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The use of behavioral insights and experimental methods has recently gained momentum among health policy-makers. There is a tendency, however, to reduce behavioral insights applications in health to “nudges,” and to reduce experiments in health to “randomized controlled trials” (RCTs). We argue that there is much more to behavioral insights and experimental methods in health economics than just nudges and RCTs. First, there is a broad and rich array of complementary experimental methods spanning the lab to the field, and all of them could prove useful in health economics. Second, there are a host of challenges in health economics, policy, and management where the application of behavioral insights and experimental methods is timely and highly promising. We illustrate this point by describing applications of experimental methods and behavioral insights to one specific topic of fundamental relevance for health research and policy: the experimental elicitation and econometric estimation of risk and time preferences. We start by reviewing the main methods of measuring risk and time preferences in health. We then focus on the “behavioral econometrics” approach to jointly elicit and estimate risk and time preferences, and we illustrate its state-of-the-art applications to health.

This chapter aims at providing an understanding of the research and devlopment (R&D) process in the pharmaceutical industry, by exploring the methodological challenges and approaches in the assessment of the determinants of innovation in the pharmaceutical industry. It (i) discusses possible methodological approaches to model occurrence of events; (ii) describes in detail competing risks duration models as the best methodological option in light of the nature of pharmaceutical R&D processes and data; (iii) concludes with an estimation strategy and overview of potential covariates that have been found to correlate with the likelihood of failure of R&D pharmaceutical projects.