Improve SSI asset imputation and calibration targets

changelog.d/1096.changed

@@ -0,0 +1 @@
+Improved SSI-sensitive liquid asset imputation predictors and added SSA SSI recipient calibration targets.

policyengine_us_data/calibration/source_impute.py

@@ -36,6 +36,8 @@
     derive_is_tipped_occupation,
 )
 from policyengine_us_data.datasets.sipp.sipp import (
+    ASSET_JOB_EARNINGS_COLUMNS,
+    ASSET_PREDICTORS,
     VEHICLE_MODEL_PREDICTORS,
     build_vehicle_training_frame,
 )
@@ -121,16 +123,7 @@
     "is_tipped_occupation",
 ]
 
-SIPP_ASSETS_PREDICTORS = [
-    "employment_income",
-    "interest_income",
-    "dividend_income",
-    "rental_income",
-    "age",
-    "is_female",
-    "is_married",
-    "count_under_18",
-]
+SIPP_ASSETS_PREDICTORS = ASSET_PREDICTORS
 
 SCF_PREDICTORS = [
     "age",
@@ -316,6 +309,164 @@ def _person_state_fips(
     return np.repeat(state_fips, counts)
 
 
+def _person_is_married(
+    data: Dict[str, Dict[int, np.ndarray]],
+    time_period: int,
+    n_persons: int,
+) -> np.ndarray:
+    """Return a person-level married flag from CPS-compatible inputs."""
+    if "is_married" in data and time_period in data["is_married"]:
+        values = np.asarray(data["is_married"][time_period])
+        if len(values) == n_persons:
+            return values.astype(np.float32)
+
+    marital_unit_id = data.get("person_marital_unit_id", {}).get(time_period)
+    if marital_unit_id is not None and len(marital_unit_id) == n_persons:
+        marital_unit_id = np.asarray(marital_unit_id)
+        counts = pd.Series(marital_unit_id).map(
+            pd.Series(marital_unit_id).value_counts()
+        )
+        return (counts.to_numpy() > 1).astype(np.float32)
+
+    return np.zeros(n_persons, dtype=np.float32)
+
+
+def _add_person_household_counts(
+    df: pd.DataFrame,
+    data: Dict[str, Dict[int, np.ndarray]],
+    time_period: int,
+) -> pd.DataFrame:
+    """Add household composition predictors to a person-level CPS frame."""
+    if "age" not in df.columns and "age" in data:
+        df["age"] = data["age"][time_period].astype(np.float32)
+
+    hh_ids_person = data.get("person_household_id", {}).get(time_period)
+    if hh_ids_person is None or "age" not in df.columns:
+        df["count_under_18"] = 0.0
+        df["count_under_6"] = 0.0
+        df["household_size"] = 1.0
+        return df
+
+    age_df = pd.DataFrame(
+        {
+            "hh": hh_ids_person,
+            "age": np.asarray(df["age"]),
+        }
+    )
+    grouped = age_df.groupby("hh")["age"]
+    df["count_under_18"] = (
+        grouped.transform(lambda values: (values < 18).sum())
+        .to_numpy()
+        .astype(np.float32)
+    )
+    df["count_under_6"] = (
+        grouped.transform(lambda values: (values < 6).sum())
+        .to_numpy()
+        .astype(np.float32)
+    )
+    df["household_size"] = grouped.transform("size").to_numpy().astype(np.float32)
+    return df
+
+
+def _add_sipp_asset_predictors(asset_df: pd.DataFrame) -> pd.DataFrame:
+    """Add SIPP-side liquid-asset model predictors without SSI receipt."""
+    asset_df = asset_df.copy()
+    asset_df["bank_account_assets"] = asset_df["TVAL_BANK"].fillna(0)
+    asset_df["stock_assets"] = asset_df["TVAL_STMF"].fillna(0)
+    asset_df["bond_assets"] = asset_df["TVAL_BOND"].fillna(0)
+    asset_df["age"] = asset_df.TAGE
+    asset_df["is_female"] = asset_df.ESEX == 2
+    asset_df["is_married"] = asset_df.EMS == 1
+
+    job_cols = [col for col in ASSET_JOB_EARNINGS_COLUMNS if col in asset_df]
+    if job_cols:
+        asset_df["employment_income"] = asset_df[job_cols].fillna(0).sum(axis=1) * 12
+    elif "TPTOTINC" in asset_df:
+        asset_df["employment_income"] = asset_df.TPTOTINC.fillna(0) * 12
+    else:
+        asset_df["employment_income"] = 0.0
+
+    asset_df["interest_income"] = (
+        asset_df["TINC_BANK"].fillna(0) + asset_df["TINC_BOND"].fillna(0)
+    ) * 12
+    asset_df["dividend_income"] = asset_df["TINC_STMF"].fillna(0) * 12
+    asset_df["rental_income"] = asset_df["TINC_RENT"].fillna(0) * 12
+    asset_df["social_security"] = asset_df["TSSSAMT"].fillna(0) * 12
+    asset_df["retirement_income"] = asset_df["TRETINCAMT"].fillna(0) * 12
