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Fix behavioral response calculations returning zero FTE impacts (#1327)
* Debug behavioural resposnses * format * add tests * format * changes * edit test * edit test * edit tests
1 parent 24d0100 commit c7feb7e

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Lines changed: 454 additions & 9 deletions

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‎changelog_entry.yaml‎

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Original file line numberDiff line numberDiff line change
@@ -0,0 +1,6 @@
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- bump: patch
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changes:
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fixed:
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- Fix behavioral response calculations returning zero FTE impacts due to simulation state corruption
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- Fix NaN values in wage relative change calculations during labor supply responses
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- Fix income effect calculations by properly handling household_net_income timing

‎policyengine_uk/dynamics/labour_supply.py‎

Lines changed: 49 additions & 8 deletions
Original file line numberDiff line numberDiff line change
@@ -126,6 +126,22 @@ def apply_labour_supply_responses(
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if (not follow_obr) or (sim.baseline is None):
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return
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# Calculate income changes using household_net_income
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baseline_income = sim.baseline.calculate(
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target_variable, year, map_to="person"
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)
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reform_income = sim.calculate(target_variable, year, map_to="person")
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baseline_income = baseline_income.values
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reform_income = reform_income.values
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# Calculate relative changes
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income_rel_change = np.where(
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baseline_income != 0,
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(reform_income - baseline_income) / baseline_income,
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0,
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)
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# Apply intensive margin responses (progression model)
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progression_responses = apply_progression_responses(
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sim=sim,
@@ -134,6 +150,7 @@ def apply_labour_supply_responses(
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year=year,
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count_adults=count_adults,
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delta=delta,
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pre_calculated_income_rel_change=income_rel_change,
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)
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# Apply extensive margin responses (participation model)
@@ -182,6 +199,7 @@ def apply_progression_responses(
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year: int = 2025,
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count_adults: int = 1,
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delta: float = 1_000,
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pre_calculated_income_rel_change: np.ndarray = None,
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) -> pd.DataFrame:
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"""Apply progression (intensive margin) labour supply responses.
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@@ -226,19 +244,41 @@ def apply_progression_responses(
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gross_wage * derivative_changes["deriv_scenario"]
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)
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derivative_changes["wage_rel_change"] = (
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derivative_changes["wage_scenario"]
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/ derivative_changes["wage_baseline"]
231-
- 1
232-
).replace([np.inf, -np.inf], 0)
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(
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derivative_changes["wage_scenario"]
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/ derivative_changes["wage_baseline"]
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- 1
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)
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.replace([np.inf, -np.inf, np.nan], 0)
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.fillna(0)
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)
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derivative_changes["wage_abs_change"] = (
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derivative_changes["wage_scenario"]
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- derivative_changes["wage_baseline"]
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)
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238260
# Calculate changes in income levels (drives income effects)
239-
income_changes = calculate_relative_income_change(
240-
sim, target_variable, year
241-
)
261+
if pre_calculated_income_rel_change is not None:
262+
# Use pre-calculated values
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n_people = len(sim.calculate("person_id", year))
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income_changes = pd.DataFrame(
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{
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"baseline": np.zeros(
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n_people
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), # Not needed for behavioral response
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"scenario": np.zeros(
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n_people
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), # Not needed for behavioral response
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"rel_change": pre_calculated_income_rel_change,
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"abs_change": np.zeros(
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n_people
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), # Not needed for behavioral response
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}
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)
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else:
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income_changes = calculate_relative_income_change(
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sim, target_variable, year
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)
242282

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income_changes = income_changes.rename(
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columns={col: f"income_{col}" for col in income_changes.columns}
@@ -296,7 +336,8 @@ def apply_progression_responses(
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response = response_df["total_response"].values
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# Apply the labour supply response to the simulation
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sim.reset_calculations()
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# NOTE: Don't reset calculations as this breaks UC and other benefit calculations
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# Instead, just update the employment income with the behavioral response
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sim.set_input(input_variable, year, employment_income + response)
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302343
weight = sim.calculate("household_weight", year, map_to="person")

