Merge branch 'master' of https://github.com/PolicyEngine/policyengine-us

 into samantharudra/issue5223

CHANGELOG.md

@@ -5,6 +5,19 @@ 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).
 
+## [1.136.1] - 2024-10-29 19:55:24
+
+### Changed
+
+- Updated policyengine-us-data to 0.11.1
+- Updated microdf-python to 0.4.3
+
+## [1.136.0] - 2024-10-29 13:25:05
+
+### Added
+
+- Capital gains tax responses.
+
 ## [1.135.0] - 2024-10-28 21:33:47
 
 ### Added
@@ -10089,6 +10102,8 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 
 
+[1.136.1]: https://github.com/PolicyEngine/policyengine-us/compare/1.136.0...1.136.1
+[1.136.0]: https://github.com/PolicyEngine/policyengine-us/compare/1.135.0...1.136.0
 [1.135.0]: https://github.com/PolicyEngine/policyengine-us/compare/1.134.0...1.135.0
 [1.134.0]: https://github.com/PolicyEngine/policyengine-us/compare/1.133.0...1.134.0
 [1.133.0]: https://github.com/PolicyEngine/policyengine-us/compare/1.132.0...1.133.0

changelog.yaml

@@ -8554,3 +8554,14 @@
     added:
     - Remaining 2025 IRS tax posted parameter updates.
   date: 2024-10-28 21:33:47
+- bump: minor
+  changes:
+    added:
+    - Capital gains tax responses.
+  date: 2024-10-29 13:25:05
+- bump: patch
+  changes:
+    changed:
+    - Updated policyengine-us-data to 0.11.1
+    - Updated microdf-python to 0.4.3
+  date: 2024-10-29 19:55:24

policyengine_us/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_us/system.py

@@ -125,6 +125,16 @@ def __init__(self, *args, **kwargs):
             )
             weekly_hours.delete_arrays(known_period)
 
+        # Capital gains responses
+
+        cg_holder = self.get_holder("long_term_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
+            )
+            cg_holder.delete_arrays(known_period)
+
 
 class Microsimulation(CoreMicrosimulation):
     default_tax_benefit_system = CountryTaxBenefitSystem
@@ -169,6 +179,16 @@ def __init__(self, *args, **kwargs):
             )
             weekly_hours.delete_arrays(known_period)
 
+        # Capital gains responses
+
+        cg_holder = self.get_holder("long_term_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
+            )
+            cg_holder.delete_arrays(known_period)
+
         self.input_variables = [
             variable
             for variable in self.input_variables
@@ -177,11 +197,13 @@ def __init__(self, *args, **kwargs):
                 "employment_income",
                 "self_employment_income",
                 "weekly_hours_worked",
+                "capital_gains",
             ]
         ] + [
             "employment_income_before_lsr",
             "self_employment_income_before_lsr",
             "weekly_hours_worked_before_lsr",
+            "capital_gains_before_response",
         ]
 
 

policyengine_us/variables/gov/simulation/capital_gains_responses.py

@@ -0,0 +1,175 @@
+from policyengine_us.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", clone_system=True
+        )
+        baseline_person = baseline_branch.populations["person"]
+        baseline_branch.tax_benefit_system.neutralize_variable(
+            "capital_gains_behavioral_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", clone_system=True
+        )
+        measurement_branch.tax_benefit_system.neutralize_variable(
+            "capital_gains_behavioral_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_behavioral_response(Variable):
+    value_type = float
+    entity = Person
+    label = "capital gains behavioral response"
+    unit = USD
+    definition_period = YEAR
+
+    def formula(person, period, parameters):
+        simulation = person.simulation
+        if simulation.baseline is None:
+            return 0
+
+        if (
+            parameters(
+                period
+            ).gov.simulation.capital_gains_responses.elasticity
+            == 0
+        ):
+            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
+
+        print(response.sum())
+
+        return response
+
+
+class capital_gains_before_response(Variable):
+    label = "capital gains before responses"
+    entity = Person
+    definition_period = YEAR
+    value_type = float
+    unit = USD
+    uprating = "calibration.gov.irs.soi.long_term_capital_gains"
+
+
+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_us/variables/household/income/person/capital_gains/long_term_capital_gains.py

@@ -13,3 +13,7 @@ class long_term_capital_gains(Variable):
         href="https://www.law.cornell.edu/uscode/text/26/1222#3",
     )
     uprating = "calibration.gov.irs.soi.long_term_capital_gains"
+    adds = [
+        "capital_gains_before_response",
+        "capital_gains_behavioral_response",
+    ]

policyengine_us/variables/input/geography.py

@@ -8,6 +8,9 @@
     medicaid_rating_areas,
     second_lowest_silver_plan_cost,
 )
+import warnings
+
+warnings.filterwarnings("ignore")
 
 label = "Geography"
 

setup.py

@@ -7,7 +7,7 @@
 
 setup(
     name="policyengine-us",
-    version="1.135.0",
+    version="1.136.1",
     author="PolicyEngine",
     author_email="[email protected]",
     long_description=readme,
@@ -35,8 +35,8 @@
     ],
     install_requires=[
         "policyengine-core>=3.10.0",
-        "policyengine-us-data==1.11.0",
-        "microdf-python",
+        "policyengine-us-data==1.11.1",
+        "microdf-python>=0.4.3",
         "tqdm",
     ],
     extras_require={