Propensity Score Matching, a Distance-Based Measure of Migration, and the Wage Growth of Young Men
Bibliographic record
Abstract
This paper estimates the effect of U.S. internal migration on real wage growth between the movers’ first and second jobs. Our analysis of migration differs from previous research in three important aspects. First, we exploit the confidential geocoding in the National Longitudinal Surveys of Youth 1979 (NLSY79) to obtain a distance-based measure. Second, we let the effect of migration on wage growth differ by schooling level. Third, we use propensity score matching to measure the effect of migration on the wages of those who move. We develop an economic model and use it to (i) assess the appropriateness of matching as an econometric method for studying migration and (ii) choose the conditioning variables used in the matching procedure. Our data set provides a rich array of variables on which to match. We find a significant effect of migration on the wage growth of college graduates of 10 percent and a marginally significant effect for high school dropouts of -12 percent. If we use a measure of migration based on moving across either county lines or state lines, the significant effects of migration for college graduates and dropouts disappear.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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 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".