\": \"uk\", # or \"us\"\n",
+ " \"time_period\": 2025,\n",
+ " \"data\": { # Required for this\n",
+ " \"people\": {\n",
+ " \"person\": {\n",
+ " \"age\": {\n",
+ " \"2025\": 30,\n",
+ " },\n",
+ " \"employment_income\": {\n",
+ " \"2025\": 30_000,\n",
+ " },\n",
+ " }\n",
+ " }\n",
+ " }\n",
+ "})\n",
+ "\n",
+ "sim.calculate()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Output schema\n",
+ "\n",
+ "`calculate_single_household` or `calculate` (when `scope=household` and `reform=None`) return the following schema."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "{'description': 'Statistics for a single household scenario.',\n",
+ " 'properties': {'full_household': {'additionalProperties': {'anyOf': [{'additionalProperties': {'additionalProperties': {'additionalProperties': {'anyOf': [{'type': 'number'},\n",
+ " {'type': 'string'},\n",
+ " {'type': 'boolean'},\n",
+ " {'items': {}, 'type': 'array'},\n",
+ " {'type': 'null'}]},\n",
+ " 'type': 'object'},\n",
+ " 'type': 'object'},\n",
+ " 'type': 'object'},\n",
+ " {'items': {'items': {'additionalProperties': {'anyOf': [{'type': 'string'},\n",
+ " {'type': 'integer'}]},\n",
+ " 'type': 'object'},\n",
+ " 'type': 'array'},\n",
+ " 'type': 'array'}]},\n",
+ " 'title': 'Full Household',\n",
+ " 'type': 'object'}},\n",
+ " 'required': ['full_household'],\n",
+ " 'title': 'SingleHousehold',\n",
+ " 'type': 'object'}"
+ ]
+ },
+ "execution_count": 3,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "from policyengine.outputs.household.single.calculate_single_household import SingleHousehold\n",
+ "\n",
+ "SingleHousehold.model_json_schema()"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "base",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.10.14"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/docs/reference/create_charts.ipynb b/docs/reference/create_charts.ipynb
new file mode 100644
index 00000000..16c0c188
--- /dev/null
+++ b/docs/reference/create_charts.ipynb
@@ -0,0 +1,7175 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Create charts for an economic comparison\n",
+ "\n",
+ "This package also comes with utilities that allow you to create charts from the economy comparison operation. In this notebook, we'll walk through them."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ " \n",
+ " \n",
+ " \n",
+ " "
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# First, set up the simulation\n",
+ "\n",
+ "from policyengine import Simulation\n",
+ "from policyengine.outputs.macro.comparison.charts import *\n",
+ "\n",
+ "sim = Simulation({\n",
+ " \"country\": \"uk\",\n",
+ " \"scope\": \"macro\",\n",
+ " \"reform\": {\n",
+ " \"gov.hmrc.income_tax.allowances.personal_allowance.amount\": 10_000,\n",
+ " },\n",
+ " \"title\": \"Lowering the personal allowance to £10,000\" # Required for charts\n",
+ "})\n",
+ "\n",
+ "from policyengine.utils.charts import add_fonts\n",
+ "\n",
+ "add_fonts() # Load the right font (Roboto Serif) in this notebook"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Budget\n",
+ "\n",
+ "The budget chart shows high-level budget changes under the reform."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "application/vnd.plotly.v1+json": {
+ "config": {
+ "plotlyServerURL": "https://plot.ly"
+ },
+ "data": [
+ {
+ "decreasing": {
+ "marker": {
+ "color": "#616161"
+ }
+ },
+ "increasing": {
+ "marker": {
+ "color": "#2C6496"
+ }
+ },
+ "measure": [
+ "relative",
+ "relative",
+ "total"
+ ],
+ "text": [
+ "£24.2bn",
+ "£0.7bn",
+ "£23.5bn"
+ ],
+ "textposition": "inside",
+ "totals": {
+ "marker": {
+ "color": "#2C6496"
+ }
+ },
+ "type": "waterfall",
+ "x": [
+ "Tax revenues",
+ "Government spending",
+ "Public sector net worth"
+ ],
+ "y": [
+ 24.18759785404004,
+ 0.7363357242798462,
+ 23.45126212976019
+ ]
+ }
+ ],
+ "layout": {
+ "annotations": [
+ {
+ "showarrow": false,
+ "text": "Source: PolicyEngine UK tax-benefit microsimulation model (version 2.18.0)",
+ "x": 0,
+ "xanchor": "left",
+ "xref": "paper",
+ "y": -0.2,
+ "yanchor": "bottom",
+ "yref": "paper"
+ }
+ ],
+ "font": {
+ "color": "black",
+ "family": "Roboto Serif"
+ },
+ "height": 600,
+ "images": [
+ {
+ "sizex": 0.15,
+ "sizey": 0.15,
+ "source": "https://raw.githubusercontent.com/PolicyEngine/policyengine-app/master/src/images/logos/policyengine/blue.png",
+ "x": 1.1,
+ "xanchor": "right",
+ "xref": "paper",
+ "y": -0.2,
+ "yanchor": "bottom",
+ "yref": "paper"
+ }
+ ],
+ "margin": {
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+ "l": 120,
+ "r": 120,
+ "t": 120
+ },
+ "modebar": {
+ "activecolor": "#F4F4F4",
+ "bgcolor": "#F4F4F4",
+ "color": "#F4F4F4"
+ },
+ "paper_bgcolor": "#F4F4F4",
+ "plot_bgcolor": "#F4F4F4",
+ "template": {
+ "data": {
+ "bar": [
+ {
+ "error_x": {
+ "color": "#2a3f5f"
+ },
+ "error_y": {
+ "color": "#2a3f5f"
+ },
+ "marker": {
+ "line": {
+ "color": "white",
+ "width": 0.5
+ },
+ "pattern": {
+ "fillmode": "overlay",
+ "size": 10,
+ "solidity": 0.2
+ }
+ },
+ "type": "bar"
+ }
+ ],
+ "barpolar": [
+ {
+ "marker": {
+ "line": {
+ "color": "white",
+ "width": 0.5
+ },
+ "pattern": {
+ "fillmode": "overlay",
+ "size": 10,
+ "solidity": 0.2
+ }
+ },
+ "type": "barpolar"
+ }
+ ],
+ "carpet": [
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+ "aaxis": {
+ "endlinecolor": "#2a3f5f",
+ "gridcolor": "#C8D4E3",
+ "linecolor": "#C8D4E3",
+ "minorgridcolor": "#C8D4E3",
+ "startlinecolor": "#2a3f5f"
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+ "baxis": {
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+ "linecolor": "#C8D4E3",
+ "minorgridcolor": "#C8D4E3",
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+ "type": "carpet"
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+ ],
+ "choropleth": [
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+ "type": "choropleth"
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+ "contour": [
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+ "colorscale": [
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+ "type": "contour"
+ }
+ ],
+ "contourcarpet": [
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+ "colorbar": {
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+ "type": "contourcarpet"
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+ "heatmap": [
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+ "histogram": [
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+ "source": [
+ "create_budget_program_comparison_chart(sim)"
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+ "## Decile\n",
+ "\n",
+ "The decile chart shows the impact of the reform on each decile of the income distribution. You can specify whether this is over income deciles, or wealth deciles, in the UK."
+ ]
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+ "## Winners and losers\n",
+ "\n",
+ "The winners and losers chart shows in each decile (and overall) the share of people who come out ahead or behind under the reform. Again, you can specify whether the deciles are income or wealth in the UK."
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+ "source": [
+ "## Poverty\n",
+ "\n",
+ "The poverty chart shows the impact of the reform on various poverty rates, split out by demographic groups you can specify. You can show the change to poverty rate by age group, gender or racial group (US-only), and you can choose between regular or deep poverty."
+ ]
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+ "create_poverty_chart(\n",
+ " sim,\n",
+ " age_group=None, # Specify None to show all age groups\n",
+ " gender=\"all\", # The rest are filters\n",
+ " racial_group=\"all\",\n",
+ " rate_relative=False, # Compare how many people are in poverty rather than the rate\n",
+ " change_relative=False, # Compare the absolute headcount rather than % change\n",
+ " poverty_rate=\"regular\",\n",
+ ")"
+ ]
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+ {
+ "cell_type": "markdown",
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+ "## Inequality\n",
+ "\n",
+ "The inequality chart shows the impact of the reform on various inequality measures: the Gini coefficient, the share of income held by the top 10% of households (by income) and the top 1% share."
