The long-run effects of parental unemployment in childhood
Bibliographic record
Abstract
Parental job loss is a large, negative shock to the household that can affect children in both the short- and long-run. Little is known, however, about how the long-run impacts of job loss on children vary with the child's age at the time of displacement. This paper provides the first empirical evidence of the long-run effects of parental unemployment on children exposed before age 10 (and as young as 2), a period thought to be critical for child development. Using administrative tax data covering the universe of children born in Canada between 1972 and 1985 and random forest proximity matching, I estimate the causal effects of parental job loss experienced at different points in childhood on a child's income attainment. I find that children exposed to parental unemployment at ages 2 to 10 experience losses of 3 to 4 rank points in average earnings attainment in adulthood (approximately $2,500 per year). These children are also 36% more likely to receive welfare as adults and 4% less likely to pursue post-secondary education. Consistent with critical periods of child development, children who experience parental job loss before age 10 experience larger reductions in income attainment than children exposed at older ages. Decomposing these estimates, I show that the majority of my treatment effects are attributable to the timing of income losses experienced during childhood, as well as unemployment-induced moves to neighourhoods with less opportunity.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".