Silver lining of the water: The role of government relief assistance in disaster recovery
Bibliographic record
Abstract
Combining three datasets, the Australian Longitudinal Census Panel of 2006 and 2011, engineering data on flood-water height, and administrative data on government relief assistance, we investigate whether and how the government’s post-disaster relief payments helped the economic recovery from riverine floods that struck the state of Queensland in Australia in 2010/11. Using a difference-in-differences methodology that compares the flooded areas with unflooded zones within Queensland whereby the flooded zones differed in their levels of flooding and the government’s relief assistance, we find that the government’s disaster relief assistance was effective in economic recovery, having led individuals residing in flooded areas with average flood height to experience a 3.4 percent rise in (self-reported) income following the disaster, relative to those individuals living in unflooded areas of the state. Our findings are robust to a battery of sensitivity tests, including migration, parallel trends, spatial spillovers, and possible confounders.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".