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From Death to Birth: Mortality Decline and Reproductive Change (1998)

Chapter: 5 The Impact of AIDS Mortality on Individual Fertility: Evidence from Tanzania

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Suggested Citation:"5 The Impact of AIDS Mortality on Individual Fertility: Evidence from Tanzania." National Research Council. 1998. From Death to Birth: Mortality Decline and Reproductive Change. Washington, DC: The National Academies Press. doi: 10.17226/5842.
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Suggested Citation:"5 The Impact of AIDS Mortality on Individual Fertility: Evidence from Tanzania." National Research Council. 1998. From Death to Birth: Mortality Decline and Reproductive Change. Washington, DC: The National Academies Press. doi: 10.17226/5842.
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Suggested Citation:"5 The Impact of AIDS Mortality on Individual Fertility: Evidence from Tanzania." National Research Council. 1998. From Death to Birth: Mortality Decline and Reproductive Change. Washington, DC: The National Academies Press. doi: 10.17226/5842.
×
Page 140
Suggested Citation:"5 The Impact of AIDS Mortality on Individual Fertility: Evidence from Tanzania." National Research Council. 1998. From Death to Birth: Mortality Decline and Reproductive Change. Washington, DC: The National Academies Press. doi: 10.17226/5842.
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Suggested Citation:"5 The Impact of AIDS Mortality on Individual Fertility: Evidence from Tanzania." National Research Council. 1998. From Death to Birth: Mortality Decline and Reproductive Change. Washington, DC: The National Academies Press. doi: 10.17226/5842.
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Suggested Citation:"5 The Impact of AIDS Mortality on Individual Fertility: Evidence from Tanzania." National Research Council. 1998. From Death to Birth: Mortality Decline and Reproductive Change. Washington, DC: The National Academies Press. doi: 10.17226/5842.
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Suggested Citation:"5 The Impact of AIDS Mortality on Individual Fertility: Evidence from Tanzania." National Research Council. 1998. From Death to Birth: Mortality Decline and Reproductive Change. Washington, DC: The National Academies Press. doi: 10.17226/5842.
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Suggested Citation:"5 The Impact of AIDS Mortality on Individual Fertility: Evidence from Tanzania." National Research Council. 1998. From Death to Birth: Mortality Decline and Reproductive Change. Washington, DC: The National Academies Press. doi: 10.17226/5842.
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Page 145
Suggested Citation:"5 The Impact of AIDS Mortality on Individual Fertility: Evidence from Tanzania." National Research Council. 1998. From Death to Birth: Mortality Decline and Reproductive Change. Washington, DC: The National Academies Press. doi: 10.17226/5842.
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Page 146
Suggested Citation:"5 The Impact of AIDS Mortality on Individual Fertility: Evidence from Tanzania." National Research Council. 1998. From Death to Birth: Mortality Decline and Reproductive Change. Washington, DC: The National Academies Press. doi: 10.17226/5842.
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Suggested Citation:"5 The Impact of AIDS Mortality on Individual Fertility: Evidence from Tanzania." National Research Council. 1998. From Death to Birth: Mortality Decline and Reproductive Change. Washington, DC: The National Academies Press. doi: 10.17226/5842.
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Suggested Citation:"5 The Impact of AIDS Mortality on Individual Fertility: Evidence from Tanzania." National Research Council. 1998. From Death to Birth: Mortality Decline and Reproductive Change. Washington, DC: The National Academies Press. doi: 10.17226/5842.
×
Page 149
Suggested Citation:"5 The Impact of AIDS Mortality on Individual Fertility: Evidence from Tanzania." National Research Council. 1998. From Death to Birth: Mortality Decline and Reproductive Change. Washington, DC: The National Academies Press. doi: 10.17226/5842.
×
Page 150
Suggested Citation:"5 The Impact of AIDS Mortality on Individual Fertility: Evidence from Tanzania." National Research Council. 1998. From Death to Birth: Mortality Decline and Reproductive Change. Washington, DC: The National Academies Press. doi: 10.17226/5842.
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Page 151
Suggested Citation:"5 The Impact of AIDS Mortality on Individual Fertility: Evidence from Tanzania." National Research Council. 1998. From Death to Birth: Mortality Decline and Reproductive Change. Washington, DC: The National Academies Press. doi: 10.17226/5842.
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Suggested Citation:"5 The Impact of AIDS Mortality on Individual Fertility: Evidence from Tanzania." National Research Council. 1998. From Death to Birth: Mortality Decline and Reproductive Change. Washington, DC: The National Academies Press. doi: 10.17226/5842.
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Suggested Citation:"5 The Impact of AIDS Mortality on Individual Fertility: Evidence from Tanzania." National Research Council. 1998. From Death to Birth: Mortality Decline and Reproductive Change. Washington, DC: The National Academies Press. doi: 10.17226/5842.
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Suggested Citation:"5 The Impact of AIDS Mortality on Individual Fertility: Evidence from Tanzania." National Research Council. 1998. From Death to Birth: Mortality Decline and Reproductive Change. Washington, DC: The National Academies Press. doi: 10.17226/5842.
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Page 155
Suggested Citation:"5 The Impact of AIDS Mortality on Individual Fertility: Evidence from Tanzania." National Research Council. 1998. From Death to Birth: Mortality Decline and Reproductive Change. Washington, DC: The National Academies Press. doi: 10.17226/5842.
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Page 156
Suggested Citation:"5 The Impact of AIDS Mortality on Individual Fertility: Evidence from Tanzania." National Research Council. 1998. From Death to Birth: Mortality Decline and Reproductive Change. Washington, DC: The National Academies Press. doi: 10.17226/5842.
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Page 157
Suggested Citation:"5 The Impact of AIDS Mortality on Individual Fertility: Evidence from Tanzania." National Research Council. 1998. From Death to Birth: Mortality Decline and Reproductive Change. Washington, DC: The National Academies Press. doi: 10.17226/5842.
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Page 158
Suggested Citation:"5 The Impact of AIDS Mortality on Individual Fertility: Evidence from Tanzania." National Research Council. 1998. From Death to Birth: Mortality Decline and Reproductive Change. Washington, DC: The National Academies Press. doi: 10.17226/5842.
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Page 159
Suggested Citation:"5 The Impact of AIDS Mortality on Individual Fertility: Evidence from Tanzania." National Research Council. 1998. From Death to Birth: Mortality Decline and Reproductive Change. Washington, DC: The National Academies Press. doi: 10.17226/5842.
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Page 160
Suggested Citation:"5 The Impact of AIDS Mortality on Individual Fertility: Evidence from Tanzania." National Research Council. 1998. From Death to Birth: Mortality Decline and Reproductive Change. Washington, DC: The National Academies Press. doi: 10.17226/5842.
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Suggested Citation:"5 The Impact of AIDS Mortality on Individual Fertility: Evidence from Tanzania." National Research Council. 1998. From Death to Birth: Mortality Decline and Reproductive Change. Washington, DC: The National Academies Press. doi: 10.17226/5842.
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Suggested Citation:"5 The Impact of AIDS Mortality on Individual Fertility: Evidence from Tanzania." National Research Council. 1998. From Death to Birth: Mortality Decline and Reproductive Change. Washington, DC: The National Academies Press. doi: 10.17226/5842.
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Page 163
Suggested Citation:"5 The Impact of AIDS Mortality on Individual Fertility: Evidence from Tanzania." National Research Council. 1998. From Death to Birth: Mortality Decline and Reproductive Change. Washington, DC: The National Academies Press. doi: 10.17226/5842.
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Page 164
Suggested Citation:"5 The Impact of AIDS Mortality on Individual Fertility: Evidence from Tanzania." National Research Council. 1998. From Death to Birth: Mortality Decline and Reproductive Change. Washington, DC: The National Academies Press. doi: 10.17226/5842.
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Suggested Citation:"5 The Impact of AIDS Mortality on Individual Fertility: Evidence from Tanzania." National Research Council. 1998. From Death to Birth: Mortality Decline and Reproductive Change. Washington, DC: The National Academies Press. doi: 10.17226/5842.
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Suggested Citation:"5 The Impact of AIDS Mortality on Individual Fertility: Evidence from Tanzania." National Research Council. 1998. From Death to Birth: Mortality Decline and Reproductive Change. Washington, DC: The National Academies Press. doi: 10.17226/5842.
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Suggested Citation:"5 The Impact of AIDS Mortality on Individual Fertility: Evidence from Tanzania." National Research Council. 1998. From Death to Birth: Mortality Decline and Reproductive Change. Washington, DC: The National Academies Press. doi: 10.17226/5842.
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Suggested Citation:"5 The Impact of AIDS Mortality on Individual Fertility: Evidence from Tanzania." National Research Council. 1998. From Death to Birth: Mortality Decline and Reproductive Change. Washington, DC: The National Academies Press. doi: 10.17226/5842.
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Suggested Citation:"5 The Impact of AIDS Mortality on Individual Fertility: Evidence from Tanzania." National Research Council. 1998. From Death to Birth: Mortality Decline and Reproductive Change. Washington, DC: The National Academies Press. doi: 10.17226/5842.
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Suggested Citation:"5 The Impact of AIDS Mortality on Individual Fertility: Evidence from Tanzania." National Research Council. 1998. From Death to Birth: Mortality Decline and Reproductive Change. Washington, DC: The National Academies Press. doi: 10.17226/5842.
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Suggested Citation:"5 The Impact of AIDS Mortality on Individual Fertility: Evidence from Tanzania." National Research Council. 1998. From Death to Birth: Mortality Decline and Reproductive Change. Washington, DC: The National Academies Press. doi: 10.17226/5842.
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Suggested Citation:"5 The Impact of AIDS Mortality on Individual Fertility: Evidence from Tanzania." National Research Council. 1998. From Death to Birth: Mortality Decline and Reproductive Change. Washington, DC: The National Academies Press. doi: 10.17226/5842.
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Suggested Citation:"5 The Impact of AIDS Mortality on Individual Fertility: Evidence from Tanzania." National Research Council. 1998. From Death to Birth: Mortality Decline and Reproductive Change. Washington, DC: The National Academies Press. doi: 10.17226/5842.
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Suggested Citation:"5 The Impact of AIDS Mortality on Individual Fertility: Evidence from Tanzania." National Research Council. 1998. From Death to Birth: Mortality Decline and Reproductive Change. Washington, DC: The National Academies Press. doi: 10.17226/5842.
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Suggested Citation:"5 The Impact of AIDS Mortality on Individual Fertility: Evidence from Tanzania." National Research Council. 1998. From Death to Birth: Mortality Decline and Reproductive Change. Washington, DC: The National Academies Press. doi: 10.17226/5842.
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Suggested Citation:"5 The Impact of AIDS Mortality on Individual Fertility: Evidence from Tanzania." National Research Council. 1998. From Death to Birth: Mortality Decline and Reproductive Change. Washington, DC: The National Academies Press. doi: 10.17226/5842.
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Suggested Citation:"5 The Impact of AIDS Mortality on Individual Fertility: Evidence from Tanzania." National Research Council. 1998. From Death to Birth: Mortality Decline and Reproductive Change. Washington, DC: The National Academies Press. doi: 10.17226/5842.
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Suggested Citation:"5 The Impact of AIDS Mortality on Individual Fertility: Evidence from Tanzania." National Research Council. 1998. From Death to Birth: Mortality Decline and Reproductive Change. Washington, DC: The National Academies Press. doi: 10.17226/5842.
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Suggested Citation:"5 The Impact of AIDS Mortality on Individual Fertility: Evidence from Tanzania." National Research Council. 1998. From Death to Birth: Mortality Decline and Reproductive Change. Washington, DC: The National Academies Press. doi: 10.17226/5842.
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Suggested Citation:"5 The Impact of AIDS Mortality on Individual Fertility: Evidence from Tanzania." National Research Council. 1998. From Death to Birth: Mortality Decline and Reproductive Change. Washington, DC: The National Academies Press. doi: 10.17226/5842.
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SThe Impact of AIDS Mortality on Individual Fertility: Evidence from Tanzania Martha Ainsworth, Deon Filmer, and Innocent Semali During the European demographic transitions, fertility decline was often but not always preceded by an aggregate decline in mortality (Matthiessen and McCann, 1978~. In sub-Saharan Africa, highlevels of child mortality are thought to be an impediment to fertility decline. Caldwell et al. (1992), for example, suggest that a decline in infant mortality to levels below 70 per 1,000 may be a prerequisite for the onset of fertility decline, based on the experience of Botswana, Kenya, and Zimbabwe. Child mortality has declined and life expectancy increased in sub-Saharan Africa in recent decades, but the spreading AIDS epidemic threatens this progress. Nearly two-thirds of the 23 million people currently infected with human immu- nodeficiency virus (HIV) worldwide live in sub-Saharan Africa (UNAIDS data, cited in Ainsworth and Over, 1997~. AIDS is fatal and is striking two key groups sexually active adults who become infected through sexual relations and very young children who are infected from their mothers at birth or while breastfeeding. The impact of AIDS on mortality is difficult to measure, as vital registration systems in sub-Saharan Africa are subject to extensive underreporting (Stover,1993~. However, the U.S. Bureau of the Census predicts that the decline in African infant and child mortality will be stalled and reversed as a result of the AIDS epidemic (Way and Stanecki, 1994~. Nicoll et al. (1994) predict that mortality of children under the age of 5 in severely affected urban areas will increase by one-third in eastern and central Africa and by as much as three- quarters in southern Africa, sharply diminishing the existing differentials in child mortality between urban and rural areas. Furthermore, levels of adult mortality in the age group 15-50 can be expected to double, triple, or even quadruple in some locales. 138

