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Why Is Infant Mortality Higher in Boys Than in Girls? A New Hypothesis Based on Preconception Environment and Evidence From a Large Sample of Twins

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Demography

An Erratum to this article was published on 09 March 2013

Abstract

Infant mortality is higher in boys than girls in most parts of the world. This has been explained by sex differences in genetic and biological makeup, with boys being biologically weaker and more susceptible to diseases and premature death. At the same time, recent studies have found that numerous preconception or prenatal environmental factors affect the probability of a baby being conceived male or female. I propose that these environmental factors also explain sex differences in mortality. I contribute a new methodology of distinguishing between child biology and preconception environment by comparing male-female differences in mortality across opposite-sex twins, same-sex twins, and all twins. Using a large sample of twins from sub-Saharan Africa, I find that both preconception environment and child biology increase the mortality of male infants, but the effect of biology is substantially smaller than the literature suggests. I also estimate the interacting effects of biology with some intrauterine and external environmental factors, including birth order within a twin pair, social status, and climate. I find that a twin is more likely to be male if he is the firstborn, born to an educated mother, or born in certain climatic conditions. Male firstborns are more likely to survive than female firstborns, but only during the neonatal period. Finally, mortality is not affected by the interactions between biology and climate or between biology and social status.

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Notes

  1. Offspring sex ratio, which is the ratio of male children to female children born to a parent, should not be confused with population sex ratio (at birth), which is the ratio of male children to female children born in a population. The world population sex ratio is estimated to be about 1.05, but offspring sex ratio varies widely across parents and population subgroups. This article is mostly concerned with offspring sex ratio.

  2. By preconception environment, I mean factors that are external to a child and that occur around the time of conception. These factors might be pure environmental hazards (such as parental exposure to chemicals) or medical factors (such as parental illnesses). Throughout the article, I use the terms preconception environment and prenatal environment interchangeably.

  3. Clearly, sex differences in mortality cannot be solely the result of the sex chromosomes XY and XX. A human cell has 22 homologous chromosome pairs in addition to the sex chromosomes, and interactions between them likely play a role in determining mortality. I therefore view sex chromosomes as well as their likely interactions with non–sex chromosomes as being entirely part of the biological process hypothesized to explain male-emale differences in mortality.

  4. See also Rosenfeld and Roberts (2004) for a review of the literature on the effect of maternal diet on offspring sex ratio.

  5. Note, however, that because sex has traditionally been treated as random, controlling for the vector X yht is irrelevant in most studies.

  6. Exposures to particular diseases or treatments, for instance, may not only lead to excess male births but also contribute to male mortality. If this is the case, then the share of male excess mortality generally attributed to biology is exaggerated. But if boys are also more likely to be born to parents of higher socioeconomic status, which would also favor their survival, then the share of male excess mortality attributed to biology is underestimated.

  7. Parental prenatal circumstances that determine offspring sex ratios, such as occupation or exposure to dioxin, might vary over time.

  8. Note that if u h and p hy were observed and controlled in Eq. (1), would measure only the effect of male biology.

  9. Additivity is consistent with models generally used by biologists and geneticists to disentangle the effects of genetic and environmental factors on health outcomes (e.g., Evans et al. 2002; Neale and Cardon 1992).

  10. Indeed, estimating the effect of child sex on mortality over a sample of opposite-sex twins allows me to control automatically for the effect of preconception environment. This is because within an opposite-sex twin pair, sex is perfectly uncorrelated with preconception environment, since there is exactly 1 male for 1 female.

  11. It is well known that twinning rates are high in the United States because of assisted reproduction. Globally, the population of twins was estimated to be 125 million in 2006 (Oliver 2006—about 1.9 % of the world population.

  12. Controls include the child’s year of birth; mother’s age at survey, education and marital status; husband’s education; household size; possession of household assets and facilities; a linear control for year of survey; and country-year fixed effects.

  13. However, I cannot fully ascertain that children born at different times to the same mother also have the same father, which implies that the sibling fixed-effect regressions mostly control for time-invariant maternal factors.

  14. Those eight zones are (1) Sahel and Sudan (Burkina Faso, Cape Verde, Chad, Gambia, Guinea-Bissau, Mali, Mauritania, Niger, Senegal, and Sudan); (2) southern-central Africa and Madagascar (Madagascar, Malawi, Mozambique, Namibia, Zambia, and Zimbabwe); (3) Central Gulf of Guinea countries and Tanzania (Benin, Côte d’Ivoire, Ghana, Tanzania, and Togo); (4) East and West Gulf of Guinea (Cameroon, Central African Republic, Equatorial Guinea, Gabon, Guinea, Liberia, Nigeria, and Sierra Leone); (5) southern Africa (Botswana, Lesotho, South Africa, and Swaziland); (6) Horn of Africa and Kenya (Djibouti, Ethiopia, Kenya, and Somalia); (7) Great Lakes countries (Burundi, Rwanda, and Uganda); and (8) central-west Africa (Angola, Congo, and Democratic Republic of Congo).

  15. Analyzing the effect of birth order on child sex using all twins (Table 6, column 1) likely understimates the effect of this variable because only the variation generated by the sample of opposite-sex twins is being used in this estimation. It is therefore better to estimate the effect of birth order using the sample of opposite-sex twins, as I do in column 5. Similarly, estimating the effects of climate and parental social status on child sex using the entire sample (columns 2–3) is likely to underestimate the true effects of these variables, given that variations are obtained only from same-sex twins. I therefore also estimate the effects of these variables using only the sample of same-sex twins (columns 6–8).

  16. The notion that opposite-sex twins are always fraternal has been challenged in recent studies showing that such twins could be identical (see, e.g., Wachtel et al. 2000).

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Acknowledgments

I am grateful to the Editor and three anonymous referees for insightful comments and suggestions that have helped improve the paper. I also thank Anna Aizer, Jason Boardman, Ken Chay, Jakina Debnam, Parfait Eloundou-Enyegue, Andrew Foster, Jeffrey Greenbaum, Blessing Mberu, Mark Pitt, Marie Patience Pongou, Yona Rubinstein, Olumide Taiwo, and Valerie Wilson for encouragement and useful comments on earlier versions of this article. I acknowledge the generous financial support of the Brown University Population Studies and Training Center, the Compton Foundation, the Hewlett Packard Foundation, and the University of Ottawa.

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  1. Department of Economics, University of Ottawa, Ottawa, ON, K1N 6N5, Canada

    Roland Pongou

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Table 10

Table 10 Sample size by country
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Pongou, R. Why Is Infant Mortality Higher in Boys Than in Girls? A New Hypothesis Based on Preconception Environment and Evidence From a Large Sample of Twins. Demography 50, 421–444 (2013). https://doi.org/10.1007/s13524-012-0161-5

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