Virtual Training on Data Disaggregation and Small Area Estimation for SDG Indicators

From 21 to 25 October, FAO's Office of Chief Statistician organized a virtual training to introduce statisticians and data analysts from African countries to small area estimation techniques.

The expected outcomes of the training were the following:

• Clarify the importance of data disaggregation in the context of the SDG monitoring framework.
• Discuss the main challenges posed by data disaggregation for SDG indicators having surveys as their main data source and illustrate the available methodological and practical solutions.
• Introduce the main SAE approaches available and discuss basic unit and area level SAE models.
• Provide practical examples based on SDG indicators under FAO custodianship, displaying the use of selected R packages to implement SAE techniques.
• Introduce the DEGURBA methodology.
• Provide practical examples based on SDG indicators under FAO custodianship, displaying the implementation of the DEGURBA methodology to produce SDG estimates by cities, towns, and rural areas.
• Identify technical assistance needs from the invited countries for the implementation of future targeted activities on SAE or the DEGURBA methodology for SDG or other national priority indicators.

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Presentations

• Preliminary session: R and Rstudio Installation and Introduction
• Data disaggregation and small area estimation for SDG indicators. Theory and practice (Day 1)
• Data disaggregation and small area estimation for SDG indicators. Theory and practice (Day 2)
• Applying the degree of urbanization. Theory and practice

Recordings

• Preliminary session (English | French [Passcode: y2VVAr2?])
• Day 1 (English | French [Passcode: SDG2024+])
• Day 2 (English | French [Passcode: SDG2024+])
• Day 3 ( English | French  [Passcode: SDG2024+]))
• Day 4 ( English | French  [Passcode: SDG2024+]))