Abstract
Composite indicators have gained popularity in various research areas. However, the determination of an appropriate weighting method is challenging. Subjective weighting methods are criticised for their potential bias that may reduce stakeholders’ trust in the results of a composite index. By contrast, objective weighting processes are perceived to provide unbiased results that may overcome trust issues. The Global Food Security Index (GFSI) is a composite indicator that measures the comparative level of food insecurity for 113 countries. The initial components of the GFSI included the affordability, availability and quality and safety components. In 2017, the GFSI added a fourth component for natural resources and resilience (NRR) as a risk to food security. The Economist Intelligence Unit’s (EIU) panel of experts uses a subjective weighting of indicators in the GFSI model. This study set out to assess whether an objective weighting of the NRR component of the GFSI significantly changed the country scores and ranks compared to the subjective weighting process. The GFSI data was analysed using a principal component analysis (PCA) to derive objectively weighted NRR scores and ranks. The objectively and subjectively weighted NRR ranks were strongly correlated (rho = 0.831), implying that the GFSI model was not strongly statistically biased. The study concluded that subjective weighting of the NRR component of the GFSI may still provide relatively fair country scores and ranks. However, an objective weighting of the NRR component could improve the reliability of the NRR component of the GFSI and build greater trust.
Similar content being viewed by others
References
Becker, W., Saisana, M., Paruolo, P., & Vandecasteele, I. (2017). Weights and importance in composite indicators: Closing the gap. Ecological Indicators, 80, 12–22.
Caccavale, O. M., & Giuffrida, V. (2020). The Proteus composite index: Towards a better metric for global food security. World Development, 126, 104709.
Candel, J. J. (2016). Putting food on the table: The European Union governance of the wicked problem of food security. Wageningen University.
Chen, P.-C., Yu, M.-M., Shih, J.-C., Chang, C.-C., & Hsu, S.-H. (2019). A reassessment of the global food security index by using a hierarchical data envelopment analysis approach. European Journal of Operational Research, 272(2), 687–698.
Decancq, K., & Lugo, M. A. (2013). Weights in multidimensional indices of wellbeing: An overview. Econometric Reviews, 32(1), 7–34.
EIU (Economist Intelligence Unit). (2017). Global food security index 2017. An annual measure of the state of global food security. The Economist Intelligence Unit Limited. Available: https://foodsecurityindex.eiu.com/Resources (Accessed 20 March 2020).
EIU (Economist Intelligence Unit). (2018). Global food security index 2018. An annual measure of the state of global food security. The Economist Intelligence Unit Limited. Available: https://foodsecurityindex.eiu.com/Resources (Accessed 20 March 2020).
EIU (Economist Intelligence Unit). (2019). Global food security index 2019. An annual measure of the state of global food security. The Economist Intelligence Unit Limited. Available: https://foodsecurityindex.eiu.com/Resources (Accessed March 01 2020).
Enaruvbe, G. O., & Atafo, O. P. (2016). Analysis of deforestation pattern in the Niger Delta region of Nigeria. Journal of Land Use Science, 11(1), 113–130.
FAO (Food and Agriculture Organisation of the United Nations). (2013). Resilient livelihoods: Disaster risk reduction for food and nutrition security. FAO. Available: http://www.fao.org/docrep/015/i2540e/i2540e00.pdf (Accessed 12 March 2020).
Freudenberg, M. (2003). Composite indicators of country performance: A critical assessment. In OECD science, technology and industry (STI) working papers. OECD Publishing.
Gan, X., Fernandez, I. C., Guo, J., Wilson, M., Zhao, Y., Zhou, B., & Wu, J. (2017). When to use what: Methods for weighting and aggregating sustainability indicators. Ecological Indicators, 81, 491–502.
Gómez-Limón, J. A., & Riesgo, L. (2009). Alternative approaches to the construction of a composite indicator of agricultural sustainability: An application to irrigated agriculture in the Duero basin in Spain. Journal of Environmental Management, 90(11), 3345–3362.
Greco, S., Ishizaka, A., Tasiou, M., & Torrisi, G. (2019). On the methodological framework of composite indices: A review of the issues of weighting, aggregation, and robustness. Social Indicators Research, 141(1), 61–94.
Headey, D., & Ecker, O. (2013). Rethinking the measurement of food security: From first principles to best practice. Food security, 5(3), 327–343.
Hendriks, S. (2015). The food security continuum: A novel tool for understanding food insecurity as a range of experiences. Food Security, 7(3), 609–619.
ICRISAT (International Crops Research Institute for the Semi-Arid Tropics). (2017). Drought-tolerant crops to the rescue in Kenya. : ICRISAT. Available: https://www.icrisat.org/drought-tolerant-crops-to-the-rescue-in-kenya/ (Accessed 28 April 2020).
