INVALIDITY OF COMMON MARKET DEFINITION TESTS IN HEALTH CARE-ENDNOTES

ENDNOTES

[1] 15 U.S.C. § 1 (Westlaw).

[2] Standard Oil Co. of New Jersey v. U.S., 221 U.S. 1, 62 (1911) (“the criteria to be resorted to in any given case for the purpose of ascertaining whether violations of the section have been committed is the rule of reason”).

[3] Board of Trade of City of Chicago v. United States, 246 U.S. 231 238 (1918).

[4] Id.

[5] Standard Oil Co. of New Jersey, at 61 (“commerce…has both a geographical and a distributive significance; that is, it includes any portion of the United States and any one of the classes of things forming a part of interstate or foreign commerce”); Bhan v. NME Hospitals, Inc., 929 F.2d 1404, 1413 (9th Cir. 1991) (“Bhan II”) (“[m]arket definition requires both a geographic and a product dimension”); Oltz v. St. Peter’s Community Hosp., 861 F.2d 1440, 1446 (9th Cir. 1988) (“[t]he term “relevant market” encompasses notions of geography as well as product use, quality, and description”).

[6] 1992 Horizontal Merger Guidelines, U.S. Department of Justice and Federal Trade Commission, available at http://www.ftc.gov/bc/docs/horizmer.htm (hereafter, “The Guidelines”), at section 1.0 (“A market is defined as a product or group of products and a geographic area in which it is produced or sold such that a hypothetical profit-maximizing firm, not subject to price regulation, that was the only present and future producer or seller of those products in that area likely would impose at least a “small but significant and nontransitory” increase in price, assuming the terms of sale of all other products are held constant. A relevant market is a group of products and a geographic area that is no bigger than necessary to satisfy this test.”).

[7] Id., at section 1.1 (“The Agency will first define the relevant product market with respect to each of the products of each of the merging firms.”).

[8] U. S. v. E. I. Du Pont de Nemours & Co., 351 U.S. 377 (1956) (the “Cellophane” case) (a product market requires “an appraisal of the ‘cross-elasticity’ of demand” [and] “no more definite rule can be declared than that commodities reasonably interchangeable by consumers for the same purposes make up that ‘part of the trade or commerce’, monopolization of which may be illegal”); Oltz v. St. Peter’s Community Hosp., 861 F.2d 1440, 1446 (9th Cir. 1988) (“[t]he product market includes the pool of goods or services that enjoy reasonable interchangeability of use and cross-elasticity of demand”); Brown Shoe Co. v. U.S., 370 U.S. 294, 325 (1962) (“[t]he outer boundaries of a product market are determined by the reasonable interchangeability of use or the cross-elasticity of demand between the product itself and substitutes for it.”).

[9] Tampa Electric Co. v. Nashville Coal Co., 365 U.S. 320, 327 (1961) (“the line of commerce, i.e., the type of goods, wares, or merchandise, etc., involved must be determined…on the basis of the facts peculiar to the case.”).

[10] Jefferson Parish Hospital Dist. No. 2 v. Hyde, 466 U.S. 2, 7 (1984) (“Jefferson Parish”) (Where a hospital had an exclusive contract with a firm of anesthesiologists, “[t]he exclusive contract had an impact on two different segments of the economy: consumers of medical services, and providers of anesthesiological services”).

[11] Summit Health, Ltd. V. Pinhas, 500 U.S. 322, 324 (1991).

[12] Bhan v. NME Hospitals, Inc., 772 F.2d 1467, 1470-71 (9th Cir. 1985) (“Bhan I”) (finding that a “nurse anesthetist administering anesthesia under the supervision of a physician may still duplicate many of the services provided by an M.D. anesthesiologist”).

[13] Oltz, 861 F.2d, at 1446.

[14] United States v. Rockford Memorial Corp., 898 F.2d 1278,1284 (7th Cir. 1990) (Posner, J.) (“If you need your hip replaced, you can’t decide to have chemotherapy instead…”).

[15] Bhan v. NME Hospitals, Inc., 772 F.2d 1467, 1470-71 (9th Cir. 1985) (“Bhan I”) (finding that a “nurse anesthetist administering anesthesia under the supervision of a physician may still duplicate many of the services provided by an M.D. anesthesiologist”).

[16] Hospital Corp. of America v. F.T.C., 807 F.2d 1381, 1888 (7th Cir., 1986) (Posner, J.).

[17] F.T.C. v. Freeman Hosp., 69 F.3d 260, 271 (8th Cir., 1995) (“A separate product market may begin to develop, for example, for certain types of tertiary care which, while serious, need not always be treated immediately and may often be scheduled in advance.”).

[18] Tampa Electric, 365 U.S., at 327 (“the area of effective competition in the known line of commerce must be charted by careful selection of the market area in which the seller operates, and to which the purchaser can practicably turn for supplies”); Standard Oil Co. of California v. United States, 337 U.S. 293, FN5 (1949) (“the ‘line of commerce’ affected need not be nationwide, at least where the purchasers cannot, as a practical matter, turn to suppliers outside their own area”). 

[19] Oltz, 861 F.2d, at 1446 (“[d]efining the relevant market is a factual inquiry ordinarily reserved for the jury”); Syufy Enterprises v. American Multicinema, Inc., 793 F.2d 990, 994 (9th Cir. 1986) (“[r]elevant market is a factual issue which is decided by the jury…”).

[20] Jefferson Parish, at 7.  

[21] Id., at FN7. 

[22] Id., at 29-30.

[23] Oltz, at 1446-47.

[24] Bhan II, at 1413 (“Bhan claims that the relevant product market is the market in which hospitals compete for anesthesia services and the geographic market is the town of Manteca. Bhan’s only evidence of the geographic market is that the hospital wanted its anesthesia providers to live in Manteca.”).

[25] Id., at 1413-14 (where nurse anesthetist Bhan argued that the geographic market for anesthesia providers at the NME hospital was confined to the town of Manteca because the hospital required those providers to reside in Manteca, “[t]he fact that the defendant hospital may wish them to reside in Manteca in no way limits the geographic area from which anesthesia services may be acquired and brought to work in Manteca…[the] result is that it cannot be said that the hospital’s policy restrains competition in a relevant market”). 

[26] Tampa Electric Co. v. Nashville Coal Co., 365 U.S. 320, 327 (1961) (“the line of commerce, i.e., the type of goods, wares, or merchandise, etc., involved must be determined…on the basis of the facts peculiar to the case.”); Brown Shoe Co. v. U.S., 370 U.S. 294, 325 (1962)(“[t]he boundaries of such a submarket may be determined by examining such practical indicia as industry or public recognition of the submarket as a separate economic entity, the product’s peculiar characteristics and uses, unique production facilities, distinct customers, distinct prices, sensitivity to price changes, and specialized vendors.”).

[27] 1992 Horizontal Merger Guidelines, U.S. Department of Justice and Federal Trade Commission, available at http://www.ftc.gov/bc/docs/horizmer.htm.

[28] Id., at section 1.0 (“A market is defined as a product or group of products and a geographic area in which it is produced or sold such that a hypothetical profit-maximizing firm, not subject to price regulation, that was the only present and future producer or seller of those products in that area likely would impose at least a “small but significant and nontransitory” increase in price, assuming the terms of sale of all other products are held constant. A relevant market is a group of products and a geographic area that is no bigger than necessary to satisfy this test.”).

[29] Guidelines, at section 1.11 (“Absent price discrimination, the Agency will delineate the product market to be a product or group of products such that a hypothetical profit-maximizing firm that was the only present and future seller of those products (“monopolist”) likely would impose at least a ‘small but significant and nontransitory’ increase in price”). 

