Housing Booms, Manufacturing Decline, and Labor Market Outcomes
Bibliographic record
Abstract
We study the extent to which manufacturing decline and local housing booms contributed to changes in labor market outcomes during the 2000s, focusing primarily on the distributional consequences across geographical areas and demographic groups. Using a local labor markets design, we estimate that manufacturing decline significantly reduced employment between 2000 and 2006, while local housing booms increased employment by roughly the same magnitude. The effects of manufacturing decline persist through 2012, but we find no persistent employment effects of local housing booms, likely because housing booms were associated with subsequent busts of similar magnitude. These results suggest that housing booms "masked" negative employment growth that would have otherwise occurred earlier in the absence of the booms. This "masking" occurred both within and between cities and demographic groups. For example, manufacturing decline disproportionately affected older men without a college education, while the housing boom disproportionately affected younger men and women, as well as immigrants. Applying our local labor market estimates to the national labor market, we find that roughly 40 percent of the reduction in employment during the 2000s can be attributed to manufacturing decline and that these negative effects would have appeared in aggregate employment statistics earlier had it not been for the large, temporary increases in housing demand.
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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 | 0.008 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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; both teacher heads agree on what is shown here.
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".