With continuing staff expansion and turnover and the introduction of new and revised standards, recommendations and expanding areas of concern for official statistics, national statistical institutions continue to need to provide training to its staff on the foundations of official statistics. SIAP/JICA residential group training courses and on-demand in-country courses address the needs for basic statistical training covering statistical methods, statistical frameworks, standards and classifications, and phases of the statistical business process for official statistics. The Fundamentals of Official Statistics programme focuses on production, analysis, use and dissemination of the core social, economic, population and environment statistics, with emphasis on the Millennium Development Goals and Sustainable Development Goals data, statistics and indicators.
Integrating the gender perspective in the the production, analysis, use and dissemination of official statistics is the goal of SIAP training courses on gender statistics. The training approach also takes the perspective that gender statistics cuts across all fields of statistics and focuses on building capacity of statistical institutions to produce and disseminate core gender statistics and indicators and to improve coordination with users of gender statistics.
In support of the capacity-building requirements of statistical institutions in implementing the 2008 SNA and the production of a core set of economic statistics endorsed by the ESCAP Committee on Statistics and the Commission, SIAP has ramped up its course offerings on economic statistics. The training programme on 2008 SNA offers basic and intermediate-level e-Learning courses twice a year and an annual regional training course on advanced topics. The programme on economic statistics addresses capacity-building priorities, such as statistical business registers and the informal economy.
SIAP coordinates implementation of the training component of the Asia-Pacific Regional Action Plan (RAP) for the Global Strategy to Improve Agricultural and Rural Statistics (Global Strategy). The Global Strategy establishes the frameworks to address intensively the lack of capacity to meet data requirements as defined in a recommended minimum set of agricultural and rural statistics. The frameworks address the need for agricultural statistics to go beyond the traditional farm level data about production to data used to monitor the role of the agricultural and rural sector in food security, environmental sustainability, and a driver for poverty reduction.
SIAP collaborates with co-implementors of the RAP-- the FAO and ADB-- in implementing the training component. Training aims to strengthen national capacity to provide training at a sustainable level. The strategy focuses on developing training assessment tools, standard syllabuses and training materials and provides training-of-trainers courses on the use of these training tools and resources as well as training in methods for producing and analysing country-determined minimum set of agricultural and rural statistics.
In the Asia-Pacific region, a lack of availability of environment statistics persists. Many countries do not have sufficient data and indicators on the state of their natural resources, factors that affect the quality of their environments and impacts of changes to the environment on economic and social well-being. The outcome of Rio+20 has highlighted the urgent need to improve capacity for producing environment statistics, for integrating these with information on other development pillars, and for producing indicators of sustainable development. SIAP works with statistical development partners in implementing a training programme series on environmental statistics and accounts (SEEA), in support of the SDGs.
SIAP conducts an annual series of Management Seminars for Heads of NSOs and a series of regional training workshops on Statistical Quality Management and Fundamental Principles of Official Statistics for deputy heads and middle-level managers of national statistical institutions.