It Ain’t Where You’re From, It’s Where You’re At: Hiring Origins, Firm Heterogeneity, and Wages.
Bibliographic record
Abstract
Sequential auction models of labor market competition predict that the wages required to successfully poach a worker from a rival employer will depend on the productivities of both the poached and poaching firms.We develop a theoretically grounded extension of the two-way fixed effects model of Abowd et al. (1999) in which log hiring wages are comprised of a worker fixed effect, a fixed effect for the "destination" firm hiring the worker, and a fixed effect for the "origin" firm, or labor market state, from which the worker was hired.This specification is shown to nest the reduced form for hiring wages delivered by semi-parametric formulations of the canonical sequential auction model of Postel-Vinay and Robin (2002b) and its generalization in Bagger et al. (2014).Fitting the model to Italian social security records, origin effects are found to explain only 0.7% of the variance of hiring wages among job movers, while destination effects explain more than 23% of the variance.Across firms, destination effects are more than 13 times as variable as origin effects.Interpreted through the lens of Bagger et al. (2014)'s model, this finding requires that workers possess implausibly strong bargaining strength.Studying a cohort of workers entering the Italian labor market in 2005, we find that differences in origin effects yield essentially no contribution to the evolution of the gender gap in hiring wages, while differences in destination effects explain the majority of the gap at the time of labor market entry.These results suggest that where a worker is hired from tends to be relatively inconsequential for their wages in comparison to where they are currently employed.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".