Wages, Skills, and Skill-Biased Technical Change: The Canonical Model Revisited
Bibliographic record
Abstract
The canonical supply-demand model of the wage returns to skill has been extremely influential; however, it has faced several important challenges. Several studies show that the standard approach sometimes produces theoretically wrong-signed elasticities of substitution, yields counterintuitive paths for skill-biased technical change (SBTC), and does not account for the observed deviations in college premia for younger vs. older workers. This paper shows that these failings can be explained by mis-measurement of relative skill prices and supplies (based on standard demographic composition-adjustments) and by inadequate ad hoc functional form assumptions about the path for SBTC. Improved estimates of skill prices and supplies that account for variation in skill across cohorts within narrowly defined groups help explain the observed deviation in the college premium for younger vs. older workers, even with perfect substitutability across age. Re-estimating the model with these prices and supplies produces a good fit with better out-of-sample prediction and robustly yields positive elasticities of substitution between high and low skill workers. The estimates suggest greater substitutability across skill and a more modest role for SBTC. We implement two new approaches to modelling SBTC. First, we study the extent to which recessions induce jumps or trend-adjustments in skill bias and find evidence that both features are important (but differ across recessions). Second, we link SBTC to direct measures of information technology investment expenditures and show that these measures explain the evolution of skill bias quite well. Together, these approaches suggest that the ad hoc assumptions for SBTC previously employed in the literature are too crude to fit the data well, leading to the incorrect conclusion that SBTC slowed during the early-1990s and under-estimates of the elasticity of substitution between high and low skill workers.
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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 | 0.002 | 0.008 |
| 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 | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".