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In order to be most cost-effective and equitable, especially when budgets are constrained, health programs must be rigorously evaluated, so policymakers can make decisions with knowledge of what works and what doesn’t. CGD offers suggestions to improve how evaluations are done, to create incentives to undertake good evaluations in the first place, and to encourage the use of evaluation results in the design of future health programs.
We examine alternative strategies for targeted sampling of health clinics for independent verification. Our results indicate that machine learning methods, particularly Random Forest, outperform other approaches and can increase the cost-effectiveness of verification activities.
As developing nations are increasingly adopting economic evaluation as a means of informing their own investment decisions, new questions emerge. The right answer to the question “which perspective?” is the one tailored to these local specifics. We conclude that there is no one-size-fits-all and that the one who pays must set or have a major say in setting the perspective.
Clear and rigorous evidence on the contributions of US global health programs is more important than ever, as the White House and lawmakers discuss and debate budgets and the future of US support to global health. Such information aids policymakers who must prioritize support to effective public health programs.
With the US Congress considering cuts to foreign assistance and aid budgets in other donor countries coming under increased pressure, evidence about what works in global development is more important than ever. Evidence should inform decisions on where to allocate scarce resources—but to do so, evaluations must be of good quality.
Evaluations are key to learning and accountability yet their usefulness depends on the quality of their evidence and analysis. This brief summarizes the key findings of a CGD Working Paper that assessed the quality of aid agency evaluations in global health. By looking at a representative sample of evaluations—both impact and performance evaluations—from major health funders, the study authors developed 10 recommendations to improve the quality of such evaluations and, consequently, increase their usefulness.
We assessed the methodological quality of global health program evaluations from five major funders between 2009 and 2014. We found that most evaluations did not meet social science methodological standards in terms of relevance, validity, and reliability. Nevertheless, good quality evaluations made it possible to identify ten recommendations for improving evaluations, including a robust finding that early planning is associated with better quality.