Inequality in Education and Economic Growth: Empirical Investigation and Foundations - Evidence from MENA Region
Bibliographic record
Abstract
This paper investigates empirically the extent of educational inequality and its impact on economic growth. Based on Barro and Lee’s (2010) data, we calculate two indicators measuring inequality of education. The sample comprises 15 countries from the MENA region over the period 1970-2010. As a second step, we applied the Kuznets curve of education for each country of the sample. As a third step, we examine the impact of education inequality on the economic growth in MENA region by using OSL and Instrumental Variables panel regressions with country fixed-effects. The findings show a decline in the Gini index within all the countries, for men and women and also for all age groups. The results also indicate that the education distribution was more unequal in the middle-income countries than in the higher-income countries in 2010. The results suggested that the shape of the Kuznets curve depends basically on the measure used to approximate the inequality. The results demonstrate also that the Gini index of men negatively and significantly affects the growth of higher-income countries. At the same time, the total Gini index influenced negatively and significantly the economic growth of all the countries, including those of high income. These results are therefore robust for the used econometric techniques. In terms of economic policy, the results suggest policymakers to focus on educational policies apt to reduce educational inequalities, especially for women, to improve the well being of the population.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".