Merge pull request #772 from PolicyEngine/pension-credit-doc

Pension Credit documentation
PolicyEngine · Oct 19, 2023 · bcf9cbf · bcf9cbf
2 parents 55efd1c + f4be3f4
commit bcf9cbf
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diff --git a/changelog_entry.yaml b/changelog_entry.yaml
@@ -0,0 +1,4 @@
+- bump: minor
+  changes:
+    added:
+    - Pension Credit documentation page.
diff --git a/docs/book/_toc.yml b/docs/book/_toc.yml
@@ -10,6 +10,7 @@ parts:
     chapters:
       - file: programs/gov/hmrc/child-benefit
       - file: programs/gov/dwp/universal-credit
+      - file: programs/gov/dwp/pension-credit
       - file: programs/gov/hmrc/national-insurance
       - file: programs/gov/hmrc/fuel-duty
       - file: programs/gov/hmrc/stamp-duty

diff --git a/docs/book/examples/income-sources.ipynb b/docs/book/examples/income-sources.ipynb
@@ -1152,15 +1152,25 @@
     "\n",
     "sim = Microsimulation()\n",
     "\n",
-    "is_pensioner_household = (sim.calculate(\"is_SP_age\", map_to=\"household\") > 0) * (sim.calculate(\"is_child\", map_to=\"household\") == 0)\n",
+    "is_pensioner_household = (\n",
+    "    sim.calculate(\"is_SP_age\", map_to=\"household\") > 0\n",
+    ") * (sim.calculate(\"is_child\", map_to=\"household\") == 0)\n",
     "household_benefits = sim.calculate(\"household_benefits\")\n",
     "household_pensions = sim.calculate(\"pension_income\", map_to=\"household\")\n",
-    "household_investment_income = sim.calculate(\"capital_income\", map_to=\"household\")\n",
-    "household_earnings = sim.calculate(\"employment_income\", map_to=\"household\") + sim.calculate(\"self_employment_income\", map_to=\"household\")\n",
+    "household_investment_income = sim.calculate(\n",
+    "    \"capital_income\", map_to=\"household\"\n",
+    ")\n",
+    "household_earnings = sim.calculate(\n",
+    "    \"employment_income\", map_to=\"household\"\n",
+    ") + sim.calculate(\"self_employment_income\", map_to=\"household\")\n",
     "total_income = sim.calculate(\"household_market_income\") + household_benefits\n",
     "\n",
-    "equivalised_income = sim.calculate(\"equiv_household_net_income\")[is_pensioner_household]\n",
-    "household_count_people = sim.calculate(\"people\", map_to=\"household\")[is_pensioner_household]\n",
+    "equivalised_income = sim.calculate(\"equiv_household_net_income\")[\n",
+    "    is_pensioner_household\n",
+    "]\n",
+    "household_count_people = sim.calculate(\"people\", map_to=\"household\")[\n",
+    "    is_pensioner_household\n",
+    "]\n",
     "equivalised_income.weights *= household_count_people.values\n",
     "household_income_decile = equivalised_income.decile_rank()\n",
     "\n",
@@ -1183,22 +1193,34 @@
     "        income_sources.append(income_source)\n",
     "        income_source_values = income_source_decodes[income_source]\n",
     "        values.append(\n",
-    "            income_source_values[is_pensioner_household][in_decile].sum() / total_income[is_pensioner_household][in_decile].sum()\n",
+    "            income_source_values[is_pensioner_household][in_decile].sum()\n",
+    "            / total_income[is_pensioner_household][in_decile].sum()\n",
     "        )\n",
-    "        cumulative_income += income_source_values[is_pensioner_household][in_decile].sum()\n",
+    "        cumulative_income += income_source_values[is_pensioner_household][\n",
