亚开行-加强亚太地区贫困统计能力的数据整合方法（英）

ADB BRIEFSNO. 341JUNE 2025ISBN 978-92-9277-322-9 (print)ISBN 978-92-9277-323-6 (PDF)ISSN 2071-7202 (print)ISSN 2218-2675 (PDF)Publication Stock No. BRF250199-2DOI: http://dx.doi.org/10.22617/BRF250199-2Data Integration Approaches to Strengthen Asia and the Pacific’s Statistical Capacity to Map Poverty KEY POINTS• More granular and timely data on poverty in developing Asia and the Pacific can inform policy design and program targeting to better address the impact of shocks on different sections of the economy and society.• By integrating poverty mapping into national statistical systems, policymakers can ensure that resources are effectively directed to areas with the greatest need. • Earlier poverty mapping methods had previously relied on applying small area estimation techniques, which combine survey-based data with censuses and administrative data.• The integration of nontraditional data sources, such as call detail records and satellite imagery, has introduced innovative methods for predicting poverty. In a data-scarce environment, this advancement allows for real-time poverty estimation, offering dynamic and cost-efficient solutions.• Despite these advancements, key challenges remain, including gaps in technical expertise, limited access to private sector data, outdated statistical regulations, and resource constraints.Christian Flora Mae SocoConsultant Asian Development BankMildred AddaweConsultant ADBArturo Martinez, Jr.Senior Statistician ADBJoseph Albert Niño BulanAssociate Statistics Officer ADBMariko ShibasakiConsultant LocationMindINTRODUCTIONAs developing Asia and the Pacific (developing Asia) seeks to accelerate progress on tackling poverty, policymakers need data to provide detailed insights into the multiple dimensions of poverty and where people are most affected. For example, goal 1 of the Sustainable Development Goals (SDGs) focuses on ending poverty “in all its forms everywhere”.Poverty indicators are typically derived from surveys on household income, expenditure, or living standards. These surveys often have sample sizes large enough to provide nationally representative estimates and reliable estimates at higher geographic levels. However, they usually lack the sample size needed for reliable estimates at more granular levels, such as municipalities and villages. This limitation hinders policymakers from effectively targeting the most vulnerable population segments for poverty reduction programs. While increasing survey sample sizes to achieve reliable estimates at granular levels is ideal, it is often impractical due to the significant additional resources required, which are not readily available to national statistical organizations (NSOs) or survey organizations. As an alternative, some countries use poverty mapping methods.ADB BRIEFS NO. 3412By identifying specific geographic areas, including those with significant pockets of poverty, geographically disaggregated data and statistics produced through poverty maps can em