Update tax-benefit targets

changelog_entry.yaml

@@ -0,0 +1,4 @@
+- bump: minor
+  changes:
+    changed:
+    - Tax-benefit targets updated from new DWP forecasts.

policyengine_uk_data/storage/tax_benefit.csv

@@ -1,44 +1,44 @@
-name,unit,reference,2018,2019,2020,2021,2022,2023,2024,2025,2026,2027,2028,2029
-attendance_allowance_count,benefit-unit-m,dwp_benefit_tables_march_2024,1.6,1.6,1.4,1.4,1.4,1.5,1.6,1.7,1.7,1.7,1.8,
-carers_allowance_count,benefit-unit-m,dwp_benefit_tables_march_2024,1.2,1.2,1.2,1.2,1.2,1.3,1.3,1.4,1.4,1.5,1.5,
-dla_count,benefit-unit-m,dwp_benefit_tables_march_2024,1.8,1.6,1.2,1.2,1.2,1.2,1.2,1.2,1.3,1.3,1.3,
-esa_count,benefit-unit-m,dwp_benefit_tables_march_2024,2.2,2.0,1.9,1.8,1.7,1.6,1.5,1.4,1.3,1.3,1.1,
-housing_benefit_count,benefit-unit-m,dwp_benefit_tables_march_2024,4.0,3.4,3.0,2.7,2.5,2.4,2.1,1.9,1.8,1.7,1.6,
-pension_credit_count,benefit-unit-m,dwp_benefit_tables_march_2024,1.7,1.6,1.5,1.4,1.4,1.3,1.3,1.3,1.2,1.2,1.1,
-pip_count,benefit-unit-m,dwp_benefit_tables_march_2024,1.9,2.2,2.4,2.5,2.9,3.2,3.5,3.9,4.2,4.4,4.7,
-universal_credit_jobseekers_count,benefit-unit-m,dwp_benefit_tables_march_2024,0.6,0.9,1.9,1.5,1.2,1.3,1.7,1.9,1.9,1.9,1.8,
-universal_credit_non_jobseekers_count,benefit-unit-m,dwp_benefit_tables_march_2024,0.7,1.3,2.1,2.6,3.1,3.5,3.8,4.2,4.4,4.5,4.8,
-winter_fuel_allowance_count,benefit-unit-m,dwp_benefit_tables_march_2024,11.6,11.4,11.1,11.3,11.4,11.6,10.8,11.0,11.0,10.9,10.9,
-attendance_allowance,gbp-bn,dwp_benefit_tables_march_2024,5.7,5.9,5.3,5.3,5.7,6.7,7.7,8.0,8.2,8.4,8.8,
-carers_allowance,gbp-bn,dwp_benefit_tables_march_2024,2.9,2.9,3.0,3.1,3.3,3.7,4.2,4.4,4.7,4.9,5.1,
-dla,gbp-bn,dwp_benefit_tables_march_2024,8.1,7.2,5.8,5.7,6.0,6.8,7.3,7.5,7.8,8.1,8.2,
-esa,gbp-bn,dwp_benefit_tables_march_2024,15.1,13.9,13.4,12.7,12.1,12.7,13.0,11.8,11.3,11.0,9.2,
-esa_contrib,gbp-bn,dwp_benefit_tables_march_2024,4.6,4.5,4.6,4.5,4.5,4.9,5.2,5.2,5.2,5.3,5.3,
-esa_income,gbp-bn,dwp_benefit_tables_march_2024,10.5,9.3,8.9,8.2,7.6,7.8,7.7,6.6,6.1,5.7,3.9,
-housing_benefit,gbp-bn,dwp_benefit_tables_march_2024,20.7,18.4,17.3,16.1,15.6,15.5,14.4,12.4,12.5,12.1,11.2,
