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Multigenerational Effects of Early-Life Health Shocks

Demography volume 56, pages 1855–1874 (2019)Cite this article

Abstract

A large literature has documented links between harmful early-life exposures and later-life health and socioeconomic deficits. These studies, however, have typically been unable to examine the possibility that these shocks are transmitted to the next generation. Our study uses representative survey data from the United States to trace the impacts of in utero exposure to the 1918 influenza pandemic on the outcomes of the children and grandchildren of those affected. We find evidence of multigenerational effects on educational, economic, and health outcomes.

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Fig. 1

Notes

  1. As mentioned in greater detail in the Data section, the first generation is composed of the parents from the Wisconsin Longitudinal Study (WLS). This is the generation that is treated, or exposed in utero to the 1918 flu epidemic. The second generation is composed of the WLS graduates and siblings, who are the focus of the WLS, and the third generation is composed of the offspring of the WLS graduates and siblings.

  2. Cohen et al. (2010) and Fletcher (2018) found no effects on overall mortality.

  3. Van den Berg and Pinger (2014) externally validated the potential for transmission across three generations by analyzing the impact of the German famine of 1916–1918 on the mental health outcomes (an index from survey questions accounting for general mental health, emotional functioning, social functioning, and vitality) of the children and grandchildren of those exposed to the famine during their SGP. The authors found that paternal (maternal) grandfathers’ (grandmothers’) exposure during their SGP is associated with better mental health in grandsons (granddaughters).

  4. Parent year of birth is recorded from the WLS graduates. We use this self-reported measure to create the indicator of flu exposure: those parents born in 1918 or 1919. To maximize sample size, we use differing waves of the WLS and reported birth years from biological siblings to supplement the reported year of birth from WLS graduates. Parent birth years are first collected for the 1992/1993 graduate wave. Missing observations are then sequentially added from graduate reports in later waves—2003 and 2011. After using all graduate-reported year of parent birth variables, we then fill in observations from identically reported measures from a selected biological sibling. Specifically, for mothers (Generation 1 females), 7,976 observations come from graduates in the 1992/1993 wave, 366 additional observations come from the 2003/2005 wave, 84 observations come from 2011 wave, and 68 observations come from the selected sibling. This leaves a base sample size of 8,494, ~94 % of which is from the graduate reported 1992/1993 wave. For fathers (Generation 1 males), 7,929 observations come from graduates in the 1992/1993 wave, 361 additional observations come from the 2003/2005 wave, 77 come from the 2011 wave, and 91 observations come from siblings, leaving a base sample size of 8,458 (again, ~94 % of which is from the graduate-reported 1992/1993 wave). From this base, 7 observations are dropped for Generation 1 female’s (WLS mother’s) year of birth because reported Generation 2’s year of birth was 10 or fewer years after Generation 1’s year of birth; 45 additional observations are lost if this threshold is increased to 15 years. For Generation 1 male’s year of birth, 2 observations are removed for identical reasons; this increases to 25 additional observations for the 15-year threshold. We attribute this reduction in the sample mostly to measurement error.

  5. The 1918 flu epidemic in Wisconsin was from September 1918 through December 1918, but it did not reach the severity experienced in many other states. According to historical records, Wisconsin had the fourth-lowest number of deaths of 25 reporting states (Burg 2000; Shors and McFadden 2009).

  6. Tables A7 and A8 in the online appendix provide evidence that our results are not driven solely by confounding between exposure to the influenza pandemic and being young mothers.

  7. The index of SES is a factor-weighted score combining data on father’s and mother’s years of schooling, father’s occupational prestige, and average parental income. Replacing this measure with average parental income (see Table A1, online appendix) does not change the effect of the flu indicator. Job prestige measures for both mother and father are based on Duncan’s socioeconomic index, which is a measure of job prestige based on income, education, and surveyed perceptions of general social standing for certain occupations (Duncan 1961).

  8. Our three generations of individuals are drawn from three nonoverlapping sets of birth years; the mean birth year for Generation 1 is ~1910, Generation 2 is ~1940, and Generation 3 is ~1965. Thus, because we are conduct the analysis separately by generation, we control for much of the time variation in the meaning of education. We also conduct the analysis stratified by sex in Tables A10–A12 (online appendix) so that we can control for the differential meaning of education in each generation.

  9. First-generation years of schooling are reported by the second-generation WLS graduates. Measurement error is likely, which may result in the insignificant coefficients of Table 2.

