euromod_linking.methods.lma_labour_alignment.imputation¶
Nearest-neighbour earnings imputation — the FALLBACK for entrants’ wages.
The method’s standard assumption is full-time take-up at the person’s own predicted hourly wage (yivwg, estimated by the national team for every working-age person). This module covers the remainder: entrants for whom no predicted wage exists, because the national wage equation excludes them (students, the self-employed, pensioners in some countries) or the value is missing.
Economic intuition¶
Someone who enters employment needs a wage before EUROMOD can tax it — but new entrants have no observed earnings. Statistical matching answers “what would this person plausibly earn?” with “what observably similar people currently earn”: each entrant is matched to the k=5 most similar continuing workers on the classic wage-equation observables (age, gender, education, marital status, region), and their earnings/months/hours are averaged with inverse-distance weights (closer matches count more). This preserves realistic wage heterogeneity across entrants — a flat assumption, everyone at the minimum wage say, would compress the bottom of the earnings distribution and mechanically overstate redistribution effects.
Donors exclude other entrants (lma==0 filter): imputing from imputed values would let assumptions compound.
Fixed methodology (matching variables, k=5, inverse-distance weighting). Deterministic: donors and recipients processed in idperson order; scaler and KNN are deterministic given pinned input order.
Attributes¶
Functions¶
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Overwrite yem_a/yemmy_a/lhw_a for recipients with donor-matched values. |
Module Contents¶
- euromod_linking.methods.lma_labour_alignment.imputation.impute_transition_incomes(df: pandas.DataFrame, eligible: pandas.Series, region_label_col: str | None, recipients: pandas.Index | None = None) tuple[pandas.DataFrame, dict][source]¶
Overwrite yem_a/yemmy_a/lhw_a for recipients with donor-matched values.
recipients defaults to every entrant (lma==1). The method normally passes only the entrants with no usable predicted wage, since the standard assumption is full-time take-up at the person’s own yivwg — this matching is the fallback for people the wage equation does not cover. Returns (df, diagnostics).
- euromod_linking.methods.lma_labour_alignment.imputation.INCOME_VARS = ('yem', 'yemmy', 'lhw')¶
- euromod_linking.methods.lma_labour_alignment.imputation.MATCHING_VARS = ('dag', 'dgn', 'deh', 'dms')¶
- euromod_linking.methods.lma_labour_alignment.imputation.N_NEIGHBORS = 5¶