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Direct and indirect estimators for data disaggregation of SDG indicators

Virtual Training on Data Disaggregation and Small Area Estimation for the SDGs 18,19, 25, 26 April 2023

Year of publication 2023

Place of publication Rome, Italy ;

Publisher FAO ;

Product type Presentation

Language English

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    Direct and indirect estimators for data disaggregation of SDG indicators
    2022 Virtual Training on Data Disaggregation and Small Area Estimation for the SDGs (20-23 September 2022)
    2022
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  • Thumbnail Image
    Presentation
    Direct and indirect estimators for data disaggregation of SDG indicators
    2022 Virtual Training on Data Disaggregation and Small Area Estimation for the SDGs (22-25 November 2022)
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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
    2022
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    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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