Learning in Retail Entry

51 Pages Posted: 24 Nov 2011 Last revised: 17 May 2019

See all articles by Nathan Yang

Nathan Yang

McGill Desautels Faculty of Management

Date Written: May 16, 2019

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.

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

Yang, Nathan, Learning in Retail Entry (May 16, 2019). NET Institute Working Paper No. 11-16. Available at SSRN: https://ssrn.com/abstract=1957992 or http://dx.doi.org/10.2139/ssrn.1957992

Nathan Yang (Contact Author)

McGill Desautels Faculty of Management ( email )

1001 Sherbrooke St. West
Montreal, Quebec H3A1G5 H3A 2M1
Canada
514-378-6233 (Phone)

HOME PAGE: http://www.mcgill.ca/desautels/nathan-yang

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