diff --git a/.github/workflows/code_changes.yaml b/.github/workflows/code_changes.yaml
index 7b7bff328..6c51b81ea 100644
--- a/.github/workflows/code_changes.yaml
+++ b/.github/workflows/code_changes.yaml
@@ -4,10 +4,10 @@ name: Code changes
on:
push:
branches:
- - master
+ - main
paths:
- - policyengine_uk/**
+ - src/**
- tests/**
- .github/**
workflow_dispatch:
diff --git a/.github/workflows/deploy.yml b/.github/workflows/deploy.yml
index 9141f4e75..c0caf0b4f 100644
--- a/.github/workflows/deploy.yml
+++ b/.github/workflows/deploy.yml
@@ -5,7 +5,7 @@ name: Deploy documentation
on:
push:
# Runs on pushes targeting the default branch
- branches: [master]
+ branches: [main]
env:
# `BASE_URL` determines, relative to the root of the domain, the URL that your site is served from.
# E.g., if your site lives at `https://mydomain.org/myproject`, set `BASE_URL=/myproject`.
diff --git a/.github/workflows/pr_code_changes.yaml b/.github/workflows/pr_code_changes.yaml
index ba99f4f9e..ecf0c35ee 100644
--- a/.github/workflows/pr_code_changes.yaml
+++ b/.github/workflows/pr_code_changes.yaml
@@ -4,10 +4,10 @@ name: Code changes
on:
pull_request:
branches:
- - master
+ - main
paths:
- - policyengine_uk/**
+ - src/**
- tests/**
- .github/**
workflow_dispatch:
diff --git a/.github/workflows/pr_docs_changes.yaml b/.github/workflows/pr_docs_changes.yaml
index 088d9f202..e769642ac 100644
--- a/.github/workflows/pr_docs_changes.yaml
+++ b/.github/workflows/pr_docs_changes.yaml
@@ -4,7 +4,7 @@ name: Docs changes
on:
pull_request:
branches:
- - master
+ - main
paths:
- docs/**
diff --git a/.github/workflows/versioning.yaml b/.github/workflows/versioning.yaml
index f92eedacf..16746f985 100644
--- a/.github/workflows/versioning.yaml
+++ b/.github/workflows/versioning.yaml
@@ -4,7 +4,7 @@ name: Versioning updates
on:
push:
branches:
- - master
+ - main
paths:
- changelog_entry.yaml
diff --git a/docs/book/validation/hbai-poverty-rates.ipynb b/docs/book/validation/hbai-poverty-rates.ipynb
new file mode 100644
index 000000000..01e3389e1
--- /dev/null
+++ b/docs/book/validation/hbai-poverty-rates.ipynb
@@ -0,0 +1,6778 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# HBAI poverty rates\n",
+ "\n",
+ "This notebook compares official HBAI poverty statistics with PolicyEngine model outputs. We examine four poverty definitions:\n",
+ "- Absolute poverty before housing costs (BHC)\n",
+ "- Absolute poverty after housing costs (AHC)\n",
+ "- Relative poverty before housing costs (BHC)\n",
+ "- Relative poverty after housing costs (AHC)\n",
+ "\n",
+ "Official data covers 2002-2023, while model projections cover 2023-2030."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Generate all data"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {
+ "tags": [
+ "hide-input"
+ ]
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ " \n",
+ " \n",
+ " \n",
+ " "
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "from IPython.core.display import HTML, display_html\n",
+ "\n",
+ "\n",
+ "def add_fonts():\n",
+ " fonts = HTML(\n",
+ " \"\"\"\n",
+ " \n",
+ " \n",
+ " \n",
+ " \"\"\"\n",
+ " )\n",
+ " return display_html(fonts)\n",
+ "\n",
+ "add_fonts()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {
+ "tags": [
+ "hide-input"
+ ]
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " "
