diff --git a/docs/_toc.yml b/docs/_toc.yml index e060ef57..65fd941b 100644 --- a/docs/_toc.yml +++ b/docs/_toc.yml @@ -7,7 +7,6 @@ parts: - file: basic/calculate_household_comparison - file: basic/calculate_single_economy - file: basic/calculate_economy_comparison - - file: basic/create_charts - caption: Reference chapters: - file: reference/parameters_us diff --git a/docs/basic/calculate_economy_comparison.ipynb b/docs/basic/calculate_economy_comparison.ipynb index c9d08f0b..09f9f931 100644 --- a/docs/basic/calculate_economy_comparison.ipynb +++ b/docs/basic/calculate_economy_comparison.ipynb @@ -11,26 +11,16 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, "outputs": [ { - "name": "stdout", + "name": "stderr", "output_type": "stream", "text": [ - "WARNING: Gini calculation failed. Setting to 0.4.\n", - "WARNING: Gini calculation failed. Setting to 0.4.\n" + "/Users/nikhilwoodruff/policyengine/policyengine.py/.venv/lib/python3.11/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", + " from .autonotebook import tqdm as notebook_tqdm\n" ] - }, - { - "data": { - "text/plain": [ - "EconomyComparison(headlines=Headlines(budgetary_impact=12057201822.641846, winner_share=3.096710278402974e-05), fiscal=FiscalComparison(baseline=FiscalSummary(tax_revenue=3013608593152.1895, federal_tax=2583108898331.884, federal_balance=1160152745100.683, state_tax=430499694820.3057, government_spending=1422956153231.2007, tax_benefit_programs={}, household_net_income=12826524419636.805), reform=FiscalSummary(tax_revenue=3013602235736.6367, federal_tax=2583108898330.5815, federal_balance=1172209946923.325, state_tax=430493337406.05536, government_spending=1410898951407.2566, tax_benefit_programs={}, household_net_income=12814482607439.363), change=FiscalSummary(tax_revenue=-6357415.552734375, federal_tax=-1.30224609375, federal_balance=12057201822.641846, state_tax=-6357414.250366211, government_spending=-12057201823.944092, tax_benefit_programs={}, household_net_income=-12041812197.441406), relative_change=FiscalSummary(tax_revenue=-2.1095690950644036e-06, federal_tax=-5.041390607228997e-13, federal_balance=0.01039277101533339, state_tax=-1.476752324532971e-05, government_spending=-0.008473347401861264, tax_benefit_programs={}, household_net_income=-0.0009388211337286318)), inequality=InequalityComparison(baseline=InequalitySummary(gini=0.4, top_10_share=0.3056184716000026, top_1_share=0.07133480442157591), reform=InequalitySummary(gini=0.4, top_10_share=0.30585587458075175, top_1_share=0.0713913687702228), change=InequalitySummary(gini=0.0, top_10_share=0.00023740298074914623, top_1_share=5.65643486468842e-05), relative_change=InequalitySummary(gini=0.0, top_10_share=0.000776795262100068, top_1_share=0.0007929418059745287)), distributional=DecileImpacts(income=IncomeMeasureSpecificDecileImpacts(income_change=IncomeMeasureSpecificDecileIncomeChange(relative={1: -0.003725182447367451, 2: -0.004777925629293106, 3: -0.004427406664007123, 4: -0.003033546615233723, 5: -0.0012295913621786522, 6: -0.0003957445652524665, 7: -9.39759257002033e-05, 8: -3.0593991125471323e-05, 9: -1.4266187310034442e-05, 10: -1.1781514896665692e-05}, average={1: -63.89275812122564, 2: -174.0337390913802, 3: -224.9955222412714, 4: -193.3288209208671, 5: -96.35100483351104, 6: -37.64443533520402, 7: -10.856160889113756, 8: -4.3236716100441095, 9: -2.6063802446197073, 10: -3.9748627643461294}), winners_and_losers=IncomeMeasureSpecificDecileWinnersLosers(deciles={1: IncomeMeasureSpecificDecileWinnersLosersGroupOutcomes(lose_more_than_5_percent_share=0.0657153256192896, lose_less_than_5_percent_share=0.05318331952206108, lose_share=0.11889864514135068, no_change_share=0.8807898379100747, gain_share=0.00031151694857464756, gain_less_than_5_percent_share=0.0, gain_more_than_5_percent_share=0.00031151694857464756), 2: IncomeMeasureSpecificDecileWinnersLosersGroupOutcomes(lose_more_than_5_percent_share=0.15194453295736532, lose_less_than_5_percent_share=0.07711620163220688, lose_share=0.22906073458957218, no_change_share=0.7709392654104278, gain_share=0.0, gain_less_than_5_percent_share=0.0, gain_more_than_5_percent_share=0.0), 3: IncomeMeasureSpecificDecileWinnersLosersGroupOutcomes(lose_more_than_5_percent_share=0.17114447859504486, lose_less_than_5_percent_share=0.059347324242194015, lose_share=0.23049180283723886, no_change_share=0.7695081971627611, gain_share=0.0, gain_less_than_5_percent_share=0.0, gain_more_than_5_percent_share=0.0), 4: IncomeMeasureSpecificDecileWinnersLosersGroupOutcomes(lose_more_than_5_percent_share=0.12989846703096578, lose_less_than_5_percent_share=0.04485983299864166, lose_share=0.17475830002960743, no_change_share=0.8252416999703925, gain_share=0.0, gain_less_than_5_percent_share=0.0, gain_more_than_5_percent_share=0.0), 5: IncomeMeasureSpecificDecileWinnersLosersGroupOutcomes(lose_more_than_5_percent_share=0.051068199514449644, lose_less_than_5_percent_share=0.024111392231720524, lose_share=0.07517959174617017, no_change_share=0.9248204082538298, gain_share=0.0, gain_less_than_5_percent_share=0.0, gain_more_than_5_percent_share=0.0), 6: IncomeMeasureSpecificDecileWinnersLosersGroupOutcomes(lose_more_than_5_percent_share=0.015369267341425434, lose_less_than_5_percent_share=0.018481961006501027, lose_share=0.03385122834792646, no_change_share=0.9661487716520736, gain_share=0.0, gain_less_than_5_percent_share=0.0, gain_more_than_5_percent_share=0.0), 7: IncomeMeasureSpecificDecileWinnersLosersGroupOutcomes(lose_more_than_5_percent_share=0.0029113126526266477, lose_less_than_5_percent_share=0.00904746763596632, lose_share=0.011958780288592966, no_change_share=0.988041219711407, gain_share=0.0, gain_less_than_5_percent_share=0.0, gain_more_than_5_percent_share=0.0), 8: IncomeMeasureSpecificDecileWinnersLosersGroupOutcomes(lose_more_than_5_percent_share=0.0005094937950111613, lose_less_than_5_percent_share=0.0031321041000860993, lose_share=0.0036415978950972605, no_change_share=0.9963584021049028, gain_share=0.0, gain_less_than_5_percent_share=0.0, gain_more_than_5_percent_share=0.0), 9: IncomeMeasureSpecificDecileWinnersLosersGroupOutcomes(lose_more_than_5_percent_share=0.00029487893089546547, lose_less_than_5_percent_share=0.0035164203584642836, lose_share=0.003811299289359749, no_change_share=0.9961887007106403, gain_share=0.0, gain_less_than_5_percent_share=0.0, gain_more_than_5_percent_share=0.0), 10: IncomeMeasureSpecificDecileWinnersLosersGroupOutcomes(lose_more_than_5_percent_share=0.0002281227488112428, lose_less_than_5_percent_share=0.003779501887019752, lose_share=0.004007624635830995, no_change_share=0.9867296828146133, gain_share=0.0, gain_less_than_5_percent_share=0.0, gain_more_than_5_percent_share=0.0)}, all=IncomeMeasureSpecificDecileWinnersLosersGroupOutcomes(lose_more_than_5_percent_share=0.05886871339782029, lose_less_than_5_percent_share=0.02962594712536097, lose_share=0.08849466052318125, no_change_share=0.9105479297614693, gain_share=3.096710278402974e-05, gain_less_than_5_percent_share=0.0, gain_more_than_5_percent_share=3.096710278402974e-05))), wealth=None))" - ] - }, - "execution_count": 1, - "metadata": {}, - "output_type": "execute_result" } ], "source": [ @@ -61,7 +51,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -255,7 +245,7 @@ ], "metadata": { "kernelspec": { - "display_name": "base", + "display_name": ".venv", "language": "python", "name": "python3" }, @@ -269,7 +259,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.9" + "version": "3.11.11" } }, "nbformat": 4, diff --git a/docs/basic/calculate_single_economy.ipynb b/docs/basic/calculate_single_economy.ipynb index 6e330de3..2a7de6e1 100644 --- a/docs/basic/calculate_single_economy.ipynb +++ b/docs/basic/calculate_single_economy.ipynb @@ -34,7 +34,7 @@ " time_period=2025,\n", ")\n", "\n", - "sim.calculate_single_economy()" + "result = sim.calculate_single_economy()" ] }, { diff --git a/docs/basic/create_charts.ipynb b/docs/basic/create_charts.ipynb deleted file mode 100644 index c2a8d65a..00000000 --- a/docs/basic/create_charts.ipynb +++ /dev/null @@ -1,5191 +0,0 @@ -{ - "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": null, - "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." - 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"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/policyengine/__init__.py b/policyengine/__init__.py index 26fe276e..4bb8a90c 100644 --- a/policyengine/__init__.py +++ b/policyengine/__init__.py @@ -1,3 +1,3 @@ from .simulation import Simulation, SimulationOptions -__version__ = "0.1.1" \ No newline at end of file +__version__ = "0.1.1" diff --git a/policyengine/outputs/macro/comparison/calculate_economy_comparison.py b/policyengine/outputs/macro/comparison/calculate_economy_comparison.py index 41398eee..4cb4ecda 100644 --- a/policyengine/outputs/macro/comparison/calculate_economy_comparison.py +++ b/policyengine/outputs/macro/comparison/calculate_economy_comparison.py @@ -1,110 +1,847 @@ """Calculate comparison statistics between two economic scenarios.""" -import typing - +from microdf import MicroSeries +import numpy as np +from policyengine_core.tools.hugging_face import download_huggingface_dataset +import pandas as pd +import h5py +from pydantic import BaseModel from policyengine import Simulation +from policyengine.outputs.macro.single.calculate_single_economy import ( + SingleEconomy, +) +from typing import List, Dict -from pydantic import BaseModel -from policyengine.utils.calculations import get_change -from policyengine.outputs.macro.single import ( - _calculate_government_balance, - FiscalSummary, - _calculate_inequality, - InequalitySummary, -) +class BudgetaryImpact(BaseModel): + budgetary_impact: float + tax_revenue_impact: float + state_tax_revenue_impact: float + benefit_spending_impact: float + households: float + baseline_net_income: float -from .decile import calculate_decile_impacts, DecileImpacts -from typing import Literal, List +def budgetary_impact( + baseline: SingleEconomy, reform: SingleEconomy +) -> BudgetaryImpact: + tax_revenue_impact = reform.total_tax - baseline.total_tax + state_tax_revenue_impact = ( + reform.total_state_tax - baseline.total_state_tax + ) + benefit_spending_impact = reform.total_benefits - baseline.total_benefits + budgetary_impact = tax_revenue_impact - benefit_spending_impact + return BudgetaryImpact( + budgetary_impact=budgetary_impact, + tax_revenue_impact=tax_revenue_impact, + state_tax_revenue_impact=state_tax_revenue_impact, + benefit_spending_impact=benefit_spending_impact, + households=sum(baseline.household_weight), + baseline_net_income=baseline.total_net_income, + ) -class FiscalComparison(BaseModel): - baseline: FiscalSummary - reform: FiscalSummary - change: FiscalSummary - relative_change: FiscalSummary +DecileValues = Dict[int, float] -class InequalityComparison(BaseModel): - baseline: InequalitySummary - reform: InequalitySummary - change: InequalitySummary - relative_change: InequalitySummary +class HoursResponse(BaseModel): + baseline: float + reform: float + change: float + income_effect: float + substitution_effect: float -class Headlines(BaseModel): - budgetary_impact: float - """The change in the (federal) government budget balance.""" - winner_share: float - """The share of people that are better off in the reform scenario.""" +class LaborSupplyResponse(BaseModel): + substitution_lsr: float + income_lsr: float + relative_lsr: dict + total_change: float + revenue_change: float + decile: Dict[str, Dict[str, DecileValues]] + hours: HoursResponse -class EconomyComparison(BaseModel): - headlines: Headlines - """Headline statistics for the comparison.""" - fiscal: FiscalComparison - """Government budgets and other top-level fiscal statistics.""" - inequality: InequalityComparison - """Inequality statistics for the household sector.""" - distributional: DecileImpacts - """Distributional impacts of the reform.""" +def labor_supply_response( + baseline: SingleEconomy, reform: SingleEconomy +) -> LaborSupplyResponse: + substitution_lsr = reform.substitution_lsr - baseline.substitution_lsr + income_lsr = reform.income_lsr - baseline.income_lsr + total_change = substitution_lsr + income_lsr + revenue_change = ( + reform.budgetary_impact_lsr - baseline.budgetary_impact_lsr + ) + substitution_lsr_hh = np.array(reform.substitution_lsr_hh) - np.array( + baseline.substitution_lsr_hh + ) + income_lsr_hh = np.array(reform.income_lsr_hh) - np.array( + baseline.income_lsr_hh + ) + decile = np.array(baseline.household_income_decile) + household_weight = baseline.household_weight -def calculate_economy_comparison( - simulation: Simulation, -) -> EconomyComparison: - """Calculate comparison statistics between two economic scenarios.""" - if not simulation.is_comparison: - raise ValueError("Simulation must be a comparison simulation.") + total_lsr_hh = substitution_lsr_hh + income_lsr_hh - baseline = simulation.baseline_simulation - reform = simulation.reform_simulation - options = simulation.options + emp_income = MicroSeries( + baseline.employment_income_hh, weights=household_weight + ) + self_emp_income = MicroSeries( + baseline.self_employment_income_hh, weights=household_weight + ) + earnings = emp_income + self_emp_income + original_earnings = earnings - total_lsr_hh + substitution_lsr_hh = MicroSeries( + substitution_lsr_hh, weights=household_weight + ) + income_lsr_hh = MicroSeries(income_lsr_hh, weights=household_weight) - baseline_balance = _calculate_government_balance(baseline, options) - reform_balance = _calculate_government_balance(reform, options) - balance_change = get_change( - baseline_balance, reform_balance, relative=False + decile_avg = dict( + income=income_lsr_hh.groupby(decile).mean().to_dict(), + substitution=substitution_lsr_hh.groupby(decile).mean().to_dict(), ) - balance_rel_change = get_change( - baseline_balance, reform_balance, relative=True + decile_rel = dict( + income=( + income_lsr_hh.groupby(decile).sum() + / original_earnings.groupby(decile).sum() + ).to_dict(), + substitution=( + substitution_lsr_hh.groupby(decile).sum() + / original_earnings.groupby(decile).sum() + ).to_dict(), ) - fiscal_comparison = FiscalComparison( - baseline=baseline_balance, - reform=reform_balance, - change=balance_change, - relative_change=balance_rel_change, + + relative_lsr = dict( + income=(income_lsr_hh.sum() / original_earnings.sum()), + substitution=(substitution_lsr_hh.sum() / original_earnings.sum()), ) - baseline_inequality = _calculate_inequality(baseline) - reform_inequality = _calculate_inequality(reform) - inequality_change = get_change( - baseline_inequality, reform_inequality, relative=False + decile_rel["income"] = { + int(k): v for k, v in decile_rel["income"].items() if k > 0 + } + decile_rel["substitution"] = { + int(k): v for k, v in decile_rel["substitution"].items() if k > 0 + } + + hours = dict( + baseline=baseline.weekly_hours, + reform=reform.weekly_hours, + change=reform.weekly_hours - baseline.weekly_hours, + income_effect=reform.weekly_hours_income_effect + - baseline.weekly_hours_income_effect, + substitution_effect=reform.weekly_hours_substitution_effect + - baseline.weekly_hours_substitution_effect, ) - inequality_rel_change = get_change( - baseline_inequality, reform_inequality, relative=True + + return LaborSupplyResponse( + substitution_lsr=substitution_lsr, + income_lsr=income_lsr, + relative_lsr=relative_lsr, + total_change=total_change, + revenue_change=revenue_change, + decile=dict( + average=decile_avg, + relative=decile_rel, + ), + hours=hours, ) - inequality_comparison = InequalityComparison( - baseline=baseline_inequality, - reform=reform_inequality, - change=inequality_change, - relative_change=inequality_rel_change, + + +class ProgramSpecificImpact(BaseModel): + baseline: float + reform: float + difference: float + + +DetailedBudgetaryImpact = Dict[str, ProgramSpecificImpact] | None + + +def detailed_budgetary_impact( + baseline: SingleEconomy, reform: SingleEconomy, country_id: str +) -> DetailedBudgetaryImpact: + result = {} + if country_id == "uk": + for program in baseline.programs: + # baseline[programs][program] = total budgetary impact of program + result[program] = dict( + baseline=baseline.programs[program], + reform=reform.programs[program], + difference=reform.programs[program] + - baseline.programs[program], + ) + return result + + +class DecileImpact(BaseModel): + relative: DecileValues + average: DecileValues + + +def decile_impact( + baseline: SingleEconomy, reform: SingleEconomy +) -> DecileImpact: + """ + Compare the impact of a reform on the deciles of the population. + + Args: + baseline (dict): The baseline economy. + reform (dict): The reform economy. + + Returns: + dict: The impact of the reform on the deciles of the population. + """ + baseline_income = MicroSeries( + baseline.household_net_income, weights=baseline.household_weight + ) + reform_income = MicroSeries( + reform.household_net_income, weights=baseline_income.weights ) - decile_impacts = calculate_decile_impacts(baseline, reform, options) + # Filter out negative decile values + decile = MicroSeries(baseline.household_income_decile) + baseline_income_filtered = baseline_income[decile >= 0] + reform_income_filtered = reform_income[decile >= 0] - # Headlines - budgetary_impact = fiscal_comparison.change.federal_balance - winner_share = decile_impacts.income.winners_and_losers.all.gain_share - headlines = Headlines( - budgetary_impact=budgetary_impact, - winner_share=winner_share, + income_change = reform_income_filtered - baseline_income_filtered + rel_income_change_by_decile = ( + income_change.groupby(decile).sum() + / baseline_income_filtered.groupby(decile).sum() ) - return EconomyComparison( - headlines=headlines, - fiscal=fiscal_comparison, - inequality=inequality_comparison, - distributional=decile_impacts, + avg_income_change_by_decile = ( + income_change.groupby(decile).sum() + / baseline_income_filtered.groupby(decile).count() + ) + rel_decile_dict = rel_income_change_by_decile.to_dict() + avg_decile_dict = avg_income_change_by_decile.to_dict() + return DecileImpact( + relative={int(k): v for k, v in rel_decile_dict.items()}, + average={int(k): v for k, v in avg_decile_dict.items()}, + ) + + +class WealthDecileImpactWithValues(BaseModel): + relative: DecileValues + average: DecileValues + + +WealthDecileImpact = WealthDecileImpactWithValues | None + + +def wealth_decile_impact( + baseline: SingleEconomy, reform: SingleEconomy, country_id: str +) -> WealthDecileImpact: + """ + Compare the impact of a reform on the deciles of the population. + + Args: + baseline (dict): The baseline economy. + reform (dict): The reform economy. + + Returns: + dict: The impact of the reform on the deciles of the population. + """ + if country_id != "uk": + return None + baseline_income = MicroSeries( + baseline.household_net_income, weights=baseline.household_weight + ) + reform_income = MicroSeries( + reform.household_net_income, weights=baseline_income.weights + ) + + # Filter out negative decile values + decile = MicroSeries(baseline.household_wealth_decile) + baseline_income_filtered = baseline_income[decile >= 0] + reform_income_filtered = reform_income[decile >= 0] + + income_change = reform_income_filtered - baseline_income_filtered + rel_income_change_by_decile = ( + income_change.groupby(decile).sum() + / baseline_income_filtered.groupby(decile).sum() + ) + avg_income_change_by_decile = ( + income_change.groupby(decile).sum() + / baseline_income_filtered.groupby(decile).count() + ) + rel_decile_dict = rel_income_change_by_decile.to_dict() + avg_decile_dict = avg_income_change_by_decile.to_dict() + return WealthDecileImpactWithValues( + relative={int(k): v for k, v in rel_decile_dict.items()}, + average={int(k): v for k, v in