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Each year billions of dollars are spent on development programs with relatively few rigorous studies of whether they actually work. In 2004, CGD set out to address this lack of good quality impact evaluations and our recommendations led to the creation of the International Initiative for Impact Evaluation (3ie) in 2009. The number and quality of impact evaluations has risen significantly, but there is still a long way to go to make sure future development interventions are based on evidence of what works.
In 2006, CGD published a working group report that addressed the insufficient number of rigorous impact evaluations of social programs in low- and middle-income countries. Last week —marking 10 years since the report’s release—CGD and J-PAL co-hosted the event, “Improving Development Policy through Impact Evaluation,” which echoed three key messages of the 2006 report: 1) conduct more and better evaluations; 2) connect evaluators and policymakers; and 3) recognize that impact evaluations are an important global public good that requires more unconstrained funding.
“3ie has made my job much easier.” This is what we heard last month from a high-ranking government official in Africa, referring to the International Initiative for Impact Evaluation (3ie), and it made us very proud. Creating 3ie was the outcome of the Evaluation Gap Working Group that we led along with Nancy Birdsall to address the limited number of rigorous impact evaluation of public policies in developing countries. As CGD celebrates its 15th year, it is worth considering what made that working group so successful, the obstacles we confronted, and the work that still remains to be done.
The International Initiative for Impact Evaluation (3ie) has announced that Emmanuel (Manny) Jimenez will be the organization’s new Executive Director starting in early 2015. The selection of Jimenez represents a key transition for 3ie, which has moved quickly from start-up to maturity in just six years.
I never cease to be astonished by the amount of energy people put into claiming that Randomized Control Trials (RCTs) are the be-all and end-all of impact evaluation methods; nor at the energy people put into claiming that RCTs are marginal, costly, and a waste of time.
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.