Gender, work, and satisfaction: a decomposition approach to job satisfaction gaps in Egypt and Tunisia
Bibliographic record
Abstract
Introduction: This study revisits the paradox of the contented female worker by analyzing gender disparities in job satisfaction in Egypt and Tunisia. Methods: Using nationally representative labor force survey data, we construct a multidimensional job satisfaction index based on eight dimensions: earnings, job security, nature of work, working hours, work schedule, work environment, commuting distance, and job-qualification match. To explain gender gaps in job satisfaction, we apply the Blinder-Oaxaca decomposition method, both with and without correcting for sample selection bias. Results: Our results show that conclusions about the existence and direction of the gender gap depend critically on accounting for selection effects. Before correcting for selection bias, women in Egypt report significantly higher job satisfaction than men, while no gender gap is observed in Tunisia-echoing the contented female worker paradox. However, once sample selection is controlled for, the paradox disappears in both countries. In Egypt, the observed gender gap is fully explained by differences in observable characteristics (endowment effect), while in Tunisia, it is largely driven by differences in returns to those characteristics (coefficient effect), highlighting structural inequalities in the labor market. Discussion: To test the robustness of our results, we also conduct the decomposition using an alternative measure of job satisfaction based on a single overall satisfaction question. The consistency of results across both measures reinforces the validity of our conclusions. Together, these findings caution against relying solely on standard models of job satisfaction and emphasize the importance of considering sample selection and multidimensional outcomes. The study underscores the need for policy interventions that promote fairer working conditions, expand access to employment benefits, and address gender-based disparities in labor markets.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".