Abstract
This paper takes a critical look at the widespread belief that, compared to public regulation, competitive markets are increasingly less reliable as ways to limit inappropriate growth in hospital spending in the United States. Setting aside the population in rural areas and small cities where competition is very unlikely, the data presented show that the fraction of the remaining U.S. population in larger cities exposed to competition is large, ranging from 58% to 89%. The paper then considers the remaining areas that could support competition but currently do not do so. The literature on how to foster competition in such currently less competitive markets is reviewed. A combination of expanded markets and incentives for aggressive (“maverick”) and disruptive hospital pricing behavior is shown to hold the most promise for a market-based solution.
Keywords
Introduction
Spending on hospital care (inpatient and outpatient/ambulatory) makes up 31% of total national health spending in the United States (Fiore et al., 2024). It is thus not surprising that policies directed at controlling overall medical spending are often directed at hospitals rather than drugs or physicians’ services. The United States spends more on hospital care per person than any other country, yet the share of GDP has barely increased from 5.4% in 2012 to 5.5% in 2023.
This is important. Hospital spending is no longer “out of control” but, instead, something has kept its growth within bounds in some settings for more than 10 years. This means that we do not need to be wholly in the dark about hopes or expectations for more acceptable growth rates, but we do need to find what might continue to hold down spending across hospital markets nationwide. Hence, the larger GDP share of hospital spending and future projections for acceleration (to 6% of GDP in 2032) have led some state and federal policymakers to look for policies to keep these costs contained.
Based on evidence and past behavior, there are two broad strategies that might be followed. One proposes to rely on competition among hospitals to hold down prices and quantities in markets where there is or could be actual hospital competition based on price. Perhaps such competition as there is in hospital markets accounts for the relatively restrained growth in spending over the past decade, but that restraint could be strengthened. The other proposes to rely on some form of state regulation or control. The most recent variant of the latter is the establishment by law of a predetermined state goal for hospital spending increases and state monitoring of individual hospital actions to check for conformance with that goal (Gudiksen et al., 2022; Pany et al., 2022).
One common theme in the discussion of political spending targets is concern that market competition cannot be relied upon to discipline price levels in many U.S. hospital markets. The Kaiser Family Foundation (KFF), for example, notes that 97% of local hospital markets in the United States are not competitive, with a nationwide trend toward greater consolidation, and with the headline: “One or two health systems controlled the entire market for inpatient hospital care in nearly half of metropolitan areas in 2022.” (Godwin et al., 2024) But is it really this bad? Is it true that very few consumers experience competitive markets for their hospital care?
One motivation for increased interest in state action of some type is the perception that not only is competition rare, but also that there has been an erosion over time of competitive structure and behavior in the hospital sector, especially in states that already have above-average levels of hospital spending. The state of Maryland has uniquely pursued a policy of state oversight and regulation of hospital spending. Recently, however, half a dozen states are in the process of implementing some kind of explicit spending growth targets.
The Best of All Possible Worlds
It is obvious that some consumers live in locations where low population density means that there are unlikely to be enough competing hospitals for market discipline. In these (rural and small city) areas, regulation may be the only way to control market power (Colmers & Glied, 2021; Glied & Chandra, 2024). The fraction of the U.S. population in rural areas and small metropolitan areas (under 400,000 in population) that are probably beyond the reach of conventional models of competition was 29% in 2022. However, in many rural areas with only one hospital in town, even that hospital is unable to generate prices and use rates high enough for fiscal soundness (Brady, 2018; Kaufman et al., 2016). Hence, the inclusion of a rural hospital bailout fund even in Trump’s BBBA.
There is a choice for the rest of the country: relying on competition to bring about the right levels and growth rates in hospital spending, prices, and use, versus turning to regulation, in which public officials attempt to identify the best outcomes and take actions to bring them about. Some states so far seem to have political support for the regulatory route, but some other states may be more disposed to turn to policy that relies on and therefore encourages market competition as the tool for determining the levels of hospital cost, access, and volume. Recent hospital spending growth, as already noted, is far from “out of control,” and so there may be some interest in the continued use of markets to shape future actions. Policymakers in those states may be the audience for a discussion of the case for and actions to encourage competitive enhancements rather than regulation.
In this paper, we take as given that some fraction of the population in rural areas and small cities will not be able to rely on competitive hospital markets. In contrast to most research on competition and hospital pricing, we do not focus on individual hospitals but instead take a market-based population approach. We show first that population exposure to competitive hospital markets, even at present, is larger than implied by much of the recent discussion of a small (and shrinking) number of larger city markets with competitive structure. So, we then ask the illuminating counterfactual question of what fraction of the population in what locations might ideally and potentially be able to experience a competitive market, one with a sufficient number of independent hospital firms of efficient scale. That is, we provide several measures both of how much competition there is at present and, more importantly, the potential for increased competition. We therefore provide evidence on the size of markets which, according to various measures, now have a structure sufficient for effective competition and of markets that could be competitive but now are not. Possible ways to shrink the gap between actual and potential competition are then suggested.
It must be acknowledged that rekindling competition where it has been extinguished by mergers or organic hospital growth, and starting a change to more competitive seller behavior, is going to be a serious challenge. Perhaps the market power horses are already out of the barn in many places in the United States and cannot be rounded up and returned. We will have a better idea of the practical feasibility of a more market-based system after we assemble the data showing the current state of play.
