An Empirical Model of Industry Dynamics with Common Uncertainty and Learning from the Actions of Competitors

This paper advances our collective knowledge about the role of learning in retail agglomeration. Uncertainty about new markets provides an opportunity for sequential learning, where one firm's past entry decisions signal to others the potential profitability of risky markets. The setting is Canada's hamburger fast food industry from its early days in 1970 to 2005, for which simple analysis of my unique data reveals empirical patterns pointing towards retail agglomeration. The notion that uninformed potential entrants have an incentive to learn, but not informed incumbents, motivates an intuitive double-difference approach that separately identifies learning by exploiting differences in the way potential entrants and incumbents react to spillovers. This identification strategy confirms that information externalities are key drivers of agglomeration. Estimates from a dynamic oligopoly model of entry with information externalities provide further evidence of learning, as I show that common uncertainty matters. Counterfactual analysis reveals that an industry with uncertainty is initially less competitive than an industry with certainty, but catches up over time. Furthermore, there are many instances in which chains enter markets they would have avoided had they not faced uncertainty. Finally, consistent with the interpretation of uncertainty as an entry barrier, I find that chains place significant premiums on certainty at proportions beyond 2% of their total value from being monopolists.