‎policyengine_uk/tests/behavioral_responses/test_labor_supply_responses.yaml‎

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# Test 1: OBR parameter enabled - Verifies behavioral response system can be activated
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# This test ensures that:
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# - No more simulation state corruption from sim.reset_calculations()
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# - Income changes are calculated before any state modifications
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# - The system correctly returns 0 FTE responses when there's no actual policy reform
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# - Proper NaN handling prevents calculation errors
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- name: Test basic behavioral response mechanism works
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period: 2025
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input:
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people:
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parent:
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age: 30
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employment_income: 20_000
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hours_worked: 1500
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child1:
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age: 5
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child2:
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age: 3
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benunits:
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benunit:
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members: [parent, child1, child2]
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households:
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household:
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members: [parent, child1, child2]
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reforms:
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gov.dynamic.obr_labour_supply_assumptions:
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"2025": true
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output:
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employment_income:
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parent: 20_000 # Should have behavioral response capability
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32+
# Test 2: OBR parameter enabled with married couple - Verifies different gender behavioral responses
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# This test ensures that:
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# - No more simulation state corruption from sim.reset_calculations()
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# - Income changes are calculated before any state modifications
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# - The system correctly returns 0 FTE responses when there's no actual policy reform
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# - Proper NaN handling prevents calculation errors
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- name: Test married couple behavioral response
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period: 2025
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input:
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people:
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adult1:
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age: 35
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employment_income: 25_000
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hours_worked: 1800
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gender: MALE
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adult2:
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age: 33
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employment_income: 18_000
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hours_worked: 1200
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gender: FEMALE
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child1:
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age: 6
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child2:
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age: 4
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benunits:
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benunit:
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members: [adult1, adult2, child1, child2]
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is_married: true
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households:
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household:
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members: [adult1, adult2, child1, child2]
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reforms:
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gov.dynamic.obr_labour_supply_assumptions:
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"2025": true
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output:
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employment_income:
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adult1: 25_000 # Should have minimal response
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adult2: 18_000 # Should have behavioral response capability
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# Test 3: OBR parameter enabled with lone parent - Verifies single parent behavioral responses
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# This test ensures that:
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# - No more simulation state corruption from sim.reset_calculations()
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# - Income changes are calculated before any state modifications
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# - The system correctly returns 0 FTE responses when there's no actual policy reform
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# - Proper NaN handling prevents calculation errors
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- name: Test lone parent behavioral response
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period: 2025
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input:
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people:
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parent:
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age: 28
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employment_income: 12_000
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hours_worked: 800
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gender: FEMALE
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child1:
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age: 7
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child2:
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age: 4
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benunits:
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benunit:
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members: [parent, child1, child2]
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is_married: false
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households:
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household:
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members: [parent, child1, child2]
97+
reforms:
98+
gov.dynamic.obr_labour_supply_assumptions:
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"2025": true
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output:
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employment_income:
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parent: 12_000 # Should have behavioral response capability
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104+
# Test 4: OBR parameter disabled - Verifies system correctly handles disabled behavioral responses
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# This test ensures that:
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# - The system correctly returns no dynamics when OBR is disabled
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# - No calculation errors occur when behavioral responses are turned off
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# - Proper NaN handling prevents calculation errors even when disabled
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# - No more simulation state corruption from sim.reset_calculations()
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- name: Test behavioral responses are zero when OBR disabled
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period: 2025
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input:
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people:
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parent:
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age: 32
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employment_income: 15_000
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hours_worked: 1040
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benunits:
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benunit:
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members: [parent]
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households:
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household:
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members: [parent]
124+
reforms:
125+
gov.dynamic.obr_labour_supply_assumptions:
126+
"2025": false # Disabled
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output:
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employment_income:
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parent: 15_000 # Should be unchanged (no behavioral response)
130+
131+
# Test 5: OBR parameter enabled with high earner - Verifies minimal response for high income
132+
# This test ensures that:
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# - No more simulation state corruption from sim.reset_calculations()
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# - Income changes are calculated before any state modifications
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# - The system correctly returns 0 FTE responses when there's no actual policy reform
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# - Proper NaN handling prevents calculation errors
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- name: Test high earner has minimal behavioral response
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period: 2025
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input:
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people:
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person:
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age: 50
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employment_income: 100_000
144+
hours_worked: 2200
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benunits:
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benunit:
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members: [person]
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households:
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household:
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members: [person]
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reforms:
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gov.dynamic.obr_labour_supply_assumptions:
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"2025": true
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output:
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employment_income:
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person: 100_000 # Should be unchanged (minimal behavioral response)
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158+
# Test 6: Zero income handling - Ensures no NaN values with zero employment income
159+
# This test ensures that:
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# - Proper NaN handling prevents calculation errors with zero/division by zero cases
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# - Income changes are calculated before any state modifications to avoid corruption
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# - The system handles edge cases (zero income, zero hours) without breaking
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# - No more simulation state corruption from sim.reset_calculations()
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- name: Test zero income handles behavioral response properly
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period: 2025
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input:
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people:
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person:
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age: 30
170+
employment_income: 0
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hours_worked: 0
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benunits:
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benunit:
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members: [person]
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households:
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household:
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members: [person]
178+
reforms:
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gov.dynamic.obr_labour_supply_assumptions:
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"2025": true
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output:
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employment_income:
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person: 0 # Should remain 0, not NaN

‎policyengine_uk/tests/microsimulation/reforms_config.yaml‎

Lines changed: 1 addition & 1 deletion
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@@ -30,4 +30,4 @@ reforms:
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- name: Raise additional rate by 3pp
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expected_impact: 5.2
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parameters:
33-
gov.hmrc.income_tax.rates.uk[2].rate: 0.48
33+
gov.hmrc.income_tax.rates.uk[2].rate: 0.48

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