Aiken, Emily L., Guadalupe Bedoya, Aidan Coville, and Joshua E. Blumenstock. 2020. “Targeting Development Aid with Machine Learning and Mobile Phone Data: Evidence from an Anti-Poverty Intervention in Afghanistan.” Proceedings of the 3rd ACM SIGCAS Conference on Computing and Sustainable Societies. 2020.

Alkire, Sabina. 2014. “Towards Frequent and Accurate Poverty Data.” OPHI Research in Progress 43b, Oxford University. Link

Amin, Samia, Jishnu Das, and Markus Goldstein, eds. 2008. Are You Being Served? New Tools for Measuring Service Delivery. Washington, DC: World Bank. https://openknowledge.worldbank.org/bitstream/handle/10986/6921/424820PUB0ISBN1LIC0disclosed0Feb131.pdf?sequence=1&isAllowed=y.

Arias, Omar, and Marcos Robles. 2007. “The Geography of Monetary Poverty in Bolivia: The Lessons of Poverty Maps.” In More Than a Pretty Picture: Using Poverty Maps to Design Better Policies and Interventions, edited by Bedi, Tara, Aline Coudouel, and Kenneth Simler, 67–89. Washington, DC: World Bank. https://openknowledge.worldbank.org/bitstream/handle/10986/6800/414470PAPER0Pr101Official0Use0Only1.pdf?sequence=1&isAllowed=y.

Asian Development Bank. 2001. Handbook for Integrating Poverty Impact Assessments in the Economic Analysis of Projects. Manila: Asian Development Bank https://www.adb.org/sites/default/files/institutional-document/31336/integrating-poverty-impact-assessment.pdf.

Ayush, Kumar, Burak Uzkent, Kumar Tanmay, Marshall Burke, David Lobell, and Stefano Ermon. 2020. “Efficient Poverty Mapping Using Deep Reinforcement Learning.” arXiv preprint arXiv:2006.04224. https://arxiv.org/abs/2006.04224.

Babenko, Boris, Jonathan Hersh, David Newhouse, Anusha Ramakrishnan, and Tom Swartz. 2017. “Poverty Mapping Using Convolutional Neural Networks Trained on High and Medium Resolution Satellite Images, with an Application in Mexico.” arXiv preprint arXiv:1711.06323. https://arxiv.org/pdf/1711.06323.pdf.

Bedi, Tara, Aline Coudouel, and Kenneth Simler, eds. 2007. More Than a Pretty Picture: Using Poverty Maps to Design Better Policies and Interventions. Washington, DC: World Bank. https://openknowledge.worldbank.org/bitstream/handle/10986/6800/414470PAPER0Pr101Official0Use0Only1.pdf?sequence=1&isAllowed=y.

Blumenstock, Joshua E. 2018. “Estimating Economic Characteristics with Phone Data.” AEA Papers and Proceedings 108: 72–76. https://par.nsf.gov/servlets/purl/10062319.

Blumenstock, Joshua, Gabriel Cadamuro, and Robert On. 2015. “Predicting Poverty and Wealth from Mobile Phone Metadata.” Science 350 (6264): 1073–1076. https://doi.org/10.1126/science.aac4420.

Chambers, Ray, and Nikos Tzavidis. 2006. “M-Quantile Models for Small Area Estimation.” Biometrika 93 (2): 255–268. https://www.jstor.org/stable/20441279?refreqid=excelsior%3A1efdc510f7c748519df160fb2e365063.

Chi, Guanghua, Han Fang, Sourav Chatterjee, and Joshua E. Blumenstock. 2021. “Micro-Estimates of Wealth for all Low- and Middle-Income Countries.” arXiv preprint arXiv:2104.07761. https://arxiv.org/abs/2104.07761.

CONEVAL (Consejo Nacional de la Política de Desarrollo Social). 2017. Metodología para la Medición de la Pobreza en los Municipios de México, 2015. Mexico City: CONEVAL. https://www.coneval.org.mx/Medicion/Documents/Pobreza_municipal/Metodologia_municipal_2015.pdf.

Corral, Paul, Kristen Himelein, Kevin McGee, and Isabel Molina. 2021. “A Map of the Poor or a Poor Map?” Policy Research Working Paper 9620, World Bank, Washington, DC. (). https://openknowledge.worldbank.org/bitstream/handle/10986/35442/A-Map-of-the-Poor-or-a-Poor-Map.pdf?sequence=1&isAllowed=y.

