Economists Incorporated, Washington, DC, United States of America.
Economics Department, The University of Kansas, Lawrence, KS, United States of America.
PLoS One. 2020 Jan 8;15(1):e0227418. doi: 10.1371/journal.pone.0227418. eCollection 2020.
We investigate the accuracy of UPP as a tool in antitrust analysis when there are cost efficiencies from a horizontal merger. We include merger-specific cost efficiencies in a tractable manner in the model and extend the standard UPP formulation to account for these efficiencies. The efficacy of the new UPP formulations is analyzed using Monte Carlo simulation of 40,000 mergers (8 scenarios, 5,000 mergers in each scenario). We find that the new UPP formulations yield substantial gains in prediction of post-merger prices, and there are substantial gains in merger screening accuracy as well. Moreover, the new UPP formulations outperform the standard UPP formulation at higher thresholds for all the standard cases in the paper. The results are robust to several additional analyses. The results show that including cost efficiencies in a manner guided by the theoretical model may yield substantial improvements in accuracy of UPP as a tool in antitrust analysis.
我们研究了当存在横向合并的成本效率时，UPP 作为反垄断分析工具的准确性。我们以一种易于处理的方式将合并特有的成本效率纳入模型，并扩展标准的 UPP 公式以考虑这些效率。我们使用 40,000 次合并的蒙特卡罗模拟（8 个场景，每个场景 5,000 次合并）来分析新 UPP 公式的效果。我们发现，新的 UPP 公式在预测合并后的价格方面有很大的收益，并且在合并筛选准确性方面也有很大的收益。此外，对于本文中的所有标准案例，新的 UPP 公式在更高的阈值下的表现均优于标准 UPP 公式。这些结果在多项其他分析中也是稳健的。结果表明，以理论模型为指导的方式纳入成本效率可能会显著提高 UPP 在反垄断分析中的准确性。
