Research

Targeted Advertising in Social Media Platforms

with Claudio Panico (Bocconi) and Carmelo Cennamo (CBS)

Social media platforms, as attention brokers, face a fundamental tension between the users they serve and the advertisers they depend on. We develop a formal model to investigate how a platform’s ability to broker user attention impacts pricing, advertising strategies, and the welfare of all participants. Our findings reveal that the welfare implications of attention brokerage are critically contingent on the platform’s model. On platforms offering high standalone benefits, such as Facebook, we find a fundamental misalignment of interests. While platform and advertiser profits grow with enhanced attention-brokering, user welfare follows an inverted-U shape, declining as users are exposed to a greater volume of lower-quality and irrelevant ads. This suggests a potential for user overexploitation. In contrast, on platforms with negligible standalone benefits, like Pinterest, market forces align the incentives of all parties. Here, the welfare of users, advertisers, and the platform all increase monotonically with the platform’s brokering ability. This research offers a novel explanation for the persistence of irrelevant ads, rooting it in this platform-mediated incentive misalignment. Furthermore, we derive clear policy implications, showing that the optimal level of attention-brokering a regulator would choose depends on the specific welfare standard being maximized. Our results demonstrate that regulatory approaches must be nuanced, as interventions appropriate for platforms with high standalone benefits may be counterproductive for others

Using Machine Learning to Estimate the Effect of General Partners on Venture Capital Performance

with Corrado Botta (Bocconi) and Mario D. Amore (Bocconi)

Despite extensive research on performance persistence in the venture capital industry, the commonly held assumption that general partners drive persistence has received limited empirical attention. In this paper, we employ machine learning methods to isolate and quantify the persistent effect (if any) of general partners on performance across multiple funds. Analyzing a panel dataset of 29,021 quarterly observations covering 722 funds managed by 811 general partners between 1997 and 2022, we document statistically significant albeit modest effects of general partners on performance persistence. These magnitudes are substantially smaller than those reported in the literature, highlighting the limited external validity of extant methods for the task at hand. Moreover, the general partner effect consistently exceeds that of venture capital firms, suggesting that individual-level analyses provide greater insights than firm-level ones. Our results indicate that most of the variation in venture capital performance is not attributable to the organizational characteristics of venture capital firms and can be explained only partially by the individuals managing them.

Similarity among Startups in Accelerators

Start-up accelerators are key players in the entrepreneurial ecosystem, offering cohort-based programs to accelerate the growth of early-stage start-ups. Despite a growing literature on the benefits of accelerator participation, little is known about whether start-ups gain from exposure to peers within accelerator cohorts. In this paper, I investigate the role of business similarity among start-ups within accelerator cohorts and argue that it may lead to improved post-acceleration performance via greater exposure to relevant knowledge and peers. I test this argument using a rich, unique dataset comprising 2,505 start-ups accelerated in 128 cohorts run by eight U.S.-based accelerators between 2005 and 2018. I find strong evidence that business similarity is positively associated with post-acceleration performance: start-ups accelerated with more similar peers raise funding more quickly and exhibit improved long-term outcomes in terms of survival, employee growth and exit via acquisition. In preliminary analyses, I also show that the effect of similarity appears to exhibit decreasing marginal returns, which may signal the presence of factors that reduce the benefit of similarity at higher levels. These results offer actionable insights for accelerator managers seeking to design effective programs and for start-ups evaluating participation in an accelerator.