{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,5]],"date-time":"2026-09-05T18:39:38Z","timestamp":1788633578239,"version":"build-2803163510"},"reference-count":72,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2025,12,1]],"date-time":"2025-12-01T00:00:00Z","timestamp":1764547200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2025,12,1]],"date-time":"2025-12-01T00:00:00Z","timestamp":1764547200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2025,11,18]],"date-time":"2025-11-18T00:00:00Z","timestamp":1763424000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003993","name":"Ministry of Agriculture, Forestry and Fisheries","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100003993","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Economics & Human Biology"],"published-print":{"date-parts":[[2025,12]]},"DOI":"10.1016\/j.ehb.2025.101552","type":"journal-article","created":{"date-parts":[[2025,11,20]],"date-time":"2025-11-20T14:16:10Z","timestamp":1763648170000},"page":"101552","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":1,"special_numbering":"C","title":["Measuring and testing vulnerability to food insecurity for prediction and targeting"],"prefix":"10.1016","volume":"59","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0863-2593","authenticated-orcid":false,"given":"Marco","family":"Letta","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1397-8537","authenticated-orcid":false,"given":"Pierluigi","family":"Montalbano","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Cristian","family":"Morales Opazo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6239-1532","authenticated-orcid":false,"given":"Federica","family":"Petruccelli","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"issue":"7903","key":"10.1016\/j.ehb.2025.101552_bib1","doi-asserted-by":"crossref","first-page":"864","DOI":"10.1038\/s41586-022-04484-9","article-title":"Machine learning and phone data can improve targeting of humanitarian aid","volume":"603","author":"Aiken","year":"2022","journal-title":"Nature"},{"key":"10.1016\/j.ehb.2025.101552_bib2","doi-asserted-by":"crossref","DOI":"10.1016\/j.jdeveco.2025.103477","article-title":"Estimating impact with surveys versus digital traces: evidence from randomized cash transfers in Togo","volume":"175","author":"Aiken","year":"2025","journal-title":"J. Dev. Econ."},{"key":"10.1016\/j.ehb.2025.101552_bib3","doi-asserted-by":"crossref","DOI":"10.1016\/j.jdeveco.2022.103016","article-title":"Program targeting with machine learning and mobile phone data: evidence from an anti-poverty intervention in Afghanistan","volume":"161","author":"Aiken","year":"2023","journal-title":"J. Dev. Econ."},{"key":"10.1016\/j.ehb.2025.101552_bib4","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1016\/j.agsy.2018.07.014","article-title":"Rainfall shocks and agricultural productivity: implication for rural household consumption","volume":"166","author":"Amare","year":"2018","journal-title":"Agric. Syst."},{"key":"10.1016\/j.ehb.2025.101552_bib5","doi-asserted-by":"crossref","DOI":"10.1016\/j.foodpol.2021.102099","article-title":"COVID-19 and food security: Panel data evidence from Nigeria","volume":"101","author":"Amare","year":"2021","journal-title":"Food Policy"},{"key":"10.1016\/j.ehb.2025.101552_bib6","doi-asserted-by":"crossref","DOI":"10.1016\/j.jdeveco.2023.103199","article-title":"Does survey mode matter? Comparing in-person and phone agricultural surveys in India","volume":"166","author":"Anderson","year":"2024","journal-title":"J. Dev. Econ."},{"key":"10.1016\/j.ehb.2025.101552_bib7","doi-asserted-by":"crossref","DOI":"10.1016\/j.worlddev.2024.106702","article-title":"Drought-sensitive targeting and child growth faltering in Southern Africa","volume":"182","author":"Baez","year":"2024","journal-title":"World Dev."