Attention presidential transition teams: the Rethinking US Development Policy team at the Center for Global Development strongly urges you to include these three big ideas in your first year budget submission to Congress and pursue these three smart reforms during your first year.
Gavi, the Vaccine Alliance, pools donor funds to increase immunization rates in developing countries. Vaccines have saved millions of lives. Results from new research at the Center for Global Development suggest Gavi could save more lives by shifting support away from lower-cost vaccines provided to middle-income countries toward more underused vaccines and support to the poorest countries.
The lack of reliable development statistics for many poor countries has led the U.N. to call for a “data revolution” (United Nations, 2013).
Afghanistan’s progress against mortality reflects the success of providing health aid that differed radically from the bulk of American aid to Afghanistan during the war. The USAID program that contributed to the decline was a multilateral effort coordinated by Afghanistan’s own Ministry of Public Health. Results were verified by random sampling, and some funding was linked to measures of performance. This internal policy experiment, however, was destined to provoke resistance. More surprising is the source of resistance to an aid program that attempted to stop simply throwing money at a problem and focus on building sustainable systems: auditors.
Context Matters for Size: Why External Validity Claims and Development Practice Don't Mix - Working Paper 336
In this paper we examine how policymakers and practitioners should interpret the impact evaluation literature when presented with conflicting experimental and non-experimental estimates of the same intervention across varying contexts. We show three things. First, as is well known, non-experimental estimates of a treatment effect comprise a causal treatment effect and a bias term due to endogenous selection into treatment. When non-experimental estimates vary across contexts any claim for external validity of an experimental result must make the assumption that (a) treatment effects are constant across contexts, while (b) selection processes vary across contexts. This assumption is rarely stated or defended in systematic reviews of evidence. Second, as an illustration of these issues, we examine two thoroughly researched literatures in the economics of education—class size effects and gains from private schooling—which provide experimental and non-experimental estimates of causal effects from the same context and across multiple contexts.
March 15, 2013
To: House Foreign Affairs Committee, Subcommittee on Asia and the Pacific