Write desired retirement contribution inputs

changelog.d/1125.changed.md

@@ -0,0 +1 @@
+Write retirement contribution source data to desired pre-limit variables and target the plain PolicyEngine-US contribution outputs after statutory caps.

policyengine_us_data/calibration/puf_impute.py

@@ -160,11 +160,11 @@
 ]
 
 CPS_RETIREMENT_VARIABLES = [
-    "traditional_401k_contributions",
-    "roth_401k_contributions",
-    "traditional_ira_contributions",
-    "roth_ira_contributions",
-    "self_employed_pension_contributions",
+    "traditional_401k_contributions_desired",
+    "roth_401k_contributions_desired",
+    "traditional_ira_contributions_desired",
+    "roth_ira_contributions_desired",
+    "self_employed_pension_contributions_desired",
 ]
 
 RETIREMENT_DEMOGRAPHIC_PREDICTORS = [
@@ -845,18 +845,9 @@ def _impute_retirement_contributions(
         n_persons = len(data["person_id"][time_period])
         return {var: np.zeros(n_persons) for var in CPS_RETIREMENT_VARIABLES}
 
-    # Extract results and apply constraints
-    limits = _get_retirement_limits(time_period)
-    age = X_test["age"].values
-    catch_up_eligible = age >= 50
-    limit_401k = limits["401k"] + catch_up_eligible * limits["401k_catch_up"]
-    limit_ira = limits["ira"] + catch_up_eligible * limits["ira_catch_up"]
+    # Extract results and apply data-domain constraints. Statutory limits
+    # are applied by PolicyEngine-US plain contribution variables.
     se_income = X_test["self_employment_income"].values
-    se_pension_cap = np.minimum(
-        se_income * limits["se_pension_rate"],
-        limits["se_pension_dollar_limit"],
-    )
-
     emp_income = X_test["employment_income"].values
 
     result = {}
@@ -866,31 +857,24 @@ def _impute_retirement_contributions(
         # Non-negativity
         vals = np.maximum(vals, 0)
 
-        # Cap 401k at year-specific limit
+        # Zero out employment-based plans for records with no employment income.
         if "401k" in var:
-            vals = np.minimum(vals, limit_401k)
-            # Zero out for records with no employment income
             vals = np.where(emp_income > 0, vals, 0)
 
-        # Cap IRA at year-specific limit
-        if "ira" in var:
-            vals = np.minimum(vals, limit_ira)
-
-        # Cap SE pension at min(25% of SE income, dollar limit)
-        if var == "self_employed_pension_contributions":
-            vals = np.minimum(vals, se_pension_cap)
+        # Zero out self-employed plans for records with no self-employment income.
+        if var == "self_employed_pension_contributions_desired":
             vals = np.where(se_income > 0, vals, 0)
 
         result[var] = vals
 
     logger.info(
         "Imputed retirement contributions for PUF: "
         "401k mean=$%.0f, IRA mean=$%.0f, SE pension mean=$%.0f",
-        result["traditional_401k_contributions"].mean()
-        + result["roth_401k_contributions"].mean(),
-        result["traditional_ira_contributions"].mean()
-        + result["roth_ira_contributions"].mean(),
-        result["self_employed_pension_contributions"].mean(),
+        result["traditional_401k_contributions_desired"].mean()
+        + result["roth_401k_contributions_desired"].mean(),
+        result["traditional_ira_contributions_desired"].mean()
+        + result["roth_ira_contributions_desired"].mean(),
+        result["self_employed_pension_contributions_desired"].mean(),
     )
 
     return result

policyengine_us_data/calibration/target_config.yaml

@@ -240,7 +240,7 @@ include:
     geo_level: national
   - variable: roth_ira_contributions
     geo_level: national
-  - variable: self_employed_pension_contribution_ald
+  - variable: self_employed_pension_contributions
     geo_level: national
 