MARTHA AINSWORTH, DEON FILMER, AND INNOCENT SEMALI 139 What will be the impact of heightened mortality from AIDS on fertility in sub-Saharan Africa? There is remarkably little empirical evidence on this issue. In fact, demographic modelers of the impact of the AIDS epidemic commonly assume no fertility response to AIDS mortality. For example, The AIDS Epi- demic and its Demographic Consequences (UN/WHO, 1991) presents seven mathematical models for the demographic consequences of the spread of HIV, none of which includes an individual fertility response. The World Bank's AIDS- adjusted population projections assume no interaction between HIV prevalence and fertility (Bos and Bulatao, 1992~. In this chapter we review the channels through which we might expect both positive and negative fertility responses to the heightened mortality of the AIDS epidemic, summarize the evidence to date, and present new evidence of the response of individual fertility behavior to heightened mortality based on three data sets from Tanzania. In the next section we provide an overview of levels of HIV infection in sub-Saharan Africa and the relation between HIV infection and mortality. This is followed by a discussion of the channels through which height- ened mortality from AIDS might induce changes in fertility. In the fourth section we present results of multivariate analysis of individual fertility using three data sets from Tanzania two national and one from the severely affected Kagera region. The results suggest that, although there is evidence of a positive effect of heightened child mortality on fertility, adult mortality at the household and com- munity level tends to be associated with lower individual fertility. These results are supported by an analysis of the effect of mortality on other indicators of fertility intentions, such as the desire for additional children and patterns of sexual behavior. EXCESS MORTALITY FROM THE AFRICAN AIDS EPIDEMIC Although sub-Saharan Africa has the highest number of current HIV infec- tions of any region in the world, the prevalence of HIV varies considerably across the continent and within countries. Figure 5-1 shows the adult seroprevalence rate (the percentage of people aged 15-50 who are HIV-positive) for HIV-1 among "low-risk" urban populations, based on HIV/AIDS Surveillance Data Base (Bureau of the Census, 1995~.1 These data are drawn from samples of pregnant women attending antenatal clinics.2 In 12 countries, over 10 percent of pregnant women in urban areas are infected with HIV. "High-risk" urban populations, 1 The discussion focuses on HIV-1 infection, which is the most prevalent variant of HIV in sub- Saharan Africa (National Research Council, 1996). 2Note, however, that these data are not necessarily indicative of seroprevalence levels in a random sample of the population; women attending antenatal clinics are often better educated and have higher incomes than the general population.

140 Percent Seropositive Less than 0.1 0.1 0.2 to 1.0 1.1 to5.0 5.1 to10.0 Over 1 0.0 No Data THE IMPACT OF AIDS MORTALITY: EVIDENCE FROM TANZANIA FIGURE 5-1 Percentage of the low-risk urban adult population infected with HIV-1, circa 1995. SOURCE: Bureau of the Census (l995:Map 2~. such as commercial sex workers and soldiers, have seroprevalence levels of 40 percent or higher in 12 countries (not shown in Figure 5-1~. Prevalence is gener- ally lower in rural areas. However, because in most countries the overwhelming share of the population is rural, even low rural rates of infection imply that the majority of AIDS deaths occur in rural areas. The number of these deaths is compounded by urban relatives who migrate to rural areas shortly before death, the magnitude of which is not known. Heterosexual transmission accounts for approximately 80-90 percent of all adult HIV infections in sub-Saharan Africa (Mann et al., 1992; National Research Council, 1996~. In many hard-hit countries, women are equally if not more likely to be infected than men. A second important transmission route is from mother to child. In Africa, roughly a quarter to a half of the children born to HIV-positive

MARTHA AINSWORTH, DEON FILMER, AND INNOCENT SEMALI 141 mothers themselves become infected, either through the birth process or through breastfeeding (Lallemant et al., 1994~. Thus, AIDS can be expected to increase mortality dramatically in Afnca both among the very young and among adults in their prime childbearing and economically active years. Way and Stanecki (1994) show a profile of age- specific mortality rates in a population in which 20 percent of adults are infected with HIV (Figure 5-2~. The baseline mortality in their comparison population without HIV is clearly not from sub-Saharan Afnca, where infant mortality ranges from 70-150 per 1,000 and where prevailing mortality among pnme-aged adults, which ranges between 5 and 8 per 1,000, is roughly eight times higher than in a developed country. Furthermore, their estimates of AIDS-related child mortality likely do not include the deaths of HIV-negative children who are put at greater risk because of the loss of their parents due to AIDS. Child mortality (ages 1-4) may be more sensitive to AIDS than is infant mortality since many infected children survive beyond 1 year of age (Valleroy et al., 1990; Way and Stanecki, 1994~. Nevertheless, Figure 5-2 illustrates the substantial impact that AIDS can have on mortality early in life and in the prime age groups. Indeed, in many cities 160 140 120 100 o O 80 113 60 40 20 without AIDS with AIDS A 0 10 20 A----- ~ -- I 9~) 40 50 Age = ~1 60 70 80 FIGURE 5-2 Hypothetical impact of HIV on age-specific mortality, assuming 20 percent of adults are infected. SOURCE: Way and Stanecki (1994:Fig. 11, p. 13).

42 THE IMPACT OF AIDS MORTALITY: EVIDENCE FROM TANZANIA in sub-Saharan Africa, seroprevalence rates among adults are even higher than the 20 percent assumed in Figure 5-2. The United Nations (1995) estimates that AIDS will increase the cumulative mortality among children under the age of 5 by 7.8 percent in the 15 most seriously affected African countries from 1980-2005. The effect increases with age: Cumulative deaths among people aged 15-34 will be 25 percent higher and among 35-49 cumulative deaths will be 61 percent higher. Because of the way in which HIV is spread to children, households with AIDS-related child mortality will also likely experience AIDS-related adult mortality. The clustering of deaths of children and prime-aged adults in the same households distinguishes AIDS from other causes of child mortality that do not threaten adults. Thus, when we consider the effects of AIDS-related child mortality on fertility, we must at the same time consider the effects of AIDS-related mortality among adults in their prime years. HOW AIDS-RELATED MORTALITY AFFECTS FERTILITY Increased mortality due to the AIDS epidemic can induce changes in indi- vidual fertility through many different channels, some biological and some be- havioral. The posited effect of higher levels of child mortality is to raise fertility and of higher adult mortality to lower fertility. Child Mortality and Fertility A couple's own child mortality can result in higher fertility through two channels: (1) abrupt cessation of breastfeeding following the child's death, which eliminates the protection afforded by breastfeeding's contraceptive effect and raises the risk of another pregnancy (the "interval effect"; and (2) by an increase in the number of births a couple must have to achieve a target number of surviv- ing children (Preston, 1978~. This latter behavioral response to child mortality may take two forms "replacement" of young children who die through addi- tional births or simply bearing more children than needed to "insure" against anticipated child mortality in the future. The "interval" and "replacement" effects of child deaths that are due to AIDS are unlikely to be strong because the parents of these children themselves are infected. In fact, it is often due to the illness and death of a child that the parents learn of their own infection. The parents may attempt to prevent future births through abstinence, contraception, or abortion; abstinence to prevent re- infection with HIV would also make a subsequent birth unlikely. The mother may also succumb to AIDS before another pregnancy can come to term, making it unlikely that one would observe her or her children's deaths in a sample of women. Thus, elevated child mortality due to AIDS will probably exert a stron- ger positive effect on fertility through the "insurance" channel by raising

