Human capital, poverty, and income distribution in developing countries
Why this work is in the frame
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Bibliographic record
Abstract
Purpose This paper aims to examine the impact of the components of human capital on the extent of poverty and income distribution in developing countries. Design/methodology/approach Data for all variables are from the World Development Report , 2006 and 2007. The least‐squares estimation technique in a multivariate linear regression is applied. It is noted that the introduction of interaction terms between income and the components of human capital yields better statistical results, as pointed out in the economic development literature. Findings Based on data from the World Bank and using a sample of 40 developing economies, it is found that the fraction of the population below the poverty line is linearly dependent upon gender parity ratio in primary and secondary schools, the prevalence of child malnutrition, per capita purchasing power parity gross national income, the maternal mortality rate, and the percentage of births attended by skilled health staff. Using another sample of 35 developing countries, it is found that income inequality linearly depends on the same explanatory variables plus the infant mortality rate and the primary school completion rate. Practical implications Statistical results of such empirical examination will assist governments in those countries identify areas that need to be improved upon in order to alleviate poverty and improve the distribution of income. Originality/value This paper provides useful information on the impact of the components of human capital on the extent of poverty and income distribution in developing countries.
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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 | 0.001 | 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 | 0.001 | 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 it