[30] James Langenfeld and Wenqing Li, Critical loss analysis in evaluating mergers, Antitrust Bulletin/Summer 2001, 299, 301 (“A critical loss analysis estimates the amount of lost sales that would make a price increase unprofitable, and then asks whether such a price increase would lead to such a loss of sales. Thus if it is applied correctly, a critical loss analysis can be a useful tool for determining whether a merger will enable firms to profitably increase price. In fact, the 1992 Department of Justice and Federal Trade. Commission Horizontal Merger Guidelines use a type of critical loss analysis to define markets.”).

[31] Joseph Farrell and Carl Shapiro, Improving Critical Loss Analysis, theantitrustsource -www. antitrustsource.com, February 2008, at 1 (“Critical Loss Analysis calculates the hypothetical monopolist’s Critical Loss, meaning the magnitude of lost sales that would (just) make it unprofitable for the hypothetical monopolist to impose a SSNIP, and compares it against the so-called Actual Loss of sales that would result from the SSNIP. If the Actual Loss would be less than the Critical Loss, the SSNIP would be profitable, so [this] would form a market.”).

[32] Kenneth L. Danger and H.E. Frech III, Critical thinking about “critical loss” in antitrust, The Antitrust Bulletin, Summer 2001, 339, 341 (“The critical sales loss is the decrease in sales resulting from a particular price increase that is just large enough to make that price increase unprofitable.  Once computed, the critical loss is compared to the sales loss expected following the particular hypothetical price increase. If the actual sales loss is expected to be larger than the critical loss, it follows that the price increase would be unprofitable.  This thought experiment connects to antitrust market definition in the following way: if the hypothetical price increase is deemed unprofitable then the candidate market is considered to be too narrow and is expanded to account for sales lost to producers located outside the candidate market.”).

[33] The Guidelines, at section 1.1 (“the Agency will begin with each product (narrowly defined) produced or sold by each merging firm and ask what would happen if a hypothetical monopolist of that product imposed at least a “small but significant and nontransitory” increase in price,

but the terms of sale of all other products remained constant. If, in response to the price

increase, the reduction in sales of the product would be large enough that a hypothetical

monopolist would not find it profitable to impose such an increase in price, then the

Agency will add to the product group the product that is the next-best substitute for the

merging firm’s product.  The price increase question is then asked for a hypothetical monopolist controlling the expanded product group….This process will continue until a group of products is identified such that a hypothetical monopolist over that group of products would profitably impose at least a “small but significant and nontransitory” increase, including the price of a product of one of the merging firms. The Agency generally will consider the relevant product market to be the smallest group of products that satisfies this test.”).

[34] Guidelines, at section 1.12.

[35] Guidelines, at section 1.21 (“In defining the geographic market or markets affected by a merger, the Agency will begin with the location of each merging firm (or each plant of a multiplant firm) and ask what would happen if a hypothetical monopolist of the relevant product at that point imposed at least a “small but significant and nontransitory” increase in price, but the terms of sale at all other locations remained constant. If, in response to the price increase, the reduction in sales of the product at that location would be large enough that a hypothetical monopolist producing or selling the relevant product at the merging firm’s location would not find it profitable to impose such an increase in price, then the Agency will add the location from which production is the next-best substitute for production at the merging firm’s location.”).

[36] Guidelines, at section 1.21. 

[37] Guidelines, at section 1.32 (“the Agency will identify other firms not currently producing or selling the relevant product in the relevant area as participating in the relevant market if their inclusion would more accurately reflect probable supply responses. These firms are termed “uncommitted entrants.” These supply responses must be likely to occur within one year and without the expenditure of significant sunk costs of entry and exit, in response to a “small but significant and nontransitory” price increase.”).

[38] James Langenfeld and Wenqing Li, Critical loss analysis in evaluating mergers, Antitrust Bulletin/Summer 2001, 299, 302-303 (“Applying the critical loss methodology involves three steps: (1) estimating the hypothetical monopolist’s per unit margin before prices would be increased; (2) determining the percentage of customers this hypothetical monopolist could lose before a price increase becomes unprofitable; and (3) estimating whether this hypothetical monopolist would lose this percentage of customers if it increased price. The first and third of these steps rely on data from the particular case examined. The second step is a purely mathematical step that relies on data from step 1.”).

[39] Kenneth L. Danger and H.E. Frech III, Critical thinking about “critical loss” in antitrust, The Antitrust Bulletin, Summer 2001, 339, 344-345.

[40] James Langenfeld and Wenqing Li, Critical loss analysis in evaluating mergers, Antitrust Bulletin/Summer 2001, 299, 302-303, supra, note 36.

[41] Wikipedia, at http://en.wikipedia.org/wiki/Price_elasticity_of_demand (“In economics and business studies, the price elasticity of demand (PED) is an elasticity that measures the nature and percentage of the relationship between changes in quantity demanded of a good and changes in its price.”).

[42] James Langenfeld and Wenqing Li, Critical loss analysis in evaluating mergers, Antitrust Bulletin/Summer 2001, 299, 302-303, supra, note 36.

[43] Kenneth L. Danger and H.E. Frech III, Critical thinking about “critical loss” in antitrust, The Antitrust Bulletin, Summer 2001, 339, 346 (“The parties to a merger often claim that the market is “exceptionally competitive.” Although the precise meaning is uncertain, suppose we interpret this as meaning perfectly competitive pricing. If literally true, then price not only equals average cost, but it also equals marginal cost…Using marginal cost at the starting point, the contribution margin is zero and the critical loss is 100%.  That is, when the monopolist raises price it would have to lose all of its sales in order for there not to be an increase in profits…”).

[44] Id., (“At a very general level, when markets are exceptionally competitive, price can be close to marginal cost. When this is true, the contribution margin is small and the critical loss is large. That is, the hypothetical monopolist can afford to lose many sales when it raises price and still have profits increase. As a result, critical loss analysis may define the antitrust market quite narrowly. Thus, the parties’ arguments that the market is exceptionally competitive may be at odds with a broad market definition.”).

[45] Id., at 351 (“The only data commonly (though not universally) available in accounting reports that resembles average variable cost is the concept of “cost of goods sold,” or “product costs”…This is the only concept related to variable costs that is allowed by generally accepted accounting practices.  The difference between this cost and the price is called the gross margin…There is wide recognition that the traditional approach doesn’t estimate true variable costs well…”).

[46] Id., at 348 (“The fact lhat average variable cost is below marginal cost comes from nothing more than cost minimization, which is a necessary component of profit maximization, and constant returns.”).

[47]James Langenfeld and Wenqing Li, Critical loss analysis in evaluating mergers, Antitrust Bulletin/Summer 2001, 299, 302-303  (“in some industries, selling additional units may require ever-increasing selling costs. These ever-increasing selling costs will increase the marginal costs of the last units sold, which, in turn, reduces the margin on the last units sold.”).

[48] Danger and Frech, supra, note 3, at 346 (“It is equally important to note that the practice of replacing marginal cost with average variable cost favors the finding of a broad market… by replacing marginal cost with average variable cost the contribution margin is biased up und the critical loss down, since average variable cost is less than marginal cost at the competitive starting point. While this substitution is sometimes made in practice because estimating marginal cost is difficult, it may substantially affect the estimate of critical loss and therefore the market definition”); Michael G. Baumann and Paul E. Godek, A new look at critical elasticity, The Antitrust Bulletin: Vol. 51, 325,  No. 2/Summer 2006 (“The critical elasticity formulae in the literature depend on the assumption that marginal costs and average variable costs are constant-and therefore equal. Under this assumption knowing average variable cost would be equivalent to knowing marginal cost.  A more realistic paradigm, consistent with the discussion above, would acknowledge that marginal cost is unknown-except that it is not equal to average variable cost and is, therefore, upward sloping at the firm’s profit-maximizing level of output…It is possible to derive a revised critical elasticity formula based on that economic paradigm of rising marginal cost… the new formula derives uniformly higher critical elasticities than those derived from previous models… since low critical elasticities imply that fewer sales must be lost to deter a given price increase, this new approach will tend to result in narrower market definitions”).