Measuring What You Can Manage
The key idea in economic analysis of hospital markets, and one with a long history, is that there is a causal connection between the structure of firms in that market—their number, size, and product mix—and the conduct and performance of hospitals in that market in terms of prices and volume. For practical reasons, the U.S. Department of Justice (DOJ) has developed rule-of-thumb measures of the extent of competition based on the structure of different markets so as to identify and target mergers that, by reducing competition, may cause some relevant measure of hospital prices to increase over the competitive level. However, as will be discussed in more detail below, neither economic theory nor empirical evidence so far provides a precise link between common measures of structure and what will happen to pricing, spending, and use. Hence, the data we will assemble on structure serve primarily as ways of prioritizing attention to that performance rather than as bulletproof predictors of firm and buyer behavior.
The most common measure of hospital competition is the Hirschman-Herfindahl Index (HHI) of concentration, the sum of the squared market shares of sellers in a market (times 100). Hospitals under common management are counted as one firm. For example, if four identical independent hospitals divide the volume in a market, the index will be 2,500, which also happens to have been the DOJ’s 2010 dividing line between moderately competitive and uncompetitive markets. The intuition here is that firms with small shares have proportionately more to gain in volume from cutting prices than firms already taking up most of the market. The HHI measure gives us a weighted average of those shares. Since hospitals are multiproduct firms, there are technically many HHIs possible for each of their possible services, inpatient admissions, or outpatient visits of different types. In practice, a hospital’s volume is usually measured by its “adjusted” inpatient admissions or discharges, with the number of inpatients treated multiplied by the ratio of total revenue to inpatient revenue; an HHI is then calculated based on this volume metric.
Another and different approach to measuring competition is simply to count the number of sellers—if it is “large enough” (based on some subjective benchmark), the market is competitive. The Bresnahan and Reiss (1991) analysis targets a number between 3 and 5 sellers (for any product) as sufficient to bring the price close to the long-run marginal cost.
The difference between these two approaches arises if firms are of different sizes—size variation or “unevenness” can lead to different HHI’s for a given number of sellers. Why do the number of sellers and unevenness both matter? The core concept for profit-maximizing firm pricing is the firm-level elasticity of demand. Elasticity rises, and profit-maximizing price falls with the number of sellers, since the percentage quantity response to a price reduction by any one firm depends on how many other firms it can draw volume from. But if some firms already have very large shares, their elasticities will be low, so the HHI measure is in effect a volume-weighted average of firm-level demand elasticities, while a simple count of sellers is unweighted. We discuss the appropriateness of weighting by existing shares below, but ultimately it is an empirical question of which metric of competition or concentration best explains price markups across markets over geography or time, and which can be changed by policy.
Some hospitals (but not others) in a market may take price-cutting actions to enhance demand for their products or pick off patients from other hospitals; such differentially aggressive behavior may well produce unevenness in market shares as an effect of competition, rather than a cause of monopoly. It can even lead to the paradox that the firm that garners the largest market share is the firm most dedicated to competitive behavior. Even the simple notion of learning by doing can produce a positive causal correlation between size and quantity—those hospitals that do more have better outcomes that attract more business (for which they can charge more). So, unevenness may be only a rough proxy for these kinds of differences across hospitals, some of which may have nothing to do with competition but others of which may. Those hospitals willing to break the local mold and do something different and better may show up in the data in this way.
In an Online Appendix, we discuss the alternative measures of competition to be used in the data analysis that follows. We consider the HHI metric, a simple count of the number of hospitals, and a “balanced diversity” measure that puts less weight on unevenness than does HHI (Ahern et al., 2024).
Choosing Among Metrics and the Missing Link
Unavoidably, there is a subjective judgment attached to different metrics of competition as to whether they represent substantial, moderate, or low competition (or concentration). The DOJ has developed some rules of thumb, which it sometimes changes—with “moderately competitive markets” in the 2010 guidelines being bounded by HHIs up to 2,500, and since 2023 (with no compelling economic rationale) modified to a limit of 1,800. There is general agreement that an HHI below 1,000 represents a highly competitive market, but the precise adverbs to use to describe markets in the range just described are not fully settled in antitrust policy, and certainly not settled in economic theory.
One ignored issue in constructing the HHI measure and in other discussions of competitiveness of markets is based on the view that small hospitals, no matter what they do, are going to have higher long-run marginal and average costs than larger hospital “plants.” Dozens of hospital cost function studies suggest that average and marginal costs become constant at a size of about 150 to 250 beds once initial fixed costs have been spread over volume. An implication is that in markets with both large and small hospitals, those with small plants should not count toward competition. Unless a smaller hospital can count on a leap in size if it lowers price, it cannot be price competitive with larger hospitals and still survive. It may still appeal, even at a high price, to those who live in the immediate neighborhood, but it cannot be an effective competitor across the local market, particularly not as a network partner. This may be a partial explanation of the finding by Pany et al. (2021) that many high priced hospitals are located in markets labeled moderately competitive by the HHI measure; just because of the much larger populations in such markets, they will have larger numbers of high priced niche hospitals (though not all of the high priced hospitals were small), but ones that are less consequential for the average hospital market price.
