Evaluating the Impact of Health Programmes *
Justine Burns, Malcolm Keswell, and Rebecca Thornton
June 22, 2009
Abstract
This paper has two broad objectives. The first objective is broadly methodological and deals
with some of the more pertinent estimation issues one should be aware of when study-
ing the impact of health status on economic outcomes. We discuss some alternatives for
constructing counterfactuals when designing health program evaluations such as random-
ization, matching and instrumental variables. Our second objective is to present a review
of the existing evidence on the impact of health interventions on individual welfare.
1 Introduction
There are a number of mechanisms through which health can affect productivity (Strauss
and Thomas 1998; Bloom et al. 2004; Weil 2006). Improved health can have a direct effect
by increasing the productivity of healthy workers, as well as an indirect effect by affecting
savings and investment (Muney and Jayachandran 2008; Yaari 1965). Most of the research
on the indirect effects involves macroeconomic studies of aggregate changes in life-expectancy
on savings, investment, and GDP, whereas the bulk of research on the direct effects of health
policy on the other hand is largely micro-focused. Moreover, this evidence is limited in scope
and generalizability partly because evaluating the impact of health interventions on individual
welfare and productivity involves time lags between the intervention, often made during infancy
or childhoood, and welfare outcomes of interest, such as employment status, usually observed
in adulthood.
A further difficulty concerns the reliability of the evidence which is available. While the
connection between income levels and health status has long been recognized as crucial for
economic growth, the causal relationship between income and health is harder to establish.
Plausibly, many economic outcomes of interest (productivity for instance) and an individual’s
health status are simultaneously determined. Thus, establishing the causal effects of health
interventions on economic outcomes requires that special attention be paid to identification
strategies.
This paper has two broad objectives. The first ob jective is broadly methodological and deals
with some of the more pertinent estimation issues one should be aware of when studying the
impact of health status on economic outcomes. If the analyst wishes to estimate the direction
and magnitude of the impact of a particular program or policy intervention on beneficiaries of
the intervention, it is necessary to assess the welfare outcomes of program beneficiaries against
some type of counterfactual. The paper begins in section 2 by discussing why the need for
counterfactuals arise in the first instance. Sections 3-5 then presents some alternatives for con-
structing counterfactuals. We begin in section 3 with the “gold standard“ of randomization
and then move to quasi-experimental matching approaches in section 4. Although methods
like propensity score matching were designed primarily as a solution to problems of identi-
fication in observational studies, the method is most useful when used in a complimentary
* University of Cape Town, University of Stellenbosch, and University of Michigan, respectively. The authors
would like to thank Duncan Thomas for useful comments. We also thank Robert Eastwood, Peter Glick, German
Mwabu as well as participants of the AERC Collaborative Research workshop on Health, Economic Growth and
Poverty Reduction held in Accra, Ghana, April 20-22, 2009. Comments can be sent to [email protected]