DANE (Departamento Administrativo Nacional de Estadística). 2020. “El DANE presenta por primera vez la medida de la pobreza multidimensional de las cabeceras municipales a nivel de manzana.” DANE, Bogota. https://www.dane.gov.co/index.php/en/actualidad-dane/5186-el-dane-presenta-por-primera-vez-la-medida-de-la-pobreza-multidimensional-de-las-cabeceras-municipales-a-nivel-de-manzana.

Dang, Hai-Anh, Dean Jolliffe, and Calogero Carletto. 2017. “Data Gaps, Data Incomparability, and Data Imputation: A Review of Poverty Measurement Methods for Data-Scarce Environments.” Policy Research Working Paper 8282, World Bank, Washington, DC. https://documents1.worldbank.org/curated/en/551171513690220305/pdf/Data-gaps-data-incomparability-and-data-imputation-a-review-of-poverty-measurement-methods-for-data-scarce-environments.pdf.

Decuyper, Adeline, Alex Rutherford, Amit Wadhwa, Jean-Martin Bauer, Gautier Krings, Thoralf Gutierrez, Vincent D. Blondel, and Miguel A. Luengo-Oroz. 2014. “Estimating Food Consumption and Poverty Indices with Mobile Phone Data.” arXiv preprint arXiv:1412.2595. https://arxiv.org/abs/1412.2595.

Demombynes, Gabriel, Chris Elbers, Jenny Lanjouw, Peter Lanjouw, Johan Mistiaen, and Berk Özler. 2002. “Producing an Improved Geographic Profile of Poverty: Methodology and Evidence from Three Developing Countries.” Discussion Paper 2002/39, World Institute for Development Economics, Helsinki. https://www.econstor.eu/handle/10419/52910.

Egedy, Tamás, and Bence Ságvári. 2021. “Urban Geographical Patterns of the Relationship between Mobile Communication, Social Networks and Economic Development—The Case of Hungary.” Hungarian Geographical Bulletin 70 (2): 129–148 ().DOI:10.15201/hungeobull.70.2.3.

Elbers, Chris, Tomoki Fujii, Peter Lanjouw, Berk Özler, and Wesley Yin. 2007. “Poverty Alleviation through Geographic Targeting: How Much Does Disaggregation Help?” Journal of Development Economics 83 (1): 198–213. https://doi.org/10.1016/j.jdeveco.2006.02.001.

Elbers, Chris, Jean O. Lanjouw, and Peter Lanjouw. 2003. “Micro-Level Estimation of Poverty and Inequality.” Econometrica 71 (1): 355–364. https://doi.org/10.1111/1468-0262.00399.

Elbers, Chris, Peter Lanjouw, and Phillippe George Leite. 2008. “Brazil within Brazil: Testing the Poverty Map Methodology in Minas Gerais.” Policy Research Working Paper 4513, World Bank, Washington, DC. https://openknowledge.worldbank.org/bitstream/handle/10986/6575/wps4513.pdf?sequence=1&isAllowed=y.

Elvidge, Christopher D., Paul C. Sutton, Tilottama Ghosh, Benjamin T. Tuttle, Kimberly E. Baugh, Budhendra Bhaduri, and Edward Bright. 2009. “A Global Poverty Map Derived from Satellite Data.” Computers & Geosciences 35 (8): 1652–1660. https://doi.org/10.1016/j.cageo.2009.01.009.

Engstrom, Ryan, Jonathan Samuel Hersh, and David Locke Newhouse. 2017. “Poverty from Space: Using High-Resolution Satellite Imagery for Estimating Economic Well-Being.” Policy Research Working Paper 8284, World Bank, Washington, DC. https://openknowledge.worldbank.org/handle/10986/29075.

Fatehkia, Masoomali, Benjamin Coles, Ferda Ofli, and Ingmar Weber. 2020. “The Relative Value of Facebook Advertising Data for Poverty Mapping.” In Proceedings of the International AAAI Conference on Web and Social Media 14: 934–938. https://ojs.aaai.org/index.php/ICWSM/article/view/7361.

Fatehkia, Masoomali, Isabelle Tingzon, Ardie Orden, Stephanie Sy, Vedran Sekara, Manuel Garcia-Herranz, and Ingmar Weber. 2020. “Mapping Socioeconomic Indicators Using Social Media Advertising Data.” EPJ Data Science 9 (1): 22. https://doi.org/10.1140/epjds/s13688-020-00235-w.