MARTHA AINSWORTH, DEON FILMER, AND INNOCENT SEMALI 143 uninfected couples' perceptions of their probable child mortality experience, increasing their estimate of the number of excess births necessary to guarantee a target number of surviving children. Multivariate studies of the relation between a couple's own child mortality and their fertility in African countries have generally confirmed a positive rela- tionship (Akin and Shariff, 1993; Anker, 1985; Benefo and Schultz, 1996; Farooq, 1985; Okojie, 1989; Snyder, 1974~. However, most of these studies have treated the couple's child mortality as exogenously determined. If one accepts the propo- sition that child health and child mortality are the outcomes of household deci- sions on health "inputs," such as consumption of food and health care, then the exogeneity of child deaths is difficult to accept. Failure to take the endogeneity of child deaths into account leads to biased estimates of the relationship with fertility. Studies that examine the impact of a woman's own child mortality on fertility are also problematic because they are confined to samples of women who have had at least one live birth, which is in effect conditioning on an endogenous variable (fertility). At least two studies have taken the endogeneity of child mortality into account using African data. Okojie (1989) found in Bendel State, Nigeria, a negative relation between a woman's predicted child survival and fertility in rural areas and among women nearing the end of their reproductive lives. Benefo and Schultz (1996) tested for and were unable to reject the exo- geneity of child mortality in Cote d'Ivoire and Ghana. When child mortality was treated as exogenous, they found a very weak replacement effect an increase in fertility of one child in response to every 4-15 child deaths, depending on the country and region. Preston (1978) points out that such weak relationships should be expected in areas with a high demand for children; if couples want as many children as possible, then a reduction in child mortality will not reduce fertility. Although Ahn and Shariff (1993) did not account for the endogeneity of child mortality, they also examined the impact of community infant and child mortality rates, which can be considered exogenous to the household. They found high infant mortality to be associated with a higher hazard of subsequent birth in Togo, but high child mortality to be negatively associated with the hazard of subsequent births in Uganda. These studies collectively suggest a positive, if sometimes weak, relation between child deaths and fertility in sub-Saharan Africa. Adult Mortality and Fertility Heightened adult mortality due to AIDS may reduce desired family size and the observed demand for children of individual women through the following channels: · AIDS mortality often occurs in young adulthood before the long-run ben- efits of earlier child investments can be realized by the parents of those infected. Heightened adult mortality rates due to AIDS will thus reduce the expected long

44 THE IMPACT OF AIDS MORTALITY: EVIDENCE FROM TANZANIA run benefits of children, in turn lowering desired family size. High adult mortal- ity may also prevent parents from investing in their children's schooling and health care. · Mortality of prime-aged adults in the household may reduce household income (at least temporarily) and raise the demand for labor of the surviving adults. This would raise the shadow cost of children and shrink the budget constraint, both of which would tend to reduce the demand for children. · High adult mortality will also leave many orphaned children to be ab- sorbed by the households of relatives. These orphaned children make additional claims on existing income and the time of adults and may reduce the demand for additional children of their own. Other Channels Through Which AIDS Morbidity and Mortality May Affect Fertility Any change in the demand for children or in biological factors affecting the supply of children because of the AIDS epidemic will be reflected in correspond- ing changes in the proximate determinants of fertility, such as contraceptive use, breastfeeding, marriage, abortion, infecundity, and sterility (Bongaarts, 1978~. Changes in the proximate determinants reflect, in most cases, individual choices or their outcomes that are joint decisions with fertility. Gregson (1994) points out many of the following effects of AIDS on the proximate determinants and, jointly, with fertility: · Fertility among infected women may decline because of illness, infertility induced by other sexually transmitted diseases (STDs), increased use of contra- ception, widowhood, and increased resort to abortion (Nicoll et al., 1994~. The use of condoms to prevent the spread of STDs, including AIDS, may reduce unwanted births. At the same time, to the extent that condoms replace more effective methods of birth control, fertility may rise. One of the major strategies to slow the spread of AIDS is to offer treat- ment for other "conventional" STDs, such as syphilis and gonorrhea, thought to facilitate transmission of HIV. This intervention would have the beneficial side effect of reducing levels of pathological sterility in many countries, which could result in higher fertility. · Other behavioral changes to prevent the spread of AIDS may include delayed age at marriage, monogamy, and increased celibacy (Caldwell et al., 1993~. These changes would be associated with lower fertility. However, HIV is also spread through breast milk, and breastfeeding is a major determinant of the period of postpartum infecundability. Any reduction in breastfeeding could re- duce the period of postpartum infecundability and raise fertility unless compen- sated for by higher contraceptive use or abstinence. · In a review of the fertility effects of HIV counseling and testing programs, . .

MARTHA AINSWORTH, DEON FILMER, AND INNOCENT SEMALI 145 Setel (1995) concludes that there is no evidence that women informed of their HIV-positive status accelerate childbearing, while a few studies show that they have somewhat lower subsequent fertility than women told they were HIV nega- tive. Finally, in the aggregate, AIDS mortality affects fertility through its impact on the age structure of women of reproductive age. But there are other aspects of the selective mortality of women of reproductive age that could affect aggregate fertility. For example, if the women who are becoming infected are also those who would have had fewer children in any event (for example, urban women), then the women with lower fertility are selectively dying and overall fertility may rise. Or, if these low-fertility women have already had the children they would have had, aggregate fertility may remain unchanged. Many demographic model- ers have assumed that women who die of AIDS will have already borne most of the children they could expect in a lifetime and therefore would have very little effect on aggregate fertility (Bos and Bulatao, 1992; Way and Stanecki, 1994~. However, infection rates in Africa are on the increase among young females, raising the possibility that many will die before having completed their lifetime fertility. A few medical researchers have found lower fertility among HIV-positive women, although they have not been able to attribute the results to biological as opposed to behavioral causes. Ryder et al. (1991) found somewhat lower fertility and higher contraceptive use among HIV-positive women than among HIV- negative women in a sample of women followed over 3 years following delivery of a live-born child in Mama Yemo Hospital in Kinshasa, Zaire. Sewankambo et al. (1995), in a recent study of 1,860 households in the rural Rakai district in Uganda, found that the birth rate among HIV-positive women aged 15-49 was 169 per 1,000, whereas that for HIV-negative women was 213 per 1,000. Using data from the same region of Uganda, Gray et al. (1995) found the prevalence of pregnancy to be lower among HIV-infected women. In addition, there is some evidence that HIV-positive mothers have a higher likelihood of spontaneous abortion (Langston et al., 1995~. To the best of our knowledge, there has been no empirical study of the behavioral response of individual fertility to increased mortality due to AIDS. Among the reasons for the lack of empirical work is the difficulty of identifying AIDS mortality, the difficulty of observing a sufficient number of adult deaths to measure their impact, the lack of longitudinal data, and the absence of observa- tions on community-level measures of mortality. An added complication is that the line of causation between child mortality and fertility runs in both direc- tions high levels of fertility and closely spaced births are thought to raise the risk of death to children and mothers. Indeed, as Nicoll et al. (1994) point out, the impact of the epidemic on child mortality depends heavily on fertility. If all infected women were to cease having children, then child mortality may increase

146 THE IMPACT OF AIDS MORTALITY: EVIDENCE FROM TANZANIA very little. Thus, there is also the need for data with sufficient instruments to separately identify the two relationships. INDIVIDUAL FERTILITY RESPONSE IN TANZANIA Tanzania, on the eastern coast of Africa, stretches to Lake Victoria in the northwest, Lake Tanganyika in the west, and Lake Nyassa in the southwest. Per capita gross national product (GNP) in the early 1990s was on the order of $100, and about three-quarters of Tanzania's 24 million people live in rural areas (World Bank, 1995~. The 1991/92 Demographic and Health Survey (DHS) estimated Tanzania's total fertility rate at 6.3, infant mortality at 92 per 1,000 and under-5 mortality at 141 per 1,000 for the 5 years preceding the survey (Ngallaba et al., 1993~. Results from the 1988 census indicate relatively higher levels of both infant and under-5 mortality 15 and 191 per 1,000, respectively (Bureau of Statistics, undated). Tanzania is among the countries most severely affected by the AIDS epi- demic in Africa and in the world. The first case of AIDS was diagnosed in 1983 in the Kagera region, on the western shore of Lake Victoria and adjacent to Uganda and Rwanda. HIV was probably in the region for a decade or more before the first diagnosis. By 1992 there was a cumulative total of 38,416 reported AIDS cases from all regions of the country since the beginning of the epidemic (Ministry of Health, cited in Mukyanuzi, 1994~. This was surely a gross undercount, but by how much we cannot be sure. As of 1990 it was estimated that between 400,000 and 800,000 people were infected, and it was anticipated that AIDS would shortly become the major cause of death among young children and prime-aged adults (World Bank, 1992~. No one has undertaken a nationally representative seroprevalence survey in Tanzania, so the true prevalence of HIV is unknown. Chin and Sonnenberg (1991) compiled a map of the estimated HIV prevalence among sexually active adults, by region, using the results of seroprevalence surveys of smaller, select samples and the reported number of cases (Figure 5-3~. At that time, the highest levels of infection were thought to be in Dar es Salaam and Kagera, followed by Mwanza and Mbeya regions. Kagera, Mwanza, and Mbeya are all along major transportation routes to adjacent countries, over which much cross-border trade passes. A population-based seroprevalence survey of Kagera region in 1987 found an infection rate of 24.2 percent among adults aged 15-54 in the main town of Bukoba (on the lake and about 100 km south of the Uganda border) (Killewo et al., 1990a). Rural rates of infection were also high: 10 percent of adults in the rural areas surrounding Bukoba and next to the lake,4.5 percent in the northwest- ern part of the region bordering Rwanda and Uganda, but less than half a percent in the southern part of the region. The infection rate among children 0-14 years of age was 3.9 percent in Bukoba, with the highest levels among the youngest children.

