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Book (stand-alone)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
2023Also available in:
No results found.The present technical report illustrates a case study on the adoption of small area estimation techniques to produce granular sub-national estimates of SDG Indicators 2.3.1 and 2.3.2, by integrating survey microdata with auxiliary information retrieved from various trustworthy geospatial information systems. The technical report provides practical guidance to national statistical offices and other institutions wanting to implement small area estimation techniques on SDG Indicators 2.3.1 and 2.3.2 or similar indicators based on surveys microdata. -
DocumentSDG Indicator 2.4.1 Guidelines on Data Analysis and Reporting
01/08/2023
2021These guidelines have been prepared in support of the methodological concept note of SDG Indicator 2.4.1, particularly the data analysis and reporting on the indicator by the Food and Agriculture Organization of the United Nations (FAO), in collaboration with the Global Strategy to improve Agricultural and Rural Statistics (GSARS). -
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.
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