The weakness of statistical systems and scarcity of relevant data for compiling indicators is a major challenge for monitoring of SDGs. Global capacity building and assistance programmes and projects are providing the opportunity to upgrade national statistical systems in many developing countries. Given the potential long lasting impact of these activities at country level, this session will be of high interest to these countries in discussing what improvement can be done in the quality of data based on findings from recent country experiences.
Following the adoption of 2030 Agenda for Sustainable Development, the United Nations Statistical Commission (UNSC) agreed on a list of 232 global indicators to track the progress of the 169 targets and 17 Sustainable Development Goals (SDGs). This includes a large number of multidimensional agricultural-related targets and global indicators to be monitored on a regular basis. The SDG agenda is ambitious since it foresees not only reducing, but eliminating hunger (indicators for values close to 0) which requires highly disaggregated data.
As a first step, concepts, definitions and adequate methodologies have to be developed and agreed at international level. For that purpose, an Inter-Agency and Expert Group (IAEG) for SDGs was put in place by the UNSC, working with specialized UN Agencies to formalize the definitions and methodologies for compiling various indicators.
On the data side, in many developing countries, the main data sources relevant to the indicators are censuses and surveys and to some extent administrative data systems. However, for indicators related to the food and agriculture sector, censuses and surveys are not always regularly conducted and administrative data is often reduced to reporting systems from decentralized branches in the field at local and regional levels. Therefore, monitoring SDG indicators in this situation of data scarcity is a major challenge in many developing countries.
To address these challenges, the international community has developed capacity building and technical assistance programmes and projects to upgrade data systems in many countries to fill data gaps for compiling the SDG indicators.
This Session will discuss some of the most important lessons learned from recent programmes to assist countries in monitoring selected SDG indicators.
Three papers will be presented during this Session:
• First paper: by Seid Yakob Mudesir, Adechian Djabar Dine, Audrier Sanou, and Naman Keita will discuss the methodological and data challenges for computing SDG indicators 2.3.1 and 2.3.2: Lessons learned from selected country experiences.
• The second paper by Aida Clara Khalil will discuss the methodologies and data requirements for disaggregation of SDG indicators.
• The third paper by Mariana Toteva and Eric Kabore will present the methodology for farm typology and its implementation in specific country context as well as some lessons learned.
Organiser: Mr Naman Keita