Health-Insurer Market Power Theory and Evidence


The US, like the Netherlands and Switzerland, among other nations, relies primarily on private health insurance to finance and reimburse for medical care. In fact, approximately 64% of the nonelderly US population enrolled in private health insurance plans in 2011. This figure is down dramatically from its height of 76% in the mid-1970s. Some researchers point out that private insurance coverage fell over time because premium hikes have vastly outweighed raises in consumer income even though the aggregate premium elasticity of demand is slightly lower than the corresponding income elasticity. Others claim the Medicaid program crowded out some private health insurance coverage. Still others propose that occupational shifts from traditionally higher coverage manufacturing jobs to lower coverage service sector jobs in the US led to some of the reduction.

Although private health insurance enrollment has declined in the past in the US, many health policy analysts expect it to increase in the future because of the recently passed Patient Protection and Affordable Care Act of 2010. The Act mandates that most US citizens purchase private health insurance, if they are not eligible for public health coverage, or pay penalties. By 2019, nearly 8 million more nonelderly citizens are expected to purchase private insurance directly from health insurers because of the mandate. As a result, a sound understanding of the health insurance product and the current operation and performance of the health insurance industry will take on even more importance in the future.

At its most basic level, health insurance is no different than any other product sold by firms and purchased by consumers. Health insurance policies are sold indirectly to consumers in the form of employer-sponsored health insurance (ESI) or are directly purchased by consumers (DPI). Of those covered by private health insurance in 2011, approximately 88% received their coverage through employers. The ensuing transaction involving the health insurance product boils down to a potential win-win situation where both market participants stand to gain.

In particular, because of the irregularity and infrequency of health-care spending, consumers typically value health insurance because it offers financial security against unexpected losses and thereby moderates swings in their income. Additionally, consumers value health insurance because it provides them with access to expensive medical treatments which they might not otherwise be able to afford out of pocket. Hence, many consumers are willing to give up their premium dollars, even when feeling quite healthy, because that initial cost pales in comparison with the dollar benefits which they expect to receive from their health insurance companies when they unexpectedly enter into a state of sickness.

Health insurers also stand to gain from the market transaction as long as the health insurance premiums charged, at least cover the costs of providing health insurance during the policy period. Costs include the expected medical benefits to be paid out and the expense load that includes claims processing, underwriting, and marketing expenditures, taxes, and profits, less any interest income earned on invested premiums. Expected medical benefits, in turn, capture the dollar amount that health insurance companies expect to reimburse medical care providers, such as hospitals, physician clinics, and drug companies, for treating patients throughout the policy period. Thus, health insurance companies can be viewed as organizations that negotiate medical care contracts with providers; mark them up to reflect expenses, profits, and risk; and then sell those policies to employers and individuals. Within that perspective, health insurance companies are paid for negotiating health-care provider contracts, reimbursing claims, and managing the associated risks, with profits as the reward for successful performance.

It is evidenced from the preceding discussion that health insurance companies simultaneously operate on different sides of two highly intertwined markets – as buyers in the market for medical services and as sellers in the market for health insurance. It is in these important roles as buyers and sellers that health insurers potentially shape the manner in which these two markets operate and perform. As discussed in the Section ‘Theoretical Aspects of Health-Insurer Market Power’, economic theory generally suggests that markets operate more efficiently when structured in a competitive manner such that individual buyers and sellers act as price takers and possess no market power. But when markets are structured noncompetitively, sellers may wield market power to the detriment of buyers, or vice versa, with inefficiencies potentially arising in either case.

In the case of health insurers, some interesting market dynamics may be involved when markets are noncompetitively structured because of the simultaneous functioning on opposite sides of the medical care (input) and health insurance (product or output) marketplaces. Indeed, against the backdrop of a baseline case where both markets are reasonably competitive, a number of different scenarios can be imagined where either the medical care provider or health insurer possesses market power and the other does not, or both possess market power in the medical services input market.

With these possible market scenarios in mind, the next section of this article reviews the theoretical aspects of market power within the context of the health insurance industry. Once the basic theory is developed, Section ‘Empirical Aspects of Health-Insurer Market Power’ discusses the empirical aspects of testing for market power effects. Section ‘Empirical Findings Regarding Health-Insurer Market Power’ reviews the empirical literature concerning market power effects in the health insurance and health-care industries. Section ’Summary and Conclusion’ is the final section for this article.

