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Death and schooling decisions over the short and long run in rural Madagascar


This paper provides strong evidence that adult mortality has a negative impact on children educational outcomes, both over the short and the long run, in rural Madagascar. The underlying longitudinal data and the difference-in-differences strategy used overcome most of the previous cross-sectional study limitations, such as failure to control for child and household pre-death characteristics and unobserved heterogeneity. This paper also pays special attention to the heterogeneity, robustness, and long-run persistence of effects. Results show that orphans are on average 10 pp less likely to attend school than their nonorphaned counterparts, this effect being even more pronounced for girls and young children from poorer households. Results on adults further show that those orphaned during childhood eventually completed less education. These findings suggest that not only do households suffering unexpected shocks resort to schooling adjustments as an immediate risk-coping strategy, but also that adversity has long-lasting effects on human capital accumulation.

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  1. Many national and international programs were launched by international organizations such as the World Bank or UN agencies, in partnership with community-based associations and NGOs, to reduce school fees and expenses, to supply uniforms, improve access to credit, or promote part-time education for orphans (see, for instance, the UNESCO program to provide education to orphans and vulnerable children, the Orphan Support Africa program or the numerous National Orphan programs in several countries from East and Southern Africa).

  2. For the sake of simplicity, any child experiencing an adult death in the household will be referred to as an “orphan” throughout this paper, even if he (resp. she) is not strictly the son (resp. daughter) of the deceased member. This is not so restrictive in the Malagasy context where most households are nuclear families. Most adult deaths registered in data are then affecting parents and, if not, a very close relative. For further details, see Section 3.

  3. Several studies point out the importance of informal insurance in developing countries where formal mechanisms are failing. This includes risk-sharing systems within extended families or community networks, likely to provide financial, human, or moral assistance and thus working as safety nets (Townsend 1995). Likewise, child fostering is a common practice among orphans. Akresh (2007) show that foster children are more likely to be enrolled in school years after adult death than their nonfoster siblings. However, the same authors put forward the reciprocity involved in those mechanisms, which is hardly sustainable in case of large and permanent shocks such as death (Townsend 1994).

  4. Using previous rounds of this survey, Rakoto-Tiana (2011) investigates children time allocation within the household and shows that there is a trade-off between schooling and domestic and productive child work, depending on the structure of the household. One interesting feature for our issue of interest is that the absence of one of the parents has a negative impact on school enrollment and seems to increase child work. Although this study does not precisely deal with adult death, it provides first evidence that child work is considered by Malagasy households as a relevant informal strategy to cope with risk.

  5. The ROR was implemented as part of the Madagascar-DIAL-INSTAT-Orstom project, established at the Malagasy National Institute of Statistics (INSTAT) and founded through the European Union, the French “Institut de Recherche pour le Développement” (ex-Orstom), and the French Ministry of Cooperation. Surveys are conducted through various rural observatories which are referred to as regions in the paper. Depending on the donors’ interest for such-and-such rural issue, some observatories were created, whereas others were phased out over time. There were a total of 13 observatories in 1999, 17 in 2000 and 2001, and 15 in 2002 and 2003. For more information, see the ROR webpage:

  6. Other reasons were refusal, temporary absence, migration, marriage, divorce, or back in the home household. The rate of missing values is 18 % for this question. However, we are rather confident with the fact that death is well reported by the remaining members of the household, so that we do not underestimate the number of deaths in this setting.

  7. The 2004 survey initially covered 14 regions. Nevertheless, four regions were phased out in 2005 and 2006. We assumed this “technical” attrition as random since it is related to a lack of funding more than to specific village or household characteristics.

  8. Most of them are “half-orphans,” and some lost a grandparent or other adult in the household. However, 76 % of the deaths recorded in the survey affect formerly resident parents of the sampled children, mostly in their working age. The average age at death is indeed 56 years for men and 54 years for women. Following results are robust if we restrict the sample to children who lost a parent. They are available from the author upon request.

  9. This is one limitation of our study since were are not able to evaluate the potential mitigating effects of informal insurance networks through fostering and migration.

  10. Gross enrollment ratios can actually be greater than 100 % as a result of grade repetition and entry at younger or older ages than the typical age at a given level

  11. Among reasons mentioned by respondents in orphans’ households for not sending them to school in 2005, shortage of labor on crops and high school fees stand in the first and second positions, which is in line with our theoretical framework.

  12. \(t\in \{0,1\}\) where 0 refers to the 2004 baseline period and 1 to the subsequent 2005 period, in what follows.

