Employment effects of on-the-job human capital acquisition
Bibliographic record
Abstract
This paper quantifies the joint effect of on-the-job training and workers' on-the-job learning decisions on aggregate employment. We present an Index of On-the-job Human Capital Acquisition (OJHCA), based on data from the OECD Program for the International Assessment of Adult Competencies. The objective of the index is to capture both formal and informal learning in the workplace. We document a strong positive association between the two components of our index, i.e., on-the-job training and on-the-job learning. We also show that the index is positively correlated with employment across OECD economies. To explain these stylized facts, we build a search and matching model with on-the-job human capital acquisition that depends on both on-the-job training provided by firms and on the workers' level of on-the-job learning. We calibrate the model to the Canadian economy and adjust the learning and training marginal costs to match cross-country levels in the human capital index. We compare the model's predictions with the data and we conclude that differences in marginal costs are necessary to match the differences observed in employment rates across countries. We also extend the model including payroll taxes and education. The model is able to reproduce the observed differences in employment rates between countries with the highest and the lowest level of OJHCA.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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 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".