Ghosh, Malay, and John N. K. Rao. 1994. “Small Area Estimation: An Appraisal.” Statistical Science 9 (1): 55–76. DOI: 10.1214/ss/1177010647.

Gram-Hansen, Bradley J., Patrick Helber, Indhu Varatharajan, Faiza Azam, Alejandro Coca-Castro, Veronika Kopackova, and Piotr Bilinski. 2019. “Mapping Informal Settlements in Developing Countries Using Machine Learning and Low Resolution Multi-Spectral Data.” In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society, 361–368. https://arxiv.org/pdf/1901.00861.pdf.

Grosh, Margaret E., Paul Glewwe. 1995. “A Guide to Living Standards Measurement Study Surveys and Their Data Sets.” Living Standards Measurement Study Working Paper 120, World Bank, Washington, DC. https://documents1.worldbank.org/curated/en/270551468764720584/pdf/multi-page.pdf.

Hastie, Trevor, Robert Tibshirani, and Jerome H. Friedman. 2009. The Elements of Statistical Learning: Data Mining, Inference, and Prediction. 2nd ed. New York: Springer.

Head, Andrew, Mélanie Manguin, Nhat Tran, and Joshua E. Blumenstock. 2017. “Can Human Development Be Measured with Satellite Imagery?” In Proceedings of the Ninth International Conference on Information and Communication Technologies and Development, article 8. https://dl.acm.org/doi/10.1145/3136560.3136576.

Heitmann, Soren, and Sinja Buri. 2019. Poverty Estimation with Satellite Imagery at Neighborhood Levels: Results and Lessons for Financial Inclusion from Ghana and Uganda. Washington, DC: World Bank. https://www.ifc.org/wps/wcm/connect/2cae89ee-dea3-4a7e-ba79-77c9011cbd0f/IFC_2019_Poverty+Estimation+with+Satellite+Imagery+at+Neighborhood+Levels.pdf?MOD=AJPERES&CVID=mHZhcxB.

Henninger, Norbert. 1998. Mapping and Geographic Analysis of Poverty and Human Welfare—Review and Assessment. Washington, DC: World Resources Institute. http://pdf.wri.org/poverty_and_human_welfare.pdf.

Henninger, Norbert, and Mathilde Snel. 2002. Where Are the Poor? Experiences with the Development and Use of Poverty Maps. Washington, DC and Arendal, Norway: World Resources Institute and UNEP/GRID-Arendal. https://files.wri.org/d8/s3fs-public/pdf/wherepoor.pdf

Hentschel, Jesko, Jean Olson Lanjouw, Peter Lanjouw, and Javier Poggi. 1998. “Combining Census and Survey Data to Study Spatial Dimensions of Poverty: A Case Study of Ecuador.” Policy Research Working Paper 1928, World Bank, Washington, DC. Link

Hentschel, Jesko, and Peter Lanjouw. 1998. “Using Disaggregated Poverty Maps to Plan Sectoral Investments.” PREM Notes 5, World Bank, Washington, DC. https://openknowledge.worldbank.org/bitstream/handle/10986/11544/multi_page.pdf?sequence=1&isAllowed=y.

Hernandez, Marco, Lingzi Hong, Vanessa Frias-Martinez, Andrew Whitby, and Enrique Frias-Martinez. 2017. “Estimating Poverty Using Cell Phone Data: Evidence from Guatemala.” Policy Research Working Paper 7969, World Bank, Washington, DC. https://openknowledge.worldbank.org/bitstream/handle/10986/26136/WPS7969.pdf?sequence=1&isAllowed=y.

Hersh, Jonathan, Ryan Engstrom, and Michael Mann. 2021. “Open Data for Algorithms: Mapping Poverty in Belize using Open Satellite Derived Features and Machine Learning.” Information Technology for Development 27 (2): 263–292. https://doi.org/10.1080/02681102.2020.1811945.

Hyman, G. 2013. “Poverty Maps from Unsatisfied Basic Needs Indicators in Latin America.” International Crisis Group. https://www.ciesin.columbia.edu/repository/povmap/methods/NBI.pdf.

Jean, Neal, Marshall Burke, Michael Xie, W. Matthew Davis, David B. Lobell, and Stefano Ermon. 2016. “Combining Satellite Imagery and Machine Learning to Predict Poverty.” Science 353 (6301): 790–794. https://doi.org/10.1126/science.aaf7894.

