time preferences for healthcare outcomes. No secondary data, however, currently exist that directly elicit health-related risk and time preferences for patients and doctors (Bradford, 2010). In this article we attempt to fill this gap by explicitly investigating whether patients and matched doctors in natural clinical settings have similar risk and time preferences for healthcare outcomes. As a robustness check, we also measure risk and time preferences in a closely comparable financial context. To the best of our knowledge, ours is the first attempt to systematically look at differences and similarities of risk and time preferences across doctors and patients in a real healthcare setting. We conduct a ‘framed experiment’ based on Harrison and List (2004) (an experiment according to Charness et al., (2013b)). Field experiments are increasingly employed in exploring preferences (Andersen et al., 2014, 2008a, 2008b; Charness et al., 2013a; Harrison et al., 2007; Sutter et al., 2011), and in comparing them across different groups of subjects (Croson and Gneezy, 2009; Harrison et al., 2009; Masclet et al., 2009). In our field experiment we measure patients’ and doctors’ risk and time preferences by adapting the multiple price list (MPL) tests proposed by Holt and Laury (2002) and Tanaka et al., (2010), respectively, to the healthcare context (Galizzi et al., 2016). In order to address any issue that can potentially arise from framing and domain-specificity in preference elicitation, we also measure patients’ and doctors’ risk and time preferences using the same MPL tests but in a closely comparable financial context. We have three main results. First, there is a significant difference in time preferences between patients and their matched doctors, with doctors discounting future health gains and financial outcomes less heavily than patients. Second, we find no systematic difference in risk preferences in the healthcare domain between patients and doctors: in our sample both patients and their matched doctors are mildly, but significantly, risk averse. Third, doctors
5 50% 200 50% 160 50% 385 50% 10 A B 6 60% 200 40% 160 60% 385 40% 10 A B 7 70% 200 30% 160 70% 385 30% 10 A B 8 80% 200 20% 160 80% 385 20% 10 A B 9 90% 200 10% 160 90% 385 10% 10 A B The MPL was adapted by presenting the lotteries as different healthcare treatments with payoffs defined as days of full health (Table 1). A risk-neutral individual should switch from the ‘safe’ option (treatment A) to the ‘risky’ option (treatment B) only when the expected utility is greater in treatment B than in A. An individual who is risk neutral chooses treatment A in rows 1-4, before switching to B in row 5. A risk averse individual switches to treatment B after row 5, while a risk lover switches before row 5. Thus, the switching point is a measure of an individual’s risk preferences. We define SwitchRiskHP (SwitchRiskHD) a variable denoting the point at which a given patient (doctor) switched from lottery A to lottery B. This ranges from 1 (switching to treatment B in the first row) to 10 (never switching to treatment B) and the higher the value, the more risk averse the patient (doctor) is. Time preferences Time preferences were measured using an adaptation of the Tanaka et al. (2010) MPL to the healthcare context. Subjects were presented with a series of six blocks of choices, each of which had five choices between two different healthcare treatments. Subjects were asked to consider their current health status and to choose between two possible hypothetical treatments, A and B, with different days of full health at different points in time (Table 2). In each block, treatment A gave a larger number of days in full health than treatment B. Treatment A, however, was offered with some delay (so-called Larger-Later option, LL) while treatment B was always available immediately (so-called Smaller-Sooner option, SS).
