The Effect of Disasters on Construction Wages: The Role Played by Spatial Proximity
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Natural hazards significantly threaten the built environment and infrastructure, resulting in a sudden and significant increase in reconstruction demand. Such an unforeseen post-disaster demand surge for reconstruction can inflate costs up to 50%, impeding prompt and efficient reconstruction efforts. The current study aimed to quantify the effect of disasters on construction wages in three Gulf Coast states (Louisiana, Texas, and Florida). To accomplish this, spatial Durbin models were utilized with a difference-in-differences specification to allow for feedback and spillover effects across counties. The results show that the impact of a disaster on construction wages works with a lag. Natural disasters caused a decrease in construction wages in the impacted counties during the disaster quarter, compared to counties that were not affected. However, construction wages increased one quarter later in the disaster-affected counties compared to the non-affected counties. The direct, indirect, and total effects of disasters on the counties' wages indicate significant feedback and spillover effects across counties when a county experiences a disaster. The findings of this study carry significant policy implications for the city’s policymakers and decision-makers.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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 it