Some important markets feature intermediaries who offer a network of upstream suppliers to downstream consumers. Examples include general contractors, who assemble networks of skilled craftsmen and subcontractors, business-to-business web sites, which assemble networks of parts suppliers, and managed care organizations, which assemble networks of hospitals and physicians. These intermediaries take advantage of their expertise and purchasing economies to identify superior suppliers and to extract better terms than could consumers shopping on their own. In some cases, such as managed care, they also provide insurance against the risk of needing the network’s services.
Sometimes, consumers may know their specific needs at the time they select their intermediary. For example, homeowners may have detailed architectural plans at the time they select their building contractors. In other situations, consumers may select their intermediary prior to knowing their specific needs. Insurance markets are an important example. Automobile owners often commit to a network of auto repair shops at the time they purchase collision insurance, even though they do not know, in advance, what kinds of repairs their car might require. Similarly, patients commit to a network of medical providers at the time they purchase their health insurance, but before they know their specific medical needs. Non-insurance examples include manufacturers who sign long-term contracts with suppliers, which in turn outsource specific manufacturing tasks as the need arises. Following Dranove and White (1996), we call these option demand markets (or OD markets). In OD markets, consumers commit to a potentially restricted network of sellers prior to knowing their needs fully, but retain the option to visit any seller in the network once their needs are known. The value that any one consumer places on a given network depends on his expectation of how well the network’s members will be able to meet his needs. This contrasts with direct purchase markets in which consumers do not eliminate any potential sellers prior to learning their needs.
The paper’s goal is to develop and validate an index of the market power of suppliers in OD markets. We consider a competitive intermediary assembling a network of suppliers on behalf of many consumers. We assume that consumers’ preferences follow the logit model of demand. We also suppose the intermediary knows the underlying logit utility function, the distribution of consumer characteristics, and the distribution of the possible states of the world that affect demand, but not the upcoming demand realizations. A straightforward calculation based on the properties of logit demand gives an estimate, for each supplier, of how much consumers in aggregate are willing to pay ex ante to retain it in the network. A simple bargaining model between the seller and the intermediary suggests that a portion of this willingness-to-pay (WTP) is captured by the seller. The WTP associated with a supplier is therefore a measure of its market power: a supplier for which WTP is high secures higher prices from the intermediary than does a supplier for which WTP is low.
After deriving the formula for WTP we validate the measure by using hospitals in the San Diego, CA metropolitan area. Following our discussion, managed care organizations (MCOs) in this market are the intermediaries and hospitals are the suppliers. Each MCO negotiates bilaterally with each hospital for inclusion in its network. Its goal is to come to agreement on rates with a set of hospitals such that the resulting network maximizes the difference between consumers’ ex ante WTP for that network and its expected payments to the hospitals that provide the services. To the extent that the MCO succeeds in doing this it can offer employers a competitive price on a health plan that employees value highly. Once the network is formed, consumers realize their health state and, for those who need hospitalization, select the hospital they most prefer from among those included in their network.
Using 1991 data on inpatient hospital services in San Diego, we estimate a multinomial hospital choice model for patients who have a free choice of hospital. This provides estimates of the parameters of patient logit demand functions. Based on these parameter values and the empirical distributions of patient characteristics and health states we compute, for each hospital, the consumers’ aggregate, ex ante WTP to retain it in the network. These WTP measures are denominated in “utils” because in estimating the logit function we intentionally select consumers who did not face varying prices across hospitals. To convert utils to dollars, we regress each hospital’s actual profits from daily hospital services provided to managed care patients (or, in some specifications, all privately insured patients) onto our estimates of consumers’ ex ante WTP. The results of this regression are consistent with the validity of the WTP measure: WTP is a highly significant predictor of hospital profits. However, this finding is subject to a number of caveats concerning limitations in our data. Further testing with more comprehensive data is needed.
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Competition and Market Power in Option Demand Markets