  13. Formally, if we denote \(S^{0}_{it}\) the potential schooling outcome of a child had he not experienced death, this identifying assumption can be written as \(\mathbb {E}[S^{0}_{i1}-S^{0}_{i0}/D=1] = \mathbb {E}[S^{0}_{i1}-S^{0}_{i0}/D=0] =\mathbb {E}[S_{i1}-S_{i0}/D=0]\). It states that there is no selection into treatment, which will be further discussed in details.

  14. Again, these results rely on the same fundamental DID identifying assumption which states that, conditional on observables and/or unobservables, there is no selection into treatment, i.e., death is as good as random. In this regression setting, this assumption can be formally rewritten \(P(D_{iht}=1/\varepsilon _{it})=P(D_{iht}=1)\).

  15. In our setting, \(D_{ih0}=0\) for every child, while \(D_{ih1}\) switches to 1 for a child in household h incurring an adult death between the two periods.

  16. In order to control for the potential inequality of treatment between household heads’ own children and other child relatives with respect to schooling.

  17. We implement the conditional logit estimator for fixed-effects panel data discrete choice model], suggested by Chamberlain (2008).

  18. This figure corresponds to the average marginal change in probability calculated from logit specification (2). The marginal effect at the mean is about 20 pp. Unfortunately, deriving marginal effects from conditional (fixed-effects) logit estimations remains quite tricky. Nevertheless, as the estimated coefficients are quite close in all specifications, we are confident with the fact that marginal changes in probability are of similar magnitude.

  19. This is partly due to the fact that child fixed-effects models are “overidentified,” since adult death shocks are measured at the household level. Thus, household fixed effects probably capture most of the unobservable heterogeneity correlated with death.

  20. This scheme builds on propensity score matching methods proposed by Heckman et al. (1998). The DID/matching combination appears to be robust by comparison to random experiments.

  21. The propensity score is computed from a probit regression where the dependent variable is a dummy equal to one if child i became orphan in 2005 and independent variables include the whole set of baseline child and household characteristics. Results are not reported but available from the author upon request.

  22. However, only 0.9 % of households were not reinterviewed for such a reason.

  23. Rakoto-Tiana (2011) specifically investigates the impact of child fostering on education outcomes in Madagascar and show mixed results depending on the structure of the host household. Young fostered children that are biologically related to the head of host household are more likely to be enrolled after fostering. This is, however, not true for nonrelated ones that might be involved in child (domestic or productive) work.

  24. Formally, we test the missing at random (MAR) assumption (Little and Rubin 1987), \(\mathbb {E}[S_{i0}/X] = \mathbb {E}[S_{i0}/X,A_{i}=1]\), where \(A_{i}\) is a dummy for nonattrition. The MAR assumption is rejected in our data.

  25. Note that we only present corrected LPM, to keep the sample size fixed and also because Heckman’s procedure is better suited to linear models.

  26. Ai and Norton (2003) show that interaction effects in nonlinear models cannot be evaluated simply by looking at the sign, magnitude, or statistical significance of interaction term coefficients, estimated by standard softwares. It requires more sophisticated computation of “true” marginal effects.

  27. Note that the scope of the analysis is here restricted to the impact of parental death, since other adult deaths which occurred in the household during childhood were not recorded.

  28. Secondary education is not rare in the adult sample. Indeed, 15.2 % of adults have engaged in some post-primary schooling, which amounts to 21.6 % of adults who went to school.

  29. Using this subsample of adults for which the exact timing of parental death and the age at end of schooling is recorded, we estimate that 12.6 % of adults who lost a parent had not finished schooling when he died, which amounts to 45.2 % of adults who lost a parent before the age of 18.

  30. In order to account for year-cohort specific trends, we more precisely interact period dummies with date of birth.

  31. We estimate the following model: \(S_{it}=\eta _{i}+\delta _{t}+ \alpha D \centerdot T_{it}+\varepsilon _{it}\), where D is a dummy equal to 1 if individual i lost a parent before age 18, and \(T_{it}\) indicates the number of years since parental death for individual i at period t. This specification is similar to Eq. 8, but postulates a constant effect \(\alpha \) of death on schooling outcomes each year after its occurrence.


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Correspondence to Jean-Noël Senne.

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Senne, JN. Death and schooling decisions over the short and long run in rural Madagascar. J Popul Econ 27, 497–528 (2014).

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  • Adult mortality
  • Orphans
  • Education
  • Longitudinal data

JEL Classifications

  • I15
  • I25
  • C23