The missing link in addressing the question of competitive market structures so far is the absence of any definitive theoretical and empirical relationship between a particular measure of market structure and the results of firm behavior, whether that is unit price, access, total revenue, or even innovation. Under some special and not especially realistic assumptions, the HHI measure tracks the Cournot model markup ratio, but that model is not generally plausible. The Federal Trade Commission (FTC) is primarily concerned with proxies for easy, if implicit, collaboration among firms on pricing or division of markets, but notes that there can be “maverick firms” which aggressively pursue lower prices and lead to changes at the market level—compared to no mavericks (US Department of Justice and Federal Trade Commission, 2010). Presumably, these mavericks must either already be efficiently sized or have prospects of growing to efficient size for their threat to high prices to be credible. This aspect of firm behavior is, however, not dependent on the number of firms or the ease of entry (presumably in response to above-normal profits); it depends not on structure but on conduct, and we have no good metrics for conduct. We may know why some firms raise prices or charge high prices, but we do not understand what leads to price cuts or low pricing generally, either at one hospital in a town or overall, across hospitals.
Room for More?
While all measures of current hospital markets will show a strong relationship between city size and measures of competition, there is a question of whether smaller cities could support enough hospitals to be competitive (or whether they (and rural areas) are natural monopolies). One way to address that question is to ask what population is needed to support a market with four or more efficiently sized hospitals, which we will define here as 250 beds (and later test the sensitivity of results to variation in this assumption). If we assume that the current nationwide average of about 250 beds per 100,000 people is needed to satisfy demand, we can conclude that a population of 400,000 is the minimum needed to support four hospitals. This is the minimum metropolitan area size consistent with four hospitals and also with a minimum HHI of 2,500.
The assumption that hospitals must be of efficient size implies that hospitals with large enough plants are the ones that should count toward measures of competition. However, it is unknown whether the presence of more or fewer small hospitals has any appreciable impact on the area-level average prices.
Descriptive Data
Table 1 shows several metrics of competition for some metropolitan areas of size greater than 400,000 population. It shows the largest 20 areas, their populations, and measures of competition (HHI, effective firms, and actual number of efficient [>250 beds] firms). It also shows the same measures for the smallest areas with populations greater than 400,000. Here, the message is that the state of competition, as measured by HHI and the other measures, is strongly related to city population. The HHI is at the highly competitive level only among the 13 largest metropolitan statistical areas (MSAs) using the older guidelines, and only 6 using the more recent ones.
Twenty Largest and Ten Smallest MSAs—Competition Metrics (2022).
Figure 1 illustrates this relationship for all 131 MSAs with large populations. Although there are a few large metro areas with high levels of concentration, among areas with populations below 2 million, virtually all (except for Oklahoma City) would be labeled highly concentrated by the HHI measure. In between, there are some moderately competitive cities such as Cincinnati.
Market concentration by MSA population.
Where competition still exists, millions of people are exposed to it—but millions are also in smaller, less competitive cities. The fraction of areas with more than four sellers (the Bresnahan Reiss measure) encompasses all of the top 20 areas but none of the bottom 10. Figure 2 shows the relationship between the number of sellers and the MSA population, and also suggests that areas with larger populations are more likely to have competitive hospital markets.
Number of large hospitals by MSA population.
The balanced diversity measures shown in Table 1 are roughly consistent with both metrics, with large effective numbers of sellers (more than 10) in large areas by population, and almost no areas with four or more effective sellers in the bottom 10 areas. In contrast to HHI, however, all the large areas by population have many effective sellers.
Table 1 also gives us details on the kinds of market theoretically large enough to support competition, but which currently do not do so. There are a few such markets among the top 20, but many are among the bottom 10 cities just above the cutoff.
One message from the summary data in Table 2 is clear: the gloomy version of prospects for competition to limit hospital prices, which are paid, directly or more often indirectly, by American consumers, is overstated by the KFF conclusion cited above. Many people still live in areas with competitive market metrics, even as those metrics are lower in the areas with smaller populations. If the HHI measures are used, the percentage of the U.S. population living in larger cities (with populations greater than 400,000) where markets are not highly concentrated was 58% in 2022 (and 41% of the total U.S. population), using the 2010 cutoff of 2,500. This fraction represents millions of people experiencing competition. If the count of cities with more than 4 efficient hospitals is used, the percentage of the population in large cities with such high competition is 70% (and 50% of the total U.S. population).
Distribution of Large MSAs and U.S. Population by HHI Category (2022).
Table 3 describes the percentage of people living in cities large enough to accommodate 4 efficiently sized hospitals, but who currently do not have competitive markets by that measure. That percentage is 30% by the hospital count measure, and 41% by the HHI measure. These are the people potentially benefiting from the rekindling of competition. Finally, Table 4 shows similar measures using the number of effective hospitals greater than 4 statistics, and gives similar conclusions.
Distribution of Large MSAs and U.S. Population by Large Hospital Numbers (2022).
Distribution of Large MSAs and U.S. Population by Number of Effective Firms (2022).
Weighting market structure metrics by population exposed to them is more than just a statistical issue. One approach to policy (call it the “legal approach”) imagines that a much above average price set by a particular hospital (or resulting from a merger of two specific hospitals) represents bad behavior and is to be targeted by antitrust action regardless of the number of other buyers in the market area potentially affected (Pany et al., 2021), while the “market level approach” says that the overall average price charged in various markets and affecting many buyers should be the issue.
