course, this is costly, but the advantage is that it yields a lot of data that allows one to unpack
the causal mechanisms explaining changes in the outcomes of interest. However, since tracking
may not always be a viable option, an alternative is to simply collect data on intermediate
indicators of long term impact in a cross-sectional survey (Ravallion, 2008).
Crucial to dealing with concerns over external validity is the need to properly understand
the programme context. This requires data, especially administrative data. Data also allows
us to understand the causal processes that underline the differences in outcomes. A researcher
may collect detailed information about the specific setting, and use survey data to try and
unpack why the outcomes occur as they do, and allow one to infer what might work in a
different context. Ravallion (2008) suggests that one should focus on intermediate behavioural
variables and not just outcome variables in this regard. In addition, it is important o have a
process evaluation conducted alongside the evaluation itself, that is, an evaluation of whether
the programme is being implemented as envisaged, whether monies are being spent as they
should, and to obtain feedback from stakeholders that might be used to adapt and improve
delivery on the ground. This kind of data is also vitally important for policy makers considering
going to scale.
Despite these concerns over external validity, policymakers frequently do use lessons from
past successful health policy interventions in designing new policies and programmes. In Sec-
tion 7, we provide a review of some of the existing evidence concerning the impact of health
interventions on individual welfare outcomes. While evidence emanating from Africa is scarce
(with the exception of Kenya perhaps), the available evidence does suggest that health inter-
ventions aimed at combatting geohelminth infections, malnutrition, and iron deficiencies have
significant positive impacts on individual productivity. In terms of other kinds of health inter-
ventions, the evidence is less well-established, suggesting scope for additional research in these
areas.
7 Existing Evidence of Health Impacts
Most evaluations in developing countries that focus on health examine either the uptake of
a certain health input (e.g., such as getting tested for HIV, using a mosquito net, going to
the clinic) or look at ways to change health behavior (e.g. through increased education or
knowledge, bargaining power). However, there are relatively few studies that look at the effects
of health on economic variables such as productivity.
There are several reasons for the limited number of studies on this topic that are related to
both the difficulty of this research question itself, as well as the context of Africa itself. As dis-
cussed in detail in section 2 above, causal inference is particularly difficult with estimating the
relationship between health and wealth and there is a vast literature outlining these challenges
(Smith 1999, Strauss 1986, Strauss and Thomas 1998). While randomized controlled trials
provide one research strategy to mitigate the challenges of causal measurement of this ques-
tion, there are additional challenges that make evaluating the relationship between health and
economic outcomes difficult, especially in Africa. We discuss each of these challenges briefly.
Returns on investments in health often take a long time to realize and often these investments
are made at early ages. Therefore, empirical analyses of the effects of early investments in
health require longitudinal data collection on individuals that can measure health inputs and
productivity after several decades. Alternatively, if only cross sectional data is available, this
requires that data be collected on intermediate indicators of long term success (Ravallion, 2008).
While the number of longitudinal studies in Africa is increasing, the number is still limited.
Existing studies such as the Cape Area Panel Study, the Malawi Diffusion and Ideational
Change Study, and the Kenya Life Panel Survey are among some examples of panel surveys
that follow individuals over time.
Other surveys, such as the Demographic Surveillance Surveys, follow individuals over time,
but often lack rich economic data; they instead focus on demographic and health indicators.
Investment in longitudinal studies would help to build our knowledge of long-term effects of
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