Izraelov, M., & Silber, J. (2019). An assessment of the global food security index. Food Security, 11(5), 1135–1152.
Jolliffe, I. T., & Cadima, J. (2016). Principal component analysis: A review and recent developments. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 374(2065), 1–16.
Kaiser, H. F. (1960). The application of electronic computers to factor analysis. Educational and Psychological Measurement, 20(1), 141–151.
Kao, C. (2010). Weight determination for consistently ranking alternatives in multiple criteria decision analysis. Applied Mathematical Modelling, 34(7), 1779–1787.
Kutcher, M. E., Ferguson, A. R., & Cohen, M. J. (2013). A principal component analysis of coagulation after trauma. The journal of trauma and acute care surgery, 74(5), 1223–1230.
Lindén, D. (2018). Exploration of implicit weights in composite indicators: The case of resilience assessment of countries' electricity supply. KTH Royal Institute of Technology.
Maricic, M., Bulajic, M., Dobrota, M., & Jeremic, V. (2016). Redesigning the global food security index: A multivariate composite I-distance indicator approach. International Journal of Food and Agricultural Economics (IJFAEC), 4(1), 69–86.
McCarthy, N., Kilic, T., Brubaker, J., Murray, S., & de la Fuente, A. (2021). Droughts and floods in Malawi: impacts on crop production and the performance of sustainable land management practices under weather extremes. Environment and Development Economics, 1–18.
Munda, G., & Nardo, M. (2005). Constructing consistent composite indicators: the issue of weights. Institute for the Protection and Security of the Citizen, Joint Research Centre. Available: https://core.ac.uk/download/pdf/38619689.pdf (Accessed 20 April 2020).
Nardo, M., Saisana, M., Saltelli, A., & Tarantola, S. (2005). Tools for composite indicators building. European Comission, Ispra, 15(1), 19–20.
OECD (Organization for Economic Cooperation and Development). (2008). Handbook on constructing composite indicators: Methodology and user guide. OECD publishing.
Parinet, B., Lhote, A., & Legube, B. (2004). Principal component analysis: An appropriate tool for water quality evaluation and management—Application to a tropical lake system. Ecological Modelling, 178(3–4), 295–311.
Paruolo, P., Saisana, M., & Saltelli, A. (2013). Ratings and rankings: Voodoo or science? Journal of the Royal Statistical Society: Series A (Statistics in Society), 176(3), 609–634.
Pérez-Escamilla, R. (2017). Food security and the 2015–2030 sustainable development goals: From human to planetary health: Perspectives and opinions. Current developments in nutrition, 1(7), e000513.
Saisana, M., Saltelli, A., & Tarantola, S. (2005). Uncertainty and sensitivity analysis techniques as tools for the quality assessment of composite indicators. Journal of the Royal Statistical Society: Series A (Statistics in Society), 168(2), 307–323.
Santeramo, F. G. (2015a). Food security composite indices: Implications for policy and practice. Development in Practice, 25(4), 594–600.
Santeramo, F. G. (2015b). On the composite indicators for food security: Decisions matter! Food Reviews International, 31(1), 63–73.
Sova, C., Flowers, K., & Man, C. (2019). Climate change and food security. Center for Strategic and International Studies (CSIS). Available: https://www.wfpusa.org/wp-content/uploads/2019/11/191015_Flowers_ClimateChangeFood_WEB.pdf (Accessed 10 April 2020).
Sweileh, W. M. (2020). Bibliometric analysis of peer-reviewed literature on food security in the context of climate change from 1980 to 2019. Agriculture & Food Security, 9(1), 1–15.
Thomas, A., D'Hombres, B., Casubolo, C., Kayitakire, F., & Saisana, M. (2017). The use of the global food security index to inform the situation in food insecure countries. Joint Research Centre (JRC). Available: https://core.ac.uk/download/pdf/146996521.pdf (Accessed 16 April 2020).
West, J. M., Julius, S. H., Kareiva, P., Enquist, C., Lawler, J. J., Petersen, B., Johnson, A. E., & Shaw, M. R. (2009). US natural resources and climate change: Concepts and approaches for management adaptation. Environmental Management, 44(6), 1001–1021.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declared that they have no conflict of interest.
Supplementary Information
ESM 1
(DOC 55 kb)
Rights and permissions
About this article
Cite this article
Odhiambo, V.O., Hendriks, S.L. & Mutsvangwa-Sammie, E.P. The effect of an objective weighting of the global food security index’s natural resources and resilience component on country scores and ranking. Food Sec. 13, 1343–1357 (2021). https://doi.org/10.1007/s12571-021-01176-6
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12571-021-01176-6