Three Essays on Economics of Innovation, Technology, and Policy

Abstract

My dissertation examines decision- and policy-making in the innovation and technology ecosystem and during the innovation process. I conduct three studies using novel, highly granular data and rigorous empirical methods.

Innovation is inherently risky, and a key question in managing the innovation process is how innovators should allocate resources when information is scarce and environments constantly change. In chapter one, I analyze how large, NIH-supported scientific labs traded off short-term productivity gains (via exploiting safe projects) against information acquisition that could improve long-term productivity (via exploring high-variance projects). I use detailed data on daily resource allocation to over 300,000 research projects from 2000–2015 and estimate a dynamic structural decision model. Results show the labs strongly valued exploration, which had a large and significant positive impact on their productivity. These findings suggest real-world decision-making sophistication and implications for how funding agencies should evaluate innovation productivity.

Innovation generates new business models and regulatory challenges. Chapter two explores whether the EU's General Data Protection Regulation (GDPR), a landmark internet privacy law, affected investments in internet infrastructure. I use state-of-the-art internet measurement data and a difference-in-differences approach to examine if the regulation's data-related restrictions slowed investments in the interconnectedness of networks in the European Economic Area (EEA) compared to non-EEA OECD networks. Results indicate zero effects on various measures, suggesting the GDPR's short-run consequences were limited to application firms, its intended targets, rather than networks, its unintended targets.

Innovation introduces new products with unclear economic properties and user behavior. Chapter three investigates user decisions around one such product: open-source software. Using data with five million months of open-source web server usage from over 150,000 US organizations between 2000-2018, I develop hazard models to study determinants of organizations' open-source software upgrade decisions. Results show a significant propensity to upgrade through users' routine administrative processes, with counter-intuitive implications for how innovators (developers) should manage releases of new products (upgrades).

My future works will continue to study a broad range of managerial and policy issues at the intersection of economics, technology, and innovation management.