Heterogeneous risk and time preferences

Alina Ferecatu,Ayse Önçüler

doi:10.1007/s11166-016-9243-x

Abstract

Assessing individuals’ time and risk preferences is crucial in domains such as health-related decisions (e.g., dieting, addictions), environmentally-friendly practices, and saving opportunities. We propose a new method to jointly elicit and estimate risk attitudes and intertemporal choices. We use a novel individual level estimation procedure based on a hierarchical Bayes methodology, which can integrate different functional forms for discounting and risk attitudes. This method provides individual level estimates, and allows us to explore the heterogeneity in the data. In addition, we report a negative correlation between risk and time preferences, implying that risk-seeking individuals are less patient and less willing to defer consumption.

Highlights

When making decisions in an intertemporal setting, individuals are known to heavily discount future outcomes and to have a strong preference for immediate gains (Loewenstein and Prelec 1992; Frederick et al 2002)
Our results suggest that individuals are generally risk-averse, with a mean constant relative risk aversion coefficient of 0.515; their discount rates are in line with previous findings on joint elicitation
The Pearson correlation between risk aversion and the model-free discount rate is 0.14, not significantly different from 0 (p = 0.17)

Summary

Introduction

When making decisions in an intertemporal setting, individuals are known to heavily discount future outcomes and to have a strong preference for immediate gains (Loewenstein and Prelec 1992; Frederick et al 2002). Chesson and Viscusi (2003) analyze the joint influence of time and uncertainty and conclude that people may have difficulties choosing the optimal precautionary measures to prevent climate change, a long term hazard, due to both ambiguity in the probability of global warming phenomena and due to the ambiguity in the timing of global warming consequences This suggests that a joint model of risk and time preferences is necessary to assess decision makers’ tradeoffs between outcomes at different points in time. The flexibility of the method in dealing with highly non-linear model specifications represents an important benefit Such individual level estimates can be used in simulation studies to assess the effectiveness of changes in public policy decisions. This is the first study that uses hierarchical Bayes modeling to jointly estimate risk and time preferences and to analyze the heterogeneity in the data

Objectives

Results

Conclusion