Risk aversion in Entrepreneurship Panels: Measurement Problems and Alternative Explanations
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Bibliographic record
Abstract
In this study, we investigate the pitfalls associated with measuring risk aversion within studies of entrepreneurial behavior. First, we raise substantial concerns as to whether standard questions employed can be used to infer risk aversion among nascent entrepreneurs. In our work we show that the US, Canadian and Swedish panel study datasets do not offer evidence that entrepreneurs are more risk averse than non‐entrepreneurs. In fact, we show that the measurements used for risk aversion in these studies are not compatible with classic expected utility theory. Furthermore, our analysis reveals that probability weighting may even counteract the respondent's risk attitude. Therefore, inferring the respondent's risk attitude from choices in the panel study datasets can be misleading in the presence of probability weighting. We therefore suggest that alternative theories of decision making under risk, like prospect theory, are relevant and should be taken into account in future studies on entrepreneurship. Copyright © 2017 John Wiley & Sons, Ltd.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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 it