Johannes Juetting | May 4, 2016 9:00am

When development economics undermines development

Why the “who” and “how” of data production matters

Experiencing the desperate state of data collection and, more broadly, National Statistical Offices in many parts of low-income countries, was an eye-opening experience. These sorts of issues were never covered in any course material.

As a junior development economist, you are trained on sampling issues, how to design questionnaires, what secondary sources one should use and general aspects of “data quality.” Data are seen as a “resource” feeding into our econometric modelling—we are looking for “instruments” to deal with endogeneity and are concerned about getting enough “high quality data” as time series to allow more robust analysis. The randomized control trial movement is a particular case in point, with lots of data collection to check whether or not a certain intervention has been successful. These data collection efforts are sometimes undertaken with national partners, sometimes not. The “how” and “who,” as well as the broader governance of data collection, matters for many development outcomes: Leaving them out of our studies risks undermining development efforts.  

One illustrative example: The largely donor-driven process in defining indicators and collecting data for the Millennium Development Goals (MDGs) led to the omission of those national statistical systems whose staff questioned the relevancy of the global indicators for national policies, possibly leading to less policy action on the ground. If we take as an example maternal mortality, we find that, in several developing regions, the large majority of data pertaining to MDG 5 is produced by international agencies through modelling, while in the developed countries this data comes from national authorities. To put it differently, in countries with the highest incidence of maternal mortality, the data are largely modeled, while in those where the problem is less severe we can rely on country data. Only recently has a movement around civil registration and vital statistical systems been created to address this important data gap.