Bibliographic record
Abstract
The Mincer earnings function is the cornerstone of a large literature in empirical economics.This paper discusses the theoretical foundations of the Mincer model and examines the empirical support for it using data from Decennial Censuses and Current Population Surveys.While data from 1940 and 1950 Censuses provide some support for Mincer's model, data from later decades are inconsistent with it.We examine the importance of relaxing functional form assumptions in estimating internal rates of return to schooling and of accounting for taxes, tuition, nonlinearity in schooling, and nonseparability between schooling and work experience.Inferences about trends in rates of return to high school and college obtained from our more general model differ substantially from inferences drawn from estimates based on a Mincer earnings regression.Important differences also arise between cohort-based and cross-sectional estimates of the rate of return to schooling.In the recent period of rapid technological progress, widely used cross-sectional applications of the Mincer model produce dramatically biased estimates of cohort returns to schooling.We also examine the implications of accounting for uncertainty and agent expectation formation.Even when the static framework of Mincer is maintained, accounting for uncertainty substantially affects the return estimates.Considering the sequential resolution of uncertainty over time in a dynamic setting gives rise to option values, which fundamentally changes the analysis of schooling decisions.In the presence of sequential resolution of uncertainty and option values, the internal rate of return -a cornerstone of classical human capital theory -is not a useful guide to policy analysis.
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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 | 0.009 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.005 |
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".