Older Workers' Training Opportunities in Times of Workplace Innovation
Bibliographic record
Abstract
Training (for workers) and innovation (for workplaces) are not free lunches. From the viewpoint of the firm, training is also highly risky, because there is uncertainty over the size of any future returns from employer-provided training. Stylized facts stress that constraints in achieving preferred working hours have major impacts on job satisfaction. Consequently hour constraints may lead to workers' job mobility and older workers' retirement. Firms internalize the risk of workers' mobility by reducing their training investments in these workers. I contrast this model with a signalling model of hour constraints where, in the face of asymmetric information over workers' quality and reliability, and so over profitability of training, workers may trade present hour constraints (at the current wage), for training (and future wage) opportunities. This set of reasoning implies that, empirically, we should observe a positive correlation between training and hour constraints at the individual level. I use two matched employer-employee datasets, for Australia and Canada respectively, to test the competing empirical implications of these two models for the link between hour constraints and training. The main result of this study is that there is little support for hour constraints as a signal of future reliability and productivity. Rather, hour constrained individuals appear to have less chance to receiving training. This result survives a number of robustness exercises that attempt to control for selection on observables and selection on unobservables that determine the hour constraint outcome. Institutional differences in the retirement funding system, and the differential appeal of outside option (the option of exiting the labour force) in Australia and Canada in the two survey years contribute to explain the different patterns of training and hour constraints older workers face in these two countries.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".