Is there a link between undernourishment, political climate and other socio-economic variables? Evidence from low-income countries
Why this work is in the frame
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Bibliographic record
Abstract
Purpose The authors investigate the role played by the political climate and other covariates on the prevalence of undernourishment for 34 low-income countries across a 21-year period. Design/methodology/approach Political climate is measured in terms of political freedoms and civil liberties. The authors follow a Granger causality approach, which looks at predictive causality (i.e. causality in a temporal sense). For the socio-economic data, the authors rely on annual time series data from the World Bank. Findings Most of the findings are in keeping with our expectations: (1) Lowering women's fertility rate lowers undernourishment; (2) undernourishment converges to its long-run equilibrium path in response to changes in income, political climate, health expenditure, fertility rate and drinking water access; (3) the effect of an instantaneous shock from income, changes to the political climate, health expenditure, fertility rate and drinking water access on undernourishment are completely adjusted in the long run. One surprising result is that there is a positive and significant relationship between the prevalence of undernourishment and political freedom. The authors offer several possible explanations for this unexpected result. Practical implications Given our results, careful attention to the co-curation of policies is desirable. As an example, the authors would advocate a more proactive role by the richer countries in terms of their commitments to foreign aid in addressing the identified problems. Originality/value The authors use advanced panel data techniques, considering a long span of time. Unlike other studies which aim to establish correlations, the authors test for Granger causality.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it