Humboldt-Universität zu Berlin >> Wirtschaftswissenschaftliche Fakultät


Subproject A14

Statistical inference methods for assessing the genetic basis of risk preferences
Head of Project : Prof. Dr. Thorsten Dickhaus

+49 30 2093 5841


+49 30 2093 5848



Humboldt-Universität zu Berlin Institut für Mathematik Unter den Linden 6 10099 Berlin, Germany






Knowledge about the genetic basis of risk preferences is informative for various reasons. First, incorporating this information into economic analyses can help to identify causal pathways, thus addressing the empirical identification problem of mutual causation. For example, it is known that risk preferences as measured from peoples’ financial decisions affect occupational choice and at the same time, chosen occupations have an effect on risk-taking behavior (Guiso and Paiella, 2008). If genetic information that is linked to risk preferences were identified, this could be used to measure the causal impact of exogenous variation in risk preferences on occupational choice. Genetic information thus improves our understanding of mechanisms that produce economically relevant individual differences. Second, economics can aid in analyzing and addressing the policy issues raised by the existence of genetic knowledge and its potential societal diffusion (see also Benjamin et al., 2008, for a detailed discussion). In this context, specific – private or societal – insurance mechanisms can either reduce or amplify the inequalities produced by genetic variation. However, due to the massive multiplicity of the statistical inference problems in genetic studies and the complicated dependency structures among genetic markers, substantial advances in statistical methodology are inevitable to face the data-analytic challenges posed by genetic association studies, cf., e. g., Yang et al. (2010) for a recent description of some possible pitfalls arising when applying multiple testing methodology to genetics data. Furthermore, combining genetic data with neuroimaging and behavioral data (e. g., by carrying out an economic game in an fMRI scanner) enables us to obtain new insights into mechanisms that shape our behavior.

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