Add poverty and inequality output types

src/policyengine/outputs/__init__.py

@@ -8,6 +8,22 @@
     DecileImpact,
     calculate_decile_impacts,
 )
+from policyengine.outputs.inequality import (
+    UK_INEQUALITY_INCOME_VARIABLE,
+    US_INEQUALITY_INCOME_VARIABLE,
+    Inequality,
+    calculate_uk_inequality,
+    calculate_us_inequality,
+)
+from policyengine.outputs.poverty import (
+    UK_POVERTY_VARIABLES,
+    US_POVERTY_VARIABLES,
+    Poverty,
+    UKPovertyType,
+    USPovertyType,
+    calculate_uk_poverty_rates,
+    calculate_us_poverty_rates,
+)
 
 __all__ = [
     "Output",
@@ -18,4 +34,16 @@
     "ChangeAggregateType",
     "DecileImpact",
     "calculate_decile_impacts",
+    "Poverty",
+    "UKPovertyType",
+    "USPovertyType",
+    "UK_POVERTY_VARIABLES",
+    "US_POVERTY_VARIABLES",
+    "calculate_uk_poverty_rates",
+    "calculate_us_poverty_rates",
+    "Inequality",
+    "UK_INEQUALITY_INCOME_VARIABLE",
+    "US_INEQUALITY_INCOME_VARIABLE",
+    "calculate_uk_inequality",
+    "calculate_us_inequality",
 ]