},{"issue":"7","key":"10.1016\/j.ehb.2025.101552_bib8","doi-asserted-by":"crossref","first-page":"976","DOI":"10.1080\/00220388.2013.785527","article-title":"The economics of poverty traps and persistent poverty: Empirical and policy implications","volume":"49","author":"Barrett","year":"2013","journal-title":"J. Dev. Stud."},{"key":"10.1016\/j.ehb.2025.101552_bib9","series-title":"Food Security","first-page":"1","article-title":"Resilience of local food systems and links to food security\u2013A review of some important concepts in the context of COVID-19 and other shocks","author":"B\u00e9n\u00e9","year":"2020"},{"key":"10.1016\/j.ehb.2025.101552_bib10","doi-asserted-by":"crossref","first-page":"153","DOI":"10.1016\/j.gloenvcha.2016.03.005","article-title":"Is resilience socially constructed? Empirical evidence from Fiji, Ghana, Sri Lanka, and vietnam","volume":"38","author":"B\u00e9n\u00e9","year":"2016","journal-title":"Glob. Environ. Change"},{"key":"10.1016\/j.ehb.2025.101552_bib11","doi-asserted-by":"crossref","unstructured":"B\u00e9n\u00e9, C., Bakker, D., Rodriguez, M.C., Even, B., Melo, J., & Sonneveld, A. (2021). Impacts of COVID-19 on people\u2019s food security: Foundations for a more resilient food system. Intl Food Policy Res Inst.","DOI":"10.2499\/p15738coll2.134295"},{"key":"10.1016\/j.ehb.2025.101552_bib12","doi-asserted-by":"crossref","unstructured":"Benson, T., Amare, M., & Ogunniyi, A. (2020). The relative commercial orientation of smallholder farmers in Nigeria: Household and crop value-chain analyses (Vol. 66). Intl Food Policy Res Inst.","DOI":"10.2499\/p15738coll2.134163"},{"issue":"6264","key":"10.1016\/j.ehb.2025.101552_bib13","doi-asserted-by":"crossref","first-page":"1073","DOI":"10.1126\/science.aac4420","article-title":"Predicting poverty and wealth from mobile phone metadata","volume":"350","author":"Blumenstock","year":"2015","journal-title":"Science"},{"key":"10.1016\/j.ehb.2025.101552_bib14","article-title":"Probing the limits of mobile phone metadata for poverty prediction and impact evaluation","author":"Blumenstock","year":"2025","journal-title":"J. Dev. Econ."},{"issue":"2","key":"10.1016\/j.ehb.2025.101552_bib15","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1023\/A:1018054314350","article-title":"Bagging predictors","volume":"24","author":"Breiman","year":"1996","journal-title":"Mach. Learn."},{"issue":"1","key":"10.1016\/j.ehb.2025.101552_bib16","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"issue":"9","key":"10.1016\/j.ehb.2025.101552_bib17","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0255519","article-title":"Multivariate random forest prediction of poverty and malnutrition prevalence","volume":"16","author":"Browne","year":"2021","journal-title":"PloS One"},{"key":"10.1016\/j.ehb.2025.101552_bib18","doi-asserted-by":"crossref","DOI":"10.1016\/j.worlddev.2022.105844","article-title":"The short-term impacts of COVID-19 on households in developing countries","volume":"153","author":"Bundervoet","year":"2022","journal-title":"World Dev."},{"key":"10.1016\/j.ehb.2025.101552_bib19","doi-asserted-by":"crossref","first-page":"146","DOI":"10.1016\/j.measurement.2017.10.065","article-title":"Food security measurement in a global context: The food insecurity experience scale","volume":"116","author":"Cafiero","year":"2018","journal-title":"Measurement"},{"key":"10.1016\/j.ehb.2025.101552_bib20","unstructured":"Calvo, C., & Dercon, S. (2005). Measuring individual vulnerability. Department of economics discussion paper series, 229. Oxford: Oxford University."},{"issue":"4","key":"10.1016\/j.ehb.2025.101552_bib21","doi-asserted-by":"crossref","first-page":"721","DOI":"10.1007\/s00355-012-0706-y","article-title":"Vulnerability to individual and aggregate poverty","volume":"41","author":"Calvo","year":"2013","journal-title":"Soc. Choice Welf."