Merge pull request #43 from PolicyEngine/api-parity

Add all remaining general economy impacts

Author: nikhilwoodruff

docs/schema.ipynb

@@ -0,0 +1,41 @@
+from policyengine import Simulation
+from microdf import MicroSeries
+
+
+def income(simulation: Simulation):
+    baseline = simulation.calculate("macro/baseline")
+    reform = simulation.calculate("macro/reform")
+
+    baseline_income = MicroSeries(
+        baseline["household"]["finance"]["household_net_income"],
+        weights=baseline["household"]["demographics"]["household_weight"],
+    )
+    reform_income = MicroSeries(
+        reform["household"]["finance"]["household_net_income"],
+        weights=baseline_income.weights,
+    )
+
+    # Filter out negative decile values
+    decile = MicroSeries(
+        baseline["household"]["finance"]["household_income_decile"]
+    )
+    baseline_income_filtered = baseline_income[decile >= 0]
+    reform_income_filtered = reform_income[decile >= 0]
+
+    income_change = reform_income_filtered - baseline_income_filtered
+    rel_income_change_by_decile = (
+        income_change.groupby(decile).sum()
+        / baseline_income_filtered.groupby(decile).sum()
+    )
+
+    avg_income_change_by_decile = (
+        income_change.groupby(decile).sum()
+        / baseline_income_filtered.groupby(decile).count()
+    )
+    rel_decile_dict = rel_income_change_by_decile.to_dict()
+    avg_decile_dict = avg_income_change_by_decile.to_dict()
+    result = dict(
+        relative={int(k): v for k, v in rel_decile_dict.items()},
+        average={int(k): v for k, v in avg_decile_dict.items()},
+    )
+    return result

policyengine/outputs/macro/comparison/decile/income.py

@@ -0,0 +1,42 @@
+from policyengine import Simulation
+from microdf import MicroSeries
+
+
+def wealth(simulation: Simulation):
+    if simulation.country != "uk":
+        return {}
+
+    baseline = simulation.calculate("macro/baseline")
+    reform = simulation.calculate("macro/reform")
+
+    baseline_income = MicroSeries(
+        baseline["household"]["finance"]["household_net_income"],
+        weights=baseline["household"]["demographics"]["household_weight"],
+    )
+    reform_income = MicroSeries(
+        reform["household"]["finance"]["household_net_income"],
+        weights=baseline_income.weights,
+    )
+
+    # Filter out negative decile values
+    decile = MicroSeries(baseline["household"]["finance"]["wealth_decile"])
+    baseline_income_filtered = baseline_income[decile >= 0]
+    reform_income_filtered = reform_income[decile >= 0]
+
+    income_change = reform_income_filtered - baseline_income_filtered
+    rel_income_change_by_decile = (
+        income_change.groupby(decile).sum()
+        / baseline_income_filtered.groupby(decile).sum()
+    )
+
+    avg_income_change_by_decile = (
+        income_change.groupby(decile).sum()
+        / baseline_income_filtered.groupby(decile).count()
+    )
+    rel_decile_dict = rel_income_change_by_decile.to_dict()
+    avg_decile_dict = avg_income_change_by_decile.to_dict()
+    result = dict(
+        relative={int(k): v for k, v in rel_decile_dict.items()},
+        average={int(k): v for k, v in avg_decile_dict.items()},
+    )
+    return result

policyengine/outputs/macro/comparison/decile/wealth.py

@@ -0,0 +1,17 @@
+from policyengine import Simulation
+
+
+def detailed_budget(simulation: Simulation):
+    baseline = simulation.calculate("macro/baseline")
+    reform = simulation.calculate("macro/reform")
+    result = {}
+    if simulation.country == "uk":
+        for program in baseline["gov"]["programs"]:
+            # baseline[programs][program] = total budgetary impact of program
+            result[program] = dict(
+                baseline=baseline["gov"]["programs"][program],
+                reform=reform["gov"]["programs"][program],
+                difference=reform["gov"]["programs"][program]
+                - baseline["gov"]["programs"][program],
+            )
+    return result

policyengine/outputs/macro/comparison/detailed_budget.py

@@ -0,0 +1,23 @@
+from policyengine import Simulation
+
+
+def inequality(simulation: Simulation):
+    baseline = simulation.calculate("macro/baseline")["household"][
+        "inequality"
+    ]
+    reform = simulation.calculate("macro/reform")["household"]["inequality"]
+
+    return dict(
+        gini=dict(
+            baseline=baseline["gini"],
+            reform=reform["gini"],
+        ),
+        top_10_pct_share=dict(
+            baseline=baseline["top_10_percent_share"],
+            reform=reform["top_10_percent_share"],
+        ),
+        top_1_pct_share=dict(
+            baseline=baseline["top_1_percent_share"],
+            reform=reform["top_1_percent_share"],
+        ),
+    )

