Minimum Wage Increases and Individual Employment Trajectories
Bibliographic record
Abstract
Using administrative employment data from the state of Washington, we use short-duration longitudinal panels to study the impact of Seattle’s minimum wage ordinance on individuals employed in low-wage jobs immediately before a wage increase. We draw counterfactual observations using nearest-neighbor matching and derive effect estimates by comparing the “treated” cohort to a placebo cohort drawn from earlier data. We attribute significant hourly wage increases and hours reductions to the policy. On net, the minimum wage increase from $9.47 to as much as $13 per hour raised earnings by an average of $8-$12 per week. The entirety of these gains accrued to workers with above-median experience at baseline; less-experienced workers saw no significant change to weekly pay. Approximately one-quarter of the earnings gains can be attributed to experienced workers making up for lost hours in Seattle with work outside the city limits. We associate the minimum wage ordinance with an 8% reduction in job turnover rates as well as a significant reduction in the rate of new entries into the workforce.
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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 | 0.001 | 0.006 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".