Revisiting zero hunger from a multidisciplinary perspective: How to measure hunger and reduce it?
DOI:
https://doi.org/10.24136/eq.3511Keywords:
global hunger index, dimensionality reduction, data imputation, partitioning around medoids clustering algorithms, genetically modified organismsAbstract
Research background: The solution to the multifaceted problem of hunger remains a challenge: about 735 million people experience hunger, especially in sub-Saharan Africa, South Asia, and parts of the Middle East. It would be expected that the measurement of the seriousness of such an important problem would be carried out with scientific objective indices. However, this is not the case. The most widely used index, the global hunger index (GHI), uses participatory methods to weigh the four correlated facets (indicators) of hunger considered: undernourishment, child stunting, child wasting, and child mortality, which translates into subjective and inaccurate results.
Purpose of the article: We aim (i) to contribute an objective and realistic weighing scheme for the GHI that, in addition, avoids the double-counting problem derived from the correlation among their indicators; and (ii) provide accurate methods for non-available data imputation.
Methods: For (i), we propose a dimensionality reduction-based weighing scheme. For (ii), in countries with more than one non-available indicator, we substitute their current tentative qualitative classification according to the GHI Hunger Severity Scale with the accurate predictions provided by a partition-around-medoids clustering algorithm. For those with only one non-available indicator and a bounded GHI, we are able to deduce their true value.
Findings & value added: Our results demonstrate that the weights provided by the above methodological proposals differ dramatically from the subjective weights used in the GHI, which leads to significant changes in the GHI ranking of countries. These findings suggest reconsidering the relative importance of hunger because GHI is a vital tool for policymakers to understand this problem, make informed decisions, prioritize resources, track progress on global goals, and design effective and efficient interventions. Finally, we advocate for the use of genetically modified organisms and high-productive agriculture as one of the main instruments to face hunger.
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References
Aiga, H. (2015). Hunger measurement complexity: Is the global hunger index reliable? Public Health, 129, 1288–1290. https://doi: 10.1016/j.puhe.2015.04.019.
View in Google Scholar
Amat Rodrigo, J. (2017). Clustering y heatmaps: aprendizaje no supervisado. Universidad de Granada. Retrieved from https://www.cienciadedatos.net/docu mentos/37_clustering_y_heatmaps#Model_based_clustering.
View in Google Scholar
Boehmke, B., & Greenwell, B. M. (2020). Hands-on machine learning with R. CRC Press.
View in Google Scholar
Cailas, M., Kerzee, R., & Bing-Canar, J. (1996). An indicator of solid waste generation potential for Illinois using principal component analysis and geographic information systems. Journal of the Air and Waste Management Association, 46, 414–421.
View in Google Scholar
Charrad, M., Ghazzali, N., Boiteau, V., & Niknafs, A. (2022). Determining the best number of clusters in a data set. Retrieved from https://cran.rproject.org/ web/packages/NbClust/NbClust.pdf.
View in Google Scholar
Chen, C. J., Fu, X. F., & Ma, X. W. (2004). Research on sustainable development with regards to the economic system and the energy system in mainland China. International Journal of Global Energy Issues, 22, 190–198.
View in Google Scholar
de Weerdt, J., Beegle, K., Friedman, J., & Gibson, J. (2014). The challenge of measuring hunger. World Bank Policy Research Working Paper Series, 6736.
View in Google Scholar
Dean, M. (2022). A practical guide to multi-criteria analysis. Technical Report. London: University College London.
View in Google Scholar
Di Pasquale, A. (2015). La elaboración de índices sintéticos de bienestar social: Validación teórica y empírica del método de agregación/ponderación. Portal de Promoción y Difusión Pública del Conocimiento Académico y Científico. Retrieved from http://nulan.mdp.edu.ar.
View in Google Scholar
Dominguez-Serrano, M., Blancas-Peral, F. J., Guerrero Casas, F. M., & Gonzalez-Lozano, M. (2011). Una revisión crítica para la construcción de indicadores sintéticos. Revista de Métodos Cuantitativos para la Economía y la Empresa, 11, 41– 70.
View in Google Scholar
FAO, IFAD, UNICEF, WFP, & WHO. (2024). In brief to the state of food security and nutrition in the world 2024 – Financing to end hunger, food insecurity and malnutrition in all its forms. FAO.
