Economies of Scale in Platforms and E-Commerce
Paper Session
Saturday, Jan. 7, 2023 2:30 PM - 4:30 PM (CST)
- Chair: Maisy Wong, University of Pennsylvania
Scale Effects for Platforms in Housing Markets
Abstract
We contribute novel estimates of scale effects for platforms by studying the 2013 mergers of three Multiple Listings Service (MLS) in Florida, the primary platforms where properties are transacted. Our differences-in-differences estimates using listings data from 2009 to 2018 demonstrate that brokerage firms that experienced an expansion in platform scale also enjoyed more revenue gains, controlling for firm fixed effects and housing market conditions. Heterogeneity analyses suggest large brokerages benefit more. Platform scale expansion also translated into faster sales.Field Experiments for Platform Regulation
Abstract
There is increasing regulatory scrutiny of digital platforms. Regulators are focusing on the ways in which platforms collect and use consumer data as well as the competitive strategies that they adopt to achieve or maintain their dominant position. Despite the perceived urgency to rein in platforms, there is surprisingly little evidence on the welfare effects that their practices generate. For academics to collaborate with platform companies to answer these questions is understandably difficult, given the reputational risks facing platforms. In this paper we describe a new approach that the literature has recently adopted to estimate how platforms affect consumers: field experiments that make use of software to manipulate and track consumer experience on platforms. We describe the approach to document new facts about search purchasing behavior on a large e-commerce platform.Matching and Network Effects in Ride Hailing
Abstract
In two-sided markets with matching, the form of the matching function determines the magnitude of network externalities and economies of scale, with important consequences for welfare and policy. Estimating the matching function, however, is typically challenging because of data limitations. In this article, I use detailed data from Uber to estimate the matching function in order to quantify network externalities and economies of density. I then present results about optimal taxes/subsidies and the effect of multi-homing in ride hailing platforms.JEL Classifications
- L1 - Market Structure, Firm Strategy, and Market Performance
- L4 - Antitrust Issues and Policies