About three months ago, J-PAL had a great conference celebrating ten years of incredible work that arguably has furthered evidence-based development work more than any other institution out there. All the videos from the event are available here, but one particular one stood out for me. This session, titledĀ J-PAL: The Next Decade happens right at the end, where J-PAL Directors Abhijit Banerjee, Esther Duflo, Rachel Glennerster and Benjamin Olken discuss what they would like to see happen over the next ten years.

Two things Prof. Banerjee said particularly resonated with me because they speak directly to the technique and underlying motivation for my PhD work, which I paraphrase below.

[9:40 – 10:30] First, he notes that there will much more extensive use of big data – data that is already collected “administratively”, and not with particular programmatic deliberation, such as cell phone call or m-pesa transfer records. One can assume that these will become more easily available This in turn will allow us to test very fine hypothesis with the power afforded by that much data – very small effects can be tested.

[13:05 – 13:55] Second, he expects to move more and more into theory-building based on the data from all these experiments. It starts with taking the results and then fitting it back to the narrative. As experiments accumulate, facts that don’t fit the story help build better stories as they are refined. New experiments are also designed to check the fit to those stories.

Remember that J-PAL is built mostly around RCTs, which take one hypothesis of causal connection within the context of a much larger story, and test that, and only that. To go from there to talking about much more freeform “big data”, and about theory-building as a natural evolution from theory-testing are two major steps away from their bed and butter. Given the complexity of the financial lives of the poor and the increasing availability of such data though, this is certainly a necessary and welcome evolution.