The Economic Impact of Monsoon Flood and Its Spillover on the Households of Bangladesh
Bibliographic record
Abstract
Bangladesh experiences mild to devastating floods during the monsoon season of every year due to its geographical location. Whatever nature these floods may possess, they can be both a curse and a blessing for the people of this country. Self-reporting of Bangladesh Household Income and Expenditure Survey (HIES) 2016 provides us with an opportunity to analyze the direct impact of the flood on the households’ development outcomes, such as income, expenditure, assets, and labor market outcomes at a microlevel. We also use the government report to identify the households that were treated in the report as being flooded but did not report as so in the HIES 2016. We use these two measures of flood exposure to estimate the full economic impact of monsoon floods and investigate any spillover effect to verify the preciseness of flood identification measure of self-reporting. Our modified control group meticulously strengthen the argument of flood impact and inaccuracy of its self-reporting by revealing households’ inhuman displacement in education and health expenditures. Though some river-centric trade centers offer employment and income increases for households, Bangladesh seems to lose its antique blessing of silt-laden flood water to replenish the fertility of flooded crop fields.
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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 | 0.001 | 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 | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".