Learning in Retail Entry

Author: Nathan Yang

Publication: International Journal of Research in Marketing, Forthcoming

Abstract:

Retailers may face uncertainty about the profitability of local markets, which provide opportunities for learning when making entry decisions. To quantify these informational benefits, I develop an empirical framework for studying dynamic retail entry with uncertainty and learning (from others). Using novel data about fast food chains, I estimate the model with a forward simulation estimation approach augmented with particle filtering as a way to flexibly account for unobserved firm beliefs about market profitability. The estimates confirm the presence of uncertainty and learning. Most importantly, simulations using the estimated model demonstrate that learning from others may indeed help mitigate some of the uncertainty.