Democracy, institutions and famines in developing and emerging countries
Bibliographic record
Abstract
The paper has a two-fold objective. First, it theoretically and empirically analyses the effects of democracy on famine mortality. Second, it examines the role played by other institutional/governance factors. The econometric exercises realised with data on a group of emerging and developing countries confirm the validity of Amartya Sen's ‘democracy prevents famine’ argument. Moreover, two main institutional indicators, computed by the World Bank, ‘control of corruption’ and ‘government effectiveness’, are found to be negatively correlated with famine mortality, suggesting that the policy environment, the level of bureaucracy and governmental capacity to take prompt decisions are relevant for reducing famine mortality. These factors are important also among countries with the same political regime. Re´sume´ Ce travail a un double objectif. D'abord, il analyse théoriquement et empiriquement les effets de la démocratie sur la mortalité causée par la famine. Deuxièmement, il examine le rôle joué par d'autres facteurs institutionnels et de gouvernance. Les études économétriques, basées sur des données d'un groupe de pays émergents, confirment la validité de l'argument que « la démocratie empêche la famine » d'Amartya Sen. En outre, deux principaux indicateurs institutionnels, « la lutte contre la corruption » et « l'efficacité du gouvernement », mesurés par la Banque mondiale, se trouvent être négativement liés à la mortalité par famine, ce qui suggère que l'environnement politique, le niveau de bureaucratie, et la capacité du gouvernement à prendre des décisions rapides, sont des facteurs déterminants pour la réduction de la mortalité par famine. Ces facteurs sont également importants parmi les pays ayant le même régime politique.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".