[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117500-en":3,"doc-seo-117500-105":30,"detail-sidebar-cat-0-en-105":92},{"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":27,"seo_description":14,"update_tm":28,"read_time":29},117500,1099514067438,"River Wang","https://ap-avatar.wpscdn.com/avatar/100002539ee87300030?x-image-process=image/resize,m_fixed,w_180,h_180&k=1780474512215547542",8,"Research & Report","Poverty Mapping in the Age of Machine Learning","Recent years have advanced poverty mapping methods, especially through modern machine-learning models applied to remotely sensed data. These approaches commonly use a validation step that compares subnational machine-learning poverty estimates with survey-based direct estimates. Yet survey-based estimates at granular levels can be imprecise, raising doubts about whether such validation truly reflects model performance. This paper tests existing validation credibility via design-based simulations using a pseudo-census from Mexico’s 2015 Intercensal Survey.","Public Disclosure Authorized Public Disclosure Authorized  \nPolicy Research Working Paper 10429  \nPoverty Mapping in the Age of Machine Learning  \nPaul Corral  \nHeath Henderson  \nSandra Segovia  \nPoverty and Equity Global Practice May 2023  \nPolicy Research Working Paper 10429  \nAbstract  \nRecent years have witnessed considerable methodological advances in poverty mapping, much of which has focused on the application of modern machine-learning approaches to remotely sensed data. Poverty maps produced with these methods generally share a common validation procedure, which assesses model performance by comparing subnational machine-learning-based poverty estimates with survey-based, direct estimates. Although unbiased, survey-based estimates at a granular level can be imprecise measures of true poverty rates, meaning that it is unclear whether the validation procedures used in machine-learning approaches are informative of actual model performance. This paper examines the credibility of existing approaches to model validation by constructing a pseudo-census from  \nthe Mexican Intercensal Survey of 2015, which is used to conduct several design-based simulation experiments. The findings show that the validation procedure often used for machine-learning approaches can be misleading in terms of model assessment since it yields incorrect information for choosing what may be the best set of estimates across different methods and scenarios. Using alternative validation methods, the paper shows that machine-learning-based estimates can rival traditional, more data intensive poverty mapping approaches. Further, the closest approximation to existing machine-learning approaches, using publicly available geo-referenced data, performs poorly when evaluated against “true” poverty rates and fails to outperform traditional poverty mapping methods in targeting simulations.  \nThis paper is a product of the Poverty and Equity Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at [http://www.worldbank.org/prwp. The authors may be contacted](http://www.worldbank.org/prwp. The authors may be contacted)[ ](http://www.worldbank.org/prwp. The authors may be contacted)[at pcorralrodas@worldbank.org](at pcorralrodas@worldbank.org)  \nThe Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent.  \nProduced by the Research Support Team  \nPoverty Mapping in the Age of Machine Learning  \nPaul Corral,∗Heath Henderson,†and Sandra Segovia‡§  \nKey words: Small area estimation, Poverty mapping, Machine learning, Satellite imagery  \nJEL classification: C13, C55, C87, C15  \n∗ The World Bank Group-Poverty and Equity Global Practice ([pcorralrodas@worldbank.org](pcorralrodas@worldbank.org))†Department of Economics and Finance, Drake University, United States of America ‡The World Bank Group  \n§ The authors acknowledge financial support from the World Bank. We thank Roy van der Weide and Isabel Molina for comments. Additionally, we thank Benu Bidani, Carlos Rodriguez-Castelan, and Johan Mistiaen for providing support and space to pursue this work. Finally, we thank the Global Solutions Group on Data for Policy. Any error or omission is the authors’ responsibility alone.  \n1 I","cbCaiaRbAsgV1Anf","https://ap.wps.com/l/cbCaiaRbAsgV1Anf","pdf",1708923,1,37,"English","en",105,"# Abstract\n# Introduction\n## Poverty maps and granular targeting\n## Small area estimation and model assumptions\n## Unit-level, area-level, and unit-context models","[{\"question\":\"Why does model validation remain a concern in machine-learning poverty mapping?\",\"answer\":\"Validation often compares machine-learning estimates to survey-based direct estimates at granular levels, but those survey estimates may be imprecise. This makes it unclear whether validation results truly represent model performance.\"},{\"question\":\"How does the paper evaluate credibility of existing validation approaches?\",\"answer\":\"It constructs a pseudo-census from Mexico’s 2015 Intercensal Survey and runs design-based simulation experiments to test how commonly used validation procedures affect model assessment and selection.\"},{\"question\":\"Do machine-learning-based poverty estimates outperform traditional poverty mapping methods?\",\"answer\":\"Using alternative validation methods, the paper finds machine-learning-based estimates can rival traditional, more data-intensive approaches in some cases. However, the closest approximation using publicly available geo-referenced data performs poorly against true poverty rates and does not outperform in targeting simulations.\"}]","Poverty Mapping in the Age of Machine Learning | PDF",1785676361,93,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":28},"poverty-mapping-in-the-age-of-machine-learning","",{"@graph":36,"@context":86},[37,54,69],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/poverty-mapping-in-the-age-of-machine-learning/117500/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05","2026-08-02",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"Why does model validation remain a concern in machine-learning poverty mapping?","Question",{"text":76,"@type":77},"Validation often compares machine-learning estimates to survey-based direct estimates at granular levels, but those survey estimates may be imprecise. This makes it unclear whether validation results truly represent model performance.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How does the paper evaluate credibility of existing validation approaches?",{"text":81,"@type":77},"It constructs a pseudo-census from Mexico’s 2015 Intercensal Survey and runs design-based simulation experiments to test how commonly used validation procedures affect model assessment and selection.",{"name":83,"@type":74,"acceptedAnswer":84},"Do machine-learning-based poverty estimates outperform traditional poverty mapping methods?",{"text":85,"@type":77},"Using alternative validation methods, the paper finds machine-learning-based estimates can rival traditional, more data-intensive approaches in some cases. However, the closest approximation using publicly available geo-referenced data performs poorly against true poverty rates and does not outperform in targeting simulations.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,124,129,132,136],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":46,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":46,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":46,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":46,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":133,"doc_module":4,"doc_module_name":46,"category_name":134,"show_sort_weight":133,"slug":135},10,"Lifestyle","lifestyle",{"id":137,"doc_module":4,"doc_module_name":46,"category_name":138,"show_sort_weight":107,"slug":139},19,"General","general"]