View in Google Scholar
FAO. (2023). Hunger and food insecurity. Food and Agriculture Organization of the United Nations.
View in Google Scholar
Fernández-Avilés, G., & Montero, J. M. (2024). Fundamentos de ciencia de datos con R. Barcelona: McGraw Hill.
View in Google Scholar
FSIN, & GRFC. (2021). 2023 Global report on food crisis. GRFC 2021.
View in Google Scholar
FSIN, & GRFC. (2022). 2023 Global report on food crisis. GRFC 2022.
View in Google Scholar
FSIN, & GRFC. (2023). 2023 Global report on food crisis. GRFC 2023.
View in Google Scholar
Hothorn, T., & Everitt, B. (2014). A handbook of statistical analyses using R. CRC Press.
View in Google Scholar
IFPRI (International Food Policy Research Institute), WHH (Welthungerhilfe), & Concern Worldwide. (2007). The challenge of hunger 2007: Global Hunger Index. Facts, determinants, and trends. Washington, DC, Bonn, & Dublin.
View in Google Scholar
IMF. (2023). World economic outlook 2023. International Monetary Fund.
View in Google Scholar
IPC. (2023). Integrated food security phase classification reports.
View in Google Scholar
IPC. (2024). Fact sheet: The IPC famine.
View in Google Scholar
Jollife, T., & Cadima, J. (2016). Principal component analysis: A review and recent developments. Philosophical Transactions, Series A, 20150202.
View in Google Scholar
Kaufman, L., & Rousseeuw, P. J. (1990). Divisive analysis (Program DIANA). In L. Kaufman & P. J. Rousseeuw (Eds.). Finding groups in data: An introduction to cluster analysis (pp. 68–125). John Wiley & Sons.
View in Google Scholar
Kemps, E., & Tiggemann, M. A. (2015). A role for mental imagery in the experience and reduction of food cravings. Frontiers in Psychiatry, 6, 193.
View in Google Scholar
Kline, P. (2014). An easy guide to factor analysis. London: Routledge.
View in Google Scholar
Lai, D. (2000). Temporal analysis of human development indicators: Principal component approach. Social Indicators Research, 51, 331–366.
View in Google Scholar
Linting, M., & van der Kooij, A. (2011). Nonlinear principal components analysis with CATPCA: A tutorial. Journal of Personality Assessment, 94(1), 12–25.
View in Google Scholar
Lu, Z., & Zhang, Y. (2012). An augmented Lagrangian approach for sparse principal component analysis. Mathematical Programming, 135, 149–193.
View in Google Scholar
Martin-Shields, C., & Stojetz, W. (2018). Food security and conflict: Empirical challenges and future opportunities for research and policy making on food security and conflict. World Development, 119, 150–164.
View in Google Scholar
Martorell, R. (2008). Malnutrition and hunger. Copenhagen Consensus 2008 Perspective Paper. Retrieved from http://www.copenhagenconsensus.com/Default .aspx?ID=1322.
View in Google Scholar
Masset, E. (2011). A review of hunger indices and methods to monitor country commitment to fighting hunger. Food Policy, 36, S102–S108.
View in Google Scholar
Montero, J. M., & Alfaro-Navarro, J. L. (2024a). Análisis de componentes principales. In G. Fernández-Avilés & J. M. Montero (Eds.). Fundamentos de ciencia de datos con R (pp. 545–556). Barcelona: McGraw Hill.
View in Google Scholar
Montero, J. M., & Alfaro-Navarro, J. L. (2024b). Análisis factorial. In G. Fernández-Avilés & J. M. Montero (Eds.). Fundamentos de ciencia de datos con R (pp. 557–578). Barcelona: McGraw Hill.
View in Google Scholar
Montero, J. M., & Fernández-Avilés, G. (2024). Análisis clúster: Clusterización no jerárquica. In G. Fernández-Avilés & J. M. Montero (Eds.). Fundamentos de ciencia de datos con R (pp. 535–544). Barcelona: McGraw Hill.
View in Google Scholar
Montero, J. M., Fernández-Avilés, G., & Mateu, J. (2015). Spatial and spatio-temporal geostatistical modeling and kriging. Chichester: John Wiley & Sons.
