Life Cycle Bias in the Estimation of Intergenerational Earnings Persistence
Bibliographic record
Abstract
The estimation of intergenerational earnings mobility is rife with measurement problems since the research does not observe permanent, lifetime earnings. Nearly all studies make corrections for mean variation in earnings because of the age differences among respondents. Recent works employ average earnings or instrumental variable methods to address the effects of measurement error as a result of transitory earnings shocks and mis-reporting. However, empirical studies of intergenerational mobility have paid no attention to the changes in earnings variance across the life cycle suggested by economic models of human capital investment. Using information from the Intergenerational Income Data from Canada and the National Longitudinal Survey and Panel Study of Income Dynamics from the United States, this study finds a strong association between age at observation and estimated earnings persistence. Part of this age-dependence is related to a general increase in transitory earnings variance during the collection of data. An independent effect of life cycle investment is also identified. These findings are then applied to the variation among intergenerational earnings persistence studies. Among studies with similar methodologies, one-third of the variance in published estimates of earnings persistence is attributable to cross-study differences in the age of responding fathers. Finally, these results call into question tests for the importance of credit constraints based on measures of earnings at different points in the life cycle.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.024 | 0.094 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".