Add LSR prototypes (#852)

* Add LSRs

* Fix import

* Fix metadata

* Adjust test

* Fix test

changelog_entry.yaml

@@ -0,0 +1,4 @@
+- bump: minor
+  changes:
+    added:
+    - U.S. progress on labour supply responses.

policyengine_uk/parameters/gov/simulation/README.md

@@ -0,0 +1 @@
+# Simulation

policyengine_uk/parameters/gov/simulation/labor_supply_responses/bounds/README.md

@@ -0,0 +1 @@
+# Bounds

policyengine_uk/parameters/gov/simulation/labor_supply_responses/bounds/effective_wage_rate_change.yaml

@@ -0,0 +1,6 @@
+description: Effective wage rate changes larger than this will be capped at this value.
+values:
+  0000-01-01: 1
+metadata:
+  unit: /1
+  label: Effective wage rate change LSR bound

policyengine_uk/parameters/gov/simulation/labor_supply_responses/bounds/income_change.yaml

@@ -0,0 +1,6 @@
+description: Net income changes larger than this will be capped at this value.
+values:
+  0000-01-01: 1
+metadata:
+  unit: /1
+  label: Income change LSR bound

policyengine_uk/parameters/gov/simulation/labor_supply_responses/income_elasticity.yaml

@@ -0,0 +1,6 @@
+description: Percent change in labor supply given a 1% change in disposable income. This applies only to employment income.
+values:
+  2020-01-01: 0
+metadata:
+  unit: /1
+  label: Income elasticity of labor supply

policyengine_uk/parameters/gov/simulation/labor_supply_responses/substitution_elasticity.yaml

@@ -0,0 +1,6 @@
+description: Percent change in labor supply given a 1% change in the effective marginal wage. This applies only to employment income.
+values:
+  2020-01-01: 0
+metadata:
+  unit: /1
+  label: Substitution elasticity of labor supply

policyengine_uk/system.py

@@ -61,6 +61,14 @@ def __init__(self, *args, **kwargs):
         if reform is not None:
             self.apply_reform(reform)
 
+        # Labor supply responses
+
+        employment_income = self.get_holder("employment_income")
+        for known_period in employment_income.get_known_periods():
+            array = employment_income.get_array(known_period)
+            self.set_input("employment_income_before_lsr", known_period, array)
+            employment_income.delete_arrays(known_period)
+
 
 class Microsimulation(CoreMicrosimulation):
     default_tax_benefit_system = CountryTaxBenefitSystem
@@ -82,6 +90,14 @@ def __init__(self, *args, **kwargs):
         if reform is not None:
             self.apply_reform(reform)
 
+        # Labor supply responses
+
+        employment_income = self.get_holder("employment_income")
+        for known_period in employment_income.get_known_periods():
+            array = employment_income.get_array(known_period)
+            self.set_input("employment_income_before_lsr", known_period, array)
+            employment_income.delete_arrays(known_period)
+
 
 class IndividualSim(CoreIndividualSim):  # Deprecated
     tax_benefit_system = CountryTaxBenefitSystem

policyengine_uk/tests/microsimulation/test_validity.py

@@ -10,6 +10,11 @@
 @pytest.mark.parametrize("year", YEARS)
 def test_not_nan(year):
     for variable in baseline.tax_benefit_system.variables:
+        requires_computation_after = baseline.tax_benefit_system.variables[
+            variable
+        ].requires_computation_after
+        if requires_computation_after:
+            continue
         if (
             baseline.tax_benefit_system.variables[variable].definition_period
             == "year"

policyengine_uk/variables/gov/simulation/labor_supply_response/labor_supply_response.py