Theoretical Aspects Of Health-Insurer Market Power

To an economist, market power means that a single seller (buyer) can, individually or with a group of other sellers (buyers), raise (lower) the product’s (input’s) price without losing all of its sales (purchases). Sellers or buyers generally attain some market power when they are few in number and possess relatively large market shares. It must also be the case that some type of industry barrier prevents new sellers or buyers from entering the market because new entrants heighten competition and typically cause an offsetting price adjustment. If these market conditions hold, a few buyers or sellers will account for a dominant share of the industry purchases or sales and hence the seller side or buyer side of the market is considered to be highly concentrated.

In the limit, a single seller of a product or input is labeled as a monopoly, whereas a single buyer of a product or input is considered a monopsony. Given that health insurers simultaneously operate in the medical services input market and health insurance output market, five potential scenarios can be imagined:

  1. Both medical care providers and health insurers do not possess market power (competitive case);
  2. Medical care providers, as sellers, possess market power, but health insurers do not in the medical services input market (monopoly case);
  3. Health insurers possess market power, but buyers (employers or individual consumers) do not in the health insurance output market (another monopoly case) (two other cases are possible (either buyers possess market power but health insurers do not or both buyers and health insurers possess market power in the output market), but their relevancy is questioned, so they are not covered in the following discussion. However, the extension of the analysis to these two cases should be evident);
  4. Health insurers possess market power, as buyers, but medical care providers do not in the medical services input market (monopsony case); and
  5. Both health insurers and medical care providers possess market power in the medical services input market (monopoly vs. monopsony or bilateral monopoly case).

Figure 1 provides a graphical illustration showing how the various market outcomes compare with the competitive outcome. (See Pauly (1988) and Scherer (1980) for a similar graphical model, although here the monopsonistic buyer also holds monopoly power in the product market.) Also, Table 1 provides a descriptive summary concerning how each of the scenarios compare to the competitive case in terms of price and quantity. In general terms, the positively sloped supply curve reflects that a higher price is necessary to attract increasing amounts of a particular type of medical service or more health insurance coverage into the marketplace. Also in general terms, the downward-sloping demand curve shows that the buyers’ maximum willingness-to-pay declines for increasing units of an input such as medical services or output such as health insurance. If the graph represents the product market for health insurance, the demand curve captures how much additional utility consumers receive from increasing amounts of health insurance coverage. If an input market, the demand curve reflects how valuable increasing amounts of the medical services are to a health insurer, which is referred to as the value of the marginal product (VMP). The demand curve declines because of the law of diminishing marginal utility and productivity.

Health-Insurer Market Power Theory And Evidence fig 1

Note that the perfectly competitive equilibrium occurs at point C, where the supply and demand curves intersect. Because both individual buyers (e.g., health insurers or consumers) and sellers (e.g., medical care providers or health insurers) are assumed to be price takers in a competitive market, they each treat the good’s price as a parameter – something outside their control. Thus, to maximize net returns – the difference between benefits and costs (net benefits represent profits to firms and consumer surplus to consumers) – sellers match up price to marginal cost (MC), whereas buyers match up price to demand (D) with price serving as the coordination device to equate supply and demand. In equilibrium, price and quantity equal PC and QC, respectively. Buyers receive the triangular area A–PC–C as ‘consumer surplus’ and sellers gain triangular area B–PC–C as ‘producer surplus.’ Note the win-win aspect of the market transaction.

Health-Insurer Market Power Theory And Evidence tab 1

The two monopoly situations are scenarios (2) and (3). In these two scenarios, the sellers (either medical care providers or health insurers) possess monopoly power but the buyers (health insurers or employers/consumers) do not in the respective market. For a monopolist, theory suggests that the marginal revenue curve (MR) lies below the corresponding downward-sloping demand curve. Marginal revenue lies below demand because price must be continually lowered to sell additional units and the revenues from the increased volume fail to compensate for the lower revenues associated with the reduced prices on the previous units. (It is supposed that the demand curve in Figure 1 is captured by the equation P = a-bQ, where Q represents quantity and P stands for price. Total revenues equal P times Q or (a-bQ)Q = aQ-bQ2. Taking the first derivative of this revenue function with respect to Q to get dTR/dQ gives marginal revenue equal to a-2bQ. It should be noticed that MR has the same intercept as demand but twice its negative slope.)