Weighting these varying metrics of competition by population affected indicates that, in contrast to the KFF headline, much of the U.S. population is still in markets with competition. This is a different and more meaningful version of the overall picture than just counting the absolute number of areas with and without competition. There is still some ambiguity here, since MSAs with more population often spread across more land area; a hospital on the south side of the Los Angeles metro area may not be a reasonable travel time alternative for someone living on the north side, but distance may not be an absolute barrier between north and south Peoria. In addition, if hospitals have limits on their capacity or their growth in capacity, the ability of one competitor to steal business from a price-increasing competitor may be circumscribed. Ultimately, the relevance of any metric of competition needs to be tested empirically by looking at its relationship with price or quantity.
Trends in Hospital Competition, by Two Measures
Was there a time, a golden age, when more people were exposed to competitive markets in hospitals than at present? The answer depends on the metric of competition. As shown in Table 5, the HHI measures show a clear trend toward fewer people in markets with values indicating moderate to high competition from 2000 to 2023. The erosion in competition was especially pronounced during the Great Recession, with the proportion dropping from 68% to 54% between 2008 and 2013; the proportion shows a much slower trend since then. The surge in consolidation during the recession does not seem to match with an increase in the hospital spending share of GDP; as noted earlier, it has barely budged then or since.
Share of MSA Population by Market Size, Concentration, and Hospital Count Over Time.
However, if we use the lower bound measure, in markets with four or more larger hospitals, there has been no change since 2000. Independent hospitals may have been lost over this 23-year period, and shares may have become more concentrated, but these changes seem limited to markets where the number of efficient sellers still remained above some competitive benchmarks. There is still a gap in some cities between the number of hospitals present and the minimum number needed for competition, but the proportion of cities in which this is the case has not changed over time. Things may not have gotten that much worse for competition, though by any measure, they have not gotten any better. Later in the paper, we discuss ways to rekindle (or kindle) increases in competition by any measure.
Hospital and Insurer Concentration, Hospital Prices, and Patient Cost Sharing
The typical concentration measures for hospitals and other markets are measures of supply-side concentration. However, because of the presence of health insurance as the vehicle to pay for hospital services, there can also be demand-side concentration in hospital markets.
Scheffler and Arnould (2017) show that when insurer concentration in markets is high, the level of hospital concentration has very little effect on prices. In contrast, in markets where insurers are not concentrated and so hypothetically have little bargaining power, there is a strong positive relationship between hospital concentration and prices. This conclusion also follows from recent structural models of hospital-insurer bargaining. The first scenario, with insurers and hospitals both concentrated, is thus one of bilateral bargaining, while the second is one of textbook monopoly. If this pattern generally holds, it would imply that efforts to encourage hospital competition should be concentrated on markets where there is low insurer concentration. Lowering of hospital prices in such areas would be expected to lead to better patient access to hospital care (rather than to higher insurer profits). In contrast, more hospital competition may be a wasted influence in markets where concentrated insurers can still mark up their premiums to cancel out much of any hospital-price-depressing effect from hospital competition. Consumers of insured hospital care can end up paying more for insurance and having less access to care from market power in either the hospital or insurer market (or both; Pauly, 1988).
Moreover, in markets with high insurer concentration, it is likely that the vehicle for holding down hospital prices in areas with high hospital concentration is for insurers to constrain quantity. In areas with low hospital concentration and high insurer concentration, the primary effect on quantity would come from high insurance premiums (relative to competitive insurance markets) discouraging insurance coverage that would have allowed for easier access. More generally, whatever the effect of insurer concentration on bargaining and prices, a further question is the extent to which variations in negotiated prices are translated into premiums. Only if lower negotiated prices mean lower premiums (rather than higher insurer profits) will there be an efficiency gain from insurer price negotiation. Since high insurer concentration generally means both hard bargaining and high premiums, there is a challenge in evaluating how well markets overall are performing. If the insurer with market power is owned by consumers, gains from price negotiation may flow to consumers—but not necessarily if insurers are investor-owned. And even in the case of buyers’ monopsony, there can still be inefficiency.
Ho and Lee (2017), in another study of hospital-insurer competitive relationships, say that higher hospital prices affect consumer surplus through their effect on insurance premiums. How would this work? The idea is that since higher hospital prices would raise insurance premiums, given insurance coverage, buyers of insurance might respond to higher premiums by choosing plans with increasing cost sharing, and then react by reducing the quantity demanded. (For hospital spending, however, the level of out-of-pocket payment is very low, less than 5%.). The other idea is that insurers facing higher hospital prices might implement stricter managed care rules, or narrower networks, that reduce quantity.
In the most definitive recent paper on hospital competition and prices, Cooper et al. (2019) examine the large Health Care Cost Institute (HCCI) commercial insurance claim database and find no relationship between hospital unit prices and the volume of care. Accordingly, in what follows, we also split the hospital-concentrated MSAs into those with high and low insurer concentration. Whether the price of hospital care moves to the competitive level and normal profit premiums follow, or whether lower hospital prices lead to lower premiums from less “double marginalization” cannot be determined a priori; it depends on the shape of the demand curve for insurance.
Generally, the combination of insurer and provider concentration will lead to higher final (insurance premium) prices and lower use of insured services because of the double marginalization issue; it would be more efficient to combine the insurer and the hospital, since that would avoid false economizing on hospital services with low real resource costs but high markups. It is that final price, for “insured hospital care,” rather than the price of hospital care alone, that should matter for consumer welfare.
