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Record W2743145133 · doi:10.2307/2696011

Immigration Reform and the Earnings of Latino Workers: Do Employer Sanctions Cause Discrimination?

2001· article· en· W2743145133 on OpenAlexaboutno aff
Cynthia Bansak, Steven Raphael

Bibliographic record

VenueIndustrial and Labor Relations Review · 2001
Typearticle
Languageen
FieldSocial Sciences
TopicMigration and Labor Dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsSanctionsEarningsImmigration reformWageImmigrationAgricultureDemographic economicsLabour economicsControl (management)EconomicsImmigration policyPolitical scienceGeographyFinance

Abstract

fetched live from OpenAlex

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,

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.946
Threshold uncertainty score0.604

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.044
GPT teacher head0.322
Teacher spread0.278 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations11
Published2001
Admission routes1
Has abstractyes

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