Accident-induced absence from work and wage ladders
Bibliographic record
Abstract
How do temporary spells of absence from work affect individuals' labor trajectory?To answer this question, we augment a 'wage ladder' model, in which individuals receive alternative take-it-or-leave-it wage offers from firms and potentially suffer accidents which may push them into temporary absence.In such an environment, during absence, individuals do not have the opportunity to receive alternative wage offers that they would have received had they remained present.To test our model's predictions and to quantify the importance of foregone opportunities to climb the wage ladder, we use linked employeremployee administrative data from Hungary, that is linked to rich individual-level administrative health records.We use unexpected and mild accidents with arguably no permanent labor productivity losses, as exogenous drivers of short periods of absence.Difference-in-Differences results show that, relative to counterfactual outcomes in the case of no accidents, (i) even short (3-12-months long) periods of absence due to accidents decrease individuals' wages for up to two years, by around 2.5 percent; and that (ii) individuals end up with lower-paying employers.The share of wage loss due to missed opportunities to switch employers is between 7-20 percent over a two-year period after returning to work, whereas at most 2 percent is due to occupation switches.Our results are robust to (a) instrumenting absence with having suffered an accident, (b) exploiting the random nature of the time of the accident, and (c) within-firm matching of individuals with and without an accident and subsequent absence spell.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".