Title: “Innovation under Ambiguity and Risk” We model innovation investments as real options and explore the implications of ambiguity—Knightian uncertainty—and risk for innovation decisions. Our model provides predictions for creating options to invest and options to wait. The ensuing empirical analysis uses a risk measure and a new outcome-independent measure of ambiguity. We find a …
Tag Archives: fall2020
Sara Moreira (Northwestern University)
Title: “Patents to Products: Product Innovation and Firm Dynamics” We study the relationship between patents and actual product innovation in the market, and how this relationship varies with firms’ market share. We use textual analysis to create a new data set that links patents to products of firms in the consumer goods sector. We find …
Petra Moser (New York University)
Title: “Women in Science: Lesson from the Baby Boom” How do children affect women in science? We investigate this question using rich biographical data, linked with patents and publications, for 83,000 American scientists in 1956 at the height of the baby boom. Our analyses reveal a unique life-cycle pattern of productivity for mothers. While other …
Raghu Rajan (University of Chicago, Booth)
Title: “Kill Zone” Venture capitalists suggest that incumbent internet platforms create a kill zone around themselves, where any competing entrant is acquired quickly. Consequently, financing new startups becomes unprofitable. We construct a simple model that rationalizes the existence of a kill zone. The price at which an acquisition is done depends on the number of …
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Adair Morse (Berkeley, Haas)
Title: “Small Business Survival Capabilities and Policy Effectiveness” Using unique City of Oakland data during COVID-19, we document that small business survival capabilities vary by firm size as a function of revenue resiliency, labor flexibility, and committed costs. Nonemployer businesses rely on low cost structures to survive 73% declines in own-store foot traffic. Microbusinesses (1-to-5 …
Jillian Grennan (Duke)
Title: “AI and High-Skilled Work: Evidence from Analysts” Policymakers fear artificial intelligence (AI) will disrupt labor markets, especially for high-skilled workers. We investigate this concern using novel, task-specific data for security analysts. Exploiting variation in AI’s power across stocks, we show analysts with portfolios that are more exposed to AI are more likely to reallocate …