Matteo Galizzi (LSE)

This week's seminar will be given by Matteo Galizzi from LSE. 

Title :  Linking experimental and survey data for a UK representative sample: Structural estimation of risk and time preferences

We report evidence from the first ‘artefactual field experiment’ that directly incorporates experimental measures for risk and time preferences for a representative sample of respondents within the Innovation Panel (IP) of the UK Household Longitudinal Survey (UKHLS, also known as Understanding Society), the world-largest multi-scope panel survey. In wave IP6 we randomly allocated a representative subsample of 708 IP respondents to an experimental module where discounting rates and a-temporal risk preferences were elicited using incentive-compatible methods. Households were independently randomly allocated to either a face-to-face or a web interview mode. Respondents also answered survey questions on risk and time attitudes. We structurally jointly estimate risk preferences under Expected Utility Theory and Rank Dependent Utility, and time preferences considering a broad class of discounting models such as exponential, hyperbolic, quasi-hyperbolic, and Weibull discounting. The models, and their finite mixtures, are estimated using Maximum Likelihood calculating individual-specific levels of daily ‘background consumption’ from linked household income data. We have three findings. First, there is high heterogeneity in the responses from our UK representative sample. Overall responses support risk aversion. The finite mixtures models suggest that about half the responses could be explained by non-constant, in particular Weibull, discounting. Second, web respondents are younger, more educated, and less likely to report poor health. Despite this, there is no significant difference between the web and the face-to-face responses and the structurally estimated parameters of risk aversion and time discounting. Third, we find little association between experimental and survey measures of risk and time preferences.