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.
This interface allows you to search for statistical classifications, guidelines and handbooks, technical reports, working papers and methodological documents, and capacity development resources. You can search by SUBJECT (general, agriculture, forestry, fishery and aquaculture, and natural resources) or use the ADVANCED SEARCH to search by keyword, country, language and lead authoring unit/office. Comments, suggestions and inquiries can be addressed to: [email protected].
You can access here all the statistical guidelines and handbooks, technical reports, working papers and methodological documents, and capacity development resources.
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Comments, suggestions and inquiries can be addressed to: [email protected].
Abstract: Forests and the forest sector are important sources of employment, livelihoods and incomes for millions across the globe, particularly in rural areas. They provide jobs in a wide range of activities related to sustainable forest management, the provision and production of timber and other wood and non-wood forest products, the protection of forest ecosystems and biodiversity, and safeguarding the benefits of forests. Despite the relevance of forests for employment and income generation, limited quantitative information is currently available on the subject. This lack of data makes it challenging to quantify the number of people employed in the forest sector, and their contribution to global employment. Notwithstanding, estimating forest-related employment involves methodological challenges such as the standardization and comparability of data collected, as well as the availability of reliable and detailed employment statistics. This study employs a new method to fill the gaps of missing data points in order to provide sound total employment estimates in the forest sector on a global scale.
Lead authoring unit/office: Forestry Division (NFO)
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. Using the surveys that are processed as part of the RuLIS database project, this brief explores patterns and trends in rural employment for women and youth with a focus on agriculture in 16 low-income and lower-middle-income countries around the world: 11 from sub-Saharan Africa, two from East Asia and the Pacific, two from Latin America and the Caribbean, and one from Central Asia.
Lead authoring unit/office: Statistics Division (ESS)
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. Using the RuLIS data, this brief focuses on the observations made in the adoption of agricultural inputs, along with improved technology such as irrigation, and mechanised tools among crop farm households in sub-Saharan Africa.
Lead authoring unit/office: Statistics Division (ESS)
Abstract: This document reflects the latest reference metadata information available on SDG-indicator 2.3.1 "Volume of production per labour unit by classes of farming / pastoral / forestry enterprise size". Last updated: February 2021.
Lead authoring unit/office: FAO
Abstract: This document reflects the latest reference metadata information available on SDG-indicator 2.3.2 "Average income of small-scale food producers, by sex and indigenous status". Last updated: February 2021.
Lead authoring unit/office: FAO
Abstract: The purpose of this note, which is part of FAO Statistics Working Paper Series, is to inform on the statistical methodology for computing and monitoring target 2.3 and measure progress in SDG indicators 2.3.1 and 2.3.2 approved by the Inter-Agency and Expert Group on the Sustainable Development Goals.
Lead authoring unit/office: Statistics Division (ESS)
Abstract: This course has been developed to support countries in computing and monitoring Indicators 2.3.1 and 2.3.2 of the 2030 Sustainable Development Goals (Labour Productivity and Income of Small-scale Food Producers), and to facilitate the understanding of the main concepts underpinning the methodology.
Lead authoring unit/office: FAO
Abstract: These Guidelines, developed as part of the Global Strategy to improve Agricultural and Rural Statistics (GSARS), seek to measure dimensions of labour force performance and job quality that are not well captured in the Decent Work framework in this setting. More specifically, the Guidelines propose Decent Work indicators that focus on youth (ages 15–24 years) within developing countries where the majority of households depend on agriculture for their well-being and a large share of the population lives in poverty.
Lead authoring unit/office: Statistics Division (ESS)
Abstract: Prepared by the Global Strategy to improve Agricultural and Rural Statistics (GSARS), this technical report presents different dimensions of employment and underemployment within agricultural households in three communities in Kenya.
Lead authoring unit/office: Statistics Division (ESS)
Abstract: Prepared by the Global Strategy to Improve Agricultural and Rural Statistics (GSARS), this document presents two case-studies, to illustrate the process of compilation of productivity indicators: i) the calculation of labour productivity based on data collected at farm-level, that is based on a pilot survey in Zambia, and ii) the construction of aggregate and country-level information on agricultural productivity from aggregate time-series.
Lead authoring unit/office: Statistics Division (ESS)