View in Google Scholar
Nigam, A. K. (2018). Global Hunger Index Revisited. Nigam, A. K. (2018). Global Hunger Index Revisited. Journal of the Indian Society of Agricultural Statistics, 72, 225–30.
View in Google Scholar
Nigam, A. K. (2019). Improving global hunger index. Agricultural Research, 8,132–139.
View in Google Scholar
Nigam, A. K. (2022). Alternatives to Global Hunger Index. In Special proceedings: 24th annual conference, 23–27 February (pp 31–34). SSCA. Retrieved from https://ssca.org.in/media/2_Arun_Nigam_Special_Procedings_28052022_FINAL_Finally_Alternatives_to_Global_Hunger_Ind_Pot2RjD.pdf.
View in Google Scholar
Panagariya, A. (2013). Does India really suffer from worse child malnutrition than sub-Saharan Africa? Economic and Political Weekly, 48(18), 98–111.
View in Google Scholar
Panigrahi, S., Rout, S., & Bari, A. (2023). Determinants of Global Hunger Index, 2023 In Congress in computer science, computer engineering, & applied computing (CSCE), Las Vegas, (pp. 102–107). IEEE. https://doi: 10.1109/CSCE60160.2023.00022.
View in Google Scholar
Premachandra, I. M. (2001). A note on DEA vs. principal component analysis: An improvement to Joe Zhu’s approach. European Journal of Operational Research, 132, 553–560.
View in Google Scholar
Rao, V. R. (2014). Applied conjoint analysis. Springer-Verlag Berlin.
View in Google Scholar
Roberts, R. J. (2009). Why we should love GMOs? Retrieved from https://www.cshl.edu/mc-events/nobel-laureate-richard-roberts-why-you-shoul d-love-gmos/.
View in Google Scholar
Roberts, R. J., & Naimy, V. (2023). Overcoming agricultural challenges with GMOs as a catalyst for poverty reduction and sustainability in Lebanon. Sustainability, 15, 16187.
View in Google Scholar
Roberts, R. J., & Naimy, V. (2024). Strategic adoption of genetically modified crops in Lebanon: A comprehensive cost–benefit analysis and implementation framework. Sustainability, 16, 8350.
View in Google Scholar
Singh, P., Kurpad, A. V., Verma, D., Nigam, A. K., Sachdev, H. S., Pandey, A., Hemalatha, R., Deb, S., Khanna. K., Awasthi, S., Toteja, G. S., Bansal, P. G., Gonmei, Z., & Bhargava, B. (2021). Global Hunger Index does not really measure hunger - An Indian perspective. Indian Journal of Medical Research, 154(3), 455–460.
View in Google Scholar
Spielman, S. E., Tuccillo, J., Folch, D. C., Schweikert, A., Davies, R., Wood, N., & Tate, E. (2020). Evaluating social vulnerability indicators: Criteria and their application to the social vulnerability index. Natural Hazards, 100, 417–436.
View in Google Scholar
Sreehari, E., & Babu, L. D. D. (2023). Global Hunger Index: A multistage coefficient estimation analysis using machine learning techniques for a hunger free society. Journal of Cleaner Production, 429, 139515.
View in Google Scholar
Thiel-Ellul, D. F., & Navarro-Jurado, E. (2014). Medición y análisis de la sostenibilidad: Indicadores sintéticos a través de métodos multicriterio y su relación con el turismo en el litoral de Andalucía. Málaga: Universidad de Málaga.
View in Google Scholar
Tobler, W. R. (1970). A computer movie simulating urban growth in the Detroit region. Economic Geography, 46, 234–240.
View in Google Scholar
von Grebmer, K., & Bernstein, J. (2020). 2020 Global hunger index: One decade to zero hunger – Linking health and sustainable food systems. International Food Policy Research Institute & Concern Worldwide.
View in Google Scholar
von Grebmer, K., Bernstein, J., de Waal, A., Prasai, N., Amin, S., & Yohannes, Y. (2015). 2015 Global hunger index: Armed conflict and the challenge of hunger. Welthungerhilfe, International Food Policy Research Institute, & Concern Worldwide.