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+ "source": [
+ "## Labor supply\n",
+ "\n",
+ "The labor supply chart shows the impact of the reform on labor supply if labor supply response assumptions have been set in the `reform`, split out by groups you can specify. You can show the change to labor supply by decile, the mechanism (or elasticity- e.g. filtering to only substitution effect-powered responses), and also by the unit (hours or earnings, though only earnings in the UK)."
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+ "source": [
+ "sim = Simulation({\n",
+ " \"country\": \"uk\",\n",
+ " \"scope\": \"macro\",\n",
+ " \"reform\": {\n",
+ " \"gov.hmrc.income_tax.allowances.personal_allowance.amount\": 10_000,\n",
+ " \"gov.simulation.labor_supply_responses.substitution_elasticity\": 0.2,\n",
+ " },\n",
+ " \"title\": \"Lowering the personal allowance to £10,000\" # Required for charts\n",
+ "})\n",
+ "\n",
+ "create_labor_supply_chart(\n",
+ " sim,\n",
+ " decile=None,\n",
+ " elasticity=\"all\",\n",
+ " unit=\"earnings\",\n",
+ " change_relative=True,\n",
+ " change_average=False,\n",
+ ")"
+ ]
+ }
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+}
diff --git a/docs/reference/simulation.ipynb b/docs/reference/simulation.ipynb
index ee3c846a..99d2e5ee 100644
--- a/docs/reference/simulation.ipynb
+++ b/docs/reference/simulation.ipynb
@@ -4,9 +4,20 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "# Simulation\n",
+ "# Simulation interface\n",
"\n",
- "The `Simulation` class is the core interface of this package. You can initialise it by passing in a dictionary that matches the `SimulationOptions` schema, and then use its `calculate` methods to ask it questions."
+ "The `Simulation` class is the core interface of this package. You can initialise it by passing in a dictionary that matches the `SimulationOptions` schema, and then use its `calculate` methods to ask it questions.\n",
+ "\n",
+ "Some of the options are straightforward and some are more complex. The straightforward ones are:\n",
+ "\n",
+ "* `country`: `uk` or `us`.\n",
+ "* `scope`: `macro` (simulating over large data to represent e.g. a country) or `household` (simulating over specific households you describe).\n",
+ "* `time_period`: the year to simulate.\n",
+ "\n",
+ "The next important features are:\n",
+ "* `reform`: the policy to use in the reform scenario if we are comparing against a different scenario.\n",
+ "* `baseline`: the policy to use in the baseline scenario if we are comparing against a different baseline scenario.\n",
+ "* `data`: either a household (if `scope` is `household`) or a large dataset name (if `scope` is `macro`)."
]
},
{
@@ -42,6 +53,61 @@
"cell_type": "markdown",
"metadata": {},
"source": [
+ "## Providing `baseline` and `reform` policies\n",
+ "\n",
+ "The `baseline` and `reform` policies are dictionaries that represent the policy to simulate. You don't have to provide a reform policy (if you don't, the simulation will just simulate the baseline policy). You also don't have to provide a baseline policy (if you don't, the simulation will just compare your reform scenario against current law).\n",
+ "\n",
+ "If you do, they should each follow this syntax:\n",
+ "\n",
+ "```json\n",
+ "{\n",
+ " \"gov.hmrc.income_tax.rate\": { // Parameter address, in the country model's `parameters/` folder\n",
+ " \"2025\": 0.2 // Value to set the parameter to in the year 2025\n",
+ " }\n",
+ "}\n",
+ "```\n",
+ "\n",
+ "You can also use this shorthand to set parameters for all years:\n",
+ "\n",
+ "```json\n",
+ "{\n",
+ " \"gov.hmrc.income_tax.rate\": 0.2\n",
+ "}\n",
+ "```\n",
+ "\n",
+ "## Providing `data`\n",
+ "\n",
+ "If you set `scope` to `macro`, you should provide either:\n",
+ "\n",
+ "* A Hugging Face `.h5` dataset address in this format: `\"hf://policyengine/policyengine-us-data/cps_2023.h5\"` (`hf://owner/dataset-name/path.h5`).\n",
+ "* An instance of `policyengine_core.data.Dataset` (advanced).\n",
+ "\n",
+ "See `policyengine.constants` for the available datasets.\n",
+ "\n",
+ "If you set `scope` to `household`, you should provide a dictionary that represents a household. This should look like:\n",
+ "\n",
+ "```json\n",
+ "{\n",
+ " \"people\": { // Entity group\n",
+ " \"person\": { // Entity name\n",
+ " \"age\": { // Variable (in the country model's `variables/` folder)\n",
+ " \"2025\": 30, // Time period and value\n",
+ " }\n",
+ " }\n",
+ " },\n",
+ " \"households\": {\n",
+ " \"household\": {\n",
+ " \"members\": [\"person\"], // Group entities need a `members` field\n",
+ " \"region\": {\n",
+ " \"2025\": \"LONDON\",\n",
+ " }\n",
+ " }\n",
+ " }\n",
+ "}\n",
+ "```\n",
+ "\n",
+ "See the country model's repository for more information on what entity types are available.\n",
+ "\n",
"## Module documentation\n",
"\n",
"```{eval-rst}\n",
diff --git a/policyengine/outputs/household/comparison/calculate_household_comparison.py b/policyengine/outputs/household/comparison/calculate_household_comparison.py
index adf81963..48c28f0c 100644
--- a/policyengine/outputs/household/comparison/calculate_household_comparison.py
+++ b/policyengine/outputs/household/comparison/calculate_household_comparison.py
@@ -8,22 +8,23 @@
from pydantic import BaseModel
from policyengine.utils.calculations import get_change
from policyengine_core.simulations import Simulation as CountrySimulation
+from policyengine.outputs.household.single.calculate_single_household import (
+ SingleHousehold,
+ fill_and_calculate,
+ FullHouseholdSpecification,
+)
from typing import Literal, List
class HouseholdComparison(BaseModel):
- household_net_income: float
- """The net income of the household."""
+ full_household_baseline: FullHouseholdSpecification
+ """The full completion of the household under the baseline scenario."""
+ full_household_reform: FullHouseholdSpecification
+ """The full completion of the household under the reform scenario."""
-def calculate_net_income(
- baseline: CountrySimulation,
- reform: CountrySimulation,
-) -> float:
- return (
- reform.calculate("household_net_income").sum()
- - baseline.calculate("household_net_income").sum()
- )
+ change: FullHouseholdSpecification
+ """The change in the household from the baseline to the reform scenario."""
def calculate_household_comparison(
@@ -33,11 +34,21 @@ def calculate_household_comparison(
if not simulation.is_comparison:
raise ValueError("Simulation must be a comparison simulation.")
- baseline = simulation.baseline_simulation
- reform = simulation.reform_simulation
-
- net_income = calculate_net_income(baseline, reform)
+ baseline_household = fill_and_calculate(
+ simulation.options.data, simulation.baseline_simulation
+ )
+ reform_household = fill_and_calculate(
+ simulation.options.data, simulation.reform_simulation
+ )
+ change = get_change(
+ baseline_household,
+ reform_household,
+ relative=False,
+ skip_mismatch=True,
+ )
return HouseholdComparison(
- household_net_income=net_income,
+ full_household_baseline=baseline_household,
+ full_household_reform=reform_household,
+ change=change,
)
diff --git a/policyengine/outputs/household/single/calculate_single_household.py b/policyengine/outputs/household/single/calculate_single_household.py
index 26631794..858a0d6c 100644
--- a/policyengine/outputs/household/single/calculate_single_household.py
+++ b/policyengine/outputs/household/single/calculate_single_household.py
@@ -7,18 +7,30 @@
from pydantic import BaseModel
from policyengine_core.simulations import Simulation as CountrySimulation
-from typing import List
+from typing import List, Dict
+from datetime import date
+from policyengine_core.variables import Variable
+from policyengine_core.entities import Entity
+from policyengine_core.model_api import YEAR, MONTH, ETERNITY, Enum
+import dpath.util
+import math
+import json
+Value = float | str | bool | list | None
+Axes = List[List[Dict[str, str | int]]]
+TimePeriodValues = Dict[str, Value]
+EntityValues = Dict[str, TimePeriodValues]
+EntityGroupValues = Dict[str, EntityValues]
+FullHouseholdSpecification = Dict[
+ str, EntityGroupValues | Axes
+] # {people: {person: {variable: {time_period: value}}}}
-class SingleHousehold(BaseModel):
- household_net_income: float
- """The net income of the household."""
+class SingleHousehold(BaseModel):
+ """Statistics for a single household scenario."""
