The Effects of Occupational Aspirations and Other Factors on the Out-Migration of Rural Youth
Bibliographic record
Abstract
Out-migration of youth from rural areas persists as one of the most serious threats to the sustainability of rural communities. This study provides a more rigorous examination than has been previously possible of whether occupational aspirations held by youth affect their long-term out-migration. The analysis is accomplished by examining the effects of occupational aspirations and known predictors of migration with five logistic regression models. We utilize data on rural youth in the 1979 National Longitudinal Survey of Youth (NLSY79) which include a measure of occupational aspirations at a youthful age and allow for a long-term measure of subsequent migration. Results show that rural youth aspiring to professional and managerial occupations are more likely to be rural out-migrants at age 35 than are youth aspiring to blue collar occupations. This greater likelihood is true even with other recognized influences on migration being controlled. Other variables introduced in our logistic models are gender, race/ethnicity, mother's education, length of residence, change in educational status, change in marital status, and actual occupation at age 35. We find that the effects of these variables on migration out of rural places largely persist when occupational aspirations are controlled. Our findings further substantiate the need for rural communities to increase career opportunities in professional and managerial occupations in order to reduce the out-migration of a large and vital segment of rural youth. Better knowledge about the odds of out-migration for other important determinants of migration should also be helpful in efforts to lessen the loss of rural youth. Keywords: rural out-migration, NLSY79, occupational aspirations, Blue collar, managerial, professional
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".