},{"key":"10.1016\/j.ehb.2025.101552_bib22","unstructured":"Capaldo J., Karfakis P., Knowles M., Smulders M. (2010) A Model of Vulnerability to Food Insecurity. ESA Working Paper Series No.10-03. Rome: Food and Agriculture Organisation of the United Nations."},{"issue":"2","key":"10.1016\/j.ehb.2025.101552_bib23","doi-asserted-by":"crossref","first-page":"178","DOI":"10.1080\/00220380500405261","article-title":"The economics of poverty traps and persistent poverty: An asset-based approach","volume":"42","author":"Carter","year":"2006","journal-title":"J. Dev. Stud."},{"key":"10.1016\/j.ehb.2025.101552_bib24","series-title":"Social Protection for the Poor and Poorest","first-page":"85","article-title":"Poverty traps and natural disasters in Ethiopia and Honduras","author":"Carter","year":"2008"},{"key":"10.1016\/j.ehb.2025.101552_bib25","series-title":"Department of Economics Discussion Paper Series","first-page":"0102","article-title":"Assessing household vulnerability to poverty from cross-sectional data: a methodology and estimates from Indonesia","author":"Chaudhuri","year":"2002"},{"key":"10.1016\/j.ehb.2025.101552_bib26","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1613\/jair.953","article-title":"SMOTE: synthetic minority over-sampling technique","volume":"16","author":"Chawla","year":"2002","journal-title":"J. Artif. Intell. Res."},{"key":"10.1016\/j.ehb.2025.101552_bib27","unstructured":"Christiaensen, L.J., & Boisvert, R.N. (2000). On measuring household food vulnerability: Case evidence from Northern Mali."},{"key":"10.1016\/j.ehb.2025.101552_bib28","doi-asserted-by":"crossref","unstructured":"Constenla-Villoslada, S., & Liu, Y., & McBride L., & Ouma, C., & Mutanda, N., & Barrett, C.B. (2025). High-frequency monitoring enables machine learning-based forecasting of acute child malnutrition for early warning. Proceedings of the National Academy of Sciences, forthcoming.","DOI":"10.1073\/pnas.2416161122"},{"issue":"1","key":"10.1016\/j.ehb.2025.101552_bib29","doi-asserted-by":"crossref","first-page":"167","DOI":"10.1007\/s11205-021-02625-7","article-title":"Vulnerability to food insecurity: a decomposition exercise for rural india using the expected utility approach","volume":"156","author":"Das","year":"2021","journal-title":"Soc. Indic. Res."},{"key":"10.1016\/j.ehb.2025.101552_bib30","doi-asserted-by":"crossref","unstructured":"Deaton, A. (1992). Understanding consumption. Oxford University Press.","DOI":"10.1093\/0198288247.001.0001"},{"issue":"3","key":"10.1016\/j.ehb.2025.101552_bib31","doi-asserted-by":"crossref","first-page":"740","DOI":"10.1257\/jel.52.3.740","article-title":"What do we learn from the weather? The new climate-economy literature","volume":"52","author":"Dell","year":"2014","journal-title":"J. Econ. Lit."},{"key":"10.1016\/j.ehb.2025.101552_bib32","doi-asserted-by":"crossref","DOI":"10.1016\/j.jdeveco.2024.103385","article-title":"Combining survey and census data for improved poverty prediction using semi-supervised deep learning","volume":"172","author":"Echevin","year":"2025","journal-title":"J. Dev. Econ."},{"issue":"6","key":"10.1016\/j.ehb.2025.101552_bib33","doi-asserted-by":"crossref","first-page":"eabe0997","DOI":"10.1126\/sciadv.abe0997","article-title":"Falling living standards during the COVID-19 crisis: quantitative evidence from nine developing countries","volume":"7","author":"Egger","year":"2021","journal-title":"Sci. Adv."},{"key":"10.1016\/j.ehb.2025.101552_bib34","doi-asserted-by":"crossref","unstructured":"FAO. 2021. The State of Food and Agriculture 2021. Making agrifood systems more resilient to shocks and stresses. Rome, FAO. Available at: https:\/\/doi.org\/10.4060\/cb4476en.","DOI":"10.4060\/cb4476en"},{"key":"10.1016\/j.ehb.2025.101552_bib35","doi-asserted-by":"crossref","unstructured":"FAO, IFAD, UNICEF, WFP and WHO. (2021). The State of Food Security and Nutrition in the World 2021. Transforming food systems for food security, improved nutrition and affordable healthy diets for all. Rome, FAO. Available at: \u3008https:\/\/doi.org\/10.4060\/cb4474en\u3009.","DOI":"10.4060\/cb4474en"},{"key":"10.1016\/j.ehb.2025.101552_bib36","unstructured":"Fatoba, I. (2021). Covid-19 Fund: Introductory Report on Fiscal Support, Palliative Analysis & Institutional Response."