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Welcome to the Toolkit on Using Small Area Estimation for 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.

As sound statistical methods are vital to overcome this challenge, Small Area Estimation constitutes an important topic in the way forward. It covers a variety of methods used to produce survey based estimates for geographical areas or domains of study in which the sample sizes are too small, or even absent,  to provide valid estimates. In order to obtain reliable estimates, additional datasets are generally brought to bear upon the process through a modelling procedure.

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. Usability of SAE for official statistics has also been carried out over the past 10 years. So how do these guidelines differ from the existing work?

The SAE4SDG Toolkit in Wiki is a space to provide information on methods to produce disaggregated data through small area estimation. It aims to complement and use the existing methodological work and case studies to encourage and enable national statistical offices to employ SAE for the monitoring of the SDGs. The Toolkit will be an evolving project/document that will incorporate newly available methods, case studies and practical examples in future versions.  The Toolkit also focuses on key steps to help countries in moving from SAE experiment to official data production. Finally, the Toolkit aims to be a space for partners to document and include references for their work on small area estimation.  




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 methods and the process around building an estimation procedure, 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 the use in official statistics is still rather limited even though the SAE methods have been around for a long time.

Challenges and limitations of producing data for SDG indicators solely based on the survey data, are illustrated and 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 synthetic 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 presented as well as an overview of statistical software and corresponding functions for small area estimation.

The example datasets were specially designed for the capacity building practical exercises whose results do not correspond to real data estimates of any area or domain.

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.

In addition, 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 covers the most common models and provides idea of extensions. Further literature is presented for readers who may be interested in more methodological aspects.

The guidelines 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.


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