Does it Take a Village? Migration among Rural South African Youth

Abstract

In a rural African context, the saying, “it takes a village to raise a child,” suggests that community characteristics are substantially important in children’s lives as they transit to adulthood. Are these contextual factors also related to youth migration? Demographers are uncertain about how community characteristics improve our understanding of an individual’s propensity to migrate, beyond individual and household factors. In many low- and middle-income country settings, youth become migrants for the first time in their lives to provide access to resources that their families need. We employ discrete-time event history models from 2003 to 2011 Agincourt Health and socio-Demographic Surveillance System in rural South Africa to test whether markers of development in a village are associated with the likelihood of youth and young adults migrating, distinguishing between becoming temporary and permanent migrants during this critical life cycle phase. We find that village characteristics indeed differentially predict migration, but not nearly as substantially as might be expected.

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Notes

  1. 1.

    As of 2018 there are 48 HDSSs in 19 countries, in Africa, Asia, and Oceania. They are part of the INDEPTH Network and more information can be found at http://www.indepth-network.org.

  2. 2.

    In this context, “rural” primarily refers to limited access to public sector services and infrastructure like tarred roads, electricity, sanitation, and water.

  3. 3.

    This is analogous to a hypothetical biomedical model where an individual acquires a disease, is cured, relapses at a later point, and then dies.

  4. 4.

    In the entire Agincourt HDSS, we estimate that there is less than 3% attrition due to those leaving for another village within Agincourt—most of which is due to marriage migration (which we address in detail below). Although it is possible to reconcile these individuals’ migration spells with the Agincourt HDSS, complete reconciliation of individual IDs is compromised because of the timing of spell exposures recorded (between moving out and recapturing individual at another census wave). The second spell in this type of move will not have information on the migrant’s history and, if it did, would very likely include an unidentifiable time gap between residence spells.

  5. 5.

    Akaike information criterion (AIC) and Bayesian information criterion (BIC) are interpreted, as measures of goodness of model fit, per the suggestions of Raftery (1996) and Burnham and Anderson (2004).

  6. 6.

    In several instances, the Hausman Test is undefined because of issues with degrees of freedom from clustering on villages and including fixed village characteristics. To our knowledge, there are no empirical guidelines in remedying the results of this test even if employing “wild bootstrapping” procedures that could potentially more effectively estimate models with village clustering and dummies.

  7. 7.

    As with Appendix Table 5, there are degrees of freedom issues with several Hausman Tests in Appendix Table 6, with no clear empirical remedy. The coefficients for time-varying village characteristics of assets and education are positively associated with permanent migration, relative to not migrating in a given year. Only for males, is village level of education positively—albeit marginally—associated with temporary migration (p<.10).

  8. 8.

    Analyses found in Appendix Table 6 found consistently positive effects of village assets and education on the chances of migration for males and females.

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Acknowledgements

We would especially like to thank Andy Foster, Margot Jackson, and David Lindstrom for their technical advice, in addition to Steve McGarvey, Leah VanWey, Zhenchao Qian, Taryn Dinkelman, and Jo Fisher for their constructive comments on earlier versions of this paper. This paper was presented at the 2016 Population Association of America annual meeting in Washington, DC. We would also like to thank Yashas Vaidya for consolidating Agincourt Health and socio-Demographic Surveillance System (Agincourt HDSS) data into a harmonized panel file used in this research. We are grateful for support of this project from the Providence/Boston Center for AIDS Research (CFAR) under NIH grant P30AI042853; the Eunice Shriver National Institute of Child Health and Human Development for 1R01HD083374 (Brown University); NIH R24HD041020, P2C HD041020 (Brown University Population Studies and Training Center); and additional support for the maintenance of the Agincourt HDSS from Wits School of Public Health, Wellcome Trust (grants 058893/Z/99/A, 069683/Z/02/Z, 069683/Z/08/Z), Medical Research Council of South Africa, and Brown University’s Population Studies and Training Center. We gratefully acknowledge the South African Medical Research Council for funding Carren Ginsburg’s Career Development Award.

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Correspondence to Tyler W. Myroniuk.

Appendix

Appendix

See Tables 4, 5 and 6

Table 4 Variation in village-level predictors over time
Table 5 Event history models, 2003–2011, 15-year-old cohorts, males and females, first migration
Table 6 Event history models repeated events, 2003–2011, 15-year-old cohorts, males and females, competing risks of temporary versus permanent migration

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Myroniuk, T.W., White, M.J., Gross, M. et al. Does it Take a Village? Migration among Rural South African Youth. Popul Res Policy Rev 37, 1079–1108 (2018). https://doi.org/10.1007/s11113-018-9493-1

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Keywords

  • Event history analysis
  • Migration
  • South Africa
  • Youth