Both fertility and HIV prevalence rates in Malawi are among the highest in the world, with
the total fertility rate at 5.7 births per woman and the HIV prevalence rate at 10.6 percent.1
Malawian women make fertility decisions in an environment characterized by high adult
and child mortality, exacerbated by mother-to-child HIV transmission. Out of a population
of about 15 million, it is estimated that 68,000 die annually from AIDS and that 560,000
children under the age of 17 have lost at least one parent to the disease.2
There are many policy interventions aimed at reducing HIV in Malawi and other SubSaharan
African countries. These include HIV testing programs, information campaigns,
and antiviral distribution programs. Evaluating the eects of such policies on outcomes such
as number of births, child mortality, and orphan-hood requires an understanding of how
women's fertility decisions are aected by the presence of HIV.
An important aspect of the environment in Malawi is that women are typically uncertain
regarding their own HIV status. An infected person can live for many years with no symptoms,
and testing was not widely available until relatively recently. The median survival
time after infection, without treatment, is about 10.4 years.3 During most of this time, an
infected person is in a clinical latency stage and experiences few or no symptoms.4
In addition to being uncertain about own HIV status, women often express beliefs about
HIV risk that dier substantially from actual risk. Studies using the Malawi Diusion and
Ideational Change Project data show that individuals in rural Malawi tend to overestimate
both the probability of being HIV-infected (Anglewicz and Kohler, 2009) and the HIV prevalence
in their community (Anglewicz, 2007). Anglewicz and Kohler (2009) attribute these
high risk assessments to overestimated probabilities of transmission. More than 95 percent
of respondents believe that transmission from a single instance of unprotected intercourse
with an infected person is highly likely or certain; however, studies estimate that it can be as
low as 1 per 1,000 encounters in the absence of an increased viral load (Gray et al., 2001).5
Women's perceptions of HIV risk and of their own HIV status aect beliefs about their
own and their children's life expectancy, which in turn may inuence life-cycle fertility
choices. In this paper, I study the determinants of women's reproductive decisions in Malawi,
taking into account uncertainty about HIV status and dierences between perceived and actual
HIV infection risk. I investigate how HIV aects fertility and simulate the impact of
dierent policy interventions, such as HIV testing programs and prevention of mother-tochild
transmission, on fertility and child mortality.
To this end, I develop a dynamic discrete-choice life-cycle fertility model in which expectations
about the life horizon and child survival depend on a perceived infection hazard.
A woman makes annual pregnancy decisions from the time of marriage until she becomes
infecund. She maximizes utility, which depends on her number of children, household consumption,
and pregnancies, subject to a per-period budget constraint. The woman faces
uncertainty regarding future income, HIV status, and the survival of herself and her children
in future periods.
A woman's perceived infection hazard is allowed to dier from her actual infection hazard
to reect the misperceptions about HIV risk observed empirically. The perceived hazard rate
for each period is a function of a woman's characteristics, such as her age, region of residence,
marital status, and schooling level. To account for unobservable factors, the hazard rate also
incorporates heterogeneity in the form of a discrete number of unobserved types.6
Given that HIV is initially asymptomatic, the model assumes that a woman does notobserve realizations of the infection process and therefore does not know her HIV status.
Assuming she knows the mortality process associated with HIV infection, however, survival
during each additional period gives her information about her status. Specically, she reduces
her subjective probabilities of having become infected in each of the past periods based on
the fact that she is still alive. According to these probabilities and given the mortality
process, she updates her survival expectations. HIV infection also increases child mortality
probabilities through mother-to-child transmission. In the model, the woman also updates
expectations about the survival of each of her children depending on the probability assigned
to her having been infected at the time of birth.