GENDER DIFFERENCES IN ATTITUDES TOWARD RISK: EVIDENCE FROM ENTREPRENUERS IN GHANA AND UGANDA
Why this work is in the frame
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Bibliographic record
Abstract
The literature on risk aversion suggests that women are less likely to be risk loving than men in financial and insurance decision-making by virtue of their sex and biological make-up. This paper tests this assertion using a unique dataset collected in Ghana and Uganda and assesses the gender differences in self-reported risk perceptions of entrepreneurs by applying a non-linear decomposition technique. The results indicate that on average, entrepreneurs in Ghana report to be less risk loving their counterparts in Uganda. Furthermore, female entrepreneurs are less likely to report to be risk loving compared to male entrepreneurs in both countries. The results from the decomposition analysis show that gender differences in risk perceptions arise mainly from the unexplained component. For Ghana in particular, the findings show that the gender differences in self-reported risk perceptions stems from differences in education and previous business experience.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it