Add ORG labor-market imputations to CPS and eCPS

policyengine_us_data/calibration/source_impute.py

@@ -1,14 +1,16 @@
 """Non-PUF QRF imputations from donor surveys.
 
-Re-imputes variables from ACS, SIPP, and SCF donor surveys.
-Only ACS includes state_fips as a QRF predictor (ACS has state
-identifiers). SIPP and SCF lack state data, so their imputations
-use only demographic and financial predictors.
+Re-imputes variables from ACS, SIPP, ORG, and SCF donor surveys.
+Only ACS and ORG include state_fips as a QRF predictor. SIPP and SCF
+lack state data, so their imputations use only demographic and
+financial predictors.
 
 Sources and variables:
     ACS  -> rent, real_estate_taxes  (with state predictor)
     SIPP -> tip_income, bank_account_assets, stock_assets,
             bond_assets  (no state predictor)
+    ORG  -> hourly_wage, is_paid_hourly,
+            is_union_member_or_covered
     SCF  -> net_worth, auto_loan_balance, auto_loan_interest
             (no state predictor)
 
@@ -27,6 +29,13 @@
 import numpy as np
 import pandas as pd
 
+from policyengine_us_data.datasets.org import (
+    ORG_BOOL_VARIABLES,
+    ORG_IMPUTED_VARIABLES,
+    build_org_receiver_frame,
+    predict_org_features,
+)
+
 logger = logging.getLogger(__name__)
 
 ACS_IMPUTED_VARIABLES = [
@@ -48,7 +57,10 @@
 ]
 
 ALL_SOURCE_VARIABLES = (
-    ACS_IMPUTED_VARIABLES + SIPP_IMPUTED_VARIABLES + SCF_IMPUTED_VARIABLES
+    ACS_IMPUTED_VARIABLES
+    + SIPP_IMPUTED_VARIABLES
+    + ORG_IMPUTED_VARIABLES
+    + SCF_IMPUTED_VARIABLES
 )
 
 ACS_PREDICTORS = [
@@ -118,13 +130,15 @@ def impute_source_variables(
     dataset_path: Optional[str] = None,
     skip_acs: bool = False,
     skip_sipp: bool = False,
+    skip_org: bool = False,
     skip_scf: bool = False,
 ) -> Dict[str, Dict[int, np.ndarray]]:
-    """Re-impute ACS/SIPP/SCF variables from donor surveys.
+    """Re-impute ACS/SIPP/ORG/SCF variables from donor surveys.
 
     Overwrites existing imputed values in data. ACS uses
-    state_fips as a QRF predictor; SIPP and SCF use only
-    demographic and financial predictors (no state data).
+    state_fips as a QRF predictor; ORG uses state plus labor-market
+    predictors; SIPP and SCF use only demographic and financial
+    predictors (no state data).
 
     Args:
         data: CPS dataset dict {variable: {time_period: array}}.
@@ -133,6 +147,7 @@ def impute_source_variables(
         dataset_path: Path to CPS h5 for Microsimulation.
         skip_acs: Skip ACS imputation.
         skip_sipp: Skip SIPP imputation.
+        skip_org: Skip ORG imputation.
         skip_scf: Skip SCF imputation.
 
     Returns:
@@ -150,6 +165,10 @@ def impute_source_variables(
         logger.info("Imputing SIPP variables")
         data = _impute_sipp(data, state_fips, time_period, dataset_path)
 
+    if not skip_org:
+        logger.info("Imputing ORG variables")
+        data = _impute_org(data, state_fips, time_period, dataset_path)
+
     if not skip_scf:
         logger.info("Imputing SCF variables")
         data = _impute_scf(data, state_fips, time_period, dataset_path)
@@ -700,3 +719,59 @@ def _impute_scf(
 
     logger.info("SCF imputation complete: %s", available_vars)
     return data
+
+
+def _impute_org(
+    data: Dict[str, Dict[int, np.ndarray]],
+    state_fips: np.ndarray,
+    time_period: int,
+    dataset_path: Optional[str] = None,
+) -> Dict[str, Dict[int, np.ndarray]]:
+    """Impute ORG-only labor-market variables onto CPS persons."""
+    pe_vars = [
+        "age",
+        "is_male",
+        "is_hispanic",
+        "cps_race",
+        "employment_income",
+        "weekly_hours_worked",
+        "self_employment_income",
+    ]
+    cps_df = _build_cps_receiver(data, time_period, dataset_path, pe_vars)
+
+    if "is_male" in cps_df.columns:
+        is_female = (~cps_df["is_male"].astype(bool)).astype(np.float32).values
+    elif "is_female" in data:
+        is_female = data["is_female"][time_period].astype(np.float32)
+    else:
+        is_female = np.zeros(len(cps_df), dtype=np.float32)
+
+    person_states = _person_state_fips(data, state_fips, time_period)
+    receiver = build_org_receiver_frame(
+        age=cps_df["age"].values,
+        is_female=is_female,
+        is_hispanic=cps_df["is_hispanic"].values,
+        cps_race=cps_df["cps_race"].values,
+        state_fips=person_states,
+        employment_income=cps_df["employment_income"].values,
+        weekly_hours_worked=cps_df["weekly_hours_worked"].values,
+    )
+    self_employment_income = (
+        cps_df["self_employment_income"].values
+        if "self_employment_income" in cps_df.columns
+        else None
+    )
+    predictions = predict_org_features(
+        receiver,
+        self_employment_income=self_employment_income,
+    )
+
+    for var in ORG_IMPUTED_VARIABLES:
+        values = predictions[var].values
+        if var in ORG_BOOL_VARIABLES:
+            data[var] = {time_period: values.astype(bool)}
+        else:
+            data[var] = {time_period: values.astype(np.float32)}
+
+    logger.info("ORG imputation complete: %s", ORG_IMPUTED_VARIABLES)
+    return data

policyengine_us_data/datasets/cps/cps.py

@@ -18,6 +18,12 @@
     align_reported_ssi_disability,
     prioritize_reported_recipients,
 )
+from policyengine_us_data.datasets.org import (
+    ORG_BOOL_VARIABLES,
+    ORG_IMPUTED_VARIABLES,
+    build_org_receiver_frame,
+    predict_org_features,
+)
 from policyengine_us_data.utils.randomness import seeded_rng
 
