The Millennium Development Goals (MDGs) served as a starting point for the new SDG framework, and indicators developed by other standards bodies helped shape its various components. All eight MDGs were brought forward to the new standard and only six of the new goals are truly original. New goals and targets nevertheless mean new monitoring elements are needed. From a statistical viewpoint such a task is challenging
– it brings the need to establish the availability of historical data that must be in place to create a timeline for new indicators: a rearview mirror to see where we have come from.
More than half of the MDG indicators (37 out of 60) were included in the new SDG standard. However, of these only 11 were incorporated ‘as is’ with their original methodology; 11 were marginally altered and 15 were adopted with a completely revised methodology. While subject matter experts have good reasons for changing indicators, or methodologies in indicators, these changes pose challenges for the producers and users of the data alike.
A case in point is the way in which child mortality is measured. In the shift from the MDG to SDG monitoring framework, infant mortality rate (IMR) has been replaced by the neonatal mortality rate (NMR). While there strong clinical reasons for this change, replacing IMR with NMR involves a trade-off with the efforts put in over the past 15 years to create a consistent dataset (particularly given that the causes of neonatal mortality are different from those afflicting older infants) and the additional challenges involved in collecting real NMR data rather than deriving it through statistical estimations.
Adopting methodologies from other tried and tested standards can counterbalance the problems associated with creating new indicators. The World Health Organization Indicator Monitoring Registry (WHO-IMR) provides a comprehensive and credible collection of well-documented and transparent indicators. Yet looking at SDG 3, which relates to health, we find that only 14 of the 24 health-related indicators have been chosen from the WHO registry. Furthermore, 9 of the 24 proposed indicators currently have no metadata on source or methodology associated with them.
Although the monitoring of the SDGs has been at the forefront of international campaigning around a revolution for sustainable development data, it is the meeting of these targets that is of much greater importance. The data needed to plan and provide resources for action extend well beyond monitoring. This raises a further challenge for data standards. Will it be possible to compare inputs, particularly international and domestic resource flows, against impacts measured by the indicators?
Most external financial resources for development are reported to the Organisation for Economic Co-operation and Development (OECD) Development Assistance Committee (DAC)’s Creditor Reporting System (CRS), with has its own system of sector classification. Domestic resource flows are generally classified in ways that are broadly compatible with the UN maintained Classifications of the Functions of Government (COFOG). There is no easily compatible mapping between the SDGs and either the CRS or COFOG classifications. Indicators dealing with social protection are a case in point: there is no equivalent sector in the CRS, yet the subject is at the heart of four SDGs.
Achieving sustainable development is not about pursuing 169 targets independently. It will involve combining resources across many crosscutting initiatives. Similarly, the data that is required to describe, meet and monitor these goals needs to be comparable and interoperable. This paper is aimed at opening up discussion on some of these challenges.