approaches by Harrison and Rutström (2008), Andersen et al. (2010), and Tanaka et al. We assume that the health-related risk preferences can be represented by a (2010). constant relative risk aversion (CRRA) utility function. The utility function of a subject in terms of healthcare payoffs x, is thus represented by (1) where is the coefficient of constant relative risk aversion in the healthcare context. s Depending on the value of a subject shows different degrees of risk aversion in the s healthcare domain that can be grouped in three main types: if risk neutral s=0 if risk averse s>0 if risk seeking s<0 Maximum Likelihood (ML) methods were used to empirically estimate risk preferences (Harrison and Rutström (2008) and Andersen et al. (2010)). From equation (1) is the utility that a subject perceives from getting a healthcare benefit Under Expected U(x) x. Utility Theory, the expected utility by a subject of a given lottery is the utility of each j=A,B outcome in that lottery, weighted by the probability of the outcome: k=1,2 pk (xkj) EUj = pkj * U (2) ∑k=1,2 with and The expected utility depends on the subject’s risk aversion parameter j=A,B k=1,2. Based on a candidate value of a latent preference index can be constructed. Our s. s Δ(EU) empirical model allows subjects in the outpatient clinics to make stochastic errors when comparing expected utilities. We include in our estimation a parameter to capture the μ stochastic error, so that the latent index is: (3) When the stochastic errors become negligible and the empirical specification reduces to μ→0 a deterministic EUT choice, where the subject always chooses the lottery with higher
approaches by Harrison and Rutström (2008), Andersen et al. (2010), and Tanaka et al. (2010). We assume that the health-related risk preferences can be represented by a constant relative risk aversion (CRRA) utility function. The utility function of a subject in terms of healthcare payoffs x, is thus represented by (1) where s is the coefficient of constant relative risk aversion in the healthcare context. Depending on the value of s a subject shows different degrees of risk aversion in the healthcare domain that can be grouped in three main types: if s=0 risk neutral if s>0 risk averse if s<0 risk seeking Maximum Likelihood (ML) methods were used to empirically estimate risk preferences (Harrison and Rutström (2008) and Andersen et al. (2010)). From equation (1) U(x) is the utility that a subject perceives from getting a healthcare benefit x. Under Expected Utility Theory, the expected utility by a subject of a given lottery j=A,B is the utility of each outcome k=1,2 in that lottery, weighted by the probability pk of the outcome: EUj = ∑k=1,2 pkj * U (xkj) (2) with j=A,B and k=1,2. The expected utility depends on the subject’s risk aversion parameter s. Based on a candidate value of s a latent preference index Δ(EU) can be constructed. Our empirical model allows subjects in the outpatient clinics to make stochastic errors when comparing expected utilities. We include in our estimation a parameter μ to capture the stochastic error, so that the latent index is: (3) When μ→0 the stochastic errors become negligible and the empirical specification reduces to a deterministic EUT choice, where the subject always chooses the lottery with higher
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