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Reducing pharmaceutical reimbursement price risk to lower national health expenditures without lowering R&D incentives

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In Japan, higher reimbursement drug prices give pharmaceutical firms stronger R&D incentives, but they also increase the financial burden on national health insurance and patients. Considering the severe financial situation that the government faces, analyzing how to achieve lower national health expenditures without lowering pharmaceutical firms’ existing R&D incentives is important. In this research, we investigate the effect of reducing the reimbursement price risk that pharmaceutical firms face on their R&D incentives. Theoretically, the presence of output price risks reduces risk-averse firms’ R&D incentives. Therefore, to the extent that pharmaceutical firms exhibit risk aversion, if creating guidelines, accelerating information disclosure and/or enabling public–private dialogue can reduce reimbursement price risks, then maintaining or even increasing R&D incentives is possible, even if the level of reimbursement drug prices is reduced. Specifically, we address (1) by how much a given level of reimbursement price risk reduces pharmaceutical firms’ R&D incentives; (2) by how much reimbursement drug prices can be reduced, keeping pharmaceutical firms’ R&D incentives constant, if one can successfully remove the risk; and (3) how the magnitude of the impact changes with the degree of price risk that firms face and with the level of their risk aversion. To this end, a hypothetical new branded drug is constructed from actual data on the Japanese drug market. Assuming that a pharmaceutical firm is an expected-utility maximizer, that the firm’s instantaneous utility function is in the form of the constant-relative-risk-aversion utility function and that its R&D incentives are quantified by the standard discounted cash flow valuation, we use simulations to compute the certainty equivalent and risk premium associated with various degrees of price risk and risk aversion. Referring to the empirical literature on risk preference, we set the parameter value for the level of relative risk aversion of a pharmaceutical firm to 3.0 and that for the discount rate to 0.08. The following results emerged. (1) In the presence of a 20% price risk regarding a reimbursement price of 100 (i.e., ranging from 80 to 120), a pharmaceutical firm’s certainty equivalent is 96.0. Hence, in the presence of a 20% price risk, a risky reimbursement drug price of 100 is equivalent to a sure reimbursement drug price of 96.0. (2) In the presence of a 20% price risk regarding a reimbursement price of 100, the price premium is 4.0. Therefore, by increasing the predictability of future prices, the reimbursement price may decrease by 4.0, while the firm’s R&D incentives remain unchanged. (3) The magnitude of the impact increases at an increasing rate with the degree of price risk and increases at a decreasing rate with the level of risk aversion.

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  1. An advantage of the RRA measure is that it is a unit-free measure. By rearranging the definition above, an elastic form is derived as follows: \( \theta_{R} = \frac{\partial U'/U'}{\partial x/x}. \)

  2. Concerning the level of RRA, more positive values are associated with greater degrees of risk aversion. whereas the zero and more negative values are related to risk neutrality and greater degree of risk loving, respectively.

  3. The formula for price revision considered here is the one used in 2012. The discrepancy rate from the market price is set to 0.06 before generic entries, to 0.1 at the time of generic entries, and to 0.18 after generic entries. The value for the weighted-average market discrepancy rate is 0.08. In this setting, the price remains at the initial level before generic entries and decreases at two-year intervals as depicted in Fig. 2. A similar price pattern is considered in Wakutsu and Nakamura [22]. Although the current formula is somewhat different from the one in 2012, the analysis presented in this paper is still applicable.

  4. A bell-shaped sales curve is ensured if \( x < y \) holds. While the height of peak drug sales depends on the values of \( x \) and \( a \), its timing is determined by the value of \( y \). Here the values are determined by an ordinary-least-square regression and set to \( x = 0.006 \), \( y = 0.32 \), and \( a = 12,500 \) as in Wakutsu and Nakamura [22].

  5. In the case of \( \theta = 1 \), we consider \( u ( {S_{t} } ) = \ln ( {S_{t} } ) \). See Wakker [21].

  6. We interviewed 17 research-oriented pharmaceutical firms in Japan between late March and early July in 2017 and collected data on the discount rate they used to evaluate an R&D project in early stages of clinical drug development such as proof-of-concept. We found that the median is 0.08 and the mean is 0.071. We tried \( r = 0.07 \) but the results were similar.


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This work was partially supported by the grant from the Health Care Science Institute and JSPS Grant-in-Aid for Scientific Research (C) (Grant number: 16K03700).

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Correspondence to Naohiko Wakutsu.

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Nakamura, H., Wakutsu, N. Reducing pharmaceutical reimbursement price risk to lower national health expenditures without lowering R&D incentives. IJEPS 13, 75–88 (2019).

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