Genoeconomics is an interdisciplinary field, combining molecular genetics and economics. It’s based on the idea that economic indicators have a genetic basis. So your financial behaviour can be traced back to your DNA, which is predictive of your (financial) behaviour. Key preferences that genoeconomic research should be able to predict would be time preference, risk aversion, and educational attainment (Fletcher, 2018). Both time preference and risk aversion can be predicted in neuroscience studies, looking at the BOLD-levels of various parts in the brain, and how they react to being presented with several choices of risk, or delay. However, education attainment, and also more macro level behaviours such as per capita income (Callaway, 2012) are slightly more difficult to defend. Although genoeconomic research in 2013 did find that two-fifths of the variance of educational attainment is explained by genetic factors (Rietveld, et al, 2013), it is difficult to see how these links would be made.
It should be mentioned that genetics isn’t new to economics. This can be seen in the use of identical twins to control for genetic background when exploring associations between educational attainment and wages (Taubman, 1976). Similar ideas have been used when exploring sibling differences as a way to control for family background factors (Fletcher, 2010, 2011a, 2014). So it’s hardly new. With regards to its applications, there have been several applications of genoeconomic methodologies to study several different phenomena. A 2012 study of tobacco taxes in the United States looked at the interaction of a single nicotinic receptor and state-level tobacco taxes to predict tobacco use, and found evidence of interaction (Fletcher, 2012). Cook and Fletcher (2015b) used three genetic variants to create a “neuroplasticity score,” showing that this score interacted with birth weight to predict adult IQ and wages using a sibling-difference design (this is known as the genetic lottery approach). Cook and Fletcher (2015a) argued that the APOE gene variant, which increases the risk for cognitive decline in later life, is unknown at the time of educational decisions. So they again used a sibling-difference design to estimate the interactive effects of educational attainment and the genetic variant in predicting cognition in later life and showed evidence that education can “rescue” genetic vulnerabilities. Thompson (2014) used a sibling-difference design to explore interactions between family income and genetic liabilities to predict educational attainments. Albert et al. (2015) showed evidence of interaction between an educational randomized controlled trial (RCT) and a genetic variant in predicting externalizing behaviours. With these small, but significant findings, a small body of research has even looked into integrating genoeconomic findings into structural economic models. Biroli (2015) extended a standard health production function model to allow measured genetic variants to affect both the health production function technology and individual preferences related to the incentives for health investment faced by individuals. Biroli found some evidence that genetic variants related to weight (FTO) change both the production function of body mass index and the level of health investment. Now this will have implications for the model, and as a result, how certain factors should be weighed. Issue is, however, that a lot more work is needed, as many single genetic variants have very weak effects on outcomes of interest. And this is true for a lot of genoeconomics. So there is plenty of application within this field. My question is just becoming, what do you do with it?
Thankfully, Benjamin et al (2012) asked this question almost 10 years before I did. They first discuss the many promises of genoeconomics, and then move onto its pitfalls. I’m going to do it the other way around.
This approach has many, and I do mean many, pitfalls. The most prominent pitfall being that even the most persuasive evidence suggests that true genotype-behaviour associations have tiny effect sizes, so current research designs in the social sciences are underpowered.
Another issue lies with the aim of genoeconomics – to uncover a causal effect between genes and economic choices/outcomes. However, so far most methodologies have been correlational in approach. So that’s not really helpful. One reason why the design remains correlational is the amount of confounding factors: the most common concern is confounding from population stratification: different groups within the sample differ in allele frequencies and also differ in their outcome for nongenetic reasons. A very famous example is the “chopsticks effect” (Lander & Schork 1994): where the authors were trying to find the genetic causes of chopstick, and found a significant association for any SNP whose allele frequencies differ appreciably between Asians and non-Asians (I don’t know what that means either…). However, it doesn’t take many braincells to figure out that the reason chopsticks are being used has everything to do with culture, and very little with genetic predisposition. What this shows us that the exclusion of confounds within genoeconomic research still has a long way to go.
So what is genoeconomic promising us? Benjamin, et al (2012) seem very excited about what genoeconomics can do for further research: “First, measuring genotypes will advance empirical analysis by providing direct and exogenous measures of preferences and abilities. Second, social scientists will use genotypic data to learn about the biological mechanisms that underlie behaviors of interest. Third, social scientists may use genetic markers as control variables, thereby improving the power of standard economic analysis. Finally, genetic information could eventually be useful for targeting social-scientific interventions, much like it is beginning to be useful for targeting medical interventions.” Now I can see the benefits of the first three on a science level. The fourth one requires a bit more explanation. The example given is that of dyslexia: if dyslexia can eventually be predicted sufficiently well by genetic screening (we’re not there yet), parents with children who have dyslexia-susceptibility genes could be given the option of enrolling their children in supplementary reading programs, years before a formal diagnosis. To add context to this example, this doesn’t pay for adults, and as a result a lot of genoeconomics is focussed on children. For adults, it is generally feasible and more accurate to measure realized preferences and abilities directly rather than relying on genetic predispositions. For this reason, in the realm of economics, targeting interventions is most likely to take the form of parents obtaining genomic information about their children and then creating a developmental environment that is most likely to cultivate the children's preferences and abilities. The question to ask then becomes: do we actually want that?
I normally have an opinion on everything, but this one I’m going to plead the fifth. The study of genes is interesting in and of itself, but complicated and often misconstrued (often not purposefully so) in what it can and cannot do for other fields (interdisciplinary work). I’ll keep my eye on this field, but as is, I’m more intrigued by neuroeconomics.
References
Albert, D., Belsky, D. W., Crowley, D. M., Latendresse, S. J., Aliev, F., Riley, B., . . . Dodge, K. A. (2015). Can genetics predict response to complex behavioral interventions? Evidence from a genetic analysis of the fast track randomized control trial. Journal of Policy Analysis and Management, 34(3), 497–518.
Benjamin, Daniel J.; Cesarini, David; Chabris, Christopher F.; Glaeser, Edward L.; Laibson, David I.; Guðnason, Vilmundur; Harris, Tamara B.; et al. (2012). "The Promises and Pitfalls of Genoeconomics" (PDF). Annual Review of Economics. 4 (1): 627–662.
Biroli, P. (2015). Genetic and economic interaction in the formation of human capital: The case of obesity. Zurich, Switzerland: University of Zurich.
Callaway, Ewen (2012). "Economics and genetics meet in uneasy union". Nature. 490 (7419): 154–5. B
Cook, C. J., & Fletcher, J. M. (2015a). Can education rescue genetic liability for cognitive decline? Social Science & Medicine, 127, 159–170.
Cook, C. J., & Fletcher, J. M. (2015b). Understanding heterogeneity in the effects of birth weight on adult cognition and wages. Journal of Health Economics, 41, 107–116. Fletcher, J. M. (2012). Why have tobacco control policies stalled? Using genetic moderation to examine policy impacts. PloS One, 7(12). Fletcher, Jason M. (2018). "Economics and genetics". Oxford Research Encyclopedia. Oxford University Press. https://oxfordre.com/economics/view/10.1093/acrefore/9780190625979.001.0001/acrefore-9780190625979-e-14 Rietveld, Cornelius A.; et al. (2013). "GWAS of 126,559 Individuals Identifies Genetic Variants Associated with Educational Attainment" (PDF). Science. 340 (1467): 1467–1471. Thompson, O. (2014). Economic background and educational attainment: The role of gene-environment interactions. Journal of Human Resources, 49(2), 263–294.
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