How Do Families and Unattached Individuals Respond to Layoffs? Evidence from Canada
Bibliographic record
Abstract
Using data from a large Canadian longitudinal dataset, we examine whether earnings of wives and teenagers increase in response to layoffs experienced by husbands. We find virtually no evidence of an “added worker effect†for the earnings of teenagers. However, we find that among families with no children of working age, wives’ earnings offset about one-fifth of the earnings losses experienced by husbands five years after the layoff. We also contrast the long-term earnings losses experienced by husbands and unattached males. Even though the former group might be less mobile geographically than the latter, we find that both groups experience roughly the same earnings losses in the long run. Furthermore, the income losses (before tax and after tax) of both groups are also very similar. However, because unattached males have much lower pre-layoff income, they experience much greater relative income shocks than (families of) laid-off husbands.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".