MARTHA AINSWORTH, DEON FILMER, AND INNOCENT SEMALI Kigoma Tabora 0 100 200 km 1 1 1 ~ Singida \ ~Kilimaniaro Ruvuma ~/ 147 me' Dar es Salaam urban 4-7% rural < 1 % urban 7-1 0% rural 1-3% urban > 10% rural > 3% HIV Prevalence urban < 4% I I rural< 1% 1 l FIGURE 5-3 Estimated HIV prevalence among sexually active adults, Tanzania, 1989. SOURCE: Chin and Sonnenberg (1991), cited in World Bank (1992~. In Western countries, the median time between infection with HIV and de- velopment of AIDS and death is roughly 10 years (Moss and Bachetti, 1989; Rutherford et al., 1990~. The incubation period is thought to be shorter in sub- Saharan Africa because of higher underlying morbidity and lower nutritional status (Killewo et al., l990b; National Research Council, 1996~. The extent to which AIDS is contributing to overall mortality is not known. Results of the 1988 Tanzanian census reveal that the unadjusted death rates for adults aged 15- 49 ranged from 3.4 to 9.4 per 1,000 across the 20 regions of mainland Tanzania (Ainsworth and Rwegarulira, 1992~. These adult death rates are based on raw census data, without any adjustments for internal consistency or underreporting. Although the levels may be underestimates, the differentials in mortality across regions point to the areas with relatively higher mortality. The highest adult mortality was recorded in Kagera region; Mbeya and Dar es Salaam, where HIV infection is also widespread, had relatively high adult death rates as well (6.3 and 6.5 per 1,000, respectively). The results revealthat tinder-5 mortalityin 1985

148 THE IMPACT OF AIDS MORTALITY: EVIDENCE FROM TANZANIA Under-5 Mortality Rate Below 160 160- 189 190 - 219 220 and above FIGURE 5-4 Under-5 mortality by district, Tanzania mainland, 1985. SOURCE: Data from Bureau of Statistics (no date). was higher around Lake Victoria and in the southern parts of Tanzania (Figure 5- 4~. These regional differentials in mortality reflect not only the effect of the AIDS epidemic but the distribution of other underlying determinants of mortality and nutritional status, such as household incomes, food prices, disease vectors (such as mosquitos), and the availability of medical care. The Model Economic models of fertility in developing countries begin with a model of a household that both produces and consumes (Becker, 1993; Schultz, 1981~. Household members derive utility from their children and from other consump- tion goods. However, children also have a potentially important contribution to household production activities and their future earnings may be an important

MARTHA AINSWORTH, DEON FILMER, AND INNOCENT SEMALI 149 long-term benefit to their parents and relatives. The demand for children is thus a function both of the benefits and the costs of raising or "producing" them. Among the costs are the value of the time of the mother or other female relatives, the costs of schooling, health care, food, clothing, and other essentials. In the more formal economic model, the "demand" for children varies with a number of factors exogenous to the household wages, prices, nonlabor income, and unob- served preferences of the parents or household for children. The supply of children is determined by biological factors such as fecundity, which varies with the woman's age. An important consideration in African settings (and in particular in light of the AIDS epidemic) is that the supply of children to the household is not necessarily dependent on fertility. Foster chil- dren and orphans can also be accepted into the household to raise the household' s utility (Ainsworth, 1996~. Even though high levels of child mortality are likely to raise fertility among parents who wish to be assured of a target number of surviving births, community levels of adult mortality are likely to reduce the demand for children by lowering their long-run expected benefits. Deaths of adults in a given household may lower fertility through decreased income and household production. Deaths of adults outside the household particularly among close relatives might also generate an unanticipated supply shock as others' children must be absorbed into the household. We thus expect both male and female adult deaths in the house- hold to lower fertility. To the extent that prime-aged males are engaged in market work, their deaths may be more likely to affect fertility through an income effect (regardless of their relation to female members of the household).3 In addition, if the death is that of a husband or partner, we expect a negative relation because of widowhood and a socially determined delay in remarriage. Prime-aged fe- males are likely to be substitutes for one another in household production (such as in child care, domestic tasks, and farming). Therefore, the death of a prime- aged female in the household will likely result in lower fertility for remaining members as the value of female time (which is scarcer within the household) increases and the cost of raising children increases indirectly. We estimate a model of the determinants of births in the past 12 months among women of reproductive age. The dependent variable equals one if the woman has given birth in the 12 months before the interview and zero otherwise. The explanatory variables of key interest to this study include different measures of mortality at the household, community, and extended family level, all of which are assumed to be exogenous: 3The relation between income and fertility is theoretically indeterminate it could be positive or negative.

150 THE IMPACT OF AIDS MORTALITY: EVIDENCE FROM TANZANIA · death of prime-aged adults in the same household according to the time since death, · death of adult relatives, · community child mortality,4 and · community mortality of prime-aged adults. A fairly standard set of fertility determinants that include factors affecting both the supply of and demand for children comprises the remaining explanatory variables. The variable definitions are somewhat specific to each data set, but include · age and age squared, to control for exposure to the risk of pregnancy; · variables that reflect the opportunity cost of a woman's time, such as her schooling and schooling squared; · variables that measure or represent the household's budget constraint; and · variables that reflect prices, such as regional or national price indices, adult and child wages, the availability of health care, schooling, and family planning infrastructure. With respect to measures of the budget constraint, two of the three data sets described below contain annual household consumption expenditure. We antici- pate that a male death affects fertility by lowering income, for which household expenditures are a proxy. Thus, results are shown for two specifications of the determinants of a recent birth. The first specification is a reduced form, in which all of the explanatory variables are assumed to be exogenous and household consumption expenditure is excluded. These results can be compared across all three data sets. The second specification, estimated for only two of the data sets, includes an endogenous regressor, the log of annual household expenditures per adult. The latter has been replaced by its predicted value using as instruments the household head's characteristics (gender, age, and schooling), the value of house- hold productive assets and dwellings, and the hectares of banana crop harvested. We estimate the relation between the probability of a live birth in the past 12 months and different indicators of adult and child mortality using probit (for the cross-sectional surveys) and random-effects probit (for the longitudinal survey) 4we do not examine the impact of women s own child mortality on their subsequent fertility because: (1) there are inadequate instruments to identify the woman s own child mortality, which is jointly endogenous with fertility; (2) the analysis would have to be limited to women who have had at least one child recently, which sharply reduces the sample and conditions the analysis on endog- enous fertility; and (3) the likelihood of observing a fertility response of women whose children die Of AIDS is low, since the mothers are infected, have higher mortality, and are less likely to be observed in the sample.

MARTHA AINSWORTH, DEON FILMER, AND INNOCENT SEMALI 151 regression (Maddala, 1987~. The probit coefficients have been transformed to facilitate interpretation. For continuous variables, the reported figures represent the percentage point increase in the probability of a birth in the past 12 months associated with a one-unit change in the explanatory variable. For binary ex- planatory variables, the coefficients have been transformed into the increase in the probability of birth when the variable equals one, compared with when it equals zero. Mortality variables are highlighted below; descriptive statistics for other variables are shown in Appendix 5-A and regression results for all variables are available from the authors. The Data Sets We use three household-level data sets collected in Tanzania in the early 1990s. Two are national and one is from the Kagera region, in the northwest. Although the three data sets are all for a single country, we are unaware of other countries with the requisite mortality data. The impacts in Tanzania are likely to be similar to those in other East African countries hard hit by the AIDS epidemic at equivalent points in the demographic transition and with similar levels of economic development. The dependent variables, measures of mortality, and other regressors in the three data sets are compared in Table 5-1. The Kagera Health and Development Survey (KHDS), conducted as part of the research project "The Economic Impact of Fatal Adult Illness due to AIDS and Other Causes in sub-Saharan Africa," and funded by the World Bank Re- search Committee, the United States Agency for International Development, and the Danish International Development Agency (Over and Ainsworth, 1989), is a longitudinal living standards survey of more than 800 households in the Kagera region. The survey was conducted from 1991 to 1993, roughly 8-10 years after the first cases of AIDS were diagnosed in the region. The sample was heavily stratified so as to capture households at high risk of adult deaths. Each household was interviewed a maximum of four times at 6- to 7-month intervals. A total of 757 households completed all four waves of the survey. The questionnaire and sampling are described in Ainsworth et al. (1992~. The KHDS questionnaire included a list of children ever born for all females aged 15-50, which was updated every wave with information on new births and on women who joined the sample. The questionnaire also collected information on the death of all household members in the 24 months before the first interview and between interviews and the death of all relatives who were not living with the household at the time of death. In addition, the data set includes several indica- tors of community-level mortality rates based on three sources: ward-level rates from the 1988 census, an enumeration of all households in the 51 survey clusters in 1991, and the results of questionnaires administered to community informants. This information allows us to examine the probability of recent births as a func- tion of adult mortality in the household and the extended household, as well as of

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154 THE IMPACT OF AIDS MORTALITY: EVIDENCE FROM TANZANIA community-level mortality of children and adults. The analysis is based on a pooled sample of 2,896 women aged 15-50 who were interviewed in waves 2-4 of the longitudinal survey. The first wave of data was dropped to permit study of the effect of a death as long ago as 30 months. On average, 13 percent of the women had given birth in the 12 months prior to each interview. The Tanzania Human Resources Development Survey (THRDS), jointly undertaken by the Department of Economics of the University of Dar es Salaam, the Government of Tanzania, and the World Bank and funded by the World Bank, the Government of Japan, and the British Overseas Development Agency, collected information from a national cross-section of nearly 5,000 households in mainland Tanzania in 1993. It included questions on fertility as well as on household and individual economic variables, although less extensively than the KHDS. A module on the mortality of household members of all ages in the past 12 months makes it possible to examine the effect of recent adult death on fertility. Each observation can also be linked with its district-level child (under- 5) mortality and its regional adult (15-49) mortality rate from the 1988 census.5 The survey design and questionnaires of the THRDS are described in Ferreira and Griffin (1995~. We use as our sample 6,234 women aged 15-50. An average of 10.4 percent of these women gave birth in the 12 months before the survey. The Tanzania Knowledge, Attitudes and Practices Survey (TKAP) collected data from a national cross section of about 4,000 households in mainland Tanza- nia in 1994. The survey design and questionnaires are described in Weinstein et al. (1995~. The TKAP has only a limited set of economic variables but a broader set of variables that reflect fertility intentions (for example, desire for an addi- tional child and measures of sexual activity). Each respondent in the household was asked whether a household member has AIDS or has died of AIDS. If the answer was negative, respondents were asked whether they knew anyone who had AIDS or had died of AIDS. From these we construct the cluster proportion of women who report knowing anyone (including household members) who has AIDS or has died of AIDS. This variable, although not measuring actual mortal- ity, reflects levels of perceived AIDS mortality in the survey communities. No other (adult) mortality questions were asked of the respondents so we cannot include deaths from other causes at the household or cluster levels. We do, however, link the data to the district child (under-5) mortality rate and the re- gional adult (15-49) mortality rate from the 1988 census. Approximately 7 percent of the women in the sample live in households in which a member has AIDS or has died of AIDS. This ranges from about 1 percent in the Dodoma 5The under-5 child mortality rate is from Bureau of statistics (no date) and the adult mortality rate is from Ainsworth and Rwegarulira (1992). The THRDS covers 49 of 103 districts in all 20 regions Of mainland Tanzania and the TKAP covers 83 districts.