@@ -0,0 +1,145 @@
+from policyengine_uk.model_api import *
+
+
+class relative_income_change(Variable):
+    value_type = float
+    entity = Person
+    label = "relative income change"
+    unit = "/1"
+    definition_period = YEAR
+    requires_computation_after = "employment_income_behavioral_response"
+
+    def formula(person, period, parameters):
+        simulation = person.simulation
+        measurement_branch = simulation.get_branch("lsr_measurement")
+        baseline_branch = simulation.get_branch("baseline").get_branch(
+            "baseline_lsr_measurement"
+        )
+        measurement_person = measurement_branch.populations["person"]
+        baseline_person = baseline_branch.populations["person"]
+        baseline_net_income = baseline_person.household(
+            "household_net_income", period
+        )
+        net_income = measurement_person.household(
+            "household_net_income", period
+        )
+        income_change_bound = parameters(
+            period
+        ).gov.simulation.labor_supply_responses.bounds.income_change
+        # _c suffix for "clipped"
+        baseline_net_income_c = np.clip(baseline_net_income, 1, None)
+        net_income_c = np.clip(net_income, 1, None)
+        relative_change = (
+            net_income_c - baseline_net_income_c
+        ) / baseline_net_income_c
+        return np.clip(
+            relative_change, -income_change_bound, income_change_bound
+        )
+
+
+class relative_wage_change(Variable):
+    value_type = float
+    entity = Person
+    label = "relative wage change"
+    unit = "/1"
+    definition_period = YEAR
+    requires_computation_after = "employment_income_behavioral_response"
+
+    def formula(person, period, parameters):
+        simulation = person.simulation
+        measurement_branch = simulation.get_branch("lsr_measurement")
+        baseline_branch = simulation.get_branch("baseline").get_branch(
+            "baseline_lsr_measurement"
+        )
+        measurement_person = measurement_branch.populations["person"]
+        baseline_person = baseline_branch.populations["person"]
+        baseline_mtr = baseline_person("marginal_tax_rate", period)
+        baseline_wage = 1 - baseline_mtr
+        mtr = measurement_person("marginal_tax_rate", period)
+        wage_rate = 1 - mtr
+        # _c suffix for "clipped"
+        baseline_wage_c = np.where(baseline_wage == 0, 0.01, baseline_wage)
+        wage_rate_c = np.where(wage_rate == 0, 0.01, wage_rate)
+        relative_change = (wage_rate_c - baseline_wage_c) / baseline_wage_c
+        wage_change_bound = parameters(
+            period
+        ).gov.simulation.labor_supply_responses.bounds.effective_wage_rate_change
+        return np.clip(relative_change, -wage_change_bound, wage_change_bound)
+
+
+class income_elasticity_lsr(Variable):
+    value_type = float
+    entity = Person
+    label = "income elasticity of labor supply response"
+    unit = GBP
+    definition_period = YEAR
+    requires_computation_after = "employment_income_behavioral_response"
+
+    def formula(person, period, parameters):
+        lsr = parameters(period).gov.simulation.labor_supply_responses
+        employment_income = person("employment_income_before_lsr", period)
+        income_change = person("relative_income_change", period)
+
+        return employment_income * income_change * lsr.income_elasticity
+
+
+class substitution_elasticity_lsr(Variable):
+    value_type = float
+    entity = Person
+    label = "substitution elasticity of labor supply response"
+    unit = GBP
+    definition_period = YEAR
+    requires_computation_after = "employment_income_behavioral_response"
+
+    def formula(person, period, parameters):
+        lsr = parameters(period).gov.simulation.labor_supply_responses
+        employment_income = person("employment_income_before_lsr", period)
+        wage_change = person("relative_wage_change", period)
+
+        return employment_income * wage_change * lsr.substitution_elasticity
+
+
+class employment_income_behavioral_response(Variable):
+    value_type = float
+    entity = Person
+    label = "income-related labor supply change"
+    unit = GBP
+    definition_period = YEAR
+
+    def formula(person, period, parameters):
+        lsr = parameters(period).gov.simulation.labor_supply_responses
+        simulation = person.simulation
+        if simulation.baseline is None:
+            return 0  # No reform, no impact
+        if lsr.income_elasticity == 0 and lsr.substitution_elasticity == 0:
+            return 0
+
+        measurement_branch = simulation.get_branch(
+            "lsr_measurement", clone_system=True
+        )  # A branch without LSRs
+        baseline_branch = simulation.get_branch("baseline").get_branch(
+            "baseline_lsr_measurement", clone_system=True
+        )  # Already created by default
+
+        # (system with LSRs) <- (system without LSRs used to calculate LSRs)
+        #                      |
+        #                      * -(baseline system without LSRs used to calculate LSRs)
+
+        for branch in [measurement_branch, baseline_branch]:
+            branch.tax_benefit_system.neutralize_variable(
+                "employment_income_behavioral_response"
+            )
+            branch.set_input(
+                "employment_income_before_lsr",
+                period,
+                person("employment_income_before_lsr", period),
+            )
+
+        return add(
+            person,
+            period,
+            [
+                "income_elasticity_lsr",
+                "substitution_elasticity_lsr",
+            ],
+        )

policyengine_uk/variables/input/income.py

@@ -13,7 +13,18 @@ class employment_income(Variable):
     unit = GBP
     reference = "Income Tax (Earnings and Pensions) Act 2003 s. 1(1)(a)"
     quantity_type = FLOW
-    category = INCOME
+    adds = [
+        "employment_income_before_lsr",
+        "employment_income_behavioral_response",
+    ]
+
+
+class employment_income_before_lsr(Variable):
+    value_type = float
+    entity = Person
+    label = "employment income before labor supply responses"
+    unit = GBP
+    definition_period = YEAR
     uprating = "calibration.programs.employment_income.budgetary_impact.UNITED_KINGDOM"