How Worker Productivity and Wages Grow with Tenure and Experience: The Firm Perspective
Bibliographic record
Abstract
How worker productivity evolves with tenure and experience is central to economics, shaping, for example, life-cycle earnings and the losses from involuntary job separation.Yet, worker-level productivity is hard to identify from observational data.This paper introduces direct measurement of worker productivity in a firm survey designed to separate the role of on-the-job tenure from total experience in determining productivity growth.Several findings emerge concerning the initial period on the job.(1) On-the-job productivity growth exceeds wage growth, consistent with wages not being allocative period-by-period.(2)Previous experience is a substitute, but a far less than perfect one, for on-the-job tenure.(3) There is substantial heterogeneity across jobs in the extent to which previous experience substitutes for tenure.The survey makes use of administrative data to construct a representative sample of firms, check for selective nonresponse, validate survey measures with administrative measures, and calibrate parameters not measured in the survey.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".