Estimating Complementarity With Large Choice Sets: An Application to Mergers

ABSTRACT Standard discrete choice demand models assume that all products are substitutes. Merger analyses based on these models may overstate consumer harm. We develop an estimator that identifies demand complementarity and remains computationally feasible with large choice sets. We apply this estimator to the chips and soda market and find a high degree of complementarity between these product groups. We show that a counterfactual merger ignoring complementarity between PepsiCo/Frito‐Lay and Dr. Pepper generates price increases for soda that are 33% larger than a model with complementarity, and that post‐merger chip prices decrease when accounting for complementarity.