Abstract

We demonstrate pitfalls when extrapolating behavioral findings from static to dynamic settings. We focus on regret theory and the design of “regret lotteries”, which have been advocated as more effective than standard incentives. Findings from one-shot settings have been used to promote regret as a tool to boost incentives in recurrent decisions across a variety of settings. Using theory and experiments, we replicate regret lotteries as the superior one-shot incentive; however, for repeated decisions we show the comparative static is entirely reversed. The results highlight the issues extrapolating from static to dynamic settings, particularly for behavioral policy design.

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)