To maximize economic profits, the monopolist-seller produces output or supplies an input up to the point where marginal profits are no longer positive (where MR equals MC) and charges the maximum price that buyers are willing to pay for that amount as indicated by the demand curve. Thus, the monopoly equilibrium occurs at MS with a price of PMS and output of QM. Note that the monopoly outcome results in a higher price and lower quantity than those predicted by the competitive outcome, C. Also, note that consumer surplus shrinks to area PMS–A–MS, whereas producer surplus expands to area B–PMS–MS–MB. The triangular area MS–C–MB represents the competitive winnings that are lost because of the monopolistic restriction of quantity.

Scenario (3) represents a situation where a monopsonist engages in negotiations with a competitive seller side. As a single buyer, the only way a monopsonist can attract additional products or inputs into the market is by paying an increasingly higher price. As a result, if all units are similarly reimbursed when finally purchased, the actual incremental costs of purchasing a particular level of inputs will be greater than the marginal cost, which assumes a price independent of the units purchased. Thus, a monopsonist’s incremental cost curve of purchasing inputs or outputs (MIC) lies above the corresponding marginal cost curve (MC) associated with a group of price-taking input buyers. (It is supposed that the supply curve in Figure 1 is captured by the equation P = c+dQ. Total costs equal P times Q or (c+dQ)Q = cQ+dQ2. Taking the first derivative of this cost function with respect to Q to get dTC/dQ gives marginal incremental cost, which equals c+2dQ. It should be noticed that MIC has the same intercept but twice the slope of the supply curve.)

To maximize economic profits, the monopsonistic health insurer continues to purchase medical services, as an input, as long as the added revenues, as reflected in D (or VMP), compensate for the added costs, as captured by MIC. Thus, in Figure 1, the health insurer purchases inputs up to the point where the MIC and D curves intersect. To attract that amount of medical services, the health insurer must pay the price indicated by point MB on the supply curve, S. Compared with the competitive case at point C, it should be noticed that the monopsonistic health insurer pays less for the medical services and purchases fewer units. Thus, in this case, the producer surplus shrinks to B–PMB–MB and consumer surplus expands to PMB–A–MS–MB. Once again some of the social winnings are lost, but this time because of a monopsonistic distortion.

Scenario (5), the bilateral monopoly situation, offers the most intriguing case. Here, a single buyer and a single seller haggle over the terms of the sale. The single seller prefers the MS outcome where seller profits are maximized but the single buyer prefers the MB outcome because buyer profits are maximized. However, it should be noticed that joint net benefits are maximized at the competitive outcome with a quantity of QC, that is, both the buyer and the seller can receive more net benefits than at their preferred outcome if they agree on the competitive output and then arrive at a mutually satisfying price to split the resulting winnings. Because neither the buyer nor the seller is able to play off the other by threatening to deal with other buyers or sellers, the resulting price depends on which party possesses a comparative advantage at bargaining or which party brings to the bargaining table something more than the other. For example, one of the parties may be operating with greater excess capacity, so the increased volume associated with the transaction is relatively attractive and therefore that party is more willing to compromise on the deal.

The exact price that evolves from the negotiation is indeterminate without knowing more about the negotiating skills of the two parties bargaining. It is not known that the upper limit would be the price that forces the buyer’s profit to zero and the lower limit would be the price that forces the seller’s profit to zero because negative profits would cause one of the firms to drop out of the deal. Alternatively stated, the price must be high enough to make the seller at least as well-off with no sale and low enough to make the buyer at least as well-off with no sale. It should be noted that the alternative to bargaining is no sale because neither the monopoly nor the monopsony outcome is relevant because each entails competitive behavior on one side of the market, which is not a characteristic of bilateral monopoly.

