Immigration Reform and the Earnings of Latino Workers: Do Employer Sanctions Cause Discrimination?
Bibliographic record
Abstract
This paper investigates whether employer sanctions for hiring undocumented workers introduced by the 1986 Immigration Reform and Control Act (IRCA) adversely affected the hourly earnings of Latino workers in the southwestern United States.We exploit the staggering of the sanctions and employee verification requirements across sectors to estimate this effect.In particular, IRCA's employer-sanctions provisions were not extended to agricultural employers until 2 years after their imposition on nonagricultural employers.Hence, Latino agricultural workers provide a control group against which to compare changes in the wages of Latinos in nonagricultural employment.We find substantial pre-post IRCA declines in the hourly earnings of Latino nonagricultural workers relative to Latinos in agriculture.This pattern, however, is considerably stronger for Latino men than Latina women.We do not observe similar intersectoral shifts in relative wages among non-Latino white workers.In fact, the relative wage changes for non-Latino white workers are of the opposite sign.Finally, the pre-post IRCA relative decline in Latino nonagricultural wages reverses the pre-IRCA trend in which the relative earnings of Latino nonagricultural workers had been increasing. 1 For discussion of employer sanctions in Canada, France,
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".