The promise and peril of youth entrepreneurship in the Middle East and North Africa
Bibliographic record
Abstract
Purpose Entrepreneurship is promoted as a solution to high rates of youth unemployment around the world and especially in the Middle East and North Africa (MENA). This paper investigates the potential for youth entrepreneurship to alleviate unemployment, focusing on Egypt, Jordan and Tunisia. Design/methodology/approach The authors examine who entrepreneurs are (in comparison to the unemployed), using multinomial logit models. The authors compare entrepreneurs' and wage workers' working conditions and earnings. They exploit panel data to assess earnings and occupational dynamics. They specifically use the Labor Market Panel Surveys of 2012 (Egypt), 2016 (Jordan), and 2014 (Tunisia), along with previous waves. Findings The authors find that entrepreneurs are the opposite of the unemployed in MENA. The unemployed are disproportionately young, educated and women. Entrepreneurs are older, less educated and primarily men. Entrepreneurship does not generally lead to higher earnings and does have fewer benefits. Originality/value Promoting youth entrepreneurship is not only unlikely to be successful in reducing youth unemployment in MENA, but also, if successful, may even be harmful to youth.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 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".