Ferdinand M. Vieider’s research while affiliated with Mohammed VI Polytechnic University and other places

We present a comprehensive meta-analysis of prospect theory (PT) parameter estimates, synthesizing data from 166 papers that report 812 estimates based on the decisions of approximately 52,000 subjects across 69 countries. We develop and implement a novel approach that enables the joint meta-analysis of multiple PT parameters, explicitly modeling potential covariation among them. Our analysis reveals that the average utility curvature (measured via the coefficient of constant relative risk aversion) is 0.33 for gains (95% credible interval [0.31, 0.36]) and 0.29 for losses (CrI [0.25, 0.32]). The average likelihood sensitivity parameter is 0.68 (CrI [0.66, 0.70]), and the average elevation parameter is 0.98 (CrI [0.95, 1.02]). These values align with the stylized facts of PT: diminishing sensitivity to changes in wealth relative to a reference point, and to changes in probabilities moving away from the endpoints of the unit interval. Beyond these central tendencies, we uncover substantial heterogeneity in parameter estimates, limiting the precision of predictions about future parameters. Among the study-level characteristics examined, the measurement method emerges as the most significant predictor of parameter variation, highlighting potential violations of procedural invariance. Nonetheless, a large portion of the heterogeneity remains unexplained even after adjusting for a wide range of study features, suggesting that subtle design differences meaningfully influence behavior. Our results thus offer not only a synthesis of existing findings but also a roadmap for future research.

Loss aversion is one of the most widely used concepts in behavioral economics. We conduct a large-scale, interdisciplinary meta-analysis to systematically accumulate knowledge from numerous empirical estimates of the loss aversion coefficient reported from 1992 to 2017. We examine 607 empirical estimates of loss aversion from 150 articles in economics, psychology, neuroscience, and several other disciplines. Our analysis indicates that the mean loss aversion coefficient is 1.955 with a 95 percent probability that the true value falls in the interval [1.820, 2.102]. We record several observable characteristics of the study designs. Few characteristics are substantially correlated with differences in the mean estimates. (JEL D81, D91)

Loss aversion is one of the most widely used concepts in behavioral economics. We conduct a large-scale interdisciplinary meta-analysis, to systematically accumulate knowledge from numerous empirical estimates of the loss aversion coefficient reported during the past couple of decades. We examine 607 empirical estimates of loss aversion from 150 articles in economics, psychology, neuroscience, and several other disciplines. Our analysis indicates that the mean loss aversion coefficient is between 1.8 and 2.1. We also document how reported estimates vary depending on the observable characteristics of the study design.

Recent papers have modeled the prevalence of risk-tolerance as shaped by growth, making testable predictions about the distribution of risk-tolerance across the globe. We test these predictions using a dataset containing a survey question capturing people’s risk-tolerance for representative samples from 78 countries. We find a negative between-country correlation between risk-tolerance and GDP per capita. Together with the positive within-country correlation between risk-tolerance and income, this results in a risk-income paradox. We further find a negative interaction effect of risk-tolerance and GDP on fertility. These findings provide support for endogenous-preference models of economic growth.

We investigate time discounting under risk. To this end, we modify a popular multiple price list (MPL) design to elicit time discounting. Structural estimations of model parameters yield several new insights. For one, we find present bias to persist under risk, contrary to some previous evidence from the psychology literature. We further confirm the robustness of inverse-S shaped probability weighting. This is important inasmuch as random choice predicts the opposite shape in our setup. We also show that correcting for probability weighting under risk impacts the assessment of discount rates. Those are systematically underestimated under the commonly used, more restrictive, expected utility.

We obtain rich measurements of risk preferences for 2939 subjects across 30 countries, and use the data to paint a picture of the distribution of risk preferences across the globe using structural equation models. Reference‐dependence and likelihood‐dependence are found to be important everywhere. Model parameters in non‐Western countries differ systematically from those in Western countries, with poorer countries substantially more risk tolerant than rich countries on average. We qualify previous findings on gender effects and cognitive ability by showing how they mainly impact likelihood‐dependence. We further add novel evidence on the correlation between risk preferences and study major. Whereas we confirm previous results on observable characteristics of subjects explaining little of overall preference heterogeneity, a few macroeconomic indicators can explain a considerable part of the between‐country heterogeneity.