In the real world, often markets are never perfectly competitive and a pure monopoly or monopsony situation, where only one seller or buyer exists, is also rare. A more likely scenario is when a few dominant sellers or buyers exist in some markets and thus these markets are said to be oligopolistic or oligopsonistic. Whether the few buyers or few sellers behave like the preceding models predict depends on whether each individual buyer or seller behaves independently or cooperates with others to extract more favorable prices from the other side of the market. Economic theory suggests that a host of factors influence if a group of sellers (or buyers) act independently or cooperatively. Among these factors are the exact number and relative size distribution of firms, height of any entry barriers, and the availability of close substitute products. These conditions are discussed in detail in the next section.

Empirical Aspects Of Health-Insurer Market Power

Researchers have employed various methods when testing for market power effects, but here the reduced-form, structureconduct-performance (SCP) approach is discussed. Although the SCP approach possesses several empirical shortcomings, it remains the most popular method when testing for market power effects in the health insurance industry. (Other techniques include structural modeling and stock market event analysis.) If suitable data exist, the following estimation equation would be specified to test for market power effects, where X stands for either price (P) or quantity (Q), MCS and MCB represent the market concentration of sellers and buyers, and D and C capture a vector of demand and costs factors, respectively.

Health-Insurer Market Power Theory And Evidence form 1

According to this monopoly theory, a direct relationship is expected between MCS and P, assuming buyer concentration is negligible and therefore has no separate impact on the market outcome. Under those same conditions, an inverse relationship is anticipated between MCS and Q. Moreover, monopsony theories predict an inverse relationship between MCB and both P and Q, based on low seller concentration. The bilateral monopoly situation, as characterized by the interaction term between the two types of concentration, MCS·MSB, is anticipated to be directly related to Q but will have an ambiguous effect on P. Recall that the latter effect depends on the relative bargaining power of the two sides of the market. Finally, the vectors D and C simply act as control variables in eqn [1], so the independent effects of market structure on P or Q can be properly isolated. Variables in D might include buyer income and the price of substitutes and complements, whereas variables in C might include any entry barriers and the state of technology. Thus, this article is not necessarily focused on the impact of those control variables on the dependent variables.

The most basic way to estimate eqn [1] is with the ordinary least squares procedure. (The interested reader will have to consult an econometric text for specifics regarding ordinary least square estimation.) For two reasons, however, ordinary least squares estimation of eqn [1] may result in biased parameter estimates. Both of the reasons deal with some right-hand side variable, or variables, in this case market concentration, being endogenously rather than exogenously determined. First, reverse causality may hold between the dependent and market concentration variables. For instance, more firms may enter the market over time and dilute seller concentration when the market price is high. Or expectations of output, as indicated by Q, may influence seller concentration. Similar examples can be cited for how the magnitude of the dependent variables may influence buyer concentration.

The other problem is that some immeasurable and therefore omitted demand or cost factor may influence both the degree of seller or buyer concentration and the price or quantity. If so, any observed statistical correlation between market concentration and price (or quantity) may only reflect an association rather than a causal relationship because of this third-variable problem. For example, the baseline health of the population may be difficult to measure. Baseline health may influence both the number of hospitals and health insurers within an area as well as the price and quantity of medical care.

Because of the potential for reverse causality or a third-variable problem, estimation of eqn [1] typically requires a panel data set and/or an instrumental variables approach. (A social or natural experiment, which allows for a control group and random assignment of participants, is preferred but the first is expensive to design and the latter is often unavailable to the researcher. See the Appendix to Article 1 in Santerre and Neun (2013) for an elementary explanation of these two approaches.) A panel data set, which covers a number of repeating cross-sections (of individuals, household, states, etc.), allows the analyst to control for unobservable heterogeneity or any omitted variables that remain constant over time. This can be accomplished by including in the estimation equation a 0/1 binary or dummy variable to represent each of the repeating observations. If all omitted variables remain fairly constant over time, the set of dummy variables does a reasonably good job of capturing the fixed differences across observations and thereby corrects for the third-variable problem.