[49] Wikipedia, supra, note 38.

[50] James Langenfeld and Wenqing Li, Critical loss analysis in evaluating mergers, Antitrust Bulletin/Summer 2001, 299, 307 (“Two broad categories of information can be combined to estimate whether the actual loss of sales resulting from a price increase would exceed the critical loss for that price increase. The first category employs the type of evidence traditionally used in defining markets. The second category is based on the margin used to make the critical loss calculation.”).

[51] Id., at 307-308 (“The first category includes evidence that buyers have shifted or have considered shifting purchases between products or locations in response to relative changes in price or other competitive variables. This evidence is often obtained by surveying market participants, including both buyers and sellers, or information obtained from documents generated by market participants and third-party sources. For example, there may be evidence that sellers base business decisions on concerns about buyers substituting between products and locations in response to relative changes in price. Alternatively, there may be evidence about the cost of customers switching products or buying similar products from more distant locations. For instance, the cost of traveling to a nearby city to make a purchase can be compared with the cost of the same product or service in the same city. If the added traveling costs are large in terms of time or expense, then it is less likely that customers in the city at issue would increase their purchases from another city ill the face of a price increase. In some cases, where past sales and price data permit, econometric analysis can also reveal the extent to which buyers would shift purchases in response to relative changes in price.”).

[52] James Langenfeld and Wenqing Li, Critical loss analysis in evaluating mergers, Antitrust Bulletin/Summer 2001, 299, 307 (“The second category of information is based on the premise that a firm can earn high margins on the last units that it sells only if (1) it already has some degree of market power or (2) it is currently coordinating its behavior with that of other firms to raise price.”).

[53] James Langenfeld and Wenqing Li, Critical loss analysis in evaluating mergers, Antitrust Bulletin/Summer 2001, 299, 308 (“Katz and Shapiro and O’Brien and Wickelgren have shown how to estimate Actual Loss using information about demand responsiveness based on firms’ premerger pricing decisions. This is what economists call ‘revealed preference’ information: inferences about preferences based directly on observed choices. Here, one can make inferences about demand sensitivity, as gauged by a real firm based on its premerger choice of price. In particular, if (before the merger) a firm chooses a high margin on its product, the firm evidently thinks that demand for its product is not very sensitive to price.”).

[54] Joseph Farrell and Carl Shapiro, Improving Critical Loss Analysis, theantitrustsource -www. antitrustsource.com, February 2008, at 4 (“The Lerner equation states that, if a product is priced so as to maximize the profits from that product, then the proportional gross margin, m (the difference between price and cost), will be the inverse of the elasticity of demand facing the product: that is, ε = 1/m“); see also Kenneth L. Danger and H.E. Frech III, Critical thinking about “critical loss” in antitrust, The Antitrust Bulletin, Summer 2001, 339, 350.

[55] Joseph Farrell and Carl Shapiro, Improving Critical Loss Analysis, theantitrustsource -www. antitrustsource.com, February 2008, at 4-5.

[56] Id., at 10.

[57] Id., at 17 (“One can undertake Critical Loss Analysis in a seemingly simpler manner, but only by ignoring key revealed-preference information and stripping the hypothetical monopolist of key business concerns shared by pre- and post-merger actual firms, thus depleting the market definition exercise of both reliability and relevance”).

[58] Kenneth L. Danger and H.E. Frech III, Critical thinking about “critical loss” in antitrust, The Antitrust Bulletin, Summer 2001, 339, 348-349 (“market definition via critical loss is highly sensitive to the degree of market power at the starting point. More preexisting market power leads to smaller critical loss estimates, thus potentially broader market definitions… If price is already at the monopoly level, then any further increase in price will result in lower profits. The critical loss is zero. So, the expected loss would necessarily be greater than the critical loss for any price increase. In this situation the critical loss analysis would suggest that the group of products over which the two firms were successfully colluding is not a relevant antitrust market, when in fact it obviously is one. This is an example of the cellophane fallacy where the analyst confuses substitution induced by monopoly prices with substitution that would preclude monopoly pricing.”).

[59] Gene C. Schaerr, The Cellophane Fallacy and the Justice Departments Guidelines for Horizontal Mergers, 94 Yale L.J. 670, 677 (1985), “In that case, the United States Supreme Court apparently determined the size of the relevant market, as defined by the number and availability of substitutes, with reference to a supra-competitive (monopoly) price rather than the lower competitive price. As a result, the Court held that the defendant had no market power when in fact it had substantial market power. The analytic error that produces this fallacy is a failure to count the market power a firm has already exercised (in raising its price above the competitive level), and instead counting only the market power the firm has not yet used.”  The “Cellophane” case is U. S. v. E. I. du Pont de Nemours & Co., 351 U.S. 377 (1956).

[60] The Guidelines, at section 1.1 (“In attempting to determine objectively the effect of a “small but significant and nontransitory” increase in price, the Agency, in most contexts, will use a price increase of five percent lasting for the foreseeable future.”).

[61] James Langenfeld and Wenqing Li, Critical loss analysis in evaluating mergers, Antitrust Bulletin/Summer 2001, 299, 310-311 (“If a relatively small number of consumers are price sensitive. then a comparison of their price sensitivity to the critical elasticity of demand can be misleading. A small price increase could lead most of these price-sensitive consumers to switch to more distant suppliers, but a larger price increase would lead very few of the substantial number of the relatively price-insensitive consumers to switch. In this case, a hypothetical monopolist could raise price substantially and lose all of the price-sensitive consumers, and still make more profits on the higher prices it can charge to price-insensitive customers. A large price increase would be profitable, when a small price increase would not.”).

[62] Kenneth Elzinga, Trial Testimony, In Re Evanston Hospital Corporation, Federal Trade Commission, Docket No. 09315, 2389, 2395 (“The person who consumes the hospital services is not basing that decision based on the different relative prices of those, because most of the consumption of hospital services is not paid for directly by the consumer but by some insurance mechanism or managed care organization…the study of markets is based on the assumption that the person who makes the choice to consume some product is also the person who pays for that product and who therefore thinks long and hard about the price of a product as well as other attributes of the product.  In the case of hospital services, there’s a disconnect between who pays for the product and who consumes the product, and for that matter, who chooses the product or the service…”).

[63] H.E. Frech III, James Langenfeld, R. Forrest McCluer, Elzinga-Hogarty Tests and Alternative Approaches for Market Share Calculations in Hospital Markets, 71 Antitrust L.J. 921, 924-925 (2004) (“The multiplicity of services also makes analyzing pricing extremely complex. In receiving hospital care, consumers receive a bundle of many services (e.g., basic hospital services, tests and other specialized procedures, drugs, devices, operating theater rentals). The definitions of these services are not necessarily the same across hospitals. A typical hospital will have at least tens and possibly hundreds of prices for each of its services for different buyers [e.g., each managed care plan will have different prices, as well as different prices for Medicare, Medicaid, and local indigent care programs]. The bases of service prices also differ. Few payers pay list fee-for-service prices, as most receive discounts off list prices. Medicare and some private payers pay a dollar amount per admission, depending on diagnosis (the DRG system), but also adjust payment upward to account for the unusual costs of the most expensive patients. These adjustments, called outlier payments, are based partly on hospital charges.  On the other hand, many payers pay on a per diem (hospital days) or capitation basis. Each of these methods provides different incentives and allocate risks differently. In addition, because the costs of treatment for capitation or per diem payers depend on the exact makeup of the consumers in the group, comparing pricing even within a category of payer can be complex. This is not to say pricing analyses in hospitals would be impossible, but they are difficult and have seldom been done.”).

