Forthcoming: American Economic Journal: Applied Economics Do Temporary-Help Jobs Improve Labor Market Outcomes for Low-Skilled Workers? Evidence from ‘Work First’
Bibliographic record
Abstract
Temporary-help jobs offer rapid entry into paid employment, but they are typically brief and it is unknown whether they foster longer-term employment. We utilize the unique structure of Detroit’s welfare-to-work program to identify the effect of temporary-help jobs on labor market advancement. Exploiting the rotational assignment of welfare clients to numerous nonprofit contractors with differing job placement rates, we find that temporary-help job placements do not improve and may diminish subsequent earnings and employment outcomes among participants. In contrast, job placements with direct-hire employers substantially raise earnings and employment over a seven quarter follow-up period. JEL: J24, J48, J62 Keywords: Temporary-help, welfare to work, job placement, low-skill workers, causal effects. 1 Temporary-help firms employ a disproportionate share of low-skilled and minority U.S. workers (U.S. Department of Labor, Bureau of Labor Statistics 2005). Within the low-wage population, employment in temporary help is especially prevalent among participants in public employment and training programs. Although the temporary-help industry accounts for less than
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.112 | 0.034 |
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".