Some academic studies find that increases in concentration caused by hospital mergers are associated with higher hospital prices. However, few of these studies account for the structure of the health insurance market and how this structure affects hospitals’ bargaining leverage and negotiating power. A recent economic paper by Eric Barrette, Gautam Gowrisankaran, and Robert Town, “Countervailing Market Power and Hospital Competition,” (Barrette et al.) studies health insurer market concentration and countervailing market power in hospital services markets. This study finds that countervailing market power is important and that higher levels of countervailing market power result in lower predicted price increases following a hospital merger. These findings may have an impact on how hospital mergers are evaluated by the Federal Trade Commission (FTC) and the courts.

Despite recognition in the Department of Justice and Federal Trade Commission 2010 Horizontal Merger Guidelines (Merger Guidelines) that powerful buyers “may constrain the ability of the merging parties to raise prices,” few empirical studies have considered the actual impact of health insurance concentration on negotiated prices between hospitals and health insurers. The Merger Guidelines clarify that the agencies “do not presume that the presence of powerful buyers alone forestalls adverse competitive effects flowing from the merger,” but, they are not clear as to how the agencies quantify the powerful buyer’s ability to limit the potential price increases by the merging parties.

Importantly, the draft revision of the Merger Guidelines released by the DOJ and FTC in July of this year does not explicitly address the powerful buyer concept or how the agencies would consider it when reviewing proposed mergers. The draft revision includes a section for consideration of markets where “terms are set by bargaining or auctions,” but it is not clear how this consideration will play out regarding countervailing market power and the powerful buyer scenario. The recent paper by Barrette et al. analyzes the relationship between health insurer market concentration and hospital prices, specifically as to how hospital bargaining leverage and hospital prices vary depending on health insurer market concentration.

The analysis of Barrette et al. is as follows. First, the authors use detailed claim-level data for inpatient services from three national commercial health insurers (United, Aetna, and Humana) to estimate a hospital choice model. The authors derive the “willingness-to-pay” on a per-person basis for each of the more than 1,000 hospitals in their data. Willingness-to-pay measures how patients and insurers value having a given hospital in their provider network. Greater willingness-to-pay indicates that the hospital is viewed by insurers and patients as having greater incremental value. Next, Barrette et al. estimate the correlation between average hospital prices and willingness-to-pay for different metropolitan areas and health insurance product types (e.g., PPO or POS). Finally, the authors measure how the correlation of willingness-to-pay and hospital price by metropolitan area relates to the concentration of health insurers in the metropolitan area.

“In other words, as health insurer concentration increases, the impact of a hospital’s bargaining leverage (i.e., willingess-to-pay) on inpatient hospital prices decreases.”

Barrette et al. find a negative correlation between health insurer concentration and willingness-to-pay on inpatient hospital prices. In other words, as health insurer concentration increases, the impact of a hospital’s bargaining leverage (i.e., willingness-to-pay) on inpatient hospital prices decreases. Thus, the higher the concentration of health insurers in an area, the lower the ability for hospitals in that area to use their bargaining leverage to obtain higher prices.

To quantify their findings, Barrette et al. calculate the expected price increase of a merger in markets with different levels of insurance market concentration. The authors find that a hypothetical hospital merger (with an average increase in willingness-to-pay of 14.4 percent) would result in a 4.3 percent price increase if it had occurred in a market in which insurer concentration is at the 25th percentile of their sample. However, this expected price increase declines to 2.8 percent if the insurer concentration is at the 50th percentile and declines further to less than 1.0 percent if the insurer concentration is at the 75th percentile.

Barrette et al.’s key finding — that mergers leading to similar increases in willingness-to-pay are expected to produce different price increases depending on health insurers concentration in the area in which the hospitals operate — provides empirical support for considering health insurer market structure (and possible health insurer countervailing market power) when assessing the competitive effects of a proposed hospital merger.

Further, the findings of Barrette et al. are pertinent to market definition as well. Some recent district court rulings pertaining to proposed hospital mergers have suggested that relevant markets should be defined “through the lens of insurers” (FTC v. Penn State Hershey Medical Center et al.) and that relevant markets have to correspond to the commercial reality of payors being the most relevant buyer (FTC v. Thomas Jefferson University et al.). While other rulings, for example FTC v. Sanford Health et al., have found that the “powerful buyer defense” is not appropriate for market definition analysis due to the lack of case law supporting that consideration. However, the district court judge in this case reaffirmed that, in the context of defining markets for mergers between health care providers, the hypothetical monopolist test should evaluate “whether an insurer could avoid a price increase.” That is, the price at issue is the price negotiated between hospitals and payors. In this regard, Barrette et al.’s findings provide empirical support for considering health insurer market structure when defining the relevant market for a proposed hospital merger. The profit-maximizing price increase for a hypothetical monopolist likely is influenced by the relationship between willingness-to-pay, health insurer market concentration, and the predicted hospital price increases.

Barrette et al.’s results can be used to conduct the hypothetical monopolist test to define markets. For example, using standard merger simulation techniques and commonly available patient discharge data, the increase in willingness-to-pay can be calculated for a hypothetical monopolist that owns all hospitals in a candidate hospital services market. Information on health insurer concentration for this candidate market also may be obtained from publicly-available sources. The findings of Barrette et al. concerning the relationship between health insurer concentration and the estimated increase in willingness-to-pay can then be used to predict the hypothetical monopolist’s profit-maximizing price increase, which can then be compared to the appropriate small but significant and non-transitory increase in price. It is still left to be seen how the FTC and the courts will consider the insights and results of Barrette et al.’s study. A likely direction, however, is that future hospital merger reviews will consider to some degree the structure of the health insurance market in the merging hospitals’ area.

Associate Director Dr. Pablo Varas has conducted economic analyses in multiple proposed hospital mergers and insurers-hospital payment disputes.