Chris Blattman

M&E: The next generation

There is an inherent tension between implementing organizations—which have specific objectives and narrow missions and mandates—and executive organizations—which provide resources to multiple implementing organizations.

…Monitoring and Evaluation (M&E) has always been an element of the accountability of implementing organizations to their funders. There has been a recent trend towards much greater rigor in evaluations to isolate causal impacts of projects and programmes and more ‘evidence base’ approaches to accountability and budget allocations Here we extend the basic idea of rigorous impact evaluation—the use of a valid counter-factual to make judgments about causality—to emphasize that the techniques of impact evaluation can be directly useful to implementing organizations (as opposed to impact evaluation being seen by implementing organizations as only an external threat to their funding).

We introduce structured experiential learning (which we add to M&E to get MeE) which allows implementing agencies to actively and rigorously search across alternative project designs using the monitoring data that provides real time performance information with direct feedback into the decision loops of project design and implementation.

Our argument is that within-project variations in design can serve as their own counter-factual and this dramatically reduces the incremental cost of evaluation and increases the direct usefulness of evaluation to implementing agencies. The right combination of M, e, and E provides the right space for innovation and organizational capability building while at the same time providing accountability and an evidence base for funding agencies.

A new paper by Lant Pritchett, Salimah Samji and Jeffrey Hammer.

15 Responses

  1. This is a great paper. My organization (in Uganda) saw a draft of the article about a year ago, and it’s been central to our thinking. We’re planning to implement a variation on this method by building and learning from a series of pilot programs. We’ll thus be able to optimize our program model, and then we aim to scale it up. Exciting stuff!

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