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Exposure to Armed Conflict and Fertility in Sub-Saharan Africa

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Demography

Abstract

Changes in fertility patterns are hypothesized to be among the many second-order consequences of armed conflict, but expectations about the direction of such effects are theoretically ambiguous. Prior research, from a range of contexts, has also yielded inconsistent results. We contribute to this debate by using harmonized data and methods to examine the effects of exposure to conflict on preferred and observed fertility outcomes across a spatially and temporally extensive population. We use high-resolution georeferenced data from 25 sub-Saharan African countries, combining records of violent events from the Armed Conflict Location and Event Data Project (ACLED) with data on fertility goals and outcomes from the Demographic and Health Surveys (n = 368,765 women aged 15–49 years). We estimate a series of linear and logistic regression models to assess the effects of exposure to conflict events on ideal family size and the probability of childbearing within the 12 months prior to the interview. We find that, on average, exposure to armed conflict leads to modest reductions in both respondents’ preferred family size and their probability of recent childbearing. Many of these effects are heterogeneous between demographic groups and across contexts, which suggests systematic differences in women’s vulnerability or preferred responses to armed conflict. Additional analyses suggest that conflict-related fertility declines may be driven by delays or reductions in marriage. These results contribute new evidence about the demographic effects of conflict and their underlying mechanisms, and broadly underline the importance of studying the second-order effects of organized violence on vulnerable populations.

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Data Availability

All data sets used are publicly available from the DHS, ACLED, and UCDP. Programming code is available upon request from Brian Thiede.

Notes

  1. The casualty figure is for all African countries from the period January 1, 2000–January 1, 2018. It includes deaths from intrastate and interstate conflicts, violent protests, terrorism, and government violence against civilians.

  2. Evidence of such weak institutions and political instability comes from the World Bank’s Governance Effectiveness and Political Stability and Absence of Violence indicators. Both indicators range between –2.50 and 2.50. For 1996–2018, our sample’s mean score is –0.75 for Governance Effectiveness and –0.65 for Political Stability and Absence of Violence.

  3. Fertility may also increase in response to perceived child mortality risk due to conflict (i.e., “insurance effects”). However, such processes would require women to perceive that conflict will increase mortality risk over sustained periods, which has not been demonstrated empirically.

  4. One study that has used high-resolution conflict and demographic data across a large multinational population focused on maternal and child health rather than fertility (Østby et al. 2018).

  5. DHS estimates are nationally representative when survey weights are applied.

  6. The DHS has implemented multiple phases of the survey, and in some cases, response options are tailored to the local context. However, the data sets and variables included in our analytic sample were sufficiently alike to permit harmonization. The use of IPUMS-DHS data also facilitated harmonization for many countries in the sample (Boyle et al. 2018).

  7. The countries (samples) in our data are Angola (2015), Benin (2001), Burkina Faso (2003), Burundi (2016–2017), Cameroon (2004), Democratic Republic of the Congo (2007), Eswatini (2006), Ethiopia (2000, 2005, 2016), Ghana (2003, 2006), Guinea (2005), Kenya (2003, 2008–2009), Lesotho (2004, 2009), Liberia (2007), Madagascar (2008), Malawi (2000, 2004, 2010, 2016), Mali (2001, 2006), Namibia (2000, 2006), Nigeria (2003, 2008), Rwanda (2005), Senegal (2005), Sierra Leone (2008), Tanzania (2004, 2015), Uganda (2001, 2006, 2016), Zambia (2007, 2013), and Zimbabwe (2005–2006, 2015).

  8. See Table A1 (online appendix) for the distribution of observations by country and the proportion of each sample exposed to conflict, as defined in the main specification.

  9. We exclude nonnumeric responses (e.g., “up to God”) from the analysis. The prevalence of such responses is typically less than 10% and has declined over time in sub-Saharan Africa (Frye and Bachan 2017).

  10. ACLED includes other categories of violent events, such as violence against civilians and violent protests or riots. We exclude such events for two reasons. First, we are interested in the effects of armed conflict specifically rather than unrest and instability in general. Second, comparable measures for these types of violence are not available in the UCDP database, which we use to test the robustness of our findings with ACLED.

  11. We account for the error around GPS coordinates in the public-use DHS data by defining a 10-km radius as the minimum buffer in our analysis. The DHS program randomly displaces the GPS coordinates for all clusters to maintain confidentiality. The coordinates are displaced by 0–2 km for urban clusters and by 0–5 km for rural clusters, with 1% of rural clusters displaced by 0–10 km (Burgert et al. 2013). Our approach is consistent with that of other high-quality sources, including the DHS program’s own geospatial data set and IPUMS-DHS.

  12. Some countries and regions in the sample did not experience conflict events during the study period. We check the robustness of our results to excluding these places from the analytic sample in Models A1–A4 of the online appendix.

  13. We assume a nine-month gestational period and that the average birth during the prior 12 months occurred at the midpoint of the interval.

  14. Models of any contraceptive use (current or previous) yield similar results.

  15. The number of children ever born is excluded as a control in the model of women’s marital status. Although premarital childbearing is common and/or increasing in some parts of the region, a substantial majority of women still do not have a child before marriage (Clark et al. 2017), raising concerns about endogeneity.

  16. Women who recently had children are less likely to be using contraception because of ongoing breastfeeding. We therefore estimate an identical model of current contraceptive use limited to the sample of women who did not have a child within the past 12 months (Model A5, Table A6; online appendix). We also estimate a comparable model that predicts unmet need for family planning (Model A6, Table A7). We find no statistically significant conflict effects in either model.

  17. Some analyses of fertility ideals have focused on this subpopulation under the assumption that such women’s ideal family size is more dynamic and less prone to ex post rationalization (Bongaarts and Casterline 2013).

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Acknowledgments

Thanks to Yosef Bodovski and Matthew Brooks for programming assistance. Thiede acknowledges the assistance provided by the Population Research Institute at Penn State, which is supported by an infrastructure grant from the Eunice Kennedy Shriver National Institute of Child Health and Human Development (P2CHD041025). Thiede and Piazza also acknowledge support from the Penn State Social Science Research Institute and the Penn State Center for Security Research and Education.

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Authors and Affiliations

  1. Department of Agricultural Economics, Sociology, and Education, The Pennsylvania State University, 111A Armsby Building, University Park, PA, 16802, USA

    Brian C. Thiede & Matthew Hancock

  2. Independent Researcher, Khartoum, Sudan

    Ahmed Kodouda

  3. Department of Political Science, The Pennsylvania State University, 321 Pond Lab, University Park, PA, 16802, USA

    James Piazza

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  1. Brian C. Thiede
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All authors contributed to the study concept and design. Data management, data analysis, and the preparation of the manuscript were led by Brian Thiede with significant contributions from all authors. All authors read and approved the final version of the manuscript.

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Correspondence to Brian C. Thiede.

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Thiede, B.C., Hancock, M., Kodouda, A. et al. Exposure to Armed Conflict and Fertility in Sub-Saharan Africa. Demography 57, 2113–2141 (2020). https://doi.org/10.1007/s13524-020-00923-2

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