This year’s Nobel Prize in economics went to Abhijit Banerjee, Esther Duflo and Michael Kremer for their work on understanding development and poverty
alleviation. A lot has been written about their research and the significance of this prize for the broader community of development economists but academic economists more generally (young and still active researchers as well increasing diversity), so
what does a non-randomista like myself who works with observational rather than experimental data have to add? I enjoyed both the commentary of Martin Sandbu in the FT and
my former WB colleague David McKenzie. Among the few things I’d like to add are the following four:
First, a setting under control of researchers
as in randomised control trials is now often treated as gold standard. And even though there is an ongoing debate on the advantages and shortcomings of this approach, it certainly has raised
the bar for inferring causality, including for non-experimental settings. And while some argue that the identification focus has gone too far, I am convinced that proper policy implications can only be drawn where endogeneity is successfully addressed.
By the way a discussion we have had also in journals such as Economic Policy, which never has published any randomised control trial.
Second, while Abhijit, Esther and Michael have applied their methodology across a large number of fields,
their approach has become dominant in the field of assessing microfinance interventions and has provided very useful insights. Having myself worked intensively in the field of financial inclusion, financial development and poverty alleviation, I see
their findings as very much complimentary to the research I have done. Their findings of a moderate but not transformative effect of microcredit (as quoted in the committee’s document as “On credit, growing evidence indicates that microfinance
programs do not have the development effects that many had thought when these programs were introduced on a large scale.”) is critical in assessing the role of finance for poverty alleviation and points to other channels, such as credit for SMEs and
job creation, through which financial deepening can contribute to poverty alleviation.
Third, the increasing importance of randomised control trials has not only forced academics out of the ivory tower but also shown the importance of
cooperation between researchers, practitioners and policy makers. Researchers’ ultimate objective should be to influence the policy debate and process; addressing questions with direct policy relevance is thus critical. This in turn implies
continuous engagement with policy makers, to gain insights into their policy concerns and to feed back into the policy process. Similarly, direct interaction with financial institutions (be they microfinance NGO or banks) is critical for both sides –
access to data and research questions for researchers and influencing the structure and behaviour of these institutions. This also applies to non-randomistas. To give one example, exploiting credit registry data (often housed at central banks) can lead to
a broader cooperation between central banks and researchers that result in important policy research, but also translation of research into policy actions and improvements in data collection.
Finally, the RCT revolution has
led to a rethinking of evaluation in the donor community. I have been myself part of this as member of the FMO advisory panel on evaluation.
And again, while little of the evaluation taking place actually relies on field experiments, the focus of development economists on experimental set-up has led to a paradigm shift in evaluation work, away from counting jobs that were supposedly created by
an intervention to focusing on additionality and contribution of an intervention to an ultimate outcome.