This course introduces the SEEA Central Framework, the international statistical standards for measuring the interactions between the environment and the economy. By providing an internationally agreed standard with agreed concepts, definitions and classifications, the SEEA is an invaluable tool for compiling integrated information on the economy and the environment. The SEEA uses concepts, definitions and classifications consistent with the SNA in order to facilitate the integration of environmental and economic statistics. By doing so, the SEEA allows users to develop indicators (including SDG indicators) and conduct analysis on the economy-environment nexus.
Timely, reliable, and comparable health statistics are fundamental to monitoring the health status of the population and for developing, implementing, and evaluating health policies and practices that address health and health care. With the adoption of the 2030 Agenda for Sustainable Development, the need for high-quality data for the over 50 health and health-related Sustainable Development Goal (SDG) indicators has increased. The COVID-19 has impacted the health and well-being of populations all over the world and further underscored the need for real-time, good quality, disaggregated data to track health. Nonetheless, for many countries, limited statistical capacity continues to pose a major challenge for monitoring the health and health-related SDGs. The objective of this e-Learning course is to address this issue by providing a general understanding of the basic concept, methods and framework required to compile and monitor the health and health-related indicators.
SIAP will be hiring a G6 level Staff Information Systems and Training Assistant. The deadline is approaching soon, so if you are interested, please refer to
The course introduces the SEEA EA, the international statistical standard for organizing data about ecosystems, measuring ecosystem services, tracking changes in ecosystem assets, and linking this information to economic and other human activity. The SEEA EA framework takes a spatial approach to organizing information on ecosystems. It supports the compilation of indicators for several global policy frameworks including the 2030 Agenda for Sustainable Development and related SDG indicators and the Post 2020 Global Biodiversity Agenda. The SEEA EA complements the accounts compiled in the SEEA Central Framework (SEEA CF) and together, SEEA EA and SEEA CF, provide a comprehensive framework for organizing data on the relationship between the environment and economy.
This course introduces concepts relevant to compiling economy wide material flow accounts (EW-MFA); it also covers the methodologies for the SDG indicators 8.4.1/12.2/1 and 8.4.2/12.2.2. EW-MFA represents a framework for describing the interaction of a domestic economy with the natural environment and the economy of the rest of the world in terms of flows of materials, waste and emissions. They are part of the broader System of Environmental Economic Accounting (SEEA) which is the international statistical standard for measuring the relationship between the economy and the environment.
The online course on crime statistics from a gender perspective will highlight basic concepts, methods and frameworks required to compile crime statistics in relation to gender and provide knowledge on the challenges and opportunities of working with different types of data sources (administrative data, sample surveys).
The course provides an opportunity for participants to explore and comprehend the techniques of data visualization for data exploration as well as for data presentation. Participants will discover, evaluate and apply the rules of data visualization on devoted cases studies and also tackle the problem of visualizing complex data. The course proposes strategies for visualizing in multi-dimensions as well as presenting the practical methods for representing statistical indicators on maps or within dashboards.
This course will focus on climate change indicators that can be compiled from environmental economic accounts. After a brief overview of climate change and, relevant polices and multilateral agreements, participants will learn how to compile various indicators that inform climate change. The focus of the course is on better understanding the relationship between climate change and economic activity. And the statistical framework that provides the concepts, definitions, and methodology for measuring this relationship is the System of Environmental Economic Accounting. In particular, participants will learn about physical supply and use tables for energy and air emissions, and indicators that can be compiled from these accounts. Other topics to be discussed include transaction accounts which can be used to derived expenditure type indicators such those on taxes on energy and pollution.
The seventh meeting of Network for the Coordination of Statistical Training in Asia and the Pacific (the Network), the subgroups will report their activities and the Network will discuss further development of their activities.
The Governing Council at its sixteenth session requested that the e-learning training modality continue, that more resources be devoted to it and that more of the Institute's training materials and resources be made accessible to users. The council will accordingly in its seventeenth session review the performance of the Institute in this regard and will consider further development of e-learning training materials and methodologies by the Institute.
The Management Seminar aims to strengthen the leadership and management abilities of the heads of national statistical offices by providing a forum to discuss, exchange views and share experiences. The theme of this year’s seminar is "Transforming Institutions is Transforming People". The seminar will identify good practices of leadership and future steps that should be taken to lead agile, resilient and responsible human resources management.
The course is designed for personnel working in the field of statistics, whose main responsibilities include data analysis of SDG indicators and related statistics with a specific target on data scientists from NSOs with an experience in both statistical modelling (regression analysis, prediction, classification, ...) and with programming or algorithmic skills. Although no programming will be required to follow and succeed in the course, the pedagogical materials include R code, in the form of reproducible markdown notebooks, as well as some Python resources and code.