The Evolution of Unobserved Skill Returns in the U.S.: A New Approach Using Panel Data
Bibliographic record
Abstract
Economists disagree about the factors driving the substantial increase in residual wage inequality in the US over the past few decades.To identify changes in the returns to unobserved skills, we make a novel assumption about the dynamics of skills rather than about the stability of skill distributions across cohorts, as is standard.We show that our assumption is supported by data on test score dynamics for older workers in the HRS.Using survey data from the PSID and administrative data from the IRS and SSA, we estimate that the returns to unobserved skills declined substantially in the late-1980s and 1990s despite an increase in residual inequality.Accounting for firm-specific pay differences yields similar results.Extending our framework to consider occupational differences in returns to skill and multiple unobserved skills, we further show that skill returns display similar patterns for workers employed in each of cognitive, routine, and social occupations.Finally, our results suggest that increasing skill dispersion, driven by rising skill volatility, explains most of the growth in residual wage inequality since the 1980s.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".