-def calculate_net_income(
- simulation: CountrySimulation,
-) -> float:
- return simulation.calculate("household_net_income").sum()
+ full_household: FullHouseholdSpecification
+ """Full variable calculations for the household."""
def calculate_single_household(
@@ -30,8 +42,163 @@ def calculate_single_household(
"This function is for single economy simulations only."
)
- net_income = calculate_net_income(simulation.baseline_simulation)
-
return SingleHousehold(
- household_net_income=net_income,
+ full_household=fill_and_calculate(
+ simulation.options.data, simulation.baseline_simulation
+ )
+ )
+
+
+def fill_and_calculate(
+ household: FullHouseholdSpecification, simulation: CountrySimulation
+):
+ """Fill in missing variables and calculate all variables for a household"""
+ # Copy the household to avoid modifying the original
+ household = json.loads(json.dumps(household))
+ household = add_yearly_variables(household, simulation)
+ household = calculate_all_variables(household, simulation)
+ household.pop("axes", None)
+ return household
+
+
+def get_requested_computations(
+ household: FullHouseholdSpecification,
+) -> List[tuple[str, str, str, str]]:
+ requested_computations = dpath.util.search(
+ {k: v for k, v in household.items() if k != "axes"},
+ "*/*/*/*",
+ # afilter=lambda t: t is None,
+ yielded=True,
+ )
+ requested_computation_data = []
+
+ for computation in requested_computations:
+ path = computation[0]
+ entity_plural, entity_id, variable_name, period = path.split("/")
+ requested_computation_data.append(
+ (entity_plural, entity_id, variable_name, period)
+ )
+
+ return requested_computation_data
+
+
+def calculate_all_variables(
+ household: FullHouseholdSpecification, simulation: CountrySimulation
+) -> FullHouseholdSpecification:
+ requested_computations = get_requested_computations(household)
+
+ for (
+ entity_plural,
+ entity_id,
+ variable_name,
+ period,
+ ) in requested_computations:
+ variable = simulation.tax_benefit_system.get_variable(variable_name)
+ result = simulation.calculate(variable_name, period)
+ population = simulation.get_population(entity_plural)
+
+ if "axes" in household:
+ count_entities = len(household[entity_plural])
+ entity_index = 0
+ for _entity_id in household[entity_plural].keys():
+ if _entity_id == entity_id:
+ break
+ entity_index += 1
+ try:
+ result = result.astype(float)
+ except:
+ pass
+ result = (
+ result.reshape((-1, count_entities)).T[entity_index].tolist()
+ )
+ # If the result contains infinities, throw an error
+ if any(
+ [
+ not isinstance(value, str) and math.isinf(value)
+ for value in result
+ ]
+ ):
+ raise ValueError("Infinite value")
+ else:
+ household[entity_plural][entity_id][variable_name][
+ period
+ ] = result
+ else:
+ entity_index = population.get_index(entity_id)
+ if variable.value_type == Enum:
+ entity_result = result.decode()[entity_index].name
+ elif variable.value_type == float:
+ entity_result = float(str(result[entity_index]))
+ # Convert infinities to JSON infinities
+ if entity_result == float("inf"):
+ entity_result = "Infinity"
+ elif entity_result == float("-inf"):
+ entity_result = "-Infinity"
+ elif variable.value_type == str:
+ entity_result = str(result[entity_index])
+ else:
+ entity_result = result.tolist()[entity_index]
+
+ household[entity_plural][entity_id][variable_name][
+ period
+ ] = entity_result
+
+ return household
+
+
+def get_household_year(household: FullHouseholdSpecification) -> str:
+ """Given a household dict, get the household's year
+
+ Args:
+ household (dict): The household itself
+ """
+
+ # Set household_year based on current year
+ household_year = date.today().year
+
+ # Determine if "age" variable present within household and return list of values at it
+ household_age_list = list(
+ household.get("people", {}).get("you", {}).get("age", {}).keys()
)
+ # If it is, overwrite household_year with the value present
+ if len(household_age_list) > 0:
+ household_year = household_age_list[0]
+
+ return str(household_year)
+
+
+def add_yearly_variables(
+ household: FullHouseholdSpecification, simulation: CountrySimulation
+) -> FullHouseholdSpecification:
+ """
+ Add yearly variables to a household dict before enqueueing calculation
+ """
+
+ variables: Dict[str, Variable] = simulation.tax_benefit_system.variables
+ entities: Dict[str, Entity] = (
+ simulation.tax_benefit_system.entities_by_singular()
+ )
+ household_year = get_household_year(household)
+
+ for variable in variables:
+ if variables[variable].definition_period in (YEAR, MONTH, ETERNITY):
+ entity_plural = entities[variables[variable].entity.key].plural
+ if entity_plural in household:
+ possible_entities = household[entity_plural].keys()
+ for entity in possible_entities:
+ if (
+ not variables[variable].name
+ in household[entity_plural][entity]
+ ):
+ if variables[variable].is_input_variable():
+ value = variables[variable].default_value
+ if isinstance(value, Enum):
+ value = value.name
+ household[entity_plural][entity][
+ variables[variable].name
+ ] = {household_year: value}
+ else:
+ household[entity_plural][entity][
+ variables[variable].name
+ ] = {household_year: None}
+ return household
diff --git a/policyengine/outputs/macro/comparison/calculate_economy_comparison.py b/policyengine/outputs/macro/comparison/calculate_economy_comparison.py
index d2ddde59..f9d04fd6 100644
--- a/policyengine/outputs/macro/comparison/calculate_economy_comparison.py
+++ b/policyengine/outputs/macro/comparison/calculate_economy_comparison.py
@@ -39,15 +39,31 @@ class InequalityComparison(BaseModel):
relative_change: InequalitySummary
+class Headlines(BaseModel):
+ budgetary_impact: float
+ """The change in the (federal) government budget balance."""
+ poverty_impact: float
+ """The relative change in the regular poverty rate."""
+ winner_share: float
+ """The share of people that are better off in the reform scenario."""
+
+
class PovertyRateMetricComparison(BaseModel):
age_group: Literal["child", "working_age", "senior", "all"]
"""The age group of the population."""
+ gender: Literal["male", "female", "all"]
+ """The gender of the population."""
racial_group: Literal["white", "black", "hispanic", "other", "all"]
"""The racial group of the population."""
relative: bool
"""Whether the poverty rate is relative to the total population, or a headcount."""
poverty_rate: Literal[
- "uk_hbai_bhc", "uk_hbai_bhc_half", "us_spm", "us_spm_half"
+ "regular",
+ "deep",
+ "uk_hbai_bhc",
+ "uk_hbai_bhc_half",
+ "us_spm",
+ "us_spm_half",
]
"""The poverty rate definition being calculated."""
baseline: float
@@ -61,6 +77,8 @@ class PovertyRateMetricComparison(BaseModel):
class EconomyComparison(BaseModel):
+ headlines: Headlines
+ """Headline statistics for the comparison."""
fiscal: FiscalComparison
"""Government budgets and other top-level fiscal statistics."""
inequality: InequalityComparison
@@ -118,7 +136,7 @@ def calculate_economy_comparison(
baseline_poverty_metrics = calculate_poverty(baseline, options)
reform_poverty_metrics = calculate_poverty(reform, options)
- poverty_metrics = []
+ poverty_metrics: List[PovertyRateMetricComparison] = []
for baseline_metric, reform_metric in zip(
baseline_poverty_metrics, reform_poverty_metrics
):
@@ -130,6 +148,7 @@ def calculate_economy_comparison(
poverty_metrics.append(
PovertyRateMetricComparison(
age_group=baseline_metric.age_group,
+ gender=baseline_metric.gender,
racial_group=baseline_metric.racial_group,
relative=baseline_metric.relative,
poverty_rate=baseline_metric.poverty_rate,
@@ -144,7 +163,25 @@ def calculate_economy_comparison(
baseline, reform, options
)
+ # Headlines
+ budgetary_impact = fiscal_comparison.change.federal_balance
+ poverty_impact = next(
+ filter(
+ lambda metric: metric.age_group == "all"
+ and metric.racial_group == "all"
+ and metric.poverty_rate == "regular",
+ poverty_metrics,
+ )
+ ).relative_change
+ winner_share = decile_impacts.income.winners_and_losers.all.gain_share
+ headlines = Headlines(
+ budgetary_impact=budgetary_impact,
+ poverty_impact=poverty_impact,
+ winner_share=winner_share,
+ )
+
return EconomyComparison(
+ headlines=headlines,
fiscal=fiscal_comparison,
inequality=inequality_comparison,
distributional=decile_impacts,
diff --git a/policyengine/outputs/macro/comparison/charts/__init__.py b/policyengine/outputs/macro/comparison/charts/__init__.py
new file mode 100644
index 00000000..2089605e
--- /dev/null
+++ b/policyengine/outputs/macro/comparison/charts/__init__.py
@@ -0,0 +1,8 @@
+from .budget import create_budget_comparison_chart
+from .budget_by_program import create_budget_program_comparison_chart
+from .decile import create_decile_chart
+from .winners_losers import create_winners_losers_chart
+from .poverty import create_poverty_chart
+from .inequality import create_inequality_chart
+from .labor_supply import create_labor_supply_chart
+from .create_all_charts import create_all_charts
diff --git a/policyengine/outputs/macro/comparison/charts/budget.py b/policyengine/outputs/macro/comparison/charts/budget.py
new file mode 100644
index 00000000..7a8d8b38
--- /dev/null
+++ b/policyengine/outputs/macro/comparison/charts/budget.py
@@ -0,0 +1,91 @@
+import plotly.express as px
+import plotly.graph_objects as go
+import typing
+
+if typing.TYPE_CHECKING:
+ from policyengine import Simulation
+
+from pydantic import BaseModel
+from policyengine.utils.charts import *
+
+
+def create_budget_comparison_chart(
+ simulation: "Simulation",
+) -> go.Figure:
+ """Create a budget comparison chart."""