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Official data: 2002-2023\n",
+ "Age groups: ['All' 'Children' 'Pensioners' 'Working age']\n"
+ ]
+ }
+ ],
+ "source": [
+ "from policyengine_uk import Microsimulation\n",
+ "import pandas as pd\n",
+ "import numpy as np\n",
+ "import plotly.express as px\n",
+ "import plotly.graph_objects as go\n",
+ "from policyengine_core.charts import format_fig, BLUE_COLOUR_SCALE, GRAY\n",
+ "\n",
+ "# Load official HBAI poverty outturn data\n",
+ "official_data = pd.read_csv('hbai_outturn.csv')\n",
+ "official_data = official_data[official_data['scenario'] == 'Baseline'].copy()\n",
+ "\n",
+ "print(f\"Official data: {official_data['year'].min()}-{official_data['year'].max()}\")\n",
+ "print(f\"Age groups: {official_data['group'].unique()}\")\n",
+ "\n",
+ "def get_age_group(age):\n",
+ " \"\"\"Map age to HBAI age groups.\"\"\"\n",
+ " if age < 18:\n",
+ " return \"Children\"\n",
+ " elif age < 66:\n",
+ " return \"Working age\"\n",
+ " else:\n",
+ " return \"Pensioners\"\n",
+ "\n",
+ "def calculate_poverty_stats_hbai_format(dataset=\"enhanced_frs\", scenario=\"PolicyEngine EFRS\"):\n",
+ " \"\"\"Calculate poverty stats in the same format as official HBAI data.\"\"\"\n",
+ " \n",
+ " sim = Microsimulation(dataset=f\"hf://policyengine/policyengine-uk-data-private/{dataset}_2023_24.h5\")\n",
+ " results = []\n",
+ " \n",
+ " # Map our variable names to HBAI format\n",
+ " poverty_map = {\n",
+ " (\"in_poverty_bhc\", True, False): (True, False), # BHC, absolute\n",
+ " (\"in_poverty_ahc\", False, False): (False, False), # AHC, absolute\n",
+ " (\"in_relative_poverty_bhc\", True, True): (True, True), # BHC, relative\n",
+ " (\"in_relative_poverty_ahc\", False, True): (False, True), # AHC, relative\n",
+ " }\n",
+ " \n",
+ " for year in range(2023, 2031):\n",
+ " # Get data for this year\n",
+ " df = sim.calculate_dataframe(\n",
+ " [\"age\", \"in_poverty_bhc\", \"in_poverty_ahc\", \n",
+ " \"in_relative_poverty_bhc\", \"in_relative_poverty_ahc\", \"person_weight\"],\n",
+ " period=year\n",
+ " )\n",
+ " \n",
+ " # Add age groups\n",
+ " df[\"age_group\"] = df[\"age\"].apply(get_age_group)\n",
+ " \n",
+ " # Calculate for each poverty type and age group\n",
+ " for pov_var, (bhc, relative) in poverty_map.items():\n",
+ " for group in [\"All\", \"Children\", \"Working age\", \"Pensioners\"]:\n",
+ " if group == \"All\":\n",
+ " group_df = df\n",
+ " else:\n",
+ " group_df = df[df[\"age_group\"] == group]\n",
+ " \n",
+ " total_pop = group_df[\"person_weight\"].sum()\n",
+ " poor_pop = group_df[group_df[pov_var[0]] == 1][\"person_weight\"].sum()\n",
+ " \n",
+ " if total_pop > 0:\n",
+ " poverty_rate = poor_pop / total_pop\n",
+ " else:\n",
+ " poverty_rate = 0\n",
+ " \n",
+ " # Add poverty rate row\n",
+ " results.append({\n",
+ " \"scenario\": scenario,\n",
+ " \"year\": year,\n",
+ " \"bhc\": bhc,\n",
+ " \"relative\": relative,\n",
+ " \"headcount\": False,\n",
+ " \"group\": group,\n",
+ " \"poverty_rate\": poverty_rate\n",
+ " })\n",
+ " \n",
+ " # Add headcount row\n",
+ " results.append({\n",
+ " \"scenario\": scenario,\n",
+ " \"year\": year,\n",
+ " \"bhc\": bhc,\n",
+ " \"relative\": relative,\n",
+ " \"headcount\": True,\n",
+ " \"group\": group,\n",
+ " \"poverty_rate\": poor_pop\n",
