Impact of Health and Literacy on Economic Growth in Morocco
Bibliographic record
Abstract
The objective of our work is to verify the impact of the improvement of health conditions and literacy on economic growth in Morocco during the period between 1980 and 2018. We based ourselves on the work of (Mankiw et al., 1992), in which they studied the impact of human capital on economic growth by integrating it as a component in the Solow model. The data we have used mainly comes from the High Commission for Planning and the World Bank. The observation that we have made is that the composite health and literacy index that we have developed has no significant impact on the growth of GDP per capita in Morocco during the period studied, which means that the literate and healthy work force does not have the expected effect on economic growth in Morocco. Thus, to be able to take advantage of its qualified and educated workforce, we suggest that the Moroccan authorities should encourage investments in sectors of activity that require this kind of workforce, because the study of the current market situation of employment in Morocco has shown that the agricultural sector and the informal sector have a significant share in the national GDP, but recruit an illiterate or low-skilled workforce.
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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 | 0.001 | 0.003 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 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".