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A nonparametric analysis of household-level food insecurity and its determinant factors: exploratory study in Ethiopia and Nigeria

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Abstract

Given the fundamental importance of food to human well-being, understanding food insecurity is crucial for sustainable development. However, due to the complex nature of food insecurity, traditional linear methods of empirical analysis may mask critical relationships between food insecurity and demographic, agricultural, and environmental factors. Here we show, using two years of household-level survey data from Ethiopia and Nigeria, that nonparametric regression (“random forest”, in this study) enables enhanced insight into the factors associated with self-reported food security and household dietary diversity score. We observe nonlinearities and thresholds in the relationships between the measures of food security, livestock ownership, and climatic conditions. The threshold-based relationships suggest that policies aimed at increasing agricultural productivity (e.g., livestock holdings) may only be beneficial up to an extent. While it is intuitive that some level of diminishing returns will exist, our nonparametric analysis could be used as a first step to discern the levels to which policies may be beneficial. Additionally, our results indicate that the random forest (and perhaps nonparametric regression and classification methods more generally) may be especially well-positioned to uncover nuances in these relationships in years with suboptimal climatic conditions (such as during the 2015 drought in Ethiopia). Ultimately, we argue that nonparametric approaches, when informed by existing theory, provide an insightful complement to inform the analysis of agricultural and development policy.

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Data availability

Survey data are publicly available at https://microdata.worldbank.org/index.php/catalog/lsms#_r=&collection=&country=66&dtype=&from=1890&page=1&ps=&sid=&sk=&sort_by=nation&sort_order=&to=2017&topic=&view=s&vk=

Climate data are publicly available at https://www.chc.ucsb.edu/data

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Acknowledgments

The authors express gratitude to the World Bank, Nigeria Bureau of Statistics, Ethiopia Central Statistical Agency, and the Climate Hazard Center at UC Santa Barbara for making the data used in this study publicly available. We thank Kathryn Grace for her helpful comments.

Funding

The authors gratefully acknowledge support from a National Science Foundation INFEWS grant (Award #1639214). MB and AV are grateful for the support from the Minnesota Population Center (#R24 HD041023), funded through grants from the Eunice Kennedy Shriver National Institute of Child Health and Human Development. MB has done most of the work on the manuscript while she was a PhD student at the University of Minnesota.

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Authors and Affiliations

  1. Institute of Urban and Regional Development, University of California - Berkeley, Berkeley, CA, 94720, USA

    Maryia Bakhtsiyarava

  2. Department of Industrial and Operations Engineering, University of Michigan, Ann Arbor, MI, 48109, USA

    Tim G. Williams & Seth D. Guikema

  3. Minnesota Population Center, University of Minnesota, Minneapolis, MN, 55455, USA

    Andrew Verdin

Authors
  1. Maryia Bakhtsiyarava
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  2. Tim G. Williams
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  3. Andrew Verdin
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  4. Seth D. Guikema
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Contributions

MB, TGW, and SDG designed the study; MB, TGW, and AV processed data and performed statistical analyses; MB, TGW, SV, and wrote the paper.

Corresponding author

Correspondence to Maryia Bakhtsiyarava.

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The authors declare no conflict of interest.

Code availability (software application or custom code)

R code to perform the analysis is available upon request.

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Bakhtsiyarava, M., Williams, T.G., Verdin, A. et al. A nonparametric analysis of household-level food insecurity and its determinant factors: exploratory study in Ethiopia and Nigeria. Food Sec. 13, 55–70 (2021). https://doi.org/10.1007/s12571-020-01132-w

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