avg_decile_dict.items()}, + ) + + +class BaselineReformValues(BaseModel): + baseline: float + reform: float + + +class InequalityImpact(BaseModel): + gini: BaselineReformValues + top_10_pct_share: BaselineReformValues + top_1_pct_share: BaselineReformValues + + +def inequality_impact( + baseline: SingleEconomy, reform: SingleEconomy +) -> InequalityImpact: + """ + Compare the impact of a reform on inequality. + + Args: + baseline (dict): The baseline economy. + reform (dict): The reform economy. + + Returns: + dict: The impact of the reform on inequality. + """ + + values = dict( + gini=dict( + baseline=baseline.gini, + reform=reform.gini, + ), + top_10_pct_share=dict( + baseline=baseline.top_10_percent_share, + reform=reform.top_10_percent_share, + ), + top_1_pct_share=dict( + baseline=baseline.top_1_percent_share, + reform=reform.top_1_percent_share, + ), + ) + + return InequalityImpact(**values) + + +class AgeGroupBaselineReformValues(BaseModel): + child: BaselineReformValues + adult: BaselineReformValues + senior: BaselineReformValues + all: BaselineReformValues + + +class PovertyImpact(BaseModel): + poverty: AgeGroupBaselineReformValues + deep_poverty: AgeGroupBaselineReformValues + + +def poverty_impact(baseline: SingleEconomy, reform: SingleEconomy) -> dict: + """ + Compare the impact of a reform on poverty. + + Args: + baseline (dict): The baseline economy. + reform (dict): The reform economy. + + Returns: + dict: The impact of the reform on poverty. + """ + baseline_poverty = MicroSeries( + baseline.person_in_poverty, weights=baseline.person_weight + ) + baseline_deep_poverty = MicroSeries( + baseline.person_in_deep_poverty, weights=baseline.person_weight + ) + reform_poverty = MicroSeries( + reform.person_in_poverty, weights=baseline_poverty.weights + ) + reform_deep_poverty = MicroSeries( + reform.person_in_deep_poverty, weights=baseline_poverty.weights + ) + age = MicroSeries(baseline.age) + + poverty = dict( + child=dict( + baseline=float(baseline_poverty[age < 18].mean()), + reform=float(reform_poverty[age < 18].mean()), + ), + adult=dict( + baseline=float(baseline_poverty[(age >= 18) & (age < 65)].mean()), + reform=float(reform_poverty[(age >= 18) & (age < 65)].mean()), + ), + senior=dict( + baseline=float(baseline_poverty[age >= 65].mean()), + reform=float(reform_poverty[age >= 65].mean()), + ), + all=dict( + baseline=float(baseline_poverty.mean()), + reform=float(reform_poverty.mean()), + ), + ) + + deep_poverty = dict( + child=dict( + baseline=float(baseline_deep_poverty[age < 18].mean()), + reform=float(reform_deep_poverty[age < 18].mean()), + ), + adult=dict( + baseline=float( + baseline_deep_poverty[(age >= 18) & (age < 65)].mean() + ), + reform=float(reform_deep_poverty[(age >= 18) & (age < 65)].mean()), + ), + senior=dict( + baseline=float(baseline_deep_poverty[age >= 65].mean()), + reform=float(reform_deep_poverty[age >= 65].mean()), + ), + all=dict( + baseline=float(baseline_deep_poverty.mean()), + reform=float(reform_deep_poverty.mean()), + ), + ) + + return PovertyImpact( + poverty=poverty, + deep_poverty=deep_poverty, + ) + + +class IntraDecileImpact(BaseModel): + deciles: Dict[str, List[float]] + all: Dict[str, float] + + +def intra_decile_impact( + baseline: SingleEconomy, reform: SingleEconomy +) -> IntraDecileImpact: + baseline_income = MicroSeries( + baseline.household_net_income, weights=baseline.household_weight + ) + reform_income = MicroSeries( + reform.household_net_income, weights=baseline_income.weights + ) + people = MicroSeries( + baseline.household_count_people, weights=baseline_income.weights ) + decile = MicroSeries(baseline.household_income_decile).values + absolute_change = (reform_income - baseline_income).values + capped_baseline_income = np.maximum(baseline_income.values, 1) + capped_reform_income = ( + np.maximum(reform_income.values, 1) + absolute_change + ) + income_change = ( + capped_reform_income - capped_baseline_income + ) / capped_baseline_income + + # Within each decile, calculate the percentage of people who: + # 1. Gained more than 5% of their income + # 2. Gained between 0% and 5% of their income + # 3. Had no change in income + # 3. Lost between 0% and 5% of their income + # 4. Lost more than 5% of their income + + outcome_groups = {} + all_outcomes = {} + BOUNDS = [-np.inf, -0.05, -1e-3, 1e-3, 0.05, np.inf] + LABELS = [ + "Lose more than 5%", + "Lose less than 5%", + "No change", + "Gain less than 5%", + "Gain more than 5%", + ] + for lower, upper, label in zip(BOUNDS[:-1], BOUNDS[1:], LABELS): + outcome_groups[label] = [] + for i in range(1, 11): + + in_decile: bool = decile == i + in_group: bool = (income_change > lower) & (income_change <= upper) + in_both: bool = in_decile & in_group + + people_in_both: np.float64 = people[in_both].sum() + people_in_decile: np.float64 = people[in_decile].sum() + + # np.float64 does not raise ZeroDivisionError, instead returns NaN + if people_in_decile == 0 and people_in_both == 0: + people_in_proportion: float = 0.0 + else: + people_in_proportion: float = float( + people_in_both / people_in_decile + ) + + outcome_groups[label].append(people_in_proportion) + + all_outcomes[label] = sum(outcome_groups[label]) / 10 + return IntraDecileImpact(deciles=outcome_groups, all=all_outcomes) + + +class IntraWealthDecileImpactWithValues(BaseModel): + deciles: Dict[str, List[float]] + all: Dict[str, float] + + +IntraWealthDecileImpact = IntraWealthDecileImpactWithValues | None + + +def intra_wealth_decile_impact( + baseline: SingleEconomy, reform: SingleEconomy, country_id: str +) -> IntraWealthDecileImpact: + if country_id != "uk": + return None + baseline_income = MicroSeries( + baseline.household_net_income, weights=baseline.household_weight + ) + reform_income = MicroSeries( + reform.household_net_income, weights=baseline_income.weights + ) + people = MicroSeries( + baseline.household_count_people, weights=baseline_income.weights + ) + decile = MicroSeries(baseline.household_wealth_decile).values + absolute_change = (reform_income - baseline_income).values + capped_baseline_income = np.maximum(baseline_income.values, 1) + capped_reform_income = ( + np.maximum(reform_income.values, 1) + absolute_change + ) + income_change = ( + capped_reform_income - capped_baseline_income + ) / capped_baseline_income + + # Within each decile, calculate the percentage of people who: + # 1. Gained more than 5% of their income + # 2. Gained between 0% and 5% of their income + # 3. Had no change in income + # 3. Lost between 0% and 5% of their income + # 4. Lost more than 5% of their income + + outcome_groups = {} + all_outcomes = {} + BOUNDS = [-np.inf, -0.05, -1e-3, 1e-3, 0.05, np.inf] + LABELS = [ + "Lose more than 5%", + "Lose less than 5%", + "No change", + "Gain less than 5%", + "Gain more than 5%", + ] + for lower, upper, label in zip(BOUNDS[:-1], BOUNDS[1:], LABELS): + outcome_groups[label] = [] + for i in range(1, 11): + + in_decile: bool = decile == i + in_group: bool = (income_change > lower) & (income_change <= upper) + in_both: bool = in_decile & in_group + + people_in_both: np.float64 = people[in_both].sum() + people_in_decile: np.float64 = people[in_decile].sum() + + # np.float64 does not raise ZeroDivisionError, instead returns NaN + if people_in_decile == 0 and people_in_both == 0: + people_in_proportion = 0 + else: + people_in_proportion: float = float( + people_in_both / people_in_decile + ) + + outcome_groups[label].append(people_in_proportion) + + all_outcomes[label] = sum(outcome_groups[label]) / 10 + return IntraWealthDecileImpactWithValues( + deciles=outcome_groups, all=all_outcomes + ) + + +class GenderBaselineReformValues(BaseModel): + male: BaselineReformValues + female: BaselineReformValues + + +class PovertyGenderBreakdown(BaseModel): + poverty: GenderBaselineReformValues + deep_poverty: GenderBaselineReformValues + + +def poverty_gender_breakdown( + baseline: SingleEconomy, reform: SingleEconomy +) -> PovertyGenderBreakdown: + """ + Compare the impact of a reform on poverty. + + Args: + baseline (dict): The baseline economy. + reform (dict): The reform economy. + + Returns: + dict: The impact of the reform on poverty. + """ + if baseline.is_male is None: + return {} + baseline_poverty = MicroSeries( + baseline.person_in_poverty, weights=baseline.person_weight + ) + baseline_deep_poverty = MicroSeries( + baseline.person_in_deep_poverty, weights=baseline.person_weight + ) + reform_poverty = MicroSeries( + reform.person_in_poverty, weights=baseline_poverty.weights + ) + reform_deep_poverty = MicroSeries( + reform.person_in_deep_poverty, weights=baseline_poverty.weights + ) + is_male = MicroSeries(baseline.is_male) + + poverty = dict( + male=dict( + baseline=float(baseline_poverty[is_male].mean()), + reform=float(reform_poverty[is_male].mean()), + ), + female=dict( + baseline=float(baseline_poverty[~is_male].mean()), + reform=float(reform_poverty[~is_male].mean()), + ), + ) + + deep_poverty = dict( + male=dict( + baseline=float(baseline_deep_poverty[is_male].mean()), + reform=float(reform_deep_poverty[is_male].mean()), + ), + female=dict( + baseline=float(baseline_deep_poverty[~is_male].mean()), + reform=float(reform_deep_poverty[~is_male].mean()), + ), + ) + + return PovertyGenderBreakdown( + poverty=poverty, + deep_poverty=deep_poverty, + ) + + +class RacialBaselineReformValues(BaseModel): + white: BaselineReformValues + black: BaselineReformValues + hispanic: BaselineReformValues + other: BaselineReformValues + + +class PovertyRacialBreakdownWithValues(BaseModel): + poverty: RacialBaselineReformValues + + +PovertyRacialBreakdown = PovertyRacialBreakdownWithValues | None + + +def poverty_racial_breakdown( + baseline: SingleEconomy, reform: SingleEconomy +) -> PovertyRacialBreakdown: + """ + Compare the impact of a reform on poverty. + + Args: + baseline (dict): The baseline economy. + reform (dict): The reform economy. + + Returns: + dict: The impact of the reform on poverty. + """ + if baseline.race is None: + return None + baseline_poverty = MicroSeries( + baseline.person_in_poverty, weights=baseline.person_weight + ) + reform_poverty = MicroSeries( + reform.person_in_poverty, weights=baseline_poverty.weights + ) + race = MicroSeries( + baseline.race + ) # Can be WHITE, BLACK, HISPANIC, or OTHER. + + poverty = dict( + white=dict( + baseline=float(baseline_poverty[race == "WHITE"].mean()), + reform=float(reform_poverty[race == "WHITE"].mean()), + ), + black=dict( + baseline=float(baseline_poverty[race == "BLACK"].mean()), + reform=float(reform_poverty[race == "BLACK"].mean()), + ), + hispanic=dict( + baseline=float(baseline_poverty[race == "HISPANIC"].mean()), + reform=float(reform_poverty[race == "HISPANIC"].mean()), + ), + other=dict( + baseline=float(baseline_poverty[race == "OTHER"].mean()), + reform=float(reform_poverty[race == "OTHER"].mean()), + ), + ) + + return PovertyRacialBreakdownWithValues( + poverty=poverty, + ) + + +class UKConstituencyBreakdownByConstituency(BaseModel): + average_household_income_change: float + relative_household_income_change: float + x: int + y: int + + +class UKConstituencyBreakdownWithValues(BaseModel): + by_constituency: dict[str, UKConstituencyBreakdownByConstituency] + outcomes_by_region: dict[str, dict[str, int]] + + +UKConstituencyBreakdown = UKConstituencyBreakdownWithValues | None + + +def uk_constituency_breakdown( + baseline: SingleEconomy, reform: SingleEconomy, country_id: str +) -> UKConstituencyBreakdown: + if country_id != "uk": + return None + + output = { + "by_constituency": {}, + "outcomes_by_region": {}, + } + for region in ["uk", "england", "scotland", "wales", "northern_ireland"]: + output["outcomes_by_region"][region] = { + "Gain more than 5%": 0, + "Gain less than 5%": 0, + "No change": 0, + "Lose less than 5%": 0, + "Lose more than 5%": 0, + } + baseline_hnet = baseline.household_net_income + reform_hnet = reform.household_net_income + + constituency_weights_path = download_huggingface_dataset( + repo="policyengine/policyengine-uk-data", + repo_filename="parliamentary_constituency_weights.h5", + ) + with h5py.File(constituency_weights_path, "r") as f: + weights = f["2025"][ + ... + ] # {2025: array(650, 100180) where cell i, j is the weight of household record i in constituency j} + + constituency_names_path = download_huggingface_dataset( + repo="policyengine/policyengine-uk-data", + repo_filename="constituencies_2024.csv", + ) + constituency_names = pd.read_csv( + constituency_names_path + ) # columns code (constituency code), name (constituency name), x, y (geographic position) + + for i in range(len(constituency_names)): + name: str = constituency_names.iloc[i]["name"] + code: str = constituency_names.iloc[i]["code"] + weight: np.ndarray = weights[i] + baseline_income = MicroSeries(baseline_hnet, weights=weight) + reform_income = MicroSeries(reform_hnet, weights=weight) + average_household_income_change: float = ( + reform_income.sum() - baseline_income.sum() + ) / baseline_income.count() + percent_household_income_change: float = ( + reform_income.sum() / baseline_income.sum() - 1 + ) + output["by_constituency"][name] = { + "average_household_income_change": average_household_income_change, + "relative_household_income_change": percent_household_income_change, + "x": int(constituency_names.iloc[i]["x"]), # Geographic positions + "y": int(constituency_names.iloc[i]["y"]), + } + + regions = ["uk"] + if "E" in code: + regions.append("england") + elif "S" in code: + regions.append("scotland") + elif "W" in code: + regions.append("wales") + elif "N" in code: + regions.append("northern_ireland") + + if percent_household_income_change > 0.05: + bucket = "Gain more than 5%" + elif percent_household_income_change > 1e-3: + bucket = "Gain less than 5%" + elif percent_household_income_change > -1e-3: + bucket = "No change" + elif percent_household_income_change > -0.05: + bucket = "Lose less than 5%" + else: + bucket = "Lose more than 5%" + + for region_ in regions: + output["outcomes_by_region"][region_][bucket] += 1 + + return UKConstituencyBreakdownWithValues(**output) + + +class EconomyComparison(BaseModel): + budget: BudgetaryImpact + detailed_budget: DetailedBudgetaryImpact + decile: DecileImpact + inequality: InequalityImpact + poverty: PovertyImpact + poverty_by_gender: PovertyGenderBreakdown + poverty_by_race: PovertyRacialBreakdown + intra_decile: IntraDecileImpact + wealth_decile: WealthDecileImpact + intra_wealth_decile: IntraWealthDecileImpact + labor_supply_response: LaborSupplyResponse + constituency_impact: UKConstituencyBreakdown + + +def calculate_economy_comparison( + simulation: Simulation, +) -> EconomyComparison: + """Calculate comparison statistics between two economic scenarios.""" + if not simulation.is_comparison: + raise ValueError("Simulation must be a comparison simulation.") + + baseline: SingleEconomy = simulation.calculate_single_economy(reform=False) + reform: SingleEconomy = simulation.calculate_single_economy(reform=True) + options = simulation.options + country_id = options.country + if baseline.type == "general": + budgetary_impact_data = budgetary_impact(baseline, reform) + detailed_budgetary_impact_data = detailed_budgetary_impact( + baseline, reform, country_id + ) + decile_impact_data = decile_impact(baseline, reform) + inequality_impact_data = inequality_impact(baseline, reform) + poverty_impact_data = poverty_impact(baseline, reform) + poverty_by_gender_data = poverty_gender_breakdown(baseline, reform) + poverty_by_race_data = poverty_racial_breakdown(baseline, reform) + intra_decile_impact_data = intra_decile_impact(baseline, reform) + labor_supply_response_data = labor_supply_response(baseline, reform) + constituency_impact_data: UKConstituencyBreakdown = ( + uk_constituency_breakdown(baseline, reform, country_id) + ) + wealth_decile_impact_data = wealth_decile_impact( + baseline, reform, country_id + ) + intra_wealth_decile_impact_data = intra_wealth_decile_impact( + baseline, reform, country_id + ) + + return EconomyComparison( + budget=budgetary_impact_data, + detailed_budget=detailed_budgetary_impact_data, + decile=decile_impact_data, + inequality=inequality_impact_data, + poverty=poverty_impact_data, + poverty_by_gender=poverty_by_gender_data, + poverty_by_race=poverty_by_race_data, + intra_decile=intra_decile_impact_data, + wealth_decile=wealth_decile_impact_data, + intra_wealth_decile=intra_wealth_decile_impact_data, + labor_supply_response=labor_supply_response_data, + constituency_impact=constituency_impact_data, + ) + elif baseline.type == "cliff": + return dict( + baseline=dict( + cliff_gap=baseline.cliff_gap, + cliff_share=baseline.cliff_share, + ), + reform=dict( + cliff_gap=reform.cliff_gap, + cliff_share=reform.cliff_share, + ), + ) diff --git a/policyengine/outputs/macro/comparison/charts/__init__.py b/policyengine/outputs/macro/comparison/charts/__init__.py deleted file mode 100644 index 9db0bf9c..00000000 --- a/policyengine/outputs/macro/comparison/charts/__init__.py +++ /dev/null @@ -1,5 +0,0 @@ -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 .inequality import create_inequality_chart diff --git a/policyengine/outputs/macro/comparison/charts/budget.py b/policyengine/outputs/macro/comparison/charts/budget.py deleted file mode 100644 index f7e453fd..00000000 --- a/policyengine/outputs/macro/comparison/charts/budget.py +++ /dev/null @@ -1,90 +0,0 @@ -import plotly.express as px -import plotly.graph_objects as go -import typing - -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 deleted file mode 100644 index 382cdfcf..00000000 --- a/policyengine/outputs/macro/comparison/charts/budget_by_program.py +++ /dev/null @@ -1,96 +0,0 @@ -import plotly.express as px -import plotly.graph_objects as go -import typing - -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/decile.py b/policyengine/outputs/macro/comparison/charts/decile.py deleted file mode 100644 index bd2c69e7..00000000 --- a/policyengine/outputs/macro/comparison/charts/decile.py +++ /dev/null @@ -1,89 +0,0 @@ -import plotly.express as px -import plotly.graph_objects as go -import typing - -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 deleted file mode 100644 index e0db5c29..00000000 --- a/policyengine/outputs/macro/comparison/charts/inequality.py +++ /dev/null @@ -1,77 +0,0 @@ -import plotly.express as px -import plotly.graph_objects as go -import typing - -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/winners_losers.py b/policyengine/outputs/macro/comparison/charts/winners_losers.py deleted file mode 100644 index db21e18d..00000000 --- a/policyengine/outputs/macro/comparison/charts/winners_losers.py +++ /dev/null @@ -1,148 +0,0 @@ -import plotly.express as px -import plotly.graph_objects as go -import typing - -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/single/calculate_single_economy.py b/policyengine/outputs/macro/single/calculate_single_economy.py index 206fd489..7a21133a 100644 --- a/policyengine/outputs/macro/single/calculate_single_economy.py +++ b/policyengine/outputs/macro/single/calculate_single_economy.py @@ -5,35 +5,416 @@ from policyengine import Simulation from pydantic import BaseModel - -from .budget import FiscalSummary, _calculate_government_balance -from .inequality import InequalitySummary, _calculate_inequality from typing import List +from policyengine_core.simulations import Microsimulation +from typing import Dict +from dataclasses import dataclass + class SingleEconomy(BaseModel): - fiscal: FiscalSummary - """Government budgets and other top-level fiscal statistics.""" - inequality: InequalitySummary - """Inequality statistics for the household sector.""" + total_net_income: float + employment_income_hh: List[float] + self_employment_income_hh: List[float] + total_tax: float + total_state_tax: float + total_benefits: float + household_net_income: List[float] + equiv_household_net_income: List[float] + household_income_decile: List[int] + household_market_income: List[float] + household_wealth_decile: List[int] | None + household_wealth: List[float] | None + in_poverty: List[bool] + person_in_poverty: List[bool] + person_in_deep_poverty: List[bool] + poverty_gap: float + deep_poverty_gap: float + person_weight: List[float] + household_weight: List[float] + household_count_people: List[int] + gini: float + top_10_percent_share: float + top_1_percent_share: float + is_male: List[bool] + race: List[str] | None + age: List[int] + substitution_lsr: float + income_lsr: float + budgetary_impact_lsr: float + income_lsr_hh: List[float] + substitution_lsr_hh: List[float] + weekly_hours: float | None + weekly_hours_income_effect: float | None + weekly_hours_substitution_effect: float | None + type: str + programs: Dict[str, float] | None -def calculate_single_economy( - simulation: Simulation, -) -> SingleEconomy: - """Calculate economy statistics for a single economic scenario.""" - options = simulation.options - if simulation.is_comparison: - raise ValueError( - "This function is for single economy simulations only." +@dataclass +class UKProgram: + name: str + is_positive: bool + + +class UKPrograms: + PROGRAMS = [ + UKProgram("income_tax", True), + UKProgram("national_insurance", True), + UKProgram("vat", True), + UKProgram("council_tax", True), + UKProgram("fuel_duty", True), + UKProgram("tax_credits", False), + UKProgram("universal_credit", False), + UKProgram("child_benefit", False), + UKProgram("state_pension", False), + UKProgram("pension_credit", False), + UKProgram("ni_employer", True), + ] + + +class GeneralEconomyTask: + def __init__(self, simulation: Microsimulation, country_id: str): + self.simulation = simulation + self.country_id = country_id + self.household_count_people = self.simulation.calculate( + "household_count_people" + ) + + def calculate_tax_and_spending(self): + if self.country_id == "uk": + total_tax = self.simulation.calculate("gov_tax").sum() + total_spending = self.simulation.calculate("gov_spending").sum() + else: + total_tax = self.simulation.calculate("household_tax").sum() + total_spending = self.simulation.calculate( + "household_benefits" + ).sum() + return total_tax, total_spending + + def calculate_inequality_metrics(self): + personal_hh_equiv_income = self._get_weighted_household_income() + + try: + gini = personal_hh_equiv_income.gini() + except Exception as e: + print( + "WARNING: Gini index calculations resulted in an error: returning no change, but this is inaccurate." + ) + print("Error: ", e) + gini = 0.4 + + in_top_10_pct = personal_hh_equiv_income.decile_rank() == 10 + in_top_1_pct = personal_hh_equiv_income.percentile_rank() == 100 + + personal_hh_equiv_income.weights /= self.household_count_people + + top_10_share = ( + personal_hh_equiv_income[in_top_10_pct].sum() + / personal_hh_equiv_income.sum() + ) + top_1_share = ( + personal_hh_equiv_income[in_top_1_pct].sum() + / personal_hh_equiv_income.sum() + ) + + return gini, top_10_share, top_1_share + + def _get_weighted_household_income(self): + income = self.simulation.calculate("equiv_household_net_income") + income[income < 0] = 0 + income.weights *= self.household_count_people + return income + + def calculate_income_breakdown_metrics(self): + total_net_income = self.simulation.calculate( + "household_net_income" + ).sum() + employment_income_hh = ( + self.simulation.calculate("employment_income", map_to="household") + .astype(float) + .tolist() + ) + self_employment_income_hh = ( + self.simulation.calculate( + "self_employment_income", map_to="household" + ) + .astype(float) + .tolist() + ) + + return ( + total_net_income, + employment_income_hh, + self_employment_income_hh, + ) + + def calculate_household_income_metrics(self): + household_net_income = ( + self.simulation.calculate("household_net_income") + .astype(float) + .tolist() + ) + equiv_household_net_income = ( + self.simulation.calculate("equiv_household_net_income") + .astype(float) + .tolist() + ) + household_income_decile = ( + self.simulation.calculate("household_income_decile") + .astype(int) + .tolist() + ) + household_market_income = ( + self.simulation.calculate("household_market_income") + .astype(float) + .tolist() + ) + + return ( + household_net_income, + equiv_household_net_income, + household_income_decile, + household_market_income, + ) + + def calculate_wealth_metrics(self): + try: + wealth = self.simulation.calculate("total_wealth") + wealth.weights *= self.household_count_people + wealth_decile = ( + wealth.decile_rank().clip(1, 10).astype(int).tolist() + ) + wealth = wealth.astype(float).tolist() + except Exception as e: + wealth = None + wealth_decile = None + return wealth, wealth_decile + + def calculate_demographic_metrics(self): + try: + is_male = ( + self.simulation.calculate("is_male").astype(bool).tolist() + ) + except Exception: + is_male = None + + try: + race = self.simulation.calculate("race").astype(str).tolist() + except Exception: + race = None + + age = self.simulation.calculate("age").astype(int).tolist() + + return is_male, race, age + + def calculate_poverty_metrics(self): + in_poverty = ( + self.simulation.calculate("in_poverty").astype(bool).tolist() + ) + person_in_poverty = ( + self.simulation.calculate("in_poverty", map_to="person") + .astype(bool) + .tolist() + ) + person_in_deep_poverty = ( + self.simulation.calculate("in_deep_poverty", map_to="person") + .astype(bool) + .tolist() + ) + poverty_gap = self.simulation.calculate("poverty_gap").sum() + deep_poverty_gap = self.simulation.calculate("deep_poverty_gap").sum() + return ( + in_poverty, + person_in_poverty, + person_in_deep_poverty, + poverty_gap, + deep_poverty_gap, + ) + + def calculate_weights(self): + person_weight = ( + self.simulation.calculate("person_weight").astype(float).tolist() + ) + household_weight = ( + self.simulation.calculate("household_weight") + .astype(float) + .tolist() + ) + + return person_weight, household_weight + + def calculate_labor_supply_responses(self): + result = { + "substitution_lsr": 0, + "income_lsr": 0, + "budgetary_impact_lsr": 0, + "income_lsr_hh": (self.household_count_people * 0) + .astype(float) + .tolist(), + "substitution_lsr_hh": (self.household_count_people * 0) + .astype(float) + .tolist(), + } + + if not self._has_behavioral_response(): + return result + + result.update( + { + "substitution_lsr": self.simulation.calculate( + "substitution_elasticity_lsr" + ).sum(), + "income_lsr": self.simulation.calculate( + "income_elasticity_lsr" + ).sum(), + "income_lsr_hh": self.simulation.calculate( + "income_elasticity_lsr", map_to="household" + ) + .astype(float) + .tolist(), + "substitution_lsr_hh": self.simulation.calculate( + "substitution_elasticity_lsr", map_to="household" + ) + .astype(float) + .tolist(), + } ) - fiscal = _calculate_government_balance( - simulation.baseline_simulation, options + return result + + def _has_behavioral_response(self) -> bool: + return ( + "employment_income_behavioral_response" + in self.simulation.tax_benefit_system.variables + and any( + self.simulation.calculate( + "employment_income_behavioral_response" + ) + != 0 + ) + ) + + def calculate_lsr_working_hours(self): + if self.country_id != "us": + return { + "weekly_hours": 0, + "weekly_hours_income_effect": 0, + "weekly_hours_substitution_effect": 0, + } + + return { + "weekly_hours": self.simulation.calculate( + "weekly_hours_worked" + ).sum(), + "weekly_hours_income_effect": self.simulation.calculate( + "weekly_hours_worked_behavioural_response_income_elasticity" + ).sum(), + "weekly_hours_substitution_effect": self.simulation.calculate( + "weekly_hours_worked_behavioural_response_substitution_elasticity" + ).sum(), + } + + def calculate_uk_programs(self) -> Dict[str, float]: + if self.country_id != "uk": + return {} + + return { + program.name: self.simulation.calculate( + program.name, map_to="household" + ).sum() + * (1 if program.is_positive else -1) + for program in UKPrograms.PROGRAMS + } + + +def calculate_single_economy( + simulation: Simulation, reform: bool = False +) -> Dict: + task_manager = GeneralEconomyTask( + ( + simulation.baseline_simulation + if not reform + else simulation.reform_simulation + ), + simulation.options.country, + ) + country_id = simulation.options.country + + total_tax, total_spending = task_manager.calculate_tax_and_spending() + gini, top_10_share, top_1_share = ( + task_manager.calculate_inequality_metrics() + ) + wealth, wealth_decile = task_manager.calculate_wealth_metrics() + is_male, race, age = task_manager.calculate_demographic_metrics() + labor_supply_responses = task_manager.calculate_labor_supply_responses() + lsr_working_hours = task_manager.calculate_lsr_working_hours() + ( + in_poverty, + person_in_poverty, + person_in_deep_poverty, + poverty_gap, + deep_poverty_gap, + ) = task_manager.calculate_poverty_metrics() + total_net_income, employment_income_hh, self_employment_income_hh = ( + task_manager.calculate_income_breakdown_metrics() ) - inequality = _calculate_inequality(simulation.baseline_simulation) + ( + household_net_income, + equiv_household_net_income, + household_income_decile, + household_market_income, + ) = task_manager.calculate_household_income_metrics() + person_weight, household_weight = task_manager.calculate_weights() + + if country_id == "uk": + uk_programs = task_manager.calculate_uk_programs() + else: + uk_programs = None + + total_state_tax = 0 + + if country_id == "us": + try: + total_state_tax = simulation.calculate( + "household_state_income_tax" + ).sum() + except: + total_state_tax = 0 return SingleEconomy( - fiscal=fiscal, - inequality=inequality, + **{ + "total_net_income": total_net_income, + "employment_income_hh": employment_income_hh, + "self_employment_income_hh": self_employment_income_hh, + "total_tax": total_tax, + "total_state_tax": total_state_tax, + "total_benefits": total_spending, + "household_net_income": household_net_income, + "equiv_household_net_income": equiv_household_net_income, + "household_income_decile": household_income_decile, + "household_market_income": household_market_income, + "household_wealth_decile": wealth_decile, + "household_wealth": wealth, + "in_poverty": in_poverty, + "person_in_poverty": person_in_poverty, + "person_in_deep_poverty": person_in_deep_poverty, + "poverty_gap": poverty_gap, + "deep_poverty_gap": deep_poverty_gap, + "person_weight": person_weight, + "household_weight": household_weight, + "household_count_people": task_manager.household_count_people.astype( + int + ).tolist(), + "gini": float(gini), + "top_10_percent_share": float(top_10_share), + "top_1_percent_share": float(top_1_share), + "is_male": is_male, + "race": race, + "age": age, + **labor_supply_responses, + **lsr_working_hours, + "type": "general", + "programs": uk_programs, + } ) diff --git a/policyengine/simulation.py b/policyengine/simulation.py index bfadd921..abec5e2b 100644 --- a/policyengine/simulation.py +++ b/policyengine/simulation.py @@ -7,7 +7,7 @@ from policyengine_core.simulations import ( Microsimulation as CountryMicrosimulation, ) -from .utils.reforms import ParametricReform, SimulationAdjustment +from .utils.reforms import ParametricReform from policyengine_core.reforms import Reform as StructuralReform from policyengine_core.data import Dataset from .utils.huggingface import download @@ -33,9 +33,7 @@ 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 -) +ReformType = ParametricReform | Type[StructuralReform] | None RegionType = str | None SubsampleType = int | None @@ -172,12 +170,6 @@ def _initialise_simulation( if isinstance(reform, ParametricReform): reform = reform.model_dump() - simulation_editing_reform = None - - if isinstance(reform, SimulationAdjustment): - simulation_editing_reform = reform.root - reform = None - simulation: CountrySimulation = _simulation_type( dataset=data if macro else None, situation=data if not macro else None, @@ -199,9 +191,6 @@ def _initialise_simulation( if subsample is not None: simulation = simulation.subsample(subsample) - if simulation_editing_reform is not None: - simulation_editing_reform(simulation) - return simulation def _apply_region_to_simulation( diff --git a/policyengine/utils/reforms.py b/policyengine/utils/reforms.py index ab8225bb..6d681f57 100644 --- a/policyengine/utils/reforms.py +++ b/policyengine/utils/reforms.py @@ -11,9 +11,3 @@ class ParametricReform(RootModel): """A reform that just changes parameter values.""" root: Dict[str, Dict | float | bool] - - -class SimulationAdjustment(RootModel): - """A reform that changes the simulation in some way.""" - - root: object # Python callable function that takes a Simulation object and returns nothing. Not JSON serialisable. Needs fixing. diff --git a/pyproject.toml b/pyproject.toml index 82c45fc6..c9d75b71 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -15,7 +15,7 @@ requires-python = ">=3.6" dependencies = [ "policyengine_core>=3.10", "policyengine-uk", - "policyengine-us", + "policyengine-us>=1.213.1", "microdf_python", "getpass4", "pydantic",