Post-2015 Consensus: Data for Development Perspective, Johnston
There is clearly an existing problem with development data provision. For example, even population figures may be uncertain because of under-counting of some groups in society, and changes to the statistical basis of GDP estimates can make large differences; Ghana became a middle-income country overnight when its estimated GDP doubled in this way.
FAO data on undernutrition – on which many poorer countries rely in the absence of official national statistics – has also been shown to be prone to significant errors.
This matters because the post-2015 agenda is likely to have a far greater monitoring burden and Jerven has shown convincingly that paying for a full set of development data is not feasible. The second issue is that, even if it was available, throwing money at the problem will not solve it. The problem of lack of statistical capacity is hard to solve and the Assessment Paper also points out the opportunity cost. The Partnership in Statistics for Development in the 21st Century (PARIS21) group estimates that there has been an increase in data-gathering exercises in Africa because of the MDGs, but a shift away from surveys not closely geared to them. As Jerven correctly argues, macroeconomic, labor and agricultural statistics have suffered in particular.
This Assessment paper is a ground-breaking attempt to delineate the issue, map the extent of the problem and make recommendations; essentially it is a wake-up call to the development community. It makes several important contributions, the most important of which is its careful enumeration of the costs of monitoring the MDG indicators. This quantification is extremely powerful in showing the need for prioritisation of targets and indicators – and so is highly complementary to the Copenhagen Consensus exercise.
Because there is not yet a definitive list of targets and indicators, a precise costing is impossible, but the Assessment Paper usefully lays out the likely minimum data requirements. However, the cost of data analysis and utilisation is excluded from these estimates and this is, of course, a necessary requirement for the information to be debated by politicians and the public.
As the author points out, many data gathering exercises are flawed. This may be due to methodological, conceptual or political problems. The potential for misunderstanding and bias is rife and poor quality data is a challenge for both academics and statisticians. Once the post-2015 list is formally agreed, Jerven’s extremely useful overview of each of the major international survey types could be expanded to include quality issues.
The methodology of the Assessment Paper is innovative and important. I am not aware of any other exercise that have attempted to cost the MDG agenda or, of course, the Post-2015 agenda. A key issue which is highlighted is the extent to which monitoring depends on surveys rather than administrative data. Also emphasised is the difficulty of getting data on the costs of surveys and, in particular, the complete lack of estimates for Multiple Indicator Cluster surveys. The impact of not doing these surveys is, of course, unknown.
The Assessment Paper has constructed a ground-breaking argument that raises to public attention a dangerous gap in the development debate. It convincingly shows that we risk repeating the mistakes of the past, in ignoring the costs of data collection in the post-MDG world. By providing a cost estimate of each of the crucial surveys and of the overall MDG exercise, the paper also kick-starts a discussion about prioritisation.