Calibrate Council Tax band household counts

changelog_entry.yaml

@@ -0,0 +1,4 @@
+- bump: patch
+  changes:
+    added:
+    - Council Tax calibration.

policyengine_uk_data/datasets/frs/frs.py

@@ -89,15 +89,6 @@ def generate(self):
         for variable in frs:
             frs[variable] = {self.dwp_frs.time_period: np.array(frs[variable])}
 
-        # Domestic rates need to be set for 2025 too
-        domestic_rates = np.array(
-            frs["domestic_rates"][self.dwp_frs.time_period]
-        )
-        frs["domestic_rates"] = {
-            self.dwp_frs.time_period: domestic_rates,
-            "2025": domestic_rates,
-        }
-
         self.save_dataset(frs)
 
         impute_brmas(self, frs)

policyengine_uk_data/storage/council_tax_bands_2024.csv

@@ -0,0 +1,12 @@
+Region,A,B,C,D,E,F,G,H,I,Total
+NORTH_EAST,667260,205250,194090,111950,60000,26180,14430,1550,0,1280700
+NORTH_WEST,1381000,700030,610850,360710,213480,103150,65240,7280,0,3441730
+SOUTH_EAST,366180,682890,1067530,836070,547270,334360,263930,39980,0,4138200
+SOUTH_WEST,469430,641160,618890,428320,289050,146600,81270,8320,0,2683030
+LONDON,148830,496490,1031190,969650,580310,295330,223990,68020,0,3813790
+EAST_MIDLANDS,800340,500990,399250,245090,152700,73090,38980,3370,0,2213820
+WEST_MIDLANDS,784110,650540,521020,299270,191300,104660,62300,6110,0,2619300
+YORKSHIRE,1068570,510140,427750,246960,156680,75040,41820,3720,0,2530680
+EAST_OF_ENGLAND,400900,595180,733990,502180,310470,170910,112790,14660,0,2841060
+WALES,214540,306120,321500,239690,199500,121740,54850,13130,5630,1476690
+SCOTLAND,497867,581576,423304,365001,362354,217954,140162,14327,0,2602545

policyengine_uk_data/tests/microsimulation/reforms_config.yaml

@@ -16,7 +16,7 @@ reforms:
   parameters:
     gov.hmrc.child_benefit.amount.additional: 25
 - name: Reduce Universal Credit taper rate to 20%
-  expected_impact: -41.3
+  expected_impact: -37.3
   parameters:
     gov.dwp.universal_credit.means_test.reduction_rate: 0.2
 - name: Raise Class 1 main employee NICs rate to 10%

policyengine_uk_data/utils/loss.py

@@ -308,6 +308,33 @@ def pe_count(*variables):
     target_names.append("dwp/pip_dl_enhanced_claimants")
     target_values.append(1_608_000)
 
+    # Council Tax band counts
+
+    ct_data = pd.read_csv(STORAGE_FOLDER / "council_tax_bands_2024.csv")
+    uk_population = (
+        sim.tax_benefit_system.parameters.gov.economic_assumptions.indices.ons.population
+    )
+    uprating = uk_population(time_period) / uk_population(2024)
+
+    # England and Wales data from https://www.gov.uk/government/statistics/council-tax-stock-of-properties-2024
+
+    # Scotland data from https://www.gov.scot/publications/council-tax-datasets/ (Number of chargeable dwellings, 2024)
+
+    for i, row in ct_data.iterrows():
+        selected_region = row["Region"]
+        in_region = sim.calculate("region").values == selected_region
+        for band in ["A", "B", "C", "D", "E", "F", "G", "H", "I"]:
+            name = f"voa/council_tax/{selected_region}/{band}"
+            in_band = sim.calculate("council_tax_band") == band
+            df[name] = (in_band * in_region).astype(float)
+            target_names.append(name)
+            target_values.append(float(row[band]) * uprating)
+        # Add total row
+        name = f"voa/council_tax/{selected_region}/total"
+        df[name] = (in_region).astype(float)
+        target_names.append(name)
+        target_values.append(float(row["Total"]) * uprating)
+
     combined_targets = pd.concat(
         [
             targets,