Saurabh Bhargava is an Associate Professor of Economics at Carnegie Mellon University, an academic affiliate of the Jameel Poverty Action Lab at MIT, and the Founder and Co-Director of the BEDR Policy Lab. Saurabh’s research area of Behavioral Economics broadly explores decision-making, and optimal policy design, in the contexts of health insurance, retirement savings, program take-up, unemployment, medical adherence, and employee reward programs. A secondary line of research involves data-driven investigations into the determinants of happiness and well-being. 

Title: Partition at Your Own Risk: Evidence on Risk-Taking Prevalence and Motives from the Field

Abstract: Clarifying the motives for risk taking is crucial for economic theory, welfare analyses, and policy formulation. In practice, attempts at such clarification in the field are frequently hindered by an inability to observe perceived risk, decision complexity, and limited generalizability. We provide new evidence on risk taking prevalence and motives from unusually rich field data describing the decisions, productivity, and beliefs of 20,133 employees across 18 large firms participating in a simple, all-or-nothing, goal-rewards program with $9.4 million in incentives. We find nearly half of employees chose lower goals than predicted by an expected utility benchmark with plausible risk preferences, leading to an average loss of 46% in potential rewards. Conservative choice persisted across financial stakes ($69 to $4,500) and employee experience and was notably larger for women, resulting in a 22% gender gap in rewards. Prominent departures from EU such as biased beliefs, non-linear decision weights, and gain-loss utility do not meaningfully improve explanatory power and we replicate choice patterns in experiments where options are explicitly represented as financial lotteries. We advance and experimentally validate a novel, heuristic, explanation that presumes risk aversion emerges from partition-dependent inference in the context of approximate pairwise comparisons. The heuristic not only explains substantially more choices in the lab and field than other benchmarks but explains most of the gender gap in conservatism. A series of experiments demonstrate how the heuristic could help to resolve seemingly contradictory empirical puzzles in insurance involving excess demand in low-risk settings (e.g., home insurance) and insufficient demand in high-risk settings (e.g., prescription drug coverage). The findings imply ostensible anomalies in the level, heterogeneity, and gender disparity of observed risk-taking across economic settings may stem from variability in heuristic adoption rather than variation in risk preferences and perceptions alone.

The paper can be found here.