When Does Data Sharing Promote Innovation?

Many innovations today are data-driven, such as self-driving cars. To improve the algorithms of these products, firms make substantial investments in data collection. However, the data is limited for an individual firm, which caps the benefits of the algorithms. Therefore, companies and policymakers ponder whether data collected by individual competing firms should be shared. More specifically, we ask the following three questions. First, when do firms voluntarily share their data? Second, if governments were to regulate data sharing, when would they mandate or prohibit data sharing to promote innovation? Third, when are firms and governments (mis)aligned in their data sharing decisions? Using a game-theoretic model, we identify two key factors that determine the answers to the questions: (i) the degree of complementarity or substitutability among firms' datasets and (ii) the performance uncertainty of the innovation. Overall, our analysis offers guidance to governments on when and how to regulate data sharing. Specifically, governments should mandate (respectively, prohibit) data sharing when firms' datasets are complements (respectively, substitutes) and the performance uncertainty is moderate (respectively, high). Our findings also shed light on several recent developments, such as the new antitrust law proposals by governments to regulate data markets