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Retooling Poverty Targeting Using Out-of-Sample Validation and Machine Learning

Citations

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Cited by:

  1. Benjamin Schwab, 2020. "In the Form of Bread? A Randomized Comparison of Cash and Food Transfers in Yemen," American Journal of Agricultural Economics, John Wiley & Sons, vol. 102(1), pages 91-113, January.
  2. Olivier Bargain & Ulugbek Aminjonov, 2020. "Poverty and COVID-19 in Developing Countries," Working Papers hal-03258229, HAL.
  3. Follett, Lendie & Henderson, Heath, 2023. "A hybrid approach to targeting social assistance," Journal of Development Economics, Elsevier, vol. 160(C).
  4. Lendie Follett & Heath Henderson, 2022. "A hybrid approach to targeting social assistance," Papers 2201.01356, arXiv.org.
  5. Villacis, Alexis & Badruddoza, Syed & Mayorga, Joaquin & Mishra, Ashok K., 2022. "Using Machine Learning to Test the Consistency of Food Insecurity Measures," 2022 Annual Meeting, July 31-August 2, Anaheim, California 322472, Agricultural and Applied Economics Association.
  6. Aminjonov, Ulugbek & Bargain, Olivier & Bernard, Tanguy, 2023. "Gimme shelter. Social distancing and income support in times of pandemic," European Economic Review, Elsevier, vol. 157(C).
  7. Altındağ, Onur & O'Connell, Stephen D. & Şaşmaz, Aytuğ & Balcıoğlu, Zeynep & Cadoni, Paola & Jerneck, Matilda & Foong, Aimee Kunze, 2021. "Targeting humanitarian aid using administrative data: Model design and validation," Journal of Development Economics, Elsevier, vol. 148(C).
  8. Liyang Tang, 2020. "Application of Nonlinear Autoregressive with Exogenous Input (NARX) neural network in macroeconomic forecasting, national goal setting and global competitiveness assessment," Papers 2005.08735, arXiv.org.
  9. Hanna, Rema & Olken, Benjamin A., 2018. "Universal Basic Incomes vs. Targeted Transfers: Anti-Poverty Programs in Developing Countries," Working Paper Series rwp18-024, Harvard University, John F. Kennedy School of Government.
  10. Li, Qing & Yu, Shuai & Échevin, Damien & Fan, Min, 2022. "Is poverty predictable with machine learning? A study of DHS data from Kyrgyzstan," Socio-Economic Planning Sciences, Elsevier, vol. 81(C).
  11. Yongtong Shao & Tao Xiong & Minghao Li & Dermot Hayes & Wendong Zhang & Wei Xie, 2021. "China's Missing Pigs: Correcting China's Hog Inventory Data Using a Machine Learning Approach," American Journal of Agricultural Economics, John Wiley & Sons, vol. 103(3), pages 1082-1098, May.
  12. Aziza Usmanova & Ahmed Aziz & Dilshodjon Rakhmonov & Walid Osamy, 2022. "Utilities of Artificial Intelligence in Poverty Prediction: A Review," Sustainability, MDPI, vol. 14(21), pages 1-39, October.
  13. Beltramo, Theresa P. & Calvi, Rossella & De Giorgi, Giacomo & Sarr, Ibrahima, 2023. "Child poverty among refugees," World Development, Elsevier, vol. 171(C).
  14. Baez, Javier E. & Kshirsagar, Varun & Skoufias, Emmanuel, 2024. "Drought-sensitive targeting and child growth faltering in Southern Africa," World Development, Elsevier, vol. 182(C).
  15. Linden McBride & Christopher B. Barrett & Christopher Browne & Leiqiu Hu & Yanyan Liu & David S. Matteson & Ying Sun & Jiaming Wen, 2022. "Predicting poverty and malnutrition for targeting, mapping, monitoring, and early warning," Applied Economic Perspectives and Policy, John Wiley & Sons, vol. 44(2), pages 879-892, June.
  16. Fisker,Peter Simonsen & Gallego-Ayala,Jordi Jose & Malmgren Hansen,David & Pave Sohnesen,Thomas & Murrugarra,Edmundo, 2022. "Guiding Social Protection Targeting Through Satellite Data in São Tomé and Príncipe," Social Protection Discussion Papers and Notes 177340, The World Bank.
  17. Crespo, Cristian, 2020. "Two become one: improving the targeting of conditional cash transfers with a predictive model of school dropout," LSE Research Online Documents on Economics 123139, London School of Economics and Political Science, LSE Library.
  18. Alessandra Garbero & Marco Letta, 2022. "Predicting household resilience with machine learning: preliminary cross-country tests," Empirical Economics, Springer, vol. 63(4), pages 2057-2070, October.
  19. Jayachandran, Seema & Biradavolu, Monica & Cooper, Jan, 2021. "Using Machine Learning and Qualitative Interviews to Design a Five-Question Women's Agency Index," IZA Discussion Papers 14221, Institute of Labor Economics (IZA).
  20. Resce, Giuliano & Vaquero-Piñeiro, Cristina, 2022. "Predicting agri-food quality across space: A Machine Learning model for the acknowledgment of Geographical Indications," Food Policy, Elsevier, vol. 112(C).