Here we note that hospital markets can fall into one of four cells, as indicated in Table 6. There are markets where both hospitals and insurers are competitive at one extreme; these are primarily large metropolitan areas. At the other extreme, there are markets where there is less hospital competition and fewer insurers; this setting for bargaining is the most common one. And then there are the interesting (if policy-ambiguous) cases of hospital competition matched with insurer monopoly (and monopsony), versus a number of areas with hospital monopolies dealing with competitive insurers.
Share of Large MSA Population by Hospital and Insurer HHI Concentration (2022).
Table 6 indicates that most larger areas with competitive hospital markets (as measured by HHI) also have competitive insurance markets, again probably reflecting the impact of population size. Not quite all—Chicago, for example, has a large population facing apparently competitive hospital markets but concentrated insurance markets. The target for intervention to renew competition and have the benefits necessarily transferred to consumers—an area with currently high HHI for hospitals but low insurer concentration—is small but still represents millions of people. The alternative case of high hospital concentration combined with high insurer concentration should have the most aggressive insurer bargaining for lower hospital prices, but ambiguity on whether any price reductions will translate into lower premiums.
In what follows, we limit our discussion to policies that impact hospital competition. The ambiguous impact of greater insurer concentration on hospital prices and insurer markups is a more complex issue.
Directions for Policy
Our review of research and our descriptive analysis of recent data have one major message: where most people live, there are enough sellers, potentially leading to competition. The best target for changing market structure can be limited to affecting the population in cities where hospital markets could have a competitive structure, but currently do not. That potential is what we now discuss.
There is general agreement that, in both the older and recent empirical literature, some measure of hospital competition in local markets, however defined, is associated with lower net prices collected by hospitals. However, especially among recent studies, there are nuances in how competition and markets are measured that need to be considered.
Much of the literature focuses on the effects of mergers on prices, finding that mergers that changed concentration over time were associated with higher prices for the merged firms. (However, this literature does not usually look at changes in overall prices at the market level where mergers have occurred.) In contrast, there are fewer recent cross-sectional studies of the association between competition and the level of market prices. The Cooper et al. study is the best such analysis and finds that, when markets are defined as the area within 15 miles of a hospital, prices are significantly higher under a monopoly (only one hospital) than when there are 2, 3, or 4 or more competing hospitals. So this study ties lower prices to the number of hospitals. The most recent comprehensive study using the HHI measure is that by Pany et al. (2021). This study takes a somewhat indirect approach to the question, asking whether the number of what are termed “high-priced hospitals” (whether or not they attract many customers) is more common in areas with high or low HHIs, other things equal. They find that a significantly larger proportion of hospitals in areas with high HHIs are high-priced, implying (but not proving) that overall prices will be higher in such areas. However, because of the larger populations in markets with lower HHIs, they find a larger fraction of their sample of high-priced hospitals is in low HHI areas.
Other, less comprehensive recent studies come to similar conclusions. For example, Seidu Dauda (2018) constructed a variety of HHI metrics based on travel time, made concentration endogenous, and found that higher concentration was associated with lower prices. In contrast, Moriya et al. (2010) find no significant effect of HHI on prices but do find a significant effect of insurer concentration.
In summary, there is evidence that the number of hospital sellers in a market affects prices, but HHI has a less consistent relationship. Regardless of the measure of market structure, other aspects of hospital behavior in addition to market structure, must also account for variations in average prices across markets. In what follows, we therefore look at prospects for increasing competition by increasing the number of hospitals and at prospects for incentivizing price cutting behavior by existing efficient-sized sellers.
How Currently Less Competitive Markets Can Become More Competitive and How States Can Help
The conceptual and data analysis presented so far finds that, under plausible assumptions about efficient hospital size and the number of efficient-sized sellers needed for competition, there are many currently uncompetitive markets that could be competitive. In what follows, we will outline possible strategies to enhance competition.
There is a literature on appropriate public policy in cities or other geographic areas where competition could occur, but currently does not seem to be present. Policies fall into several categories, which we will discuss:
Regulation in disguise
Piecemeal competition on pricing and entry
Broaden the market
Releasing mavericks
Enhancing price transparency
Direct state action
One set of solutions in effect introduces price regulation (what markets are supposed to avoid), but in disguise. For example, Avik Roy proposes capping hospital prices for the commercially insured in markets with high HHIs at the rates paid by Medicare Advantage plans. The issue is that those payers, although nominally private (and earning substantial private profits), track original administered price Medicare payment rates closely (Roy, 2019). What is unclear is why a hospital is willing to admit me when I go on Medicare Advantage at a third of the payment rate as when I was commercially insured, but what is clear is that this arrangement outsources the determination of hospital prices to a public plan. Perhaps the hospital surmises that if it rejected me at Medicare Advantage rates, I would just switch to original Medicare, and it would have to take me at the same low payment, but whatever the reason, there is a lot more going on in the process of determining Medicare Advantage payment rates than just hospital competition.
