The Interplay of Economic, Environmental, and Political Factors on Life Expectancy in Somalia
Bibliographic record
Abstract
Life expectancy in Somalia remains critically low due to the complex interplay of economic, environmental, and political factors.This study aims to examine the determinants of life expectancy in Somalia from 1985 to 2022.Using the Autoregressive Distributed Lag (ARDL) model and VECM Granger causality tests, the analysis incorporates GDP, income inequality, CO₂ emissions, institutional quality, and internal conflict as key explanatory variables.The ARDL bounds test confirms a long-run cointegration relationship among these factors.The results reveal that GDP has a positive and significant impact on life expectancy (coefficient = 0.0675, p = 0.0163), while income inequality (-27.582,p = 0.0257) and CO₂ emissions (-0.176, p = 0.0324) negatively affect life expectancy in the long run.Additionally, weak institutional quality (-2.187, p = 0.000) and internal conflict (-0.103, p = 0.002) significantly reduce life expectancy.The error correction term is negative and highly significant (-1.536, p = 0.000), indicating a strong adjustment toward equilibrium.The study concludes that fostering inclusive economic growth, improving governance, reducing inequality, and addressing environmental degradation are essential to improving life expectancy in Somalia.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".