[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-113226-en":3,"doc-seo-113226-105":29,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":13,"seo_description":14,"update_tm":27,"read_time":28},113226,4398048950312,"Violet","https://ap-avatar.wpscdn.com/avatar/400002538284de19e3c?_k=1778320343897328908",8,"Research & Report","Nowcasting Global Poverty - Article","This paper evaluates methods for nowcasting country-level poverty rates, emphasizing approaches that use statistical learning on large-scale country data from the World Development Indicators and Google Earth Engine. Methods are tested by withholding observed poverty rates and measuring prediction accuracy on the held-out data. A simple GDP-based approach—scaling the last observed welfare distribution by real GDP per capita growth—matches more complex models using 1,000+ variables. The GDP method also outperforms direct poverty-rate prediction models, even when the last survey is up to five years old, implying limited gains from added modeling complexity in this setting.","Pub lic Disclosure Authorized Pub lic Disclosure Authorized  \nThe World Bank Economic Review, 36(4), 2022, 835–856  \n[https://doi.org10.1093/wber/lhac017](https://doi.org10.1093/wber/lhac017)  \nArticle  \nNowcasting Global Poverty  \nDaniel Gerszon Mahler, R. Andrés Castañeda Aguilar,  \nand David Newhouse  \nAbstract  \nThis paper evaluates different methods for nowcasting country-level poverty rates, including methods that apply statistical learning to large-scale country-level data obtained from the World Development Indicatorsand Google Earth Engine. The methods are evaluated by withholding measured poverty rates and determining how accurately the methods predict the held-out data. A simple approach that scales the last observed welfare distribution by a fraction of real GDP per capita growth performs nearly as well as models using statistical learning on 1,000+ variables. This GDP-based approach outperforms all models that predict poverty rates directly, even when the last survey is up to five years old. The results indicate that in this context, the additional complexity introduced by applying statistical learning techniques to a large set of variables yields only marginal improvements in accuracy.  \nJEL classification: C53, D31, I32, O10  \nKeywords: poverty, nowcasting, machine learning, measurement  \n1. Introduction  \nTimely and comparable poverty estimates are vital to assess countries’ development progress. International poverty estimates serve as a public good for researchers and inform the development community on efforts to meet the first Sustainable Development Goal, to end extreme poverty by 2030 . Within international development organizations, national development agencies, and NGOs, they also inform the allocation of resources and the development of strategic priorities.  \nYet timely and comparable estimates of poverty are lacking for many reasons. In some countries, fragility, conflict, and violence make it difficult to conduct household expenditure surveys altogether, while in other countries, lack of financial resources is the main obstacle. Even when surveys are frequently conducted, the time it takes to field a survey, collect, process, and analyze the data often implies a two-year  \nDaniel Gerszon Mahler (corresponding author) is with the World Bank Data Group in Washington, DC. His email address is [dmahler@worldbank.org. R. Andr](dmahler@worldbank.org. R. Andr)és Castañeda Aguilar is with the World Bank Data Group in Washington, DC. His email address is [acastanedaa@worldbank.org. David Newhouse](acastanedaa@worldbank.org. David Newhouse) is with the World Bank Data Group in Washington, DC. His [email address is dnewhouse@worldbank.org. The research for this article was supported financially by the UK government](email address is dnewhouse@worldbank.org. The research for this article was supported financially by the UK government)[ ](email address is dnewhouse@worldbank.org. The research for this article was supported financially by the UK government)through the Data and Evidence for Tackling Extreme Poverty (DEEP) Research Programme and by the World Bank through a Research Support Budget grant. The authors thank Aart Kraay, Andres Fernando Chamorro Elizondo, Benjamin Stewart, Benu Bidani, Christoph Lakner, Dean Jolliffe, Lucas Kitzmueller, Marta Schoch, Minh Cong Nguyen, Nishant Yonzan, Nobuo Yoshida, and Samuel Kofi Tetteh Baah for insightful comments. The authors are also grateful for feedback received during the special IARIW-World Bank Conference “New Approaches to Defining and Measuring Poverty in a Growing World,”the CCS-UN Workshop “Nowcasting in International Organizations,” and the 2021 ECINEQ Conference. A supplementary online appendix is available with this article at The World Bank Economic Review website.  \n© 2022 International Bank for Reconstruction and Development / The World Bank. Published by Oxford University Press  \nDownloaded from [https://academic.oup.com/wber/article/36/4/835/675002","cbCaicdAoVWw1tzs","https://ap.wps.com/l/cbCaicdAoVWw1tzs","pdf",1265504,1,22,"English","en",105,"# Introduction\n## Objective and key definitions\n## Predictors and data sources\n## Evaluation strategy","[{\"question\":\"What does the paper mean by “nowcasting” and “nearcasting” poverty?\",\"answer\":\"Nowcasts refer to poverty estimates for the present year, relying only on variables with little time lag or variables that are themselves nowcasted. Nearcasts refer to estimates for the preceding year, relying on data produced with about a one-year time lag.\"},{\"question\":\"How are the poverty nowcasting methods evaluated in the paper?\",\"answer\":\"The evaluation withholds measured poverty rates and tests how accurately each method predicts the held-out data. This validation uses the past poverty estimates as training by pretending a subset is missing.\"},{\"question\":\"Why does the GDP-based approach perform well compared with complex machine-learning models?\",\"answer\":\"The paper finds that scaling the last welfare distribution by a fraction of real GDP per capita growth performs nearly as well as models using statistical learning on more than 1,000 variables. It also outperforms models that predict poverty rates directly, even with surveys up to five years old.\"}]",1784501664,55,{"code":4,"msg":30,"data":31},"ok",{"site_id":24,"language":23,"slug":32,"title":13,"keywords":33,"description":14,"schema_data":34,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":27},"nowcasting-global-poverty-article","",{"@graph":35,"@context":85},[36,53,68],{"@type":37,"itemListElement":38},"BreadcrumbList",[39,43,47,50],{"item":40,"name":41,"@type":42,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":44,"name":45,"@type":42,"position":46},"https://docshare.wps.com/document/","Document",2,{"item":48,"name":12,"@type":42,"position":49},"https://docshare.wps.com/document/research-report/",3,{"item":51,"name":13,"@type":42,"position":52},"https://docshare.wps.com/document/nowcasting-global-poverty-article/113226/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":23,"description":14,"dateModified":61,"datePublished":62,"encodingFormat":60,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":40,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-07-20","2026-07-19",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What does the paper mean by “nowcasting” and “nearcasting” poverty?","Question",{"text":75,"@type":76},"Nowcasts refer to poverty estimates for the present year, relying only on variables with little time lag or variables that are themselves nowcasted. Nearcasts refer to estimates for the preceding year, relying on data produced with about a one-year time lag.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How are the poverty nowcasting methods evaluated in the paper?",{"text":80,"@type":76},"The evaluation withholds measured poverty rates and tests how accurately each method predicts the held-out data. This validation uses the past poverty estimates as training by pretending a subset is missing.",{"name":82,"@type":73,"acceptedAnswer":83},"Why does the GDP-based approach perform well compared with complex machine-learning models?",{"text":84,"@type":76},"The paper finds that scaling the last welfare distribution by a fraction of real GDP per capita growth performs nearly as well as models using statistical learning on more than 1,000 variables. 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