You are viewing an old version of this page. View the current version.

Compare with Current View Page History

« Previous Version 46 Next »

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.





What to expect

The SAE4SDG Toolkit targets practitioners and technical staff in National Statistical Offices and other institutions within the National Statistical System that are interested in using SAE for the monitoring of the SDGs. While the Toolkit provides information on SAE models and the process around building the models, it also offers discussions around elements that help countries make the transition from SAE experimentation to production.  This is to respond to the challenges that he use in official statistics is still rather limited even though the method has been around for a long time.

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. The shared example data and R code can be used to replicate the examples and to give guidance for real applications. Furthermore, an overview of recommended literature, projects and case studies by country and agency is provided as well as an overview of statistical software providing functions for small area estimation.

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

Please note that the provided data cannot be used for real analysis and the results of the practical exercises cannot be interpreted in any kind.

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 complemented over time. Case studies are not presented yet for all SDGs.

What not to expect

There is a wide range of small area estimation methods and the research is growing. As most other guidelines, this Toolkit only mentions the most basic models and provides idea of extensions. For interest in more methodological aspects, further literature is presented. The guidelines also do not have any research goal, instead existing literature and software is cited. For special issues in applications, the collaboration with experts may be recommendable.

The same applies to the presented case studies. There is a wide range of case studies available. However, the focus should be on case studies that are actually used as official statistics or at least as experimental statistics. Thus, the collaboration with national statistical offices is needed to find out to which case studies this applies.


  • No labels