Abstract: Many technology products and platform markets are characterized by indirect network effects. These network effects arise when the benefit from using a product or a service increases with the use of a complementary product or service. For example, the demand for mobile handsets is a function of the software apps available on it. Similarly, the demand for gaming consoles is a function of the games available on it. Indirect network effects are central to platform markets and are a major driver of important managerial questions such as how to price the two products (e.g. Adobe PDF Reader is given away free so that the value of Adobe PDF writer increases) and whether to allow interoperability of competing platforms (e.g. iOS versus Android). They are also a critical factor in policy questions such as whether the emergence of a monopoly platform is a natural outcome of indirect network effects or unfair competition. Research Questions While there has been a lot of theoretical work on indirect network effects, empirical validation and quantification of the magnitude of such indirect network effects has only been a recent phenomenon. In the literature on hardware-software indirect network effects, network effects have been quantified as a function of the number of software applications available on a platform. However, this can be a poor approximation especially when software quality is heterogeneous. For example, Facebook and Hi5 are both social networking apps but a well-functioning Facebook app is likely to contribute more to sales of a mobile platform than Hi5. Indeed, Microsoft paid Facebook to develop a Facebook app for its mobile platform because it recognized the indirect network effects generated by Facebook’s app is likely to be high. Empirical work that recognizes or quantifies heterogeneous indirect network effects has been absent in the literature. I plan to fill this gap in the research by measuring heterogeneous indirect network effects in the market for mobile handsets. Specifically, the proposed research answers the following research questions: (1) What is the impact of apps on sales of mobile handsets? How much more do top apps contribute towards the sales of mobile handsets relative to the median app on the platform? To this end, we will develop a structural model of consumer demand for both handsets and apps, and use it to estimate the impact of individual apps on smartphone sales. (2) How does platform incompatibility and the resulting absence of certain software applications on certain hardware affect consumer welfare, hardware sales, and competition between platforms? The advantage of the structural model is that we can run policy simulations and conduct counterfactual analysis to determine the impact of network effects on consumer welfare and platform revenues.