Fix Universal Credit caseload targets

changelog.d/452.fixed.md

@@ -0,0 +1 @@
+Correct Universal Credit caseload calibration by preserving the published family-type values, adding explicit 2025 and 2026 household totals from DWP deductions statistics, and removing dead OBR jobseeker-split dispatch logic.

policyengine_uk_data/targets/build_loss_matrix.py

@@ -52,7 +52,6 @@
     compute_two_child_limit,
     compute_uc_by_children,
     compute_uc_by_family_type,
-    compute_uc_jobseeker,
     compute_uc_outside_cap,
     compute_uc_payment_dist,
     compute_uk_population,
@@ -393,15 +392,6 @@ def _compute_column(target: Target, ctx: _SimContext, year: int) -> np.ndarray |
     ):
         return compute_ss_ni_relief(target, ctx)
 
-    # UC jobseeker splits
-    if name in (
-        "obr/universal_credit_jobseekers",
-        "obr/universal_credit_non_jobseekers",
-        "obr/universal_credit_jobseekers_count",
-        "obr/universal_credit_non_jobseekers_count",
-    ):
-        return compute_uc_jobseeker(target, ctx)
-
     # UC outside benefit cap
     if name == "obr/universal_credit_outside_cap":
         return compute_uc_outside_cap(target, ctx)

policyengine_uk_data/targets/compute/__init__.py

@@ -7,7 +7,6 @@
     compute_two_child_limit,
     compute_uc_by_children,
     compute_uc_by_family_type,
-    compute_uc_jobseeker,
     compute_uc_outside_cap,
     compute_uc_payment_dist,
 )
@@ -77,7 +76,6 @@
     "compute_two_child_limit",
     "compute_uc_by_children",
     "compute_uc_by_family_type",
-    "compute_uc_jobseeker",
     "compute_uc_outside_cap",
     "compute_uc_payment_dist",
     "compute_uk_population",

policyengine_uk_data/targets/compute/benefits.py

@@ -98,24 +98,6 @@ def compute_uc_payment_dist(target, ctx) -> np.ndarray:
     return ctx.household_from_family(in_band)
 
 
-def compute_uc_jobseeker(target, ctx) -> np.ndarray:
-    """Compute UC jobseeker / non-jobseeker splits."""
-    family = ctx.sim.populations["benunit"]
-    uc = ctx.sim.calculate("universal_credit")
-    on_uc = uc > 0
-    unemployed = family.any(ctx.sim.calculate("employment_status") == "UNEMPLOYED")
-
-    if "non_jobseekers" in target.name:
-        mask = on_uc * ~unemployed
-    else:
-        mask = on_uc * unemployed
-
-    if "_count" in target.name:
-        return ctx.household_from_family(mask)
-    else:
-        return ctx.household_from_family(uc * mask)
-
-
 def compute_uc_outside_cap(target, ctx) -> np.ndarray:
     """Compute OBR UC outside benefit cap."""
     uc = ctx.sim.calculate("universal_credit")

policyengine_uk_data/targets/sources/dwp.py

@@ -168,20 +168,37 @@ def get_targets() -> list[Target]:
         "couple_no_children": 231.368,
         "couple_with_children": 839.379,
     }
-    undercount_relative = 1.27921 / sum(_UC_BY_FAMILY_TYPE.values())
+    # This vintage sums to 6.096 million; 2025-26 totals are targeted separately.
     for family_type, count_k in _UC_BY_FAMILY_TYPE.items():
         targets.append(
             Target(
                 name=f"dwp/uc/claimants_{family_type}",
                 variable="universal_credit",
                 source="dwp",
                 unit=Unit.COUNT,
-                values={2025: count_k * (1 + undercount_relative) * 1e3},
+                values={2025: count_k * 1e3},
                 is_count=True,
                 reference_url="https://stat-xplore.dwp.gov.uk/",
             )
         )
 
+    # DWP UC deductions statistics, Table 1: total households are households
+    # with a deduction divided by their share. This gives 6.38m in April 2025,
+    # 6.60m in May-August, 6.96m in September-November, and 7.17m in
+    # December 2025-February 2026. The 2025 value is the calendar-year average;
+    # the 2026 value represents the December-February plateau.
+    targets.append(
+        Target(
+            name="dwp/uc/households",
+            variable="universal_credit",
+            source="dwp",
+            unit=Unit.COUNT,
+            values={2025: 6_700_000, 2026: 7_200_000},
+            is_count=True,
+            reference_url="https://www.gov.uk/government/statistics/universal-credit-quarterly-statistics-29-april-2013-to-12-february-2026/universal-credit-deductions-statistics-march-2025-to-february-2026",
+        )
+    )
+
     # Two-child limit statistics (April 2025 publication, modeled at 2026)
     targets.append(
         Target(

policyengine_uk_data/tests/test_target_registry.py

@@ -8,6 +8,7 @@
 """
 
 from policyengine_uk_data.targets import get_all_targets
+from policyengine_uk_data.targets.build_loss_matrix import _compute_column
 
 
 def test_registry_loads():
@@ -62,6 +63,24 @@ def test_dwp_pip_targets():
     assert "dwp/pip_dl_enhanced_claimants" in names
 
 
+def test_dwp_uc_households_target_uses_count_fallback():
+    """The UC household total should be registered and produce a matrix column."""
+    target = next(
+        target
+        for target in get_all_targets(year=2025)
+        if target.name == "dwp/uc/households"
+    )
+
+    class DummyCtx:
+        @staticmethod
+        def pe_count(variable):
+            assert variable == "universal_credit"
+            return [1, 0, 1]
+
+    assert target.values[2025] == 6.7e6
+    assert _compute_column(target, DummyCtx(), 2025) == [1, 0, 1]
+
+
 def test_voa_council_tax_targets():
     """VOA council tax band targets should exist."""
     targets = get_all_targets(year=2025)