Two interrelated advances in genetics have occurred which have ushered in the growing field of genoeconomics. The first is a rapid expansion of so-called big data featuring genetic information collected from large population–based samples. The second is enhancements to computational and predictive power to aggregate small genetic effects across the genome into single summary measures called polygenic scores (PGSs). Together, these advances will be incorporated broadly with economic research, with strong possibilities for new insights and methodological techniques.
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Economics and Genetics
Jason M. Fletcher
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Gene–Environment Interplay in the Social Sciences
Rita Dias Pereira, Pietro Biroli, Titus Galama, Stephanie von Hinke, Hans van Kippersluis, Cornelius A. Rietveld, and Kevin Thom
Nature (one’s genes) and nurture (one’s environment) jointly contribute to the formation and evolution of health and human capital over the life cycle. This complex interplay between genes and environment can be estimated and quantified using genetic information readily available in a growing number of social science data sets. Using genetic data to improve our understanding of individual decision making, inequality, and to guide public policy is possible and promising, but requires a grounding in essential genetic terminology, knowledge of the literature in economics and social-science genetics, and a careful discussion of the policy implications and prospects of the use of genetic data in the social sciences and economics.
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Happiness and Productivity in the Workplace
Mahnaz Nazneen and Daniel Sgroi
Happiness has become an important concept in economics as a target for government policy at the national level. This is mirrored in an increasing understanding of the microeconomic effects of increased happiness. While correlational studies have for many years documented a relationship between individual-level happiness and productivity, more recent work provides causal evidence that a positive shock to happiness can boost productivity significantly. These studies include three strands of research. The first provides a number of longitudinal surveys that have generated evidence linking happiness to productivity but run the risk of confounding happiness with other related variables that may be driving the relationship. The second includes laboratory experiments that simulate a workplace under tightly controlled conditions, and this strand has established a clear relationship between positive happiness shocks and rises in productivity. The third involves examining experimental field data, which sacrifices the control of laboratory experiments but offers greater realism. However, there is still work to be done generalizing these findings to more complex work environments, especially those that involve cooperative and team-based tasks where increases in happiness may have other consequences.
