108 CHAPTER 5


Case, Paxson, and Ableidinger (2004) and Case and Ardington (2006) indi- cate that male death is likely to occur in poor households. If so, large impacts of the death of working males have to be interpreted carefully. It is still pos- sible that we pick up the effect of poverty on school dropouts. One important cause of girls’ decisions to drop out of school in South Africa is pregnancy. The question then arises of whether the probability of becoming pregnant during schooling is correlated with prime-age adult mortality in the household. For example, it is possible that poor families are more likely to have pregnant teenagers and face higher prime-age adult deaths than wealthy families. Again this factor was controlled for by including the value of durable goods in the household. However, it is possible for some households to have members with high biological fecundity (propensity to reproduce), which increases both the likelihood of teenage pregnancy and adult sexual activity (including extramarital sex), possibly leading to a higher incidence of HIV/ AIDS-related deaths. Another potential problem arises from a positive corre- lation between a pregnancy shock (implying a negative shock to enrollment) and a prime-age adult mortality shock in the household. In the preceding cross-sectional analysis, data limitations make it hard to control the resulting potential downward bias. I was thus forced to set this problem aside. Next I analyze the transition of adolescents from school to the labor force using panel data for 1998–2004. Table 5.5 shows the relationship between age in 1998 and activity in 2004—whether or not an individual is in school (employed and unemployed). Interestingly, the transition to the labor market starts above ages 12–14 in 1998. In the preliminary analysis, I used a sample aged 7–13, but it was found that children aged 7–12 (in 1998) did not contribute to the estimation owing to a very small number of transitions. Therefore—as long as enrollment is used as a measure of schooling status, as opposed to more sophisticated measures, such as attendance, grade progression, and test scores—it is not feasible to identify the transition from school to nonschool status using younger children. Therefore I focus on adolescents (defined as those aged 14–19) in this study.


Table 5.6 gives the estimation results for the conditional (fixed-effect) logit model.17 In this analysis I ignore other activities as an endogenous choice variable since the number of observations in this group is only seven for 1998 (rendering the estimation unstable). In all specifications, I also include dum- mies for maternal and paternal deaths.18


17 Unobserved fecundity is controlled as fixed effects are eliminated in the conditional logit


estimation. 18 I include parents in all ages, not restricting them to ages 15–64.


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