Bibliographic record
Abstract
This paper estimates the consequences of the decline of the Brazilian manufacturing sector for displaced workers. I estimate that earnings decline by nearly 50% after displacement relative to one year prior. About a quarter of the initial earnings loss is attributable to reduced hours of work rather than lower wages. However, hours recover fully within one year of displacement, while wages remain about a third lower. Allowing the displacement effect to differ by age yields a surprising U-shaped curve. Middle aged workers are hit hardest by a layoff, with younger and older workers relatively better off. For workers aged 35-40, the initial earnings loss reaches 70%. This is a surprising finding because most theories of job loss predict a negative relationship between the wage loss on displacement and the length of tenure on the pre-displacement job, which is increasing in age. I account for these facts with a simple model in which the ratio of specific to general human capital reaches a peak at middle age. Young workers have little specific capital and a low specific-general human capital ratio. In the early years of one's career, specific capital (whether due to investments in specific skills or in search) accumulates much more rapidly than general human capital. Around ages 35 to 40, this trend reverses and the returns to general skills rise more rapidly. Thus, the accumulation of general skills serves to reduce the effect of job displacement at older ages despite increasing average job tenure. These findings suggest that major market reforms may have larger than anticipated effects because the primary losers are workers in the middle of their working life. This is also important from a welfare perspective because these workers are the most likely to fall through the cracks of social safety nets, which typically target younger and older workers.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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 | 0.000 |
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".