Justin Sandefur


Justin SandefurJustin Sandefur is a research fellow at the Center for Global Development. His research focuses on the interface of law and development in sub-Saharan Africa. From 2008 to 2010, he served as an adviser to the Tanzanian government to set up the country’s National Panel Survey to monitor poverty dynamics and agricultural production. He has also worked on a project with the Kenyan Ministry of Education to bring rigorous impact evaluation into the Ministry’s policymaking process by scaling up proven small-scale reforms. His recent papers concentrate on education in Kenya, and his research includes the examinations through randomized controlled trials of new approaches to conflict resolution in Liberia, efforts to curb police extortion and abuse in Sierra Leone, and an initiative to expand land titling in urban slums in Tanzania.




Papers Published in World Economics:


Costing a Data Revolution

The lack of reliable development statistics for many poor countries has led the U.N. to call for a “data revolution” (United Nations, 2013). One fairly narrow but widespread interpretation of this revolution is for international aid donors to fund a coordinated wave of household surveys across the developing world, tracking progress on a new round of post-2015 Sustainable Development Goals. We use data from the International Household Survey Network (IHSN) to show (i) the supply of household surveys has accelerated dramatically over the past 30 years and that (ii) demand for survey data appears to be higher in democracies and more aid-dependent countries. We also show that given existing international survey programs, the cost to international aid donors of filling remaining survey gaps is manageable--on the order of $300 million per year. We argue that any aid-financed expansion of household surveys should be complemented with (a) increased access to data through open data protocols, and (b) simultaneous support for the broader statistical system, including routine administrative data systems.

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