Abstract

National Statistical Offices (NSOs) have the mandate to collect, analyze and disseminate official statistics for monitoring the development of socio-economic indicators that help policy makers to make better decisions. However, traditional data sources used to produce official statistics face several limitations when used to provide information for disaggregated population groups. To address the challenge of “leave no one behind”, required for implementing the Sustainable Development Goals (SDG) and the achievement of the 2030 Agenda, NSOs have to address several issues to adopt innovative methods of data integration that combine information from different data sources (household surveys, population censuses, administrative records, satellite imagery, and social media) to produce not only geographically disaggregated official statistics, but also information for vulnerable populations allocated all along the countries.
The growing need for disaggregated and timely statistics for specific geographic areas and small groups of the population can be adequately fulfilled by innovative procedures such as small area estimation and record linkage; and by using surveys and nontraditional data such as remote sensing and mobile phone positioning data, which allows for achieving accuracy beyond the limits of the survey sample approach. Many countries have begun to use these techniques to provide official statistics of poverty, unemployment, crime, and victimization, among others. In this context, the World Bank, the UN Regional Commissions, and the Inter-Secretariat Working Group on Household Surveys have been collaborating on capacity-building projects to support countries in producing more disaggregated data for SDG monitoring.
The session will bring speakers from national statistical offices, academia, regional and international organizations together to:
- Share experiences of NSOs in adopting a culture of data integration for a broad range of indicators; and discuss challenges in countries
- Discuss the need for more research on data integration methods to support low- and middle-income countries, when data access, data availability and technical capacity are often lacking
- Review challenges in building capacities of NSOs in small area estimation and other types of data integration and how different communities can collaborate to support countries