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
6 Receive 360 € in 1 month Receive 60 € today A B 7 Receive 360 € in 1 month Receive 120 € A B 8 Receive 360 € in 1 month Receive 180 € today A B 9 Receive 360 € in 1 month Receive 240 € today A B 10 Receive 360 € in 1 Receive 300 € A B 11 Receive 360 € in 3 months Receive 60 € today A B 12 Receive 360 € in 3 months Receive 120 € today A B 13 Receive 360 € in 3 months Receive 180 € today A B 14 Receive 360 € in 3 months Receive 240 € A B 15 Receive 360 € in 3 months Receive 300 € today A B 16 Receive 900 € in 1 week Receive 150 € today A B 17 Receive 900 € in 1 week Receive 300 € today A B 18 Receive 900 € in 1 week Receive 450 € today A B 19 Receive 900 € in 1 week Receive 600 € today A B 20 Receive 900 € in 1 week Receive 750 € A B 21 Receive 900 € in 1 month Receive 150 € A B 22 Receive 900 € in 1 Receive 300 € today A B 23 Receive 900 € in 1 month Receive 450 € today A B 24 Receive 900 € in 1 month Receive 600 € A B 25 Receive 900 € in 1 month Receive 750 € today A B 26 Receive 900 € in 3 months Receive 150 € A B 27 Receive 900 € in 3 months Receive 300 € A B 28 Receive 900 € in 3 months Receive 450 € today A B 29 Receive 900 € in 3 months Receive 600 € today A B 30 Receive 900 € in 3 months Receive 750 € today A B Q1.13. Please of the following hypothetical scenarios. Suppose you need to choose between two medical treatments, A and B. Each treatment has two possible outcomes in terms of how long the effect will last. You know the probabilities with which this will happen. Irrespective of which treatment you choose, for as long as their effect lasts you are in full health. When the effect of the treatment is gone, you go back to your initial state of health, i.e. the state you where before you started the treatment that is the same regardless of the treatment you chose, and no further treatment will be allowed.
5 360 days in full health starting in 1 week 300 days in full health starting today A B 6 360 days in full health starting in 1 month 60 days in full health starting today A B 7 360 days in full health starting in 1 month 120 days in full health starting today A B 8 360 days in full health starting in 1 180 days in full health starting today A B 9 360 days in full health starting in 1 month 240 days in full health starting today A B 10 360 days in full health starting in 1 month 300 days in full health starting today A B 11 360 days in full health starting in 3 months 60 days in full health starting today A B 12 360 days in full health starting in 3 months 120 days in full health starting today A B 13 360 days in full health starting in 3 months 180 days in full health starting A B 14 360 days in full health starting in 3 months 240 days in full health starting today A B 15 360 days in full health starting in 3 months 300 days in full health starting today A B 16 900 days in full health starting in 1 week 150 days in full health starting today A B 17 900 days in full health starting in 1 week 300 days in full health starting today A B 18 900 days in full health starting in 1 week 450 days in full health starting A B 19 900 days in full health starting in 1 week 600 days in full health starting today A B 20 900 days in full health starting in 1 week 750 days in full health starting A B 21 900 days in full health starting in 1 month 150 days in full health starting today A B 22 900 days in full health starting in 1 month 300 days in full health starting A B 23 900 days in full health starting in 1 month 450 days in full starting health today A B 24 900 days in full health starting in 1 month 600 days in full health starting today A B 25 900 days in full health starting in 1 month 750 days in full health starting today A B 26 900 days in full health starting in 3 months 150 days in full health starting today A B 27 900 days in full health starting in 3 months 300 days in full health starting today A B 28 900 days in full health starting in 3 months 450 days in full health starting today A B
9.5 360 € in 3 months 300 € today A B 10.1 900 € in 1 week 150 € today A B 10.2 900 € in 1 week 300 € A B 10.3 900 € in 1 week 450 € today A B 10.4 900 € in 1 week 600 € A B 10.5 900 € in 1 week 750 € today A B 11.1 900 € in 1 month 150 € A B 11.2 900 € in 1 month 300 € today A B 11.3 900 € in 1 month 450 € A B 11.4 900 € in 1 month 600 € today A B 11.5 900 € in 1 month 750 € A B 12.1 900 € in 3 months 150 € today A B 12.2 900 € in 3 months 300 € A B 12.3 900 € in 3 months 450 € today A B 12.4 900 € in 3 months 600 € today A B 12.5 900 € in 3 months 750 € today A B