The International Initiative for Impact Evaluation (3ie) works to improve the lives of people in the developing world by supporting the production and use of evidence on what works, when, why and for how much. 3ie is a new initiative which responds to demands for better evidence, and will enhance development effectiveness by promoting better-informed policies. 3ie finances high-quality impact evaluations and campaigns to inform better programme and policy design in developing countries.The 3ie Working Paper series covers both conceptual issues related to impact evaluation and findings from specific studies or systematic reviews. The views in the papers are those of the authors, and cannot be taken to represent the views of 3ie, its members, or any of its funders.This Working Paper was written by Howard White and Daniel Phillips, 3ie, and edited by Catherine Robinson. Production and cover design by Radhika Menon and Mukul Soni, 3ie.
AcknowledgementsThe authors would like to thank members of the cross-college working group on impact evaluation, especially Clare Chandler, who offered advice and useful information which helped in the paper's development. We would like to thank Fred Carden, Rick Davies, Katherine Hay, Lina Payne, Wendy Olsen, John Mayne, Bruno Marchal, Robert Brinkerhoff, Julia Betts, Karl Hughes, Kimberly Bowman and Eric Djimeu for constructive criticism, ideas and information that have led to revisions of the paper. We are also grateful to colleagues from 3ie whose ideas and information have contributed to our work.
AbstractWith the results agenda in the ascendancy in the development community, there is an increasing need to demonstrate that development spending makes a difference, that it has an impact. This requirement to demonstrate results has fuelled an increase in the demand for, and production of, impact evaluations. There exists considerable consensus among impact evaluators conducting large n impact evaluations involving tests of statistical difference in outcomes between the treatment group and a properly constructed comparison group. However, no such consensus exists when it comes to assessing attribution in small n cases, i.e. when there are too few units of assignment to permit tests of statistical difference in outcomes between the treatment group and a properly constructed comparison group.We examine various evaluation approaches that could potentially be suitable for small n analysis and find that a number of them share a methodological core which could provide a basis for consensus. This common core involves the specification of a theory of change together with a number of further alternative causal hypotheses. Causation is established beyond reasonable doubt by collecting evidence to validate, invalidate, or revise the hypothesised explanations, with the goal of rigorously evidencing the links in the actual causal chain.We argue that, properly applied, approaches which undertake these steps can be used to address attribution of cause and effect. However, we also find that more ne...