Growth and Innovation in Platform Ecosystems

A large and growing portion of the economy transpires within software platforms. These environments are bounded high growth economies that embed many novel features: the platform owner sets rules for participation; R&D occurs rapidly, with generations of innovations building on one another; there are profit and adoption tradeoffs in open versus closed code. This paper introduces and develops a new model of platform growth, inspired by the standard growth literature. At each moment in continuous time, the platform owner must choose (i) how much source code to open and make available to a developer community, (ii) how fast to absorb developer code into the platform, (iii) how much to tax developer output, and (iv) how much to invest in the platform itself. Without any public code, the control problem reduces to the growth model of Solow (1956). Our platform model captures an infinitely recursive R&D ecosystem with innovation spillovers across developers. To optimize growth, the platform owner must internalize this recursive externality. Developers also innovate less the sooner they lose their code, but the platform grows faster with earlier code expropriation. This model maps optimal growth insights from a social planner’s macroeconomic policy to control parameters of a profit and innovation seeking firm. We currently conclude that larger platforms should impose higher developer fees and taxes, expropriate less, and save more. This is consistent with existing platforms. ∗Funding from the National Science Foundation (Grant SES #0925004) is gratefully acknowledged.