While tourism is a priority sector in Asia and the Pacific for its revenue- and job-generating potential, countries and communities are often left to deal with the adverse impact of unbalanced tourism, economic overdependence, and environmental pressures. To appropriately balance tourism-related policies in the economic, social, and environmental spheres, there is a need for a comprehensive analysis of data in these broad but interlinked domains. The Statistical Framework for Measuring the Sustainability of Tourism (SF-MST) provides an integrated approach to assess tourism’s economic, environmental, and social dimensions. It provides a coherent structure for organizing data using common concepts, definitions, classifications, and reporting rules. The broad coverage of statistical topics in this framework facilitates discussion of tourism impacts and supports policymaking, including at local and destination level. It provides a consistent and comparable framing for the variety of measures relevant to comprehensively assess the sustainability of tourism. The SF-MST was endorsed by the United Nations Statistics Commission in 2024, at which time Member States also noted the need for capacity building to support its implementation.
The need for harmonized, high-quality disaster-related statistics has become increasingly critical as hazardous events and disasters of growing intensity and frequency pose greater risks to society. To support the production of high-quality disaster-related statistics, the Global Disaster-related Statistics Framework (G-DRSF) was endorsed by the United Nations Statistical Commission (UNSC) at its 57th Session in March 2026 as the first global statistical framework for the production of disaster-related statistics. The G-DRSF is a crucial element in the disaster data ecosystem, connecting data producers and disaster risk reduction policymakers. By providing internationally agreed upon concepts, definitions, classifications and measurement recommendations, it helps countries to generate empirical insights that contribute to a better understanding of disaster risks and informing resilience building.
The Workshop on Data Governance, Innovation and Quality Assurance in a Changing Data Ecosystem for the Asia-Pacific Region will take place from 13 to 16 October 2026 in Chiba, Japan. The workshop is jointly organised by the United Nations Statistics Division (UNSD) and the United Nations Statistical Institute for Asia and the Pacific (SIAP), with the support of the Office of the Director-General for Policy Planning on Statistical Policy, Ministry of Internal Affairs and Communications (MIC), Government of Japan. The workshop is also supported by the United Nations Development Account 18th tranche project, which will facilitate the participation of selected beneficiary countries in the Asia-Pacific region. The workshop will be conducted in English. Official statistics are essential for informed decision-making, public accountability and public trust. As demand grows for more timely, granular and integrated data, national statistical offices are increasingly expected to draw on administrative records, geospatial information, data science, artificial intelligence and other new and alternative data sources. These developments offer important opportunities to modernise statistical production and better meet user needs, while also raising challenges related to governance, data access, confidentiality, transparency, interoperability, methodological soundness and quality assurance.
Population and housing censuses are the most comprehensive and authoritative data sources on the size and characteristics of a country’s population. They form a cornerstone of national statistical systems, generating essential social, economic, and demographic data. These data provide the baseline evidence required for planning, policy development, implementation, and monitoring across geographical levels, sectors, as well as for research and analysis. Censuses also underpin many Sustainable Development Goal (SDG) indicators, enabling countries to track progress toward national and global development targets. Since its first publication in 1958, the Principles and Recommendations for Population and Housing Censuses have been a pillar of the decennial World Programmes on population and housing censuses, setting out the standards, concepts, and methods that national statistics offices or other national institutions in charge of population censuses should follow to plan, organize, conduct, and use population and housing censuses. In March 2025, the United Nations Statistical Commission endorsed the 4th revision of these Principles and Recommendations. As with previous versions, the revised guidance covers key census elements (characteristics, methodology, and content) and addresses several key emerging issues, such as the transition to alternative approaches from traditional methods, including the use of administrative data and other data sources, the integration of geospatial information with census data, emergency preparedness and contingency planning, and quality assurance.
