Learning in Retail Entry
51 Pages Posted: 24 Nov 2011 Last revised: 17 May 2019
Date Written: May 16, 2019
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.
Keywords: Keywords: Bayesian Learning; Dynamic Discrete Choice; Location Intelligence; Market Structure; Retail Strategy; Social Learning; Unobserved Heterogeneity.
JEL Classification: C73, D83, L13, L66, L81, R00
Suggested Citation: Suggested Citation