Language, Agglomeration, and Hispanic Homeownership
Bibliographic record
Abstract
As of the fourth quarter of 2005, 76 percent of white non-Hispanic families owned homes, but only 50 percent of Hispanic families. We argue that low rates of homeownership in Hispanic communities create a self-reinforcing mechanism that contributes to this large disparity. In part, this occurs because proximity to other homeowners belonging to a family’s social network improves access to information about how to become a homeowner. Role model effects may also be relevant. We investigate these issues using household-level data on out-of-state movers from the 2000 Decennial Census. Three especially important results are obtained. First, proximity to Hispanic homeowners in the 1995 place of residence increases the propensity of a Hispanic family to own a home in 2000. Second, that effect is especially strong with respect to proximity to weak English speaking Hispanic homeowners. Third, these patterns hold regardless of the Hispanic family’s own ability to speak English. From a policy perspective, these results suggest that local programs designed to promote homeownership among weak English-speaking Hispanic families likely increase Hispanic homeownership beyond just the immediate program participants.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".