early health investments. Political stability and funding are two challenges to conducting these
longitudinal surveys, and the studies in South Africa, Malawi, Kenya, and Ghana are examples
of countries that have had stable governments and the presence of researchers; however, more
effort should be made to expand this list and to further understand linkages in other regions
and countries.
There are further challenges to conducting studies that evaluate the causal relationship
between health and productivity. In conducting randomized controlled trials, it is important to
consider ethical implications of withholding some treatment from the control group. Internal
review boards consider it unethical to withhold life-saving treatment from a study population
and thus interventions must carefully consider these implications for the research. For example,
we have limited evidence of the effects of ARVs on economic productivity of HIV-infected
individuals. One of the reasons for this is that it could be viewed as unethical to have a
study population of HIV positive individuals for whom some of them are in a control group,
receiving no ARVs. Researchers who have examined this research question have used quasi-
experimental methods to study the effects of treatment on economic behavior (Habyarimana et
al. 2008; Thirumurthy et al. 2005). In addition to using these non-experimental methodologies,
there are several other possibilities for researchers. First, encouragement designed evaluations
can be conducted where treatment is not withheld from individuals; rather, individuals are
given randomized encouragement such as subsidies or reminders to get their treatment. The
randomized subsidy can then be used as an instrument for the treatment itself. A second
approach that could be useful to explore is to partner with medical randomized controlled
studies to study economic outcomes. For example, following individuals in phase III medical
trials over time could be one promising avenue. If a drug or vaccine is found to be effective,
these individuals could be followed over time to study longer-term effects of good health.
7.1 What have we learned from health evaluations to date?
One of the difficulties with evaluating the impact of health interventions on individual welfare
and productivity is the time lag involved between the intervention, often made at a relatively
young age, and welfare outcomes of interest, such as employment, income and poverty status in
adult life. Consequently, in this arena, collecting data on intermediate outcomes such as school
enrollment rates, labour market participation, and test scores aimed at measuring cognitive
ability becomes important. Insofar as positive outcomes in these respects are associated with
better long term prospects as an adult, they provide some evidence for the impact of health
interventions on productivity. In this section, we briefly review some of the available evidence
concerning the impact of health interventions on individual productivity.7 The evaluation
methods used in these studies encompass the entire range of evaluation methods reviewed
earlier in this paper.
7.1.1 Nutritional supplementation
There is overwhelming and consistent evidence that malnutrition during the early years of a
childs life is associated with lower cognitive levels and academic achievement, as well as higher
dropout rates (Grantham-McGregor, 2007). Malnutrition which occurs in utero, or during the
early years of a childs life can have serious and long lasting impacts on child development
outcomes, and most often manifests itself as stunting. Longitudinal studies in developing
countries have indicated that stunted children are less likely to be enrolled in school (Beasley
et al, 2000), more likely to enrol late (Brooker et al, 1999; Moock and Leslie, 1986), and more
likely to attain lower grades for their age 8 (Moock and Leslie, 1986; Jamison, 1986; Clark et
al, 1990; Hutchinson et al, 1997). Part of the advantage that well nourished children enjoy
is that they enter school earlier and thus have more time to learn, and they also appear to
7 This section draws heavily on Burns (2007)
8 The relationship between stature and age-appropriate grade is reduced with progression through school,
which is compatible with a higher dropout rate for more stunted children.
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