-pip,gbp-bn,dwp_benefit_tables_march_2024,10.6,12.5,13.7,15.2,17.6,21.6,25.4,27.8,30.2,32.8,35.3,
-statutory_maternity_pay,gbp-bn,dwp_benefit_tables_march_2024,2.4,2.5,2.5,2.6,2.7,2.8,3.2,3.3,3.3,3.4,3.5,
-capital_gains_tax,gbp-bn,obr_march_2024_efo,,,,,16.9,14.8,15.2,16.2,18.9,21.3,23.5,
-child_benefit,gbp-bn,obr_march_2024_efo,,,,,11.6,12.5,13.5,13.5,13.5,13.5,13.5,
-council_tax,gbp-bn,obr_march_2024_efo,,,,,40.9,43.6,45.9,48.2,50.7,53.4,56.3,
-council_tax_england,gbp-bn,obr_march_2024_efo,,,,,36.3,38.7,40.8,43.0,45.3,47.8,50.4,
-council_tax_scotland,gbp-bn,obr_march_2024_efo,,,,,2.7,2.8,2.9,3.0,3.1,3.2,3.3,
-council_tax_wales,gbp-bn,obr_march_2024_efo,,,,,1.9,2.0,2.1,2.2,2.3,2.4,2.5,
-domestic_rates,gbp-bn,obr_march_2024_efo,,,,,0.4,0.4,0.4,0.4,0.4,0.4,0.4,
-fuel_duties,gbp-bn,obr_march_2024_efo,,,,,25.1,24.6,24.7,27.3,27.6,28.0,28.2,
-income_tax,gbp-bn,obr_march_2024_efo,,,,,250.5,279.2,302.7,315.8,331.4,348.7,363.3,
-jobseekers_allowance,gbp-bn,obr_march_2024_efo,,,,,0.3,0.3,0.2,0.1,0.1,0.1,0.1,
-ni_employee,gbp-bn,obr_march_2024_efo,,,,,74.0,70.7,55.5,57.2,59.4,61.4,63.2,
-ni_employer,gbp-bn,obr_march_2024_efo,,,,,103.1,108.5,112.7,116.4,120.4,124.7,128.9,
-pension_credit,gbp-bn,obr_march_2024_efo,,,,,4.9,5.4,5.8,5.9,5.7,5.4,5.3,
-sdlt_residential,gbp-bn,obr_march_2024_efo,,,,,11.7,8.5,9.3,10.2,11.9,13.9,15.9,
-stamp_duty_land_tax,gbp-bn,obr_march_2024_efo,,,,,15.4,11.5,12.8,13.8,15.8,18.1,20.4,
-state_pension,gbp-bn,obr_march_2024_efo,,,,,109.8,124.6,138.1,146.0,150.5,153.2,158.0,
-tax_credits,gbp-bn,obr_march_2024_efo,,,,,8.9,7.3,1.6,-0.1,-0.1,-0.1,-0.1,
-tv_licence_fee,gbp-bn,obr_march_2024_efo,,,,,3.7,3.7,3.9,4.0,4.0,4.0,4.1,
-universal_credit,gbp-bn,obr_march_2024_efo,,,,,42.6,51.8,67.0,74.3,77.6,80.8,87.2,
-universal_credit_jobseekers,gbp-bn,obr_march_2024_efo,,,,,9.2,11.1,17.4,19.3,19.7,20.0,20.1,
-universal_credit_non_jobseekers,gbp-bn,obr_march_2024_efo,,,,,33.4,40.7,49.5,55.0,57.9,60.8,67.1,
-vat,gbp-bn,obr_march_2024_efo,,,,,159.7,169.6,173.9,180.3,188.0,195.9,204.1,
-winter_fuel_allowance,gbp-bn,obr_march_2024_efo,,,,,2.0,2.0,1.9,1.9,1.9,1.9,2.0,
-private_school_students,person-k,obr_march_2024_efo,557,557,557,557,557,557,557,557,557,557,557,
