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Daniel Hedblom, Brent R Hickman, John A List

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We develop theory and a tightly-linked field experiment to explore the supply side implications of corporate social responsibility (CSR). Our natural field experiment, in which we created our own firm and hired actual workers, generates a rich data set on worker behavior and responses to both pecuniary and CSR incentives. Making use of a novel identification framework, we use these data to estimate a structural principal-agent model. This approach permits us to compare and contrast treatment and selection effects of both CSR and financial incentives. Using data from more than 110 job seekers, we find strong evidence that when a firm advertises work as socially-oriented, it attracts employees who are more productive, produce higher quality work, and have more highly valued leisure time. In terms of enhancing the labor pool, for example, CSR increases the number of applicants by 25 percent, an impact comparable to the effect of a 36 percent increase in wages. We also find an economically important complementarity between CSR and wage offers, highlighting the import of using both to hire and motivate workers. Beyond lending insights into the supply side of CSR, our research design serves as a framework for causal inference on other forms of non-pecuniary incentives and amenities in the workplace, or any other domain more generally.

Christopher Cotton, Brent R Hickman, John A List, Joseph Price, Sutanuka Roy

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We conduct a field experiment across three diverse school districts to structurally identify student motivation and study productivity parameters in a model of adolescent human capital development. By observing study time, homework task completion, and test results, we can identify individual and demographic variations in motivation and study time effectiveness. Struggling students typically do not lack motivation but rather struggle to convert study time into completed assignments and proficiency improvements. The study also attending a higher-performing school is associated with both higher productivity and higher motivation relative to peers with similar observables in lower-performing schools. Counterfactual analyses estimates that school quality differences account for a substantial share of the racial differences in test scores, and considers the impact of alternative policies aimed at reducing racial performance gaps.

Aaron Bodoh-Creed, Brent R Hickman, John A List, Ian Muir, Gregory Sun

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In this paper, we provide a suite of tools for empirical market design, including optimal nonlinear pricing in intensive-margin consumer demand, as well as a broad class of related adverse selection models. Despite significant data limitations, we are able to derive informative bounds on demand under counterfactual price changes. These bounds arise because empirically plausible DGPs must respect the Law of Demand and the observed shift(s) in aggregate demand resulting from a known exogenous price change(s). These bounds facilitate robust policy prescriptions using rich, internal data sources similar to those available in many real-world applications. Our partial identification approach enables viable nonlinear pricing design while achieving robustness against worst-case deviations from baseline model assumptions. As a side benefit, our identification results also provide useful, novel insights into optimal experimental design for pricing RCTs.