Housing Booms and Busts, Labor Market Opportunities, and College Attendance
Bibliographic record
Abstract
We study how the recent national housing boom and bust affected college enrollment and attainment during the 2000s.We exploit cross-city variation in local housing booms, and use a variety of data sources and empirical methods, including models that use plausibly exogenous variation in housing demand identified by sharp structural breaks in local housing prices.We show that the housing boom improved labor market opportunities for young men and women, thereby raising their opportunity cost of college-going.According to standard human capital theories, this effect should have reduced college-going overall, but especially for persons at the margin of attendance.We find that the boom substantially lowered college enrollment and attainment for both young men and women, with the effects concentrated at two-year colleges.We find that the positive employment and wage effects of the boom were generally undone during the bust.However, attainment for the particular cohorts of college-going age during the housing boom remain persistently low after the end of the bust, suggesting that reduced educational attainment may be an enduring effect of the housing cycle.We estimate that the housing boom explains roughly 30 percent of the recent slowdown in college attainment.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".