Smoking provides one example of economic analysis that can improve the study of how genetic variation influences phenotypic variation. Traditional heritability studies suggest at least some genetic component to lung cancer (Lichtenstein et al., 2000); molecular genetics identifies a locus of lung cancer susceptibility on chromosome 6q23-25 (Bailey-Wilson et al., 2004). The genetic susceptibility to lung cancer is undoubtedly amplified by cigarette smoking, an economic decision affected by advertising, social norms, cigarette prices, consumer income, and tax rates on cigarettes (Cutler and Glaeser, 2005). Economics can explain how social institutions—like the market for cigarettes—interact with genes to jointly generate important health phenotypes like lung cancer.
More generally, economic institutions may either reduce or amplify the inequalities produced by genetic variation. In some situations, social transfers partially offset genetic factors—for example, when individuals with illness receive extra insurance-based resources to treat or manage their illness. The second subfield uses genetic information to identify causal mechanisms. This subfield will recognize a central fact of empirical economics: the ubiquity of mutual causation—for example, health influences wealth and vice versa (Case, Lubotsky, and Paxson, 2002). Genetic measures can help to separate the causal effect in a particular direction.
For example, a robust literature argues that height, even in adolescence, increases earnings (Persico, Postlewaite, and Silverman, 2004). However, this literature is plagued by difficulty in controlling for the fact that height also reflects better health and nutrition in wealthier families. If height-linked alleles were identified, then they could, in principle, be used to measure the causal impact of exogenous variation in height. More formally, such research would analyze allele variation across siblings to identify the causal effect of genetic predispositions for height (controlling for household background characteristics). To take another example, Ding, Lehrer, Rosenquist, and Audrain-McGovern (2005) address the causal effect of health on educational outcomes, using genetic predictors of health to ameliorate confounding by third factors potentially correlated with both health and educational outcomes.
More generally, cognition-linked alleles will contribute to understanding of the cognitive factors that influence income, or the extent to which cognitive factors influence decision making about savings and wealth. Genetic research will also identify biological mechanisms that interact with environmental factors to jointly influence behavior. We anticipate that crude concepts like “risk aversion” (unwillingness to take risks) and “patience” (willingness to delay gratification) that are central to economic analyses will be decomposed into much more useful subcomponents associated with particular neural mechanisms and their environmental and genetic antecedents (Plomin, Corley, Caspi, Fulker, and DeFries, 1998).