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Merged
MaxGhenis merged 1 commit into from
Jun 7, 2026
+978 −217
Merged

Add default rerandomized takeup target handler #258

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232 changes: 27 additions & 205 deletions src/microplex_us/pipelines/us.py
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Original file line number Diff line number Diff line change
Expand Up @@ -109,6 +109,28 @@
evaluate_policyengine_us_target_set,
slice_policyengine_us_target_evaluation_report,
)
from microplex_us.policyengine.takeup import (
DEFAULT_MEDICAID_TAKEUP_RATE,
DEFAULT_PREGNANCY_RATE,
DEFAULT_VOLUNTARY_FILING_RATE,
EITC_TAKEUP_CHILD_COUNT_HELPER_COLUMN,
VOLUNTARY_FILING_AGE_HEAD_HELPER_COLUMN,
VOLUNTARY_FILING_WAGE_INCOME_HELPER_COLUMN,
WIC_TAKEUP_CATEGORY_BREASTFEEDING,
WIC_TAKEUP_CATEGORY_CHILD,
WIC_TAKEUP_CATEGORY_INFANT,
WIC_TAKEUP_CATEGORY_NONE,
WIC_TAKEUP_CATEGORY_POSTPARTUM,
WIC_TAKEUP_CATEGORY_PREGNANT,
_load_microplex_eitc_takeup_rates,
_load_microplex_medicaid_takeup_rates,
_load_microplex_pregnancy_rates,
_load_microplex_takeup_rate,
_load_microplex_voluntary_filing_rates,
_load_microplex_wic_nutritional_risk_rates,
_load_microplex_wic_takeup_rates,
_microplex_seeded_rng,
)
from microplex_us.policyengine.target_profiles import (
PolicyEngineUSTargetCell,
resolve_policyengine_us_target_profile,
Expand Down Expand Up @@ -301,202 +323,6 @@
"is_union_member_or_covered",
)