However, the analyst still may have to be concerned with the possibility of reverse causation and any omitted variables that do change over time. For example, the baseline health of the population may be systematically worsening or improving because of some confounding factor that cannot be easily observed and measured. In this case, an instrumental variable approach should be employed and either implemented on a cross-sectional basis or incorporated within a fixed effects framework. A good instrumental variable is one that is highly correlated with the suspected endogenous right-hand side variable but uncorrelated with the dependent variable.

For example, suppose that the impact of health-insurer buyer concentration on the price of hospital services is empirically examined and assume that the seller side is fairly competitive in all of the hospital services markets under investigation. A good instrument, in this case, is highly correlated with health-insurer concentration but not correlated with the price of hospital services. With that in mind, some researchers have used the size distribution of employers in the market area as an instrument. The reasoning is that health insurance companies may be attracted to areas with more medium and large-sized employers and employer size is unlikely to directly influence the price of hospital services.

This section has briefly reviewed the technique used by most researchers to test for the market power effects of health insurers as a way of providing some context to the next section that describes the empirical findings. The instrumental variables technique, although econometrically fairly powerful, is often difficult to implement in practice because suitable instruments are hard to find. This is particularly true for studies relating to health care where many variables, such as health status, health insurance coverage, and medical care utilization, are highly interrelated. The researcher must typically be ingenious with respect to uncovering an instrumental variable that influences the suspected endogenous variable but not the dependent variable in the estimation equation. It should be noticed in eqn [1] that at least three instruments may be necessary because both concentration measures as well as their interaction are likely endogenous.

Empirical Findings Regarding Health-Insurer Market Power

To estimate eqn [1], the analyst must identify the degree of market concentration in a particular market. Thus, defining the relevant market area is an important consideration. A relevant market area contains both a product and a geographical dimension. In an output market, the relevant product market considers all of the substitute products that buyers might switch to if any one product’s price is raised by a nontrivial amount for a nontemporary period of time. These substitute products may satisfy similar needs or fulfill similar functions. For example, with respect to health insurance, analysts must consider if indemnity plans, health maintenance organizations (HMOs), and preferred provider organizations (PPOs) are substitutes or not. (In the past, researchers treated indemnity, HMO, and PPO plans as separate markets. More recently, the distinction between these plans have become blurred in practice, in part because most health insurers offer multiple products and buyers are willing to switch among products depending on relative prices. Also, many of these health insurance products now contain many features of the others.) In addition, for larger employer/firms, the analysts may consider if self-insured plans are reasonable substitutes for fully insured plans that are purchased from health insurance companies.

Similarly, the relevant geographical output market considers all other locations that buyers might switch to if the price of the product is increased by a significant amount for a meaningful period of time. For some products, the market may be very local in nature, but for others, the relevant geographical market may be regional, national, or even international in scope. Although many health insurers such as Aetna and Cigna operate nationally, most experts agree that the market for health insurance is local in nature because employers and consumers want access to a local network of providers. For example, consumers in Philadelphia wish access to a network of providers in that city so they likely are unwilling to purchase their insurance from a health insurer with provider network established in Boston. Consequently, the geographical market for health insurance is often defined as the metropolitan statistical area (MSA) for research and policy purposes. The important take-away for defining the relevant market area is that current purchasing patterns may not properly reflect the relevant market area because the switching of buyers to new products and locations will not take place until the change in the product’s price actually occurs. Thus, one must consider potential substitute products and locations when defining the relevant market area.

Once the relevant market is identified, the degree of market concentration must be assessed. Customary measures of market concentration are the Herfindahl–Hirschman Index (HHI) of market concentration and the number of firms in the market. The HHI is computed by the squaring and adding, in percentage terms, the market shares of all firms in the industry. It ranges from 0 to 10 000 with the latter reflecting only one firm in the market.

The HHI is preferred to other measures such as the concentration ratio, which is an indicator of the percentage of output produced by the industry leaders, because it captures the relative size distribution of output among the leading firms. The value of the HHI decreases with a larger number of equally sized firms, so values closer to zero indicate a less concentrated market. The Federal Trade Commission and Department of Justice considers an HHI more than 2500 as representing a highly concentrated market or a market characterized as a tight oligopoly. In contrast, a market with an HHI more than 1500 but less than 2500 is interpreted as being mildly concentrated or a loose oligopoly. To put these numbers in some perspective, the American Medical Association (2011) reports that the health-insurer HHI is greater than 2500 in most MSAs of the US.