+ if not simulation.is_comparison:
+ raise ValueError("Simulation must be a comparison simulation.")
+
+ economy = simulation.calculate_economy_comparison()
+
+ if simulation.options.country == "uk":
+ x_values = [
+ "Tax revenues",
+ "Government spending",
+ "Public sector net worth",
+ ]
+ y_values = [
+ economy.fiscal.change.federal_tax,
+ economy.fiscal.change.government_spending,
+ economy.fiscal.change.federal_balance,
+ ]
+ else:
+ x_values = [
+ "Federal tax revenues",
+ "State tax revenues",
+ "Federal government spending",
+ ]
+ y_values = [
+ economy.fiscal.change.federal_tax,
+ economy.fiscal.change.state_tax,
+ economy.fiscal.change.government_spending,
+ ]
+
+ y_values = [value / 1e9 for value in y_values]
+
+ net_change = round(economy.fiscal.change.federal_balance / 1e9, 1)
+
+ if net_change > 0:
+ description = f"raise ${net_change}bn"
+ elif net_change < 0:
+ description = f"cost ${-net_change}bn"
+ else:
+ description = "have no effect on government finances"
+
+ chart = go.Figure(
+ data=[
+ go.Waterfall(
+ x=x_values,
+ y=y_values,
+ measure=["relative"] * (len(x_values) - 1) + ["total"],
+ textposition="inside",
+ text=[f"${value:.1f}bn" for value in y_values],
+ increasing=dict(
+ marker=dict(
+ color=BLUE,
+ )
+ ),
+ decreasing=dict(
+ marker=dict(
+ color=DARK_GRAY,
+ )
+ ),
+ totals=dict(
+ marker=dict(
+ color=BLUE if net_change > 0 else DARK_GRAY,
+ )
+ ),
+ ),
+ ]
+ ).update_layout(
+ title=f"{simulation.options.title} would {description}",
+ yaxis_title="Budgetary impact ($bn)",
+ uniformtext=dict(
+ mode="hide",
+ minsize=12,
+ ),
+ )
+
+ return format_fig(
+ chart, country=simulation.options.country, add_zero_line=True
+ )
diff --git a/policyengine/outputs/macro/comparison/charts/budget_by_program.py b/policyengine/outputs/macro/comparison/charts/budget_by_program.py
new file mode 100644
index 00000000..27eb23de
--- /dev/null
+++ b/policyengine/outputs/macro/comparison/charts/budget_by_program.py
@@ -0,0 +1,97 @@
+import plotly.express as px
+import plotly.graph_objects as go
+import typing
+
+if typing.TYPE_CHECKING:
+ from policyengine import Simulation
+
+from pydantic import BaseModel
+from policyengine.utils.charts import *
+
+
+def create_budget_program_comparison_chart(
+ simulation: "Simulation",
+) -> go.Figure:
+ """Create a budget comparison chart."""
+ if not simulation.is_comparison:
+ raise ValueError("Simulation must be a comparison simulation.")
+
+ if not simulation.options.country == "uk":
+ raise ValueError("This chart is only available for the UK.")
+
+ economy = simulation.calculate_economy_comparison()
+
+ change_programs = economy.fiscal.change.tax_benefit_programs
+
+ change_programs = {
+ program: change_programs[program]
+ for program in change_programs
+ if round(change_programs[program] / 1e9, 1) != 0
+ }
+
+ labels = [
+ simulation.baseline_simulation.tax_benefit_system.variables.get(
+ program
+ ).label
+ for program in change_programs
+ ]
+
+ x_values = labels
+ y_values = [
+ round(change_programs[program] / 1e9, 1) for program in change_programs
+ ]
+
+ total_from_programs = round(sum(y_values))
+ total_overall = round(economy.fiscal.change.federal_balance / 1e9)
+
+ if total_from_programs != total_overall:
+ x_values.append("Other")
+ y_values.append(total_overall - total_from_programs)
+
+ x_values.append("Combined")
+ y_values.append(total_overall)
+
+ if total_overall > 0:
+ description = f"raise ${total_overall}bn"
+ elif total_overall < 0:
+ description = f"cost ${-total_overall}bn"
+ else:
+ description = "have no effect on government finances"
+
+ chart = go.Figure(
+ data=[
+ go.Waterfall(
+ x=x_values,
+ y=y_values,
+ measure=["relative"] * (len(x_values) - 1) + ["total"],
+ textposition="inside",
+ text=[f"${value:.1f}bn" for value in y_values],
+ increasing=dict(
+ marker=dict(
+ color=BLUE,
+ )
+ ),
+ decreasing=dict(
+ marker=dict(
+ color=DARK_GRAY,
+ )
+ ),
+ totals=dict(
+ marker=dict(
+ color=BLUE if total_overall > 0 else DARK_GRAY,
+ )
+ ),
+ ),
+ ]
+ ).update_layout(
+ title=f"{simulation.options.title} would {description}",
+ yaxis_title="Budgetary impact (bn)",
+ uniformtext=dict(
+ mode="hide",
+ minsize=12,
+ ),
+ )
+
+ return format_fig(
+ chart, country=simulation.options.country, add_zero_line=True
+ )
diff --git a/policyengine/outputs/macro/comparison/charts/create_all_charts.py b/policyengine/outputs/macro/comparison/charts/create_all_charts.py
new file mode 100644
index 00000000..eb6b2e3a
--- /dev/null
+++ b/policyengine/outputs/macro/comparison/charts/create_all_charts.py
@@ -0,0 +1,65 @@
+import plotly.express as px
+import plotly.graph_objects as go
+import typing
+
+if typing.TYPE_CHECKING:
+ from policyengine import Simulation
+
+from pydantic import BaseModel
+from policyengine.utils.charts import *
+from .budget import create_budget_comparison_chart
+from .budget_by_program import create_budget_program_comparison_chart
+from .decile import create_decile_chart
+from .winners_losers import create_winners_losers_chart
+from .poverty import create_poverty_chart
+from .inequality import create_inequality_chart
+from .labor_supply import create_labor_supply_chart
+from typing import Any
+
+
+class MacroCharts(BaseModel):
+ budget: Any
+ budget_programs: Any
+ decile_income_relative: Any
+ decile_income_average: Any
+ decile_wealth_relative: Any
+ decile_wealth_average: Any
+ winners_and_losers_income_decile: Any
+ winners_and_losers_wealth_decile: Any
+
+
+def create_all_charts(
+ simulation: "Simulation",
+) -> MacroCharts:
+ """Create all charts."""
+ if not simulation.is_comparison:
+ raise ValueError("Simulation must be a comparison simulation.")
+ if not simulation.options.scope == "macro":
+ raise ValueError(
+ "This function is only available for macro simulations."
