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Climate anomalies and birth rates in sub-Saharan Africa

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Abstract

Research on climate change and human population growth has focused mainly on how population size affects greenhouse gas emissions. Much less is known about how changes in temperature and precipitation, and corresponding socioeconomic impacts, influence population growth despite plausible rationale for such effects. We examine this relationship using birth histories from 1982 through 2017 in 23 sub-Saharan African countries, combined with high-resolution historical climate records. Our analyses show that exposure to climatic variability is associated with changes in the probability of childbearing, at least in the short run. Women exposed to spells of above-average temperatures and below-average precipitation experience significant reductions in the probability of fertility in the subsequent year. The association between precipitation anomalies and birth rates is particularly robust, though the estimated magnitude of effects is modest. We find substantively meaningful variation in both temperature and precipitation effects between demographic groups and across countries. Our results underscore the need to consider changes in fertility among the adaptive strategies households will employ in the face of environmental change. They also highlight the need to incorporate feedbacks between climate and fertility in models of population change and greenhouse gas emissions.

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Data availability

Data are available upon reasonable request.

Code availability

Code is available upon reasonable request.

Notes

  1. We model the effects of short-term climatic variability—i.e., the deviation of temperatures and precipitation during one- or multi-year intervals from the long-term mean—rather than longer term changes in climate. Estimates of the effects of short-term variability can be used to form plausible though imperfect expectations about the consequences of longer term climatic change. Our approach is common in the social science literature on climate change impacts (Gray and Wise 2016; Mueller et al. 2014; Nawrotzki et al. 2015).

  2. The net effects of these changes are likely to vary according to whether children are viewed as “net costs” or “net benefits,” and whether the climate shock in question is expected to have a temporary or long-term impact on households’ economic status (Lee and Kramer 2002).

  3. Prior research has also suggested that excess temperatures may increase interpersonal violence and psychological distress (Burke et al. 2018; Mares and Moffett 2016), implying a potential direct link between heat and conflict among household members.

  4. The effects of heat on women’s health may also reduce gestational length (Barreca and Schaller 2020). These effects have greater implications for the timing of births than the number of births that occur, although the acceleration (displacement) of births will influence observed fertility rates on a monthly or even annual basis (e.g., the acceleration of a birth from January to December).

  5. One anonymous reviewer noted the low availability of air conditioning in the region, which prior research suggests may help to moderate the effects of ambient conditions (especially temperature) on health and reproductive outcomes (Barreca et al. 2016; Barreca et al. 2018).

  6. We discuss the limitations to the DHS and our analysis in the concluding section of the paper.

  7. Although the article describing the data was published in 2006, the dataset had been updated through 2016 at the time of analysis.

  8. The location of DHS clusters is displaced by 0–2 km for urban communities and 0–5 km for rural communities, with the location of 1% of rural communities displaced by up to 10 km. The use of a 10-km buffer is therefore appropriate, since it ensures that the area for which the spatial mean is calculated contains the true location of the cluster of interest.

  9. The DHS data permit the modeling of births at a monthly resolution, but such data magnify significant and spatially heterogeneous misreporting of birth month and pose challenges for measurement and statistical modeling.

  10. We assume that primary school attainment is determined prior to age 14, the earliest age at which climate exposures are measured. Attainment of higher levels of education may occur at later ages, and therefore during the exposure periods included in our models. We therefore exclude controls and interactions for higher levels of education to avoid endogeneity issues (Randell & Gray 2019).

  11. The use of categorical indicators imposes arbitrary thresholds, and these results should therefore also be interpreted with some caution.

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Funding

We acknowledge the support of a research experience for undergraduates (REU) sponsored by The Pennsylvania State University and Project Drawdown during the summer of 2019. Thiede also acknowledges assistance provided by the Population Research Institute at Penn State University, which is supported by an infrastructure grant from the Eunice Kennedy Shriver National Institute of Child Health and Human Development (P2CHD041025).

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Thiede designed the study, led the analysis, and led the drafting of the paper. Ronnkvist contributed to data analysis and the drafting of the paper. Armao contributed to data analysis and the drafting of the paper. Burka contributed to data analysis and the drafting of the paper.

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Correspondence to Brian C. Thiede.

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Thiede, B.C., Ronnkvist, S., Armao, A. et al. Climate anomalies and birth rates in sub-Saharan Africa. Climatic Change 171, 5 (2022). https://doi.org/10.1007/s10584-021-03273-z

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