  10. Richter and Robling (2013: table 12) found a similar effect for in utero female exposure to flu in the first trimester.

  11. Mothers and fathers of WLS graduates are assumed to be married.

  12. Table A2 in the online appendix repeats the estimation of Table 3 while also controlling for an indicator of own flu exposure and own year-of-birth measures.

  13. Table A6 in the online appendix includes a number of alternative measures for years of schooling in place of the simple count used in Table 4.

  14. Table 3 includes selected siblings of the WLS graduates. These siblings do not have to be high school graduates.

  15. This finding differs from that of Richter and Robling (2013), who found that maternal exposure was tied to daughters’ outcomes and paternal exposure tied to sons’ outcomes.

  16. Table A14 in the online appendix tests indicators for a number self-reported health conditions. A weak positive association is shown between Generation 1 female flu exposure and an indicator of cardiovascular disease during the 1992/1993 wave of the WLS (Table A15, panel A, online appendix). This association, however, is reduced in magnitude and statistical significance for later-life waves of the WLS (Table A15, online appendix, panels B and C).

  17. The Dutch famine influenced maternal nutritional status directly through caloric restriction; the 1918 flu influenced similar nutrition through symptoms, such as appetite loss, vomiting, and/or diarrhea.

  18. When we omit those with no family income during the 1992 wave of the WLS, negative effects from Generation 1 female flu exposure are primarily seen in second-generation males. This indicates, however, a greater frequency of no income among second-generation females from Generation 1 flu exposure. Additionally, when looking at net worth in the 2011 wave of the WLS, we see no difference by Generation 2’s sex.

  19. Analysis of the WLS graduate/sibling children (i.e., the third generation) restricts the sample to those children who are biological children and who were age 35 or older by the 2003/2004 wave of the WLS.

  20. For Generation 1 female exposure in column 4, the p value for a difference in coefficients between panel A of Table 4 and Table 6 is 0.411, implying that the effect in the third generation is statistically indistinguishable from that in the second generation.

  21. We specifically explore the confounding effects of younger mothers in Tables A7 and A8 (online appendix). These tables show that younger mothers are indeed initially disadvantaged (i.e., fewer years of schooling); however, this young-mother disadvantage does not persist in subsequent generations. Rather, only those mothers born in the 1918–1919 range have significant negative effects on later generations.

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Acknowledgments

This research uses data from the Wisconsin Longitudinal Study (WLS) of the University of Wisconsin–Madison. Since 1991, the WLS has been supported principally by the National Institute on Aging (AG-9775, AG-21079, AG-033285, and AG-041868), with additional support from the Vilas Estate Trust, the National Science Foundation, the Spencer Foundation, and the Graduate School of the University of Wisconsin–Madison. Since 1992, data have been collected by the University of Wisconsin Survey Center. A public-use file of data from the Wisconsin Longitudinal Study is available from the Wisconsin Longitudinal Study, University of Wisconsin–Madison, 1180 Observatory Drive, Madison, WI 53706; and at http://www.ssc.wisc.edu/wlsresearch/data/. The opinions expressed herein are those of the authors. Forgues acknowledges support from the National Institute on Aging Training Grant T32 AG00129 at the University of Wisconsin–Madison. Authorship for this article is alphabetical.

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Authors and Affiliations

  1. Department of Economics, University of California, Merced, Merced, CA, USA

    C. Justin Cook

  2. La Follette School of Public Affairs, University of Wisconsin–Madison, Madison, WI, USA

    Jason M. Fletcher

  3. Department of Sociology, University of Wisconsin–Madison, Madison, WI, USA

    Jason M. Fletcher & Angela Forgues

  4. Agricultural and Applied Economics, University of Wisconsin–Madison, Madison, WI, USA

    Jason M. Fletcher

  5. Population Health Sciences, University of Wisconsin–Madison, Madison, WI, USA

    Jason M. Fletcher

  6. Center for Demography and Ecology, University of Wisconsin–Madison, Madison, WI, USA

    Jason M. Fletcher & Angela Forgues

  7. Center for Demography of Health and Aging, University of Wisconsin–Madison, Madison, WI, USA

    Jason M. Fletcher & Angela Forgues

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  1. C. Justin Cook
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Correspondence to C. Justin Cook.

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Cook, C.J., Fletcher, J.M. & Forgues, A. Multigenerational Effects of Early-Life Health Shocks. Demography 56, 1855–1874 (2019). https://doi.org/10.1007/s13524-019-00804-3

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