Bibliographic record
Abstract
Using a unique data set and a novel identification strategy, we estimate the effect of minimum wage increases on job vacancy postings. Using occupation-specific county-level vacancy data from the Conference Board’s Help Wanted Online for 2005-2018, we find that state-level minimum wage increases lead to substantial declines in existing and new vacancy postings in occupations with a larger share of workers who earn close to the prevailing minimum wage. We estimate that a 10 percent increase in the state-level effective minimum wage reduces vacancies by 2.4 percent in the same quarter, and the cumulative effect is as large as 4.5 percent a year later, in these occupations relative to the rest. The negative effect on vacancies is more pronounced for occupations where workers typically have lower educational attainment (high school or less) and in counties with higher poverty rates. We argue that our focus on vacancies versus on employment has a distinct advantage of highlighting a mechanism through which minimum wage hikes affect labor markets. Our finding of a negative effect on vacancies is not inconsistent with the wide range of findings in the literature about the effect of minimum wage change on employment, which is driven by changes in both hiring and separation margins.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".