Lee, Kamwoo, and Jeanine Braithwaite. 2020. “High-Resolution Poverty Maps in Sub-Saharan Africa.” arXiv preprint arXiv:2009.00544. https://arxiv.org/ftp/arxiv/papers/2009/2009.00544.pdf.

Lee, Myeong, Rachael Dottle, Carlos Espino, Imam Subkhan, Ariel Rokem, and Afra Mashhadi. 2017. “A Tool for Estimating and Visualizing Poverty Maps.” Myeong Lee (website). https://myeonglee.com/publications/tool-estimating-and-visualizing-poverty-maps#:~:text=%22Poverty%20maps%22%20are%20designed%20to,different%20dimensions%20of%20poverty%20determinants.

Leidig, Mathias, and Richard M. Teeuw. 2015. “Quantifying and Mapping Global Data Poverty.” PLOS ONE 10 (11): e0142076. https://doi.org/10.1371/journal.pone.0145591.

Malgioglio, Silvia, Nobuo Yoshida, and Johannes Hoogeveen. 2019. “New Monitoring Methods and Tools Make Development More Effective.” Data Blog, October 29, 2019. https://blogs.worldbank.org/opendata/new-monitoring-methods-and-tools-make-development-more-effective.

Molina, Isabel, and J. N. K. Rao. 2010. “Small Area Estimation of Poverty Indicators.” Canadian Journal of Statistics 38 (3): 369–385. https://doi.org/10.1002/cjs.10051.

Nguyen, Minh, Paul Andres Corral Rodas, João Pedro Azevedo, and Qinghua Zhao. 2018. “SAE: A Stata Package for Unit Level Small Area Estimation.” Policy Research Working Paper 8630, World Bank, Washington, DC. https://openknowledge.worldbank.org/bitstream/handle/10986/30650/WPS8630.pdf?sequence=1&isAllowed=y.

Piaggesi, Simone, Laetitia Gauvin, Michele Tizzoni, Ciro Cattuto, Natalia Adler, Stefaan Verhulst, Andrew Young, Rhiannan Price, Leo Ferres, and André Panisson. 2019. “Predicting City Poverty Using Satellite Imagery.” In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 90–96. https://openaccess.thecvf.com/content_CVPRW_2019/papers/cv4gc/Piaggesi_Predicting_City_Poverty_Using_Satellite_Imagery_CVPRW_2019_paper.pdf.

Puttanapong, Nattapong, Arturo M. Martinez Jr., Mildred Addawe, Joseph Bulan, Ron Lester Durante, and Marymell Martillan. 2020. “Predicting Poverty Using Geospatial Data in Thailand.” ADB Economics Working Paper Series 630, Asian Development Bank, Manila. https://www.adb.org/sites/default/files/publication/666711/ewp-630-predicting-poverty-geospatial-data-thailand.pdf.

Rodas, Paul Corral, Isabel Molina, and Minh Nguyen. 2021. “Pull Your Small Area Estimates up by the Bootstraps.” Journal of Statistical Computation and Simulation 91 (16): 3304–3357. https://doi.org/10.1080/00949655.2021.1926460.

Šćepanović, Sanja, Igor Mishkovski, Pan Hui, Jukka K. Nurminen, and Antti Ylä-Jääski. 2015. “Mobile Phone Call Data as a Regional Socio-Economic Proxy Indicator.” PLOS ONE 10 (4): e0124160. https://doi.org/10.1371/journal.pone.0124160.

SDSN (Sustainable Development Solutions Network) TReNDS (Thematic Research Network on Data and Statistics). 2018. Household Surveys Shape Policy Investments. Global Partnership for Sustainable Development Data. https://www.data4sdgs.org/sites/default/files/2018-11/LSMS%20Case%20Study.pdf.

Serajuddin, Umar, Hiroki Uematsu, Christina Wieser, Nobuo Yoshida, and Andrew Dabalen. 2015. “Data Deprivation: Another Deprivation to End.” Policy Research Working Paper 7252, World Bank, Washington, DC. https://openknowledge.worldbank.org/handle/10986/21867.

Smith-Clarke, Chris, and Licia Capra. 2016. “Beyond the Baseline: Establishing the Value in Mobile Phone Based Poverty Estimates.” In Proceedings of the 25th International Conference On World Wide Web, 425–434. https://doi.org/10.1145/2872427.2883076.

Smith-Clarke, Christopher, Afra Mashhadi, and Licia Capra. 2014. “Poverty on the Cheap: Estimating Poverty Maps Using Aggregated Mobile Communication Networks.” In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, 511–520. https://doi.org/10.1145/2556288.2557358.

