Exploiting Data from Field Experiments

This chapter gives an introduction on how to exploit data from field experiments and aims to provide an intuitive understanding for managers and researchers alike. We outline the relevance and hurdles in identifying causal effects compared to observing purely correlational associations in studies which take place in the real world. We further provide a framework to classify different kinds of field experiments, such as quasi field experiments and natural field experiments. The core of this chapter focuses on giving an understanding of three standard econometric methods to exploit data from field experiments: difference-in-differences, regression discontinuity, and instrumental variables. For each method, we provide an intuitive understanding of the core features and its critical assumptions. We complement those explanations with an in-depth look at one practical application of each method in a field experiment setting and with a variety of practical examples from recently published research. Lastly, we provide a brief overview on how to implement each method in standard software packages such as STATA, R, and SPSS.