Abstract

We demonstrate potential pitfalls when extrapolating behavioral findings identified from one-shot choices to repeated settings. As a case study, we examine the use of “regret lotteries” as a behavioral tool to boost motivation. Based on findings from one-shot settings, presenting counterfactual information generates the potential for regret, which can be used to increase a lottery’s value. This result has motivated the increasing use of “regret lotteries” in the field to incentivize recurrent decisions like exercise, compliance with company directives, and vaccine take-up. We formally outline how anticipated regret and learning can differentially motivate people’s decisions under risk. While the former is predicted to increase the valuation of regret lotteries and lead them to be superior in one-shot settings, the latter is a countervailing force that can reverse the behavioral effect in dynamic settings, leading to the superiority of standard lotteries in sequential choice. Using a controlled experiment and multiple robustness treatments, we show that while regret lotteries may be valued more in one-shot decisions, in repeated choice the effect is entirely reversed. These findings have implications for incentive and policy design, highlighting the need for caution when extrapolating one-shot findings to inherently repeated settings.

Abstract

Decision and game-theoretic models of unawareness often assume risk preferences to be orthogonal to varying awareness. We study the effects of exposure to unawareness on risk preferences using a novel experimental task. The task has solutions that are difficult to find, but easy to verify and so exposes subjects to unawareness in a natural way. We find that increased exposure to unawareness alone does not affect risk taking. The role of context, however, is shown to be important. For treatments inducing higher unawareness, subjects are more risk averse when the risk elicitation task is framed in the same context as the complex task versus framed in a neutral way; we observe no such differences for the control treatment. We propose a novel decision theoretic model that guides the interpretation of the experimental results.

Abstract

Wages in the public sector might respond to political, rather than market, forces, resulting in persistent differences relative to the private sector. Economists have long been interested in studying public-private wage gaps, as the presence of a gap could redirect resources in ways that have major impacts for the economy. This paper studies the public-private wage gap while controlling for selection bias, which has been the main empirical challenge in this literature. I focus on the Brazilian labor market, where workers can only obtain a government job by securing a sufficiently high score in public-sector exams. I construct a novel dataset with over 2 million individuals that took an exam at least once. Using a fuzzy regression discontinuity design exploring discontinuities around the approval cutoffs, I find a large and significant public-sector wage premium. The estimated coefficients are almost twice as large as OLS coefficients, resulting from a negative selection bias term. Finally, a survey experiment with Brazilian undergraduates show that students who prefer to work in the public sector expect a positive and large public-private wage gap, while those not interested in the public sector expect a (small) negative wage gap. The survey results lend support to an interpretation of negative selection on unobserved ability or productivity.

Media: InfoMoney (Brazil)