  21. Emily Aiken & Suzanne Bellue & Dean Karlan & Christopher R. Udry & Joshua Blumenstock, 2021. "Machine Learning and Mobile Phone Data Can Improve the Targeting of Humanitarian Assistance," NBER Working Papers 29070, National Bureau of Economic Research, Inc.
  22. Jayachandran, Seema & Biradavolu, Monica & Cooper, Jan, 2023. "Using machine learning and qualitative interviews to design a five-question survey module for women’s agency," World Development, Elsevier, vol. 161(C).
  23. Saha, Shree & Narayanan, Sudha, 2022. "A simplified measure of nutritional empowerment: Using machine learning to abbreviate the Women’s Empowerment in Nutrition Index (WENI)," World Development, Elsevier, vol. 154(C).
  24. de Blasio, Guido & D'Ignazio, Alessio & Letta, Marco, 2022. "Gotham city. Predicting ‘corrupted’ municipalities with machine learning," Technological Forecasting and Social Change, Elsevier, vol. 184(C).
  25. Stefanía D’Iorio & Liliana Forzani & Rodrigo García Arancibia & Ignacio Girela, 2023. "Predictive Power of Composite Socioeconomic Indices in Regression and Classification: Principal Components and Partial Least Squares," Working Papers 246, Red Nacional de Investigadores en Economía (RedNIE).
  26. Adel Daoud & Felipe Jordán & Makkunda Sharma & Fredrik Johansson & Devdatt Dubhashi & Sourabh Paul & Subhashis Banerjee, 2023. "Using Satellite Images and Deep Learning to Measure Health and Living Standards in India," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 167(1), pages 475-505, June.
  27. Garbero, Alessandra & Sakos, Grayson & Cerulli, Giovanni, 2023. "Towards data-driven project design: Providing optimal treatment rules for development projects," Socio-Economic Planning Sciences, Elsevier, vol. 89(C).
  28. Henderson, Heath & Follett, Lendie, 2022. "Targeting social safety net programs on human capabilities," World Development, Elsevier, vol. 151(C).
  29. Bargain, Olivier & Aminjonov, Ulugbek, 2021. "Poverty and COVID-19 in Africa and Latin America," World Development, Elsevier, vol. 142(C).
  30. Scognamillo, Antonio & Song, Chun & Ignaciuk, Adriana, 2023. "No man is an Island: A spatially explicit approach to measure development resilience," World Development, Elsevier, vol. 171(C).
  31. Austin Nichols, 2018. "Implementing machine learning methods in Stata," London Stata Conference 2018 08, Stata Users Group.
  32. Ratzanyel Rincón, 2023. "Quarterly multidimensional poverty estimates in Mexico using machine learning algorithms/Estimaciones trimestrales de pobreza multidimensional en México mediante algoritmos de aprendizaje de máquina," Estudios Económicos, El Colegio de México, Centro de Estudios Económicos, vol. 38(1), pages 3-68.
  33. Paolo Verme, 2023. "Predicting Poverty with Missing Incomes," Working Papers 642, ECINEQ, Society for the Study of Economic Inequality.
  34. Wang, Hanjie & Feil, Jan-Henning & Yu, Xiaohua, 2023. "Let the data speak about the cut-off values for multidimensional index: Classification of human development index with machine learning," Socio-Economic Planning Sciences, Elsevier, vol. 87(PA).
  35. Guido de Blasio & Alessio D'Ignazio & Marco Letta, 2020. "Predicting Corruption Crimes with Machine Learning. A Study for the Italian Municipalities," Working Papers 16/20, Sapienza University of Rome, DISS.
  36. Daoud, Adel & Johansson, Fredrik, 2019. "Estimating Treatment Heterogeneity of International Monetary Fund Programs on Child Poverty with Generalized Random Forest," SocArXiv awfjt, Center for Open Science.
  37. Damian Kozbur, 2020. "Analysis of Testing‐Based Forward Model Selection," Econometrica, Econometric Society, vol. 88(5), pages 2147-2173, September.
  38. Montorsi, Carlotta & Fusco, Alessio & Van Kerm, Philippe & Bordas, Stéphane P.A., 2024. "Predicting depression in old age: Combining life course data with machine learning," Economics & Human Biology, Elsevier, vol. 52(C).
  39. Knippenberg, Erwin & Jensen, Nathaniel & Constas, Mark, 2019. "Quantifying household resilience with high frequency data: Temporal dynamics and methodological options," World Development, Elsevier, vol. 121(C), pages 1-15.
  40. Bargain, Olivier B. & Aminjonov, Ulugbek, 2020. "Between a Rock and a Hard Place: Poverty and COVID-19 in Developing Countries," IZA Discussion Papers 13297, Institute of Labor Economics (IZA).
  41. Della Guardia, Anne & Lake, Milli & Schnitzer, Pascale, 2022. "Selective inclusion in cash transfer programs: Unintended consequences for social cohesion," World Development, Elsevier, vol. 157(C).
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