+name,unit,reference,2018,2019,2020,2021,2022,2023,2024,2025,2026,2027,2028,2029
+attendance_allowance_count,benefit-unit-m,dwp_benefit_tables_march_2025,1.4,1.4,1.3,1.3,1.3,1.4,1.5,1.6,1.6,1.7,1.7,1.7
+carers_allowance_count,benefit-unit-m,dwp_benefit_tables_march_2026,0.8,0.8,0.9,0.8,0.9,0.9,0.9,1.0,1.0,1.0,1.0,1.0
+dla_count,benefit-unit-m,dwp_benefit_tables_march_2027,1.8,1.5,1.2,1.2,1.2,1.2,1.3,1.3,1.3,1.3,1.3,1.3
+esa_count,benefit-unit-m,dwp_benefit_tables_march_2028,2.2,2.0,1.9,1.8,1.7,1.6,1.4,1.0,0.8,0.8,0.8,0.8
+housing_benefit_count,benefit-unit-m,dwp_benefit_tables_march_2029,4.0,3.4,3.0,2.7,2.5,2.4,2.1,1.6,1.4,1.4,1.4,1.4
+pension_credit_count,benefit-unit-m,dwp_benefit_tables_march_2030,1.7,1.6,1.5,1.4,1.4,1.4,1.3,1.4,1.3,1.2,1.2,1.2
+pip_count,benefit-unit-m,dwp_benefit_tables_march_2031,1.9,2.2,2.3,2.5,2.8,3.2,3.5,3.8,4.1,4.3,4.5,4.6
+universal_credit_jobseekers_count,benefit-unit-m,dwp_benefit_tables_march_2032,0.6,0.8,1.8,1.4,1.1,1.2,1.4,1.6,1.6,1.5,1.5,1.5
+universal_credit_non_jobseekers_count,benefit-unit-m,dwp_benefit_tables_march_2033,0.7,1.3,2.2,2.7,3.1,3.6,4.1,4.7,5.1,5.2,5.3,5.4
+winter_fuel_allowance_count,benefit-unit-m,dwp_benefit_tables_march_2034,11.6,11.4,11.1,11.3,11.4,11.6,1.3,1.3,1.2,1.2,1.2,1.2
+attendance_allowance,gbp-bn,dwp_benefit_tables_march_2035,5.7,5.9,5.3,5.3,5.7,6.7,7.7,8.3,8.9,9.3,9.6,10.0
+carers_allowance,gbp-bn,dwp_benefit_tables_march_2036,2.9,2.9,3.0,3.1,3.2,3.7,4.2,4.5,4.8,4.9,5.0,5.1
+dla,gbp-bn,dwp_benefit_tables_march_2037,8.1,7.2,5.8,5.7,6.0,6.9,7.6,8.0,8.4,8.7,8.8,8.7
+esa,gbp-bn,dwp_benefit_tables_march_2038,15.1,13.9,13.4,12.7,12.1,12.4,12.3,7.9,4.9,5.1,5.2,5.3
+esa_contrib,gbp-bn,dwp_benefit_tables_march_2039,4.6,4.5,4.6,4.5,4.5,4.9,5.2,5.0,4.9,5.1,5.2,5.3
+esa_income,gbp-bn,dwp_benefit_tables_march_2040,10.5,9.3,8.8,8.2,7.6,7.4,7.1,2.9,0.0,0.0,0.0,0.0
+housing_benefit,gbp-bn,dwp_benefit_tables_march_2041,20.7,18.4,17.3,16.1,15.6,15.8,15.4,12.2,11.5,11.7,12.0,12.6
+pip,gbp-bn,dwp_benefit_tables_march_2042,10.6,12.5,13.7,15.2,17.6,21.7,25.8,28.5,31.6,33.2,35.0,37.6
+statutory_maternity_pay,gbp-bn,dwp_benefit_tables_march_2043,2.4,2.5,2.5,2.6,2.7,2.9,3.0,3.2,3.2,3.3,3.4,3.5