MARTHA AINSWORTH, DEON FILMER, AND INNOCENT SEMALI 155 region to almost 20 percent in the Kagera region. The sample mean of the cluster proportion of women respondents who knew anyone who had AIDS or had died of AIDS is equal to about 47 percent, with a range from about 29 percent in the Rukwa region to almost 88 percent in the Kagera region. It should be noted that the TKAP did not include an income or expenditure module and therefore we are unable to condition on these variables in the analy- sis. The regressions do include some measures of household wealth (such as type of flooring in the dwelling, ownership of a bicycle or a car), which indicate to some extent potential income effects. Nor were community and facility data collected in the 1994 TKAP. However, in 1991 and 1992 all the clusters included in the TKAP were administered a community and health facility questionnaire as a part of the Tanzania Demographic and Health Survey (TDHS) whose sampling frame and questionnaire are described in Ngallaba et al. (1993~. Therefore, the community and facility data included in the regressions are from approximately 2 years prior to the individual-level data. The community and facility variables used here are described in Beegle (1995~. The sample used is that of 3,950 women aged 15-49. Of these women, 19.4 percent gave birth in the past 12 months.6 The Impact of Child Death Rates on Recent Fertility The marginal effect of a one-unit change in the community-level child mor- tality on the probability of a birth in the past 12 months is presented in Table 5-2. Note that the child mortality variable for the THRDS and the TKAP is defined in the same way and comes from the same source, the 1988 census. An increase in the under-5 child mortality of 10 per 1,000 (from 177 to 187 per 1,000 in the THRDS, for example) is associated with an increase in fertility of 0.3 to 0.5 percent not a very strong effect. The KHDS child mortality variable is based on the ratio of the number of children who died who were under age 15 per 1,000 population in the commu- nity. The source of this information is the KHDS community questionnaire. In every wave, the community respondents were asked how many people of differ- ent ages had died in the community since the last wave (or in the case of wave 1, in the past 12 months). This information was divided by the total population of 6The means of the dependent variables are quite different across the three data sets, part of which can be accounted for by the stratification of the samples. When weighted, the mean percentage of women who gave birth in the past 12 months is 15.0 percent for the KHDS (wave 1), 11.4 percent for the THRDS, and 19.5 percent for the TKAP. An additional factor is the phrasing of the questions. Although the KHDS and TKAP variables were obtained from a full fertility history of each woman, the THRDS fertility variable is based on asking each woman whether she gave birth in the past 12 months.

156 THE IMPACT OF AIDS MORTALITY: EVIDENCE FROM TANZANIA TABLE 5-2 Marginal Effecta of Child Mortality on Recent Fertility, Women Aged 15-50b (dependent variable: births in the past 12 months) Marginal Effect of Child Mortality Rate Mean Child Mortality (Standard Excluding Including Data Set Variable Deviation) Expenditure ExpenditureC KHDS, Death of children <15 5.97 0.1133 0.0948 1991- per 1,000 population, (5.42) 1993 community level THRDS, Deaths of children <5 177.15 0.0275d 0.0292d 1993 per 1,000, 1988 census, (40.54) district level TKAP, Deaths of children <5 186.26 0.047d 1994 per 1,000, 1988 census, (40.52) district level NOTE: , data not available. aThe derivative of the probability of birth in the past 12 months with respect to the explanatory variable, evaluated at the means of all of the independent variables and multiplied by 100 (see Appendix 5-B). bFor the TKAP survey, women aged 15-49. CPredicted log expenditures per adult are included as an explanatory variable. Vindicates probit parameter estimate is significantly different from zero at the 5 percent level. the community from the door-to-door enumeration in early 1991, updated for in- and out-migration and births and deaths, and annualized. The average death rate in the sample of women was about 6 children under age 15 per 1,000 population. An increase in the child mortality rate of 10 children per 1,000 population would raise the probability of birth in the last 12 months by one percentage point, although this result is not statistically significant. The positive correlation for child deaths in all three samples is expected and consistent with the literature. The inclusion of controls for household expenditure per adult do not much alter the results. The Impact of Adult Death Rates on Recent Fertility Higher levels of adult mortality at the community level are associated with lower recent fertility in all three data sets (Table 5-3~. However, the results are statistically significant only in the THRDS when the predicted log of expenditure per adult is included. An increase in the adult mortality of 5 per 1,000 would

MARTHA AINSWORTH, DEON FILMER, AND INNOCENT SEMALI TABLE 5-3 Marginal Effecta of Adult Death Rates on Recent Fertility, Women Aged 15-50b (dependent variable: births in the past 12 months) 157 Marginal Effect of Adult Death Rate Mean Adult Mortality (Standard Excluding Including Data Set Variable Deviation) Expenditure ExpenditureC KHDS, Deaths of adults 9.73 -0.0343 -0.0540 1991-1993 15 and older per (6.55) 1,000 population, community level THRDS, Deaths of adults 5.63 -0.551 _0.854d 1993 15-49 per 1,000, (1.34) 1988 census, regional level TKAP, Deaths of adults 5.46 -0.781 1994 15-49 per 1,000 (1.55) 1988 census, regional level NOTE: , data not available. aThe derivative of the probability with respect to the explanatory variable evaluated at the means of all the independent variables and multiplied by 100 (see Appendix 5-B). bFor the TKAP survey, women aged 15-49. CPredicted log expenditures per adult are included as an explanatory variable. Vindicates that the probit parameter estimate is significantly different from zero at the 5 percent level. reduce the percentage of women giving birth from 10.4 to 6.2 percent. Such an increase would represent a doubling of adult mortality, which is not unexpected in areas hard hit by the AIDS epidemic. The community-level mortality for adults and children (Tables 5-2 and 5-3) are jointly significant at the 5 percent level for the THRDS and at 11 percent for the TKAP. For the KHDS, they are not jointly significant. The Impact of Deaths of Household Members and Relatives Higher levels of community adult mortality are associated with lower recent fertility in the Tanzanian data sets, possibly because of the reduced expected long-run benefits of children as adults. We would expect adult deaths in a

158 THE IMPACT OF AIDS MORTALITY: EVIDENCE FROM TANZANIA particular household to lower the fertility of surviving women for additional reasons lower income, greater demand on the time of surviving women in home production, and the need to care for orphaned children who may substitute for the woman's own children. Only the KHDS and the THRDS collected information on deaths in the household. During all four waves of the KHDS, from 6 months before the first wave of household interviews until the end of the last interview, 268 household members died among households interviewed (Ainsworth et al., 1995~. Of these, 47 percent (126 persons) were adults aged 15-50, and roughly half of them were reported by their relatives to have died of AIDS. One-third of adult deaths were attributed to other illness or unknown causes. The mean proportion of women in the KHDS with male and female adult deaths in their household in various time frames is reported in Table 5-4. Deaths of adult relatives include relatives who were household members as well as nonresident relatives. The one-round THRDS recorded a total of 324 deaths of household members in the 12 months before the survey, of which only 118 (36 percent) were adults aged 15-50. The main causes of death among adults were illness (88 percent), traffic accidents (6 percent), and childbirth (3 percent). About 1.3 percent of the women lived in a household where there had been a male adult death and an equal number where there had been a female adult death (age 15-50) in the past 12 months.7 Because the birth and death variables used in this analysis are measured over discrete time intervals, it is not always obvious which of these two events oc- curred first. Adult deaths in the past 12 months (measured in both the KHDS and the THRDS) could have occurred before conception, during pregnancy, or after a recent birth. However, deaths more than 12 months in the past (measured in the KHDS only) occurred before any recent birth and in most cases before concep- tion. Turning first to the results for the KHDS, both the deaths of adult household members and the deaths of adult relatives are associated with lower recent fertil- ity (see Table 5 5~.8 Only the death of women in the household and of both male and female relatives are significantly related to recent fertility, however. Women in households where another female adult died 0-12 months ago have a three 7The relative rarity of the deaths of prime-aged adults in the THRDS (with 5,000 households) underscores the difficulty of studying the impact of adult deaths from AIDS, even in a country with an AIDS epidemic. In contrast, the KHDS obtained information on 126 adult deaths (age 15-50) from a much smaller sample of about 800 households over a 2-year period by selecting areas with known high adult mortality and by stratifying the sample according to the anticipated risk of adult deaths. This required an extensive enumeration of 29,000 households, from which the survey sample was selected. 8The KHDS results are based on separate regressions using death of adult household members in one specification and death of relatives in another. The two sets of mortality variables were not entered in the same regression.

