Common ownership -where two firms are at least partially owned by the same investor -and its impact on product market outcomes has recently drawn a lot of attention from scholars and practitioners alike. Theoretical and empirical research suggests that common ownership can lead to higher prices. This paper focuses on implications for market entry. To estimate the effect of common ownership on entry decisions, we focus on the pharmaceutical industry. In particular, we consider the entry decisions of generic pharmaceutical firms into drug markets opened up by the end of regulatory protection in the US. We first provide a theoretical framework that shows that a higher level of common ownership between the brand firm (incumbent) and potential generic entrant reduces the generic's incentives to entry. We provide robust evidence for this prediction. The effect is large: a one-standard-deviation increase in common ownership decreases the probability of generic entry by 9-13%. We extend our basic theoretical framework and allow for multiple entrants. Our model shows that for sufficiently high levels of common ownership, the classical idea of entry decisions being strategic substitutes can be reversed into being strategic complements. Our empirical results provide some support for these predictions.
We develop a structural model to address the question whether, and to what extent, expert panelists engage in herd behavior when voting on important policy questions. Our data comes from FDA advisory committees voting on questions concerning the approval of new drug applications. We utilize a change in the voting procedure from sequential to simultaneous voting to identify herding. Estimates suggest that around half of the panelists are willing to vote against their private assessment if votes from previous experts indicate otherwise and, on average, 9 percent of the sequential votes are actual herd-votes. Temporary committee members are more prone to herding than regular (standing) members. We find that simultaneous voting improves information aggregation given our estimates.
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