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 experiment’ based on Harrison and List (2004) (an ‘framed 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
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
D-P%20RP%20TP%20JHE%20RR2%204%20October%202016.docx