[64] Kenneth Elzinga, Trial Testimony,  In Re Evanston Northwestern Hospital, F.T.C. Docket No. 09315, at tr. 2387:18-24 (“People who travel outside their home turf for hospital services usually do so for one of two reasons or a combination of the two.  There’s some particular service or amenity that they associate with that distant hospital that’s important to them, or they may have family who lives some distance away and they travel to that hospital.  People who consume travel –hospital services close to home typically are there either because their doctor places them at that hospital or, for purposes of their own convenience or the convenience of their family, it is very important for them to be hospitalized close to home.  So, unlike products like coal and beer that will move about in response to the market signals –prices change and coal gets shipped to a different location; prices change and beer gets shipped to a different location –here, the prices of hospital services do not drive most people to change the location of where they consume hospital services. If they consume hospital services close to home, it’s because their doctor placed them there, their doctor practices at those hospitals, they want to be at a hospital close to home, because they think that’s important for their own care or for the convenience of their family members. If they want to go far away to a hospital, it’s because of something unique in that

situation.  And consequently, the normal things that we associate with market signals through price don’t affect those decisions, and therefore, the ability of particular payor hospitals to raise prices is not disciplined or thwarted by the travel patterns that we observe looking at patient flow data.”);  Hospital Corp. of America v. F.T.C., 807 F.2d 1381, 1387-8 (1986) (Posner, J.) (“if, in the event that hospital prices in Chattanooga rose above the competitive level, persons desiring hospital services in Chattanooga would switch to hospitals in other cities…this would mean that the Chattanooga hospital market, which is to say the set of hospital-services providers to which consumers in Chattanooga can feasibly turn, includes hospitals in other cities…[but g]oing to another city is out of the question in medical emergencies; and even when an operation or some other hospital service can be deferred, the patient’s doctor will not (at least not for reasons of price) send the patient to another city, where the doctor is unlikely to have hospital privileges.”);

[65] Elzinga & Hogarty, The Problem of Geographic Market Delineation in Antitrust Suits, 18 Antitrust Bull. 45 (1973).

[66] The Guidelines, Introduction.

[67] Elzinga & Hogarty, The Problem of Geographic Market Delineation Revisited: the Case of Coal, 23 Antitrust Bull. 1, 2 (1978) (“In our original article, we proposed delineating geographic markets by the application of two tests: (1) “Little Out From Inside” (LOFI) and (2) “Little In From Outside” (LIFO). The LOFI test posed the question: What is the smallest geographic region required to account for nearly all shipments from a given producing area? This question concerned the “supply side” and ensured that significant “exports” to other regions were taken into account. The ratio associated with LOFI is the sales of sellers located in the producing area to customers in that same area divided by the total sales of these firms to all destinations. The LIFO test posed the question: Of total purchases within the region identified by the LOFI test do nearly all emanate from within that region itself? This question dealt with the “demand side” and guaranteed that significant “imports” from other regions would not be overlooked. The ratio associated with LIFO is the sales of firms in the producing area to customers located in that area divided by the total purchases these customers make from all sellers wherever located. If both these tests could be satisfied simultaneously, then a distinct geographic market has been identified.”).

[68] Id., (“we suggested that “nearly all” shipments be interpreted to mean 90 percent for a “strong” market and 75 percent for a “weak” market.”).

[69] Elzinga & Hogarty, The Problem of Geographic Market Delineation in Antitrust Suits, 18 Antitrust Bull. 45, 49 & 73 (1973) (“price data are of little use in geographic market delineation-at least for antimerger enforcement-for two reasons, one practical, the other conceptual…The practical difficulty is determining, in two geographic territories, “the price” at which the prodnct sells in each area, as well as “the transportation rate” between the two areas. One need only consider a few of the court-designated relevant product lines-shoes, metal cans and glass bottles, commercial banking, beer, and grocery retailing-to realize that assigning “a price” to these products is a complex task…[thus] there are both conceptual and operational problems in translating price data into the estimation of market areas. On the other hand, all of the demand and supply elements that affect price also affect quantity-or shipments-and these figures can be used in estimating market areas. Specifically, the only data required to estimate market areas-at least in most cases-are shipments data in physical terms (e.g., barrels)…”).

[70] Elzinga & Hogarty, The Problem of Geographic Market Delineation in Antitrust Suits, 18 Antitrust Bull. 45 (1973) (“The 75 per cent figure is obviously somewhat arbitrary. It represents a conservative estimate of the percentage of shipments which encompass the primary demand and supply forces and may understate the true scope of the relevant market. Some might argue that the figure should be, say, 90 per ceent, on the grounds that “overlooking” 25 per cent of the shipments is overlooking too much. The 75 per cent benchmark reflects our view that this satisfactorily encompasses the primary demand-supply forces at reasonable costs of estimation. Those objecting to this benchmark can readily substitute a higher (or lower) one into the estimating procedure.”).

[71] In re Adventist Health System/West, et al., 117 F.T.C. 224, 1994 WL 16010985, ¶ 48 (F.T.C.) (“Under the Elzinga-Hogarty test, a recognizable, but weak, market is attained when both LOFI and LIFO statistics reach 75 percent.  A market is characterized as strong if both LOFI and LIFO reach 90 percent.”); California v. Sutter Health System, 130 F.Supp.2d 1109, 1121 (N.D. Cal., 2001) (“A LIFO and LOFI of 75% is considered a weak indication of the existence of a market and a LIFO and LOFI of 90% is considered a strong indication of a market.”); F.T.C. v. Freeman Hosp., 911 F.Supp. 1213, 1218, 1221 (1995, W.D.Mo.) (In a motion by the FTC for a preliminary injunction restraining the merger of two of the three hospitals in Joplin, Missouri, and where both sides experts applied the Elzinga-Hogarty test as their primary analytical tool, the FTC’s expert using an 80% threshold for relevant market and the hospitals’ expert using a 90% threshold, the court noted that, “While this [the FTC’s] criterion exceeds the 75 percent ‘weak market’ inclusion criterion, it is closer to a ‘weak” than a “strong’ market standard…In considering the competing market definitions, the Court gives significantly greater weight to Dr. Lynk’s [the hospitals’] relevant geographic market.”

[72] H.E. Frech III, James Langenfeld, R. Forrest McCluer, Elzinga-Hogarty Tests and Alternative Approaches for Market Share Calculations in Hospital Markets, 71 Antitrust L.J. 921, 927-928 (2004) (“However, no one has articulated an economic rationale for 90 percent, 75 percent, or any other percentage. Indeed, there are significant problems with relying on this type of “bright-line” interpretation of patient flows. Flows of patients measured by some arbitrary and static level of migration in or out of any area do not necessarily imply that consumers who are not migrating would change their behavior and become migrants in response to a small price increase.  Some migration among hospitals is for reasons other than price sensitivity. Consumers migrate from small towns to larger cities for higher perceived quality or more sophisticated services. They also migrate because they have family, friends, or business relations near the hospitals. This type of migration does not indicate that the distant hospitals constrain each other’s price and quality.”).

[73] Tampa Electric Co. v. Nashville Coal Co., 365 U.S. 320, 327 (1961) (“the area of effective competition in the known line of commerce must be charted by careful selection of the market area in which the seller operates, and to which the purchaser can practicably turn for supplies,”) citing Standard Oil Co. v. United States, 337 U.S. 293 (1949).

[74] Hospital Corp. of America v. F.T.C., 807 F.2d 1381, 1387-8 (1986) (Posner, J.) (“if, in the event that hospital prices in Chattanooga rose above the competitive level, persons desiring hospital services in Chattanooga would switch to hospitals in other cities…this would mean that the Chattanooga hospital market, which is to say the set of hospital-services providers to which consumers in Chattanooga can feasibly turn, includes hospitals in other cities…[but g]oing to another city is out of the question in medical emergencies; and even when an operation or some other hospital service can be deferred, the patient’s doctor will not (at least not for reasons of price) send the patient to another city, where the doctor is unlikely to have hospital privileges.”).

