Add capital gains responses (#979)

* Add capital gains responses

* Add changes

* Versioning

.gitignore

@@ -50,4 +50,6 @@ policyengine_uk/calibration/*.h5
 **/*.csv
 **/*.pkl
 **/*.log
-**/ukmod.json
+**/ukmod.json
+
+*.ipynb

CHANGELOG.md

@@ -5,6 +5,12 @@ All notable changes to this project will be documented in this file.
 The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/), 
 and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html).
 
+## [2.12.0] - 2024-10-23 14:47:21
+
+### Added
+
+- Capital Gains Tax elasticities.
+
 ## [2.11.0] - 2024-10-23 10:15:26
 
 ### Added
@@ -1548,6 +1554,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 
 
+[2.12.0]: https://github.com/PolicyEngine/openfisca-uk/compare/2.11.0...2.12.0
 [2.11.0]: https://github.com/PolicyEngine/openfisca-uk/compare/2.10.0...2.11.0
 [2.10.0]: https://github.com/PolicyEngine/openfisca-uk/compare/2.9.0...2.10.0
 [2.9.0]: https://github.com/PolicyEngine/openfisca-uk/compare/2.8.0...2.9.0

changelog.yaml

@@ -1299,3 +1299,8 @@
     added:
     - Benefit uprating for 2025/26.
   date: 2024-10-23 10:15:26
+- bump: minor
+  changes:
+    added:
+    - Capital Gains Tax elasticities.
+  date: 2024-10-23 14:47:21

policyengine_uk/parameters/gov/simulation/capital_gains_responses/elasticity.yaml

@@ -0,0 +1,6 @@
+description: Elasticity of capital gains with respect to the capital gains marginal tax rate.
+values:
+  2000-01-01: 0
+metadata:
+  unit: /1
+  label: capital gains elasticity

policyengine_uk/system.py

@@ -74,6 +74,16 @@ def __init__(self, *args, **kwargs):
             self.set_input("employment_income_before_lsr", known_period, array)
             employment_income.delete_arrays(known_period)
 
+        # Capital gains responses
+
+        cg_holder = self.get_holder("capital_gains")
+        for known_period in cg_holder.get_known_periods():
+            array = cg_holder.get_array(known_period)
+            self.set_input(
+                "capital_gains_before_response", known_period, array
+            )
+            employment_income.delete_arrays(known_period)
+
 
 class Microsimulation(CoreMicrosimulation):
     default_tax_benefit_system = CountryTaxBenefitSystem
@@ -105,3 +115,14 @@ def __init__(self, *args, **kwargs):
                     "employment_income_before_lsr", known_period, array
                 )
                 employment_income.delete_arrays(known_period)
+
+        # Capital gains responses
+
+        for simulation in list(self.branches.values()) + [self]:
+            cg_holder = self.get_holder("capital_gains")
+            for known_period in cg_holder.get_known_periods():
+                array = cg_holder.get_array(known_period)
+                self.set_input(
+                    "capital_gains_before_response", known_period, array
+                )
+                employment_income.delete_arrays(known_period)

policyengine_uk/variables/gov/hmrc/capital_gains_tax/capital_gains_tax.py

@@ -1,4 +1,5 @@
 from policyengine_uk.model_api import *
+from policyengine_core.holders import Holder
 
 
 class capital_gains_tax(Variable):