+    "            in_decile\n",
+    "        ].sum()\n",
     "    # Add 'other income'\n",
     "    deciles.append(decile)\n",
     "    income_sources.append(\"Other\")\n",
-    "    values.append(1 - cumulative_income / total_income[is_pensioner_household][in_decile].sum())\n",
+    "    values.append(\n",
+    "        1\n",
+    "        - cumulative_income\n",
+    "        / total_income[is_pensioner_household][in_decile].sum()\n",
+    "    )\n",
     "\n",
-    "df = pd.DataFrame({\n",
-    "    \"Decile\": deciles,\n",
-    "    \"Income source\": income_sources,\n",
-    "    \"Value\": values,\n",
-    "})\n",
+    "df = pd.DataFrame(\n",
+    "    {\n",
+    "        \"Decile\": deciles,\n",
+    "        \"Income source\": income_sources,\n",
+    "        \"Value\": values,\n",
+    "    }\n",
+    ")\n",
     "\n",
     "# Order by state support, other income, pensions, investment, earnings\n",
-    "df[\"Income source\"] = pd.Categorical(df[\"Income source\"], [\"State support\", \"Other\", \"Pensions\", \"Investment\", \"Earnings\"])\n",
+    "df[\"Income source\"] = pd.Categorical(\n",
+    "    df[\"Income source\"],\n",
+    "    [\"State support\", \"Other\", \"Pensions\", \"Investment\", \"Earnings\"],\n",
+    ")\n",
     "df = df.sort_values([\"Decile\", \"Income source\"], ascending=[True, False])\n",
     "\n",
     "import plotly.express as px\n",
@@ -1217,7 +1239,7 @@
     "    yaxis=dict(\n",
     "        tickformat=\".0%\",\n",
     "        title=\"Percentage of income\",\n",
-    "        tickvals=[0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, .7, .8, .9, 1],\n",
+    "        tickvals=[0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1],\n",
     "    ),\n",
     "    xaxis=dict(\n",
     "        title=\"Income decile\",\n",
@@ -1228,7 +1250,7 @@
     "fig = format_fig(fig).update_layout(\n",
     "    title=\"Sources of income for pensioner households\",\n",
     ")\n",
-    "fig\n"
+    "fig"
    ]
   }
  ],

diff --git a/docs/book/programs/gov/dcms/bbc/tv-licence.ipynb b/docs/book/programs/gov/dcms/bbc/tv-licence.ipynb
@@ -127,16 +127,19 @@
     "dcms = system.parameters.gov.dcms\n",
     "\n",
     "df = pd.DataFrame()\n",
-    "df['Date'] = [\n",
-    "    parameter.instant_str for parameter in dcms.bbc.tv_licence.colour.values_list\n",
+    "df[\"Date\"] = [\n",
+    "    parameter.instant_str\n",
+    "    for parameter in dcms.bbc.tv_licence.colour.values_list\n",
     "]\n",
-    "df['Full TV Licence Fee'] = [\n",
-    "    f'£{parameter.value:.2f}' for parameter in dcms.bbc.tv_licence.colour.values_list\n",
+    "df[\"Full TV Licence Fee\"] = [\n",
+    "    f\"£{parameter.value:.2f}\"\n",
+    "    for parameter in dcms.bbc.tv_licence.colour.values_list\n",
     "]\n",
-    "df['Blind TV Licence Fee'] = [\n",
-    "    f'£{0.5 * parameter.value:.2f}' for parameter in dcms.bbc.tv_licence.colour.values_list\n",
+    "df[\"Blind TV Licence Fee\"] = [\n",
+    "    f\"£{0.5 * parameter.value:.2f}\"\n",
+    "    for parameter in dcms.bbc.tv_licence.colour.values_list\n",
     "]\n",
-    "df.sort_values('Date').set_index('Date')"
+    "df.sort_values(\"Date\").set_index(\"Date\")"
    ]
   },
   {
@@ -311,24 +314,29 @@
    ],
    "source": [
     "import pandas as pd\n",
+    "\n",
     "# aged discount\n",
