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Merged
MaxGhenis merged 1 commit into from
Jun 3, 2026
Merged

Populate eCPS-supported export inputs #196

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899 changes: 849 additions & 50 deletions src/microplex_us/data_sources/cps.py
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246 changes: 246 additions & 0 deletions src/microplex_us/data_sources/puf.py
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Original file line number Diff line number Diff line change
Expand Up @@ -229,10 +229,58 @@
)
MIN_PE_STYLE_SOCIAL_SECURITY_QRF_TRAINING_RECORDS = 100
PE_PUF_PERSON_EXPANSION_RANDOM_SEED = 64
QBI_PUF_RANDOM_SEED = 64
QBI_SIMULATION_RANDOM_SEED = 42

QBI_QUALIFICATION_PROBABILITIES = {
"self_employment_income": 0.8,
"farm_operations_income": 0.95,
"farm_rent_income": 0.5,
"rental_income": 0.4,
"estate_income": 0.5,
"partnership_s_corp_income": 0.85,
}
QBI_SSTB_PROBABILITY_BY_RAW_COLUMN = {
"E00900": 0.20,
"E26270": 0.15,
"E26390": 0.10,
"E26400": 0.10,
}
QBI_REIT_PTP_PROBABILITY = 0.07
QBI_REIT_PTP_LOG_NORMAL_MU = 8.04
QBI_REIT_PTP_LOG_NORMAL_SIGMA = 1.20
QBI_BDC_PROBABILITY = 0.003
QBI_BDC_LOG_NORMAL_MU = 8.71
QBI_BDC_LOG_NORMAL_SIGMA = 1.00
QBI_PROFIT_MARGIN_BETA_A = 2.0
QBI_PROFIT_MARGIN_BETA_B = 3.0
QBI_PROFIT_MARGIN_SCALE = 0.20
QBI_PROFIT_MARGIN_SHIFT = 0.05
QBI_HAS_EMPLOYEES_LOGIT_INTERCEPT = -3.1
QBI_HAS_EMPLOYEES_LOGIT_SLOPE_PER_DOLLAR = 1.2e-6
QBI_RENTAL_LABOR_RATIO_BETA_A = 1.5
QBI_RENTAL_LABOR_RATIO_BETA_B = 8.0
QBI_RENTAL_LABOR_RATIO_SCALE = 0.08
QBI_NON_RENTAL_LABOR_RATIO_BETA_A = 2.0
QBI_NON_RENTAL_LABOR_RATIO_BETA_B = 2.0
QBI_NON_RENTAL_LABOR_RATIO_SCALE = 0.25
QBI_DEPRECIATION_PROXY_SIGMA = 0.8
QBI_UBIA_MULTIPLE_OF_QBI = 4.0
QBI_UBIA_SIGMA = 1.0
QBI_QUALIFICATION_COLUMNS = tuple(
f"{variable}_would_be_qualified" for variable in QBI_QUALIFICATION_PROBABILITIES
)
QBI_BOOLEAN_COLUMNS = (
"business_is_sstb",
"sstb_self_employment_income_would_be_qualified",
*QBI_QUALIFICATION_COLUMNS,
)

JOINT_HEAD_SHARE_ALLOCATION = {
"employment_income": 0.6,
"self_employment_income": 0.6,
"sstb_self_employment_income": 0.6,
"sstb_self_employment_income_before_lsr": 0.6,
}

JOINT_EQUAL_SHARE_ALLOCATION = (
Expand Down Expand Up @@ -262,6 +310,12 @@
"charitable_noncash",
"ira_deduction",
"student_loan_interest",
"w2_wages_from_qualified_business",
"unadjusted_basis_qualified_property",
"sstb_w2_wages_from_qualified_business",
"sstb_unadjusted_basis_qualified_property",
"qualified_reit_and_ptp_income",
"qualified_bdc_income",
)

PUF_DEMOGRAPHIC_HELPER_COLUMNS = (
Expand Down Expand Up @@ -296,6 +350,7 @@
"pension_income",
"social_security",
"social_security_retirement",
*QBI_BOOLEAN_COLUMNS,
*PUF_DEMOGRAPHIC_HELPER_COLUMNS,
}

Expand Down Expand Up @@ -1171,6 +1226,11 @@ def map_puf_variables(
if medical_expense_floor is not None:
for variable, fraction in MEDICAL_EXPENSE_CATEGORY_BREAKDOWNS.items():
result[variable] = medical_expense_floor.fillna(0) * fraction
result = _add_puf_qbi_simulation_columns(
result,
raw_puf=puf,
random_seed=random_seed,
)