MARTHA AINSWORTH, DEON FILMER, AND INNOCENT SEMALI TABLE 5-4 Descriptive Statistics for Household-Level Mortality Variables, KHDS, Women 15-50 (n = 2,896) 159 Male Deaths Female Deaths Mortality Standard Standard Variable Mean Deviation Mean Deviation Death of household member aged 15-50 0-12 months ago 0.033 (0.178) 0.038 (0.191) 12-18 months ago 0.023 (0.150) 0.029 (0.168) 18-24 months ago 0.037 (0.220) 0.051 (0.220) 24-30 months ago 0.040 (0.197) 0.060 (0.238) Death of a relative aged 15-50 last 30 months Sibling 0.078 (0~268) 0.090 (0.286) Parent 0.016 (0.125) 0.024 (0.154) Spouse 0.026 (0.159) NOTE: , data not available. percentage point lower probability of having had a birth in the past year and for those in households where a female adult died 18-24 months ago the probability is two percentage points lower. These results are statistically significant and large compared with the average probability of a birth in the past year of 13 percent. The signs on the results for female deaths in other time periods are also negative, but not statistically significant. The results for male deaths are not statistically significant, although the effect of death in each period is negative. As a group, the female deaths are jointly significant at the 5 percent level; the male death variables are not jointly statistically significant. The death of close relatives of both genders (siblings, husband, parents) in the past 30 months is associated with lower fertility. The death of an adult brother, sister, or husband leads to a drop in the probability of a birth in the past year of two to three percentage points. The results for deaths of parents and other relatives of the head or spouse (not shown) are not statistically significant. The death of a spouse is significantly associated with lower fertility, but loses the significance when household consumption per adult is included. The deaths of all relatives are jointly statistically significant, both with and without including the log of consumption per adult. In summary, the deaths of female household members and close relatives are associated with lower recent fertility, whereas deaths of adult males in the house- hold have no apparent relation with fertility, irrespective of whether controls for household resources are included. These results suggest that female deaths and

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MARTHA AINSWORTH, DEON FILMER, AND INNOCENT SEMALI 161 the deaths of adult siblings, in particular, raise the opportunity costs of time of surviving women, lowering their fertility. The negative (generally insignificant) effect of husband's death on fertility may be operating purely through lower exposure to the risk of pregnancy, but evidently not through its indirect effect on household resources. In the lower half of Table 5-5, the coefficients on deaths of adults in the past 12 months for the THRDS are not individually statistically significant, nor are they jointly so. The signs and magnitudes of the (insignificant) marginal effects are greatly different from the KHDS results. What can account for this? It must be kept in mind that the marginal effects are evaluated at the mean values of the explanatory variables, so that samples with identical probit coefficients but dif- ferent means on the independent variables may have very different marginal effects. Other factors explaining the difference in results are that deaths were far more prevalent in the KHDS survey, and the survey questions on adult deaths differed. The KHDS asked about the date of every death, on which basis the time since death could be determined. The THRDS simply asked for a list of all deaths in the past 12 months. Mortality and Other Measures of Fertility Intentions Results discussed in the previous section suggest a negative relation between adult mortality at the community and household level and fertility, but a positive relation between community levels of child mortality and fertility. However, the results are not always statistically significant. Conception and birth occur 9- months apart; retrospective data on deaths often do not extend back far enough in time to study this issue easily. In this section we consider the impact of deaths on several "proximate" indicators of fertility intentions or outcomes that are likely to respond to mortality with a shorter lag: · the desire for an additional child; and · measures of recent sexual activity. If mortality affects fertility outcomes through behavioral channels, then to be consistent with the results on fertility in the previous section, heightened adult mortality from AIDS or other causes would be associated with a reduction in desire for additional children and a reduction in sexual activity. Heightened child mortality would be associated with increased desire for additional children and greater sexual activity. Irrespective of whether a reduction in sexual activity is due to a change in fertility intentions or an attempt to prevent HIV infection, it can be expected to reduce fertility. Among the three data sets, only the TKAP obtained information on these variables, but the questions were asked of both women and men. Table 5-6

162 ca ;^ ~ ~ X C) o ~ . ·_4 .= ¢ x a' VO Cq o .= a' _. o Cq a' Cq a' o o o a' r. . ·bC ¢ E~ ca s~ 4= _I X o V, ca ~ s~ o C) ca V s~ .= o ~o ~ ¢ ~ ~ C . . o ;^ s~ _4 ~ ~o * * * o o ~o o CMo .. ... oo oo 1 1 * * CM ~oo CMo ~o~ oo ~oo 1 * * * o oo ~ oo ooo l o .. . O0 ~ ~1- ~ o oo 11 00 5 ~t m) ~t 0 0 ~ 0 00 0 ~0o 11 ~ ~, ~o ~o ~ca _ ~ i_== ~ <= o' r Cq Cq

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164 THE IMPACT OF AIDS MORTALITY: EVIDENCE FROM TANZANIA presents the relation between measures of mortality and three dependent vari- ables: weeks. · Fertility intentions: A dichotomous variable (0/1) equal to one if the respondent would ever like another child. For women, this variable is the answer to a direct question; for men, it is equal to one if the current family size is smaller than the respondent's ideal family size and zero otherwise. · Onset of sexual activities: Among the sample of men and women aged 15-19, a dichotomous variable equal to one if the respondent has ever had sexual intercourse. · Recent sexual activity: The frequency of sexual intercourse in the past 4 Models for the first two of these dependent variables are estimated using a probit regression, whereas the models for the third dependent variable are estimated using a Tobit regression with a lower limit of zero. Among women aged 15-49 and men aged 15-59, 62 percent and 88 percent, respectively, would like another child. The mean number of acts of sexual intercourse in the past 4 weeks was 4.3 for women and 4.8 for men, including about 40 percent of female and 30 percent of the male respondents who reported no sexual intercourse in the past 4 weeks. Among those aged 15-19, 53 percent of women and 64 percent of men had ever had sexual intercourse. These dependent variables are regressed on the same set of explanatory variables as in the fertility regressions and include three measures of adult mor- tality: the proportion of households in the sample cluster who know someone who has AIDS or who has died of AIDS, the district-level under-5 mortality rate, and the region-level adult (aged 15-49) mortality rate from the 1988 census.9 Unfortunately, the regressions do not control for household resource availability because the TKAP survey did not collect consumption information. The results for women and men are reported in Table 5-6. Among both men and women, perceptions of high mortality due to AIDS are associated with a significant reduction in the desire for additional children. Spe- cifically, an increase of 0.10 in the proportion of respondents who know someone who has AIDS or has died of AIDS is associated with a reduction of 2.1 percent- age points in the probability of wanting another child among women, and a 0.6 point fall for men. Child mortality, surprisingly, has no significant relation to the 9In a separate specification, the first of these variables was replaced by a dummy variable for whether a household member had AIDS or had died of AIDS. The coefficient was never significant in regressions for any of the three dependent variables, for men or women. The coefficients on the under-5 and adult mortality are qualitatively unaffected when the AIDS variables at the household and cluster level are excluded from the regressions.

MARTHA AINSWORTH, DEON FILMER, AND INNOCENT SEMALI 165 desire for additional children among men or women, confirming the weak rela- tionship found earlier and in other studies. The results for adult and child death rates are unaffected by exclusion of the AIDS awareness variable from the re- gression. Among women aged 15-19, higher community adult death rates are associ- ated with a significantly lower probability of ever having sexual intercourse. Anecdotes suggest that as the result of the AIDS epidemic, increasingly younger girls are being sought for sex because they are presumed to be uninfected. How- ever, to the contrary, the results in Table 5-6 provide evidence that fewer teenage girls are sexually active in high-mortality communities. An increase in the adult mortality of 1 per 1,000 (about a 20 percent increase over the mean level) is associated with a four percentage point reduction in the probability of ever hav- ing sexual intercourse, from 53 to 49 percent. A doubling of the adult mortality rate, which is typical in hard-hit areas, would reduce the onset of sexual activity among women by about 20 percentage points. Adult mortality is not significantly correlated with the onset of sexual activity among men aged 15-59, however. Finally, higher levels of adult mortality in the community are associated with a reduction in the frequency of sexual intercourse among women, whereas among men the perception of higher mortality due to AIDS is associated with less intercourse. An increase of 5 per 1,000 in the community adult mortality rate corresponds to a decrease in sexual activity from 4.3 to 3.3 in the past month among women. Among men, an increase of 0.10 in the proportion of those knowing of someone who has AIDS or has died of AIDS (from 0.5 to 0.6) is associated with a reduction in sexual activity from 4.8 to 4.5 in the past month. Thus, women' s sexual activity responds to the adult mortality rate, whereas men' s sexual activity responds to the perception of AIDS deaths in the community. The results in Table 5-6 suggest that greater personal experience with AIDS and AIDS mortality in the community leads to a lower reported desire for children among both men and women and a decrease in sexual activity among men. Higher adult mortality rates in the community, which are the result of high levels of HIV infection, are associated with delayed onset of sexual activity and a reduction in the frequency of sex among women. All of these outcomes would be associated with lower fertility. CONCLUSIONS This chapter has examined the impact of increased mortality that is due to the AIDS epidemic on individual fertility behavior. Models of the demographic impact of the epidemic have generally ignored the possibility of a behavioral fertility response to higher mortality from AIDS. The AIDS epidemic will raise mortality in two main groups children under 5 and sexually active adults, gen- erally in the age group 15-50. However, child mortality is already very high in most sub-Saharan countries, whereas adult mortality in the prime ages is among

166 THE IMPACT OF AIDS MORTALITY: EVIDENCE FROM TANZANIA the lowest of any age group. The AIDS epidemic will marginally raise child mortality, but could as much as quadruple underlying mortality among prime- aged adults. We use three recent data sets from Tanzania to explore the relation between various measures of mortality and the probability of a birth in the past 12 months. Individuals' perceptions of mortality trends are likely to be influenced by mortal- ity in their communities as well as deaths in their households and extended family. The results confirm the positive but weak relation between community levels of child mortality and recent fertility found elsewhere in the literature. Thus, an increase in child mortality due to AIDS can be expected to contribute to higher fertility. Community levels of adult mortality are negatively correlated with recent fertility (as expected), but are often not statistically significant at conventional levels. Deaths of female adults within the past 24 months are significantly associated with lower recent fertility of surviving women in the same household. However, a male death as long as 30 months ago is not correlated with the recent fertility of surviving women in the same household. It was not anticipated that very recent adult deaths would influence fertility, but this appears to be true for the deaths of women in the past 12 months. Because AIDS deaths are the outcome of extended illness, it is likely that mortality among household members and relatives can be anticipated. This could account for the unexpected finding that recent fertility responds to recent deaths. The relation of the deceased adult to the surviving woman does matter. Women who lost a brother or sister within the last 30 months had significantly lower recent fertility. The effect of the loss of a husband in the past 30 months was also negative, but only marginally signifi- cant. Analysis of the impact of mortality on other indicators of fertility intentions tends to reinforce the hypothesis that higher adult mortality will lead to a de- crease in fertility. Higher personal awareness of AIDS and AIDS mortality are associated with a reduced desire for additional children among both men and women and a decrease in sexual activity among men. Higher levels of adult mortality in the community are associated with a lower probability that women aged 15-19 have ever had sexual intercourse and with a reduction of sexual activity among women. Taken together, these results suggest that there will indeed be a behavioral response to heightened mortality because of AIDS and that it will, on net, be negative. An important caveat is that the three data sets used in this study allowed us to consider the fertility response only within 30 months of an adult death in one sample, 12 months in another, and quite vaguely in the third. Con- ceivably, with a longer time lag, one might observe a compensating increase in fertility for the preceding period of low fertility. However, the negative relation- ship between recent fertility and community-level adult death rates is not subject to this problem and implies that even in the long run the fertility response to adult

MARTHA AINSWORTH, DEON FILMER, AND INNOCENT SEMALI 167 mortality will be negative. Although we have identified evidence of behavioral response, the total effect of heightened mortality due to AIDS on fertility will include both behavioral and biological components, the latter reflecting the im- pact of HIV infection on fecundity. APPENDIX Appendix tables begin on the following page.

