Модель монополистической конкуренции с гетерогенными потребителями
Standard measures of competitive toughness fail to capture the fact that, as consumers optimize intertemporally, firms operating today compete with (yet non-existent) businesses which will be started tomorrow. We develop a two-tier CES model of dynamic monopolistic competition in which the impact of product differentiation on the market outcome depends crucially on the elasticity of intertemporal substitution (EIS). The degree of product differentiation per se fails to serve as a meaningful indicator of competitive toughness: what matters is its cross-effect with EIS. We also extend the model to the case of non-CES preferences to capture variable markups.
Many industries are made of a few big firms, which are able to manipulate the market outcome, and of a host of small businesses, each of which has a negligible impact on the market. We provide a general equilibrium framework that encapsulates both market structures. Due to the higher toughness of competition, the entry of big firms leads them to sell more through a market expansion effect generated by the shrinking of the monopolistically competitive fringe. Furthermore, social welfare increases with the number of big firms because the pro-competitive effect associated with entry dominates the resulting decrease in product diversity.
We propose a model of monopolistic competition with additive preferences and variable marginal costs. Using the concept of "relative love for variety," we provide a full characterization of the free-entry equilibrium. When the relative love for variety increases with individual consumption, the market generates pro-competitive effects. When it decreases, the market mimics anti-competitive behavior. The constant elasticity of substitution is the only case in which all competitive effects are washed out. We also show that our results hold true when the economy involves several sectors, firms are heterogeneous, and preferences are given by the quadratic utility and the translog.