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Demography
Published in: Demography 3/2019

06-05-2019

The Effects of Conflict on Fertility: Evidence From the Genocide in Rwanda

Authors: Kati Kraehnert, Tilman Brück, Michele Di Maio, Roberto Nisticò

Published in: Demography | Issue 3/2019

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Abstract

Our study analyzes the fertility effects of the 1994 genocide in Rwanda. We study the effects of violence on both the duration time to the first birth in the early post-genocide period and on the total number of post-genocide births per woman up to 15 years following the conflict. We use individual-level data from Demographic and Health Surveys, estimating survival and count data models. This article contributes to the literature on the demographic effects of violent conflict by testing two channels through which conflict influences fertility: (1) the type of violence exposure as measured by the death of a child or sibling, and (2) the conflict-induced change in local demographic conditions as captured by the change in the district-level sex ratio. Results indicate the genocide had heterogeneous effects on fertility, depending on the type of violence experienced by the woman, her age cohort, parity, and the time horizon (5, 10, and 15 years after the genocide). There is strong evidence of a child replacement effect. Having experienced the death of a child during the genocide increases both the hazard of having a child in the five years following the genocide and the total number of post-genocide births. Experiencing sibling death during the genocide significantly lowers post-genocide fertility in both the short-run and the long-run. Finally, a reduction in the local sex ratio negatively impacts the hazard of having a child in the five years following the genocide, especially for older women.
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Appendix
Available only for authorised users
Footnotes
1
A commune in Rwanda in 1991 denoted a local administrative unit akin to a district.
 
2
Caldwell (2004) noted that economic shocks generally have negative short-term effects on fertility.
 
3
For a detailed account of the historical evolution of the tensions between Hutu and Tutsi, see Prunier (1999), Newbury (1988), Mamdani (2001), and Desforges (1999).
 
4
In the literature, the term replacement effect includes both the physiological effect (associated with the truncation of lactation and the shortening of the length of the postpartum amenorrheic period) and the volitional replacement effect. The former, however, would apply only to cases of infant death and cannot explain the relation between child death and fertility (Palloni and Rafalimanana 1999).
 
5
Conflict may affect the marriage market in ways that go beyond the decline in the sex ratio (La Mattina 2017). First, conflict may decrease women’s utility of being unmarried because of deteriorating economic conditions and increased risk of becoming a victim of sexual violence, thus increasing fertility. Second, the genocide may delay the age of first marriage, which would decrease fertility.
 
6
We retain only women in the analysis sample for whom fertility information is available for at least five years during the post-genocide period. Thus, the age of the women included in our sample is slightly different for each wave. For instance, consider the 2010 wave that interviewed women aged 15–49 in 2010. When we restrict the sample to women aged 10–45 years in 1994, the regression sample consists of women who were aged 25–49 in 2010.
 
7
Components of the wealth index include durables and housing characteristics. This wealth index provides a proxy for long-term economic well-being because many durables and housing characteristics are typically held by households for many years and are infrequently replaced (Sahn and Stifel 2000).
 
8
Accuracy tests on the sibling mortality module in the DHS are discussed in de Walque and Verwimp (2010).
 
9
For comparability, our analysis applies the administrative structure in place in 1991 to all DHS waves, when Rwanda’s administrative structure consisted of 11 prefectures and 145 communes.
 
10
The duration time is parameterized in terms of the set of covariates, including the conflict proxy, but the particular distributional form of the duration time is not parameterized. Also, there is no constant term; the latter is absorbed in h0(t), which is not directly estimated in the model.
 
11
Using June 1995 as a starting point allows us to exclude children conceived during the genocide, potentially through rape, from our analysis.
 
12
The Cox regression analysis includes also right-censored observations, thus overcoming problems associated with censoring and preventing bias in our estimates.
 
13
Previous fertility is defined as the number of children born before June 1995 and the percentage of children ever lost before the genocide.
 
14
In Eq. (2), we use prefecture fixed effects (instead of commune fixed effects, as in Eq. (1)) because the sex ratio varies at the commune level.
 
15
To compute the hazard ratio from the Cox coefficients, the following formula is applied: \( 100\times \left[\frac{\left({e}^{\upbeta \times 1}-{e}^{\upbeta \times 0}\right)}{e^{\upbeta \times 0}}\right] \) where β is the estimated regression coefficient.
 
16
An F test rejects the null hypothesis that the coefficients are equal, with the p value being .06.
 
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Metadata
Title
The Effects of Conflict on Fertility: Evidence From the Genocide in Rwanda
Authors
Kati Kraehnert
Tilman Brück
Michele Di Maio
Roberto Nisticò
Publication date
06-05-2019
Publisher
Springer US
Published in
Demography / Issue 3/2019
Print ISSN: 0070-3370
Electronic ISSN: 1533-7790
DOI
https://doi.org/10.1007/s13524-019-00780-8

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