},{"issue":"4","key":"10.1016\/j.ehb.2025.101552_bib37","doi-asserted-by":"crossref","first-page":"1074","DOI":"10.1111\/joes.12216","article-title":"Identifying vulnerability to poverty: a critical survey","volume":"32","author":"Gallardo","year":"2018","journal-title":"J. Econ. Surv."},{"key":"10.1016\/j.ehb.2025.101552_bib38","first-page":"1","article-title":"Predicting household resilience with machine learning: preliminary cross-country tests","author":"Garbero","year":"2022","journal-title":"Empir. Econ."},{"issue":"2","key":"10.1016\/j.ehb.2025.101552_bib39","doi-asserted-by":"crossref","first-page":"418","DOI":"10.1093\/wber\/lhz021","article-title":"Call me maybe: experimental evidence on frequency and medium effects in microenterprise surveys","volume":"34","author":"Garlick","year":"2020","journal-title":"World Bank Econ. Rev."},{"key":"10.1016\/j.ehb.2025.101552_bib40","doi-asserted-by":"crossref","DOI":"10.1016\/j.foodpol.2021.102153","article-title":"High-frequency phone surveys on COVID-19: good practices, open questions","volume":"105","author":"Gourlay","year":"2021","journal-title":"Food Policy"},{"key":"10.1016\/j.ehb.2025.101552_bib41","doi-asserted-by":"crossref","unstructured":"Hastie, T., & Tibshirani, R., & Friedman, J. (2009). The elements of statistical learning: data mining, inference, and prediction. Springer Science & Business Media.","DOI":"10.1007\/978-0-387-84858-7"},{"key":"10.1016\/j.ehb.2025.101552_bib42","doi-asserted-by":"crossref","unstructured":"Haushofer, J., Niehaus, P., Paramo, C., Miguel, E., & Walker, M. (2022). Targeting impact versus deprivation. CEGA Working Paper Series, No. WPS-199. Center for Effective Global Action. University of California, Berkeley. Text. https:\/\/doi.org\/10.5072\/FK22F7TN60.","DOI":"10.3386\/w30138"},{"issue":"37","key":"10.1016\/j.ehb.2025.101552_bib43","doi-asserted-by":"crossref","first-page":"11423","DOI":"10.1073\/pnas.1512215112","article-title":"Measuring development resilience in the world\u2019s poorest countries","volume":"112","author":"Headey","year":"2015","journal-title":"Proc. Natl. Acad. Sci."},{"key":"10.1016\/j.ehb.2025.101552_bib44","unstructured":"Hoddinott, J., & Quisumbing, M.A.R. (2003). Data sources for microeconometric risk and vulnerability assessments. World Bank, Social Protection."},{"key":"10.1016\/j.ehb.2025.101552_bib45","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1016\/j.foodpol.2019.03.001","article-title":"Alternatives to calorie-based indicators of food security: an application of machine learning methods","volume":"84","author":"Hossain","year":"2019","journal-title":"Food Policy"},{"key":"10.1016\/j.ehb.2025.101552_bib46","doi-asserted-by":"crossref","DOI":"10.1016\/j.gfs.2020.100468","article-title":"Improved storage mitigates vulnerability to food-supply shocks in smallholder agriculture during the COVID-19 pandemic","volume":"28","author":"Huss","year":"2021","journal-title":"Glob. Food Secur."},{"key":"10.1016\/j.ehb.2025.101552_bib47","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1016\/j.foodpol.2019.01.011","article-title":"Advancing a new index for measuring household vulnerability to food insecurity","volume":"84","author":"Ibok","year":"2019","journal-title":"Food Policy"},{"issue":"25","key":"10.1016\/j.ehb.2025.101552_bib48","doi-asserted-by":"crossref","first-page":"3603","DOI":"10.1080\/00036841003670754","article-title":"Vulnerability and poverty dynamics in Vietnam","volume":"43","author":"Imai","year":"2011","journal-title":"Appl. Econ."