SAE4SDG

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 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 guidelines SAE4SDG in form of a Wiki is to complement and use the existing work to encourage and enable national statistical offices to use SAE for the monitoring of the SDGs. In contrast to the projects mentioned-above, these guidelines will be an evolving project/document that will add newly available methods, case studies and discussions in future versions.

What to expect

SAE4SDG targets practitioners and technical staff in national statistical offices and other institutions that are interested in using SAE for the monitoring of the SDGs. While some background in statistical analysis is helpful, the guidelines rather give an idea on how to conduct the analysis than explaining the statistical models in detail. For more details of the statistical methods, references will be provided.  

The limitations of direct estimation, i.e., the estimation solely based on the survey data, are illustrated and used to motivate the usage of small area estimation.

The guidelines follow a production framework suggested by Tzavidis et al (2018). For each production step, explanations are provided and three examples are conducted along the production steps. The shared example data and R code can be used to replicate the examples and, in the best case, to transfer it to the own use case. Furthermore, an overview of recommended literature, projects and case studies by country and agency are provided as well as an overview of statistical software providing functions for small area estimation.

After producing statistical indicators, these usually need to be visualized and communicated to the public or policy makers. Therefore, some ideas for this task are shared.

Last but not least, case studies are collected and assigned to the 17 Sustainable Development Goals. This list is part of a long term evolution and should be filled over time. Case studies are not presented yet for all SDGs.

The choice of described methods and provided code is based on the nature of the majority of the SDG indicators that are based on survey data and availability in standard statistical software. The usage of the last criteria is a try to prevent the promotion of methods that may fit best for the case but are not easily applicable.

The objectives of SAE4SDG can be summarized as follows:

• Centralize relevant documents of references
• Provide practical tools with accompanying case studies for countries to use SAE for SDG monitoring
• Promote standardized SAE applications and the assessment of the methodologies
• Promote software that supports the application
• Encourage the provision of SAE methodology in standard statistical software