View in Google Scholar
von Grebmer, K., Bernstein, J., Delgado, C., Smith, D., Wiemers, M., Schiffer, T., Hanano, A., Towey, O., Ní Chéilleachair, R., Foley, C., Gitter, S., Ekstrom, K., & Fritschel, H. (2021). 2021 Global hunger index: Hunger and food systems in conflict settings. Welthungerhilfe & Concern Worldwide.
View in Google Scholar
von Grebmer, K., Bernstein, J., Geza, W., Ndlovu, M., Wiemers, M., Reiner, L., Bachmeier, M., Hanano, A. Chéilleachair, R. N., Foley, C., Sheehan, T., Gitter, S., Larocque, G., & Fritschel, H. (2023). 2023 Global hunger index: The power of youth in shaping food systems. International Food Policy Research Institute & Concern Worldwide.
View in Google Scholar
von Grebmer, K., Bernstein, J., Geza, W., Ndlovu, M., Wiemers, M., Reiner, L., Bachmeier, M., Hanano, A. Chéilleachair, R. N., Foley, C., Sheehan, T., Gitter, S., Larocque, G., & Fritschel, H. (2023). 2023 Global hunger index by severity-map. Retrieved from https://www.welthungerhilfe.org/fileadmin/pictures/public ations/en/studies_analysis/2023_Global_Hunger_Index_poster_map_EN.pdf.
View in Google Scholar
von Grebmer, K., Bernstein, J., Hammond, L., Patterson, F., Klaus, L., Fahlbusch, J., Sonntag, A., Fahlbusch, J., Towey, O., Foley, C., Gitter, S., Ekstrom, K. & Fritschel, H. (2018). 2018 Global hunger index: Forced migration and hunger. Welthungerhilfe, International Food Policy Research Institute, & Concern Worldwide.
View in Google Scholar
von Grebmer, K., Bernstein, J., Hossain, N., Brown, T., Prasai, N., Yohannes, Y., Towey, O., Patterson, F., Sonntag, A., Zimmermann, S. M., & Foley, C. (2017). 2017 Global hunger index: The inequalities of hunger. Welthungerhilfe, International Food Policy Research Institute, & Concern Worldwide.
View in Google Scholar
von Grebmer, K., Bernstein, J., Mukerji, R., Patterson, F., Wiemers, M., Chéilleachair, R. N., Foley, C., Gitter, S., Ekstrom, K., & Fritschel, H. (2019). 2019 Global hunger index: The challenge of hunger and climate change. Welthungerhilfe, International Food Policy Research Institute, & Concern Worldwide.
View in Google Scholar
von Grebmer, K., Bernstein, J., Prasai, N., Yin, S., & Yohannes, Y. (2016). 2016 Global hunger index: Getting to zero hunger. Welthungerhilfe, International Food Policy Research Institute, & Concern Worldwide.
View in Google Scholar
von Grebmer, K., Bernstein, J., Wiemers, M., Reiner, L., Bachmeier, M., Hanano, A., Chéilleachair, R. N., Foley, C., Sheehan, T., Gitter, S., Larocque, G., & Fritschel, H. (2023). 2023 Global hunger index: The challenge of hunger and malnutrition. Welthungerhilfe.
View in Google Scholar
von Grebmer, K., Bernstein, J., Wiemers, M., Reiner, L., Bachmeier, M., Hanano, A., Towey, O., Chéilleachair, R. N., Foley, C., Gitter, S., Larocque, G., Fritschel, H., & Resnick, D. (2022). 2022 Global hunger index: Food systems transformation and local governance. International Food Policy Research Institute & Concern Worldwide.
View in Google Scholar
von Grebmer, K., Fritschel, H., Nestorova, B., Olofinbiyi, T., Pandya-Lorch, R., & Yohannes, Y. (2008). Global hunger index: The challenge of hunger 2008. International Food Policy Research Institute & Concern Worldwide.
View in Google Scholar
von Grebmer, K., Headey, D., Béné, C., Haddad, L., Olofinbiyi, T., Wiesmann, D., Fritschel, H., Yin, S., Yohannes, Y., Foley, C., von Oppeln, C., Béné, C., Haddad, L., & Iseli, B. (2013). 2013 Global hunger index: The challenge of hunger – Building resilience to achieve food and nutrition security (Vol. 79). International Food Policy Research Institute & Concern Worldwide.
