The Impact of Aggregate and Sectoral Fluctuations on Training Decisions
Bibliographic record
Abstract
The literature on training has pointed out that macroeconomic fluctuations can have a positive or a negative effect on training decisions. On the one hand, the opportunity cost to train is lower during downturns, and thus training should be counter-cyclical. On the other hand, a positive shock may be related to the adoption of new technologies and increased returns to skill, making training incidence pro-cyclical. The first contribution of this paper is to document, using the Canadian panel of Workplace and Employee Survey (WES), that (i) training moves counter-cyclically with aggregate output fluctuations (more training in downturns), while at the same time (ii) the relative position of sectoral output has a positive impact on training decisions (more training in sectors doing relatively better). This second fact is novel and unexplored. Overall, the results show that the firms' decisions to train are quite complex; in order to fully understand them, one needs to take into account not only the change in aggregates, but also the relative position of each sector in the economy. The second contribution of the paper is to illustrate the mechanisms at work by incorporating training decisions into a standard Mortensen-Pissarides model. In the standard model, production takes place if workers' productivity is above a reservation threshold. In our extension, this threshold gets expanded into a whole interval within which production takes place if workers are trained. The quantitative analysis from the calibrated model illustrates the counter-cyclical opportunity cost adjustment from aggregate shocks and the pro-cyclical adjustment coming from sectoral reallocation.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".