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FAO releases key methodological documents to support the production and use of disaggregated data for the SDGs

These tools are an essential resource for any country working to produce SDG disaggregated data and design evidence-based inclusive policies


With the adoption of the 2030 Agenda, Member States have pledged to "leave no one behind" and called for “the social, economic and political inclusion of all regardless of age, sex, disability, race, ethnicity, origin, religion, economic status or other”. This principle is at the core of the SDG Monitoring Framework, and embodied in many SDG indicators for which FAO is the designated custodian agency: Prevalence of undernourishment, Prevalence of food insecurity, Productivity and income of small-scale food producers, Agricultural sustainability, Women’s ownership of agricultural land and Women’s equal rights to land ownership.

FAO provides a significant contribution in supporting countries’ efforts in monitoring the 2030 Agenda, being the custodian agency for 21 SDG indicators and contributing agency for an additional five. Disaggregation of FAO-relevant SDG indicators is fundamental to make sure that required policies and plans are formulated based on sound statistical evidence, and resources target the areas and the population groups that are most in need.

As a member of the UN working group on data disaggregation and the UN Task Force on small area estimation, FAO is well positioned to support countries who lack the capacity to report SDG indicators with the level of disaggregation required by the 2030 Agenda. In this article, we introduce a collection of tools and resources designed by FAO and its partners to support countries in the production of disaggregated SDG indicators, with the ultimate goal to promote evidence-based and inclusive policy making processes to leave no one behind.

  • Data disaggregation methods using survey data

In 2021, FAO's Office of Chief Statistician published the Guidelines on data disaggregation for SDG Indicators using survey data (FAO, 2021), which offer methodological and practical guidance for the production of direct and indirect disaggregated estimates of SDG indicators having surveys as their main data source. The publication provides tools to assess the accuracy of these estimates and presents strategies for the improvement of output quality, including small area estimation (SAE) methods.

The guidelines are complemented by two technical reports, each one presenting a case study for a specific SDG indicator:  SDG Indicator 2.1.2 and SDG Indicator 5.a.1.


Indirect estimation of food insecurity

The first one, An indirect estimation approach for disaggregating SDG indicators using survey data - Case study based on SDG Indicator 2.1.2 (FAO, 2022), discusses the adoption of the so-called projection estimator to produce indirect disaggregated estimates of SDG indicator 2.1.2 by integrating data from two independent surveys. The case study is based on microdata from Malawi and presents the steps and software packages to replicate the discussed estimation technique in other countries and contexts.


Small area estimation of women’s ownership of agricultural land

The second one, Using small area estimation for data disaggregation of SDG indicators – A case study based on SDG Indicator 5.a.1 (FAO, 2022), presents a case study based on the use of a SAE approach to produce disaggregated estimates of SDG Indicator 5.a.1 by sex and at granular sub-national level. In particular, the report discusses a possible model-based technique to integrate a household or agricultural survey measuring the indicator of interest with census microdata, in order to borrow strength from a more comprehensive data source and produce estimates of higher quality. The discussed estimation approach could be extended or customized for the integration of survey data with alternative data sources, such as administrative records, and/or geospatial information, and for the disaggregation of other (SDG) indicators based on survey microdata.


Integration of survey data with geospatial information for the production of small area estimates of productivity and income of small-scale food producers

In addition, FAO has released a third technical report presenting two case studies on SDG Indicators 2.3.1 and 2.3.2, measuring the labour productivity and income of small-scale food producers respectively. The report titled Integrating surveys with geospatial data through small area estimation to disaggregate SDG indicators at subnational level Case study on SDG Indicators 2.3.1 and 2.3.2 investigates the integration of survey microdata with auxiliary geospatial information to increase the precision of estimates produced at detailed sub-national level. The case study on indicator 2.3.1 has also been featured in an article recently published in a Special Edition of the Statistical Journal of the International Association of Official Statistics (IAOS).


Disaggregation by geographic location

Finally, FAO has collaborated with other UN agencies – the European Commission, the International Labour Organization (ILO), the Organisation for Economic Co-operation and Development (OECD), the United Nations Human Settlements Programme (UN-Habitat),  and The World Bank, to develop a harmonized methodology to facilitate international statistical comparisons and to disaggregate the entire territory of a country along an urban-rural continuum. In Applying the Degree of Urbanisation — A methodological manual to define cities, towns and rural areas for international comparisons (2021), the authors develop a degree of urbanisation classification, which defines cities, towns and semi-dense areas, and rural areas.

This manual can be complemented by the Global Human Settlement Layer (GHSL) implemented by the European Commission, and the e-learning course The Degree of Urbanisation, developed by the same organization in 2022.