Analysis of Human Capital Effects: A Systematic Review of the Literature
Bibliographic record
Abstract
Economic theory presents human capital as playing a driving role in the process of economic and social development in the countries. Indeed, human capital is presented in several research works as a factor that promotes accelerated growth and sustainable development. Microeconomic analyzes also suggest that investments that improve the level of human capital contribute to improving the distribution of income, but also reduce poverty. However, these conclusions seem not to be shared by all researchers. This article aims to enlighten researchers and especially young researchers on the results of the analyzes of human capital’s effects on economic growth, income inequality, poverty and welfare. In order to achieve this objective, we collected information through search engines. This strategy required the definition of four equations with Boolean operators linking the key words of the study, i.e., ‘education’ with ‘economic growth’, ‘income inequality’, ‘poverty’ and ‘welfare’. Based on the results obtained, we note the existence of a consensus around the effects of human capital on poverty and welfare. However, the results obtained for agricultural productivity, economic growth and income inequality remain mixed. One observation made in the literature is the use of education quantity as a proxy of human capital. As the definition of human capital is broader, education quantity cannot be a good proxy. We suggest some avenues for new research based on a more global human capital index, because this one takes into account other dimensions such as stunting, mortality, average number of years of education and education quality,
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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 | 0.009 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.018 | 0.020 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".