Domestic production in the Pacific is limited by the lack of resources and technologies, geographic isolation, high costs, and insufficient economies of scale. As such, Pacific economies are heavily reliant on international trade to supply their basic commodities and more complex machinery, and to expand their market base. International merchandise trade statistics (IMTS) is crucial to monitor and analyze trade flows, production and consumption. Timely and high-quality trade statistics will better guide the development of trade and economic policies and infrastructure planning. As trade datasets grow in volume and complexity, manual data cleaning, validation, and compilation could be slow, prone to error, lack an audit trail, and not methodologically repeatable. Automating parts of the statistical production process offers reproducibility and reduces human error, standardizes methods across reporting cycles and across countries, and allows small teams to focus on processes that need more careful manual deliberation (such as data cleaning procedures and analysis).This hybrid workshop, organized by the Pacific Community (SPC) and United Nations Statistical Institute for Asia and the Pacific (SIAP), aims to increase capacities of national statistical systems to compile international merchandise trade statistics by automating parts of their workflow process.
Adopted by the 56th Session of the United Nations Statistical Commission, the 2025 System of National Accounts (2025 SNA) serves as the internationally recognized standard for compiling measures of economic activity. The basic theoretical framework of the 2008 SNA is retained in the updated framework. The 2025 SNA gives more prominence to certain aspects of the economy, such as globalization, digitalization, informality, and emerging financial issues. The updated framework also provides a broader perspective in relation to the complex interplay among economic, social, and environmental factors.The 2025 SNA and accompanying guidance notes provide clarifications and elaborations on specific issues. These are complemented by published compilation guides and handbooks, with further guidance currently under development to support implementation. International organizations are called to design training courses, both in-person and online, to assist countries in understanding the conceptual changes and clarifications, and in identifying priorities.
Household consumption and expenditure surveys (HCES) have been typically undertaken to compile information for important macroeconomic indicators, such as consumer price indices, and to provide input into national accounts. With time, the use of HCES has been extended to welfare and poverty analysis, and more recently to food security and nutrition analysis. The food data collected in HCES provides core information for these types of analyses. However, the data is comprehensive and complex to process; users, based on their needs or interests, often tend to follow different approaches when preparing the data for analysis. When data from the same survey is processed independently for different uses, it quite often leads to inconsistent results. Such practice is inefficient and costly. Recognizing the importance of having a standard approach to preparing HCES data for analysis, the United Nations Committee of Experts in Food Security, Agricultural and Rural Statistics (UN-CEAG) developed unified guidelines on how to prepare food consumption data from HCES for “all uses” including for the compilation of information on poverty, economy and food security. The Guidelines provide countries with standard methods for processing food data from HCES, to increase efficiency, quality and consistency when the data is further used for national accounts, consumer price indices, poverty and food security analyses. These are the foundation for this e-learning course.
Effective measurement of poverty is essential for understanding the scale, depth and distribution of deprivation, and for designing policies that respond to the needs of the poorest and most vulnerable. Accurate, timely, and comparable poverty statistics enable governments to better target interventions, monitor progress, and assess the impact of economic and social shocks. In the context of the Sustainable Development Goals, particularly SDG 1 (No Poverty), robust poverty measurement is indispensable for ensuring that policy responses are evidence-based and effective. Consistent methodologies and measurements across countries and population groups are critical for producing reliable poverty estimates and for SDG monitoring. In this regard, the World Bank’s revision of the international poverty line in 2025, from USD 2.15 to USD 3.00 per person per day, to align with the 2021 purchasing power parities (PPP) and updated global price data represented an important development. While this update improved the relevance of the global poverty benchmark1, it introduced significant challenges for comparability, trend analysis, and the communication of results. It also requires that countries recalibrate poverty estimates and reassess historical series.
