Small Area Estimation to Improve SDG Data: A Case Study on Labor Productivity in Food Production

What is it about?

This research discusses the significance of producing high-quality disaggregated estimates for Sustainable Development Goal (SDG) indicators, focusing on the limitations of traditional sample surveys. It highlights the challenges posed by insufficient sample sizes and inadequate coverage of all disaggregation domains necessary for reliable direct estimates of SDG indicators. To address these issues, the research explores the use of small area estimation (SAE) techniques, which integrate survey data with auxiliary information from geospatial information systems. A case study is presented using a Fay-Herriot area-level SAE model to estimate SDG Indicator 2.3.1, which measures average labour productivity of small-scale food producers. The model leverages area-level auxiliary data, offering ease of implementation without needing access to survey microdata. The research emphasizes the potential of modern geospatial systems to enhance the production of frequent and granular SDG indicator estimates, advocating for new data integration strategies as stipulated by the 2030 Agenda for Sustainable Development.

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Photo by Nuril Fikriyah on Unsplash

Photo by Nuril Fikriyah on Unsplash

Why is it important?

This study investigates the use of small area estimation (SAE) techniques to produce disaggregated estimates of Sustainable Development Goal (SDG) indicators, specifically focusing on SDG Indicator 2.3.1, which measures the average labor productivity of small-scale food producers. The research is significant as it addresses the challenges faced by traditional sample surveys, which often lack the capacity to provide reliable and detailed data for all relevant sub-populations. By exploring SAE methods, the study contributes to enhancing the quality and granularity of SDG monitoring, which is crucial for effectively tracking progress towards the 2030 Agenda for Sustainable Development. Key Takeaways: 1. The research demonstrates that sample surveys alone are insufficient for producing reliable estimates of SDG indicators across all disaggregation domains, prompting the need for indirect estimation approaches such as small area estimation (SAE) techniques. 2. Findings reveal that area-level SAE models, specifically the Fay-Herriot model used in the study, effectively integrate survey data with geospatial auxiliary information, allowing for high-frequency and granular disaggregated estimates without the need for survey microdata. 3. The study highlights the potential of modern geospatial information systems and other big data sources in enhancing data integration strategies, aligning with the 2030 Agenda's call for innovative data solutions to improve the monitoring of SDG indicators.