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Tools of the trade: recent tests of matching estimators through the evaluation of job-training programs

Jed Friedman's picture
Of all the impact evaluation methods, the one that consistently (and justifiably) comes last in the methods courses we teach is matching. We de-emphasize this method because it requires the strongest assumptions to yield a valid estimate of causal impact. Most importantly this concerns the assumption of unconfoundedness, namely that selection into treatment can be accurately captured solely as a function of observable covariates in the data.

What makes bureaucracies work better? Lessons from the Nigerian Civil Service

Markus Goldstein's picture
Given Jed's post last week on thinking through performance incentives for health workers, and the fact that the World Bank is in the throes of a reform process itself, a fascinating new paper from Imran Rasul and Daniel Rogger on autonomy and performance based incentives in Nigeria gives us some other food for thought.   In a nutshell, Rasul and Rogger f

Doing Experiments with Socially Good but Privately Bad Treatments

David McKenzie's picture
Most experiments in development economics involve giving the treatment group something they want (e.g. cash, health care, schooling for their kids) or at least offering something they might want and can choose whether or not to take up (e.g. business training, financial education). Indeed among the most common justifications for randomization is that there is not enough of the treatment for everyone who wants it, leading to oversubscription or randomized phase-in designs.

Do financial incentives undermine the motivation of public sector workers? Maybe, but where is the evidence from the field?

Jed Friedman's picture
These past weeks I’ve visited several southern African nations to assist on-going evaluations of health sector pay-for-performance reforms. It’s been a whirlwind of government meetings, field trips, and periods of data crunching. We’ve made good progress and also discovered roadblocks – in other words business as usual in this line of work. One qualitative data point has stayed with me throughout these weeks, the paraphrased words of one clinic worker: “I like this new program because it makes me feel that the people in charge of the system care about us.”

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