How Much Is Employment Increased by Cutting Labor Costs? Estimating the Elasticity of Job Creation
Bibliographic record
Abstract
In search and bargaining models, the effect of higher wages on employment is determined by the elasticity of the job creation curve.In this paper, we use U.S. data over the 1970-2007 period to explore whether labor market outcomes abide by the restrictions implied by such models and to evaluate the elasticity of the job creation curve.The main difference between a job creation curve and a standard demand curve is that the former represents a relationship between wages and employment rates, while the latter represents a relationship between wages and employment levels.Although this distinction is quite simple, it has substantive implications for the identification of the effect of higher wages on employment.The main finding of the paper is that U.S. labor market outcomes observed at the city-industry level appear to conform well to the restrictions implied by search and bargaining theory and, using 10-year differences, we estimate the elasticity of the job creation curve with respect to wages to be -0.3.We interpret this relatively low elasticity as reflecting a low propensity for individuals to become more entrepreneurial and create more jobs when labor costs are lower and variable profits are higher.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".