Sparks, Corey, and Joey Campbell. 2014. “An Application of Bayesian Methods to Small Area Poverty Rate Estimates.” Population Research and Policy Review 33 (3): 455–477. http://dx.doi.org/10.1007/s11113-013-9303-8.

Statistics South Africa. 2020. “Mapping Vulnerability to Covid-19.” https://www.statssa.gov.za/?p=13875.

Steele, Jessica E., Pål Roe Sundsøy, Carla Pezzulo, Victor A. Alegana, Tomas J. Bird, Joshua Blumenstock, Johannes Bjelland, et al. 2017. “Mapping Poverty Using Mobile Phone and Satellite Data.” Journal of The Royal Society Interface 14 (127): 20160690. https://doi.org/10.1098/rsif.2016.0690.

Tarozzi, Alessandro, and Angus Deaton. 2007. “Using Census and Survey Data to Estimate Poverty and Inequality for Small Areas.” The Review of Economics and Statistics 91 (4): 773–792. http://dx.doi.org/10.2139/ssrn.997829.

Tingzon, Isabelle, Ardie Orden, Stephanie Sy, Vedran Sekara, Ingmar Weber, Masoomali Fatehkia, Manuel Garcia-Herranz, and Dohyung Kim. 2019. “Mapping Poverty in the Philippines Using Machine Learning, Satellite Imagery, and Crowd-Sourced Geospatial Information.” In AI for Social Good ICML 2019 Workshop. https://pdfs.semanticscholar.org/9d96/bbd1bab6f66015096336052bd86662e14c6d.pdf.

United States Agency for International Development. 2022. “Demographic and Health Survey (DHS).” https://dhsprogram.com/Methodology/Survey-Types/DHS.cfm.

World Bank. 2008. Nicaragua Poverty Assessment. Washington, DC: World Bank. https://documents1.worldbank.org/curated/en/235491468297893170/pdf/397360ESW0vol110gray0cover01PUBLIC1.pdf.

World Bank. 2020. “Tools for Poverty and Equity Rapid Data Collection.” World Bank Poverty Global Practice Virtual Summer University, Washington, DC, July 7, 2020. Link

World Bank. 2022. “About LSMS.” https://www.worldbank.org/en/programs/lsms/overview

World Bank. 2022. “SWIFT—Rapid Poverty Assessment Tool.” World Bank, Washington, DC. Link

World Bank. n.d. Survey of Well-being via Instant and Frequent Tracking (SWIFT): Estimating Consumption for Household Poverty Measurement. Washington, DC: World Bank. https://www.ifc.org/wps/wcm/connect/64f11adb-ab01-4207-93cd-dd2cc51af16c/SWIFT-booklet-05.pdf?MOD=AJPERES&CVID=m9Or9Ia.

Yeh, Christopher, Anthony Perez, Anne Driscoll, George Azzari, Zhongyi Tang, David Lobell, Stefano Ermon, and Marshall Burke. 2020. “Using Publicly Available Satellite Imagery and Deep Learning to Understand Economic Well-Being in Africa.” Nature Communications 11 (1): article 2583. https://www.nature.com/articles/s41467-020-16185-w.

Yoshida, Nobuo. 2017. “SWIFT: Survey of Well-being via Instant and Frequent Tracking.” https://slideplayer.com/slide/12436138/.

Yoshida, Nobuo, X. Chen, S. Takamatsu, K. Yoshimura, S. Malgioglio, S. Shivakumaran, K. Zhang, and D. Aron. 2021. “The Concept and Empirical Evidence of SWIFT Methodology (Draft).” World Bank, Washington, DC.

Yoshida, Nobuo, R. Munoz, A. Skinner, C. Kyung-eun Lee, M. Brataj, W. Durbin, D. Sharma, and C. Wieser. 2015. Swift Data Collection Guidelines Version 2. Washington, DC: World Bank. https://documents1.worldbank.org/curated/en/591711545170814297/pdf/97499-WP-P149557-OUO-9-Box391480B-ACS.pdf.

Zhao, Xizhi, Bailang Yu, Yan Liu, Zuoqi Chen, Qiaoxuan Li, Congxiao Wang, and Jianping Wu. 2019. “Estimation of Poverty Using Random Forest Regression with Multi-Source Data: A Case Study in Bangladesh.” Remote Sensing 11 (4): 375. https://doi.org/10.3390/rs11040375.