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Predicting poverty and malnutrition for targeting, mapping, monitoring, and early warning

Author

Listed:
  • Linden McBride
  • Christopher B. Barrett
  • Christopher Browne
  • Leiqiu Hu
  • Yanyan Liu
  • David S. Matteson
  • Ying Sun
  • Jiaming Wen

Abstract

Increasingly plentiful data and powerful predictive algorithms heighten the promise of data science for humanitarian and development programming. We advocate for embrace of, and investment in, machine learning methods for poverty and malnutrition targeting, mapping, monitoring, and early warning while also cautioning that distinct objectives require distinct data and methods. In particular, we highlight the differences between poverty and malnutrition targeting and mapping, the differences between structural and stochastic deprivation, and the modeling and data challenges of early warning system development. Overall, we urge careful consideration of the purpose and use cases of machine learning informed models.

Suggested Citation

  • 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.
    • Handle: RePEc:wly:apecpp:v:44:y:2022:i:2:p:879-892
    DOI: 10.1002/aepp.13175
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    1. Joachim De Weerdt, 2010. "Moving out of Poverty in Tanzania: Evidence from Kagera," Journal of Development Studies, Taylor & Francis Journals, vol. 46(2), pages 331-349.
    2. David Coady & Margaret Grosh & John Hoddinott, 2004. "Targeting of Transfers in Developing Countries : Review of Lessons and Experience," World Bank Publications - Books, The World Bank Group, number 14902, December.
    3. Carter, Michael R. & May, Julian, 1999. "Poverty, livelihood and class in rural South Africa," World Development, Elsevier, vol. 27(1), pages 1-20, January.
    4. Chris Elbers & Jean O. Lanjouw & Peter Lanjouw, 2003. "Micro--Level Estimation of Poverty and Inequality," Econometrica, Econometric Society, vol. 71(1), pages 355-364, January.
    5. Clare Balboni & Oriana Bandiera & Robin Burgess & Maitreesh Ghatak & Anton Heil, 2023. "Why Do People Stay Poor?," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 137(2), pages 785-844.
    6. 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).
    7. Grosh, M.E. & Baker, J.L., 1995. "Proxy Means Tests for Targetting Social Programs. Simulations and Speculation," Papers 118, World Bank - Living Standards Measurement.
    8. Bob Baulch & John Hoddinott, 2000. "Economic mobility and poverty dynamics in developing countries," Journal of Development Studies, Taylor & Francis Journals, vol. 36(6), pages 1-24.
    9. Ryan Engstrom & Jonathan Hersh & David Newhouse, 2022. "Poverty from Space: Using High Resolution Satellite Imagery for Estimating Economic Well-being," The World Bank Economic Review, World Bank, vol. 36(2), pages 382-412.
    10. Martin Ravallion, 2020. "On Measuring Global Poverty," Annual Review of Economics, Annual Reviews, vol. 12(1), pages 167-188, August.
    11. Carter, Michael R. & Lybbert, Travis J., 2012. "Consumption versus asset smoothing: testing the implications of poverty trap theory in Burkina Faso," Journal of Development Economics, Elsevier, vol. 99(2), pages 255-264.
    12. Kathleen Beegle & Joachim De Weerdt & Stefan Dercon, 2011. "Migration and Economic Mobility in Tanzania: Evidence from a Tracking Survey," The Review of Economics and Statistics, MIT Press, vol. 93(3), pages 1010-1033, August.
    13. Christopher B. Barrett & Teevrat Garg & Linden McBride, 2016. "Well-Being Dynamics and Poverty Traps," Annual Review of Resource Economics, Annual Reviews, vol. 8(1), pages 303-327, October.
    14. Njuguna, Christopher & McSharry, Patrick, 2017. "Constructing spatiotemporal poverty indices from big data," Journal of Business Research, Elsevier, vol. 70(C), pages 318-327.
    15. Michael Carter & Christopher Barrett, 2006. "The economics of poverty traps and persistent poverty: An asset-based approach," Journal of Development Studies, Taylor & Francis Journals, vol. 42(2), pages 178-199.
    16. Esther Rolf & Jonathan Proctor & Tamma Carleton & Ian Bolliger & Vaishaal Shankar & Miyabi Ishihara & Benjamin Recht & Solomon Hsiang, 2020. "A Generalizable and Accessible Approach to Machine Learning with Global Satellite Imagery," NBER Working Papers 28045, National Bureau of Economic Research, Inc.
    17. Baez,Javier E. & Kshirsagar,Varun & Skoufias,Emmanuel, 2019. "Adaptive Safety Nets for Rural Africa : Drought-Sensitive Targeting with Sparse Data," Policy Research Working Paper Series 9071, The World Bank.
    18. Felix Naschold & Christopher B. Barrett, 2011. "Do Short‐Term Observed Income Changes Overstate Structural Economic Mobility?," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 73(5), pages 705-717, October.
    19. Linden McBride & Austin Nichols, 2018. "Retooling Poverty Targeting Using Out-of-Sample Validation and Machine Learning," The World Bank Economic Review, World Bank, vol. 32(3), pages 531-550.
    20. Christopher Yeh & Anthony Perez & Anne Driscoll & George Azzari & Zhongyi Tang & David Lobell & Stefano Ermon & Marshall Burke, 2020. "Using publicly available satellite imagery and deep learning to understand economic well-being in Africa," Nature Communications, Nature, vol. 11(1), pages 1-11, December.
    21. Chris Browne & David S Matteson & Linden McBride & Leiqiu Hu & Yanyan Liu & Ying Sun & Jiaming Wen & Christopher B Barrett, 2021. "Multivariate random forest prediction of poverty and malnutrition prevalence," PLOS ONE, Public Library of Science, vol. 16(9), pages 1-23, September.
    22. Tara Bedi & Aline Coudouel & Kenneth Simler, 2007. "More Than a Pretty Picture : Using Poverty Maps to Design Better Policies and Interventions," World Bank Publications - Books, The World Bank Group, number 6800, December.
    23. Christopher Barrett & Paswel Phiri Marenya & John Mcpeak & Bart Minten & Festus Murithi & Willis Oluoch-Kosura & Frank Place & Jean Claude Randrianarisoa & Jhon Rasambainarivo & Justine Wangila, 2006. "Welfare dynamics in rural Kenya and Madagascar," Journal of Development Studies, Taylor & Francis Journals, vol. 42(2), pages 248-277.
    24. Lentz, E.C. & Michelson, H. & Baylis, K. & Zhou, Y., 2019. "A data-driven approach improves food insecurity crisis prediction," World Development, Elsevier, vol. 122(C), pages 399-409.
    25. Cissé, Jennifer Denno & Barrett, Christopher B., 2018. "Estimating development resilience: A conditional moments-based approach," Journal of Development Economics, Elsevier, vol. 135(C), pages 272-284.
    26. Mude, Andrew G. & Barrett, Christopher B. & McPeak, John G. & Kaitho, Robert & Kristjanson, Patti, 2009. "Empirical forecasting of slow-onset disasters for improved emergency response: An application to Kenya's arid north," Food Policy, Elsevier, vol. 34(4), pages 329-339, August.
    27. Masaki,Takaaki & Newhouse,David Locke & Silwal,Ani Rudra & Bedada,Adane & Engstrom,Ryan, 2020. "Small Area Estimation of Non-Monetary Poverty with Geospatial Data," Policy Research Working Paper Series 9383, The World Bank.
    28. 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.
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    Cited by:

    1. Newhouse,David Locke & Merfeld,Joshua David & Ramakrishnan,Anusha Pudugramam & Swartz,Tom & Lahiri,Partha, 2022. "Small Area Estimation of Monetary Poverty in Mexico Using Satellite Imagery and Machine Learning," Policy Research Working Paper Series 10175, The World Bank.
    2. Binh Tang & Yanyan Liu & David S. Matteson, 2022. "Predicting poverty with vegetation index," Applied Economic Perspectives and Policy, John Wiley & Sons, vol. 44(2), pages 930-945, June.
    3. 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).
    4. Merfeld, Joshua D. & Newhouse, David & Weber, Michael & Lahiri, Partha, 2022. "Combining Survey and Geospatial Data Can Significantly Improve Gender-Disaggregated Estimates of Labor Market Outcomes," IZA Discussion Papers 15390, Institute of Labor Economics (IZA).
    5. Ola Hall & Mattias Ohlsson & Thortseinn Rognvaldsson, 2022. "Satellite Image and Machine Learning based Knowledge Extraction in the Poverty and Welfare Domain," Papers 2203.01068, arXiv.org.
    6. Ola Hall & Francis Dompae & Ibrahim Wahab & Fred Mawunyo Dzanku, 2023. "A review of machine learning and satellite imagery for poverty prediction: Implications for development research and applications," Journal of International Development, John Wiley & Sons, Ltd., vol. 35(7), pages 1753-1768, October.
    7. Yujun Zhou & Erin Lentz & Hope Michelson & Chungmann Kim & Kathy Baylis, 2022. "Machine learning for food security: Principles for transparency and usability," Applied Economic Perspectives and Policy, John Wiley & Sons, vol. 44(2), pages 893-910, June.
    8. 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.

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