Dealing with Endogeneity: A Nontechnical Guide for Marketing Researchers

This chapter provides a nontechnical summary of how to deal with endogeneity in regression models for marketing research applications. When researchers want to make causal inference of a marketing variable (e.g., price) on an outcome variable (e.g., sales), using observational data and a regression approach, they need the marketing variable to be exogenous. If the marketing variable is driven by factors unobserved by the researcher, such as the weather or other factors, then the assumption that the marketing variable is exogenous is not tenable, and the estimated effect of the marketing variable on the outcome variable may be biased. This is the essence of the endogeneity problem in regression models. The classical approach to address endogeneity is based on instrumental variables (IVs). IVs are variables that isolate the exogenous variation in the marketing variable. However, finding IVs of good quality is challenging. We discuss good practice in finding IVs, and we examine common IV estimation approaches, such as the two-stage least squares approach and the control function approach. Furthermore, we consider other implementation challenges, such as dealing with endogeneity when there is an interaction term in the regression model. Importantly, we also discuss when endogeneity matters and when it does not matter, as the “cure” to the problem can be worse than the “disease.”