+ )
+
+ return MacroCharts(
+ budget=create_budget_comparison_chart(simulation).to_dict(),
+ budget_programs=create_budget_program_comparison_chart(
+ simulation
+ ).to_dict(),
+ decile_income_relative=create_decile_chart(
+ simulation, "income", True
+ ).to_dict(),
+ decile_income_average=create_decile_chart(
+ simulation, "income", False
+ ).to_dict(),
+ decile_wealth_relative=create_decile_chart(
+ simulation, "wealth", True
+ ).to_dict(),
+ decile_wealth_average=create_decile_chart(
+ simulation, "wealth", True
+ ).to_dict(),
+ winners_and_losers_income_decile=create_winners_losers_chart(
+ simulation, "income"
+ ).to_dict(),
+ winners_and_losers_wealth_decile=create_winners_losers_chart(
+ simulation, "wealth"
+ ).to_dict(),
+ )
diff --git a/policyengine/outputs/macro/comparison/charts/decile.py b/policyengine/outputs/macro/comparison/charts/decile.py
new file mode 100644
index 00000000..d17c5bf2
--- /dev/null
+++ b/policyengine/outputs/macro/comparison/charts/decile.py
@@ -0,0 +1,90 @@
+import plotly.express as px
+import plotly.graph_objects as go
+import typing
+
+if typing.TYPE_CHECKING:
+ from policyengine import Simulation
+
+from pydantic import BaseModel
+from policyengine.utils.charts import *
+from typing import Literal
+
+
+def create_decile_chart(
+ simulation: "Simulation",
+ decile_variable: Literal["income", "wealth"],
+ relative: bool,
+) -> go.Figure:
+ """Create a budget comparison chart."""
+ if not simulation.is_comparison:
+ raise ValueError("Simulation must be a comparison simulation.")
+
+ economy = simulation.calculate_economy_comparison()
+
+ if decile_variable == "income":
+ data = economy.distributional.income.income_change
+ else:
+ data = economy.distributional.wealth.income_change
+
+ if relative:
+ data = data.relative
+ else:
+ data = data.average
+
+ avg_change = sum(data.values()) / len(data)
+
+ if relative:
+ text = [f"{value:.1%}" for value in data.values()]
+ avg_change = round(avg_change, 3)
+ else:
+ text = [f"${value:,.0f}" for value in data.values()]
+ avg_change = round(avg_change)
+
+ avg_change_str = (
+ f"${abs(avg_change):,.0f}"
+ if not relative
+ else f"{abs(avg_change):.1%}"
+ )
+
+ if avg_change > 0:
+ description = (
+ f"increase the net income of households by {avg_change_str}"
+ )
+ elif avg_change < 0:
+ description = (
+ f"decrease the net income of households by {avg_change_str}"
+ )
+ else:
+ description = "have no effect on household net income"
+
+ chart = go.Figure(
+ data=[
+ go.Bar(
+ x=list(data.keys()),
+ y=list(data.values()),
+ text=text,
+ marker=dict(
+ color=[
+ BLUE if value > 0 else DARK_GRAY
+ for value in data.values()
+ ]
+ ),
+ )
+ ]
+ ).update_layout(
+ title=f"{simulation.options.title} would {description}",
+ yaxis_title=f"Average change to net income ({'%' if relative else '$'})",
+ yaxis_tickformat="$,.0f" if not relative else ".0%",
+ xaxis_title=(
+ "Income decile" if decile_variable == "income" else "Wealth decile"
+ ),
+ uniformtext=dict(
+ mode="hide",
+ minsize=12,
+ ),
+ xaxis_tickvals=list(data.keys()),
+ )
+
+ return format_fig(
+ chart, country=simulation.options.country, add_zero_line=True
+ )
diff --git a/policyengine/outputs/macro/comparison/charts/inequality.py b/policyengine/outputs/macro/comparison/charts/inequality.py
new file mode 100644
index 00000000..5fa00d39
--- /dev/null
+++ b/policyengine/outputs/macro/comparison/charts/inequality.py
@@ -0,0 +1,78 @@
+import plotly.express as px
+import plotly.graph_objects as go
+import typing
+
+if typing.TYPE_CHECKING:
+ from policyengine import Simulation
+
+from pydantic import BaseModel
+from policyengine.utils.charts import *
+
+
+def create_inequality_chart(
+ simulation: "Simulation",
+ relative: bool,
+) -> go.Figure:
+ """Create a budget comparison chart."""
+ if not simulation.is_comparison:
+ raise ValueError("Simulation must be a comparison simulation.")
+
+ economy = simulation.calculate_economy_comparison()
+
+ x_values = [
+ "Gini index",
+ "Top 10% share",
+ "Top 1% share",
+ ]
+
+ if relative:
+ data = economy.inequality.relative_change
+ else:
+ data = economy.inequality.change
+
+ y_values = [
+ data.gini,
+ data.top_10_share,
+ data.top_1_share,
+ ]
+
+ if all(value < 0 for value in y_values):
+ description = f"lower inequality"
+ elif all(value > 0 for value in y_values):
+ description = f"raise inequality"
+ else:
+ description = "have an ambiguous effect on inequality"
+
+ if not relative:
+ y_values = [value * 100 for value in y_values]
+
+ chart = go.Figure(
+ data=[
+ go.Bar(
+ x=x_values,
+ y=y_values,
+ text=[
+ f"{value:.1%}" if relative else f"{value:.1f}pp"
+ for value in y_values
+ ],
+ marker=dict(
+ color=[
+ BLUE if value < 0 else DARK_GRAY for value in y_values
+ ]
+ ),
+ ),
+ ]
+ ).update_layout(
+ title=f"{simulation.options.title} would {description}",
+ yaxis_title="Change" + (" (%)" if relative else ""),
+ yaxis_ticksuffix="pp" if not relative else "",
+ yaxis_tickformat=".0%" if relative else ".1f",
+ uniformtext=dict(
+ mode="hide",
+ minsize=12,
+ ),
+ )
+
+ return format_fig(
+ chart, country=simulation.options.country, add_zero_line=True
+ )
diff --git a/policyengine/outputs/macro/comparison/charts/labor_supply.py b/policyengine/outputs/macro/comparison/charts/labor_supply.py
new file mode 100644
index 00000000..c3946e60
--- /dev/null
+++ b/policyengine/outputs/macro/comparison/charts/labor_supply.py
@@ -0,0 +1,126 @@
+import plotly.express as px
+import plotly.graph_objects as go
+import typing
+
+if typing.TYPE_CHECKING:
+ from policyengine import Simulation
+ from policyengine.outputs.macro.comparison.calculate_economy_comparison import (
+ LaborSupplyMetricImpact,
+ )
+
+from pydantic import BaseModel
+from policyengine.utils.charts import *
+from typing import Literal
+
+
+def create_labor_supply_chart(
+ simulation: "Simulation",
+ decile: Literal[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, "all"] | None,
+ elasticity: Literal["income", "substitution", "all"] | None,
+ unit: Literal["earnings", "hours"] | None,
+ change_relative: bool = True,
+ change_average: bool = False,
+) -> go.Figure:
+ """Create a budget comparison chart."""
+ if not simulation.is_comparison:
+ raise ValueError("Simulation must be a comparison simulation.")
+
+ economy = simulation.calculate_economy_comparison()
+
+ def lsr_filter(comparison: "LaborSupplyMetricImpact"):
+ if decile is not None:
+ if comparison.decile != decile:
+ return False
+ if elasticity is not None:
+ if comparison.elasticity != elasticity:
+ return False
+ if unit is not None:
+ if comparison.unit != unit:
+ return False
+ return True
+
+ overall_lsr_impact = list(
+ filter(
+ lambda comparison: comparison.decile == "all"
+ and comparison.elasticity == "all"
+ and comparison.unit == "earnings",
+ economy.labor_supply,
+ )
+ )[0].relative_change
+
+ overall_lsr_impact = round(overall_lsr_impact, 3)
+
+ if overall_lsr_impact > 0:
+ description = f"raise labor supply by {overall_lsr_impact:.1%}"
+ elif overall_lsr_impact < 0:
+ description = f"lower labor supply by {-overall_lsr_impact:.1%}"
+ else:
+ description = "have no effect on labor supply"
+
+ lsr_impacts = list(
+ filter(
+ lsr_filter,
+ economy.labor_supply,
+ )
+ )
+
+ if decile is None:
+ x_values = [lsr.decile for lsr in lsr_impacts]
+ x_titles = list(range(1, 11))
+ elif elasticity is None:
+ x_values = [lsr.elasticity for lsr in lsr_impacts]
+ x_titles = ["Income", "Substitution", "All"]
+ elif unit is None:
+ x_values = [lsr.unit for lsr in lsr_impacts]
+ x_titles = ["Earnings", "Hours"]
+
+ if change_relative:
+ y_values = [lsr.relative_change for lsr in lsr_impacts]
+ elif change_average:
+ y_values = [lsr.average_change for lsr in lsr_impacts]
+ else:
+ y_values = [lsr.change / 1e9 for lsr in lsr_impacts]
+
+ colors = [BLUE if value > 0 else DARK_GRAY for value in y_values]
+ text = [
+ (
+ f"{value:.1%}"
+ if change_relative
+ else (
+ f"{value:,.1%}"
+ if change_relative
+ else (
+ f"${value:.1f}bn"
+ if not change_average
+ else f"${value:,.0f}"
+ )
+ )
+ )
+ for value in y_values
+ ]
+
+ fig = go.Figure(
+ data=[
+ go.Bar(
+ x=x_values,
+ y=y_values,
+ marker=dict(color=colors),
+ text=text,
+ )
+ ]
+ ).update_layout(
+ title=f"{simulation.options.title} would {description}",
+ yaxis_title="Labor supply change"
+ + (" ($bn)" if not change_average else ""),
+ yaxis_tickformat=(".0%" if change_relative else ",.0f"),
+ yaxis_ticksuffix=(
+ " ($bn)" if not change_average and not change_relative else ""
+ ),
+ xaxis_title="Group",
+ xaxis_tickvals=x_values,
+ xaxis_ticktext=x_titles,
+ )
+
+ return format_fig(
+ fig, country=simulation.options.country, add_zero_line=True
+ )
diff --git a/policyengine/outputs/macro/comparison/charts/poverty.py b/policyengine/outputs/macro/comparison/charts/poverty.py
new file mode 100644
index 00000000..271071fb
--- /dev/null
+++ b/policyengine/outputs/macro/comparison/charts/poverty.py
@@ -0,0 +1,139 @@
+import plotly.express as px
+import plotly.graph_objects as go
+import typing
+
+if typing.TYPE_CHECKING:
+ from policyengine import Simulation
+ from policyengine.outputs.macro.comparison.calculate_economy_comparison import (
+ PovertyRateMetricComparison,
+ )
+
+from pydantic import BaseModel
+from policyengine.utils.charts import *
+from typing import Literal
+
+
+def create_poverty_chart(
+ simulation: "Simulation",
+ age_group: str,
+ gender: str,
+ racial_group: str,
+ poverty_rate: str,
+ rate_relative: bool,
+ change_relative: bool = True,
+) -> go.Figure:
+ """Create a budget comparison chart."""
