Political economy, sectoral shocks, and border enforcement
Bibliographic record
Abstract
In this paper, we examine the correlation between sectoral shocks and border enforcement in the United States, the U.S. government's main policy instrument for combating illegal immigration. We see whether border enforcement falls following positive shocks to sectors that are intensive in the use of undocumented labour, as would be consistent with political economy models of illegal immigration. We find that border enforcement is negatively correlated with lagged relative price changes in the apparel, fruits and vegetables, and livestock industries and with housing starts in western United States, suggesting that authorities relax border enforcement when demand forundocumented labour is high. Economie politique, chocs sectoriels et vigilance aux frontières. Dans ce mémoire, les auteurs examinent la corrélation entre les chocs sectoriels et la vigilance aux frontières aux Etats‐Unis. La vigilance aux frontières est le principal instrument de politique publique utilisé par le gouvernement américain pour combattre l'immigration illégale. On se demande si la vigilance aux frontières se relâche à la suite de chocs positifs dans des secteurs qui utilisent relativement plus de travailleurs illégaux, ainsi que le suggèrent les modèles d'économie politique de l'immigration illégale. Les principaux résultats indiquent que la vigilance aux frontières est co‐reliée négativement (avec un délai) avec les changements de prix relatifs dans les secteurs du vêtement, des fruits et légumes, et du bétail, ainsi qu'avec le nombre de mises en chantier dans la construction domiciliaire dans l'ouest des Etats‐Unis. Voilà qui suggère que les autorités relâchent la vigilance aux frontières quand la demande de travailleurs illégaux augmente.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".