# Map filing status code to string
filing_status_map = {
Expand Down Expand Up @@ -1460,6 +1520,187 @@ def _numeric_series(df: pd.DataFrame, column: str) -> pd.Series:
return df[column].fillna(0).astype(float)


def _bernoulli_lognormal_sample(
n: int,
*,
probability: float,
log_mean: float,
log_sigma: float,
rng: np.random.Generator,
) -> np.ndarray:
positive = rng.binomial(1, probability, size=n).astype(bool)
return np.where(
positive,
rng.lognormal(mean=log_mean, sigma=log_sigma, size=n),
0.0,
)


def _conditional_lognormal_sample(
flag: np.ndarray,
target_mean: pd.Series | np.ndarray,
*,
log_sigma: float,
rng: np.random.Generator,
) -> np.ndarray:
flag_array = np.asarray(flag, dtype=bool)
target = np.maximum(np.asarray(target_mean, dtype=float), 0.0)
positive_target = target > 0.0
mu = np.zeros_like(target, dtype=float)
mu[positive_target] = np.log(target[positive_target]) - (log_sigma**2 / 2.0)
draws = rng.lognormal(mean=mu, sigma=log_sigma, size=len(target))
return np.where(flag_array & positive_target, draws, 0.0)


def _simulate_qbi_w2_wages_and_ubia(
puf: pd.DataFrame,
*,
seed: int = QBI_SIMULATION_RANDOM_SEED,
) -> tuple[np.ndarray, np.ndarray]:
"""Simulate PE-US-data-style Section 199A W-2 wages and UBIA support."""
rng = np.random.default_rng(seed)
qbi = sum(
_numeric_series(puf, income_type) * probability
for income_type, probability in QBI_QUALIFICATION_PROBABILITIES.items()
).to_numpy(dtype=float)

margins = (
rng.beta(QBI_PROFIT_MARGIN_BETA_A, QBI_PROFIT_MARGIN_BETA_B, qbi.size)
* QBI_PROFIT_MARGIN_SCALE
+ QBI_PROFIT_MARGIN_SHIFT
)
revenues = np.maximum(qbi, 0.0) / margins
logit = (
QBI_HAS_EMPLOYEES_LOGIT_INTERCEPT
+ QBI_HAS_EMPLOYEES_LOGIT_SLOPE_PER_DOLLAR * revenues
)
employee_probability = np.where(
revenues == 0.0,
0.0,
1.0 / (1.0 + np.exp(-logit)),
)
has_employees = rng.binomial(1, employee_probability)

rental_income = _numeric_series(puf, "rental_income")
is_rental = rental_income.to_numpy(dtype=float) > 0.0
labor_ratios = np.where(
is_rental,
rng.beta(
QBI_RENTAL_LABOR_RATIO_BETA_A,
QBI_RENTAL_LABOR_RATIO_BETA_B,
qbi.size,
)
* QBI_RENTAL_LABOR_RATIO_SCALE,
rng.beta(
QBI_NON_RENTAL_LABOR_RATIO_BETA_A,
QBI_NON_RENTAL_LABOR_RATIO_BETA_B,
qbi.size,
)
* QBI_NON_RENTAL_LABOR_RATIO_SCALE,
)
w2_wages = revenues * labor_ratios * has_employees

depreciation_proxy = _conditional_lognormal_sample(
is_rental,
rental_income,
log_sigma=QBI_DEPRECIATION_PROXY_SIGMA,
rng=rng,
)
is_capital_intensive = is_rental | (depreciation_proxy > 0.0)
ubia = _conditional_lognormal_sample(
is_capital_intensive,
QBI_UBIA_MULTIPLE_OF_QBI * np.maximum(qbi, 0.0),
log_sigma=QBI_UBIA_SIGMA,
rng=rng,
)
return w2_wages, ubia


def _add_puf_qbi_simulation_columns(
mapped_puf: pd.DataFrame,
*,
raw_puf: pd.DataFrame,
random_seed: int,
) -> pd.DataFrame:
"""Populate PE-US-data-style Section 199A support columns from PUF."""
result = mapped_puf.copy()
rng = np.random.default_rng(QBI_PUF_RANDOM_SEED + int(random_seed))
for variable, probability in QBI_QUALIFICATION_PROBABILITIES.items():
result[f"{variable}_would_be_qualified"] = rng.random(len(result)) < probability

w2_wages, ubia = _simulate_qbi_w2_wages_and_ubia(
result,
seed=QBI_SIMULATION_RANDOM_SEED,
)
result["w2_wages_from_qualified_business"] = w2_wages
result["unadjusted_basis_qualified_property"] = ubia

