In committing to the realization of the 2030 Agenda for Sustainable Development, Member States recognized that the dignity of the individuals is fundamental and that the Agenda’s Goals and targets should be met for all nations and people and for all segments of society. Ensuring that these commitments are translated into effective action requires a precise understanding of the target populations and progress made in addressing their particular priorities.

To properly measure this, statistics need to be presented for different population groups and geographical areas. The Global Sustainable Development Goal (SDG) indicator framework has included an overarching principle of data disaggregation: Sustainable Development Goal indicators should be disaggregated, where relevant, by income, sex, age, race, ethnicity, migratory status, disability and geographic location, or other characteristics, in accordance with the Fundamental Principles of Official Statistics.

Small area estimation (SAE) techniques have been proved to be very useful to provide reliable disaggregated data for some SDG indicators, such as poverty estimation, food insecurity and undernutrition, health related indicators, unemployment rate, etc.

Under the guidance of IAEG-SDGs and ISWGHS, a draft set of Toolkit has been developed by UNSD on small area estimation for SDGs. The Toolkit provides guidance to countries, based on national experiences, on the use of small area estimation for SDG data disaggregation. The Toolkit covers essential elements of the use of small area estimation including the use of input data, modelling, data validation and dissemination. The Toolkit also provides practical guidance to countries on how to move from SAE experiments to official production.

In this regard, the UN Statistics Division (as the secretariat of both the Inter-Secretariat Working Group on Household Surveys and IAEG-SDGs working on SAE) is (a) fianlising the SAE Toolkit for SDG data disaggregation; and (b) developing a set of eLearning courses on small area estimation for SDGs data disaggregation, in collaboration with ECLAC Statistics Division and UNFPA .