policyengine_uk/variables/gov/hmrc/capital_gains_tax/responses.py

@@ -0,0 +1,163 @@
+from policyengine_uk.model_api import *
+from policyengine_core.simulations import *
+
+
+class relative_capital_gains_mtr_change(Variable):
+    value_type = float
+    entity = Person
+    label = "relative change in capital gains tax rate"
+    unit = "/1"
+    definition_period = YEAR
+
+    def formula(person, period, parameters):
+        simulation: Simulation = person.simulation
+        baseline_branch = simulation.get_branch("baseline").get_branch(
+            "baseline_cgr_measurement"
+        )
+        baseline_person = baseline_branch.populations["person"]
+        baseline_branch.tax_benefit_system.neutralize_variable(
+            "capital_gains_behavioural_response"
+        )
+        baseline_branch.set_input(
+            "capital_gains_before_response",
+            period,
+            person("capital_gains_before_response", period),
+        )
+        baseline_mtr = baseline_person(
+            "marginal_tax_rate_on_capital_gains", period
+        )
+        del simulation.branches["baseline"].branches[
+            "baseline_cgr_measurement"
+        ]
+
+        measurement_branch = simulation.get_branch("cgr_measurement")
+        measurement_branch.tax_benefit_system.neutralize_variable(
+            "capital_gains_behavioural_response"
+        )
+        measurement_branch.set_input(
+            "capital_gains_before_response",
+            period,
+            person("capital_gains_before_response", period),
+        )
+        measurement_person = measurement_branch.populations["person"]
+        reform_mtr = measurement_person(
+            "marginal_tax_rate_on_capital_gains", period
+        )
+        del simulation.branches["cgr_measurement"]
+
+        # Handle zeros in tax rates to prevent log(0)
+        min_rate = 0.001
+        baseline_mtr_adj = np.maximum(baseline_mtr, min_rate)
+        reform_mtr_adj = np.maximum(reform_mtr, min_rate)
+
+        # Calculate log difference
+        return np.log(reform_mtr_adj) - np.log(baseline_mtr_adj)
+
+
+class capital_gains_elasticity(Variable):
+    value_type = float
+    entity = Person
+    label = "elasticity of capital gains realizations"
+    unit = "/1"
+    definition_period = YEAR
+
+    def formula(person, period, parameters):
+        gov = parameters(period).gov
+        return gov.simulation.capital_gains_responses.elasticity
+
+
+class capital_gains_behavioural_response(Variable):
+    value_type = float
+    entity = Person
+    label = "capital gains behavioral response"
+    unit = GBP
+    definition_period = YEAR
+
+    def formula(person, period, parameters):
+        simulation = person.simulation
+        if simulation.baseline is None:
+            return 0
+
+        capital_gains = person("capital_gains_before_response", period)
+        tax_rate_change = person("relative_capital_gains_mtr_change", period)
+        elasticity = person("capital_gains_elasticity", period)
+
+        # Calculate response using log differences
+        response_factor = np.exp(elasticity * tax_rate_change) - 1
+        response = capital_gains * response_factor
+
+        return response
+
+
+class capital_gains_before_response(Variable):
+    label = "capital gains before responses"
+    entity = Person
+    definition_period = YEAR
+    value_type = float
+    unit = GBP
+    uprating = "calibration.programs.capital_gains.total"
+
+
+class adult_index_cg(Variable):
+    value_type = int
+    entity = Person
+    label = "index of adult in household, ranked by capital gains"
+    definition_period = YEAR
+
+    def formula(person, period, parameters):
+        return (
+            person.get_rank(
+                person.household,
+                -person("capital_gains_before_response", period),
+                condition=person("is_adult", period),
+            )
+            + 1
+        )
+
+
+class marginal_tax_rate_on_capital_gains(Variable):
+    label = "capital gains marginal tax rate"
+    documentation = "Percent of marginal capital gains that do not increase household net income."
+    entity = Person
+    definition_period = YEAR
+    value_type = float
+    unit = "/1"
+
+    def formula(person, period, parameters):
+        mtr_values = np.zeros(person.count, dtype=np.float32)
+        simulation = person.simulation
+        DELTA = 1_000
+        adult_index_values = person("adult_index_cg", period)
+        for adult_index in [1, 2]:
+            alt_simulation = simulation.get_branch(
+                f"adult_{adult_index}_cg_rise"
+            )
+            mask = adult_index_values == adult_index
+            for variable in simulation.tax_benefit_system.variables:
+                variable_data = simulation.tax_benefit_system.variables[
+                    variable
+                ]
+                if (
+                    variable not in simulation.input_variables
+                    and not variable_data.is_input_variable()
+                ):
+                    alt_simulation.delete_arrays(variable)
+            alt_simulation.set_input(
+                "capital_gains",
+                period,
+                person("capital_gains", period) + mask * DELTA,
+            )
+            alt_person = alt_simulation.person
+            household_net_income = person.household(
+                "household_net_income", period
+            )
+            household_net_income_higher_earnings = alt_person.household(
+                "household_net_income", period
+            )
+            increase = (
+                household_net_income_higher_earnings - household_net_income
+            )
+            mtr_values += where(mask, 1 - increase / DELTA, 0)
+
+            del simulation.branches[f"adult_{adult_index}_cg_rise"]
+        return mtr_values

policyengine_uk/variables/household/income/income.py

@@ -430,3 +430,7 @@ class capital_gains(Variable):
     value_type = float
     unit = GBP
     uprating = "calibration.programs.capital_gains.total"
+    adds = [
+        "capital_gains_before_response",
+        "capital_gains_behavioural_response",
+    ]

setup.py

@@ -4,7 +4,7 @@
 
 setup(
     name="PolicyEngine-UK",
-    version="2.11.0",
+    version="2.12.0",
     author="PolicyEngine",
     author_email="nikhil@policyengine.org",
     classifiers=[