IDEAS home Printed from https://ideas.repec.org/a/eee/wdevel/v122y2019icp399-409.html
   My bibliography  Save this article

A data-driven approach improves food insecurity crisis prediction

Author

Listed:
  • Lentz, E.C.
  • Michelson, H.
  • Baylis, K.
  • Zhou, Y.

Abstract

Globally, over 800 million people are food insecure. Current methods for identifying food insecurity crises are not based on statistical models and fail to systematically incorporate readily available data on prices, weather, and demographics. As a result, policymakers cannot rapidly identify food insecure populations. These problems delay responses to mitigate hunger. We develop a replicable, near real-time model incorporating spatially and temporally granular market data, remotely-sensed rainfall and geographic data, and demographic characteristics. We train the model on 2010–2011 data from Malawi and forecast 2013 food security. Our model correctly identifies the food security status of 83 to 99% of the most food insecure village clusters in 2013, depending on the food security measure, while the prevailing approach correctly identifies between 0 and 10%. Our results show the power of modeling food insecurity to provide early warning and suggest model-driven approaches could dramatically improve food insecurity crisis response.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:wdevel:v:122:y:2019:i:c:p:399-409
    DOI: 10.1016/j.worlddev.2019.06.008
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0305750X19301603
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.worlddev.2019.06.008?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Basu, Kaushik, 1996. "Relief programs: When it may be better to give food instead of cash," World Development, Elsevier, vol. 24(1), pages 91-96, January.
    2. Hidrobo, Melissa & Hoddinott, John & Kumar, Neha & Olivier, Meghan, 2018. "Social Protection, Food Security, and Asset Formation," World Development, Elsevier, vol. 101(C), pages 88-103.
    3. Hyman, Glenn & Larrea, Carlos & Farrow, Andrew, 2005. "Methods, results and policy implications of poverty and food security mapping assessments," Food Policy, Elsevier, vol. 30(5-6), pages 453-460.
    4. Jones, Andrew D. & Shrinivas, Aditya & Bezner-Kerr, Rachel, 2014. "Farm production diversity is associated with greater household dietary diversity in Malawi: Findings from nationally representative data," Food Policy, Elsevier, vol. 46(C), pages 1-12.
    5. Vaitla, Bapu & Coates, Jennifer & Glaeser, Laura & Hillbruner, Christopher & Biswal, Preetish & Maxwell, Daniel, 2017. "The measurement of household food security: Correlation and latent variable analysis of alternative indicators in a large multi-country dataset," Food Policy, Elsevier, vol. 68(C), pages 193-205.
    6. 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.
    7. Alex Nikulkov & Christopher B Barrett & Andrew G Mude & Lawrence M Wein, 2016. "Assessing the Impact of U.S. Food Assistance Delivery Policies on Child Mortality in Northern Kenya," PLOS ONE, Public Library of Science, vol. 11(12), pages 1-15, December.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Backer, David & Billing, Trey, 2024. "Forecasting the prevalence of child acute malnutrition using environmental and conflict conditions as leading indicators," World Development, Elsevier, vol. 176(C).
    2. Delprato, Marcos & Frola, Alessia & Antequera, Germán, 2022. "Indigenous and non-Indigenous proficiency gaps for out-of-school and in-school populations: A machine learning approach," International Journal of Educational Development, Elsevier, vol. 93(C).
    3. Maryia Bakhtsiyarava & Tim G. Williams & Andrew Verdin & Seth D. Guikema, 2021. "A nonparametric analysis of household-level food insecurity and its determinant factors: exploratory study in Ethiopia and Nigeria," Food Security: The Science, Sociology and Economics of Food Production and Access to Food, Springer;The International Society for Plant Pathology, vol. 13(1), pages 55-70, February.
