Research Article: The Effects of International Monetary Fund Loans on Health Outcomes

Date Published: July 22, 2008

Publisher: Public Library of Science

Author(s): Megan Murray, Gary King

Abstract: Megan Murray and Gary King discuss a new study that finds that IMF economic reform programs are associated with worsened tuberculosis outcomes in post-communist Eastern European countries.

Partial Text: Founded in the wake of the Great Depression of the 1930s, the International Monetary Fund (IMF) was established in 1945 when government representatives met and agreed on a “framework of international economic cooperation” ( designed to prevent future economic crises. Its mission was threefold: to ensure the stability of the exchange rate, to promote economic growth, and to provide financial assistance in the form of short-term loans to countries experiencing balance-of-payments difficulties. When countries borrow from the Fund, they are required to agree to conditions set by the organization, a process that the IMF refers to as “conditionality.” These conditions entail the adoption of economic policies or “structural adjustment programs” that are meant to redress the problems that led to the need for the loan and therefore to enable prompt repayment. While the conditions vary for different loans, most impose some regimen of fiscal austerity through reduced government spending, removing barriers to international trade, cutting government subsidies, and privatization.

What kind of impact might IMF loans, and their conditionalities, have upon health outcomes? A new study in this issue of PLoS Medicine attempts to address this question by examining IMF programs and tuberculosis (TB) outcomes in post-communist countries [1].

Given the often vituperative debate between the IMF and its critics about the health impacts of IMF loans, the need for evidence in support of charges and counter-charges becomes ever more apparent. But what kind of evidence would shed light on these health impacts?

In the new study, David Stuckler and colleagues delve into this difficult methodological terrain [1]. They provide new evidence linking IMF loans to the enormous increases in TB incidence, prevalence, and mortality that occurred in some former Soviet Union and Eastern European countries during the post-communist period of the early to mid-1990s. After controlling for a host of variables, they find that IMF loans are associated with a 16.6% rise in annual TB mortality. This estimate did not change after adjusting for factors expected to mediate the impact of the loans, such as HIV prevalence, incarceration rates, and variables reflecting macroeconomic policy changes. Although IMF loans were associated with a fall in directly observed therapy (DOTS) population coverage levels, controlling for this variable had no effect on the strength of the association between loans and TB deaths. This result emphasizes the complex and confusing pathways by which macroeconomic policies lead to specific health effects.

The new study raises many important issues, particularly related to the policy implications of the conclusions. But are the study findings correct? Should we regard them as meeting the evidence-based standards of the best clinical research?

Although these limitations seem stark by the standards of a randomized clinical trial (RCT), we should not necessarily discount the study’s policy implications. If the assumptions underlying this work are correct, the authors are estimating a causal effect among all “subjects” (i.e., countries) and time periods of interest. In contrast, the patients included in RCTs are typically not representative of, and certainly not randomly selected from, the populations to which the treatment would be applied. This leaves us with a key question: is the potential for bias larger when random assignment to treatment is impossible, as in Stuckler and colleagues’ study and other observational studies, or when random selection of trial participants from the target population is impossible, as with most RCTs? Failure to either randomly assign or randomly treat can lead to biases of any size. As a result, one type of study should not be automatically favored over the other [5,6]. RCTs themselves are prone to many weaknesses, such as problems of compliance, missing data, measurement error, and post-treatment bias, all of which require modeling assumptions of their own and lead to substantial uncertainties of other kinds.



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