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Book (stand-alone)An indirect estimation approach for disaggregating SDG indicators using survey data
Case study based on SDG Indicator 2.1.2
2022Also available in:
No results found.As the custodian United Nations (UN) agency of 21 Sustainable Development Goal (SDG) indicators, and a member of the Inter-Agency and Expert Group on SDG Indicators (IAEG-SDGs) and the Working Group on data disaggregation, the Food and Agriculture Organization of the United Nations (FAO) has been working to support countries in reporting SDG indicators at the required disaggregation level. To this end, FAO Office of Chief Statistician (OCS) has developed 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 estimates of SDG indicators having surveys as their main or preferred data source. This technical report presents a case study based on the so-called “projection estimator”, allowing the integration of two independent surveys for the production of synthetic disaggregated estimates. In particular, the publication presents a practical exercise focused on the production of disaggregated estimates for SDG Indicator 2.1.2, on the Prevalence of Moderate or Severe Food Insecurity in the population based on the Food Insecurity Experience Scale (FIES). This application – based on survey microdata from Malawi – expands and enriches the brief practical exercise presented in the Guidelines. -
Book (series)Disaggregating data for development: a cost-effective approach to SDG Indicators 2.1.2, 2.3.1 and 2.3.2 in Latin America using small area estimation 2025
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No results found.This paper presents the experience of the Food and Agriculture Organization of the United Nations (FAO) in providing technical assistance to four countries in Latin America – Brazil, Chile, Colombia and Ecuador – to produce small area estimates for three Sustainable Development Goal (SDG) indicators: SDG Indicator 2.1.2, on the prevalence of moderate and severe food insecurity in the population based on the Food Insecurity Experience Scale (FIES); SDG Indicator 2.3.1, measuring the average value of agricultural production per labour unit; and SDG Indicator 2.3.2, on the average income of small-scale food producers.The paper describes the methodological details and results of the case studies developed, showing how small area estimation can be used to increase the precision of estimates at the subnational level and produce predictions in estimation domains excluded from the sample. It discusses the policy implications of having SDG estimates at the subnational level, and how countries can use this information to formulate programmes and allocate funds. The paper concludes with recommendations on how small area estimation can be incorporated in the processes implemented at the national level to produce agriculture and food security statistics. -
Book (stand-alone)Using small area estimation for data disaggregation of SDG indicators
Case study based on SDG Indicator 5.a.1
2022Also available in:
No results found.This technical report presents a case study based on the use of a small area estimation (SAE) approach to produce disaggregated estimates of SDG Indicator 5.a.1 by sex and at granular sub-national level. In particular, after introducing the framework for using SAE techniques, 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 also 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.
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