   # === NATIONAL — IRS SOI domain-constrained dollar targets (restored: |rel_err| < 15%) ===

policyengine_us_data/datasets/cps/cps.py

@@ -1512,40 +1512,25 @@ def add_personal_income_variables(cps: h5py.File, person: DataFrame, year: int):
     # nearly all of RETCB_VAL and left IRA contributions at $0.
     #
     # The proportional approach uses BEA/FRED and IRS SOI shares to
-    # split contributions into DC (401k) and IRA pools, then splits
-    # each pool into traditional/Roth using administrative fractions.
-    # See imputation_parameters.yaml for sources.
-    from policyengine_us_data.utils.retirement_limits import (
-        get_retirement_limits,
-    )
-
-    limits = get_retirement_limits(year)
-    LIMIT_401K = limits["401k"]
-    LIMIT_401K_CATCH_UP = limits["401k_catch_up"]
-    LIMIT_IRA = limits["ira"]
-    LIMIT_IRA_CATCH_UP = limits["ira_catch_up"]
-    CATCH_UP_AGE = 50
-    catch_up_eligible = person.A_AGE >= CATCH_UP_AGE
-    limit_401k = LIMIT_401K + catch_up_eligible * LIMIT_401K_CATCH_UP
-    limit_ira = LIMIT_IRA + catch_up_eligible * LIMIT_IRA_CATCH_UP
-
+    # split contributions into self-employed pension, DC (401k), and
+    # IRA pools, then splits each pool into traditional/Roth using
+    # administrative fractions. See imputation_parameters.yaml for
+    # sources.
     retirement_contributions = person.RETCB_VAL
     has_wages = person.WSAL_VAL > 0
     has_se = person.SEMP_VAL > 0
     has_earned_income = has_wages | has_se
 
-    # 1) Self-employed pension: cap at min(25% of SE income, dollar
-    #    limit) so dual-income filers keep a remainder for 401(k)/IRA.
-    se_rate = p["se_pension_contribution_rate"]
-    se_dollar_cap = p["se_pension_contribution_dollar_limit"][year]
-    se_pension_cap = np.minimum(person.SEMP_VAL * se_rate, se_dollar_cap)
-    cps["self_employed_pension_contributions"] = np.where(
+    # 1) Self-employed pension: allocate a share without applying statutory
+    #    limits. PolicyEngine-US applies those limits.
+    se_share = p["se_pension_share_of_retirement_contributions"]
+    cps["self_employed_pension_contributions_desired"] = np.where(
         has_se,
-        np.minimum(retirement_contributions, se_pension_cap),
+        retirement_contributions * se_share,
         0,
     )
     remaining = np.maximum(
-        retirement_contributions - cps["self_employed_pension_contributions"],
+        retirement_contributions - cps["self_employed_pension_contributions_desired"],
         0,
     )
 
@@ -1561,17 +1546,15 @@ def add_personal_income_variables(cps: h5py.File, person: DataFrame, year: int):
     # earned income (including SE-only filers).
     ira_pool = np.where(has_earned_income, remaining - dc_pool, 0)
 
-    # DC pool: split into traditional/Roth 401(k), cap at combined
-    # 401(k) limit.
-    dc_capped = np.minimum(dc_pool, limit_401k)
-    cps["traditional_401k_contributions"] = dc_capped * (1 - roth_dc_share)
-    cps["roth_401k_contributions"] = dc_capped * roth_dc_share
-
-    # IRA pool: split into traditional/Roth IRA, cap at combined
-    # IRA limit.
-    ira_capped = np.minimum(ira_pool, limit_ira)
-    cps["traditional_ira_contributions"] = ira_capped * trad_ira_share
-    cps["roth_ira_contributions"] = ira_capped * (1 - trad_ira_share)
+    # DC pool: split into desired traditional/Roth 401(k) contributions.
+    # The statutory elective deferral limit is applied in policyengine-us.
+    cps["traditional_401k_contributions_desired"] = dc_pool * (1 - roth_dc_share)
+    cps["roth_401k_contributions_desired"] = dc_pool * roth_dc_share
+
+    # IRA pool: split into desired traditional/Roth IRA contributions.
+    # The statutory IRA limit is applied in policyengine-us.
+    cps["traditional_ira_contributions_desired"] = ira_pool * trad_ira_share
+    cps["roth_ira_contributions_desired"] = ira_pool * (1 - trad_ira_share)
     # Allocate capital gains into long-term and short-term based on aggregate split.
     cps["long_term_capital_gains"] = person.CAP_VAL * (p["long_term_capgain_fraction"])
     cps["short_term_capital_gains"] = person.CAP_VAL * (

