The (Statistical) Power of Incentives

Abstract

I study the optimal design of monetary incentives in experiments where incentives are a treatment variable. I propose a novel framework called the Budget Minimization problem in which a researcher chooses the level of incentives that allows her to detect a predicted treatment effect while minimizing her expected budget. The Budget Minimization problem builds upon the power analysis and structural modeling. It extends the standard optimal design approach by explicitly incorporating the budget as a part of the objective function. I prove theoretically that the problem has an interior solution under fairly mild conditions. To showcase the practical applications of the Budget Minimization problem, I provide examples of its implementation in several well-known experiments. I also offer a practical guide to assist researchers in utilizing the proposed framework. The Budget Minimization problem contributes to the experimental economists’ toolkit for an optimal design, however, it also challenges some conventional design recommendations.