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Nonlinear Pricing in Yellow Pages

This paper proposes a methodology for analyzing nonlinear pricing data with an application to yellow page advertising. First, we develop a model that incorporates some features of this industry such as a free minimal advertisement size offered to all businesses. The model structure is then defined by the distribution of businesses’ types, the inverse demand function and the publisher’s cost function. Under the assumption of a multiplicative inverse demand function, we show that the model structure is nonparametrically identified up to the cost function, which is identified through its marginal cost at the total amount produced. Next, we propose a simple nonparametric procedure to estimate the type distribution and the inverse demand function. We establish the asymptotic properties of our two-step nonparametric estimator, whose first step converges at the parametric rate. The method is applied to analyze nonlinear pricing data in yellow page advertising. The empirical results show an important heterogeneity in businesses’ tastes for advertising. Some counterfactuals assess the cost of asymmetric information and the gain or loss of nonlinear pricing in presence of asymmetric information relative other pricing rules.