Prediction and Identification in Two-Sided Markets

35 Pages Posted: 25 Jan 2018 Last revised: 28 Mar 2018

Andre Boik

University of California, Davis - Department of Economics

Multiple version iconThere are 2 versions of this paper

Date Written: March 23, 2018

Abstract

I introduce a reduced form two-sided market model to study prediction and identification in two-sided markets. The model generates the hallmark features of two-sided markets: potentially below cost or even negative prices to one side of the market, and the “see-saw” or “waterbed” effect of a tendency for price movements across sides to be negatively correlated. I show that the standard “one-sided” model of complements is a special case of the two-sided model, and that it generates those same hallmark features of two-sided markets. The model of complements also performs well in predicting price effects even when the data is actually generated by the two-sided market model: the “wrong” model often delivers correct answers and can be used to estimate market power and pass through rates. I show that even a naive one-sided model that ignores any relationship across goods/groups can perform well when prices to one side of the market are censored at zero, a very common outcome in two-sided markets. The main cost to using a model of complements to estimate cross-group effects in a two-sided market is that it invites the use of invalid instruments. I show that these findings are consistent with the empirical regularities and identification strategies in the existing two-sided market and indirect network effects literatures. I consider a number of applications of these results.

Suggested Citation

Boik, Andre, Prediction and Identification in Two-Sided Markets (March 23, 2018). Available at SSRN: https://ssrn.com/abstract=3104846 or http://dx.doi.org/10.2139/ssrn.3104846

Andre Boik (Contact Author)

University of California, Davis - Department of Economics ( email )

One Shields Drive
Davis, CA 95616-8578
United States

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