Abstract. Methods used to predict post-merger outcomes assume that synergies are common knowledge and imply that synergies will be at least partially passed through to consumers, potentially offsetting anticompetitive merger effects. However, the common knowledge assumption is inconsistent with other features of the merger review process and its implications are potentially inconsistent with the evidence of merger retrospectives. We relax the assumption in a simple model of post-merger competition and show that strategic incentives can lead a merged firm to not pass through quite large synergies arising in both horizontal and vertical mergers.

Abstract. Some argue that large platforms, such as Alphabet/Google, Amazon, Apple, Facebook and Microsoft (or GAFAM), are unusual in their number, pace and concentration of technology mergers, with the potential to harm market competition. Using a unique taxonomy developed by S&P Global Market Intelligence, we compare the M&A activities of GAFAM to other top acquirers from 2010 to 2020. We find: (i) GAFAM completed more tech acquisitions per firm than other groups of top acquirers, and acquired younger and more consumer-facing firms on average. (ii) The top 25 private equity firms outpaced GAFAM in tech acquisitions per firm since 2018. (iii) GAFAM acquisitions are less concentrated across tech categories than other top acquirer groups, due, in part, to an “acquire-adjacent-and-then-expand” strategy. (iv) Over time, more and more GAFAM and other top acquirers acquire in the same categories. (v) No evidence suggesting that a GAFAM acquisition in a category, compared to similar categories without GAFAM acquisitions, is correlated with a slowdown in the number of new acquirers acquiring in that category. Overall, we find that technology acquisitions do not shield GAFAM from competition, at least not from other GAFAM members or other firms that acquire in the same categories.

Abstract. Policymakers are increasingly concerned that incumbent acquisitions of small or young firms may slow down rather than speed up innovation, but it is difficult to identify which firms are related in the fast-changing space of technological innovation. This paper proposes a new, data-driven method to classify patent data into tech-business zones on a probabilistic basis, using patent assignee information. After combining M&A data from S&P Global Market Intelligence with PatentsView data from the US Patent and Trademark Office, we discuss how the zone classification can aid merger reviews and other lines of research.

Abstract. Mergers and acquisitions (M&A) provide an important way for firms to expand technologically. In this paper, we examine how public firms listed in North American stock exchanges acquire technology companies during 2010-2020. Combining data from Compustat, CRSP, Refinitiv, and S&P on M&A, and utilizing a unique S&P taxonomy that classifies M&A by tech categories and business verticals, we show that (i) only 13.1% of public firms engage in tech M&A but such acquisitions are widespread across sectors of the economy; (ii) larger and older firms are more likely to acquire tech companies; (iii) transactions in each M&A-active tech category tend to be led by acquirers from a specific sector, to varying extents over time; (iv) the majority of target companies fall outside the acquirer's core area of business; and (v) firms are, in part, driven to acquire because they face increased competition in their core areas.

Abstract. I examine the short-run impact of taxing ride-sharing trips on the price and usage of ride-sharing across different community areas in Chicago and investigate whether the tax had unequal effects on community areas with different racial compositions. I document significant heterogeneity in price increases due to the tax across the community area of departure as well as across destination points, providing evidence that this was correlated with community areas’ differential access to alternatives to ride-sharing, such as public transit. Clustering community areas based on their racial composition reveals that Black areas experienced particularly high price increases and percentage reductions in ride-sharing usage. Overall, the burden of the tax fell more heavily on minority-concentrated areas. These findings highlight the potential trade-offs between addressing negative externalities and exacerbating inequalities in urban policy, and suggest the need for further research on the impact of platforms’ taxation on racial inequality.

Abstract. I examine how venture capitalists’ (VCs') investments in competing startups impact their outcomes. Theoretically, I show that if VCs improve their screening practices by investing in a particular business area, they may favor the startup subsequently invested in that area, at the expense of that initially financed. Using Crunchbase data and S&P’s unique technology taxonomy, I find that these subsequent startups raise more venture capital and secure follow-on funding more often than those not sharing a VC with a competitor. This, however, hurts initial startups. The result is only partially explained by improved screening by VCs, indicating how investing in competing startups significantly affects their monitoring practices.

Abstract. I study the effects of a tax imposed by the city of Chicago, which levied taxes on ride-sharing but not traditional taxi trips. I document a significant increase in ride-sharing prices, particularly for single rides starting or ending in downtown Chicago, which faced the largest tax increment. Tax pass-through rates were above 100% for single rides and even more pronounced for shared rides in the downtown area. This pattern can be explained by the fact that ride-sharing companies are multi-product peer-to-peer platforms and riders may substitute single with shared rides. The tax steered riders towards the shared service downtown, leading to a slight increase in traditional taxi usage. Conversely, areas outside downtown witnessed reduced ride-sharing use, with no rise in demand for taxis. This is consistent with a more intense competition between taxi and ride-sharing services downtown. Lastly, although the tax alleviated congestion, the magnitude of this benefit remains modest and constrained to the downtown area.

Abstract. Industries with significant scale economies or learning-by-doing may come to be dominated by a single firm. Economists have studied how likely this is to happen, and whether it is efficient, using models where buyers are price or quantity takers, even though these industries are often also characterized by buyer-seller negotiations. We extend the dynamic "learning-by-doing and forgetting" model of Besanko, Doraszelski, Kryukov, and Satterthwaite (2010) to allow for Nash-in-Nash bargaining over prices. Price-taking and the social planner solution are captured as special cases. We show that sellers’ dynamic incentives, market concentration and welfare can change sharply, and non-monotonically, as one moves away from the price-taking assumption. We study the implications of buyer bargaining power for the existence of multiple equilibria, the design of subsidy policies and the welfare effects of policies designed to increase competition.

Abstract. Economic agents often have private verifiable information about multiple attributes characterizing their product or service. Since selectively disclosing only certain attributes can make information withholding more salient, we experimentally study how the dimensionality of the information space affects receivers’ skepticism about undisclosed information and hence senders’ disclosure incentives. We find that increasing the number of attributes from one to two leads to more disclosure of unfavorable information. By eliciting players’ beliefs, we show that this effect is driven by senders expecting more skepticism about undisclosed dimensions. Nonetheless, receivers do not exhibit any significant behavior change, making senders’ strategy sub-optimal.

Abstract. We study the effects of technology venture acquisitions on investment in the acquired firm’s business area. Using data on acquisitions and venture capital funding in the U.S. from Crunchbase, we consider the ventures acquired between 2014 and 2016 alongside a set of comparable control ventures that remained independent as of 2016. By modeling each venture as a point in the technology space, we leverage textual analysis to track investments in business areas similar to acquired or control ventures. Our difference-in-differences analysis shows that acquisitions stimulate venture capital investment, particularly in areas with fewer ventures and more intense past or expected acquisition activity. This suggests that tech acquisitions signal the potential of a business area. Contrary to antitrust concerns, we find that acquisitions by big tech platforms and other large acquirers have a similar positive effect, whereas private equity buyouts lead to an even greater increase in venture capital activity.