raw_sstb_sources = pd.DataFrame(
{
column: pd.to_numeric(raw_puf[column], errors="coerce").fillna(0.0)
if column in raw_puf.columns
else pd.Series(0.0, index=result.index, dtype=float)
for column in QBI_SSTB_PROBABILITY_BY_RAW_COLUMN
},
index=result.index,
)
largest_qbi_source = raw_sstb_sources.idxmax(axis=1)
has_any_qbi_source = raw_sstb_sources.abs().sum(axis=1).gt(0.0)
probability_sstb = (
largest_qbi_source.map(QBI_SSTB_PROBABILITY_BY_RAW_COLUMN)
.fillna(0.0)
.where(has_any_qbi_source, 0.0)
)
is_sstb = rng.binomial(n=1, p=probability_sstb).astype(bool)
result["business_is_sstb"] = is_sstb

legacy_self_employment_income = _numeric_series(result, "self_employment_income")
result["sstb_self_employment_income_before_lsr"] = np.where(
is_sstb,
legacy_self_employment_income,
0.0,
)
result["sstb_self_employment_income"] = result[
"sstb_self_employment_income_before_lsr"
]
result["self_employment_income"] = np.where(
is_sstb,
0.0,
legacy_self_employment_income,
)
result["sstb_self_employment_income_would_be_qualified"] = np.where(
is_sstb,
result["self_employment_income_would_be_qualified"].astype(bool),
False,
)
result["sstb_w2_wages_from_qualified_business"] = np.where(
is_sstb,
w2_wages,
0.0,
)
result["sstb_unadjusted_basis_qualified_property"] = np.where(
is_sstb,
ubia,
0.0,
)

result["qualified_reit_and_ptp_income"] = _bernoulli_lognormal_sample(
len(result),
probability=QBI_REIT_PTP_PROBABILITY,
log_mean=QBI_REIT_PTP_LOG_NORMAL_MU,
log_sigma=QBI_REIT_PTP_LOG_NORMAL_SIGMA,
rng=rng,
)
result["qualified_bdc_income"] = _bernoulli_lognormal_sample(
len(result),
probability=QBI_BDC_PROBABILITY,
log_mean=QBI_BDC_LOG_NORMAL_MU,
log_sigma=QBI_BDC_LOG_NORMAL_SIGMA,
rng=rng,
)
return result


def _default_cps_reference_year(target_year: int) -> int:
return min(max(target_year - 1, 2021), 2023)

Expand Down Expand Up @@ -1942,6 +2183,10 @@ def _add_derived_income_columns(df: pd.DataFrame) -> pd.DataFrame:
result = normalize_dividend_columns(result)
employment_income = _numeric_series(result, "employment_income")
self_employment_income = _numeric_series(result, "self_employment_income")
sstb_self_employment_income = _numeric_series(
result,
"sstb_self_employment_income",
)
taxable_interest_income = _numeric_series(result, "taxable_interest_income")
ordinary_dividend_income = _numeric_series(result, "ordinary_dividend_income")
short_term_capital_gains = _numeric_series(result, "short_term_capital_gains")
Expand Down Expand Up @@ -1970,6 +2215,7 @@ def _add_derived_income_columns(df: pd.DataFrame) -> pd.DataFrame:
result["income"] = (
employment_income
+ self_employment_income
+ sstb_self_employment_income
+ result["interest_income"]
+ result["dividend_income"]
+ rental_income
Expand Down
16 changes: 12 additions & 4 deletions src/microplex_us/manifests/pe_source_impute_blocks.json
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Original file line number Diff line number Diff line change
Expand Up @@ -167,7 +167,9 @@
"TPTOTINC",
"TVAL_BANK",
"TVAL_STMF",
"TVAL_BOND"
"TVAL_BOND",
"TVEH_NUM",
"THVAL_VEH"
],
"direct_columns": {
"age": "TAGE",
Expand All @@ -176,7 +178,9 @@
"weight": "WPFINWGT",
"bank_account_assets": "TVAL_BANK",
"stock_assets": "TVAL_STMF",
"bond_assets": "TVAL_BOND"
"bond_assets": "TVAL_BOND",
"household_vehicles_owned": "TVEH_NUM",
"household_vehicles_value": "THVAL_VEH"
},
"sum_columns_contains": {},
"indicator_columns": {
Expand Down Expand Up @@ -214,12 +218,16 @@
"count_under_18",
"bank_account_assets",
"stock_assets",
"bond_assets"
"bond_assets",
"household_vehicles_owned",
"household_vehicles_value"
],
"target_variables": [
"bank_account_assets",
"stock_assets",
"bond_assets"
"bond_assets",
"household_vehicles_owned",
"household_vehicles_value"
],
"predictors": [
"employment_income",
Expand Down
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