    4. 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.
    5. 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.
    6. 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).
    7. 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).
    8. Aslihan Arslan & Romina Cavatassi & Marup Hossain, 2022. "Food systems and structural and rural transformation: a quantitative synthesis for low and middle-income countries," Food Security: The Science, Sociology and Economics of Food Production and Access to Food, Springer;The International Society for Plant Pathology, vol. 14(1), pages 293-320, February.
    9. 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).
    10. Aslihan Arslan, Romina Cavatassi, Marup Hossain, 2022. "Research Series 69: Structural and rural transformation and food systems: a quantitative synthesis for LMICs," IFAD Research Series 320720, International Fund for Agricultural Development (IFAD).
    11. Wang,Dieter & Andree,Bo Pieter Johannes & Chamorro Elizondo,Andres Fernando & Spencer,Phoebe Girouard, 2020. "Stochastic Modeling of Food Insecurity," Policy Research Working Paper Series 9413, The World Bank.
    12. Vu, Khoa & Vuong, Nguyen Dinh Tuan & Vu-Thanh, Tu-Anh & Nguyen, Anh Ngoc, 2022. "Income shock and food insecurity prediction Vietnam under the pandemic," World Development, Elsevier, vol. 153(C).
    13. 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.
    14. Wang, Dieter & Andrée, Bo Pieter Johannes & Chamorro, Andres Fernando & Spencer, Phoebe Girouard, 2022. "Transitions into and out of food insecurity: A probabilistic approach with panel data evidence from 15 countries," World Development, Elsevier, vol. 159(C).
    15. Kurt B. Waldman & Stacey Giroux & Jordan P. Blekking & Kathy Baylis & Tom P. Evans, 2020. "Smallholder food storage dynamics and resilience," Food Security: The Science, Sociology and Economics of Food Production and Access to Food, Springer;The International Society for Plant Pathology, vol. 12(1), pages 7-20, February.
    16. Meerza, Syed Imran Ali & Meerza, Syed Irfan Ali & Ahamed, Afsana, 2021. "Food Insecurity Through Machine Learning Lens: Identifying Vulnerable Households," 2021 Annual Meeting, August 1-3, Austin, Texas 314072, Agricultural and Applied Economics Association.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Vu, Khoa & Vuong, Nguyen Dinh Tuan & Vu-Thanh, Tu-Anh & Nguyen, Anh Ngoc, 2022. "Income shock and food insecurity prediction Vietnam under the pandemic," World Development, Elsevier, vol. 153(C).
    2. Upton, Joanna & Constenla-Villoslada, Susana & Barrett, Christopher B., 2022. "Caveat utilitor: A comparative assessment of resilience measurement approaches," Journal of Development Economics, Elsevier, vol. 157(C).
    3. Ouoba, Youmanli & Sawadogo, Natéwindé, 2022. "Food security, poverty and household resilience to COVID-19 in Burkina Faso: Evidence from urban small traders’ households," World Development Perspectives, Elsevier, vol. 25(C).
    4. Bapu Vaitla & Jennifer Denno Cissé & Joanna Upton & Girmay Tesfay & Nigussie Abadi & Daniel Maxwell, 2020. "How the choice of food security indicators affects the assessment of resilience—an example from northern Ethiopia," Food Security: The Science, Sociology and Economics of Food Production and Access to Food, Springer;The International Society for Plant Pathology, vol. 12(1), pages 137-150, February.
    5. Ricardo Sibrian & Marco d’Errico & Patricia Palma de Fulladolsa & Flavia Benedetti-Michelangeli, 2021. "Household Resilience to Food and Nutrition Insecurity in Central America and the Caribbean," Sustainability, MDPI, vol. 13(16), pages 1-30, August.
    6. Lentz, Erin C. & Michelson, Hope C. & Baylis, Kathy, 2017. "An Approach to Improving Early Warning Systems: Using Spatially and Temporally Rich Data to Predict Food Insecurity Crises in Malawi," 2017 Annual Meeting, July 30-August 1, Chicago, Illinois 258223, Agricultural and Applied Economics Association.
