Working Papers

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Organizational Barriers to Transforming Large Finance Corporations: Cloud Adoption and the Importance of Technological Architecture (with M. Iansiti)

Abstract: This paper studies the impact of technological architecture around data storage and processing on the performance of large financial corporations after being exposed to more stringent data privacy regulations. A modular approach to cloud adoption – which reflects in the lack of data interoperability and reliance on microservices architecture – significantly constrains corporations’ ability to adapt after the GDPR became enforceable. We hypothesize that a modular approach to cloud adoption leads to uncontrolled scaling and data silos that hinder coordination and regulatory compliance. Using a difference-in-differences regression design, we find that establishment revenues lower by 30% among corporations substantially exposed to GDPR. Other corporations do not experience similar losses. We also find evidence consistent with theory using two alternative measures based on cloud vendor configurations.

Local Labor Market Frictions and Platform-Dependent Entrepreneurship

Abstract: This paper explores how local labor market frictions affect the performance of entrepreneurial complementors in a large platform marketplace. Empirically, we show that shop-level revenues were significantly higher when entrepreneurs started their platform-dependent businesses in rural areas and slacker labor markets measured by less frequent quits and fewer job openings at the state level. We also find that labor market slack suppresses the positive association between local wages and entrepreneurial revenues on the platform. These findings suggest that platform-dependent entrepreneurs entering the platform as complementors when local labor market frictions were higher eventually perform better. On the other hand, local labor market frictions disconnect the revenues of platform-dependent entrepreneurs from local wage dynamics.

Sampling Bias in Entrepreneurial Experiments (with R. Koning and R. Nanda)

Abstract: Using data from a prominent online platform for launching new digital products, we document that ‘sampling bias’—defined as the difference between a startup’s target customer base and the actual sample on which early ‘beta tests’ are conducted—has a systematic and persistent impact on the venture’s success. Specifically, we show that products with a female-focused target market launching on a typical day, when nine in ten users on this platform are men, experience 45% less growth a year after launch than those for whom the target market is more male-focused. By isolating exogenous variation in the composition of beta testers unrelated to the characteristics of launched products on that day, we find that on days when there are unexpectedly more women beta testers on the platform—reducing the amount of sampling bias for female-focused products—the gender-performance gap shrinks towards zero. Our results highlight how sampling bias can lead to fewer successfully commercialized innovations for consumers who are underrepresented among early users.