 
@@ -77,6 +83,8 @@ def generate(self):
         add_rent(self, cps, person, household)
         logging.info("Adding tips")
         add_tips(self, cps)
+        logging.info("Adding ORG labor-market inputs")
+        add_org_labor_market_inputs(cps)
         logging.info("Adding auto loan balance, interest and wealth")
         add_auto_loan_interest_and_net_worth(self, cps)
         logging.info("Added all variables")
@@ -1828,6 +1836,54 @@ def add_tips(self, cps: h5py.File):
     self.save_dataset(cps)
 
 
+def add_org_labor_market_inputs(cps: h5py.File) -> None:
+    """Impute ORG-derived wage and union inputs onto CPS persons."""
+    household_ids = np.asarray(cps["household_id"], dtype=np.int64)
+    person_household_ids = np.asarray(
+        cps["person_household_id"],
+        dtype=np.int64,
+    )
+    household_state_fips = np.asarray(cps["state_fips"], dtype=np.float32)
+    household_index = {
+        int(household_id): i for i, household_id in enumerate(household_ids)
+    }
+    person_state_fips = np.array(
+        [
+            household_state_fips[household_index[int(household_id)]]
+            for household_id in person_household_ids
+        ],
+        dtype=np.float32,
+    )
+
+    receiver = build_org_receiver_frame(
+        age=cps["age"],
+        is_female=cps["is_female"],
+        is_hispanic=cps["is_hispanic"],
+        cps_race=cps["cps_race"],
+        state_fips=person_state_fips,
+        employment_income=cps["employment_income"],
+        weekly_hours_worked=cps["weekly_hours_worked"],
+    )
+    self_employment_income = np.asarray(
+        cps.get(
+            "self_employment_income",
+            np.zeros(len(receiver), dtype=np.float32),
+        ),
+        dtype=np.float32,
+    )
+    predictions = predict_org_features(
+        receiver,
+        self_employment_income=self_employment_income,
+    )
+
+    for variable in ORG_IMPUTED_VARIABLES:
+        values = predictions[variable].values
+        if variable in ORG_BOOL_VARIABLES:
+            cps[variable] = values.astype(bool)
+        else:
+            cps[variable] = values.astype(np.float32)
+
+
 def add_overtime_occupation(cps: h5py.File, person: DataFrame) -> None:
     """Add occupation categories relevant to overtime eligibility calculations.
     Based on:

policyengine_us_data/datasets/cps/extended_cps.py

@@ -7,6 +7,10 @@
 from policyengine_core.data import Dataset
 
 from policyengine_us_data.datasets.cps.cps import CPS, CPS_2024, CPS_2024_Full
+from policyengine_us_data.datasets.org import (
+    ORG_IMPUTED_VARIABLES,
+    apply_org_domain_constraints,
+)
 from policyengine_us_data.datasets.puf import PUF, PUF_2024
 from policyengine_us_data.storage import STORAGE_FOLDER
 from policyengine_us_data.utils.mortgage_interest import (
@@ -85,6 +89,10 @@ def _supports_structural_mortgage_inputs() -> bool:
     # Hours/employment
     "weekly_hours_worked",
     "hours_worked_last_week",
+    # ORG labor-market variables
+    "hourly_wage",
+    "is_paid_hourly",
+    "is_union_member_or_covered",
     # Previous year income
     "employment_income_last_year",
     "self_employment_income_last_year",
@@ -336,6 +344,28 @@ def _apply_post_processing(predictions, X_test, time_period, data):
         for col in ss_cols:
             predictions[col] = reconciled[col]
 
+    org_cols = [c for c in predictions.columns if c in ORG_IMPUTED_VARIABLES]
+    if org_cols:
+        n_half = len(data["person_id"][time_period]) // 2
+        weekly_hours = (
+            predictions["weekly_hours_worked"].values
+            if "weekly_hours_worked" in predictions.columns
+            else data["weekly_hours_worked"][time_period][n_half:]
+        )
+        receiver = pd.DataFrame(
+            {
+                "employment_income": X_test["employment_income"].values,
+                "weekly_hours_worked": np.asarray(weekly_hours, dtype=np.float32),
+            }
+        )
+        constrained = apply_org_domain_constraints(
+            predictions[org_cols],
+            receiver,
+            self_employment_income=X_test["self_employment_income"].values,
+        )
+        for col in org_cols:
+            predictions[col] = constrained[col]
+
     return predictions
 
 

policyengine_us_data/datasets/org/__init__.py

@@ -0,0 +1,10 @@
+from .org import (
+    ORG_BOOL_VARIABLES,
+    ORG_IMPUTED_VARIABLES,
+    ORG_PREDICTORS,
+    apply_org_domain_constraints,
+    build_org_receiver_frame,
+    get_org_model,
+    load_org_training_data,
+    predict_org_features,
+)