The Bell Curve Revisited: Testing Controversial Hypotheses with Molecular Genetic Data

Dalton Conley, Benjamin Domingue

Sociological Science, July 5, 2016
DOI 10.15195/v3.a23

In 1994, the publication of Herrnstein’s and Murray’s The Bell Curve resulted in a social science maelstrom of responses. In the present study, we argue that Herrnstein’s and Murray’s assertions were made prematurely, on their own terms, given the lack of data available to test the role of genotype in the dynamics of achievement and attainment in U.S. society. Today, however, the scientific community has access to at least one dataset that is nationally representative and has genome-wide molecular markers. We deploy those data from the Health and Retirement Study in order to test the core series of propositions offered by Herrnstein and Murray in 1994. First, we ask whether the effect of genotype is increasing in predictive power across birth cohorts in the middle twentieth century. Second, we ask whether assortative mating on relevant genotypes is increasing across the same time period. Finally, we ask whether educational genotypes are increasingly predictive of fertility (number ever born [NEB]) in tandem with the rising (negative) association of educational outcomes and NEB. The answers to these questions are mostly no; while molecular genetic markers can predict educational attainment, we find little evidence for the proposition that we are becoming increasingly genetically stratified.

Creative Commons LicenseThis work is licensed under a Creative Commons Attribution 4.0 International License.
Dalton Conley: Department of Sociology, Princeton University

Benjamin Domingue: Graduate School of Education, Stanford University

Acknowledgements: Funding for this study was provided by the Russell Sage Foundation, Grant 83-15-29. This research uses data from the HRS, which is sponsored by the National Institute on Aging (Grants NIA U01AG009740, RC2AG036495, and RC4AG039029) and conducted by the University of Michigan.

  • Citation: Conley, Dalton, and Benjamin Domingue. 2016. “The Bell Curve Revisited: Testing Controversial Hypotheses with Molecular Genetic Data.” Sociological Science 3: 520-539.
  • Received: January 19, 2016
  • Accepted: February 22, 2016
  • Editors: Stephen Morgan
  • DOI: 10.15195/v3.a23

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3 Reactions to The Bell Curve Revisited: Testing Controversial Hypotheses with Molecular Genetic Data

  1. Mel Bartley July 5, 2016 at 8:56 am #

    Very interesting analysis. One question: If Reitveld et al. show that their genetic score only explains 3-4% of variance in years of education, how can a 1 SD change in your score predict a 0.5 year difference in education? Maybe its just because I am a sociologist….

    • Mazirian July 11, 2016 at 12:54 pm #

      The SD for years of education in the study was about 2.5 years. If the polygenic score explains 4% of the variance in years of education, the correlation between the polygenic score and years of education is equal to the square root of 3%, or 0.20. This means that a change of 1 SD in the polygenic score predicts a difference of 0.20*2.5=0.5 years in education.

  2. John Hamilton Bradford July 8, 2016 at 12:24 am #

    This is a fascinating study! My comments below pertain to Proposition #1, that “The effect of genetic endowment is increasing over time with the rise of a meritocratic society”.

    The data reported here do not seem to unequivocally contravene H&M’s hypothesis. The authors focus on the interaction terms, PGS x Birth Year (i.e. cohort), and show that interaction coefficient is slightly negative (b=-0.006, p=0.47), indicating a weakening of the association between genetic endowment and educational attainment, broadly conceived. The finding is that PGSs (polygenic scores) are (slightly) less predictive of educational attainment in later cohorts.

    This isn’t that surprising, given educational inflation – over time, higher percentages of the population achieve any given level of educational attainment. In addition, as shown in Table 3 and mentioned in the Discussion section, this decline in importance of genetic endowment is restricted only to the ‘lower half of the educational distribution’. In contrast, genetic endowment (measured by PGSs) has become even more important across cohorts in predicting the ‘transition from a completed college degree to graduate education’ (534). Isn’t this what we’d expect to happen as the level of educational attainment at the lower half of the distribution becomes increasingly decoupled from cognitive ability?

    H&M argued that cognitive ability is becoming more important in determining one’s life chances. The authors of this paper don’t actually test this hypothesis. They instead create polygenic scores *of educational attainment* (!) rather than cognitive ability – based on the GWAS of Rietveld et al. (2013) – and find that genetic predictors of *educational attainment* become (slightly) less predictive of educational attainment, on average, i.e. for high school and college. But again, they also report that the association of this genetic correlate (of educational attainment) and educational attainment has actually become stronger for transitions into graduate and professional schools from college.

    If I’m not mistaken, the association between cognitive ability (as measured say by standardized tests, SAT, ACT, GRE, AFQT and NEA reports on reading and math ability) and educational attainment has weakened over time. It is possible that cognitive ability is becoming increasingly salient in determining SES as H&M maintain, and at the same time, educational attainment is becoming less salient, simply because the relationship between cognitive ability and educational attainment is becoming weaker. In other words, educational attainment, at least at the lower levels, is less salient in determining relative status.

    The authors disaggregated the effect of the interaction term (PGS x Cohort) on different levels of educational attainment, and found some interesting results. It would also be interesting to disaggregate each level of educational attainment by discipline. Perhaps the association between genetic endowment has become stronger in some fields, like the STEM fields, but not in others?

    Perhaps a more straightforward test of H&M’s hypotheses would be to find a PGS for cognitive ability, and then to see whether the association between PGS and something like wealth, income, occupational status, etc. increases or decreases over time.

    Finally, it is interesting to consider how these findings bear upon the issue of meritocracy and the transition from societies based primarily on ascribed status to achieved status. An unchanging or declining ability of genetic endowment to predict status can be given different valences and interpretations, depending on our assumptions about the past. The finding that we aren’t becoming a more meritocratic society (based on the negative interaction term coefficient) is also consistent with the hypothesis that previous cohorts (or societies, etc.) were a lot more meritocratic than we thought. This may sound implausible, but it is consistent with Gregory Clark’s work (e.g. The Son Also Rises)- that the rate of intergenerational social mobility seems to be (an extremely slow) constant, except in places like India where it is even slower due to barriers preventing inter-caste marriages. His findings are consistent with the hypothesis that social status (phenotype) is largely heritable, due to some underlying social competency (genotype).