DEFAULT_ACA_TAKEUP_RATE = 0.672
DEFAULT_DC_PTC_TAKEUP_RATE = 0.32
DEFAULT_EARLY_HEAD_START_TAKEUP_RATE = 0.09
DEFAULT_EITC_TAKEUP_RATES_BY_CHILDREN = {0: 0.65, 1: 0.86, 2: 0.85, 3: 0.85}
DEFAULT_HEAD_START_TAKEUP_RATE = 0.30
DEFAULT_MEDICAID_TAKEUP_RATE = 0.93
DEFAULT_MEDICAID_TAKEUP_RATES_BY_STATE = {
"AK": 0.88,
"AL": 0.92,
"AR": 0.79,
"AZ": 0.95,
"CA": 0.78,
"CO": 0.99,
"CT": 0.89,
"DC": 0.99,
"DE": 0.86,
"FL": 0.98,
"GA": 0.73,
"HI": 0.88,
"IA": 0.84,
"ID": 0.78,
"IL": 0.85,
"IN": 0.99,
"KS": 0.92,
"KY": 0.87,
"LA": 0.79,
"MA": 0.94,
"MD": 0.95,
"ME": 0.92,
"MI": 0.91,
"MN": 0.89,
"MO": 0.89,
"MS": 0.75,
"MT": 0.83,
"NC": 0.94,
"ND": 0.91,
"NE": 0.79,
"NH": 0.84,
"NJ": 0.74,
"NM": 0.84,
"NV": 0.93,
"NY": 0.86,
"OH": 0.82,
"OK": 0.77,
"OR": 0.92,
"PA": 0.64,
"RI": 0.94,
"SC": 0.93,
"SD": 0.88,
"TN": 0.92,
"TX": 0.76,
"UT": 0.53,
"VA": 0.82,
"VT": 0.93,
"WA": 0.98,
"WI": 0.91,
"WV": 0.83,
"WY": 0.70,
}
DEFAULT_SNAP_TAKEUP_RATE = 0.82
DEFAULT_TANF_TAKEUP_RATE = 0.22
DEFAULT_VOLUNTARY_FILING_RATE = 0.05
DEFAULT_VOLUNTARY_FILING_RATES = {
"no_children": {
"zero": {"under_65": 0.20, "age_65_plus": 0.05},
"low": {"under_65": 0.24, "age_65_plus": 0.04},
"medium": {"under_65": 0.0, "age_65_plus": 0.0},
"high": {"under_65": 0.0, "age_65_plus": 0.005},
},
"with_children": {
"zero": {"under_65": 0.50, "age_65_plus": 0.075},
"low": {"under_65": 0.60, "age_65_plus": 0.06},
"medium": {"under_65": 0.0, "age_65_plus": 0.0},
"high": {"under_65": 0.025, "age_65_plus": 0.0037},
},
}
WIC_TAKEUP_CATEGORY_PREGNANT = "PREGNANT"
WIC_TAKEUP_CATEGORY_POSTPARTUM = "POSTPARTUM"
WIC_TAKEUP_CATEGORY_BREASTFEEDING = "BREASTFEEDING"
WIC_TAKEUP_CATEGORY_INFANT = "INFANT"
WIC_TAKEUP_CATEGORY_CHILD = "CHILD"
WIC_TAKEUP_CATEGORY_NONE = "NONE"
DEFAULT_WIC_TAKEUP_RATES = {
WIC_TAKEUP_CATEGORY_PREGNANT: 0.456,
WIC_TAKEUP_CATEGORY_POSTPARTUM: 0.689,
WIC_TAKEUP_CATEGORY_BREASTFEEDING: 0.663,
WIC_TAKEUP_CATEGORY_INFANT: 0.784,
WIC_TAKEUP_CATEGORY_CHILD: 0.460,
WIC_TAKEUP_CATEGORY_NONE: 0.0,
}
DEFAULT_WIC_NUTRITIONAL_RISK_RATES = {
WIC_TAKEUP_CATEGORY_PREGNANT: 0.913,
WIC_TAKEUP_CATEGORY_POSTPARTUM: 0.933,
WIC_TAKEUP_CATEGORY_BREASTFEEDING: 0.889,
WIC_TAKEUP_CATEGORY_INFANT: 0.950,
WIC_TAKEUP_CATEGORY_CHILD: 0.752,
WIC_TAKEUP_CATEGORY_NONE: 0.0,
}
DEFAULT_PREGNANCY_RATE = 0.041
EITC_TAKEUP_CHILD_COUNT_HELPER_COLUMN = "_mp_eitc_child_count_for_takeup"
VOLUNTARY_FILING_AGE_HEAD_HELPER_COLUMN = "_mp_voluntary_filing_age_head"
VOLUNTARY_FILING_WAGE_INCOME_HELPER_COLUMN = "_mp_voluntary_filing_wage_income"


def _stable_string_hash(value: str) -> np.uint64:
"""Deterministic string hash for reproducible MP stochastic inputs."""
with warnings.catch_warnings():
warnings.filterwarnings("ignore", "overflow encountered", RuntimeWarning)
hashed = np.uint64(0)
for byte in value.encode("utf-8"):
hashed = hashed * np.uint64(31) + np.uint64(byte)
hashed = hashed ^ (hashed >> np.uint64(33))
hashed = hashed * np.uint64(0xFF51AFD7ED558CCD)
hashed = hashed ^ (hashed >> np.uint64(33))
return hashed


def _microplex_seeded_rng(
variable_name: str,
*,
salt: str | None = None,
) -> np.random.Generator:
key = variable_name if salt is None else f"{variable_name}:{salt}"
seed = int(_stable_string_hash(key)) % (2**63)
return np.random.default_rng(seed=seed)


def _load_microplex_takeup_rate(variable_name: str, year: int) -> float:
"""Load MP-owned scalar take-up assumptions for PE dataset inputs."""
if variable_name == "aca":
return DEFAULT_ACA_TAKEUP_RATE
if variable_name == "dc_ptc":
return DEFAULT_DC_PTC_TAKEUP_RATE
if variable_name == "early_head_start":
return DEFAULT_EARLY_HEAD_START_TAKEUP_RATE
if variable_name == "head_start":
return 0.40 if year <= 2020 else DEFAULT_HEAD_START_TAKEUP_RATE
if variable_name == "snap":
return DEFAULT_SNAP_TAKEUP_RATE
if variable_name == "tanf":
return DEFAULT_TANF_TAKEUP_RATE
raise KeyError(f"Unknown Microplex take-up rate: {variable_name!r}")


