The regional workshop aims to raise awareness and build technical capacity of national experts in Asia and the Pacific region in terms of the importance and use of sex-disaggregated statistics in agriculture, and for compilation, use and reporting of SDG indicators 5.a.1 and 5.a.2. Further details on the course are in the attached Concept Note.
This course was jointly organised by the Institute and the Japan International Cooperation Agency of the Government of Japan. It is designed to strengthen the innovative capabilities of national statistical systems to explore new data sources such as geo-spatial information, big data, and alternative administrative data, and apply alternative/non-traditional methods for SDG statistics. The program will also develop ability to use the non-traditional supplementary data for disaggregation of SDG statistics.
The course is aimed at strengthening the technical capacity of national statistical offices to compile the SDG indicators under FAO custodianship and report them at national and international levels.
This regional course aims to better equip national and international stakeholders in the new SDGs statistical monitoring system. It is designed to enhance institutional capacity to identify, collect, analyse and disseminate labour market information and other indicators related to decent work. It emphasises the 19th International Conference of Labour Statisticians (ICLS) resolution on statistics of work, employment and labour underutilization as an important pillar of the new SDG indicator framework, not only for targets in Goal 8 but also in other Goals related to the decent work agenda.
The workshop will provide an opportunity to participants to learn the basic concepts, compilation and analysis related to gender statistics through hands-on sessions, with the overall aim of enhancing the capacity to produce a set of gender related indicators, to track and monitor Sustainable Development Goals (SDGs).
Training course will feature an intensive workshop on the Advanced Data Planning Tool (ADAPT), an innovative web-based planning tool for National Statistical Offices, national planning agencies, and other data producers to document data demands and supply and identify the data gaps in policy, planning and monitoring frameworks.
This course introduce fundamental knowledge on poverty statistics. This course will help you understand how to compile and monitor Sustainable Development Goals (SDGs) indicators related to poverty.
To enhance the capacity of entities which belong to the National Statistics Office, the Ministries of Agriculture and/or the Ministries of Food through provision of training to statistical staff, in order to increase their knowledge and to develop their skills to produce data, apply appropriate statistical methods to produce Post-Harvest Loss (PHL) statistics and indicators including the Global Food Loss Index (GFLI), interpret and utilise these for monitoring the progress in achieving the Sustainable Development Goals (SDGs).
The course is aimed at strengthening the technical capacity of entities of national statistical systems to produce a set of education-related statistics, including indicators, to formulate, implement and evaluate esucation policy and track progress towards the achievement of Sustainable Development Goal 4 (SDG4).
Development of evidence-based sustainable development strategies and policies relies on available and reliable statistics. Mainstreaming the measurement of environment concerns as part of the regular data collection programs of national statistical offices would give policy makers the means with which to make balanced policy choices for sustainable development. Such measurements will also assist with monitoring national and international sustainable development initiatives, including the sustainable development goals, the SAMOA Pathway for Small Island Developing States and the Pacific SDG Roadmap.
The Network facilitates information sharing and promotes coordination among national statistical training institutions, regional and international statistical training providers, and donor agencies that provide funding for statistical trainings in the region. At its organizational meeting, the Network members recognized that better coordination would provide an environment within which training recipients and training providers can use a common language and a set of tools to describe priority needs and, identify and fill training gaps in the region.