policyengine/outputs/macro/comparison/inequality.py

@@ -0,0 +1,109 @@
+from policyengine import Simulation
+from microdf import MicroSeries
+import numpy as np
+
+
+def labor_supply_response(simulation: Simulation) -> dict:
+    baseline = simulation.calculate("macro/baseline")
+    reform = simulation.calculate("macro/reform")
+
+    substitution_lsr = (
+        reform["household"]["labor_supply"]["substitution_lsr"]
+        - baseline["household"]["labor_supply"]["substitution_lsr"]
+    )
+    income_lsr = (
+        reform["household"]["labor_supply"]["income_lsr"]
+        - baseline["household"]["labor_supply"]["income_lsr"]
+    )
+    total_change = substitution_lsr + income_lsr
+    revenue_change = (
+        reform["household"]["labor_supply"]["budgetary_impact_lsr"]
+        - baseline["household"]["labor_supply"]["budgetary_impact_lsr"]
+    )
+
+    substitution_lsr_hh = np.array(
+        reform["household"]["labor_supply"]["substitution_lsr_hh"]
+    ) - np.array(baseline["household"]["labor_supply"]["substitution_lsr_hh"])
+    income_lsr_hh = np.array(
+        reform["household"]["labor_supply"]["income_lsr_hh"]
+    ) - np.array(baseline["household"]["labor_supply"]["income_lsr_hh"])
+    decile = np.array(
+        baseline["household"]["finance"]["household_income_decile"]
+    )
+    household_weight = baseline["household"]["demographics"][
+        "household_weight"
+    ]
+
+    total_lsr_hh = substitution_lsr_hh + income_lsr_hh
+
+    emp_income = MicroSeries(
+        baseline["household"]["finance"]["employment_income_hh"],
+        weights=household_weight,
+    )
+    self_emp_income = MicroSeries(
+        baseline["household"]["finance"]["self_employment_income_hh"],
+        weights=household_weight,
+    )
+    earnings = emp_income + self_emp_income
+    original_earnings = earnings - total_lsr_hh
+    substitution_lsr_hh = MicroSeries(
+        substitution_lsr_hh, weights=household_weight
+    )
+    income_lsr_hh = MicroSeries(income_lsr_hh, weights=household_weight)
+
+    decile_avg = dict(
+        income=income_lsr_hh.groupby(decile).mean().to_dict(),
+        substitution=substitution_lsr_hh.groupby(decile).mean().to_dict(),
+    )
+    decile_rel = dict(
+        income=(
+            income_lsr_hh.groupby(decile).sum()
+            / original_earnings.groupby(decile).sum()
+        ).to_dict(),
+        substitution=(
+            substitution_lsr_hh.groupby(decile).sum()
+            / original_earnings.groupby(decile).sum()
+        ).to_dict(),
+    )
+
+    relative_lsr = dict(
+        income=(income_lsr_hh.sum() / original_earnings.sum()),
+        substitution=(substitution_lsr_hh.sum() / original_earnings.sum()),
+    )
+
+    decile_rel["income"] = {
+        int(k): v for k, v in decile_rel["income"].items() if k > 0
+    }
+    decile_rel["substitution"] = {
+        int(k): v for k, v in decile_rel["substitution"].items() if k > 0
+    }
+
+    hours = dict(
+        baseline=baseline["household"]["labor_supply"]["weekly_hours"],
+        reform=reform["household"]["labor_supply"]["weekly_hours"],
+        change=reform["household"]["labor_supply"]["weekly_hours"]
+        - baseline["household"]["labor_supply"]["weekly_hours"],
+        income_effect=reform["household"]["labor_supply"][
+            "weekly_hours_income_effect"
+        ]
+        - baseline["household"]["labor_supply"]["weekly_hours_income_effect"],
+        substitution_effect=reform["household"]["labor_supply"][
+            "weekly_hours_substitution_effect"
+        ]
+        - baseline["household"]["labor_supply"][
+            "weekly_hours_substitution_effect"
+        ],
+    )
+
+    return dict(
+        substitution_lsr=substitution_lsr,
+        income_lsr=income_lsr,
+        relative_lsr=relative_lsr,
+        total_change=total_change,
+        revenue_change=revenue_change,
+        decile=dict(
+            average=decile_avg,
+            relative=decile_rel,
+        ),
+        hours=hours,
+    )