Another kind of regulation in disguise is the proposal by Glied and Altman (2017). They note that antitrust enforcement has often failed to preserve competition in the hospital sector, especially for midsize hospitals that are being forced out or captured by large systems. They propose a combination of regulation and competition that relies either on price regulation for tertiary care services (when antitrust fails) or a requirement that a single (regulated) insurer be given the power to cover all or most privately insureds and then can set hospital prices as a monopsonist. The idea is that even when a city has relatively many mid-sized community hospitals, there is still a natural monopoly for tertiary care services rendered by “must-have” hospitals that will need to be in every network, and whose price must be regulated.
Competition by Bits and Pieces
Rather than compete for the full line of thousands of services offered even by modest community hospitals, there is the possibility that new entrants may be able to offer smaller subsets of those services at lower prices. In theory, the most attractive services to pick off would indeed be those that, in typical hospital pricing, carry high markups, with high marginal profits used (sometimes) to cross-subsidize other services. The most successful example is probably free-standing ambulatory surgery centers, which appear to offer lower prices and equally good quality as hospitals for same-day surgery. Hospitals have successfully objected to such firms precisely on the grounds that they can charge less by avoiding overhead used to finance other essential hospital services, including the rescue services that may occasionally be needed if complications arise from the ambulatory procedure. Obviously, this phenomenon must reflect mispricing by the hospital, overcharging for the surgery and undercharging for the rescue service. Another variant is the spread of mini hospitals, which largely replicate hospital ER services and some portion of follow-on care (including medically supervised recovery overnight if needed; Bjorkgren, 2024).
The problem is that these substitutes usually pick off relatively low-priced treatments of lower complexity, which is not the main source of revenue in a full-service hospital. They definitely can constrain overpricing for hospital-provided ambulatory care, however.
In the latter case, proposals to pay hospitals for ambulatory services on a site-neutral basis, rather than pay both a facility fee and a procedure fee as is common in original Medicare, are advocated as a way of lowering costs (Cooper & Gaynor, 2021). While the same biased pricing also applies to commercially insured patients, there is an unanswered question—How does the hospital determine the facility fee for such services if it wishes to sell to price-conscious buyers? Probably many hospitals now just copy their Medicare payments for all customers, but in principle, a commercial insurance network should not agree to pay a facility fee unless there is some perceived advantage to using the hospital outpatient service compared to a stand-alone alternative.
Broaden the Market
When there are at present too few hospitals in a market area to bring about lower prices, one strategy suggested by Barak Richman (2023) is for insurers to send their insureds to other locations, virtually or physically, where there are low-priced, high-quality hospitals. We know that consumers in general have strong preferences for hospitals close to where they live, and they like face-to-face services; they would require large improvements in quality or large monetary savings to go beyond 15 miles or to the computer. There may be ways to change these preferences, but so far, they do not exist.
The Maverick Strategy
As the previous data shows, there are many hospitals located even in the MSAs with high HHIs. That means that a new entry is not necessarily needed to change prices. What is needed is for at least some of those hospitals to change to a policy of price reduction. The 2010 FTC guidelines call such firms “mavericks” and note that they sometimes do emerge. The guidelines tell lawyers to oppose a merger that would take out a maverick, without exactly saying what a maverick is (Owings, 2013). In a more affirmative state policy, the states might want to encourage maverick behavior as well as preserve it if it has already appeared. But what kind of behavior might call for this special treatment?
There are at least two ways to define maverick firms in the context of hospital markets. The simplest and most literal interpretation would be a hospital that, for whatever reason, pursues a low price (price reduction or price undercutting) strategy, thus putting downward pressure on prices of all hospitals in the market. There is no theory about why some firms might choose to behave this way (other than “culture” or personality of management). Presumably, it would be a hospital with excess capacity, so it could absorb more business if lower prices brought it in. It might be a seller with a lower-than-average marginal cost (for some reason) or a higher-than-average firm-level demand elasticity (for some reason), but for the present, it is hard to specify tangible characteristics of potential mavericks or how they might be fostered,
Another interpretation links the notion of beneficial competitive behavior to the work of Clay Christensen on disruptive innovation. (Christensen, 1997) The idea here is that there is some metric of quality that all firms and customers abide by, but a maverick firm offers a product that is modestly inferior quality by that metric but more attractive on some other dimension—either lower price or something else.
One dimension of hospital care that strongly affects patient choice is distance to the hospital; people seem to place a very high value on using a hospital in the neighborhood. One could then view Richman’s suggestion as proposing a hospital further away (even in another city), as a less desirable feature, but in return for both lower prices and higher clinical quality. The key issue is whether the price reduction is worth the loss of nearness.
Are there other dimensions for Christensen’s type of disruption through slightly lower quality but much cheaper health care? Christensen has written on the subject of disruptive competition in health care, but (according to my criticism) produced an anodyne discussion of cost and quality without identifying something to give up to get something (low price, other aspects of quality) of higher value (Smith, 2007). In a critique (Pauly, 2008), one of us suggested that care by nurse practitioners might be an example, but ran into a storm of criticism based on the claim that they provide as high a level of quality as do physicians. In health care, no one wants to talk out loud about reducing quality that might reduce health, adding a little in return for much lower prices or many better amenities—even though there is strong empirical evidence that middle-class people are willing to risk their health for money or time saving. Perhaps the safest example of tradeoffs between quality and price is indeed the narrow network that compels patients to travel longer distances for lower prices. To some extent, one could also interpret managed care firms as entities that traded more hassle for lower premiums (but the backlash against them argues that the tradeoff was not acceptable). Pushing care out of the hospital and into the home and onto the family may have some promise, though not all families may regard an obligation to provide space and care as a benefit. But it does not seem that a real maverick health system with a disruptive bargain-basement cost-reducing innovation is likely to appear anytime soon unless things change. Quite the contrary, if there is any trend, it is to concierge care, which adds more quality at a higher price, potentially efficient if the quality associated with greater personal attention is worth the price, but not a development that will contain spending growth.