+    asset_df["non_ssi_income"] = (
+        asset_df["employment_income"]
+        + asset_df["social_security"]
+        + asset_df["retirement_income"]
+    )
+    asset_df["household_weight"] = asset_df.WPFINWGT
+
+    asset_df["is_under_18"] = asset_df.TAGE < 18
+    asset_df["is_under_6"] = asset_df.TAGE < 6
+    grouped = asset_df.groupby("SSUID")
+    asset_df["count_under_18"] = grouped["is_under_18"].transform("sum")
+    asset_df["count_under_6"] = grouped["is_under_6"].transform("sum")
+    asset_df["household_size"] = grouped["PNUM"].transform("count")
+    return asset_df
+
+
+def _add_cps_asset_predictors(
+    cps_asset_df: pd.DataFrame,
+    data: Dict[str, Dict[int, np.ndarray]],
+    time_period: int,
+) -> pd.DataFrame:
+    """Add CPS-side predictors aligned to the SIPP liquid-asset model."""
+    cps_asset_df = cps_asset_df.copy()
+    n_persons = len(cps_asset_df)
+
+    if "is_male" in cps_asset_df.columns:
+        cps_asset_df["is_female"] = (~cps_asset_df["is_male"].astype(bool)).astype(
+            np.float32
+        )
+    elif "is_female" in data:
+        cps_asset_df["is_female"] = data["is_female"][time_period].astype(np.float32)
+    else:
+        cps_asset_df["is_female"] = 0.0
+
+    cps_asset_df["is_married"] = _person_is_married(
+        data,
+        time_period,
+        n_persons,
+    )
+    cps_asset_df = _add_person_household_counts(cps_asset_df, data, time_period)
+
+    for var in [
+        "employment_income",
+        "interest_income",
+        "dividend_income",
+        "rental_income",
+        "social_security",
+        "pension_income",
+        "retirement_distributions",
+    ]:
+        if var in cps_asset_df.columns:
+            continue
+        if var in data:
+            cps_asset_df[var] = data[var][time_period].astype(np.float32)
+        else:
+            cps_asset_df[var] = 0.0
+
+    cps_asset_df["retirement_income"] = cps_asset_df["pension_income"].fillna(
+        0
+    ) + cps_asset_df["retirement_distributions"].fillna(0)
+    cps_asset_df["non_ssi_income"] = (
+        cps_asset_df["employment_income"].fillna(0)
+        + cps_asset_df["social_security"].fillna(0)
+        + cps_asset_df["retirement_income"].fillna(0)
+    )
+
+    for predictor in SIPP_ASSETS_PREDICTORS:
+        if predictor not in cps_asset_df.columns:
+            cps_asset_df[predictor] = 0.0
+        cps_asset_df[predictor] = cps_asset_df[predictor].fillna(0).astype(np.float32)
+
+    return cps_asset_df
+
+
 @pipeline_node(
     PipelineNode(
         id="acs_qrf",
@@ -571,56 +722,30 @@ def _impute_sipp(
             "TAGE",
             "ESEX",
             "EMS",
-            "TPTOTINC",
+            "TSSSAMT",
+            "TRETINCAMT",
             "TVAL_BANK",
             "TVAL_STMF",
             "TVAL_BOND",
             "TINC_BANK",
             "TINC_STMF",
             "TINC_BOND",
             "TINC_RENT",
-        ]
+        ] + ASSET_JOB_EARNINGS_COLUMNS
         asset_df = pd.read_csv(
             STORAGE_FOLDER / "pu2023.csv",
             delimiter="|",
             usecols=asset_cols,
         )
         asset_df = asset_df[asset_df.MONTHCODE == 12]
-
-        asset_df["bank_account_assets"] = asset_df["TVAL_BANK"].fillna(0)
-        asset_df["stock_assets"] = asset_df["TVAL_STMF"].fillna(0)
-        asset_df["bond_assets"] = asset_df["TVAL_BOND"].fillna(0)
-        asset_df["age"] = asset_df.TAGE
-        asset_df["is_female"] = asset_df.ESEX == 2
-        asset_df["is_married"] = asset_df.EMS == 1
-        asset_df["employment_income"] = asset_df.TPTOTINC * 12
-        asset_df["interest_income"] = (
-            asset_df["TINC_BANK"].fillna(0) + asset_df["TINC_BOND"].fillna(0)
-        ) * 12
-        asset_df["dividend_income"] = asset_df["TINC_STMF"].fillna(0) * 12
-        asset_df["rental_income"] = asset_df["TINC_RENT"].fillna(0) * 12
-        asset_df["household_weight"] = asset_df.WPFINWGT
-        asset_df["is_under_18"] = asset_df.TAGE < 18
-        asset_df["count_under_18"] = (
-            asset_df.groupby("SSUID")["is_under_18"]
-            .sum()
-            .loc[asset_df.SSUID.values]
-            .values
-        )
+        asset_df = _add_sipp_asset_predictors(asset_df)
 