168 THE IMPACT OF AIDS MORTALITY: EVIDENCE FROM TANZANIA APPENDIX 5-A: SUMMARY STATISTICS FOR THE THREE SAMPLES (a) Kagera Sample of Women Aged 15-50 in Waves 2, 3, and 4 (n = 2,896) Standard Variable Mean Deviation Woman-level variables Birth in the past 12 months (o/l)a 0.128 0.334 Age 27.064 10.18 Age squared 836.07 620.22 Years of education 4.907 2.965 Years of education squared 32.87 26.51 Household-level variables Water from closed sourceb (0/1) 0.126 0.332 Dwelling has a toilet or latrine (0/1) 0.967 0.18 No household member owns any land (0/1) 0.0317 0.175 Acres of land owned by all household members 5.413 5.763 Value of land owned by all household members (/1,000,000) 0.678 2.29 Community-level variables Dispensary in community (0/1) 0.307 0.461 Health center in community (0/1) 0.0849 0.279 Hospital in community (0/1) 0.0521 0.222 Urban community (0/1) 0.198 0.398 Motorable road in community (0/1) 0.963 0.19 Road is sometimes impassable (0/1) 0.46 0.5 Number of primary schools in community 1.315 0.542 Family planning within 5 kilometers of community (0/1) 0.693 0.461 Price index 1.262 0.245 No child wage reported in community (0/1) 0.427 0.495 Child wage for clearing land in community (/1,000) 0.0869 0.0954 Adult male wage for clearing land in community (/1,000) 0.266 0.297 Karagwe district (0/1) 0.139 0.346 Muleba district (0/1) 0.157 0.364 Biharamulo district (0/1) 0.0808 0.273 Ngara district (0/1) 0.111 0.314 Household expenditure variables Log of annual household expenditure -2.106 0.718 per adult Identifying variables for annual household (hh) expenditure per adult (not in fertility equations) Head of hh male (0/1) 0.762 0.426 Head's age 48.18 15.64 Head's age squared 2565.9 1593.9 Head's years of schooling 4.55 3.09 Head's schooling squared 30.28 34.46 HH member owns dwelling (0/1) 0.933 0.251

MARTHA AINSWORTH, DEON FILMER, AND INNOCENT SEMALI (a) Kagera Sample of Women Aged 15-50 in Waves 2, 3, and 4 (n = 2,896) (con tin uedf) 169 Standard Variable Mean Deviation Value of owned and occupied dwelling (/1,000,000) Value of farm equipment (/1,000,000) Value of farm buildings (/1,000,000) Value of livestock (/1,000,000) Value of business assets (/1,000,000) Hectares of banana crop harvested (averaged over all waves observed) 0.459 0.0194 0.00159 0.0732 0.0382 0.888 0.776 atoll), dummy variable. bClosed source includes indoor plumbing, inside standpipe, water vendor, water truck or tanker service, neighboring household, private outside standpipe or tap, public standpipe. (b) Tanzania National Sample, THRDS (n = 6,037) Standard Variable Mean Deviation Woman-level variables Birth last 12 months (0/1) 0.105 0.306 Age 27.75 9.098 Age squared 852.7 561.8 Years of schooling 5.625 3.331 Years of schooling squared 44.73 36.92 Household-level variables Toilet or latrine (0/1) 0.963 0.188 Water from closed source (0/1) 0.593 0.491 Any farmland owned by hh member (0/1) 0.350 0.477 Hectares of farmland owned by hh member 0.973 2.398 Male adult (15-50) death in household in past 12 months (0/1) 0.0129 0.113 Female adult (15-50) death in household in past 12 months (0/1) 0.0133 0.114 Community-level variables Distance to nearest public road (cluster median) 0.390 0.963 Land area (sq km)/populationa 2.480 3.755 Number of primary schools/populationa 4.272 1.622 Number of hospitals/populationa 0.140 0.107 Number of health centers/populationa 0.132 0.073 Number of doctors/populationa 0.500 0.559 Number of nurses/populationa 29.43 31.00 Rural cluster (0/1) 0.426 0.495 North highland zoneb (0/1) 0.122 0.327

170 THE IMPACT OF AIDS MORTALITY: EVIDENCE FROM TANZANIA (b) Tanzania National Sample, THRDS (n = 6,037) (continue]) Variable Standard Mean Deviation Central zoneb (0/1) 0.061 0.239 South highland zoneb (0/1) 0.135 0.342 Southern zoneb (0/1) 0.100 0.300 Lake zoneb (0/1) 0.251 0.434 Under-5 mortality rate 177.15 40.54 Adult mortality rate 5.632 1.341 Household expenditure variables Log of annual household expenditure per adult (/1000000) -1.662 0.728 Identifying variables for annual household expenditure per adult (not in fertility equations) Head of hh male (0/1) 0.851 0.356 Head's age 43.91 12.76 Head's age squared 2090.8 1218.6 Head's years of schooling 5.519 3.783 Head's years of schooling squared 44.77 46.79 HH member owns dwelling (0/1) 0.699 0.459 Value of owned and occupied dwelling(/1,000,000) 0.004 0.010 Owned dwelling walls of mud or wood (0/1) 0.318 0.465 Owned dwelling floors of earth or wood (0/1) 0.463 0.4987 Owned dwelling roof of grass or mud (0/1) 0.335 0.472 Owned dwelling windows with glass or screens (0/1) 0.124 0.330 Total number of cows, bulls or oxen currently owned 2.622 12.78 aVariables calculated at the district level. Land per capita is scaled upward by a factor of 100; the other variables are scaled upward by a factor of 10,000. bCoastal zone (Tanya, Morogoro, Coast, Dar es Salaam regions), north highland zone (Arusha and Kilimanjaro regions), central zone (Dodoma and Singida regions), south highland zone (Iringa, Mbeye, and Rukwa regions), southern zone (Lindi, Mtwara, and Ruvumba regions), lake zone (Tabora, Kigoma, Shinyanga, Kagera, Mwanza, and Mara regions).

MARTHA AINSWORTH, DEON FILMER, AND INNOCENT SEMALI (c) TKAP Sample of Women Aged 15-49 and Sample of Men Aged 15-59 171 Women Aged 15-49 (n = 3,950) Men Aged 15-59 (n= 1,948) Standard Standard Variable Mean Deviation MeanDeviation Individual-level variables Birth in past year (0/1) 0.194 0.396 Want another child (0/1) 0.616 0.486 0.8760.329 Ever had sexual intercourse, ages 15-19 (0/1) 0.528 0.500 0.6350.482 Frequency of sexual intercourse in past 4 weeks 4.314 5.943 4.8337.159 Age 28.001 8.980 31.01411.641 Age squared 864.68 546.19 1097.31806.03 Years of schooling 4.485 3.926 5.7104.368 Years of schooling squared 35.526 208.791 51.677305.408 Household-level variables Water from closed source (0/1) 0.361 0.480 0.4230.494 Flush or pit toilet facility 0.906 0.292 0.9210.269 Floor of parquet, finished wood, or cement (0/1) 0.228 0.419 0.2680.443 Household member owns a bicycle (0/1) 0.339 0.473 0.3550.479 Household member owns a car (0/1) 0.019 0.136 0.0170.128 Community-level variables Rural HH (0/1) 0.732 0.443 0.6920.462 North highland zone (o/l)a 0.109 0.312 0.0950.293 Central zone (o/l)a 0.087 0.281 0.0930.290 South highland zone (o/l)a 0.148 0.355 0.1840.388 Southern zone (o/l)a 0.079 0.271 0.0820.275 Lake zone (o/l)a 0.368 0.482 0.2780.448 Road is seasonal or is a path (o/l)a 0.238 0.426 0.2160.411 Distance to primary school (km)b 0.660 1.907 0.7242.007 Village has one or more health workers (o/l)b 0.375 0.484 0.3650.481 Distance to nearest health facilityb 4.372 7.071 4.5437.490 Nearest health facility is a pharmacy (o/l)b 0.063 0.244 0.0800.271

72 THE IMPACT OF AIDS MORTALITY: EVIDENCE FROM TANZANIA (c) TKAP Sample of Women Aged 15-49 and Sample of Men Aged 15-59 (con tin uedf) Women Aged 15-49 (n = 3,950) Men Aged 15-59 (n= 1,948) Standard Standard Variable Mean Deviation Mean Deviation Nearest health facility is a hospital (o/l)b 0.128 0.335 0.115 0.320 Nearest health facility is a health center (o/l)b 0.170 0.375 0.192 0.394 Number of family planning methods available at nearest facilityb 2.209 1.486 2.204 1.534 Pill available at nearest facility (o/l)b 0.751 0.443 0.710 0.454 Injections available at nearest facility (o/l)b 0.298 0.457 0.327 0.469 Condoms available at nearest facility (o/l)b 0.799 0.401 0.809 0.393 IUD available at nearest facility (o/l)b 0.218 0.413 0.245 0.430 aCoastal zone (Tanya, Morogoro, Coast, and Dar es Salaam regions), north highland zone (Arusha and Kilimanjaro regions), central zone (Dodoma and Singida regions), south highland zone (Iringa, Mbeye, and Rukwa regions), southern zone (Lindi, Mtwara, and Ruvumba regions), lake zone (Tabora, Kigoma, Shinyanga, Kagera, Mwanza, and Mara regions). bCommunity and facility data from 1991/92 Tanzania Demographic Health Survey (see Beegle, 1995, for details).