def _load_microplex_medicaid_takeup_rates(year: int) -> dict[str, float]:
"""Load MP-owned Medicaid take-up rates by state abbreviation."""
_ = year
return dict(DEFAULT_MEDICAID_TAKEUP_RATES_BY_STATE)


def _load_microplex_eitc_takeup_rates(year: int) -> dict[int, float]:
"""Load MP-owned EITC take-up rates by capped qualifying-child count."""
_ = year
return dict(DEFAULT_EITC_TAKEUP_RATES_BY_CHILDREN)


def _load_microplex_voluntary_filing_rates(year: int) -> dict:
"""Load MP-owned voluntary filing rate table."""
_ = year
return {
children: {wage: dict(age_rates) for wage, age_rates in wage_rates.items()}
for children, wage_rates in DEFAULT_VOLUNTARY_FILING_RATES.items()
}


def _load_microplex_wic_takeup_rates(year: int) -> dict[str, float]:
"""Load MP-owned WIC take-up rates by demographic category."""
_ = year
return dict(DEFAULT_WIC_TAKEUP_RATES)


def _load_microplex_wic_nutritional_risk_rates(year: int) -> dict[str, float]:
"""Load MP-owned WIC nutritional-risk rates by demographic category."""
_ = year
return dict(DEFAULT_WIC_NUTRITIONAL_RISK_RATES)


def _load_microplex_pregnancy_rates(year: int) -> dict[str, float]:
"""Load pregnancy rates by state abbreviation, matching PE-US-data when present."""
_ = year
try:
from policyengine_us_data.db.etl_pregnancy import (
get_state_pregnancy_rates,
)

rates = get_state_pregnancy_rates()
except Exception:
LOGGER.warning(
"Failed to load state pregnancy rates; using national fallback",
exc_info=True,
)
return {}

return {str(state).upper(): float(rate) for state, rate in rates.items()}


PUF_SUPPORT_CLONE_OVERRIDDEN_VARIABLES: tuple[str, ...] = (
"partnership_s_corp_income",
"interest_deduction",
Expand Down Expand Up @@ -582,9 +408,7 @@ def _load_microplex_pregnancy_rates(year: int) -> dict[str, float]:
)
)

PUF_SUPPORT_CLONE_CPS_MEASURED_OVERLAP_VARIABLES: tuple[str, ...] = (
"social_security",
)
PUF_SUPPORT_CLONE_CPS_MEASURED_OVERLAP_VARIABLES: tuple[str, ...] = ("social_security",)
PUF_SUPPORT_CLONE_DONOR_ONLY_COLLAPSE_EXCLUDED_VARIABLES: tuple[str, ...] = (
"employment_income_before_lsr",
)
Expand Down Expand Up @@ -6780,9 +6604,9 @@ def _finalize_puf_support_clone_frame(
identity_override = set(
cps_passthrough_summary.get("identity_reconciled_variables", ())
)
donor_only_collapse_variables = (
integrated_set - preclone_columns
) - set(PUF_SUPPORT_CLONE_DONOR_ONLY_COLLAPSE_EXCLUDED_VARIABLES)
donor_only_collapse_variables = (integrated_set - preclone_columns) - set(
PUF_SUPPORT_CLONE_DONOR_ONLY_COLLAPSE_EXCLUDED_VARIABLES
)
irs_detail_override = (
integrated_set
& set(self.config.puf_support_clone_collapse_irs_detail_variables)
Expand Down Expand Up @@ -11746,9 +11570,7 @@ def capped_at(values: pd.Series, caps: pd.Series) -> pd.Series:
else first_present("employment_income_before_lsr")
)
elif "employment_income" in result.columns:
result["employment_income_before_lsr"] = first_present(
"employment_income"
)
result["employment_income_before_lsr"] = first_present("employment_income")
elif "wage_income" in result.columns:
result["employment_income_before_lsr"] = first_present("wage_income")
else:
Expand Down
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