policyengine_us_data/datasets/cps/extended_cps.py

@@ -56,10 +56,6 @@
 from policyengine_us_data.utils.dataset_validation import (
     assert_no_computed_policyengine_us_variables_exported,
 )
-from policyengine_us_data.utils.retirement_limits import (
-    get_retirement_limits,
-    get_se_pension_limits,
-)
 from policyengine_us_data.utils.randomness import seeded_rng
 
 logger = logging.getLogger(__name__)
@@ -154,11 +150,11 @@ def _supports_structural_mortgage_inputs() -> bool:
     "taxable_sep_distributions",
     "tax_exempt_sep_distributions",
     # Retirement contributions
-    "traditional_401k_contributions",
-    "roth_401k_contributions",
-    "traditional_ira_contributions",
-    "roth_ira_contributions",
-    "self_employed_pension_contributions",
+    "traditional_401k_contributions_desired",
+    "roth_401k_contributions_desired",
+    "traditional_ira_contributions_desired",
+    "roth_ira_contributions_desired",
+    "self_employed_pension_contributions_desired",
     # Social Security sub-components
     "social_security_retirement",
     "social_security_disability",
@@ -753,50 +749,34 @@ def _impute_cps_only_variables(
 
 
 def apply_retirement_constraints(predictions, X_test, time_period):
-    """Enforce IRS contribution limits on retirement variable predictions.
+    """Clean retirement contribution predictions for data-domain eligibility.
 
     Args:
         predictions: DataFrame of QRF predictions for retirement
             contribution variables.
         X_test: DataFrame with at least ``age``,
             ``employment_income``, and ``self_employment_income``.
-        time_period: Tax year (int) for IRS limit look-up.
+        time_period: Tax year (int), accepted for API compatibility.
 
     Returns:
-        DataFrame with constrained values (same columns).
+        DataFrame with cleaned values (same columns).
     """
-    limits = get_retirement_limits(time_period)
-    se_limits = get_se_pension_limits(time_period)
-
-    age = X_test["age"].values
-    catch_up = age >= 50
     emp_income = X_test["employment_income"].values
     se_income = X_test["self_employment_income"].values
 
-    limit_401k = limits["401k"] + catch_up * limits["401k_catch_up"]
-    limit_ira = limits["ira"] + catch_up * limits["ira_catch_up"]
-    se_pension_cap = np.minimum(
-        se_income * se_limits["se_pension_rate"],
-        se_limits["se_pension_dollar_limit"],
-    )
-
-    # Explicit mapping: variable -> (cap array, zero_mask or None).
+    # Explicit mapping: variable -> zero_mask or None. Statutory limits
+    # are applied by PolicyEngine-US plain contribution variables.
     _CONSTRAINT_MAP = {
-        "traditional_401k_contributions": (limit_401k, emp_income == 0),
-        "roth_401k_contributions": (limit_401k, emp_income == 0),
-        "traditional_ira_contributions": (limit_ira, None),
-        "roth_ira_contributions": (limit_ira, None),
-        "self_employed_pension_contributions": (
-            se_pension_cap,
-            se_income == 0,
-        ),
+        "traditional_401k_contributions_desired": emp_income == 0,
+        "roth_401k_contributions_desired": emp_income == 0,
+        "traditional_ira_contributions_desired": None,
+        "roth_ira_contributions_desired": None,
+        "self_employed_pension_contributions_desired": se_income == 0,
     }
 
     result = predictions.clip(lower=0)
     for var in result.columns:
-        cap, zero_mask = _CONSTRAINT_MAP.get(var, (None, None))
-        if cap is not None:
-            result[var] = np.minimum(result[var].values, cap)
+        zero_mask = _CONSTRAINT_MAP.get(var)
         if zero_mask is not None:
             result.loc[zero_mask, var] = 0
 
