The great recession and its effect on authorized and unauthorized Mexican agricultural workers in the United States: Who settles in the U.S.?
Bibliographic record
Abstract
Using the National Agricultural Workers Survey (NAWS) and binary logistic regression analysis, I determine the odds of settlement in the U.S. during the Great Recession (2008–2010) of authorized and unauthorized agricultural workers from Mexico who were in the U.S. long enough—and at the right time—to be interviewed by the NAWS. Both groups were more likely to settle in the U.S. during 2008–2010 than their counterparts during the pre-recession (2005–2007) suggesting that the economic crisis had deterred circular-return migration to Mexico and/or discouraged new immigrants from migrating to the U.S during the recession. The odds of settlement of the two groups were also affected by region of settlement within the U.S. Authorized agricultural migrants interviewed in the Eastern, Midwestern, and Northwestern states were significantly less likely to settle in the U.S. than their counterparts from the reference state California. The long history of immigrant settlement in California and a largely year-round growing season probably accounts for this difference. Conversely, unauthorized migrants interviewed in the Northwestern, Midwestern, and Southeastern states were significantly more likely to settle in the U.S. than their unauthorized counterparts from California, suggesting major differences in settlement patterns of authorized versus unauthorized Mexican migrants, which could have major implications for farm labor availability in the future. A third model with region of origin of migrants within Mexico and a fourth model with demographic, human capital, and other variables are included to further determine odds of settlement in the U.S. of authorized and unauthorized agricultural migrants. Keywords: unauthorized agricultural workers; return migration; Mexico; migrant settlement Resume A l'aide du National Agricultural Workers Survey (NAWS) et de l'analyse de la regression logistique binaire, je determine les chances d'etablissement des travailleurs agricoles autorises et non-autorises, originaire du Mexique, installes aux Etats-Unis durant la Grande Recession (2008-2010), qui etaient aux Etats-Unis depuis assez longtemps—et au bon moment—pour etre interviewes par le NAWS. Les deux groupes etaient plus susceptibles de s'installer aux Etats-Unis en 2008-2010 que leurs homologues durant la pre-recession (2005-2007) suggerant que la crise economique a dissuade la migration circulaire de retour vers le Mexique et/ou decourage les nouveaux immigrants d'aller vers les Etats-Unis durant la recession. Les chances d'etablissement des deux groupes furent aussi affectees par les regions d'etablissement a l'interieur des Etats-Unis. Les migrants agricoles autorises interviewes dans les etats de l'est, du centre-ouest et du nord-ouest avaient beaucoup moins de chances de s'installer aux Etats-Unis que leurs homologues de l'etat mentionne, la Californie. La longue histoire de l'etablissement des immigrants en Californie et leur large accroissement durant toute l'annee expliquent probablement la difference. Reciproquement, les migrants non-autorises interviewes dans les etats du nord-ouest, du centre-ouest et du sud-est avaient des chances plus significatives de s'installer aux Etats-Unis que leurs homologues non-autorises de Californie, suggerant des differences majeures dans l'occupation du territoire des migrants mexicains autorises par rapports aux non-autorises, ce qui pourrait avoir des implications majeures sur les implications du travail agricole a l'avenir. Un troisieme modele avec les regions d'origine des migrants au Mexique et un quatrieme modele avec la demographie, le capital humain et autres variables sont inclus pour determiner les chances les plus probables pour que les migrants agricoles autorises et non-autorises s'installent aux Etats-Unis.
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How this classification was reachedexpand
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 | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".