Donor-impute CPS demographic, occupation, and TTOC features on PUF clones

changelog.d/impute-cps-clone-features.changed.md

@@ -0,0 +1 @@
+Donor-impute race, Hispanic status, sex, and occupation-based CPS features onto the PUF clone half of the extended CPS so subgroup analyses and overtime-eligibility inputs better align with PUF-imputed incomes.

policyengine_us_data/calibration/source_impute.py

@@ -28,6 +28,10 @@
 
 import numpy as np
 import pandas as pd
+from policyengine_us_data.datasets.cps.tipped_occupation import (
+    derive_any_treasury_tipped_occupation_code,
+    derive_is_tipped_occupation,
+)
 
 from policyengine_us_data.datasets.org import (
     ORG_BOOL_VARIABLES,
@@ -80,6 +84,7 @@
     "age",
     "count_under_18",
     "count_under_6",
+    "is_tipped_occupation",
 ]
 
 SIPP_ASSETS_PREDICTORS = [
@@ -109,6 +114,8 @@
     "NONE": 0,
 }
 
+SIPP_JOB_OCCUPATION_COLUMNS = [f"TJB{i}_OCC" for i in range(1, 8)]
+
 
 def _encode_tenure_type(df: pd.DataFrame) -> pd.DataFrame:
     """Convert tenure_type enum strings to numeric codes."""
@@ -381,6 +388,12 @@ def _impute_sipp(
     sipp_df["age"] = sipp_df.TAGE
     sipp_df["household_weight"] = sipp_df.WPFINWGT
     sipp_df["household_id"] = sipp_df.SSUID
+    sipp_df["treasury_tipped_occupation_code"] = (
+        derive_any_treasury_tipped_occupation_code(sipp_df[SIPP_JOB_OCCUPATION_COLUMNS])
+    )
+    sipp_df["is_tipped_occupation"] = derive_is_tipped_occupation(
+        sipp_df["treasury_tipped_occupation_code"]
+    )
 
     sipp_df["is_under_18"] = sipp_df.TAGE < 18
     sipp_df["is_under_6"] = sipp_df.TAGE < 6
@@ -398,6 +411,7 @@ def _impute_sipp(
         "count_under_18",
         "count_under_6",
         "age",
+        "is_tipped_occupation",
         "household_weight",
     ]
     tip_train = sipp_df[tip_cols].dropna()
@@ -428,6 +442,12 @@ def _impute_sipp(
     else:
         cps_tip_df["count_under_18"] = 0.0
         cps_tip_df["count_under_6"] = 0.0
+    if "treasury_tipped_occupation_code" in data:
+        cps_tip_df["is_tipped_occupation"] = derive_is_tipped_occupation(
+            data["treasury_tipped_occupation_code"][time_period]
+        ).astype(np.float32)
+    else:
+        cps_tip_df["is_tipped_occupation"] = 0.0
 
     qrf = QRF()
     logger.info(

policyengine_us_data/datasets/cps/cps.py

@@ -25,6 +25,10 @@
     predict_org_features,
 )
 from policyengine_us_data.utils.randomness import seeded_rng
+from policyengine_us_data.datasets.cps.tipped_occupation import (
+    derive_treasury_tipped_occupation_code,
+    derive_is_tipped_occupation,
+)
 
 
 class CPS(Dataset):
@@ -487,6 +491,9 @@ def children_per_parent(col: str) -> pd.DataFrame:
     cps["is_full_time_college_student"] = person.A_HSCOL == 2
 
     cps["detailed_occupation_recode"] = person.POCCU2
+    cps["treasury_tipped_occupation_code"] = derive_treasury_tipped_occupation_code(
+        person.PEIOOCC
+    )
     add_overtime_occupation(cps, person)
 
 
@@ -1785,6 +1792,9 @@ def add_tips(self, cps: h5py.File):
     raw_data = self.raw_cps(require=True).load()
     raw_person = raw_data["person"]
     cps["is_married"] = raw_person.A_MARITL.isin([1, 2]).values
+    cps["is_tipped_occupation"] = derive_is_tipped_occupation(
+        derive_treasury_tipped_occupation_code(raw_person.PEIOOCC)
+    )
     raw_data.close()
 
     cps["is_under_18"] = cps.age < 18