Attitudes to Price Risk and Uncertainty: The Earnest Search for Identification and Policy Relevance
Bibliographic record
Abstract
After several decades of neglect, the food crises of 2007–08 and 2010–11 have brought food price volatility back on the policy agenda. The study of price volatility, however, is really the study of price risk and uncertainty as they relate to individuals, households, and firms. Because the study of behavior in the face of risk and uncertainty has mostly focused on behavior in the face of income risk and uncertainty, we first review the theoretical and empirical literatures on behavior in the face of price risk and uncertainty. Then, because policy recommendations are only as good as the empirical findings on which they are based, and because market‐level phenomena such as price risk do not lend themselves well to randomization, we discuss the ways in which experimental economics can inform our understanding of price risk. Finally, because expected utility—the workhorse model used to study behavior in the face of risk and uncertainty—fails to account for a number of behaviors, we discuss how insights from behavioral economics could be incorporated into the study of price risk, with the ultimate goal of generating more policy‐relevant findings. Après avoir été négligée pendant plusieurs décennies, la volatilité des prix alimentaires a été remise à l'ordre du jour par les crises alimentaires de 2007‐2008 et 2010‐2011. L'étude de la volatilité des prix est toutefois équivalente à l'étude du risque et de l'incertitude relatifs aux prix et de leurs conséquences en ce qui a trait aux individus, aux ménages, et aux firmes. Comme l'étude du comportement face au risque et à l'incertitude a surtout porté sur le risque et l'incertitude relatifs au revenu, nous offrons tout d'abord une revue de la littérature portant sur le risque et l'incertitude relatifs aux prix. Ensuite, parce que les recommandations de politique économique ne sont aussi bonnes que les résultats empiriques sur lesquelles elles sont fondées, et parce que les phénomènes de marché comme la volatilité des prix sont difficiles à étudier par randomisation, nous expliquons comment les économies expérimentale et comportementale peuvent améliorer notre compréhension du comportement face au risque et à l'incertitude des prix. Finalement, parce que la théorie de l'utilité espérée—le modèle de référence en ce qui a trait à l'étude du risque et de l'incertitude—peine à expliquer plusieurs comportements, nous présentons la façon dont certains résultats en économie comportementale peuvent être incorporés dans l'étude du risque et de l'incertitude relatifs aux prix, dans le but de générer des recommandations de politique économique qui sont plus utiles.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.014 | 0.053 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.011 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 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".