Senior Economist, Development Research Group, World Bank
JED FRIEDMAN is a senior economist in the Development Research Group (Poverty and Inequality Team) at the World Bank. His research interests include the measurement of poverty dynamics and the interactions between poverty and health and his recent articles have appeared in the Review of Economics and Statistics, the Journal of Development Economics, and the American Journal of Public Health, among others. Jed's current work involves investigating the effectiveness of malaria control programs in India, Nigeria, and Zambia; national health financing reforms in Kyrgyzstan, Zambia, and Zimbabwe; and conditional cash transfers in the Philippines. Jed holds a B.A. in Philosophy from Stanford University and a Ph.D. in Economics from the University of Michigan.
Blogging on: Development Impact
- More to do on measuring hunger
- Involving local non-state capacity to improve service delivery: it can be more difficult than it appears
- External validity as seen from other quantitative social sciences - and the gaps in our practice
- Towards a more systematic approach to external validity: understanding site-selection bias
- Should impact evaluation be justified by clinical equipoise or policy equipoise?
- Will that successful intervention over there get results over here? We can never answer with full certainty, but a few steps may help
- Challenges in counting the world’s hungry
- The often (unspoken) assumptions behind the difference-in-difference estimator in practice
- Policy learning with impact evaluation and the “science of delivery”
- Measuring the rate at which we discount the future: a comparison of two new field-based approaches
- Behind low rates of participation in micro-insurance: a misunderstanding of the insurance concept?
- Tools of the trade: recent tests of matching estimators through the evaluation of job-training programs
- Do financial incentives undermine the motivation of public sector workers? Maybe, but where is the evidence from the field?
- Using spatial variation in program performance to identify causal impact
- Learning from cross-disciplinary impact evaluation: the Family Rewards CCT program in New York City
- Caution when applying impact evaluation lessons across contexts: the case of financial incentives for health workers
- Q&A with Arun Agrawal, Editor of World Development Part II
- Q&A with Arun Agrawal, Editor of World Development Part I
- Tools of the trade: when to use those sample weights
- Trying to measure what workers actually do: the task approach to job content
- Thinking about the placebo effect as a “meaning response” and the implication for policy evaluation
- Incentives in the public sector: Some lessons from recent failures
- Feigning illness to improve care: Recent lessons from standardized patients in rural
- Sorting through heterogeneity of impact to enhance policy learning
- Weighting for external validity, then waiting for election results
Blogging on: Development Impact
- regarding the study above
- re: Great book that covers this topic
- Stuart, agreed on the higher bar for deception...
- Stefano, thanks very much for
- Sean, thanks very much for
- Re: "regression to the mean"
- Hi Lant, thanks so much for
- Hi Jessica, thanks very much
- Hi Heather, great comment,
- Hi Dan, thanks for the question! In this case...
- Hi Bob, thanks very much for
- Hi Alexander, thanks for
- Hello Jean and Rob, thanks
- Hello Aake, thanks for the insightful question
- Aha, a true randomista!