Upward Pricing Pressure (“UPP”) is a tool that antitrust enforcers have used to estimate the potential price impact of mergers in markets with differentiated products. Additionally, antitrust enforcers are using UPP as an initial screen to estimate whether a merger may be likely to harm competition in a relevant market. For example, the Federal Trade Commission (“FTC”) estimated a gross upward pricing pressure index (“GUPPI”) for different local geographic areas in its review of the proposed Dollar Tree/Family Dollar merger and used these GUPPI scores as an initial screen for determining whether the proposed merger would likely harm competition in a specific geographic area. However, UPP computations do not directly predict post-merger prices or provide an estimate of accuracy of price prediction. Further, the standard application of UPP does not include cost efficiencies from a proposed merger. We extend the standard UPP formulation to include merger-specific cost efficiencies and find that the inclusion of these merger-specific cost efficiencies may yield substantial improvements in accuracy of UPP, both as a price predictor and as a merger screening tool in antitrust analysis.
UPP does not claim to provide the exact amount that the merged firm will raise prices in post-merger equilibrium. Rather, UPP provides a measure of the initial incentive to raise prices, holding fixed other economic environment parameters, such as price and level of output of other firms, demand determinants, and so on. Thus, once the market re-equilibrates to a new post-merger equilibrium, the actual change in prices may be different from the initial incentive to raise prices.
Cost efficiencies often are a motivation for mergers. The FTC and Department of Justice (“DOJ”) Horizontal Merger Guidelines (“Guidelines”) state that “[i]n a unilateral effects context, incremental cost reductions may reduce or reverse any increases in the merged firm’s incentive to elevate price.” These claimed cost efficiencies must be merger specific and verifiable for the FTC and DOJ to include them in their analyses. Thus, at least in principle, merger-specific efficiencies should be incorporated into post-merger price predictions relating to unilateral effects.
In a recent paper, Tarun Sabarwal and I model a modified UPP formulation that includes merger-specific cost efficiencies in various functional forms. We use the theoretical framework of Sonia Jaffe and E. Glen Weyl (“The First-Order Approach to Merger Analysis,” American Economic Journal: Microeconomics, November 2013). In our model, cost efficiencies are made merger-specific by requiring them to be zero if output of either firm in the merger is zero. In other words, cost efficiencies are activated only for the merged firm and only when outputs of both merging firms are positive. To analyze the efficacy of this modified UPP formulation vis-à-vis more widely used versions, we use Monte Carlo simulation. For the demand side, we use four standard functional forms that have been widely used in merger analyses. These are Logit demand, Log-Linear demand, Linear demand, and Almost Ideal demand. We also use two different cost formulations for a total of eight different scenarios. For each scenario we estimate 5,000 mergers for a total of 40,000 mergers simulations. We find that with the inclusion of merger-specific cost efficiencies, as well as a more accurate first-order approximation to compute UPP, there are substantial gains in prediction of post-merger equilibrium prices.
The merger simulations also reveal that different measures of UPP yield different post-merger price predictions. We find that the modified UPP formulations (which include cost efficiencies) have lower median post-merger price predictions. For example, the UPP formulation incorporating merger-specific efficiencies and pass-through results in a median post-merger price increase of 1.2 percent compared to a median post-merger price increase of 10.7 percent for the UPP with no merger-specific efficiencies or pass-through. The distribution of these post-merger price predictions also varies considerably based on the measure of UPP – eighty percent of the post-merger predicted prices for the UPP formulation incorporating merger-specific efficiencies and pass-through are between -61.9 percent and 25.3 percent while eighty percent of the post-merger predicted prices for the UPP with no merger-specific efficiencies or pass-through are between 2.1 percent and 27.5 percent.
We also use our simulation data to investigate the accuracy of different UPP formulations as pre-merger screening tools. UPP is being used increasingly as a pre-merger screening tool by antitrust agencies both in the United States and worldwide, mainly because it is relatively quick and easy to implement, requires less information than some other measures, and is grounded in economic theory. The typical use of UPP is to flag a merger for further scrutiny if the UPP calculation is above a given threshold, such as five percent. As UPP is not a perfect predictor of post-merger prices, this can lead to false positives and false negatives. We find that the probability of making a merger screening error decreases substantially when UPP formulations that include cost efficiencies are considered compared to UPP formulations that do not include cost efficiencies. For example, we find that the total probability of making a merger screening error decreases 96 percent when using the UPP formulation incorporating merger-specific efficiencies and pass-through compared to UPP formulations with no merger-specific efficiencies or pass-through.
In sum, we find that the modified UPP formulations including merger-specific cost efficiencies yield substantial gains in post-merger price prediction and merger screening accuracy. These 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.