Why the Linear Utility Function is a Risky Choice in Discrete-Choice Experiments

This article assesses how the form of the utility function in discrete-choice experiments (DCEs) affects estimates of willingness-to-pay (WTP). The utility function is usually assumed to be linear in its attributes. Non-linearities, in the guise of interactions and higher-order terms, are applied only rather ad hoc. This paper sheds some light on this issue by showing that the linear utility function can be a risky choice in DCEs. For this purpose, a DCE conducted in Switzerland to assess preferences for statutory social health insurance is estimated in two ways: first, using a linear utility function; and second, using a non-linear utility function specified according to model specification rules from the econometrics and statistics literature. The results show that not only does the non-linear function outperform the linear specification with regard to goodness-of-fit, but it also generates significantly different WTP. Hence, the functional form of the utility function may have significant impact on estimated WTP. In order to produce unbiased estimates of preferences and to make adequate decisions based on DCEs, the form of the utility function should become more prominent in future experiments.Discrete-Choice Experiment, Preference Measurement, Health Insurance, Model Specification

A Pharmaceutical Innovation – Is it Worth the Money? Whose Money?

This study seeks to provide evidence for deciding whether or not a pharmaceutical innovation should be included in the benefit list of social health insurance. A discrete choice experiment (DCE) was conducted in Germany to measure preferences for modern insulin therapy. Of the 1,100 individuals interviewed in 2007, 200 suffered from type 1 diabetes, 150 from insulin-treated type 2 diabetes, and 150 from insulin-naive type 2 diabetes. The long-acting insulin analogue ”Insulin Detemir” is compared to human insulin as the status quo. The DCE contains two price attributes, copayment and increased contributions to health insurance. As one would expect, non-affected non-diabetics and insulin-naive diabetics exhibit higher willingness-to-pay (WTP) values through copayment (adjusted for probability of contracting diabetes), while affected type 1 and insulin-treated type 2 diabetics have higher WTP through increased contributions. However, WTP values exceed the extra treatment cost in both financing alternatives, justifying inclusion of the innovation in the benefit list from a cost-benefit point of view.Health insurance, discrete-choice experiment, preferences, diabetes

Fine Tuning of Health Insurance Regulation: Unhealthy Consequences for an Individual Insurer

This paper sheds light on some unexpected consequences of health insurance regulation that may pose a big challenge to insurers’ risk management. Because mandated uniform contributions to health insurance trigger risk selection efforts risk adjustment (RA) schemes become necessary. A good deal of research into the optimal RA formula has been performed (Ellis and Van de Ven [2000]). A recent proposal has been to add ”Hospitalization exceeding three days during the previous year” as an indicator of high risk (Beck et al. [2006]). Applying the new formula to an individual Swiss health insurer, its payments into the RA scheme are postdicted to explode, reaching up to 13 percent of premium income. Its mistake had been to successfully implement Managed Care, resulting in low rates of hospitalization. The predicted risk management response is to extend hospital stays beyond three days, contrary to stated policy objectives also of the United States.Health insurance, regulation, risk adjustment, risk management

Why the Linear Utility Function is a Risky Choice in Discrete-Choice Experiments

This article assesses how the form of the utility function in discrete-choice experiments (DCEs) affects estimates of willingness-to-pay (WTP). The utility function is usually assumed to be linear in its attributes. Non-linearities, in the guise of interactions and higher-order terms, are applied only rather ad hoc. This paper sheds some light on this issue by showing that the linear utility function can be a risky choice in DCEs. For this purpose, a DCE conducted in Switzerland to assess preferences for statutory social health insurance is estimated in two ways: first, using a linear utility function; and second, using a non-linear utility function specified according to model specification rules from the econometrics and statistics literature. The results show that not only does the non-linear function outperform the linear specification with regard to goodness-of-fit, but it also generates significantly different WTP. Hence, the functional form of the utility function may have significant impact on estimated WTP. In order to produce unbiased estimates of preferences and to make adequate decisions based on DCEs, the form of the utility function should become more prominent in future experiments

A Pharmaceutical Innovation - Is it Worth the Money? Whose Money?

This study seeks to provide evidence for deciding whether or not a pharmaceutical innovation should be included in the benefit list of social health insurance. A discrete choice experiment (DCE) was conducted in Germany to measure preferences for modern insulin therapy. Of the 1,100 individuals interviewed in 2007, 200 suffered from type 1 diabetes, 150 from insulin-treated type 2 diabetes, and 150 from insulin-naive type 2 diabetes. The long-acting insulin analogue ”Insulin Detemir” is compared to human insulin as the status quo. The DCE contains two price attributes, copayment and increased contributions to health insurance. As one would expect, non-affected non-diabetics and insulin-naive diabetics exhibit higher willingness-to-pay (WTP) values through copayment (adjusted for probability of contracting diabetes), while affected type 1 and insulin-treated type 2 diabetics have higher WTP through increased contributions. However, WTP values exceed the extra treatment cost in both financing alternatives, justifying inclusion of the innovation in the benefit list from a cost-benefit point of view

Capping risk adjustment?

When premiums are community-rated, risk adjustment (RA) serves to mitigate competitive insurers’ incentive to select favorable risks. However, unless fully prospective, it also undermines their incentives for efficiency. By capping its volume, one may try to counteract this tendency, exposing insurers to some financial risk. This in term runs counter the quest to refine the RA formula, which would increase RA volume. Specifically, the adjuster, “Hospitalization or living in a nursing home during the previous year” will be added in Switzerland starting 2012. This paper investigates how to minimize the opportunity cost of capping RA in terms of increased incentives for risk selection