         asset_train_cols = [
-            "employment_income",
-            "interest_income",
-            "dividend_income",
-            "rental_income",
             "bank_account_assets",
             "stock_assets",
             "bond_assets",
-            "age",
-            "is_female",
-            "is_married",
-            "count_under_18",
             "household_weight",
+            *SIPP_ASSETS_PREDICTORS,
         ]
         asset_train = asset_df[asset_train_cols].dropna()
         asset_train = asset_train.loc[
@@ -641,39 +766,18 @@ def _impute_sipp(
                 "interest_income",
                 "dividend_income",
                 "rental_income",
+                "social_security",
+                "pension_income",
+                "retirement_distributions",
                 "age",
                 "is_male",
             ],
         )
-        if "is_male" in cps_asset_df.columns:
-            cps_asset_df["is_female"] = (~cps_asset_df["is_male"].astype(bool)).astype(
-                np.float32
-            )
-        else:
-            cps_asset_df["is_female"] = 0.0
-        if "is_married" in data:
-            cps_asset_df["is_married"] = data["is_married"][time_period].astype(
-                np.float32
-            )
-        else:
-            cps_asset_df["is_married"] = 0.0
-        cps_asset_df["count_under_18"] = (
-            cps_tip_df["count_under_18"]
-            if "count_under_18" in cps_tip_df.columns
-            else 0.0
+        cps_asset_df = _add_cps_asset_predictors(
+            cps_asset_df,
+            data,
+            time_period,
         )
-        for cap_var in [
-            "interest_income",
-            "dividend_income",
-            "rental_income",
-        ]:
-            if cap_var not in cps_asset_df.columns:
-                if cap_var in data:
-                    cps_asset_df[cap_var] = data[cap_var][time_period].astype(
-                        np.float32
-                    )
-                else:
-                    cps_asset_df[cap_var] = 0.0
 
         asset_vars = [
             "bank_account_assets",
@@ -738,12 +842,11 @@ def _impute_sipp(
             ).astype(np.float32)
         else:
             cps_vehicle_df["is_female"] = 0.0
-        if "is_married" in data:
-            cps_vehicle_df["is_married"] = data["is_married"][time_period].astype(
-                np.float32
-            )
-        else:
-            cps_vehicle_df["is_married"] = 0.0
+        cps_vehicle_df["is_married"] = _person_is_married(
+            data,
+            time_period,
+            len(cps_vehicle_df),
+        )
         for cap_var in [
             "interest_income",
             "dividend_income",

policyengine_us_data/calibration/target_config.yaml

@@ -207,6 +207,12 @@ include:
     geo_level: national
   - variable: ssi
     geo_level: national
+  - variable: person_count
+    geo_level: national
+    domain_variable: ssi
+  - variable: person_count
+    geo_level: national
+    domain_variable: age,ssi
   - variable: tanf
     geo_level: national
   - variable: spm_unit_count

policyengine_us_data/calibration/unified_matrix_builder.py

@@ -2110,8 +2110,26 @@ def _query_targets(self, target_filter: dict) -> pd.DataFrame:
 
         if "domain_variables" in target_filter:
             dvs = target_filter["domain_variables"]
-            ph = ",".join(f"'{dv}'" for dv in dvs)
-            and_conditions.append(f"tv.domain_variable IN ({ph})")
+            exact_ph = ",".join(f"'{dv}'" for dv in dvs)
+            single_constraint_dvs = [dv for dv in dvs if "," not in str(dv)]
+            if single_constraint_dvs:
+                component_ph = ",".join(f"'{dv}'" for dv in single_constraint_dvs)
+                and_conditions.append(
+                    "("
+                    f"tv.domain_variable IN ({exact_ph}) "
+                    "OR EXISTS ("
+                    "SELECT 1 FROM stratum_constraints sc_domain "
+                    "WHERE sc_domain.stratum_id = tv.stratum_id "
+                    "AND sc_domain.constraint_variable NOT IN ("
+                    "'state_fips', 'congressional_district_geoid', "
+                    "'tax_unit_is_filer', 'ucgid_str'"
+                    ") "
+                    f"AND sc_domain.constraint_variable IN ({component_ph})"
+                    ")"
+                    ")"
+                )
+            else:
+                and_conditions.append(f"tv.domain_variable IN ({exact_ph})")
 
         if "variables" in target_filter:
             vs = ",".join(f"'{v}'" for v in target_filter["variables"])