View in Google Scholar
von Grebmer, K., Nestorova, B., Quisumbing, A., Fertziger, R., Fritschel, H., Pandya-Lorch, R., & Yohannes, Y. (2009). 2009 Global hunger index: The challenge of hunger – Focus on financial crisis and gender inequality. International Food Policy Research Institute & Concern Worldwide.
View in Google Scholar
von Grebmer, K., Ringler, C., Rosegrant, M. W., Olofinbiyi, T., Wiesmann, D., Tolulope Olofinbiyi, Doris Wiesmann, Fritschel, H., Badiane, O., Torero, M., Yohannes, Y., Thompson, J., von Oppeln, C., & Rahall, J. (2012). 2012 Global hunger index: The challenge of hunger – Ensuring sustainable food security under land, water, and energy stresses. International Food Policy Research Institute & Concern Worldwide.
View in Google Scholar
von Grebmer, K., Saltzman, A., Birol, E., Wiesmann, D., Prasai, N., Yin, S., Yohannes, Y., Menon, P., Thompson, J., & Sonntag, A. (2014). 2014 Global hunger index: The challenge of hidden hunger. Welthungerhilfe, International Food Policy Research Institute, & Concern Worldwide.
View in Google Scholar
von Grebmer, K., Torero, M., Olofinbiyi, T., Fritschel, H., Wiesmann, D., & Yohannes, Y. (2010). 2010 Global hunger index: The challenge of hunger – Focus on the crisis of child undernutrition. International Food Policy Research Institute & Concern Worldwide.
View in Google Scholar
von Grebmer, K., Torero, M., Olofinbiyi, T., Fritschel, H., Wiesmann, D., Yohannes, Y., Schofield, L., & von Oppeln, C. (2011). 2011 Global hunger index: The challenge of hunger – Taming price spikes and excessive food price volatility. International Food Policy Research Institute & Concern Worldwide.
View in Google Scholar
Vyas, S., & Kumaranayake, L. (2006). Constructing socio-economic status indices: How to use principal components analysis. Health Policy and Planning, 21, 459–468.
View in Google Scholar
Weismann, D., Biesalski, H. K., von Grebmer, K., & Bernstein, J. (2015). Methodological review and revision of the global hunger index. ZEF Working Paper Series, 139.
View in Google Scholar
WFP. (2023). Afghanistan: Hunger and food insecurity overview. World Food Programme.
View in Google Scholar
Wiesmann, D. (2004). An international nutrition index: Concept and analyses of food insecurity and undernutrition at country levels. Development Economics and Policy Series, 39.
View in Google Scholar
Wiesmann, D. (2006). A global hunger index: Measurement concept, ranking of countries and trends. Food Consumption and Nutrition Division Discussion Paper, 212.
View in Google Scholar
Wiesmann, D., von Braun, J., & Feldbrügge, T. (2000). An international nutrition index: Successes and failures in addressing hunger and malnutrition. ZEF Discussion Papers on Development Policy, 26.
View in Google Scholar
Wiesmann, D., Weingärtner, L., & Schöninger, I. (2006). The challenge of hunger: Global hunger index – Facts, determinants, and trends. Bonn: International Food Policy Research Institute.
View in Google Scholar
World Bank. (2023). World development indicators. World Bank.
View in Google Scholar
Wubneh, M. (1987). A multivariate analysis of socio-economic characteristics of urban areas in Ethiopia. African Urban Quarterly, 2, 425–433.
View in Google Scholar
Yadav, A. K., Srivastava, M., & Pal, Ch. (2002). Constructing development index for primary education in India: An inter-state comparison. Margin, 35, 55–65.
View in Google Scholar
Zhou, X., Tang, X., & Zhang, R. (2020). Impact of green finance on economic development and environmental quality: A study based on provincial panel data from China. Environmental Science and Pollution Research, 27, 19915–19932.
View in Google Scholar
Zhu, J. (1998). Data envelopment analysis vs. principal component analysis: An illustrative study of economic performance of Chinese cities. European Journal of Operational Research, 111, 50–61.
View in Google Scholar
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