[75] H.E. Frech III, James Langenfeld, R. Forrest McCluer, Elzinga-Hogarty Tests and Alternative Approaches for Market Share Calculations in Hospital Markets, 71 Antitrust L.J. 921, 947 (2004) (“major problems with the “rank, then combine” approach and the use of bright line standards still occur, as evidenced by the defendants’ expert in Tenet being unable find a market based on a 75 or 90 percent LIFO/LOFI for even the entire state of Missouri…Given these results, we suggest that the courts refrain from using a bright line rule of thumb for interpreting E-H results. We believe that arbitrary choices, such as 90 percent LIFO/LOFI tests, are particularly inappropriate. Analyzing patient flows as an approximation of where competition exists makes some sense. However, constructing an up-or-down test of market definition based on pre-ordained percentages of patient flow strikes us as an attempt to create a bright line where none exists. In our case study, using the 90 percent with the “rank then combine” method led to including zip codes from all over the State of California in the relevant market for a merger of two hospitals located a few miles away from each other.”) (the “Tenet case” is FTC v. Tenet Health Care Corp., 186 F.3d 1045 [8th Cir. 1999], the reference to the “defendants’ expert” is to the Expert Report of Barry Harris, cited in footnote 31 of this Frech, Langenfeld and McCluer citation).

[76] Elzinga & Hogarty, The Problem of Geographic Market Delineation in Antitrust Suits, 18 Antitrust Bull. 45, 75-76 (1973) (“The proposed estimating procedure is less useful as the product market is less clearly defined. For example, applying this procedure to commercial banking is unworkable since to speak of the shipments or sales of “commercial banking” (as opposed to “trust services”) is meaningless. This is both a weakness and strength of the estimating procedure.  Its strength is its requirement of parties to Section 7 suits to delineate more accurately the relevant product market. As indicated earlier, this estimating procedure hinges upon a clearly delineated product market.”).

[77] In the Matter of Evanston Northwestern Healthcare Corporation, F.T.C. Docket No. 9315, (Aug. 6, 2007), available at http:// www.ftc.gov/os/adjpro/d9315/070806opinion.pdf.

[78] Kenneth Elzinga, Trial Testimony, In Re Evanston Hospital Corporation, Federal Trade Commission, Docket No. 09315, 2384:20-25 (“The tag names applied to the inapplicability of the Elzinga-Hogarty Test using patient flow data in a hospital merger would be –the first one is a fallacy, it’s the silent majority fallacy. The second one I identified in my expert report, what I call the payor problem…”).

[79] Cory S. Capps, David Dranove, Shane Greenstein, and Mark Satterwaite, The Silent Majority Fallacy of the Elzinda-Hogarty Criteria: A Critique and New Approach To Analyzing Hospital Mergers, NBER Working Paper Series, Working Paper 8216, Nation Bureau of Economic Research, 1050 Massachusetts Avenue, Cambridge, MA 02138, available at http://www.nber.org/papers/w8216, at page 1 (“There is a potential error in relying on consumer outflows to justify mergers between providers of goods and services.  The E/H approach draws a conclusion about the entire market from the behavior of those consumers who express displeasure with their local sellers by travelling elsewhere.  This is a valid logical leap when travelers and non-travelers have similar demands and related market experiences.  However, if the two groups differ on dimensions other than location, the E/H gives rise to what we call the “silent majority fallacy.”  That is, if travelers and non-travelers display fundamentally different demand behavior, either because they differ in their taste for travel or their need for local/non-local services, then there is no necessary relationship between the market experiences of these two groups post-merger.  If travelers differ significantly from non-travelers, then the presence of a minority of travelers does not imply that local firms lack market power vis-à-vis the majority of consumers who are non-travelers.”).

[80] Kenneth Elzinga, Trial Testimony,  In Re Evanston Northwestern Hospital, F.T.C. Docket No. 09315, 2386:2-2387:13 (“in my view, the existence of travelers, patients who travel some distance to consume hospital services, does not discipline or affect the prices of those who do not travel, what I call the silent majority… [the] assumption of the Elzinga-Hogarty Test, using patient flow data, is if the price of hospital services in Charlottesville were to go up, say, because the two hospitals merged, this would induce or push even more patients to flow outside or travel outside the area for hospital services. That actually doesn’t happen in hospital markets or the market for hospital services, and that’s what I call the silent majority fallacy, that these people who don’t travel are not protected by those who do, whereas in the case of the shipments of other goods and services that I’ve looked at, the shipment of beer out of the State of North Carolina will affect the price of beer inside the State of North Carolina”); at tr. 2393:1:13 (“there is this silent majority, and if patient flow data show that a non-trivial number of people travel to a distant hospital, the problem in the Elzinga-Hogarty Test, using patient flow data, is that one might assume from it — assume incorrectly — that the existence of those traveling patients protects and disciplines the prices paid by the silent majority who don’t travel, and these economists, Greg Werden and others and myself, are persuaded that in that regard, the Elzinga-Hogarty Test, using patient flow data, is misleading in trying to establish the contours of a relevant geographic market area”).

[81] Id., at tr. 2387:18-24 (“People who travel outside their home turf for hospital services usually do so for one of two reasons or a combination of the two.  There’s some particular service or amenity that they associate with that distant hospital that’s important to them, or they may have family who lives some distance away and they travel to that hospital.  People who consume travel –hospital services close to home typically are there either because their doctor places them at that hospital or, for purposes of their own convenience or the convenience of their family, it is very important for them to be hospitalized close to home.  So, unlike products like coal and beer that will move about in response to the market signals –prices change and coal gets shipped to a different location; prices change and beer gets shipped to a different location –here, the prices of hospital services do not drive most people to change the location of where they consume hospital services. If they consume hospital services close to home, it’s because their doctor placed them there, their doctor practices at those hospitals, they want to be at a hospital close to home, because they think that’s important for their own care or for the convenience of their family members. If they want to go far away to a hospital, it’s because of something unique in that

situation.  And consequently, the normal things that we associate with market signals through price don’t affect those decisions, and therefore, the ability of particular payor hospitals to raise prices is not disciplined or thwarted by the travel patterns that we observe looking at patient flow data. That’s really where the Elzinga-Hogarty Test goes awry”).

[82] Id., at tr. 2395 (“In the case of hospital services, there’s a disconnect between who pays for the product and who consumes the product, and for that matter, who chooses the product or service, and that disconnect to my mind is – makes the use of patient flow data in hospital merger cases all the more suspect”); Id., at 2389, tr. 16-21 (“The person who consumes the hospital services is not basing that decision based on the different relative prices of those, because most of the consumption of hospital services is not paid for directly by the consumer but by some insurance mechanism or managed care organization”);  Hospital Corp. of America v. F.T.C., 807 F.2d 1381, 1388 (7th Cir., 1986) (Posner, J.) (“the demand for hospital services by patients and their doctors is highly inelastic under competitive conditions. This is not only because people place a high value on their safety and comfort and because many of their treatment decisions are made for them by their doctor, who doesn’t pay their hospital bills; it is also because most hospital bills are paid largely by insurance companies or the federal government rather than by the patient.”).

[83] Emergency Medical Treatment and Stabilization Act, 42 U.S.C. § 1395dd (b)(1) (Westlaw):  (“Necessary stabilizing treatment for emergency medical conditions and labor (1) In general, If any individual (whether or not eligible for benefits under this subchapter) comes to a hospital and the hospital determines that the individual has an emergency medical condition, the hospital must provide either— (A) within the staff and facilities available at the hospital, for such further medical examination and such treatment as may be required to stabilize the medical condition, or (B) for transfer of the individual to another medical facility in accordance with subsection (c) of this section”).