Based on our earlier results, finding only a weak relationship between hospital market structure and prices, it may be that changing state policy to encourage the maverick behavior of existing efficient-sized hospitals will do more to lower prices than trying to change the structure. Mergers should still be blocked, but the possibilities for more than rearguard action are likely to flow from somehow getting hospitals to behave differently.
Price Transparency
The single most powerful piece of evidence that hospital markets are not perfectly competitive is that they violate the economic Law of One Price. Even in areas with low HHIs, the prices paid for ostensibly similar services vary substantially across sellers on average and even vary within sellers among different insurer buyers. This pattern has led to the hope that better information to buyers about where low-priced sellers can be found could lead to shifts of business to those sellers, thus lowering the average price. The proper theory to explain what is going on is still missing, and the evidence on experiments with public policy that compels sellers who are unwilling to disclose their prices is mixed. There may or may not be something there.
There are two kinds of hospital services buyers who might pay attention to price variation. One set would be individual consumers with private high-deductible insurance plans (HDHPs). At least for “shoppable” services, they could gain if it becomes easier to find a good deal. The other set is insurers trying to set up networks. While the specific price to be paid in a network is usually negotiated on a face-to-face basis, the argument is that if all insurers know who got the lowest price and what it is, they can ask for that price too. Of course, it is likely that the lowest price went to the largest insurer, so a smaller insurer cannot necessarily do much with that knowledge (which it probably could have sleuthed out anyway). Because full information for individual buyers is also full information for competitive hospitals in a market, transparency can be a highly effective tool for sellers to conspire and police each other in oligopoly behavior; the government can help to sustain cartel stability.
Some evidence is favorable. The first states to pass their own price transparency laws were those with larger deductibles and higher HDHP shares, suggesting some rationality in the political system (Palaniappan & Pauly, 2025). Across market areas, spending growth was slower in markets with higher deductibles. But there is some unfavorable evidence. In a study of one larger employer that instituted an HDHP, spending fell, but there was no movement to lower-priced sellers—insureds cut use even of cheap sellers. (Brot-Goldberg et al., 2017) Across people with HDHPs for long periods of time, the pattern that emerges is a fall in spending when people switch in from a lower deductible plan, but no slower growth in spending thereafter for those paying more out of pocket for more costly new technology.
Some speculate that the advent of plans with somewhat higher cost-sharing has contributed to the slowdown in spending growth, although the percentage of total and hospital spending out of pocket has been very stable (and low) during this time period. A definite “maybe” as a cause of low spending—but is it one that states can further advance in market areas with weak competitive structures? As noted, those areas with just a few big sellers are prime candidates to use the information on rivals’ prices for nefarious purposes. Rather than compel the dissemination of information through high prices by law, an alternative would be to encourage or even subsidize low-priced sellers to advertise that fact, a behavior that has already started (Palaniappan & Pauly, 2025).
Direct State Interventions
We may have to wait a long time for natural market competition to erode hospitals’ market power in smaller cities, but are there positive steps that states eager to pursue market solutions can implement? On the fiscal side, a state could seek to establish a network of low-priced sellers as part of its state and local employee benefit plan, perhaps even paying a modest subsidy on top of low prices and awarding honors to the most price-effective hospitals in a town. Employers are the ultimate buyers of care and insurance for most workers, and there may be ways to reward especially aggressive or effective companies that follow the Richman strategy or, in other ways, try to channel their workers toward lower-priced, high-quality sellers for their own good. Picking and choosing winners among local hospitals by the government is likely to create backlash, so it is better for Pharmacy Benefit Managers (PBMs) to be given this task.
The historical evidence strongly suggests that hospital spending growth has been driven by the introduction of more costly but presumably more beneficial technology. Studies of cost effectiveness are supposed to help insurers pick and choose. Identification and use of cost sharing for the middle class to discourage low-value users (value-based insurance) has always seemed like a hopeful goal; maybe some states can get it to work or publicize it when it does. Legal protection for hospitals that deny access in low-value cases may help.
Real gains will require changes in hospital management behavior within markets. So, think of a medium-sized city with three or four health systems, usually anchored by a teaching facility. Managements will usually be pleased to compete on the basis of quality, since higher quality services at their institution will make them feel good and be the envy of their peers. But higher quality and, especially, being first with the latest quality-improving technology, will not slow price growth, much less bring prices down. Some dimensions of quality, though important and possibly cost reducing, will also not be attractive—Who wants to be CEO of the hospital whose slogan is “we make fewer mistakes than the others”? Then, too, there is the strong temptation to consumers to judge quality by price unless they have strong evidence to the contrary.
One might have thought that the value-based care approach, still underway but somewhat muted because of past failure, would have led to a focus on price. After all, value is usually defined as health benefit divided by price, and so attacking the denominator has some rationale, even if it is not usually mentioned. However, to the present, the value-based approach in health care has tended to focus much more on the numerator in your system and on claiming that it is much higher, with less low-effectiveness care, than at other systems in town. Perhaps recognition of the spread of high-deductible health plans will lead some hospital systems to train their physicians in explaining tradeoffs among alternative treatment paths of different costs.