The numbers of refugees and internally displaced persons (IDPs) have increased rapidly in recent years, with UNHCR estimating over 117.8 million forcibly displaced persons by the end of 2025. Similarly, the issue of statelessness continues to persist in society, with 4.5 million people currently estimated to be stateless. With the growing prominence of displacement and statelessness, there is increasing interest at national, regional and international levels in statistics on refugees, IDPs and stateless persons, including complete, accurate, timely and internationally comparable estimates on the numbers of people impacted, their well-being and vulnerabilities. Good-quality statistics are a requirement for developing, implementing and monitoring national, regional and international policies and agreements concerning refugees, IDPs and stateless persons. The course introduces the “International Recommendations on Refugee Statistics” (IRRS), the “International Recommendations on IDP Statistics” (IRIS) and the “International Recommendations on Statelessness Statistics” (IROSS). These sets of recommendations were endorsed by the United Nations Statistical Commission in 2018 (IRRS), 2020 (IRIS) and 2023 (IROSS), respectively. The course is spread over six substantive modules and provides details on the statistical framework for compiling refugee, IDP and stateless persons statistics. It introduces concepts, definitions, classifications and methodologies relevant to collecting, compiling, analyzing and disseminating data and indicators on refugees, IDPs and stateless persons, including Sustainable Development Goal (SDG) indicators. The course also includes information on data sources, institutional mechanisms, and coordination.
This course is an advanced course in data visualization, conceived as an extension of SIAP’s facilitated course “Data Visualization for Official Statistics and SDG Indicators,” last conducted in 2024, or, alternatively, of the self-paced course “Principles of Data Visualization for Official Statistics and SDG Indicators,” available on SIAP’s e-learning platform. This advanced course focuses on methods for producing high-quality graphics for monitoring and publishing official statistics and SDG indicators. It highlights advanced topics in data visualization such as visualizing and representing high-dimensional datasets, visualizing uncertainty, and visualizing networks, among others. It also addresses important issues such as gender and ethical considerations in graphical communication. Additionally, the course proposes resources and practical insights on storytelling and the construction of visual narratives for diverse audiences, promoting inclusive data visualization. This course provides an opportunity for participants to improve their data visualization techniques, both for data exploration and for the (re)presentation of complex, unstructured, or high-dimensional datasets. It includes case studies and practical strategies, as well as hands-on methods for representing statistical indicators on maps or within dashboards.
The System of Environmental-Economic Accounting (SEEA) provides the agreed upon statistical framework for measuring the relationship between the environment and the economy. It is comprised of two complimentary international statistical standards: the SEEA Central Framework (SEEA CF) and the SEEA Ecosystem Accounting (SEEA EA). The SEEA CF takes a resource-based approach to measuring the supply and use of environmental resources and availability of environmental assets. The SEEA EA takes a holistic and spatially explicit approach to compiling accounts on ecosystems. The SEEA can be flexibly implemented by countries prioritizing accounts based on availability of information and policy priorities. The accounts are designed to respond to data needs for multiple policy initiatives including global initiatives on sustainable development, on mitigating and adapting to climate change and biodiversity. In addition, the Global Set of Climate Change Statistics and Indicators with its Self-Assessment Tool (CISAT) offers an opportunity to comprehensively assess data availability as well as the needs and gaps, in an effort to priorities key steps to address the data challenges
Climate change is one of the most pressing issues of our time. Its impacts on well-being are far reaching, including impacts on health and the economy. Humans have contributed to climate change largely through economic activities which are intrinsically linked to climate change. Our supply and use of energy for example has led to increased emissions of greenhouse gases which have resulted in global warming. A better understanding of the relationship between the economy and climate change through the compilation of relevant indicators is key to mitigating and adapting to climate change. 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. 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. Further details on course content follow below.
Across Asia and the Pacific, violence against women and girls is taking increasingly complex and lethal forms—from gender-related killings(femicide/feminicide) to rapidly evolving technology-facilitated abuse—yet both remain insufficiently measured due to persistent gaps in definitions, reporting, and integrated data systems.In 20241, at least 17,400 women and girls in Asia were killed by an intimate partner or family member—approximately 48 every day—according to joint United Nations Office on Drugs and Crime (UNODC) and United Nations Entity for Gender Equality and the Empowerment of Women (UN Women) estimates. While this represents a decline from the previous year, it does not signal true progress. Instead, it reflects ongoing weaknesses in national data and reporting systems, including incomplete identification and classification of femicide and limited capacity to capture gender-related motives.
