Race, State Surveillance, and Policy Spillover: Do Restrictive Immigration Policies Affect Citizen Earnings?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract This paper investigates whether restrictive immigration policy affects earnings among White, African-American, and Latinx US citizens. Incorporating sociological theories of race that point to state surveillance of Black and Latinx bodies as a linchpin of racial inequality, we ask: Do immigration policies that expand the reach of law enforcement spill over to lower or to raise earnings of employed US citizens? If so, are the effects of these policies greater for Latinx and African-American citizens compared to their White counterparts? Are the effects of these policies stronger among Latinx and African-American men—who are more directly targeted by surveillance policing as a function of their gender—than for co-ethnic women? To investigate these questions, we combine two nationally representative longitudinal datasets—the 1979 National Longitudinal Survey of Youth and the 1997 National Longitudinal Survey of Youth. We find that immigration policies that expand the reach of law enforcement raise wages among native-born Whites. However, we also find that state policies enhancing immigration law enforcement decrease wages among Latinx and African-American citizens compared to Whites. We find no gender/race interactions influencing spillover effects of immigration policy on earnings.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it