},{"key":"10.1016\/j.ehb.2025.101552_bib49","unstructured":"International Monetary Fund. 2022. World Economic Outlook: War Sets Back the Global Recovery. Washington, DC, April."},{"issue":"6301","key":"10.1016\/j.ehb.2025.101552_bib50","doi-asserted-by":"crossref","first-page":"790","DOI":"10.1126\/science.aaf7894","article-title":"Combining satellite imagery and machine learning to predict poverty","volume":"353","author":"Jean","year":"2016","journal-title":"Science"},{"key":"10.1016\/j.ehb.2025.101552_bib51","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.worlddev.2019.04.010","article-title":"Quantifying household resilience with high frequency data: temporal dynamics and methodological options","volume":"121","author":"Knippenberg","year":"2019","journal-title":"World Dev."},{"key":"10.1016\/j.ehb.2025.101552_bib52","unstructured":"Lantz, B. (2019). Machine learning with R: expert techniques for predictive modeling. Packt Publishing Ltd."},{"key":"10.1016\/j.ehb.2025.101552_bib53","doi-asserted-by":"crossref","first-page":"399","DOI":"10.1016\/j.worlddev.2019.06.008","article-title":"A data-driven approach improves food insecurity crisis prediction","volume":"122","author":"Lentz","year":"2019","journal-title":"World Dev."},{"key":"10.1016\/j.ehb.2025.101552_bib54","unstructured":"L\u00f8vendal C.R., Knowles M. (2005) Tomorrow\u2019s hunger: a framework for analyzing vulnerability to food insecurity. Food and Agriculture Organization of the United Nations, ESA Working Paper No.05-07. Agricultural and Development Economics Division, Rome."},{"key":"10.1016\/j.ehb.2025.101552_bib55","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1016\/j.worlddev.2018.07.018","article-title":"Households\u2019 vulnerability from trade in Vietnam","volume":"112","author":"Magrini","year":"2018","journal-title":"World Dev."},{"issue":"3","key":"10.1016\/j.ehb.2025.101552_bib56","first-page":"531","article-title":"Retooling poverty targeting using out-of-sample validation and machine learning","volume":"32","author":"McBride","year":"2018","journal-title":"World Bank Econ. Rev."},{"key":"10.1016\/j.ehb.2025.101552_bib57","first-page":"1","article-title":"Predicting poverty and malnutrition for targeting, mapping, monitoring, and early warning","author":"McBride","year":"2021","journal-title":"Appl. Econ. Perspect. Policy"},{"issue":"9","key":"10.1016\/j.ehb.2025.101552_bib58","doi-asserted-by":"crossref","first-page":"1489","DOI":"10.1016\/j.worlddev.2011.02.009","article-title":"Trade openness and developing countries\u2019 vulnerability: Concepts, misconceptions, and directions for research","volume":"39","author":"Montalbano","year":"2011","journal-title":"World Dev."},{"issue":"2","key":"10.1016\/j.ehb.2025.101552_bib59","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1257\/jep.31.2.87","article-title":"Machine learning: an applied econometric approach","volume":"31","author":"Mullainathan","year":"2017","journal-title":"J. Econ. Perspect."},{"issue":"7","key":"10.1016\/j.ehb.2025.101552_bib60","doi-asserted-by":"crossref","first-page":"1243","DOI":"10.1007\/s10113-015-0772-7","article-title":"Assessing the determinants of poverty and vulnerability of smallholder farmers in a changing climate: the case of Morogoro region, Tanzania","volume":"15","author":"Mutabazi","year":"2015","journal-title":"Reg. Environ. Change"},{"key":"10.1016\/j.ehb.2025.101552_bib61","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1016\/j.worlddev.2013.11.006","article-title":"Measuring exposure to downside risk with an application to Thailand and Vietnam","volume":"71","author":"Povel","year":"2015","journal-title":"World Dev."},{"key":"10.1016\/j.ehb.2025.101552_bib62","first-page":"18","article-title":"Food supply chain disruption due to natural disasters: entities, risks, and strategies for resilience","author":"Reddy","year":"2016","journal-title":"ERIA Discuss."