[84] Kenneth Elzinga, Trial Testimony,  In Re Evanston Northwestern Hospital, F.T.C. Docket No. 09315, at 2390:5-21 (“My reading of the literature on this persuades me that the critical variable in selecting a hospital or the critical variables is where your doctor places you or where your doctor is willing to work with you when you’re a patient there, that’s critical. Most people don’t want to change doctors…in response to a price incentive at a particular hospital. Family locations are fundamental. And also, in the case of people who travel, it’s typically not in response to a price incentive but some particular service or amenity at the distant hospital, and that’s why the existence of the patient flow data does not give you very much information in fact, it gives you misleading information about the contours of the relevant geographic market”).

[85] Hospital Corp. of America v. F.T.C., 807 F.2d 1381, 1387-8 (1986) (Posner, J.) (“if, in the event that hospital prices in Chattanooga rose above the competitive level, persons desiring hospital services in Chattanooga would switch to hospitals in other cities…this would mean that the Chattanooga hospital market, which is to say the set of hospital-services providers to which consumers in Chattanooga can feasibly turn, includes hospitals in other cities…[but g]oing to another city is out of the question in medical emergencies; and even when an operation or some other hospital service can be deferred, the patient’s doctor will not (at least not for reasons of price) send the patient to another city, where the doctor is unlikely to have hospital privileges.”).

[86] Hospital Corp. of America v. F.T.C., 807 F.2d 1381, 1387-8 (1986) (Posner, J.) (“if, in the event that hospital prices in Chattanooga rose above the competitive level, persons desiring hospital services in Chattanooga would switch to hospitals in other cities…this would mean that the Chattanooga hospital market, which is to say the set of hospital-services providers to which consumers in Chattanooga can feasibly turn, includes hospitals in other cities…[but g]oing to another city is out of the question in medical emergencies; and even when an operation or some other hospital service can be deferred, the patient’s doctor will not (at least not for reasons of price) send the patient to another city, where the doctor is unlikely to have hospital privileges.”);

F.T.C. v. Freeman Hosp., 69 F.3d 260, 271 (8th Cir., 1995) (“A separate product market may begin to develop, for example, for certain types of tertiary care which, while serious, need not always be treated immediately and may often be scheduled in advance.”).

[87] Public Discharge Data Set, available from the California Office of Statewide Health Planning and Development, Patient Data Section (PDS), Suite 270, 400 R Street, Sacramento, CA 95811-6213

[88] Hospital Corp. of America v. F.T.C., 807 F.2d 1381, 1888 (7th Cir., 1986) (Posner, J.) (“Going to another city is out of the question in medical emergencies…”).

[89] Kenneth Elzinga, Trial Testimony,  In Re Evanston Northwestern Hospital, F.T.C. Docket No. 09315, 2386:2-2387:13 Kenneth Elzinga, Trial Testimony,  In Re Evanston Northwestern Hospital, F.T.C. Docket No. 09315, at 2395:24-2396:10 (“I’ll give you an example from my own family. A couple months ago, my wife had a very serious fall and was hospitalized in two different hospitals, started at one and was moved to a surgical intensive care unit at the University of Virginia Hospital, in the — so, she was a consumer of hospital services, but in neither choice was she the decision-maker as to where she would be the consumer of hospital services, that was made by doctors.  And in addition, my wife and I paid no attention to relative prices, because we’re insured for these services…”).

[90] Hospital Corp. of America v. F.T.C., 807 F.2d 1381, 1888 (7th Cir., 1986) (Posner, J.) (“Going to another city is out of the question in medical emergencies…”).

[91] “Out-Of-County patients are those admitted to hospitals located in counties other than the county of residence of the patient.  “In-County patients were those admitted to hospitals within their county of residence.

[92] Emergency Medical Treatment and Stabilization Act, 42 U.S.C. § 1395dd (Westlaw).

[93] Elzinga & Hogarty, The Problem of Geographic Market Delineation in Antitrust Suits, 18 Antitrust Bull. 45, 75-76 (1973) (“The Legal Restriction Element: If an area, such as a state or city, has unique laws affecting the flow of the product into or out of that area, this is a factor limiting the geographic market to that area. In this case, states have distinctive laws regarding the taxation, labeling, advertising, capping, etc. of beer. This allegedly affected the ability of beer to move into Wisconsin from areas ontside of the state.”).

[94] California v. Sutter Health System, 130 F.Supp.2d 1109 (N.D. Cal., 2001); F.T.C. v. Freeman Hosp., 911 F.Supp. 1213, 1218 (1995, W.D.Mo.).

[95] F.T.C. v. Freeman Hosp., 69 F.3d 260, 271 (8th Cir., 1995) (“A separate product market may begin to develop, for example, for certain types of tertiary care which, while serious, need not always be treated immediately and may often be scheduled in advance.”).

[96] Parikh v. Franklin Medical Center, 940 F.Supp. 395, 402-3 (D.Mass., 1996) (Where an anesthesiologist sued a community hospital to enforce his exclusive provider contract, alleging an expansive geographic market that included tertiary hospitals, the Court found that defendant’s expert, “appropriately limited the product market to those services regularly provided at community hospitals. Complex surgical procedures performed more than 75% of the time at tertiary-care hospitals do not fall into this category. A true picture of the product market requires this minor excision.”).

[97] In re Adventist Health Sys., 117 F.T.C. 224 (1994), at ¶¶ 58, 60 (“Of the 2711 patients who left the Ukiah-Willits-Lakeport area for inpatient care in 1987, 1045 (or 39 percent) had a specific diagnosis or received a specific procedure that complaint counsel allege was not delivered at any hospital in the area (CX- 88). Dr. Melnick conceded that some of these 1045 patients could have been treated at hospitals in the area (Tr. 851); however, some procedures such as neurosurgery, cardiac surgery and organ transplants are not available in the area and would have to be provided by tertiary care hospitals… Adjusting market data by omitting patients with unique needs not provided by hospitals in the market is theoretically sound…”).

[98] In re Adventist Health System/West, et al., 117 F.T.C. 224, 1994 WL 16010985, ¶ 55 (F.T.C.)

(“California regulations require hospitals to file DRG reports with OSHPD that classify each patient according to cause of hospitalization (Tr. 730-32). Each DRG is assigned a weight that indicates the relative resource requirements for treating the typical patient with that particular DRG and Dr. Melnick testified that a higher DRG-weight implies a more complex case (Tr. 731).”).

[99] Kenneth Elzinga, Trial Testimony,  In Re Evanston Northwestern Hospital, F.T.C. Docket No. 09315, 2386:2-2387:13 Kenneth Elzinga, Trial Testimony,  In Re Evanston Northwestern Hospital, F.T.C. Docket No. 09315, at 2395:24-2396:10 (“I’ll give you an example from my own family. A couple months ago, my wife had a very serious fall and was hospitalized in two different hospitals, started at one and was moved to a surgical intensive care unit at the University of Virginia Hospital, in the — so, she was a consumer of hospital services, but in neither choice was she the decision-maker as to where she would be the consumer of hospital services, that was made by doctors.  And in addition, my wife and I paid no attention to relative prices, because we’re insured for these services…”).

[100] See California v. Sutter Health Sys., 84 F. Supp. 2d 1057 (N.D. Cal.), revised, 130 F. Supp. 2d 1109 (N.D. Cal. 2001); In re Adventist Health Sys., 117 F.T.C. 224 (1994). 

[101] 22 Cal. Adm. Code § 70103 (Westlaw). “License Required. (a) No person, firm, partnership, association, corporation, political subdivision of the state or other governmental agency shall establish, operate or maintain a hospital, or hold out, represent, or advertise by any means that it operates a hospital, without first obtaining a license from the Department.”

[102] 22 Cal. Adm. Code §§ 70491-70499 (Westlaw).

