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Child fostering in a changing climate: evidence from sub-Saharan Africa

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Abstract

An extensive social science literature has examined the effects of climate change on human migration. Prior studies have focused largely on the out-migration of working age adults or entire households, with less attention to migration and other forms of geographic mobility among other age groups, including youth. In this study, we focus on the implications of climate variability for the movement of children by examining the association between climate exposures and the in- and out-fostering of children in sub-Saharan Africa. We link high-resolution temperature and precipitation records to data from the Demographic and Health Surveys for 23 sub-Saharan African countries. We fit a series of regression models to measure the overall associations between climate exposures and each outcome and then evaluate whether these associations are moderated by socioeconomic status, the number of children in the household, and the prevalence of fostering in each country. Precipitation is positively associated with in-fostering overall, and these effects are especially strong among households that already have at least one child and in countries where child fostering is common. We find no overall relationship between either temperature or precipitation exposures and out-fostering, but we do detect significant effects among households with many children and those with more educated heads. In sum, our findings suggest that climate variability can influence child mobility, albeit in complex and in some cases context-specific ways. Given the socioeconomic and health implications of fostering, these results underline another pathway through which climate exposures can affect children’s well-being. More broadly, this study shows that new attention to the links between climate variability, child fostering, and other understudied forms of spatial mobility is needed to fully understand the effects of climate change on human populations.

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Notes

  1. While it is possible that fostering may occur within very small geographic areas (e.g., within a village or neighborhood), we assume that a change in guardianship involves a move across a non-trivial distance in most cases.

  2. Temperature and precipitation anomalies capture short-term climate variability (i.e., short-term deviations from long-term climate averages), which is distinct from climate change (i.e., long-term shifts in the temperature and precipitation distributions). Despite these differences, it is currently standard practice among population-environment scholars to measure the demographic impacts of climate variability and (implicitly or explicitly) use those estimates to inform expectations about the likely effects of future climate change (e.g., Bohra-Mishra et al., 2014; Davenport et al., 2017; Thiede & Strube, 2020).

  3. Importantly, however, Mueller et al. (2020a) also find evidence of displacing effects in their study. For example, stresses associated with precipitation deficits increase out-migration in Botswana and Kenya.

  4. One other exception is a recent simulation of the links between rainfall and return migration in Thailand by Entwisle and colleagues (2020). This analysis highlighted the economic demands that returning migrants may place on receiving households.

  5. Relatedly, in some situations, children may be fostered in anticipation of future shocks (Kielland, 2016).

  6. For example, while all households are at risk of in-fostering, out-fostering can only occur among households with children.

  7. The motivations for this sequence of interaction models were determined before the analysis, drawing on prior demographic research and considering what DHS data could be used appropriately. As described below, we do not include moderators (or controls) that could have been plausibly affected by the focal climate exposure terms.

  8. The household sample weight is variable hv005 in the original DHS files, and is used as recommended by the DHS program. According to DHS documentation, “The household weight…for a particular household is the inverse of its household selection probability multiplied by the inverse of the household response rate in the stratum” (Croft et al., 2018: 1.31).

  9. We exclude 18,102 households with a large number (9+) of children to avoid outliers that we expect may reflect measurement error in many cases.

  10. Information on who the child resides with (e.g., other parent, relatives, and others) is only available for some DHS surveys and is therefore not used in the analysis.

  11. This assumption is important to our analysis and merits at least two points of elaboration. First, we are making assumptions about the initial decision to in- or out-foster a child, which is distinct from questions about the duration of fostering. The latter is important but cannot be analyzed precisely using our data—a limitation we discuss more in the concluding section. Second, previous research does not provide clear guidance about the presence and length of potentially lagged effects. We therefore tested the robustness of our findings in a supplementary model that included additional controls for temperature and precipitation anomalies in each of the five years prior to the survey (Models 9–12 and 17–20). Our main conclusions about the effects of temperature and precipitation in year t−1 did not change after controlling for conditions in these earlier years.

  12. This measurement strategy does not capture intra-annual variation in temperature and precipitation, differences in which may be masked by annual averages. This limitation could be substantively meaningful in some situations. For example, a location that experienced alternating periods of much-above and much-below average temperatures or precipitation could have the same annual anomaly as a location that experienced average conditions consistently.

  13. We use the term province to describe all first-level sub-national administrative units.

  14. We henceforth refer to the number of children in the household for brevity. We do not measure the number of children born to non-surveyed women in the household (i.e., older women without birth histories) because of challenges measuring non-resident children.

  15. We do not include controls (e.g., household wealth) or moderators that are measured at the time of the survey and potentially influenced by the focal climate exposures and fostering decisions. Such variables constitute poor control variables (Angrist & Pischke, 2009). We also note that the DHS collects relatively limited information on household social and economic dynamics (e.g., excluding detailed income or consumption records), which is appropriate given its focus on demographic and health outcomes but imposes some limitations on our analysis. We ran an additional robustness check controlling for at least one woman in the household working in agriculture (Models 13-16 and 21-24). Results from these analyses were consistent with our main findings.

  16. We take the average across all years of data for countries with multiple surveys in the analytic sample.

  17. We are likewise unable to detect climate effects on very short-term fostering arrangements that occurred between the exposure period and the DHS interview.

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Funding

Ronnkvist acknowledges support from the University of Wisconsin-Madison’s University Fellowship. Support for this fellowship is provided by the Graduate School, part of the Office of Vice Chancellor for Research and Graduate Education at the University of Wisconsin-Madison, with funding from the Wisconsin Alumni Research Foundation and the UW-Madison. Ronnkvist also acknowledges the support of the Center for Demography and Ecology (P2C HD047873 & T32 HD007014). Thiede acknowledges the 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). Thiede’s work was also supported by the USDA National Institute of Food and Agriculture and Multistate Research Project #PEN04623 (Accession #1013257). Barber acknowledges support from The Pennsylvania State University’s College of Agricultural Sciences undergraduate research award program.

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  1. University of Wisconsin - Madison, Madison, USA

    Sara R. Ronnkvist

  2. The Pennsylvania State University, State College, USA

    Brian C. Thiede

  3. Independent Researcher, State College, USA

    Emma Barber

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Ronnkvist, S.R., Thiede, B.C. & Barber, E. Child fostering in a changing climate: evidence from sub-Saharan Africa. Popul Environ 45, 29 (2023). https://doi.org/10.1007/s11111-023-00435-2

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