In recent years, the data landscape has evolved rapidly, creating for National Statistical Offices (NSOs) both significant opportunities for innovation and important challenges in maintaining data quality and reliability. In response to these developments, the Statistical Institute for Asia and the Pacific (SIAP) and the Ministry of Data And Statistics (MODS) are organizing a Regional Training on Data Science for Official Statistics, recognizing both the opportunities and the challenges associated with integrating Data Science methods and tools into NSO processes. The overall objective of the week-long training is to build the capacity of NSOs to integrate Data Science methods into the production of Official Statistics. It seeks to strengthen participants’ skills in handling, analyzing, and visualizing data sources, with a particular focus on reproducible methods and tools for monitoring and reporting Official Statistics and Sustainable Development Goals (SDGs) indicators.
The training aims to strengthen the capacity of heads of National Statistics Offices to lead their own organisations and position them in the larger data ecosystem. This training session follows similar sessions run successfully by PARIS21 and SIAP in the region.
PARIS21, SIAP
Regional Training
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Türkiye
Pacific SIDS Energy Statistics Training Workshop
The United Nations Statistical Institute for Asia and the Pacific (SIAP) and the United Nations Statistics Division (UNSD), in cooperation with the Department of Economic and Social Affairs (DESA) Small Island Developing States (SIDS) Unit and the Secretariat for the Pacific Community, are organizing an Energy Statistics Training Workshop for Pacific SIDS. The Workshop will be held in Port Moresby, Papua New Guinea from 29 April – 1 May 2026. The Workshop will be an opportunity for data providers, policy makers and others to come together to accelerate the region’s renewable energy transition. The workshop will build national capacity to produce, disseminate and use high-quality energy statistics to monitor progress. The Workshop will be held in conjunction with the Pacific SIDS Regional Symposium: Transition towards 100% renewable energy. The Symposium will strengthen capacity and catalyse investment in renewable energy systems in the region through scalable pilot initiatives and SIDS-SIDS peer learning.
National statistical systems are increasingly using administrative data to compile official statistics. Such data can be utilized to better meet the increasing demands for new statistics and indicators that are highly disaggregated. Administrative data is not collected for the primary purpose of compiling official statistics, and statisticians need to ensure that the data meets certain criteria before using it to produce official statistics. This course provides an overview of administrative data, a discussion of data quality issues and institutional mechanisms to ensure that administrative data can be used in the production of official statistics. The course builds upon content developed for in-person training courses conducted by United Nations Statistics Division (UNSD) and to which members of the Collaborative on Administrative Data have provided valuable input.
The 2025 System of National Accounts (2025 SNA) and Balance of Payments and Integrated Investment Position Manual, 7th edition (BPM7) provide more visibility to digital activities, products and transactions in macroeconomic accounts given its tremendous impact on production, consumption, prices, trade, labour, and other aspects of the economy. Measurement issues associated with digitalization are elaborated in the 2025 SNA and BPM7, as well as in the accompanying guidance notes and manuals. Some national statistical organizations have initiated experimental estimates on capturing digital activities, while others have integrated questions on the use of digital products and services in their regular household and business surveys.This webinar series builds on outreach missions of the Intersecretariat Working Group on National Accounts and Advisory Expert Group members and regional commissions to increase awareness on the developments in the 2025 SNA. This collaboration between the Economic and Social Commission for Asia and the Pacific, through the Statistics Division and Statistical Institute for Asia and the Pacific (SIAP), and the ASEAN Secretariat contributes to advancing regional dialogue on the complexities of measurement of digitalization and better understanding of methodological approaches.
Identifying and responding to the needs of users for data and statistics is at the heart of the mission of National Statistical Offices (NSOs) and national statistical systems. NSOs already undertake many activities interacting with different users such as preparing press releases to aid journalists and communicate with the public at large and organizing launch events/workshops to disseminate important results from surveys and censuses. In general, the NSO interacts with users towards the tail end of the statistical production process when the data is disseminated. There is an opportunity for national statistical offices to further improve their collaboration with users by engaging more systematically throughout the statistical production process. User engagement is the process of conducting a dialogue with users of official statistics to understand their needs and improve the products, services and operation of a statistical organization accordingly.
