In response to a growing demand for high-quality and internationally-comparable statistics, FAO develops, implements and promotes methods and standards to guide national data producers in generating and using sound statistics. In particular, the Organization is committed to provide national statistical systems with internationally recognized definitions, concepts and classifications as well as methodological guidance for the production of high quality statistics related to food and agriculture.
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Abstract: With the adoption of the 2030 Agenda for Sustainable Development, the production of high quality disaggregated estimates of Sustainable Development Goal (SDG) indicators has taken greater significance. In this context, sample surveys are characterized by samples that are either not large enough to guarantee reliable direct estimates for all relevant sub-populations, or that do not cover all possible disaggregation domains. To address these issues, indirect estimation approaches such as small area estimation (SAE) techniques can be adopted. The literature on the use of SAE in official statistics is broad and in continuous progress, yet the number of case studies on SAE methods applied to SDG indicators can still be expanded. After a brief review of the main SAE approaches available along with their principal fields of application, the present paper aims contributing to fill this gap by presenting a case study on SAE to produce disaggregated estimates of SDG Indicator 2.3.1, measuring average labour productivity of small-scale food producers. The discussed empirical exercise is based on a Fay-Herriot area-level SAE model, integrating survey data with area-level auxiliary information retrieved from multiple trustworthy geospatial information systems. Area-level SAE models have the advantage of being easy to implement and do not require accessing survey microdata and unit-level auxiliary information. These characteristics, jointly with the great potentials offered by modern geospatial information systems, offer the possibility of producing good quality disaggregated estimates of SDG indicators at high frequency and granular disaggregation level.
Lead authoring unit/office: Office of Chief Statistician (OCS)
Abstract: These guidelines are intended to assist countries in understanding the agronomic parameters involved in the computation of the agricultural component of the Sustainable Development Goal (SDG) indicator 6.4.1 on the change in water use efficiency over time. They provide a detailed explanation of the calculation process for calculation by countries willing to generate a more accurate estimation using their national data. The guidelines provide the minimum standard method using an estimated or default value proposed by FAO, as well as the available methodologies to progressively improve the accuracy through a monitoring ladder for countries that have more comprehensive and accurate data on their main crops areas and productions.
Lead authoring unit/office: Land and Water Division (NSL)
Abstract: 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.
Lead authoring unit/office: Office of Chief Statistician (OCS)
Abstract: FAO is committed to scaling up support to countries to ensure that high quality and comparable data for the SDGs are produced and used in support to decision-making and SDG national, regional and global monitoring. The Organization has developed regional roadmaps to ensure that this support is embedded in FAO’s cooperation framework and activities at country level. This document, prepared by FAO’s Office of the Chief Statistician, contains a wealth of relevant information which can support countries in producing, analyzing and using SDG indicators, as well as understanding how they can receive support from FAO.
Lead authoring unit/office: Office of Chief Statistician (OCS)
Abstract: 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). It complements the 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.
Lead authoring unit/office: Office of Chief Statistician (OCS)
Abstract: On the road to Glasgow COP26, FAO Land and Water Division, as coordinator of the UN-Water Expert Group on Water Scarcity, is now releasing a new Brief on Water Use Efficiency (WUE) to advance climate goals through water-energy nexus lens, across all sectors, to ensure sustainable use of freshwater in preventing water scarcity. This Brief provides an analytical basis for water-related policy interventions to implement Sustainable Development Goal (SDG) target 6.4.1, increasing water-use efficiency across all sectors and addresses: who will benefit from the adoption of water-use efficiency measures? what’s the difference between water efficiency and efficient use?
Lead authoring unit/office: Land and Water Division (NSL)
Abstract: RuLIS is a tool to support policies for reducing rural poverty, jointly developed by the Food and Agriculture Organization of the United Nations (FAO) Statistics Division, the World Bank and the International Fund for Agricultural Development (IFAD). RuLIS brings together harmonized indicators and comparable data across countries and over time on rural incomes, livelihoods and rural development and allows monitoring the status and progressr of Sustainable Development Goals (SDG) indicators. SDG 5.a1 measures women’s ownership rights and control over agricultural land. Through this indicator it is possible to assess the extent of women’s disadvantages in ownership and tenure rights over agricultural land, providing a basis for policy measures aimed at securing women equal opportunities and access to economic resources. This brief is the first analysis that employs a harmonized methodology for measuring tenure rights over agricultural land based on RuLIS data.
Abstract: The global indicator on water stress tracks the level of pressure that human activities exert over natural freshwater resources, indicating the environmental sustainability of the use of water resources. A high level of water stress has negative effects on social and economic development, increasing competition and potential conflict among users. This calls for effective supply and demand management policies. Securing environmental flow requirements is essential to maintaining ecosystem health, resilient, and available for future generations. This indicator addresses the environmental component of target 6.4. In this report, you can learn more about the progress on the level of water stress globally, by country, and by major basin. More information and the methodological guidance can be found at: www.fao.org/sustainable-development-goals/ indicators/642 This report is part of a series that tracks progress towards the various targets set out in SDG 6 using the SDG global indicators. To learn more about water and sanitation in the 2030 Agenda for Sustainable Development, and the Integrated Monitoring Initiative for SDG 6, visit our website: www.sdg6monitoring.org
Lead authoring unit/office: Land and Water Division (NSL)
Abstract: The global indicator on water-use efficiency tracks to what extent a country’s economic growth is dependent on the use of water resources, and enables policy and decision-makers to target interventions at sectors with high water use and low levels of improved efficiency over time. This indicator addresses the economic component of target 6.4. In this report, you can learn more about the global and country progress on water-use efficiency. More information and methodological guidance can be found at: www.fao.org/sustainable-development-goals/ indicators/641 This report is part of a series that tracks progress towards the various targets set out in SDG 6 using the SDG global indicators. To learn more about water and sanitation in the 2030 Agenda for Sustainable Development, and the Integrated Monitoring Initiative for SDG 6, visit our website: www.sdg6monitoring.org
Lead authoring unit/office: Land and Water Division (NSL)
Abstract: Virtual Training Series conducted by the FAO Statistics Division (ESS) on SDG indicator 2.4.1, "Proportion of Agricultural Area under Productive and Sustainable Agriculture”.
Lead authoring unit/office: Statistics Division (ESS)