},{"issue":"1","key":"10.1016\/j.ehb.2025.101552_bib63","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s40008-019-0174-y","article-title":"Analysis of households\u2019 vulnerability to food insecurity and its influencing factors in East Hararghe, Ethiopia","volume":"8","author":"Sileshi","year":"2019","journal-title":"J. Econ. Struct."},{"key":"10.1016\/j.ehb.2025.101552_bib64","first-page":"1","article-title":"Predicting poverty with vegetation index","author":"Tang","year":"2021","journal-title":"Appl. Econ. Perspect. Policy"},{"key":"10.1016\/j.ehb.2025.101552_bib65","unstructured":"Upton, J., Tennant, E., Florella, K., & Barrett, C.B. (2021). Household Resilience, and Rural Food Systems: Evidence from Southern and Eastern Africa. Applied Economics and Policy Working Paper Series, No.2021-17. Available at: https:\/\/papers.ssrn.com\/sol3\/papers.cfm?abstract_id=3992671."},{"key":"10.1016\/j.ehb.2025.101552_bib66","doi-asserted-by":"crossref","DOI":"10.1016\/j.jdeveco.2022.102873","article-title":"Caveat utilitor: a comparative assessment of resilience measurement approaches","volume":"157","author":"Upton","year":"2022","journal-title":"J. Dev. Econ."},{"key":"10.1016\/j.ehb.2025.101552_bib67","doi-asserted-by":"crossref","first-page":"1479","DOI":"10.1002\/aepp.13475","article-title":"A machine learning-based exploration of resilience and food security","volume":"46","author":"Villacis","year":"2024","journal-title":"Appl. Econ. Perspect. Policy"},{"key":"10.1016\/j.ehb.2025.101552_bib68","doi-asserted-by":"crossref","DOI":"10.1016\/j.worlddev.2022.105838","article-title":"Income shock and food insecurity prediction Vietnam under the pandemic","volume":"153","author":"Vu","year":"2022","journal-title":"World Dev."},{"key":"10.1016\/j.ehb.2025.101552_bib69","unstructured":"World Bank. 2016. \u201cInternational Development Association Project Appraisal Document. National Safety Nets Project.\u201d Report No: PAD 1687."},{"key":"10.1016\/j.ehb.2025.101552_bib70","unstructured":"World Bank. 2020. Nigeria Development Update, June 2020: Nigeria in Times of COVID-19 - Laying Foundations for a Strong Recovery. World Bank, Washington, DC. \u00a9 World Bank."},{"issue":"4","key":"10.1016\/j.ehb.2025.101552_bib71","doi-asserted-by":"crossref","first-page":"1364","DOI":"10.1093\/erae\/jbad017","article-title":"From necessity to opportunity: lessons for integrating phone and in-person data collection","volume":"50","author":"Zezza","year":"2023","journal-title":"Eur. Rev. Agric. Econ."},{"key":"10.1016\/j.ehb.2025.101552_bib72","article-title":"Machine learning for food security: principles for transparency and usability","volume":"1","author":"Zhou","year":"2021","journal-title":"Appl. Econ. Perspect. Policy"}],"container-title":["Economics & Human Biology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1570677X25000851?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1570677X25000851?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2025,12,19]],"date-time":"2025-12-19T14:20:36Z","timestamp":1766154036000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1570677X25000851"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,12]]},"references-count":72,"alternative-id":["S1570677X25000851"],"URL":"https:\/\/doi.org\/10.1016\/j.ehb.2025.101552","relation":{},"ISSN":["1570-677X"],"issn-type":[{"value":"1570-677X","type":"print"}],"subject":[],"published":{"date-parts":[[2025,12]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Measuring and testing vulnerability to food insecurity for prediction and targeting","name":"articletitle","label":"Article Title"},{"value":"Economics & Human Biology","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.ehb.2025.101552","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2025 The Authors. Published by Elsevier B.V.","name":"copyright","label":"Copyright"}],"article-number":"101552"}}