Wages and Employment: The Canonical Model Revisited
Bibliographic record
Abstract
The basic canonical model fails to predict the aggregate college premium outside of the original sample period (1963-1987) or to account for the observed deviations in college premia for younger vs. older workers.This paper documents that these failings are due to mis-measurement of the relevant prices and quantities when using composition adjustment methods to construct relative skill prices and supplies, which ignore cohort effects that are particularly important in the 1980s and 1990s.Re-estimating the model with prices and quantities that incorporate cohort effects produces a good fit for the out of sample prediction and explains the observed deviation in the college premium for younger vs. older workers even with perfect substitutability across age.Moreover, the estimated elasticity of substitution between high and low skill is higher and there is a much smaller role for skill-biased technical change in explaining the path of the college wage premium.The elasticity of substitution is also an important parameter for the broader literature on education and wages, especially in assessing general equilibrium responses to government policies.In the case of a tuition subsidy, price responses can undo most of the direct (partial equilibrium) effect of the subsidy on enrolment, so that general equilibrium enrolment responses are substantially weaker.The higher elasticity estimated in this paper, produces much weaker general equilibrium relative price changes and stronger enrolment effects.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.017 | 0.003 |
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".