src/policyengine/outputs/inequality.py

@@ -0,0 +1,276 @@
+"""Inequality analysis output types."""
+
+from typing import Any
+
+import numpy as np
+import pandas as pd
+from pydantic import ConfigDict
+
+from policyengine.core import Output, Simulation
+
+
+def _gini(values: np.ndarray, weights: np.ndarray) -> float:
+    """Calculate weighted Gini coefficient.
+
+    Args:
+        values: Array of income values
+        weights: Array of weights
+
+    Returns:
+        Gini coefficient between 0 (perfect equality) and 1 (perfect inequality)
+    """
+    # Handle edge cases
+    if len(values) == 0 or weights.sum() == 0:
+        return 0.0
+
+    # Sort by values
+    sorted_indices = np.argsort(values)
+    sorted_values = values[sorted_indices]
+    sorted_weights = weights[sorted_indices]
+
+    # Cumulative weights and weighted values
+    cumulative_weights = np.cumsum(sorted_weights)
+    total_weight = cumulative_weights[-1]
+    cumulative_weighted_values = np.cumsum(sorted_values * sorted_weights)
+    total_weighted_value = cumulative_weighted_values[-1]
+
+    if total_weighted_value == 0:
+        return 0.0
+
+    # Calculate Gini using the area formula
+    # Gini = 1 - 2 * (area under Lorenz curve)
+    lorenz_curve = cumulative_weighted_values / total_weighted_value
+    weight_fractions = sorted_weights / total_weight
+
+    # Area under Lorenz curve using trapezoidal rule
+    area = np.sum(weight_fractions * (lorenz_curve - weight_fractions / 2))
+
+    return float(1 - 2 * area)
+
+
+class Inequality(Output):
+    """Single inequality measure result - represents one database row.
+
+    This is a single-simulation output type that calculates inequality
+    metrics for a given income variable, optionally filtered by
+    demographic variables.
+    """
+
+    model_config = ConfigDict(arbitrary_types_allowed=True)
+
+    simulation: Simulation
+    income_variable: str
+    entity: str = "household"
+
+    # Optional demographic filters
+    filter_variable: str | None = None
+    filter_variable_eq: Any | None = None
+    filter_variable_leq: Any | None = None
+    filter_variable_geq: Any | None = None
+
+    # Results populated by run()
+    gini: float | None = None
+    top_10_share: float | None = None
+    top_1_share: float | None = None
+    bottom_50_share: float | None = None
+
+    def run(self):
+        """Calculate inequality metrics."""
+        # Get income variable info
+        income_var_obj = (
+            self.simulation.tax_benefit_model_version.get_variable(
+                self.income_variable
+            )
+        )
+
+        # Get target entity data
+        target_entity = self.entity
+        data = getattr(self.simulation.output_dataset.data, target_entity)
+
+        # Map income variable to target entity if needed
+        if income_var_obj.entity != target_entity:
+            mapped = self.simulation.output_dataset.data.map_to_entity(
+                income_var_obj.entity,
+                target_entity,
+                columns=[self.income_variable],
+            )
+            income_series = mapped[self.income_variable]
+        else:
+            income_series = data[self.income_variable]
+
+        # Get weights
+        weight_col = f"{target_entity}_weight"
+        if weight_col in data.columns:
+            weights = data[weight_col]
+        else:
+            weights = pd.Series(np.ones(len(income_series)))
+
+        # Apply demographic filter if specified
+        if self.filter_variable is not None:
+            filter_var_obj = (
+                self.simulation.tax_benefit_model_version.get_variable(
+                    self.filter_variable
+                )
+            )
+
+            if filter_var_obj.entity != target_entity:
+                filter_mapped = (
+                    self.simulation.output_dataset.data.map_to_entity(
+                        filter_var_obj.entity,
+                        target_entity,
+                        columns=[self.filter_variable],
+                    )
+                )
+                filter_series = filter_mapped[self.filter_variable]
+            else:
+                filter_series = data[self.filter_variable]
+
+            # Build filter mask
+            mask = filter_series.notna()
+            if self.filter_variable_eq is not None:
+                mask &= filter_series == self.filter_variable_eq
+            if self.filter_variable_leq is not None:
+                mask &= filter_series <= self.filter_variable_leq
+            if self.filter_variable_geq is not None:
+                mask &= filter_series >= self.filter_variable_geq
+
+            # Apply mask
+            income_series = income_series[mask]
+            weights = weights[mask]
+
+        # Convert to numpy arrays
+        values = np.array(income_series)
+        weights_arr = np.array(weights)
+
+        # Remove NaN values
+        valid_mask = ~np.isnan(values) & ~np.isnan(weights_arr)
+        values = values[valid_mask]
+        weights_arr = weights_arr[valid_mask]
+
+        # Calculate Gini coefficient
+        self.gini = _gini(values, weights_arr)
+
+        # Calculate income shares
+        if len(values) > 0 and weights_arr.sum() > 0:
+            total_income = np.sum(values * weights_arr)
+
+            if total_income > 0:
+                # Sort by income
+                sorted_indices = np.argsort(values)
+                sorted_values = values[sorted_indices]
+                sorted_weights = weights_arr[sorted_indices]
+
+                # Cumulative weight fractions
+                cumulative_weights = np.cumsum(sorted_weights)
+                total_weight = cumulative_weights[-1]
+                weight_fractions = cumulative_weights / total_weight
+
+                # Top 10% share
+                top_10_mask = weight_fractions > 0.9
+                self.top_10_share = float(
+                    np.sum(
+                        sorted_values[top_10_mask]
+                        * sorted_weights[top_10_mask]
+                    )
+                    / total_income
+                )
+
+                # Top 1% share
+                top_1_mask = weight_fractions > 0.99
+                self.top_1_share = float(
+                    np.sum(
+                        sorted_values[top_1_mask] * sorted_weights[top_1_mask]
+                    )
+                    / total_income
+                )
+
+                # Bottom 50% share
+                bottom_50_mask = weight_fractions <= 0.5
+                self.bottom_50_share = float(
+                    np.sum(
+                        sorted_values[bottom_50_mask]
+                        * sorted_weights[bottom_50_mask]
+                    )
+                    / total_income
+                )
+            else:
+                self.top_10_share = 0.0
+                self.top_1_share = 0.0
+                self.bottom_50_share = 0.0
+        else:
+            self.top_10_share = 0.0
+            self.top_1_share = 0.0
+            self.bottom_50_share = 0.0
+
+
+# Default income variables for each country
+UK_INEQUALITY_INCOME_VARIABLE = "equiv_hbai_household_net_income"
+US_INEQUALITY_INCOME_VARIABLE = "household_net_income"
+
+
+def calculate_uk_inequality(
+    simulation: Simulation,
+    income_variable: str = UK_INEQUALITY_INCOME_VARIABLE,
+    filter_variable: str | None = None,
+    filter_variable_eq: Any | None = None,
+    filter_variable_leq: Any | None = None,
+    filter_variable_geq: Any | None = None,
+) -> Inequality:
+    """Calculate inequality metrics for a UK simulation.
+
+    Args:
+        simulation: The simulation to analyse
+        income_variable: Income variable to use (default: equiv_hbai_household_net_income)
+        filter_variable: Optional variable to filter by
+        filter_variable_eq: Filter for exact match
+        filter_variable_leq: Filter for less than or equal
+        filter_variable_geq: Filter for greater than or equal
+
+    Returns:
+        Inequality object with Gini and income share metrics
+    """
+    inequality = Inequality(
+        simulation=simulation,
+        income_variable=income_variable,
+        entity="household",
+        filter_variable=filter_variable,
+        filter_variable_eq=filter_variable_eq,
+        filter_variable_leq=filter_variable_leq,
+        filter_variable_geq=filter_variable_geq,
+    )
+    inequality.run()
+    return inequality
+
+
+def calculate_us_inequality(
+    simulation: Simulation,
+    income_variable: str = US_INEQUALITY_INCOME_VARIABLE,
+    filter_variable: str | None = None,
+    filter_variable_eq: Any | None = None,
+    filter_variable_leq: Any | None = None,
+    filter_variable_geq: Any | None = None,
+) -> Inequality:
+    """Calculate inequality metrics for a US simulation.
+
+    Args:
+        simulation: The simulation to analyse
+        income_variable: Income variable to use (default: household_net_income)
+        filter_variable: Optional variable to filter by
+        filter_variable_eq: Filter for exact match
+        filter_variable_leq: Filter for less than or equal
+        filter_variable_geq: Filter for greater than or equal
+
+    Returns:
+        Inequality object with Gini and income share metrics
+    """
+    inequality = Inequality(
+        simulation=simulation,
+        income_variable=income_variable,
+        entity="household",
+        filter_variable=filter_variable,
+        filter_variable_eq=filter_variable_eq,
+        filter_variable_leq=filter_variable_leq,
+        filter_variable_geq=filter_variable_geq,
+    )
+    inequality.run()
+    return inequality