Theoretically, the HHI works best as a measure of market concentration when the products sold by the various firms are reasonably similar. However, when firms sell differentiated products, the HHI loses some of its appeal because niche markets may develop with some firms potentially establishing varying degrees of market power in the various niches. For example, local HMOs may not have a substantial competitive effect on those HMOs possessing a national geographic scope. In this case, the number of firms may provide a better measure of the degree of market competition because the market takes on features similar to the economist’s notion of monopolistic competition. Monopolistic competition holds when a large number of firms offering differentiated products coexist in a market and entry barriers are low or nonexistent. As a point of References:, greater than 200 health insurance companies operate in the typical US state.

Table 2 lists chronologically 17 empirical studies in the economics literature to date regarding the market power effects of health insurers on health-care provider behavior. Note in the table that information is provided for the unit of analysis and method used in each study followed by some abbreviated findings for each article. A number of caveats should be noted. First, although the author(s) may have used an instrumental variables (IV) approach rather than ordinary least squares (OLS), the actual instrument or instruments used may have been weak in a theoretical or statistical sense. Recall that a good instrument must be correlated with the suspected endogenous independent variable but uncorrelated with the dependent variable. But in practice, some instruments are better at achieving that result than others. As a result, some statistical bias from reverse causality or a third-variable problem may still remain even though an IV procedure is employed if a weak instrument is used.

Health-Insurer Market Power Theory And Evidence tab 2

Second, notice that most of the earlier papers deal with Blue Cross (BC) plans. That early focus likely reflects that BC plans dominated many areas and data were available because most plans were organized on a nonprofit basis at the time. However, since the late 1980s, many BC plans have converted to for-profit status to gain access to equity capital so data have become more proprietary in nature. Third, only a few studies simultaneously control for both insurer and provider market concentration and none allow for an interaction term. Finally, it should be pointed out that some studies are conducted using national data for the US, whereas others are performed with data from particular states or areas.

With these caveats in mind, it appears to be the case that a majority of the relevant studies, reported in Table 2, find that a greater dominance of health insurers, as reflected in a higher market share or greater market concentration, results in a lower negotiated hospital price. Thus, it might appear that ample statistical evidence exists to suggest that health insurers possess and exercise market power in the hospital services market (i.e., a movement from point C to MB in Figure 1). However, an inverse relation between health-insurer market power and provider prices may not necessarily reflect monopsonistic exploitation, that is, instead of greater health-insurer market power resulting in a movement from point C to MB in Figure 1, it may actually be the case that the provider market adjusts from MS to C in response to greater health-insurer buyer pressure. If so, health insurers may actually be exercising monopoly-busting power by forcing dominant hospitals to lower price and produce more services. It follows that empirical evidence is required for both the change in price and the quantity to assess whether health insurers exercise monopsony power in provider markets.

With this perspective in mind, several articles analyze the quantity aspect of health-insurer market power effects. The first study, by Feldman and Wholey (2001), finds that greater HMO market power leads to a lower hospital price but also causes increased hospital output. Bates and Santerre (2008) extend the Feldman and Wholey study by examining the effects of both HMO and PPO market concentration on various measures of hospital output at the MSA level. They find that increased HMO and PPO market concentration leads to a more inpatient and outpatient care, respectively. Finally, Bates et al. (2006) find that greater health-insurer market concentration is associated with the hospital services industry using its resources in a more technically efficient manner (i.e., getting more output from the same inputs). These three papers, especially when considered together with the other studies finding lower negotiated hospital prices in response to greater health-insurer market concentration, imply fairly strongly that health insurers exercise monopoly busting rather than monopsony power in the hospital services industry.

