These drug sales are high volume, accounting for approximately 18.5 percent of the U.S. prescription drug market in 2002. Before 1990, Medicaid received no special rebates, but thanks to federal legislation passed that year, it is entitled to discounted prices in the form of rebates on each item.
These rebates are tied to the prices pharmaceutical companies charge non-Medicaid buyers of the same drug, a link that gives firms a profit incentive to adjust their prices compared to what they would choose without the legislation. Those adjustments not only affect company revenue but also impact the overall cost and quantity of Medicaid drugs for the government. They also affect drug costs and quantities for non-Medicaid buyers.
Calculating how companies should respond to the rebates and determining the impact of their responses on drug costs and demand have proved to be difficult problems. Now, however, Peter Klibanoff, an associate professor of managerial economics and decision sciences at the Kellogg School of Management, and Tapas Kundu, a research associate at the University of Oslo, have developed a model that analyzes the pricing strategies and allows policymakers to calculate the overall social costs of the two different approaches that the legislation mandated be used to calculate the rebates. “We are the first to fully work out what a pharma’s optimal response to these rebate policies would be,” Klibanoff says. “And to my knowledge we’re the first to ask if this would be good or bad for social welfare.”
The legislation requires two calculations of the rebate amount for each transaction: a minimum price rule and an average price rule. Whichever amount is higher, the government receives as its discount. Under the minimum price rule, the rebate is calculated as the difference between the minimum price for which a manufacturer sells a drug and the drug’s average price. Under the average price rule, the rebate is simply a fraction of the average price of any particular drug. Klibanoff and Kundu set out to determine how pharmaceutical manufacturers would guarantee their own best interests in dealing with each of these rebate rules and see what could be said about the social benefits of the policy.
Modeling the Problem
The pair used standard economic theory to build the model of manufacturers’ likely responses to the pricing process, and took a strictly analytical approach to the issue. “We have looked at previous researchers’ analyses, but we didn’t go back to the data,” Klibanoff says. “Our main contribution was modeling the problem in the way we did and fully solving it out.”
There is often a range of prices charged for a single drug, varying according to the organizations—such as HMOs, hospitals, or retail pharmacies—that buy the medications. Klibanoff and Kundu’s model shows how—in order to maximize their profits—drug makers would adjust their prices in response to the two ways of calculating rebates.
Under the minimum price rule, Klibanoff says, manufacturers would definitely want to increase the lowest prices they charged for a given drug before the government imposed the rule for its Medicaid patients. But for channels where that drug already commanded higher prices, pharmas would react to the minimum price rule by leveling off those prices or even decreasing them.
By contrast, the model shows that pharmas’ optimal response in the case of the average price rule would be to shift all the prices in the same direction—up or down. “If the demand from consumers covered by Medicaid is sufficiently insensitive to price, all prices would decrease,” Klibanoff explains. “But if Medicaid consumers are just as price-sensitive as consumers not covered by Medicaid, all prices would increase.”
By incorporating how drug makers and purchasers optimally respond to pricing regulations, the model provides guidance to government regulators on how their pricing policies will affect social welfare by taking into account both the well-being of consumers and the profits of the drug manufacturers.
“We’re saying: ‘Here are conditions under which society as a whole would benefit from these rebate rules and here are circumstances under which it would not’,” Klibanoff explains.
“The analysis of these policies is surprisingly intricate, even in a relatively simple setting such as ours,” Klibanoff and Kundu point out in their paper. “This suggests that great care is needed when implementing [most-favored-customer] rules and that making provisions for data collection to support follow-up empirical work measuring the pricing and demand response has high potential value in avoiding mistakes or helping fine-tune the policy.”
The paper also emphasizes the model’s value beyond the specific case of Medicaid. “Understanding the effects of these regulations is not simply of interest for evaluation of Medicaid policy, but is also important as a guide to future regulation,” the pair writes. “For example, recently there has been debate about the appropriate regulatory regime to govern drug purchases and reimbursement under Medicare.”
Klibanoff adds a specific recommendation. “Hopefully, for future policies of this kind and future decisions related to reimbursement, the model points out the need to build a monitoring system to see if the policy is creating good or harm,” he says. “The model can help focus those efforts.”
“It should encourage policymakers to think of strategic effects rather than just the direct effects that will arise from policies,” Klibanoff says. “Our paper shows that there may be a very sophisticated response to pricing policy on the part of the manufacturers and that, as a result, such a policy might be a good or bad idea. The devil’s in the details, and we provide some of the details you want to look at.”
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