[103] 22 Cal. Adm. Code § 70495 (Westlaw). “Intensive Care Service Staff. (a) A physician with training in critical care medicine shall have overall responsibility for the intensive care service. This physician or his designated alternate shall be responsible for: (1) Implementation of established policies and procedures, (2) Development of a system for assuring physician coverage, (3) Final decision regarding admissions to and discharges from the unit, and (4) Assuring there is continuing education for the medical staff and nursing personnel.”

[104] Cal. Bus. & Prof. Code § 2052 (Westlaw).

[105] The American Board of Pediatrics requirements for board certification in General Pediatrics include: 1) graduate of an approved medical school, 2) licensed to practice medicine in the United States or Canada, 3) completion of three years of post-graduate specialty training in pediatrics, 4) take and pass the certification examination.  See the requirements at https://www.abp.org/ABPWebSite/.

[106] Emergency Medical Treatment and Stabilization Act, 42 U.S.C. § 1395dd (b)(1) (Westlaw):  (“Necessary stabilizing treatment for emergency medical conditions and labor (1) In general, If any individual (whether or not eligible for benefits under this subchapter) comes to a hospital and the hospital determines that the individual has an emergency medical condition, the hospital must provide either— (A) within the staff and facilities available at the hospital, for such further medical examination and such treatment as may be required to stabilize the medical condition, or (B) for transfer of the individual to another medical facility in accordance with subsection (c) of this section”).

[107] Emergency Medical Treatment and Stabilization Act, 42 U.S.C. § 1395dd (c) (Westlaw).

[108] Kenneth Elzinga, Trial Testimony,  In Re Evanston Northwestern Hospital, F.T.C. Docket No. 09315, 2386:2-2387:13 Kenneth Elzinga, Trial Testimony,  In Re Evanston Northwestern Hospital, F.T.C. Docket No. 09315, at 2395:24-2396:10 (“I’ll give you an example from my own family. A couple months ago, my wife had a very serious fall and was hospitalized in two different hospitals, started at one and was moved to a surgical intensive care unit at the University of Virginia Hospital, in the — so, she was a consumer of hospital services, but in neither choice was she the decision-maker as to where she would be the consumer of hospital services, that was made by doctors.  And in addition, my wife and I paid no attention to relative prices, because we’re insured for these services…”).

[109] Id., at page 7, (“Among other things, the 1982 Merger Guidelines advanced a new approach to market definition…The Guidelines approach to market definition focused on a central enforcement related question (would a merger result in a price increase?) through the use of the hypothetical monopolist concept.”).

[110] The seven cases were: California v. Sutter Health Sys., 84 F. Supp. 2d 1057 (N.D. Cal.), revised, 130 F. Supp. 2d 1109 (N.D. Cal. 2001); FTC v. Tenet Healthcare Corp., 186 F.3d 1045 (8th Cir. 1999); United States v. Long Island Jewish Med. Ctr., 983 F. Supp. 121 (E.D.N.Y. 1997); FTC v. Butterworth Health Corp., 946 F. Supp. 1285, 1300-1301 (W.D. Mich. 1996); United States v. Mercy Health Services, 902 F. Supp. 968 (N.D. Iowa 1995); FTC v. Freeman Hosp., 911 F. Supp. 1213 (W.D. Mo.); In re Adventist Health Sys., 117 F.T.C. 224 (1994). 

[111] Improving Health Care: A Dose of Competition, A Report by the Federal Trade Commission and the Department of Justice, July 2004, at Chapter 4, page 10 (“Problems with its [critical loss analysis] application have led some commentators to question the value of critical loss analysis as an antitrust tool”) (citations omitted).

[112] David Scheffman, Malcolm Coate, and Louis Silvia, 20 Years of Merger Guidelines Enforcement at the FTC: An Economic Perspective, http://www.justice.gov/atr/hmerger/12881.htm, at pages 19 and 25 (the authors, all economists with the FTC, opined that, “If petroleum markets illustrate the easy applicability of Guidelines analysis to market definition, hospitals markets lie at the other end of the spectrum. Geographic and sometimes product market analyses are complicated by complex transactions between hospitals and third party payors and heterogeneity of hospitals. Transactions prices are determined in a relatively small number of bilaterally negotiated contracts between individual hospitals and managed care payors, thus the kinds of quantitative data necessary for estimation of relevant demand curves are generally not readily available… An important lesson from Tenet is that the Guidelines approach to market definition can sometimes impose heavy burdens on the government, especially in cases like hospitals where pre-merger price cost margins are relatively high, resulting in critical loss estimates being small. Even the opinions of sophisticated buyers, if unsupported by quantitative analyses, may not be enough to be persuasive.”).

[113] Among these were: California v. Sutter Health Sys., 84 F. Supp. 2d 1057 (N.D. Cal.), revised, 130 F. Supp. 2d 1109 (N.D. Cal. 2001); FTC v. Tenet Healthcare Corp., 186 F.3d 1045 (8th Cir. 1999); United States v. Long Island Jewish Med. Ctr., 983 F. Supp. 121 (E.D.N.Y. 1997); United States v. Mercy Health Services, 902 F. Supp. 968 (N.D. Iowa 1995); FTC v. Freeman Hosp., 911 F. Supp. 1213 (W.D. Mo.); In re Adventist Health Sys., 117 F.T.C. 224 (1994). 

[114] In re Adventist Health Sys., 117 F.T.C. 224 (1994), at ¶ 17 (“The acquisition of UGH by AHS/West and Ukiah Adventist increased the market share of AHS/West, the largest provider of acute care hospital services in the southeastern Mendocino/western Lake County area from approximately 38% to approximately 71%, and increased the two-firm concentration ratio from approximately 71% to approximately 94%. As a result of the acquisition, the Herfindahl-Hirschmann Index increased by over 2500 points, from approximately 3100 points to approximately 5600 points. In the southeastern Mendocino area, the acquisition of UGH by AHS/West and Ukiah Adventist increased the market share of AHS/West from approximately 49% to approximately 92%, and increased the two-firm concentration ratio from approximately 92% to 100%. As a result of the acquisition, the Herfindahl-Hirschmann Index increased by over 4200 points, from approximately 4340 points to approximately 8580 points”).

[115] Id., at 238.

[116] Id., at 251.

[117] Id., at 250 (“Under the Elzinga-Hogarty test, a recognizable, but weak, market is attained when both LOFI and LIFO statistics reach 75 percent. A market is characterized as strong if both LOFI and LIFO reach 90 percent…”).

[118] Id., at 252 (“By Dr. Melnick’s calculation, the residents of the Ukiah-Willits-Lakeport area who were hospitalized outside that area in 1987 were classified in DRGs that have a 38 percent greater severity weight than those residents seeking hospital services at facilities within the geographic market, and this suggested to him that patients leaving the area are different from those staying in the area since the intensity of services delivered to them by hospitals outside the area is much higher”).

[119] Id., at 253 (“Dr. Lynk’s criticism of Dr. Melnick’s DRG weight and ‘unique diagnosis and procedure’ analyses is convincing, and I conclude that they do not accurately measure the competitively insignificant outmigration from the Ukiah-Willits-Lakeport area.”).

[120] Wikipedia, at http://en.wikipedia.org/wiki/Diagnosis-related_group, (“Diagnosis-related group (DRG) is a system to classify hospital cases into one of approximately 500 groups, also referred to as DRGs, expected to have similar hospital resource use, developed for Medicare as part of the prospective payment system.”).

[121] Id., at 252.