However, some limited evidence suggests that the situation may be different in the physician services market. More specifically, although Feldman and Wholey (2001) and Schneider et al. (2008) find no relationship between health-insurer market power and physician pricing and output, Dafny et al. (2012) show that greater health-insurer market concentration is related to a reduction in both physician earnings and employment as a monopsony model suggests. The study by Dafny et al. (2012) comes across as being particularly persuasive because it uses a data set of 11 million people in various employer-sponsored health insurance plans across the nation over an 8-year period and specifies plan-fixed effects along with using a plausible instrumental variables approach. Dafny et al. (2012) findings also agree with basic intuition because physician markets are much less concentrated than hospital services markets and, unlike nurses, physicians are not unionized. Given these two conditions, health insurers may be able to exploit physicians. It will be interesting to see if future studies offer collaborative evidence.

The literature on the relationship between health-insurer market concentration and insurer behavior pales in comparison with the previous literature. It should be noted in Table 3 that only six studies to date have focused on this particular topic and that these studies are relatively recent in comparison with the research on the previous topic. All but one study suggest that health insurers exercise market power by raising premiums and/or lowering output when the market for health insurance is more concentrated.

Health-Insurer Market Power Theory And Evidence tab 3

Dafny’s (2010) study is particularly convincing because it shows that health insurers charge higher premiums to more profitable employers. Economic theory suggests that only firms with market power can practice price discrimination of that kind. In addition, Dafny et al. (2012) find that health insurance premiums spiked upward in areas where the health insurer market concentration suddenly shot up because of a merger between Aetna and Prudential in 1998. Finally, Bates et al. (2012) show that the number of people with individually purchased health insurance (but not ESI) is lower in states where health-insurer market concentration is greater, particularly when no state rate review regulations exist. All in all, the evidence, although relatively limited, seems to suggest that health insurers are able to exercise market power in their output market. (Empirically examining the impact of mergers on premiums and profits provides another way of observing whether health insurers possess market power. Feldman et al. (1996) find that premiums increase in the most competitive market areas 1 year after mergers among HMOs. Hilliard et al. (2011) show that rivals’ returns increase in response to a merger in market areas where the premerger HHI is high and the post-merger change in the HHI is large. Thus, both of these papers suggest health insurers engage in anticompetitive behavior.)

Summary And Conclusion

Whether health insurers possess and exercise market power remains an important issue for the US because the recently passed health insurance reform continues to rely heavily on a private health insurance industry. As discussed in this article, economic theory suggests that sellers and buyers may exploit their situation by raising prices above and lowering prices below the competitive level in the output and input markets, respectively, when the relevant market is highly concentrated. In both cases, these price distortions can lead to allocative inefficiency and large firms gaining at the expense of consumers or suppliers. The health insurance industry simultaneously plays critical roles as an important buyer of health care provider services and as a health insurance provider to the public. Consequently, firms in the health insurance industry potentially can exercise both monopoly and monopsony power.

The empirical evidence to date suggests that health insurers may possess monopsony power in many physician services markets of the US. At least, one highly credible study finds that physicians are paid less and fewer physicians are employed when health insurers possess more market power in their area. However, studies focusing on the hospital services industry suggest the opposite. These studies find that health insurers, when they attain more market power, are able to bust the monopoly power of hospitals, thereby creating lower prices and more hospital services. Further complicating the analysis, recent research seems to have concluded that health insurers possess and exercise market power in their output market, that is, premiums are higher and fewer people are insured in areas where the health insurance industry is more highly concentrated. Consequently, it appears that health insurers, when they possess market power, are not passing along any cost savings from the hospital or physician services markets to buyers of health insurance.

Normally, reducing the market power of an industry, such as health insurance, would mean that suppliers and buyers, in this case physicians and consumer/patients, will unambiguously benefit. However, reducing market power also mean health insurers will be less able to hold the market power of hospitals in check. Given this trade-off, it is unclear how health policy authorities should craft public policies affecting the health insurance industry. For example, should public authorities level the playing field by allowing physicians to join unions so they can negotiate collectively to countervail the market power of health insurers? Or, should antitrust laws be enforced more aggressively toward health insurers or hospitals, or toward both? Or, would a profit tax on health insurers (and hospitals) be a better idea? How about a public health insurance option? According to the existing empirical literature, health policy analysts may have to confront these sorts of questions if economic efficiency is desired and a private health insurance system continues to be relied on in the US.


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Comparisons of Health Insurance Systems
Health Microinsurance Programs in Developing Countries