policyengine/outputs/macro/comparison/labor_supply_response.py

@@ -0,0 +1,71 @@
+from policyengine import Simulation
+from microdf import MicroSeries
+
+
+def age(simulation: Simulation):
+    baseline = simulation.calculate("macro/baseline")["household"]["finance"]
+    reform = simulation.calculate("macro/reform")["household"]["finance"]
+    baseline_demographics = simulation.calculate("macro/baseline")[
+        "household"
+    ]["demographics"]
+
+    baseline_poverty = MicroSeries(
+        baseline["person_in_poverty"],
+        weights=baseline_demographics["person_weight"],
+    )
+    baseline_deep_poverty = MicroSeries(
+        baseline["person_in_deep_poverty"],
+        weights=baseline_demographics["person_weight"],
+    )
+    reform_poverty = MicroSeries(
+        reform["person_in_poverty"], weights=baseline_poverty.weights
+    )
+    reform_deep_poverty = MicroSeries(
+        reform["person_in_deep_poverty"], weights=baseline_poverty.weights
+    )
+    age = MicroSeries(baseline_demographics["age"])
+
+    poverty = dict(
+        child=dict(
+            baseline=float(baseline_poverty[age < 18].mean()),
+            reform=float(reform_poverty[age < 18].mean()),
+        ),
+        adult=dict(
+            baseline=float(baseline_poverty[(age >= 18) & (age < 65)].mean()),
+            reform=float(reform_poverty[(age >= 18) & (age < 65)].mean()),
+        ),
+        senior=dict(
+            baseline=float(baseline_poverty[age >= 65].mean()),
+            reform=float(reform_poverty[age >= 65].mean()),
+        ),
+        all=dict(
+            baseline=float(baseline_poverty.mean()),
+            reform=float(reform_poverty.mean()),
+        ),
+    )
+
+    deep_poverty = dict(
+        child=dict(
+            baseline=float(baseline_deep_poverty[age < 18].mean()),
+            reform=float(reform_deep_poverty[age < 18].mean()),
+        ),
+        adult=dict(
+            baseline=float(
+                baseline_deep_poverty[(age >= 18) & (age < 65)].mean()
+            ),
+            reform=float(reform_deep_poverty[(age >= 18) & (age < 65)].mean()),
+        ),
+        senior=dict(
+            baseline=float(baseline_deep_poverty[age >= 65].mean()),
+            reform=float(reform_deep_poverty[age >= 65].mean()),
+        ),
+        all=dict(
+            baseline=float(baseline_deep_poverty.mean()),
+            reform=float(reform_deep_poverty.mean()),
+        ),
+    )
+
+    return dict(
+        poverty=poverty,
+        deep_poverty=deep_poverty,
+    )

policyengine/outputs/macro/comparison/poverty/age.py

@@ -0,0 +1,55 @@
+from policyengine import Simulation
+from microdf import MicroSeries
+
+
+def gender(simulation: Simulation):
+    baseline = simulation.calculate("macro/baseline")["household"]["finance"]
+    reform = simulation.calculate("macro/reform")["household"]["finance"]
+    baseline_demographics = simulation.calculate("macro/baseline")[
+        "household"
+    ]["demographics"]
+
+    if baseline_demographics["is_male"] is None:
+        return {}
+    baseline_poverty = MicroSeries(
+        baseline["person_in_poverty"],
+        weights=baseline_demographics["person_weight"],
+    )
+    baseline_deep_poverty = MicroSeries(
+        baseline["person_in_deep_poverty"],
+        weights=baseline_demographics["person_weight"],
+    )
+    reform_poverty = MicroSeries(
+        reform["person_in_poverty"], weights=baseline_poverty.weights
+    )
+    reform_deep_poverty = MicroSeries(
+        reform["person_in_deep_poverty"], weights=baseline_poverty.weights
+    )
+    is_male = MicroSeries(baseline_demographics["is_male"])
+
+    poverty = dict(
+        male=dict(
+            baseline=float(baseline_poverty[is_male].mean()),
+            reform=float(reform_poverty[is_male].mean()),
+        ),
+        female=dict(
+            baseline=float(baseline_poverty[~is_male].mean()),
+            reform=float(reform_poverty[~is_male].mean()),
+        ),
+    )
+
+    deep_poverty = dict(
+        male=dict(
+            baseline=float(baseline_deep_poverty[is_male].mean()),
+            reform=float(reform_deep_poverty[is_male].mean()),
+        ),
+        female=dict(
+            baseline=float(baseline_deep_poverty[~is_male].mean()),
+            reform=float(reform_deep_poverty[~is_male].mean()),
+        ),
+    )
+
+    return dict(
+        poverty=poverty,
+        deep_poverty=deep_poverty,
+    )