@@ -836,11 +816,11 @@ def reconcile_ss_subcomponents(predictions, total_ss):
 
 
 _RETIREMENT_VARS = {
-    "traditional_401k_contributions",
-    "roth_401k_contributions",
-    "traditional_ira_contributions",
-    "roth_ira_contributions",
-    "self_employed_pension_contributions",
+    "traditional_401k_contributions_desired",
+    "roth_401k_contributions_desired",
+    "traditional_ira_contributions_desired",
+    "roth_ira_contributions_desired",
+    "self_employed_pension_contributions_desired",
 }
 
 _SS_SUBCOMPONENT_VARS = {

policyengine_us_data/datasets/cps/imputation_parameters.yaml

@@ -21,7 +21,15 @@ long_term_capgain_fraction: 0.880
 # Used to split CPS RETCB_VAL (a single bundled total) into
 # account-type-specific variables.
 #
-# DC vs IRA share of non-SE retirement contributions.
+# Self-employed pension share of retirement contributions.
+# Self-employed pension: $30.13B (IRS SOI Publication 1304, Table 1.4,
+# TY 2023, "Payments to a Keogh plan")
+# Combined employee DC + IRA + self-employed pension: $655.53B
+# Share: $30.13B / $655.53B = 4.6%
+# https://www.irs.gov/statistics/soi-tax-stats-individual-statistical-tables-by-size-of-adjusted-gross-income
+se_pension_share_of_retirement_contributions: 0.046
+
+# DC vs IRA share of remaining non-SE retirement contributions.
 # Employee DC: $567.9B (BEA/FRED Y351RC1A027NBEA minus W351RC0A144NBEA)
 # Total IRA: $57.5B (IRS SOI Tables 5 & 6, TY 2022)
 # Combined: $625.4B
@@ -46,8 +54,9 @@ roth_share_of_dc_contributions: 0.15
 # https://www.irs.gov/statistics/soi-tax-stats-accumulation-and-distribution-of-individual-retirement-arrangements
 traditional_share_of_ira_contributions: 0.392
 
-# SE pension contribution cap.
-# SEP-IRA / Solo 401(k) contributions are capped at the lesser of
+# SE pension statutory parameters retained for retirement-limit utilities.
+# These are not used to reduce desired source contribution data.
+# SEP-IRA / Solo 401(k) contributions are limited to the lesser of
 # a percentage of net SE earnings and a dollar limit.
 # The 25% rate is technically ~20% for sole proprietors after the
 # deduction-for-half-of-SE-tax adjustment, but 25% is the standard

policyengine_us_data/datasets/puf/puf.py

@@ -692,7 +692,7 @@ def preprocess_puf(puf: pd.DataFrame) -> pd.DataFrame:
     puf["taxable_ira_distributions"] = puf.E01400
     puf["tax_exempt_interest_income"] = puf.E00400
     puf["tax_exempt_pension_income"] = puf.E01500 - puf.E01700
-    puf["traditional_ira_contributions"] = puf.E03150
+    puf["traditional_ira_contributions_desired"] = puf.E03150
     puf["unrecaptured_section_1250_gain"] = puf.E24515
 
     puf["foreign_tax_credit"] = puf.E07300
@@ -835,7 +835,7 @@ def preprocess_puf(puf: pd.DataFrame) -> pd.DataFrame:
     "taxable_ira_distributions",
     "tax_exempt_interest_income",
     "tax_exempt_pension_income",
-    "traditional_ira_contributions",
+    "traditional_ira_contributions_desired",
     "unrecaptured_section_1250_gain",
     "foreign_tax_credit",
     "amt_foreign_tax_credit",