     "aged_discount = {\n",
     "    \"Reformed value\": [0, 0.25, 0.50, 0.75],\n",
     "    \"Current value\": [],\n",
     "    \"Change against current\": [],\n",
     "    \"Tax revenues impact (£m)\": [91.7, 68.8, 45.9, 22.9],\n",
-    "    \"Reference\": []\n",
+    "    \"Reference\": [],\n",
     "}\n",
     "reference_list_ad = [\n",
     "    \"https://policyengine.org/uk/policy?focus=policyOutput.netIncome&reform=27903&region=uk&timePeriod=2023&baseline=1\",\n",
     "    \"https://policyengine.org/uk/policy?focus=policyOutput.netIncome&reform=27083&region=uk&timePeriod=2023&baseline=1\",\n",
     "    \"https://policyengine.org/uk/policy?focus=policyOutput.netIncome&reform=19090&region=uk&timePeriod=2023&baseline=1\",\n",
-    "    \"https://policyengine.org/uk/policy?focus=policyOutput.netIncome&reform=27909&region=uk&timePeriod=2023&baseline=1\"\n",
+    "    \"https://policyengine.org/uk/policy?focus=policyOutput.netIncome&reform=27909&region=uk&timePeriod=2023&baseline=1\",\n",
     "]\n",
     "for i in range(len(aged_discount[\"Reformed value\"])):\n",
     "    aged_discount[\"Current value\"] += [\"100%\"]\n",
-    "    aged_discount[\"Change against current\"] += [f\"{aged_discount['Reformed value'][i]-1:.0%}\"]\n",
-    "    aged_discount[\"Reformed value\"][i] = f\"{aged_discount['Reformed value'][i]:.0%}\"\n",
+    "    aged_discount[\"Change against current\"] += [\n",
+    "        f\"{aged_discount['Reformed value'][i]-1:.0%}\"\n",
+    "    ]\n",
+    "    aged_discount[\"Reformed value\"][\n",
+    "        i\n",
+    "    ] = f\"{aged_discount['Reformed value'][i]:.0%}\"\n",
     "    aged_discount[\"Reference\"] += [\n",
     "        f\"<a href=\\\"{reference_list_ad[i]}\\\">Budgetary impact of changing aged discount to {aged_discount['Reformed value'][i]}</a>\"\n",
     "    ]\n",
@@ -337,14 +345,25 @@
     "df_aged_discount[\"Reformed policy\"] = \"Aged discount\"\n",
     "\n",
     "# minimum age\n",
-    "age_list = list(range(70,81))\n",
+    "age_list = list(range(70, 81))\n",
     "age_list.remove(75)\n",
     "min_age = {\n",
     "    \"Reformed value\": age_list,\n",
     "    \"Current value\": [],\n",
     "    \"Change against current\": [],\n",
-    "    \"Tax revenues impact (£m)\": [-19.6, -11.4, -7.7, -2.4, -1.5, 1.3, 2.2, 3.7, 7.1, 91.7],\n",
-    "    \"Reference\": []\n",
+    "    \"Tax revenues impact (£m)\": [\n",
+    "        -19.6,\n",
+    "        -11.4,\n",
+    "        -7.7,\n",
+    "        -2.4,\n",
+    "        -1.5,\n",
+    "        1.3,\n",
+    "        2.2,\n",
+    "        3.7,\n",
+    "        7.1,\n",
+    "        91.7,\n",
+    "    ],\n",
+    "    \"Reference\": [],\n",
     "}\n",
     "reference_list_minage = [\n",
     "    \"https://policyengine.org/uk/policy?focus=policyOutput.netIncome&reform=27932&region=uk&timePeriod=2023&baseline=1\",\n",
@@ -356,13 +375,13 @@
     "    \"https://policyengine.org/uk/policy?focus=policyOutput.netIncome&reform=27951&region=uk&timePeriod=2023&baseline=1\",\n",
     "    \"https://policyengine.org/uk/policy?focus=policyOutput.netIncome&reform=27953&region=uk&timePeriod=2023&baseline=1\",\n",
     "    \"https://policyengine.org/uk/policy?focus=policyOutput.netIncome&reform=27955&region=uk&timePeriod=2023&baseline=1\",\n",