+ if not simulation.is_comparison:
+ raise ValueError("Simulation must be a comparison simulation.")
+
+ economy = simulation.calculate_economy_comparison()
+
+ def poverty_filter(comparison: "PovertyRateMetricComparison"):
+ if age_group is not None:
+ if comparison.age_group != age_group:
+ return False
+ if racial_group is not None:
+ if comparison.racial_group != racial_group:
+ return False
+ if gender is not None:
+ if comparison.gender != gender:
+ return False
+ if poverty_rate is not None:
+ if comparison.poverty_rate != poverty_rate:
+ return False
+ if rate_relative is not None:
+ if comparison.relative != rate_relative:
+ return False
+ return True
+
+ poverty_rates = list(
+ filter(
+ poverty_filter,
+ economy.poverty,
+ )
+ )
+
+ if len(poverty_rates) == 0:
+ raise ValueError("No data found for the selected filters.")
+
+ overall_poverty_rate = list(
+ filter(
+ lambda comparison: comparison.age_group == "all"
+ and comparison.racial_group == "all"
+ and comparison.gender == "all"
+ and comparison.poverty_rate == (poverty_rate or "regular"),
+ economy.poverty,
+ )
+ )[0].relative_change
+
+ overall_poverty_rate = round(overall_poverty_rate, 3)
+
+ if overall_poverty_rate > 0:
+ description = f"raise the {'deep ' if poverty_rate == 'deep' else ''}poverty rate by {overall_poverty_rate:.1%}"
+ elif overall_poverty_rate < 0:
+ description = f"lower the {'deep ' if poverty_rate == 'deep' else ''}poverty rate by {-overall_poverty_rate:.1%}"
+ else:
+ description = "have no effect on the poverty rate"
+
+ if age_group is None:
+ x_values = [poverty.age_group for poverty in poverty_rates]
+ x_titles = ["Child", "Working-age", "Senior", "All"]
+ elif gender is None:
+ x_values = [poverty.gender for poverty in poverty_rates]
+ x_titles = ["Male", "Female", "All"]
+ elif racial_group is None:
+ x_values = [poverty.racial_group for poverty in poverty_rates]
+ x_titles = ["White", "Black", "Hispanic", "Other", "All"]
+ elif poverty_rate is None:
+ x_values = [poverty.poverty_rate for poverty in poverty_rates]
+ x_titles = ["Regular", "Deep", "All"]
+ elif rate_relative is None:
+ x_values = [poverty.relative for poverty in poverty_rates]
+ x_titles = ["Relative", "Headcount", "All"]
+
+ if change_relative:
+ y_values = [poverty.relative_change for poverty in poverty_rates]
+ elif not rate_relative:
+ y_values = [poverty.change for poverty in poverty_rates]
+ else:
+ y_values = [poverty.change * 100 for poverty in poverty_rates]
+
+ colors = [BLUE if value > 0 else DARK_GRAY for value in y_values]
+ text = [
+ (
+ f"{value:.1%}"
+ if change_relative
+ else (f"{value:,.0f}" if not rate_relative else f"{value:.1f}pp")
+ )
+ for value in y_values
+ ]
+
+ fig = go.Figure(
+ data=[
+ go.Bar(
+ x=x_values,
+ y=y_values,
+ marker=dict(color=colors),
+ text=text,
+ )
+ ]
+ ).update_layout(
+ title=f"{simulation.options.title} would {description}",
+ yaxis_title="Poverty rate change"
+ + (" (%)" if rate_relative and not change_relative else ""),
+ yaxis_tickformat=(
+ ".0%"
+ if change_relative
+ else (",.0f" if not rate_relative else ".1f")
+ ),
+ yaxis_ticksuffix=(
+ "pp" if (rate_relative and not change_relative) else ""
+ ),
+ xaxis_title="Group",
+ xaxis_tickvals=x_values,
+ xaxis_ticktext=x_titles,
+ )
+
+ return format_fig(
+ fig, country=simulation.options.country, add_zero_line=True
+ )
diff --git a/policyengine/outputs/macro/comparison/charts/winners_losers.py b/policyengine/outputs/macro/comparison/charts/winners_losers.py
new file mode 100644
index 00000000..9d17d495
--- /dev/null
+++ b/policyengine/outputs/macro/comparison/charts/winners_losers.py
@@ -0,0 +1,149 @@
+import plotly.express as px
+import plotly.graph_objects as go
+import typing
+
+if typing.TYPE_CHECKING:
+ from policyengine import Simulation
+
+from pydantic import BaseModel
+from policyengine.utils.charts import *
+from typing import Literal, Dict
+
+
+COLOR_MAP = {
+ "Gain more than 5%": BLUE,
+ "Gain less than 5%": BLUE_95,
+ "No change": LIGHT_GRAY,
+ "Lose less than 5%": MEDIUM_LIGHT_GRAY,
+ "Lose more than 5%": DARK_GRAY,
+}
+
+FORMATTED_KEYS = {
+ "gain_more_than_5_percent_share": "Gain more than 5%",
+ "gain_less_than_5_percent_share": "Gain less than 5%",
+ "no_change_share": "No change",
+ "lose_less_than_5_percent_share": "Lose less than 5%",
+ "lose_more_than_5_percent_share": "Lose more than 5%",
+}
+
+
+def create_winners_losers_chart(
+ simulation: "Simulation",
+ decile_variable: Literal["income", "wealth"],
+) -> go.Figure:
+ """Create a budget comparison chart."""
+ if not simulation.is_comparison:
+ raise ValueError("Simulation must be a comparison simulation.")
+
+ economy = simulation.calculate_economy_comparison()
+
+ if decile_variable == "income":
+ data = economy.distributional.income.winners_and_losers
+ else:
+ data = economy.distributional.wealth.winners_and_losers
+
+ all_decile_data = {}
+ for key in FORMATTED_KEYS.keys():
+ all_decile_data[FORMATTED_KEYS[key]] = data.all.model_dump()[key]
+
+ all_decile_chart = go.Bar(
+ x=list(all_decile_data.values()),
+ y=["All"] * len(all_decile_data),
+ name="All deciles",
+ orientation="h",
+ yaxis="y",
+ xaxis="x",
+ showlegend=False,
+ text=[f"{value:.1%}" for value in all_decile_data.values()],
+ marker=dict(color=[COLOR_MAP[key] for key in all_decile_data.keys()]),
+ )
+
+ x_values = []
+ y_values = []
+ color_values = []
+ text = []
+ hover_text = []
+ for outcome_type in FORMATTED_KEYS.keys():
+ for decile in range(1, 11):
+ value = data.deciles[decile].model_dump()[outcome_type]
+ x_values.append(value)
+ y_values.append(decile)
+ color_values.append(COLOR_MAP[FORMATTED_KEYS[outcome_type]])
+ text.append(f"{value:.1%}")
+ hover_text.append(
+ f"{FORMATTED_KEYS[outcome_type]}, {decile}: {value:.1%}"
+ )
+
+ decile_chart = go.Bar(
+ x=x_values,
+ y=y_values,
+ name="Deciles",
+ orientation="h",
+ yaxis="y2",
+ xaxis="x2",
+ text=text,
+ textposition="inside",
+ marker=dict(
+ color=color_values,
+ ),
+ customdata=hover_text,
+ hovertemplate="%{customdata}",
+ # Need to sort out showlegend, currently fiddly.
