Employer Learning, Productivity, and the Earnings Distribution: Evidence from Performance Measures
Bibliographic record
Abstract
Pay distributions fan out with experience. The leading explanations for this pattern are that over time, either employers learn about worker productivity but productivity remains fixed or workers' productivities themselves evolve heterogeneously. We propose a dynamic specification that nests both employer learning and dynamic productivity heterogeneity. We estimate this model on a 20-year panel of pay and performance measures from a single, large firm. The advantage of these data is that they provide us with repeat measures of productivity, some of which have not yet been observed by the firm when it sets wages. We use our estimates to investigate how learning and dynamic productivity heterogeneity jointly contribute to the increase in pay dispersion with age. We find that both mechanisms are important for understanding wage dynamics. The dispersion of pay increases with experience primarily because productivity differences increase. Imperfect learning, however, means that wages differ significantly from individual productivity all along the life cycle because firms continuously struggle to learn about a moving target in worker productivity. Our estimates allow us to calculate the degree to which imperfect learning introduces a wedge between the private and social incentives to invest in human capital. We find that these disincentives exist throughout the life cycle but increase rapidly after about 15 years of experience. Thus, in contrast to the existing literature on employer learning, we find that imperfect learning might have especially large effects on investments among older workers.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 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) | 0.003 | 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".