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FAO is the custodian UN agency for of 21 SDG Indicators across 6 Goals and is a contributing agency for a further 5. In this capacity, the Organization is supporting countries’ efforts in monitoring the 2030 Sustainable Development Agenda. FAO has a dedicated webpage on the SDGs, where interested users can find out more about the work of the Organization on Indicators under its custodianship.


Since 2015, FAO – through an active engagement with Inter-Agency and Expert Group on SDG Indicators (IAEG-SDG SDGs) – has progressively ensured the successful methodological development of all indicators under its custodianship, which are now all classified either in TIER I or TIER II. In addition, the Organization has coordinated a broad range of capacity development activities in support of SDG monitoring, including global, regional and national training workshops. Besides training, FAO has produced a series of learning material and 15 e-learning courses covering 18 SDG Indicators, which are disseminated through the FAO E-learning Academy.

The methodological work undertaken by FAO also focuses on data disaggregation and small area estimation (SAE) for SDG Indicators. In this respect, the Organization has recently released a publication to provide Guidelines on data disaggregation using survey data. The Guidelines offer a comprehensive overview of survey methods and tools that member countries can adopt for the production of direct and indirect disaggregated estimates of SDG indicators using household and/or agricultural surveys as the main supporting data source. The publication addresses the limitation main limitations posed by most surveys, either having samples that are often not large enough to guarantee reliable direct estimates for all sub-populations of interest, or that do not cover all possible disaggregation domains.

The Guidelines start with discussing the presentation of the main statistical challenges posed by data disaggregation in the context of the implementation of the 2030 Agenda for Sustainable Development. Subsequently, technical solutions to define sampling strategies for direct domain estimation and methods relying on the use of auxiliary information are discussed. The guidelines also propose sampling designs that guarantee a sufficient number of sampling units for every subpopulation or domain for which disaggregated data must be produced, thus allowing the calculation of direct disaggregated estimates. Pros and cons of each approach are extensively discussed along with their context of applicability. Moreover, methods for measuring sampling accuracy are provided. The estimation and dissemination of quality indicators assessing estimates accuracy represents a fundamental step in the production of disaggregated estimates and has the potential of increasing the transparency of NSOs and consequently the public confidence in official statistics. In addition, direct estimates presenting large sampling errors are an indication of the need to either resort to SAE techniques or revisit the sampling design.

A large section of the guidelines is dedicated to present an indirect approach for producing disaggregated estimates relying on the integrated use of two independent surveys. This method allows integrating a small survey, measuring a target variable with a small measurement error, and a more extensive survey, collecting variables of general use, at least one of which is highly correlated with the target variable (proxy variable). The described estimator – i.e. the projection estimator - is operationalized for the production of disaggregated synthetic estimates of the SDG Indicator 2.1.2: Prevalence of Moderate and Severe Food Insecurity based on the Food Insecurity Experience Scale (FIES) in Malawi. The guidelines end with a chapter providing an overview of small area estimation (SAE) techniques, as one of the possible approaches to produce indirect disaggregated estimates. Being SAE methods based on model assumptions, the publication also discusses the tools for the validation and interpretation of obtained results.Main reference:

Starting from methods presented in the Guidelines, the FAO has released  two additional practical publications discussing the application of indirect estimation approaches (including SAE) to produce disaggregated estimates of SDG indicators 2.1.2, on the prevalence of moderate and severe food insecurity in the population based on the Food Insecurity Experience Scale (FIES), and 5.a.1, on women's ownership and secure rights over agricultural land.

Main references:

FAO, 2021. Guidelines on data disaggregation for SDG Indicators using survey data.

Future work on SAE

FAO is developing additional case studies using the methodological tools described in the guidelines. In particular, the FAO is applying the projection estimator and SAE techniques to other countries and other FAO-relevant SDG indicators based on survey data to verify the robustness of the results and to further enhance and extend the methodology for the disaggregation of the SDG Indicators. In addition, the case studies will further explore the integration of survey data with additional data sources such as censuses, administrative data and geo-spatial information. Methodologies FAO, 2022. An indirect estimation approach for disaggregating SDG indicators using survey data. Case study based on SDG indicator 2.1.2.

FAO, 2022. Using small area estimation for data disaggregation of SDG indicators. Case study based on SDG indicator 5.a.1.

Future work on SAE

Small area techniques and software presented in the guidelines and implemented in the forthcoming case studies technical reports will be used to provide technical assistance to countries requesting FAO’s support for producing disaggregated SDG Indicators. 

Information provided by Ms. Clara Aida Khalil, FAO Statistics DivisionOffice of the Chief Statistician