+capital_gains_tax,gbp-bn,obr_march_2024_efo,,,,,16.9,14.5,13.3,19.7,19.4,20.2,23.1,25.5
+child_benefit,gbp-bn,obr_march_2024_efo,,,,,11.6,12.5,13.5,13.5,13.5,13.5,13.5,
+council_tax,gbp-bn,obr_march_2024_efo,,,,,40.9,43.3,46.4,48.9,51.4,54.2,57.0,60.0
+council_tax_england,gbp-bn,obr_march_2024_efo,,,,,36.3,38.3,41.2,43.5,45.8,48.4,51.0,53.7
+council_tax_scotland,gbp-bn,obr_march_2024_efo,,,,,2.7,2.9,3.0,3.1,3.2,3.3,3.4,3.5
+council_tax_wales,gbp-bn,obr_march_2024_efo,,,,,1.9,2.1,2.2,2.3,2.4,2.5,2.6,2.7
+domestic_rates,gbp-bn,obr_march_2024_efo,,,,,0.4,0.5,0.5,0.5,0.5,0.5,0.5,0.5
+fuel_duties,gbp-bn,obr_march_2024_efo,,,,,25.1,24.8,24.4,24.4,27.0,27.3,27.3,27.0
+income_tax,gbp-bn,obr_march_2024_efo,,,,,250.5,275.7,307.1,328.4,354.5,377.1,384.1,396.7
+jobseekers_allowance,gbp-bn,obr_march_2024_efo,1.3,0.7,1.0,0.5,0.3,0.3,0.3,0.2,0.3,0.2,0.2,0.3
+ni_employee,gbp-bn,obr_march_2024_efo,,,,,74,61.8,43.9,46.1,47.6,49.1,50.7,52.5
+ni_employer,gbp-bn,obr_march_2024_efo,,,,,103.1,108.3,115.3,145.8,150.1,154.4,158.9,163.6
+pension_credit,gbp-bn,obr_march_2024_efo,5.1,5.1,5.1,4.8,4.9,5.5,5.9,5.9,5.9,5.7,5.7,5.7
+sdlt_residential,gbp-bn,obr_march_2024_efo,,,,,11.7,8.6,10.1,11.3,13.4,15.6,17.8,19.3
+stamp_duty_land_tax,gbp-bn,obr_march_2024_efo,,,,,15.4,11.6,13.6,14.1,17.2,19.9,22.5,24.5
+state_pension,gbp-bn,obr_march_2024_efo,,,,,109.8,124.6,138.1,146,150.5,153.2,158,
+tax_credits,gbp-bn,obr_march_2024_efo,,,,,8.9,7.3,1.6,0,0,0,0,0
+tv_licence_fee,gbp-bn,obr_march_2024_efo,,,,,3.7,3.7,3.9,4,4,4,4.1,
+universal_credit,gbp-bn,obr_march_2024_efo,8.1,18.4,38.2,40.6,41.9,52.1,65.3,75.8,83.1,84.0,85.9,88.9
+universal_credit_jobseekers,gbp-bn,obr_march_2024_efo,2.0,3.0,16.6,13.0,9.8,11.1,13.1,15.3,16.0,15.4,15.1,15.5
+universal_credit_non_jobseekers,gbp-bn,obr_march_2024_efo,6.2,15.3,21.7,27.6,32.1,41.0,52.2,60.6,67.1,68.6,70.9,73.4
+vat,gbp-bn,obr_march_2024_efo,,,,,159.7,168.9,170.3,178.8,186.6,193.9,201.7,209.8
+winter_fuel_allowance,gbp-bn,obr_march_2024_efo,2.0,2.0,2.0,2.0,2.0,2.0,0.3,0.3,0.3,0.3,0.3,0.3
+private_school_students,person-k,obr_march_2024_efo,557,557,557,557,557,557,557,557,557,557,557,