-    "    \"https://policyengine.org/uk/policy?focus=policyOutput.netIncome&reform=27960&region=uk&timePeriod=2023&baseline=1\"\n",
+    "    \"https://policyengine.org/uk/policy?focus=policyOutput.netIncome&reform=27960&region=uk&timePeriod=2023&baseline=1\",\n",
     "]\n",
     "for i in range(len(min_age[\"Reformed value\"])):\n",
     "    min_age[\"Current value\"] += [75]\n",
     "    min_age[\"Change against current\"] += [min_age[\"Reformed value\"][i] - 75]\n",
     "    min_age[\"Reference\"] += [\n",
-    "        f\"<a href=\\\"{reference_list_minage[i]}\\\">Budgetary impact of changing minimum age for aged discount to {age_list[i]}</a>\"\n",
+    "        f'<a href=\"{reference_list_minage[i]}\">Budgetary impact of changing minimum age for aged discount to {age_list[i]}</a>'\n",
     "    ]\n",
     "\n",
     "df_min_age = pd.DataFrame(min_age)\n",
@@ -372,32 +391,36 @@
     "fee_list = list(range(157, 162))\n",
     "fee_list.remove(159)\n",
     "licence_fee = {\n",
-    "        \"Reformed value\": fee_list,\n",
-    "        \"Current value\": [],\n",
-    "        \"Change against current\": [],\n",
-    "        \"Tax revenues impact (£m)\": [-52.1, -26, 26, 52.1],\n",
-    "        \"Reference\": []\n",
+    "    \"Reformed value\": fee_list,\n",
+    "    \"Current value\": [],\n",
+    "    \"Change against current\": [],\n",
+    "    \"Tax revenues impact (£m)\": [-52.1, -26, 26, 52.1],\n",
+    "    \"Reference\": [],\n",
     "}\n",
     "reference_list_fee = [\n",
     "    \"https://policyengine.org/uk/policy?focus=policyOutput.netIncome&reform=27089&region=uk&timePeriod=2023&baseline=1\",\n",
     "    \"https://policyengine.org/uk/policy?focus=policyOutput.netIncome&reform=27087&region=uk&timePeriod=2023&baseline=1\",\n",
     "    \"https://policyengine.org/uk/policy?focus=policyOutput.netIncome&reform=26981&region=uk&timePeriod=2023&baseline=1\",\n",
-    "    \"https://policyengine.org/uk/policy?focus=policyOutput.netIncome&reform=26983&region=uk&timePeriod=2023&baseline=1\"\n",
+    "    \"https://policyengine.org/uk/policy?focus=policyOutput.netIncome&reform=26983&region=uk&timePeriod=2023&baseline=1\",\n",
     "]\n",
     "for i in range(len(licence_fee[\"Reformed value\"])):\n",
     "    licence_fee[\"Current value\"] += [\"£159\"]\n",
-    "    licence_fee[\"Change against current\"] += [f\"£{licence_fee['Reformed value'][i]-159}\"]\n",
+    "    licence_fee[\"Change against current\"] += [\n",
+    "        f\"£{licence_fee['Reformed value'][i]-159}\"\n",
+    "    ]\n",
     "    licence_fee[\"Reformed value\"][i] = f\"£{licence_fee['Reformed value'][i]}\"\n",
     "    licence_fee[\"Reference\"] += [\n",
     "        f\"<a href=\\\"{reference_list_fee[i]}\\\">Budgetary impact of changing licence fee to {licence_fee['Reformed value'][i]}</a>\"\n",
     "    ]\n",
     "\n",
-    "df_licence_fee =pd.DataFrame(licence_fee)\n",
+    "df_licence_fee = pd.DataFrame(licence_fee)\n",
     "df_licence_fee[\"Reformed policy\"] = \"TV licence fee\"\n",
     "\n",
     "# concat df\n",
     "df_reform = pd.concat([df_aged_discount, df_min_age, df_licence_fee])\n",
-    "df_reform.set_index([\"Reformed policy\", \"Current value\", \"Reformed value\"]).style.format(lambda x: x)\n"
+    "df_reform.set_index(\n",
+    "    [\"Reformed policy\", \"Current value\", \"Reformed value\"]\n",
+    ").style.format(lambda x: x)"
    ]
   }
  ],