+ )
+
+ fig = go.Figure(
+ data=[
+ all_decile_chart,
+ decile_chart,
+ ]
+ )
+
+ winner_share = round(
+ economy.distributional.income.winners_and_losers.all.gain_share, 3
+ )
+
+ if winner_share > 0:
+ description = f"raise the net income of {winner_share:.1%} of people"
+ elif winner_share < 0:
+ description = (
+ f"decrease the net income of {-winner_share:.1%} of people"
+ )
+ else:
+ description = "have no effect on household net income"
+
+ fig.update_layout(
+ barmode="stack",
+ grid=dict(
+ rows=2,
+ columns=1,
+ ),
+ yaxis=dict(
+ title="",
+ tickvals=["All"],
+ domain=[0.91, 1],
+ ),
+ xaxis=dict(
+ title="",
+ tickformat=".0%",
+ anchor="y",
+ matches="x2",
+ showgrid=False,
+ showticklabels=False,
+ fixedrange=True,
+ ),
+ xaxis2=dict(
+ title="Population share",
+ tickformat=".0%",
+ anchor="y2",
+ fixedrange=True,
+ ),
+ yaxis2=dict(
+ title="Population share",
+ tickvals=list(range(1, 11)),
+ anchor="x2",
+ domain=[0, 0.85],
+ ),
+ title=f"{simulation.options.title} would {description}",
+ )
+
+ return format_fig(fig, country=simulation.options.country)
diff --git a/policyengine/outputs/macro/comparison/decile.py b/policyengine/outputs/macro/comparison/decile.py
index 61dc7381..a7041e7c 100644
--- a/policyengine/outputs/macro/comparison/decile.py
+++ b/policyengine/outputs/macro/comparison/decile.py
@@ -125,11 +125,11 @@ def calculate_income_specific_decile_winners_losers(
BOUNDS = [
(-np.inf, -0.05),
- (-0.05, 0),
+ (-0.05, -1e-3),
(-np.inf, -1e-3),
(-1e-3, 1e-3),
(1e-3, np.inf),
- (0, 0.05),
+ (1e-3, 0.05),
(0.05, np.inf),
]
LABELS = [
diff --git a/policyengine/outputs/macro/comparison/labor_supply.py b/policyengine/outputs/macro/comparison/labor_supply.py
index 6f1b8126..fd8269e6 100644
--- a/policyengine/outputs/macro/comparison/labor_supply.py
+++ b/policyengine/outputs/macro/comparison/labor_supply.py
@@ -25,6 +25,8 @@ class LaborSupplyMetricImpact(BaseModel):
"""The change in the labor supply metric value."""
relative_change: float
"""The relative change in the labor supply metric value."""
+ average_change: float
+ """The average change in the labor supply metric value (per household)."""
def calculate_labor_supply_impact(
@@ -53,6 +55,8 @@ def calculate_labor_supply_impact(
)
)
+ return lsr_metrics
+
def calculate_specific_lsr_metric(
baseline: Microsimulation,
@@ -82,19 +86,25 @@ def calculate_specific_lsr_metric(
else:
variable = "weekly_hours_worked"
- baseline_values = baseline.calculate(baseline_variable)
- reform_values = reformed.calculate(baseline_variable) + reformed.calculate(
- variable
- )
+ baseline_values = baseline.calculate(baseline_variable, map_to="household")
+ reform_values = reformed.calculate(
+ baseline_variable, map_to="household"
+ ) + reformed.calculate(variable, map_to="household")
if decile == "all":
in_decile = np.ones_like(baseline_values, dtype=bool)
else:
- in_decile = reformed.calculate("household_income_decile") == decile
+ in_decile = (
+ reformed.calculate("household_income_decile").values == decile
+ )
baseline_total = (baseline_values * in_decile).sum()
reform_total = (reform_values * in_decile).sum()
+ households = (
+ in_decile * baseline.calculate("household_weight").values
+ ).sum()
change = reform_total - baseline_total
+ average_change = change / households
relative_change = change / baseline_total
return LaborSupplyMetricImpact(
@@ -104,6 +114,7 @@ def calculate_specific_lsr_metric(
baseline=baseline_total,
reform=reform_total,
change=change,
+ average_change=average_change,
relative_change=relative_change,
)
diff --git a/policyengine/outputs/macro/single/budget.py b/policyengine/outputs/macro/single/budget.py
index c349c364..d5fbdb1a 100644
--- a/policyengine/outputs/macro/single/budget.py
+++ b/policyengine/outputs/macro/single/budget.py
@@ -35,10 +35,12 @@ class FiscalSummary(BaseModel):
"""The total tax revenue collected by the government."""
federal_tax: float
"""The total tax revenue collected by the federal (or national) government."""
+ federal_balance: float
+ """Federal taxes subtract spending."""
state_tax: float
"""The total tax revenue collected by the state government."""
government_spending: float
- """The total spending by the government on modeled programs."""
+ """The total spending by the (federal) government on modeled programs."""
tax_benefit_programs: dict[str, float]
"""The total revenue change to the government from each tax-benefit program."""
household_net_income: float
@@ -73,6 +75,7 @@ def calculate_government_balance(
return FiscalSummary(
tax_revenue=total_tax,
federal_tax=national_tax,
+ federal_balance=national_tax - total_spending,
state_tax=total_state_tax,
government_spending=total_spending,
tax_benefit_programs=tb_programs,
diff --git a/policyengine/outputs/macro/single/poverty.py b/policyengine/outputs/macro/single/poverty.py
index 91aa8639..f0f2c93c 100644
--- a/policyengine/outputs/macro/single/poverty.py
+++ b/policyengine/outputs/macro/single/poverty.py
@@ -15,10 +15,17 @@ class PovertyRateMetric(BaseModel):
"""The age group of the population."""
racial_group: Literal["white", "black", "hispanic", "other", "all"]
"""The racial group of the population."""
+ gender: Literal["male", "female", "all"]
+ """The gender of the population."""
relative: bool
"""Whether the poverty rate is relative to the total population, or a headcount."""
poverty_rate: Literal[
- "uk_hbai_bhc", "uk_hbai_bhc_half", "us_spm", "us_spm_half"
+ "regular",
+ "deep",
+ "uk_hbai_bhc",
+ "uk_hbai_bhc_half",
+ "us_spm",
+ "us_spm_half",
]
"""The poverty rate definition being calculated."""
