Abstract
We introduce a model of (platform-mediated) many-to-many matching in which agents' preferences are both vertically and horizontally differentiated. We first show how the model can be used to derive the profit-maximizing matching plans under customized pricing. We then investigate the implications for targeting and welfare of uniform pricing (be it explicitly mandated or induced by privacy regulation), preventing the platform from conditioning prices on agents' profiles. The model can be applied to study ad exchanges, online retailing, and media markets.
Reference
Renato Gomes, and Alessandro Pavan, “Price customization and targeting in matching markets”, The RAND Journal of Economics, vol. 55 (2), n. Summer, 2024, pp. 230–265.
Published in
The RAND Journal of Economics, vol. 55 (2), n. Summer, 2024, pp. 230–265