MARTHA AINSWORTH, DEON FILMER, AND INNOCENT SEMALI APPENDIX 5-B: ESTIMATION OF THE DETERMINANTS OF FERTILITY Specification and Estimation 173 The probit model assumes an underlying linear index function (Bi*) for woman i of the following form: Bi* = Taxi + Vi' (1) where Xi is a set of explanatory variables and vi is an error term that is distributed normally with mean 0 and with variance ceil (which is normalized to 1 in the estimation). Actual (observed) birth in the last 12 months (Bi) is given by Bi = 1 if Bi* 2 0, Bi = 0 otherwise. (2) The probit model is a convenient (and commonly used) method to estimate a model with a dichotomous outcome. However, it is sensitive to misspecification. A non-normal distribution of vi or omitted variables can cause the parameter estimates to be biased. In this chapter we check the robustness of our results to certain types of violations of the underlying assumptions (see below). However, we leave more formal tests for future work. Two main issues arise in the estimation of this model. First, when including income in the analysis of fertility, it must be treated as (potentially) endogenous. Second, in the Kagera sample, the fact that there are as many as three observa- tions per woman in the sample may affect our results. We address below how these problems were dealt with. The Potential Endogeneity of Household Consumption In the estimation of the determinants of having had a birth in the past 12 months for two of the data sets (KHDS and THRDS), we estimate models that include the effect of household permanent income. Consistent with what is done in this literature we use the log of total household consumption expenditures per adult as a proxy for income. Household expenditures cannot necessarily be assumed as exogenous to fertility decisions. For example, if children themselves contribute to household income then the causal relationship runs in both direc- tions.~° Including an endogenous right-hand side regressor will lead to inconsistent crobit parameter estimates. Smith and Blundell (1986) and Rivers and Vuong {OFor recent discussions of these and other explanations of the endogeneity of income in African contexts see Benefo and Schultz (1996), as well as Montogomery et al. (1995).

174 THE IMPACT OF AIDS MORTALITY: EVIDENCE FROM TANZANIA (1988) propose an erogeneity test for a model with a dichotomous dependent variable and a potentially endogenous continuous explanatory variable. In the present case, we rewrite equation (1) to include the log of household expenditures per adult: Bi* = Taxi + YEi + Vi where Bi = 1 if Bi* 2 0, Bi = 0 otherwise. Ei = 8iXi + 82Zi + Hi' where Bi is the event of having a birth, Xi is a set of exogenous variables, Ei is the log of household expenditure per adult, and 0, it, hi, and 82 are parameters to be estimated. The set of variables Zi are the identifying instruments, that is they affect fertility only through their effect on Ei. Smith and Blundell show that an exogeneity test for E is a t-test of the significance of the parameter or in the following probit regression: Bi* = ~Xi+7Ei+ ~Ui+Vi, (5) where ui are the residuals estimated from equation (4~. If the estimate of or is significantly different from zero then we must treat Ei as endogenous. The set of instrumental variables we use differs somewhat for the two samples. They are in general, however, characteristics of the head of the house- hold and the value and/or characteristics of household assets. In both analyses the set includes the sex, age squared, education, and education squared of the head of the household. For the KHDS it includes in addition to the head's characteristics the value of farm equipment, the value of farm buildings, the value of livestock, the value of business assets, a dummy for whether or not a household member owns the dwelling, and if so the value of the dwelling, and the area of banana crops harvested. For the THRDS it includes in addition to the head's character- istics the total number of cows, bulls, and oxen currently owned, a dummy for whether or not a household member owns the dwelling, and if so the value of the dwelling and a series of dummies equal to one if the dwelling has walls made of mud or wood, has a floor made of earth or wood, has a roof made of grass or mud, and has windows or screens. The validity of these variables as instruments for income is dependent on the assumption that they affect the probability of a birth in the past year only through their impact on income (or its proxy, expenditures). The head of the household may or may not be the husband of the woman in question, and therefore his or her characteristics (conditional on the women's characteristics) are unlikely to affect the probability of a birth in the past 12 months directly. Characteristics and the value of the dwelling and productive assets are potentially good measures of

MARTHA AINSWORTH, DEON FILMER, AND INNOCENT SEMALI 175 "exogenous" wealth if there is a low turnover in the ownership of these assets, that is that they capture that part of income that is not endogenously related to the probability of birth in the past year. But, if only the characteristics of the head are used as instruments, none of the results of this chapter are substantially changed. The first-stage regressions perform well in the sense that they explain a good part of the variance of the dependent variable. The adjusted it-squared in the KHDS sample is 0.32 and in the THRDS is 0.39. The instruments perform well in the sense that they explain a reasonable part of this variance. The incremental R square of adding the set of instruments in the first-stage regression is about 0.05 in the KHDS sample and about 0.08 in the THRDS sample. In addition, both sets of instruments are jointly significantly different from zero at the 99 percent level. When the residual from the first-stage estimation is included in the second- stage probit regression for birth in the past year (equation 5), the l-tests reject exogeneity in both samples (and in both model specifications in the KHDS analy- sis). l l Therefore, to control for the endogeneity of expenditure, we estimate equa- tion (2) in a first step and then use the predicted value of Ei (that is Ei) to estimate Bi* = DXi + Hi + Vi (6) In the THRDS results, the asymptotic covariance matrix derived from this probit is then adjusted for the fact that Ei is a variable predicted from an auxiliary regression. This is done using the formula given in Maddala (1983:245~. Multiple Observations on a Single Woman in the KHDS Random-Effects Probit The KHDS data were collected over four interviews, separated by approxi- mately 6 months each. In our current analysis we have pooled the data across waves 2, 3, and 4, and therefore a woman can appear up to three times in the sample. To be able to include deaths up to 30 months of age, we exclude wave 1 observations from this analysis. Estimating this model as a probit regression on pooled data produces consistent but inefficient estimates (Maddala, 1987~. To improve the efficiency of the estimates, we estimate a random-effects probit model (reviewed in Maddala, 1987~. The random-effects probit model derives from a decomposition of the error iiIn the KHDS model with deaths of household members, the ~ statistic has a value of 2.711 (t statistics derived from a pooled probit estimation with huber standard errors). In the THRDS it has a value of 2.903. We note that the derivation of this test was done under the assumption of cross- sectional data.

176 THE IMPACT OF AIDS MORTALITY: EVIDENCE FROM TANZANIA term given in equation (1). Including subscripts for time periods, equation (1) for woman i in time period t becomes Bit* = Exit + Wi + V it ~ (7) where Bit= 1 if Bit* 2 0, Bit = 0 otherwise, where wi is a woman-specific time- invariant unobserved variable that is distributed normally with mean zero and variance ceil. This model is estimated using a method proposed by Butler and Moffitt (1982). In general these results are very similar to the pooled sample with simple probit estimation. The estimate of the share of the variance that is woman specific (p) is equal to approximately 0.34 and is significantly different from zero. However, the point estimates, as well as the statistical significance of these, are not very different. One Observation per Woman A woman can appear either one, two, or three times in the sample. If the number of times a woman is present in the sample is related to the issues under study, this could potentially bias our results. To investigate the sensitivity of our estimates to this, we estimate the models using only one observation, selected at random over the three waves, per woman. The biggest difference between these estimates and those in the random- effects model is that the community mortality variables are no longer significant, although the signs remain. In general the household-level mortality variables exhibit the same patterns, although the effect of a female death in the past 18-24 months is no longer significant, and the effect of the death of a husband in the past 30 months is larger. Transforming the Results from A Probit or Tobit Regression In Tables 5-1 through 5-6, the probit parameter estimates have all been transformed to correspond to the marginal effect of a change in one of the inde- pendent variables on the expected value of the dependent variable (i.e., the prob- ability that it equals one). If the underlying model for the observed variable B is Bi * = pXi + Vi, (8) where Bi = 1 if Bi * 2 0, Bi = 0 otherwise, then the change in the probability that B equals 1 due to a change in one of the X's, bE(B)I6X~, is equal to pif(z), where pi is the probit parameter estimate on A, f() is the standard normal probability

MARTHA AINSWORTH, DEON FILMER, AND INNOCENT SEMALI 177 density function, and z is equal to ~X. In the reported results, OX is evaluated at the means of the X' s. The Tobit parameter estimates have been transformed to correspond to the marginal effect of a change in the independent variables on the expected value of the dependent vanable. If the underlying model for the observed variable T is Ti* = DXi + Pi, ~ ~. ~ ~ (9) where If= ~,~~ it ~,~~ > (), ~ ~ = () otherwise, then the change in the expected value of T due to a change in one of the X's, bE(l~l6X~, is equal to piF(z), where pi is the Tobit parameter estimate on X, F() is the standard normal cumulative distn- bution function, and z is equal to Xp/c,, where c, is the standard deviation of £. McDonald and Moffitt (1980) recommend evaluating this at the mean of the X's. ACKNOWLEDGMENTS We thank Ed Bos, Barney Cohen, Will Dow, Tom Mernck, Mark Montgom- ery, and participants in World Bank and Committee on Population seminars for comments on earlier drafts. The opinions expressed in this chapter are those of the authors and do not necessarily reflect the policy of The World Bank or its member governments. This research was financed by The World Bank Research Committee, RPO#680-46. REFERENCES Ahn, N., and A. Shariff 1993 A Comparative Study of Fertility Determinants in Togo and Uganda: A Hazards Model Analysis. Paper prepared for the XIInd General Population Conference of the IUSSP, Montreal, Canada, August 24-September 1. Ainsworth, M. 1996 Economic Aspects of Child Fostering in Cote d'Ivoire. Pp. 25-62 in T. Paul Schultz, ea., Research in Population Economics volume 8. Greenwich, Conn.: JAI Press. Ainsworth, M., and A.A. Rwegarulira 1992 Coping with the Impact of the AIDS Epidemic in Tanzania: Survivor Assistance. AFTPN Working Paper no. 6. The World Bank, Washington, D.C. Ainsworth, M., G. Koda, G. Lwihula, P. Mujinja, M. Over, and I. Semali 1992 Measuring the Impact of Fatal Adult Illness in Sub-Saharan Africa: An Annotated House- hold Questionnaire. Living Standards Measurement Study Working Paper no. 90. The World Bank, Washington, D.C. Ainsworth, M., S. Ghosh, and I. Semali 1995 The Impact of Adult Deaths on Household Composition in Kagera Region, Tanzania. Paper presented at the Annual Meetings of the Population Association of America, San Francisco, Calif., April 6. Ainsworth, M., and M. Over 1997 Confronting AIDS: Public Priorities in a Global Epidemic. New York: Oxford Univer- sity Press.

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The last 35 years or so have witnessed a dramatic shift in the demography of many developing countries. Before 1960, there were substantial improvements in life expectancy, but fertility declines were very rare. Few people used modern contraceptives, and couples had large families. Since 1960, however, fertility rates have fallen in virtually every major geographic region of the world, for almost all political, social, and economic groups. What factors are responsible for the sharp decline in fertility? What role do child survival programs or family programs play in fertility declines? Casual observation suggests that a decline in infant and child mortality is the most important cause, but there is surprisingly little hard evidence for this conclusion. The papers in this volume explore the theoretical, methodological, and empirical dimensions of the fertility-mortality relationship. It includes several detailed case studies based on contemporary data from developing countries and on historical data from Europe and the United States.

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