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The Impact of Family Transitions on Child Fostering in Rural Malawi

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Demography

Abstract

Despite the frequency of divorce and remarriage across much of sub-Saharan Africa, little is known about what these events mean for the living arrangements of children. We use longitudinal data from rural Malawi to examine the effects of family transitions on the prevalence and incidence of child fostering, or children residing apart from their living parents. We find that between 7 % and 15 % of children aged 3–14 are out-fostered over the two-year intersurvey period. Although divorce appears to be a significant driver of child fostering in the cross-sectional analysis, it is not significantly associated with the incidence of out-fostering. In contrast, maternal remarriage has both a lagged and an immediate effect on the incidence of out-fostering. Furthermore, the likelihood of out-fostering is even higher among children whose mother remarried and had a new child during the intersurvey period. Using longitudinal data collected from living mothers rather than from children’s current foster homes offers new insights into the reasons children are sent to live with others besides their parents.

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Notes

  1. The survey rounds collected from 1998 to 2006 are also known as the Malawi Diffusion and Ideational Change Project (MDICP).

  2. The data do not enable us to identify whether women who had a nonmarital birth later married the father of that child.

  3. The bivariate probit regression with selection is estimated using the heckprob command in Stata (StataCorp 2009). Predicted probabilities of out-fostering are obtained from the post-estimation command predict var, pmargin, which estimates the univariate predicted probability of success for the main dependent variable. This regression is based on the assumption of two latent variables, and , which represent the probability of being out-fostered and of selection into the longitudinal sample, respectively. From these latent variables, three probabilities can be estimated. First, is the probability that an observation is not censored and is in the longitudinal sample:

    Second, is the probability of an uncensored success, or the probability that an observation is observed and fostered:

    And, third, is the probability of an uncensored failure, or the probability that an observation is observed and not fostered:

    The joint estimation leads to the log likelihood function:

  4. The exclusion criterion, whether the respondent was successfully interviewed in 2004, is significantly correlated with attrition from the 2008 sample (R = .1818) but is uncorrelated with out-fostering in the analytic sample (R = –.0019).

  5. In the selection equation, we found that children whose mothers were not married in 2006 were significantly more likely to attrit from the sample, consistent with prior research by Anglewicz (2012) on migration out of the study area. Children who were Tumbuka or in the other ethnic group category were significantly more likely than Yao children to be included in the longitudinal sample, as were children whose mothers were successfully interviewed in 2004.

  6. Unless otherwise stated, all predicted probabilities presented in this article hold all variables constant at either the reference category or the mean value.

  7. We estimate our “upper bound” of prevalent child fostering in 2008 as the percentage of children aged 0–14 who were not coresident with their mother, plus those children of the same age who were listed by their mother in the 2006 household roster but who were not matched in their mother’s 2008 roster.

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Acknowledgements

The data used in this article were collected with support from the NIH (R01-HD37276, R01-HD050142, and R01-HD/MH-41713-0) and the Rockefeller Foundation (RF-99009 #199). Monica Grant was also supported by an NIH core grant to the Center for Demography and Ecology at the University of Wisconsin–Madison (R24 HD047873). We thank Philip Anglewicz, Ruben Castro, Sophia Chae, Hans-Peter Kohler, and Serena Tang for assistance linking the children’s data across survey rounds. We also thank Marcia Carlson, Shelley Clark, Rachel Goldberg, and the anonymous reviewers at Demography for comments on previous versions of this article, and Russell Dimond for statistical computing assistance.

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  1. Department of Sociology, Center for Demography and Ecology, University of Wisconsin–Madison, Madison, WI, USA

    Monica J. Grant

  2. Department of Health and Behavioral Sciences, University of Colorado Denver, Denver, CO, USA

    Sara Yeatman

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  1. Monica J. Grant
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Correspondence to Monica J. Grant.

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Grant, M.J., Yeatman, S. The Impact of Family Transitions on Child Fostering in Rural Malawi. Demography 51, 205–228 (2014). https://doi.org/10.1007/s13524-013-0239-8

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