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 Résumé À l'aide du National Agricultural Workers Survey (NAWS) et de l'analyse de la régression logistique binaire, je détermine les chances d'établissement des travailleurs agricoles autorisés et non-autorisés, originaire du Mexique, installés aux États-Unis durant la Grande Récession (2008-2010), qui étaient aux États-Unis depuis assez longtemps—et au bon moment—pour être interviewés par le NAWS. Les deux groupes étaient plus susceptibles de s'installer aux États-Unis en 2008-2010 que leurs homologues durant la pré-récession (2005-2007) suggérant que la crise économique a dissuadé la migration circulaire de retour vers le Mexique et/ou découragé les nouveaux immigrants d'aller vers les États-Unis durant la récession. Les chances d'établissement des deux groupes furent aussi affectées par les régions d'établissement à l'intérieur des États-Unis. Les migrants agricoles autorisés interviewés dans les états de l'est, du centre-ouest et du nord-ouest avaient beaucoup moins de chances de s'installer aux États-Unis que leurs homologues de l'état mentionné, la Californie. La longue histoire de l'établissement des immigrants en Californie et leur large accroissement durant toute l'année expliquent probablement la différence. Réciproquement, les migrants non-autorisés interviewés dans les états du nord-ouest, du centre-ouest et du sud-est avaient des chances plus significatives de s'installer aux États-Unis que leurs homologues non-autorisés de Californie, suggérant des différences majeures dans l'occupation du territoire des migrants mexicains autorisés par rapports aux non-autorisés, ce qui pourrait avoir des implications majeures sur les implications du travail agricole à l'avenir. Un troisième modèle avec les régions d'origine des migrants au Mexique et un quatrième modèle avec la démographie, le capital humain et autres variables sont inclus pour déterminer les chances les plus probables pour que les migrants agricoles autorisés et non-autorisés s'installent aux États-Unis.
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 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".