value: float
@@ -41,6 +48,15 @@ def calculate_poverty(
poverty_metrics = []
age = simulation.calculate("age")
+ if options.country == "uk":
+ gender = simulation.calculate("gender")
+ else:
+ gender = simulation.calculate("is_male").map(
+ {
+ True: "MALE",
+ False: "FEMALE",
+ }
+ )
person_weight = simulation.calculate("person_weight").values
if options.country == "us":
racial_groups = ["white", "black", "hispanic", "other", "all"]
@@ -49,56 +65,57 @@ def calculate_poverty(
for age_group in ["child", "working_age", "senior", "all"]:
lower_age, upper_age = AGE_BOUNDS[age_group]
in_age_group = (age >= lower_age) & (age < upper_age)
- for racial_group in racial_groups:
- if racial_group != "all":
- in_racial_group = simulation.calculate("race") == racial_group
- else:
- in_racial_group = np.ones_like(age, dtype=bool)
- for relative in [True, False]:
- for poverty_rate in [
- "uk_hbai_bhc",
- "uk_hbai_bhc_half",
- "us_spm",
- "us_spm_half",
- ]:
- if not poverty_rate.startswith(options.country):
- continue
+ for gender_group in ["male", "female", "all"]:
+ in_gender = (gender_group == "all") | (
+ gender == gender_group.upper()
+ )
+ for racial_group in racial_groups:
+ if racial_group != "all":
+ in_racial_group = (
+ simulation.calculate("race") == racial_group
+ )
+ else:
+ in_racial_group = np.ones_like(age, dtype=bool)
+ for relative in [True, False]:
+ for poverty_rate in ["regular", "deep"]:
+ if poverty_rate in (
+ "regular",
+ "uk_hbai_bhc",
+ "us_spm",
+ ):
+ in_poverty = simulation.calculate(
+ "in_poverty", map_to="person"
+ )
+ elif poverty_rate in (
+ "deep",
+ "uk_hbai_bhc_half",
+ "us_spm_half",
+ ):
+ in_poverty = simulation.calculate(
+ "in_deep_poverty", map_to="person"
+ )
- if poverty_rate == "uk_hbai_bhc":
- in_poverty = simulation.calculate(
- "in_poverty", map_to="person"
- )
- elif poverty_rate == "uk_hbai_bhc_half":
- in_poverty = simulation.calculate(
- "in_deep_poverty", map_to="person"
- )
- elif poverty_rate == "us_spm":
- in_poverty = simulation.calculate(
- "in_poverty", map_to="person"
+ in_group = np.array(
+ in_age_group & in_racial_group & in_gender
)
- elif poverty_rate == "us_spm_half":
- in_poverty = simulation.calculate(
- "in_deep_poverty", map_to="person"
- )
-
- in_group = np.array(in_age_group & in_racial_group)
- total_in_group = (in_group * person_weight).sum()
- total_in_group_in_poverty = (
- in_group * in_poverty * person_weight
- ).sum()
- if relative:
- result = total_in_group_in_poverty / total_in_group
- else:
- result = total_in_group_in_poverty
+ total_in_group = (in_group * person_weight).sum()
+ total_in_group_in_poverty = (
+ in_group * in_poverty * person_weight
+ ).sum()
+ if relative:
+ result = total_in_group_in_poverty / total_in_group
+ else:
+ result = total_in_group_in_poverty
- poverty_metrics.append(
- PovertyRateMetric(
- age_group=age_group,
- racial_group=racial_group,
- relative=relative,
- poverty_rate=poverty_rate,
- value=result,
+ poverty_metrics.append(
+ PovertyRateMetric(
+ age_group=age_group,
+ gender=gender_group,
+ racial_group=racial_group,
+ relative=relative,
+ poverty_rate=poverty_rate,
+ value=result,
+ )
)
- )
return poverty_metrics
diff --git a/policyengine/simulation.py b/policyengine/simulation.py
index 09036e7b..f62a9011 100644
--- a/policyengine/simulation.py
+++ b/policyengine/simulation.py
@@ -42,10 +42,17 @@
HouseholdComparison,
calculate_household_comparison,
)
+from .outputs.macro.comparison.charts.create_all_charts import (
+ create_all_charts,
+ MacroCharts,
+)
+from typing import Any, Tuple
CountryType = Literal["uk", "us"]
ScopeType = Literal["household", "macro"]
-DataType = str | dict | None # Needs stricter typing
+DataType = (
+ str | dict | Any | None
+) # Needs stricter typing. Any==policyengine_core.data.Dataset, but pydantic refuses for some reason.
TimePeriodType = int
ReformType = (
ParametricReform | SimulationAdjustment | Type[StructuralReform] | None
@@ -70,6 +77,10 @@ class SimulationOptions(BaseModel):
None,
description="How many, if a subsample, households to randomly simulate.",
)
+ title: str | None = Field(
+ "[Analysis title]",
+ description="The title of the analysis (for charts). If not provided, a default title will be generated.",
+ )
class Simulation:
@@ -342,3 +353,7 @@ def calculate_single_household(self) -> SingleHousehold:
def calculate_household_comparison(self) -> HouseholdComparison:
"""Calculate comparison statistics between two household scenarios."""
return calculate_household_comparison(self)
+
+ def create_all_charts(self) -> MacroCharts:
+ """Create all macro charts for the simulation."""
+ return create_all_charts(self)
diff --git a/policyengine/utils/calculations.py b/policyengine/utils/calculations.py
index b6c24e7e..0a9aa709 100644
--- a/policyengine/utils/calculations.py
+++ b/policyengine/utils/calculations.py
@@ -1,5 +1,6 @@
from typing import Dict
from pydantic import BaseModel
+import numpy as np
Output = Dict[str, float | None]
@@ -8,6 +9,7 @@ def get_change(
x: Output | Dict[str, Output],
y: Output | Dict[str, Output],
relative: bool,
+ skip_mismatch: bool = False,
) -> Output | Dict[str, Output]:
"""Take two objects of nested str-float relations and create a similarly-structured object with the differences."""
if isinstance(x, BaseModel):
@@ -21,12 +23,28 @@ def get_change(
for key in x:
if isinstance(x[key], dict):
result[key] = get_change(x[key], y[key], relative=relative)
+ elif isinstance(x[key], list):
+ try:
+ result[key] = list(np.array(y[key]) - np.array(x[key]))
+ except:
+ result[key] = None
elif x[key] is None and y[key] is None:
result[key] = None
elif x[key] is None:
- raise ValueError(f"Key {key} is None in x but not in y")
+ if skip_mismatch:
+ result[key] = None
+ else:
+ raise ValueError(f"Key {key} is None in x but not in y")
elif y[key] is None:
- raise ValueError(f"Key {key} is None in y but not in x")
+ if skip_mismatch:
+ result[key] = None
+ else:
+ raise ValueError(f"Key {key} is None in y but not in x")
+ elif isinstance(x[key], str) or isinstance(y[key], str):
+ if x[key] == y[key]:
+ result[key] = 0
+ else:
+ result[key] = f"{x[key]} -> {y[key]}"
elif not relative:
result[key] = y[key] - x[key]
else:
diff --git a/policyengine/utils/charts.py b/policyengine/utils/charts.py
index bf3a665b..9f624c47 100644
--- a/policyengine/utils/charts.py
+++ b/policyengine/utils/charts.py
@@ -21,6 +21,7 @@ def add_fonts():
BLUE_PRIMARY = BLUE = "#2C6496"
BLUE_PRESSED = "#17354F"
BLUE_98 = "#F7FAFD"
+BLUE_95 = "#D8E6F3"
TEAL_LIGHT = "#D7F4F2"
TEAL_ACCENT = "#39C6C0"
TEAL_PRESSED = "#227773"
@@ -29,6 +30,7 @@ def add_fonts():
GRAY = "#808080"
LIGHT_GRAY = "#F2F2F2"
MEDIUM_DARK_GRAY = "#D2D2D2"
+MEDIUM_LIGHT_GRAY = "#BDBDBD"
WHITE = "#FFFFFF"
TEAL_98 = "#F7FDFC"
BLACK = "#000000"
@@ -118,10 +120,71 @@ def format_fig(
margin_t=120,
margin_l=120,
margin_r=120,
+ uniformtext=dict(
+ mode="hide",
+ minsize=12,
+ ),
+ )
+
+ # Auto-format currency
+
+ if country == "uk":
+ currency = "£"
+ else:
+ currency = "$"
+
+ fig.update_layout(
+ title=correct_text_currency(fig.layout.title.text or "", currency),
+ yaxis_title=correct_text_currency(
+ fig.layout.yaxis.title.text or "", currency
+ ),
+ yaxis_ticksuffix=correct_text_currency(
+ fig.layout.yaxis.ticksuffix or "", currency
+ ),
+ xaxis_title=correct_text_currency(
+ fig.layout.xaxis.title.text or "", currency
+ ),
+ xaxis_ticksuffix=correct_text_currency(
+ fig.layout.xaxis.ticksuffix or "", currency
+ ),
)
+
+ fig.update_layout(
+ title=wrap_text(fig.layout.title.text or ""),
+ )
+
+ for trace in fig.data:
+ if trace.text is not None:
+ trace.text = [
+ correct_text_currency(t, currency) for t in trace.text
+ ]
+
return fig
+def wrap_text(text: str, max_length: int = 80) -> str:
+ """Wrap text to a maximum length, respecting spaces."""
+ if len(text) <= max_length:
+ return text
+
+ split_text = text.split(" ")
+ wrapped_text = ""
+ line_length = 0
+ for word in split_text:
+ if line_length + len(word) > max_length:
+ wrapped_text += "
"
+ line_length = 0
+ wrapped_text += word + " "
+ line_length += len(word) + 1
+
+ return wrapped_text
+
+
+def correct_text_currency(text: str, currency: str) -> str:
+ """Correct text to match the currency symbol."""
+ return text.replace("$", currency).replace(f"{currency}-", f"-{currency}")
+
+
def cardinal(n: int) -> int:
"""Convert an integer to a cardinal string."""
ending_number = n % 10