Collaborative Research: Differentiated Product Oligopolies and the Antitrust Litigation Model

合作研究：差异化产品寡头垄断与反垄断诉讼模式

• 批准号: 0003686
• 负责人: Bart Wilson
• 金额: --
• 依托单位: Cybernomics Incorporated
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2000
• 资助国家: 美国
• 起止时间: 2000-08-01 至 2001-10-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0003686&HistoricalAwards=false
• 关键词: Collaborative; Research; Differentiated; Product; Oligopolies

Increasingly, U.S. antitrust authorities evaluate horizontal mergers in terms of how proposed consolidations may affect the strategic situation in specific oligopoly models. Most prominent is the analysis of horizontal mergers in markets where products are differentiated. In such contexts Department of Justice (DOJ) staff have developed an Antitrust Litigation Model (ALM) merger simulation as a screening device to help determine when competitive problems might arise. The DOJ staff use the ALM to assess the effects of a merger in a two step process. First, staff collect price and market share data, and estimate some demand parameters. Second, these data are inserted into the model to generate imputed cost parameters and post-merger predictions. Despite the convenience of the ALM's predictions, a number of questions critical to the usefulness of this approach remain unanswered. First, the relationship between price predictions generated under model-specific assumptions and outcomes in more general naturally occurring circumstances is not clear. Second, even on the domain of the theory, the incentives that drive predicted unilateral effects are subtle and may have little explanatory power. Third, the model is used to distinguish between relatively subtle differences in market outcomes. Some information about the potential accuracy of pre-merger demand parameter estimates would thus be useful. Laboratory methods provide an ideal medium for examining these important questions. In the laboratory the demand system, the nature of strategic interactions and the underlying equilibrium predictions can be specified a priori. Thus, the relationship between predictions and outcomes can be examined explicitly. This proposal describes three experiments designed to provide some insight into the importance of the assumptions underlying the ALM. First, suppose that the underlying demand system for a model is logit and that sellers are Bertrand competitors, the assumptions of the ALM. An experiment conducted under these "best shot" circumstances could evaluate the correlation between actual and predicted prices and market shares, as well as the accuracy of the models' imputed cost measures. Further, analysis of post-merger performance would allow examination of the capacity of theoretical comparative statics effects to predict behavioral outcomes Second, although a logit demand system offers analytic advantages, there is no a priori reason to believe that many natural demand systems are logit. An experiment would examine the effects of the changes in the underlying demand system. Starting with identical observable pre-merger information, we can evaluate the importance of the underlying demand specification on pre- and post-merger performance by shifting the demand system from logit to linear. A third experiment assesses the importance of assuming that sellers are Bertrand (quantity-setting) competitors. Although economists typically assume that strategic interactions are Bertrand when products are differentiated, there is no good reason for supposing that this assumption reflects the nature of competition in natural contexts. Holding the observable market information fixed, we can evaluate the importance of an assumption about the nature of strategic interactions.

美国反垄断机构越来越多地根据拟议的合并如何影响特定寡头垄断模式中的战略形势来评估横向合并。 最突出的是对产品差异化市场中横向合并的分析。 在这种情况下，司法部 (DOJ) 工作人员开发了反垄断诉讼模型 (ALM) 合并模拟作为筛选工具，帮助确定何时可能出现竞争问题。 DOJ 工作人员使用 ALM 通过两步流程评估合并的影响。 首先，工作人员收集价格和市场份额数据，并估计一些需求参数。 其次，将这些数据插入模型中以生成估算成本参数和合并后预测。 尽管 ALM 的预测很方便，但对该方法的实用性至关重要的许多问题仍未得到解答。 首先，在特定模型假设下生成的价格预测与更一般的自然发生情况下的结果之间的关系尚不清楚。 其次，即使在理论领域，推动预测单边效应的激励因素也是微妙的，可能没有什么解释力。 第三，该模型用于区分市场结果中相对细微的差异。 因此，有关合并前需求参数估计的潜在准确性的一些信息将是有用的。 实验室方法为研究这些重要问题提供了理想的媒介。 在实验室中，需求系统、战略互动的性质和潜在的均衡预测可以先验地指定。 因此，可以明确地检查预测和结果之间的关系。 该提案描述了三个实验，旨在深入了解 ALM 基础假设的重要性。 首先，假设模型的基础需求系统是 logit，并且卖方是 Bertrand 竞争对手，这是 ALM 的假设。 在这些“最佳机会”情况下进行的实验可以评估实际价格和预测价格与市场份额之间的相关性，以及模型估算成本衡量指标的准确性。 此外，对合并后绩效的分析将允许检查理论比较静态效应预测行为结果的能力。 其次，尽管逻辑需求系统提供了分析优势，但没有先验理由相信许多自然需求系统是逻辑的。 实验将检验潜在需求系统变化的影响。 从相同的可观察的合并前信息开始，我们可以通过将需求系统从对数系统转变为线性系统来评估潜在需求规范对合并前和合并后绩效的重要性。 第三个实验评估了假设卖家是伯特兰（数量设定）竞争对手的重要性。 尽管经济学家通常假设当产品具有差异性时，战略互动就是贝特朗（Bertrand），但没有充分的理由假设这一假设反映了自然环境中竞争的本质。 保持可观察的市场信息固定，我们可以评估关于战略互动性质的假设的重要性。