An Economically Correct But Visionary Solution
The economic argument against monopoly is that prices higher than marginal cost ultimately distort consumption, and that a potential win-win solution is for buyers to pay sellers a lump sum in return for a seller’s promise to charge marginal cost. This kind of major reorganization is probably beyond the realm of the possible, but Lakdawalla and Sood (2013) suggest an approximation that relies on the form of insurance contract as a force for good. Extending their idea to hospital pricing, the idea here would be for insurers and hospitals to contract for lower hospital marginal prices (either directly or as volume discounts) in return for lump sum payments to hospitals for cutting their unit prices. Even though paying the bonus returns to the seller would reduce the wealth gain to buyers from a lower price, the lower price itself can help avoid distortions (like forcing consumers to pass up a decent neighborhood hospital and travel to another for a lower price). That kind of price-cutting bonus could be offered by or mandated by the government, but one may envision even privately agreed contracts between hospitals and insurers taking this “Netflix” form. Some state Medicaid drug programs have already shown the way, but perhaps this model could be tried for hospital inpatient care as well. It would be worth a shot.
Markets Versus Regulation, Revisited
It remains to be seen whether hospitals can hold their spending close to the same rates of growth as those embodied in state-chosen spending plans. Some hospitals will inevitably rise above the mean, and it appears that there will be no credit for those who hold growth well below the target, so, at a bare minimum, some finger-pointing is likely to happen. But if market forces can keep average spending growth below that of targets, and if the targets are not tightened so much as to guarantee violation, there may not need to be a clash. Spending growth targets that do not constrain do require the political resources needed to set and monitor them (not an insignificant amount), but otherwise do no harm. The history of hospital spending growth over the past decade or so suggests that hospitals can cope with GDP-based targets—they only need to keep doing whatever it was they were doing (or not doing). However, recent government forecasts peg future hospital spending growth as higher than national GDP growth (Keehan et al., 2025), so the honeymoon might end.
If a state policymaker were successful in using changes in market structure to bring about lower spending without constraining regulation, there would be evidence of success: a change in concentration. If, instead, surmising that structure has less to do with performance, the state encouraged Christensen-like disruption, there would be little evidence to show for it. Perhaps more patients sent to more distant but lower-priced hospitals could be counted, and slower rates of adoption of low- value innovations could be tracked. Hospitals that charged lower prices and were more aggressive in seeking patients from greater distances might be identified. But disruptive pricing policy in a market is not easy to see from the outside (even though insiders feel it acutely), and the only way to posit a cause of such a change in pricing behavior would be to identify exogenous changes in reimbursement incentives to protect and even reward the not-quite-as- convenient-but-much-less-costly alternative.
An important gap in knowledge is associated with the focus of much of the research in this area on specific hospital mergers. While mergers can themselves sometimes change HHI measures (and much less frequently the number of sellers threshold), there is little understanding of what even a substantial merger between two hospitals would do to the pricing policies of other hospitals in the market area. In theory, such a merger should cause their prices to increase as well (and blocking the merger would keep their prices low), but evidence on the effect of such market-wide effects of hospital-specific shocks to market structure is sparse. In a recent study of a hospital merger in the Netherlands (Roos et al., 2019), attention is paid to rival hospitals, but no price effects are reported.
It should be noted that greater seller competition will probably be associated with higher administrative costs than if there were a single regulated monopoly seller in each market. The tradeoff between potentially greater consumer choice and innovation against such nonmedical costs needs to be considered.
It is easy at this point to revert to platitudes about mindset changes (for hospital managers, for insurers, for consumers). Yet sometimes platitudes contain wisdom, and that may be the case here. Hospital systems will have to turn away from their current embrace of giantism (“Big Med”) toward targeting a cost-effectiveness filter for resource deployment.
What we can say is that, despite considerable consternation in the policy literature, hospital competition remains a potentially powerful force for much of the U.S. population, perhaps strong enough to account for the restrained hospital spending growth of the recent past. However, there are some cities where there is now less competition than the amount that would be economically feasible, and there are, in concept, some steps that could be taken at the state and local level to increase competitive pressure in such markets. As an alternative to increased price or spending regulations, a focus on encouraging competition where it could exist but currently does not, along with fostering maverick price- cutting behavior in all markets, might protect the overall hospital sector from an outbreak of cost inflation, and so be good policy.
Supplemental Material
sj-docx-1-mcr-10.1177_10775587261452330 – Supplemental material for Kindling Hospital Competition to Control Costs
Supplemental material, sj-docx-1-mcr-10.1177_10775587261452330 for Kindling Hospital Competition to Control Costs by Mark Pauly and Carol Tao in Medical Care Research and Review
Supplemental Material
sj-docx-2-mcr-10.1177_10775587261452330 – Supplemental material for Kindling Hospital Competition to Control Costs
Supplemental material, sj-docx-2-mcr-10.1177_10775587261452330 for Kindling Hospital Competition to Control Costs by Mark Pauly and Carol Tao in Medical Care Research and Review
Footnotes
Funding
The authors received no financial support for the research, authorship, and/or publication of this article.
Declaration of Conflicting Interests
The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
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