[122] Id., at 253 (“Dr. Lynk criticized Dr. Melnick’s unique diagnosis and DRG weight analyses for patients seeking treatment inside and outside his proposed relevant geographic market, arguing that Dr. Melnick’s claim that average DRG weight differed between the two groups of patients ignored the significant DRG overlap between groups…The flaw in Dr. Melnick’s diagnosis and procedure analysis is that…if a patient left the area, went down to Santa Rosa for treatment of a malignant neoplasm in his upper left, and his next-door neighbor stayed put and was treated for a malignant neoplasm of the lower left at Ukiah Valley, the Melnick Uniqueness Analysis would say there’s no competitive overlap here. …”).

[123] Id., at 256 (“I reject complaint counsel’s adjustment to the LIFO figure for the Ukiah-Willits area because it is based on planning documents whose accuracy is unverifiable and because it was not calculated or referred to by Dr. Melnick in his testimony with respect to the geographic market and competitive injury. Since I have also rejected Dr. Melnick’s adjustment to the Ukiah-Willits-Lakeport LIFO figure, I conclude that both Ukiah-Willits and Ukiah-Willits-Lakeport are such weak geographic markets that they do not describe the competitive interaction between hospitals in the Ukiah-Santa Rosa area”).

[124] Id., at 257. 

[125] Id., at 262 (“Taking into consideration Dr. Melnick’s Elzinga-Hogarty analysis and other record evidence relating to patient outmigration, e.g. (F 63), I conclude that the relevant geographic market is Ukiah-Willits-Lakeport-Santa Rosa”).

[126] Wikipedia, at http://en.wikipedia.org/wiki/Major_Diagnostic_Category, (“The Major Diagnostic Categories (MDC) are formed by dividing all possible principal diagnoses (from ICD-9-CM) into 25 mutually exclusive diagnosis areas.  The diagnoses in each MDC correspond to a single organ system or etiology and, in general, are associated with a particular medical specialty. …MDC codes, like DRG codes, are primarily a claims and administrative data element unique to the United States medical care reimbursement system.”).

[127] Wikipedia, at http://en.wikipedia.org/wiki/Diagnosis-related_group, (“Diagnosis-related group (DRG) is a system to classify hospital cases into one of approximately 500 groups, also referred to as DRGs, expected to have similar hospital resource use, developed for Medicare as part of the prospective payment system.  DRGs are assigned by a “grouper” program based on ICD diagnoses, procedures, age, sex, and the presence of complications or comorbidities.  DRGs have been used since 1983 to determine how much Medicare pays the hospital, since patients within each category are similar clinically and are expected to use the same level of hospital resources.”).

[128]  International Classification of Diseases, 9th Revision, Clinical Modification (ICD-9-CM), available on CD-ROM from the U.S. Government Printing Office and others.

[129]  Kaiser patients may be compelled to travel great distances to use only Kaiser facilities.  See Adventist, at 261 (“Although they were disgruntled, Dr. Falk treated several patients from Lake and Mendocino Counties at the Kaiser facility in Santa Rosa who were required by Kaiser to be treated there…”).

[130] Children’s hospitals specialize in that product submarket, as seen in the statement of the industry trade group, NACHRI at http://www.childrenshospitals.net/AM/Template.cfm?Section=About_Us1&Template=/CM/HTMLDisplay.cfm&ContentID=34959 (“The National Association of Children’s Hospitals and Related Institutions is an organization of children’s hospitals with 218 members in the United States, Canada, Australia, the United Kingdom, Italy, China, Mexico and Puerto Rico.  NACHRI promotes the health and well-being of all children and their families through support of children’s hospitals and health systems that are committed to excellence in providing health care to children.  NACHRI works to ensure all children’s access to health care and children’s hospitals’ continuing ability to provide services needed by children. Children’s hospitals work to ensure the health of all children through clinical care, research, training and advocacy.”).

[131] David Scheffman, Malcolm Coate, and Louis Silvia, 20 Years of Merger Guidelines Enforcement at the FTC: An Economic Perspective, supra, at note 93.

[132] Elzinga & Hogarty, The Problem of Geographic Market Delineation in Antitrust Suits, 18 Antitrust Bull. 45, 75-76 (1973) (“The proposed estimating procedure is less useful as the product market is less clearly defined. For example, applying this procedure to commercial banking is unworkable since to speak of the shipments or sales of “commercial banking” (as opposed to “trust services”) is meaningless. This is both a weakness and a strength of the estimating procedure.  Its strength is its requirement of parties to Section 7 suits to delineate more accurately the relevant product market. As indicated earlier, this estimating procedure hinges upon a clearly delineated product market.”).

[133] California v. Sutter Health Sys., 130 F. Supp. 2d 1109 (N.D. Cal. 2001).

[134] Id., at 1120 (“Although the Merger Guidelines are not binding, courts have often adopted the standards set forth in the Merger Guidelines in analyzing antitrust issues. See Tenet, 186 F.3d at 1053 (referencing Merger Guidelines analysis in evaluating geographic market); United States v. Mercy Health Services, 902 F.Supp. 968, 980 (N.D.Iowa 1995) (utilizing Merger Guideline SSNIP analysis in determining geographic market)”).

[135] Id., (“The analytical process often begins with an application of the Elzinga-Hogarty test (“E-H test”), a two-part test which examines current market behavior through an analysis of hospital service areas and patient flow data.”).

[136] Id., at 1121 (“Dr. Langenfeld determined that the 85% draw area of Summit and Alta Bates encompassed his proposed Inner East Bay geographic market…”).

[137] Id., at 1121 (“Defendants’ expert, Margaret Guerin-Calvert, derived the hospitals’ service area from the same data, but chose a 90% threshold level of significance for inclusion.”).

[138] Id., at 1124, (“Elzinga and Hogarty were of the view that results of 75% constitute at least weak evidence of the existence of a market, they subsequently revised their evaluation and determined that a 90% result, rather than a range, more appropriately indicates the existence of a geographic market. See Elzinga & Hogarty, The Problems of Geographic Market Delineation Revisited: the Case of Coal, 23 Antitrust Bulletin 1 (1978)”).

[139] Id., at 1123.

[140] Id., at 1119 (“Although Kaiser hospitals may not directly provide services to non-member patients, plaintiff concedes for purposes of this case that they do provide viable substitutes for services offered at other hospitals in the region; if faced with an anticompetitive price increase, patients may choose to join the Kaiser network for acute inpatient services.”).

[141] Id., at 1120, (“E-H test results reflect only current market behavior, and are insufficient to determine where patients could practically turn for acute inpatient services if faced with a future anticompetitive price increase. See Mercy Health, 902 F.Supp. at 978 (“[the E-H test] does not pretend to answer the question of what would happen if there was an attempt to exercise market power by one of the market participants.”).

[142] Id., at 1124.

[143] Id., at 1123, (“the Court finds that the proper scope of the merging parties’ hospital service area is more accurately reflected by the methodology employed by defendant’s expert. This area encompasses the Inner East Bay and extends east into Contra Costa County to include those zip codes in which Valley Memorial, Sutter Delta, and Kaiser-Walnut Creek are located.”).

[144] Id., at 1125-26 (“Although both experts determined that neither Alta Bates nor Summit drew a significant number of patients from San Francisco, the question in this case is whether hospitals in San Francisco serve as practical alternatives for patients who reside in the Inner East Bay. The fact that the combined service area of San Francisco hospitals overlaps completely with the combined service area of Summit and Alta Bates indicates that patients located within the merging parties’ service area could practically turn to San Francisco hospitals for acute inpatient services in the event of an anticompetitive price increase.”).

[145] Public Discharge Data Set, available from the California Office of Statewide Health Planning and Development, Patient Data Section (PDS), Suite 270, 400 R Street, Sacramento, CA 95811-6213

[146] “Urgent Admissions” are those not scheduled at least twenty four hours prior to admission but not admitted through the emergency department.

[147] “Out-of County Patients” refers to those patients admitted to a California non-federal hospital located in a county other than the county in which that patient resides.  “In-County Patients” refers to those patients admitted to a hospital located in the same county in which that patient resides.