+ " })\n",
+ " \n",
+ " return pd.DataFrame(results)\n",
+ "\n",
+ "# Calculate model results for both datasets\n",
+ "frs_model = calculate_poverty_stats_hbai_format(\"frs\", \"PE FRS\")\n",
+ "\n",
+ "efrs_model = calculate_poverty_stats_hbai_format(\"enhanced_frs\", \"PE EFRS\")\n",
+ "\n",
+ "# Combine all data\n",
+ "model_data = pd.concat([frs_model, efrs_model], ignore_index=True)\n",
+ "all_data = pd.concat([official_data, model_data], ignore_index=True)\n",
+ "\n",
+ "# Helper functions for visualisation\n",
+ "def get_poverty_description(bhc, relative):\n",
+ " \"\"\"Get human-readable poverty type description.\"\"\"\n",
+ " housing = \"BHC\" if bhc else \"AHC\"\n",
+ " pov_type = \"Relative\" if relative else \"Absolute\"\n",
+ " return f\"{pov_type} poverty {housing}\"\n",
+ "\n",
+ "def show_poverty_analysis(data, bhc, relative):\n",
+ " \"\"\"Show table and faceted chart for a specific poverty type.\"\"\"\n",
+ " \n",
+ " poverty_desc = get_poverty_description(bhc, relative)\n",
+ " \n",
+ " # Filter for specific poverty type (rates only, not headcounts)\n",
+ " subset = data[\n",
+ " (data['bhc'] == bhc) & \n",
+ " (data['relative'] == relative) & \n",
+ " (data['headcount'] == False)\n",
+ " ].copy()\n",
+ " \n",
+ " # Convert to percentages\n",
+ " subset['poverty_rate'] = subset['poverty_rate'] * 100\n",
+ " \n",
+ " # Show comparison table for 2023\n",
+ " comparison_2023 = subset[subset['year'] == 2023].pivot_table(\n",
+ " index='group',\n",
+ " columns='scenario',\n",
+ " values='poverty_rate'\n",
+ " ).round(1)\n",
+ " \n",
+ " # Create faceted line chart including 'All' group\n",
+ " fig = px.line(\n",
+ " subset,\n",
+ " x='year',\n",
+ " y='poverty_rate',\n",
+ " color='scenario',\n",
+ " facet_col='group',\n",
+ " facet_col_wrap=2, # Wrap after 2 columns for better layout\n",
+ " title=poverty_desc,\n",
+ " labels={'poverty_rate': 'Poverty rate (%)', 'year': 'Year'},\n",
+ " markers=True,\n",
+ " color_discrete_map={\n",
+ " 'Baseline': GRAY,\n",
+ " 'PolicyEngine FRS': BLUE_COLOUR_SCALE[1],\n",
+ " 'PolicyEngine EFRS': BLUE_COLOUR_SCALE[2],\n",
+ " 'PE FRS': BLUE_COLOUR_SCALE[1],\n",
+ " 'PE EFRS': BLUE_COLOUR_SCALE[2]\n",
+ " },\n",
+ " height=600 # Increased height to accommodate 4 panels\n",
+ " )\n",
+ " \n",
+ " # Add vertical line at 2023\n",
+ " fig.add_vline(x=2023, line_dash=\"dot\", line_color=\"gray\", opacity=0.5)\n",
+ " \n",
+ " fig = format_fig(fig)\n",
+ " fig.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Absolute poverty before housing costs (BHC)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [
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diff --git a/docs/book/validation/hbai.md b/docs/book/validation/hbai.md
index 5c7d81cba..2e8c2c46b 100644
--- a/docs/book/validation/hbai.md
+++ b/docs/book/validation/hbai.md
@@ -1,4 +1,4 @@
-# HBAI
+# HBAI components
```{note}
PolicyEngine UK includes variables that match to the Households Below Average Income (HBAI) income concepts.
diff --git a/myst.yml b/myst.yml
index b4a11aab8..48b55db46 100644
--- a/myst.yml
+++ b/myst.yml
@@ -24,6 +24,7 @@ project:
- title: Validation
children:
- file: docs/book/validation/hbai
+ - file: docs/book/validation/hbai-poverty-rates
- title: Policy
children:
- file: docs/book/policy/model-baseline