Welcome to the Toolkit for using Small Area Estimation for the SDGs!
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 Sustainable Development Goal (SDG) indicator framework has included an overarching principle of data disaggregation: SDG 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.
To enable national statistical offices to estimate disaggregated indicators, guidelines are needed to support the process. The idea of writing guidelines on how to use statistical methods and, in particular small area estimation (SAE), to receive disaggregated statistical indicators is not new. Some focus on methodological aspects, others provide methodology in a specific program language or focus on a specific topic as poverty mapping. Several statistical institutions conducted projects on the evaluation of the usability of SAE for official statistics. In 2020, the Asian Development Bank even published practical guidelines especially focusing on the monitoring process of the Sustainable Development Goal with SAE. So how do these guidelines differ from the existing work?
The idea of the SAE4SDG Toolkit in Wiki is to complement and use the existing methodological work and case studies to encourage and enable national statistical offices to use SAE for the monitoring of the SDGs. The Toolkit will be an evolving project/document that will incorporate newly available methods, case studies and discussions in future versions.