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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".