Financial Vulnerability and Personal Finance Outcomes of Natural Disasters
Bibliographic record
Abstract
I evaluate the effects of hurricanes of varying intensity on the financial condition of a typical resident in both affected and unaffected census tracts, where the degree of affect is determined by the relative location of a census tract?s boundary with buffers around the tracks of hurricane eyes that occurred in the years 2000-2014. The primary question in the article is whether financial vulnerability, or, alternatively, ?financial preparedness,? affects post-hurricane disaster financial outcomes. {{p}} I find that hurricanes tend to lower credit scores, for the most, but outcomes are far from uniform across categories of hurricanes. I attribute these differences largely to number of disasters in each quarter of the study period, levels of disaster aid, and media coverage and political interest. In some cases I surmise that those in the 25-mile buffer may benefit from economic stimulus that follows a hurricane, but do not have damages and other economic losses to the same extent as those within a 15-mile buffer. Modeling hurricanes as ?treatments? and interacting them with variables from consumer credit reports, I find that the financial vulnerability of residents in affected census tracts is associated with poorer financial outcomes. Considering lags, financial vulnerability is shown to have a considerable impact on post-hurricane personal finance outcomes.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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; 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".