Stratifying Disaster: State Aid, Institutional Processes, and Inequality in American Communities
Bibliographic record
Abstract
Abstract Disaster aid is an increasingly costly form of social spending and an often-overlooked way that welfare states manage new forms of risk related to climate change. In this article, I argue that disaster aid programs engender racial and socioeconomic inequalities through a process of assistance access constituted by distinct state logics, administrative burdens, and bureaucratic actors. I test this claim empirically by analyzing 5.37 million applicant records from FEMA’s Individuals and Households Program (IHP) from 2005 to 2016. Results demonstrate that key institutional features—the conditions of eligibility and sufficiency, burdens of proof, and assessments by contracted inspectors—combine in a stepwise process to funnel permanent repair resources to homeowners in whiter communities, while temporary rental aid is granted disproportionately to households in communities of color. Analyses of denial codes suggest racial disparities in appraisals of disaster damage. Among those approved for aid, more benefits accrue to those from comparatively higher income communities, and a decoupling of permanent and temporary housing aid further stratifies socioeconomic growth during recovery. Theoretically, this research advances an account of institutional processes transferable to other analyses of social programs, and it introduces climate risk as a new form of social risk against which welfare states insure citizens.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".