policyengine_uk_data/tests/test_population.py

@@ -0,0 +1,12 @@
+def test_child_limit():
+    from policyengine_uk import Microsimulation
+    from policyengine_uk_data.datasets import EnhancedFRS_2022_23
+
+    # Initialize simulation
+    sim = Microsimulation(dataset=EnhancedFRS_2022_23)
+
+    population = sim.calculate("people", 2025).sum() / 1e6
+    POPULATION_TARGET = 69.5  # Expected UK population in millions, per ONS 2022-based estimate here: https://www.ons.gov.uk/peoplepopulationandcommunity/populationandmigration/populationprojections/bulletins/nationalpopulationprojections/2022based
+    assert (
+        abs(population / POPULATION_TARGET - 1) < 0.02
+    ), f"Expected UK population of {POPULATION_TARGET:.1f} million, got {population:.1f} million."

policyengine_uk_data/utils/incomes_projection.py

@@ -29,15 +29,11 @@
 statistics = pd.concat(dfs)
 statistics = statistics[statistics.value.notnull()]
 
-OBR_BASELINE = {  # Policy baseline that targets are based on.
-    "gov.dwp.winter_fuel_payment.eligibility.require_benefits": False,
-}
-
 
 def create_target_matrix(
     dataset: str,
     time_period: str,
-    reform=OBR_BASELINE,
+    reform=None,
 ) -> np.ndarray:
     """
     Create a target matrix A, s.t. for household weights w, the target vector b and a perfectly calibrated PolicyEngine UK:

policyengine_uk_data/utils/loss.py

@@ -23,15 +23,11 @@
 statistics = pd.concat(dfs)
 statistics = statistics[statistics.value.notnull()]
 
-OBR_BASELINE = {  # Policy baseline that targets are based on.
-    "gov.dwp.winter_fuel_payment.eligibility.require_benefits": False,
-}
-
 
 def create_target_matrix(
     dataset: str,
     time_period: str,
-    reform=OBR_BASELINE,
+    reform=None,
 ) -> np.ndarray:
     """
     Create a target matrix A, s.t. for household weights w, the target vector b and a perfectly calibrated PolicyEngine UK:
@@ -117,7 +113,7 @@ def pe_count(*variables):
     df["obr/pension_credit"] = pe("pension_credit")
     df["obr/stamp_duty_land_tax"] = pe("expected_sdlt")
     df["obr/state_pension"] = pe("state_pension")
-    df["obr/tax_credits"] = pe("tax_credits")
+    # df["obr/tax_credits"] = pe("tax_credits")
     df["obr/tv_licence_fee"] = pe("tv_licence")
 
     uc = sim.calculate("universal_credit")
@@ -153,6 +149,38 @@ def pe_count(*variables):
         "NORTHERN_IRELAND": "northern_ireland",
     }
     age = sim.calculate("age")
+
+    # Ensure local populations are consistent with national population
+    local_population_total = 0
+    for pe_region_name, region_name in region_to_target_name_map.items():
+        for lower_age in range(0, 90, 10):
+            upper_age = lower_age + 10
+            name = f"ons/{region_name}_age_{lower_age}_{upper_age - 1}"
+            local_population_total += (
+                demographics[demographics.name == name][
+                    str(time_period)
+                ].values[0]
+                * 1e3
+            )
+
+    population_scaling_factor = (
+        demographics[demographics.name == "ons/uk_population"][
+            str(time_period)
+        ].values[0]
+        * 1e6
+        / local_population_total
+    ) * 0.9
+
+    for pe_region_name, region_name in region_to_target_name_map.items():
+        for lower_age in range(0, 90, 10):
+            upper_age = lower_age + 10
+            name = f"ons/{region_name}_age_{lower_age}_{upper_age - 1}"
+            statistics.loc[
+                (statistics.name == name)
+                & (statistics.time_period == int(time_period)),
+                "value",
+            ] *= population_scaling_factor
+
     for pe_region_name, region_name in region_to_target_name_map.items():
         for lower_age in range(0, 90, 10):
             upper_age = lower_age + 10
@@ -164,6 +192,8 @@ def pe_count(*variables):
             )
             df[name] = household_from_person(person_in_criteria)
 
+    df["ons/uk_population"] = household_from_person(age >= 0)
+
     targets = (
         statistics[statistics.time_period == int(time_period)]
         .set_index("name")