Adverse Selection in Household Investment in Education in Egypt: Can the World Bank Policy Recommendations Offer a Solution for Egyptian Households?
Bibliographic record
Abstract
The Egyptian labour market gives preference to graduates of higher levels of education to fill remunerative jobs, which has encouraged households from all income levels to invest in post-secondary education. However, this investment failed to pay off in labour market outcomes as the number of graduates exceeded the number of remunerative jobs leading a substantial proportion of graduates to either accept low-paid jobs or remain unemployed until they secure remunerative ones. Paradoxically, in response to these unfavourable outcomes, households have continued to invest heavily in higher levels of education to give their children a competitive advantage to secure one of the few remunerative jobs, a behaviour best described as a version of "adverse selection." This situation has posed a challenge for Egyptian policy makers who faced the problems of increased demand for higher education and more unemployed educated youth. To address this challenge, the World Bank has consistently advised the Egyptian government to: a) reduce government intervention in labour markets; b) decrease public subsides, while encouraging private investment in post-secondary education; and c) improve the quality of education to match the needs of private employers and increase demand for educated youth. This thesis examines whether such recommendations could address labour market challenges faced by educated youth and avoid encouraging household spending. Toward this goal, the effect of changes in Egypt's policy direction, from a centrally-planned to a market economy, on the education-labour market relationship and on public and private investment in education was studied. Through interviews with a sample of Egyptian households, the determinants of Egyptian household spending on education were also examined to better understand how the quality and/or outcomes of higher education have influenced such spending. Evidence suggests that for the World Bank's policies to tackle the underlying causes of "adverse selection," they must go beyond attributing education-labour market problems to the quality of education and to distortions caused by government education and labour market policies and focus on the need to promote the creation of good quality jobs. Fortunately, a recent shift by the World Bank to focus on creating good jobs may be a positive development.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".