policyengine_us_data/db/etl_national_targets.py

@@ -659,15 +659,15 @@ def extract_national_targets(year: int = DEFAULT_YEAR):
             "year": 2024,
         },
         {
-            "variable": "self_employed_pension_contribution_ald",
+            "variable": "self_employed_pension_contributions",
             "value": RETIREMENT_CONTRIBUTION_TARGETS[
-                "self_employed_pension_contribution_ald"
+                "self_employed_pension_contributions"
             ]["value"],
             "source": RETIREMENT_CONTRIBUTION_TARGETS[
-                "self_employed_pension_contribution_ald"
+                "self_employed_pension_contributions"
             ]["source"],
             "notes": RETIREMENT_CONTRIBUTION_TARGETS[
-                "self_employed_pension_contribution_ald"
+                "self_employed_pension_contributions"
             ]["notes"],
             "year": 2024,
         },

policyengine_us_data/storage/calibration_targets/soi_metadata.py

@@ -14,7 +14,7 @@
         ),
         "source_year": 2023,
     },
-    "self_employed_pension_contribution_ald": {
+    "self_employed_pension_contributions": {
         "value": 30.130848e9,
         "source": "https://www.irs.gov/statistics/soi-tax-stats-individual-statistical-tables-by-size-of-adjusted-gross-income",
         "notes": (

policyengine_us_data/utils/loss.py

@@ -137,9 +137,9 @@
     #
     # traditional_ira_contributions: IRS SOI Publication 1304, Table 1.4
     # (TY 2023), "IRA payments" deduction — $13.77B (col DU, row
-    # "All returns, total"). This is the actual above-the-line
-    # deduction claimed on returns. The variable flows directly into
-    # the ALD with no deductibility logic in policyengine-us, so the
+    # "All returns, total"). This is the above-the-line deduction
+    # claimed on returns. The variable flows directly into the ALD
+    # with no deductibility logic in policyengine-us, so the
     # target must match the deduction, not total contributions.
     # https://www.irs.gov/statistics/soi-tax-stats-individual-statistical-tables-by-size-of-adjusted-gross-income
     "traditional_ira_contributions": RETIREMENT_CONTRIBUTION_TARGETS[
@@ -159,15 +159,15 @@
     # https://corporate.vanguard.com/content/dam/corp/research/pdf/how_america_saves_report_2024.pdf
     "traditional_401k_contributions": 482.7e9,
     "roth_401k_contributions": 85.2e9,
-    # self_employed_pension_contribution_ald: IRS SOI Publication
+    # self_employed_pension_contributions: IRS SOI Publication
     # 1304, Table 1.4 (TY 2023), "Payments to a Keogh plan" —
     # $30.13B (col DM, row "All returns, total"). Includes
     # SEP-IRAs, SIMPLE-IRAs, and traditional Keogh/HR-10 plans.
-    # Targeting the ALD (not the input) because policyengine-us
-    # applies a min(contributions, SE_income) cap.
+    # Targeting the contribution output because policyengine-us applies
+    # statutory limits before the ALD formula.
     # https://www.irs.gov/statistics/soi-tax-stats-individual-statistical-tables-by-size-of-adjusted-gross-income
-    "self_employed_pension_contribution_ald": RETIREMENT_CONTRIBUTION_TARGETS[
-        "self_employed_pension_contribution_ald"
+    "self_employed_pension_contributions": RETIREMENT_CONTRIBUTION_TARGETS[
+        "self_employed_pension_contributions"
     ]["value"],
     # roth_ira_contributions: IRS SOI IRA Accumulation Tables 5 & 6
     # (TY 2022, latest published). Total Roth IRA contributions =

policyengine_us_data/utils/national_target_parity.py

@@ -61,7 +61,7 @@
     "traditional_ira_contributions",
     "traditional_401k_contributions",
     "roth_401k_contributions",
-    "self_employed_pension_contribution_ald",
+    "self_employed_pension_contributions",
     "roth_ira_contributions",
 }