Natural Disasters and Human Development in Asia–Pacific: The Role of External Debt
Bibliographic record
Abstract
The average country in Asia–Pacific experiences more natural disasters than average countries of other developing regions. This paper presents stylized facts on natural disasters, human development, and external debt in Asia–Pacific. The paper also contains estimates of the effects that natural disasters have on human development. Controlling for country- and time-fixed effects, the dynamic panel model estimates show that external debt has a mitigating effect on the adverse impacts that natural disasters have on human development; in countries with low external debt-to-GDP ratios, natural disasters significantly decrease the human development index, but not so in countries with high external debt-to-GDP ratios. External debt (i.e., borrowing from abroad) is a financial contract for obtaining resources from abroad (i.e., imports of goods and services). When a country experiencing a natural disaster borrows from abroad to increase imports of goods and services, the population suffers less when a natural disaster strikes. Natural disasters destroy goods and capital (e.g., food, machinery, buildings, and roads) in the countries in which they occur. If imports of goods and services do not increase, then the population has less goods and services to consume following a natural disaster. By increasing imports, which are mirrored on the financial side by an increase in external debt, the population of a country that was struck by a natural disaster can experience consumption smoothing. As the incidence of natural disasters increases globally, a policy recommendation for disaster-prone countries, supported by the empirical results of this paper, is the need for deeper and innovative mechanisms of access to international financing, including reforms in both domestic and international financial systems. The paper’s most significant contribution is the unique lens through which it analyzes the often-studied subject of natural disasters. Rather than looking at disasters as merely adverse events and debt as an unwelcome obligation in isolation, it connects the two and uncovers the paradoxically positive and beneficial role a healthy level of external debt can play in mitigating the adverse effects of these disasters. It provides a fresh perspective, a shift in thinking that may immensely benefit external debt and disaster management policies.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".