Matteo Tranchero

Matteo Tranchero (Working), Finding Diamonds in the Rough: Data-Driven Opportunities and Pharmaceutical Innovation.

Abstract: Big data are increasingly used to make predictions about uncertain investments, thereby helping firms identify innovation opportunities without the need for domain knowledge. This trend has led to questions about which firms will primarily benefit from the availability of these data-driven predictions. Contrary to existing research suggesting that data-driven predictions level the playing field for firms lacking domain knowledge, I argue—using a simple theoretical framework—that these predictions actually reinforce the competitive advantage of firms with domain knowledge. In innovation contexts, where returns are skewed and not all leads can be pursued, domain knowledge helps evaluate predictions and avoid false positives. I test this idea in the context of pharmaceutical innovation, exploiting the features of genome-wide association studies (GWASs) that provide data-driven predictions about new drug targets. The results show that GWASs stimulate corporate investments, but around one-third of these resources are misallocated toward false positive predictions. Companies lacking domain knowledge react more strongly but are disproportionally likely to fall into the trap of false positives. Instead, domain knowledge helps firms make fewer investments that target only the best opportunities. Together, the results show that even if data-driven predictions hold value when searching for innovations, domain knowledge remains the crucial source of competitive advantage in the age of big data.

Abhishek Nagaraj and Matteo Tranchero (Working), How Does Data Access Shape Science? The Impact of Federal Statistical Research Data Centers on Economics Research.

Abstract: This study examines the impact of access to confidential administrative data on the rate, direction, and policy relevance of economics research. To study this question, we exploit the progressive geographic expansion of the U.S. Census Bureau's Federal Statistical Research Data Centers (FSRDCs). FSRDCs boost data diffusion, help empirical researchers publish more articles in top outlets, and increase citation-weighted publications. Besides direct data usage, spillovers to non-adopters also drive this effect. Further, citations to exposed researchers in policy documents increase significantly. Our findings underscore the importance of data access for scientific progress and evidence-based policy formulation.

Johannes Hoelzemann, Gustavo Manso, Abhishek Nagaraj, Matteo Tranchero (Working), The Streetlight Effect in Data-Driven Exploration.

Abstract: We examine innovative contexts like scientific research or technical R\&D where agents must search across many potential projects of varying and uncertain returns. Is it better to possess incomplete but accurate data on the value of some projects, or might there be cases where it is better to explore on a blank slate? While more data usually improves welfare, we present a theoretical framework to understand how it can unexpectedly decrease it. In our model of the streetlight effect, we predict that when data shines a light on attractive but not optimal projects, it can severely narrow the breadth of exploration and lower individual and group payoffs. We test our predictions in an online lab experiment and show that the availability of data on the true value of one project can lower individual payoffs by 17% and reduce the likelihood of discovering the optimal outcome by 54% compared to cases where no data is provided. Suggestive empirical evidence from genetics research illustrates our framework in a real-world setting: data on moderately promising genetic targets delays valuable discoveries by 1.6 years on average. Our paper provides the first systematic examination of the streetlight effect, outlining the conditions under which data leads agents to look under the lamppost rather than engage in socially beneficial exploration.