    7. Hossain, Marup & Mullally, Conner & Asadullah, M. Niaz, 2019. "Alternatives to calorie-based indicators of food security: An application of machine learning methods," Food Policy, Elsevier, vol. 84(C), pages 77-91.
    8. Abay, Kibrom A. & Abay, Mehari H. & Berhane, Guush & Chamberlin, Jordan, 2022. "Social protection and resilience: The case of the productive safety net program in Ethiopia," Food Policy, Elsevier, vol. 112(C).
    9. Upton, Joanna & Tennant, Elizabeth & Florella, Kathryn J. & Barrett, Christopher B., 2021. "A Comparative Assessment of Resilience Measuremen tApproaches," Working Papers 316614, Cornell University, Department of Applied Economics and Management.
    10. Otilia Vanessa Cordero-Ahiman & Jorge Leonardo Vanegas & Christian Franco-Crespo & Pablo Beltrán-Romero & María Elena Quinde-Lituma, 2021. "Factors That Determine the Dietary Diversity Score in Rural Households: The Case of the Paute River Basin of Azuay Province, Ecuador," IJERPH, MDPI, vol. 18(4), pages 1-16, February.
    11. Murendo, Conrad & Kairezi, Grace & Mazvimavi, Kizito, 2020. "Resilience capacities and household nutrition in the presence of shocks. Evidence from Malawi," World Development Perspectives, Elsevier, vol. 20(C).
    12. Tesfaye, Wondimagegn & Tirivayi, Nyasha, 2020. "Crop diversity, household welfare and consumption smoothing under risk: Evidence from rural Uganda," World Development, Elsevier, vol. 125(C).
    13. Kassie, Menale & Fisher, Monica & Muricho, Geoffrey & Diiro, Gracious, 2020. "Women’s empowerment boosts the gains in dietary diversity from agricultural technology adoption in rural Kenya," Food Policy, Elsevier, vol. 95(C).
    14. Holla,Alaka & Bendini,Maria Magdalena & Dinarte Diaz,Lelys Ileana & Trako,Iva, 2021. "Is Investment in Preprimary Education Too Low ? Lessons from (Quasi) ExperimentalEvidence across Countries," Policy Research Working Paper Series 9723, The World Bank.
    15. Malerba, Daniele, 2020. "Poverty alleviation and local environmental degradation: An empirical analysis in Colombia," World Development, Elsevier, vol. 127(C).
    16. Ryckembusch, David & Frega, Romeo & Silva, Marcio Guilherme & Gentilini, Ugo & Sanogo, Issa & Grede, Nils & Brown, Lynn, 2013. "Enhancing Nutrition: A New Tool for Ex-Ante Comparison of Commodity-based Vouchers and Food Transfers," World Development, Elsevier, vol. 49(C), pages 58-67.
    17. Sukhwinder Singh & Andrew D. Jones & Ruth S. DeFries & Meha Jain, 2020. "The association between crop and income diversity and farmer intra-household dietary diversity in India," Food Security: The Science, Sociology and Economics of Food Production and Access to Food, Springer;The International Society for Plant Pathology, vol. 12(2), pages 369-390, April.
    18. Alloush, Mohamad & Taylor, J. Edward & Gupta, Anubhab & Rojas Valdes, Ruben Irvin & Gonzalez-Estrada, Ernesto, 2017. "Economic Life in Refugee Camps," World Development, Elsevier, vol. 95(C), pages 334-347.
    19. Sékou Amadou Traoré & Christoph Reiber & Bekele Megersa & Anne Valle Zárate, 2018. "Contribution of cattle of different breeds to household food security in southern Mali," Food Security: The Science, Sociology and Economics of Food Production and Access to Food, Springer;The International Society for Plant Pathology, vol. 10(3), pages 549-560, June.
    20. Issahaku, Gazali & Abdulai, Awudu, "undated". "Adaptation to Climate Change and its influence on Household Welfare in Ghana," 2017 Annual Meeting, July 30-August 1, Chicago, Illinois 259938, Agricultural and Applied Economics Association.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:wdevel:v:122:y:2019:i:c:p:399-409. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/worlddev .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.