A Large-Scale Analysis of Mergers and Merger Simulations

摘要近年来，美国反垄断机构每年审查2000多起潜在的并购交易。他们的任务是权衡市场力量的危害与可能传递给消费者的潜在协同效应，并说服法院阻止他们预测会对消费者造成重大损害的合并，或者命令合并方采取补救措施。这些项目将研究美国反垄断执法的两个方面。首先，该项目将记录已获批准的并购的实际价格和数量效应，研究多个行业中规模足够大的交易。这种量化将有助于了解当前政策的效果，并可能指出未来调整的途径。其次，该项目将研究预测预期并购价格效应的标准工具之一的有效性：并购模拟。将对合并模拟的预测能力进行系统评估，并描述预测与实际观测不同的原因。该项目的结果将对反垄断政策产生重要影响，并将为美国各种行业的合并评估提供工具。本项目将首先利用合并完成日期前后的数据以及各种控制因素，估算合并对合并方和非合并方的价格和数量影响。该项目将把这些效应（跨并购和跨市场内部并购）与集中度指标（如赫芬达尔-赫希曼指数（HHI）和naïve HHI变化）联系起来。将会有一份关于价格变动时间路径的文件。其次，该项目将仅使用合并完成前的数据来估计数据集中每个合并的需求结构模型，并在各种需求规范中进行选择。然后，该项目将使用这些估计值、隐含成本和静态贝叶斯-纳什均衡假设来预测合并后的价格。最后，通过使用合并后数据估计结构模型的参数，将预测误差分解为需求侧变化和供给侧变化（如协同效应和行为）的预测误差。这种分解可以为如何在实践中调整合并模拟提供指导。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

AbstractAntitrust agencies in the United States reviewed over two thousand prospective mergers and acquisitions per year in recent years. Their task is to trade off harms from market power with potential synergies that may be passed on to consumers—and to convince a court to either block mergers that they predict will cause significant consumer harm or to order that the merging parties adopt remedies. These projects will study two aspects of antitrust enforcement in the US. First, the project will document the realized price and quantity effects of mergers that have been approved, studying a near-universe of sufficiently large deals in a number of industries. This quantification will help inform the effects of current policy and may point to avenues for future adjustments. Second, the project will study the efficacy of one of the standard tools for predicted price effects of prospective mergers: the merger simulation. There will be systematic assessment of the predictive power of mergers simulations and characterize the reasons why predictions differ from realized observations. The results of the project will have important implications for antitrust policy, and will provide tools for conducting merger evaluations in the US for a variety of industries.The project will first estimate the price and quantity effect of mergers—on both merging parties and non-merging parties—using data before and after the completion date of the merger and a variety of controls. The project will correlate these effects, both across mergers and within-mergers across markets, with measures of concentration, such as the Herfindahl-Hirschman Index (HHI) and the naïve change of the HHI. There will be a documentation of the time path of the change in prices. Second, the project will estimate a structural model of demand for each of the mergers in the dataset using only data before the merger was consummated, choosing among a variety of demand specifications. The project will then predict post-merger prices using these estimates, the implied costs, and the assumption of static Bayesian-Nash equilibrium. Finally, there will be a decomposition of the prediction error into that due to demand-side changes and supply-side changes (such as synergies and conduct) by estimating parameters of the structural model using post-merger data. This decomposition may provide guidance for how to adjust merger simulations in practice.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.