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Food systems and structural and rural transformation: a quantitative synthesis for low and middle-income countries

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Abstract

Structural and rural transformation in a country are intricately linked to food system outcomes. Structural transformation captures a country’s level of dependence on agriculture, while rural transformation captures the productivity in the agricultural sector. Specifically, the agri-food system and employment transitions accompany country transformations and shape the spatial distribution of populations by influencing where people live, work and eat, all of which closely relate to food system transitions. We create a food systems index (FSI) capturing a rich set of drivers established in the literature. Using country level data from 85 low and middle-income countries (LMIC’s), we analyse the linkages between food system, structural and rural transformations as well as spatial population distributions. We also analyse a large number of policy relevant variables using machine-learning methodology to shed light on patterns related to institutions, female empowerment, infrastructure and health. Our analysis indicates that rural-dominant countries in the lowest FSI group will see their youth populations more than double in the next 30 years, indicating that their food system investments today will affect one third of global youth in the future. Medium FSI countries need to invest more in the semi-rural and peri-urban areas. We find that structural transformation is a necessary but not sufficient condition for desirable food system outcomes. Rural transformation by itself without structural transformation is not enough either. For LMIC’s, broad development interventions such as financial and digital connectivity as well as women’s empowerment loom more important than narrowly focused interventions regarding progress in the food system.

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Data availability

Data are available upon reasonable request.

Code availability

Stata code is available upon reasonable request.

Notes

  1. These are historical and observational characterisations of economies, and do not reflect value judgements or environmental sustainability dimension of the transition (e.g. preference for external input use as opposed to low-input/organic agriculture).

  2. Please see Box 2.2. in IFAD (2019) for details of the methodology to develop the ROS.

  3. This dimension does not directly capture environmental externalities of the FS due to lack of comparable data for all countries, though the ND-GAIN captures groundwater and freshwater health, biodiversity and ecological footprint of each country indirectly accounting for this to a certain extent.

  4. The rating from 0 to 100 on each indicator is determined by the following formula: ((country’s individual score on the indicator—the lowest score on the indicator)/(highest score—lowest score on the indicator))*100. With this formula, the country with the highest score rates 100 and the one with the lowest rates zero, with all other countries in between, relative to their distance from both the lowest and highest scores.

  5. A fivefold cross-validation method randomly splits the sample into 5 equal size sub-samples. Then, out of the 5 sub-samples, four subsamples are used to train the model (training data) and the remaining one sub-sample is used for testing the model (test data). The cross-validation process is then repeated 5 times (once for each fold), such that each of the 5 sub-samples are used exactly once as the test data. Finally, the 5 sets of result (one from each fold) are averaged to produce results.

  6. Note that the desirability directions of dimensions remain the same as before, i.e., an increase indicates an improvement in desirability of food system outcome for all dimensions except for the demand dimension, where an increase indicates higher stress to the food system.

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Funding

This research is funded by the International Fund for Agricultural Development (IFAD). The opinions expressed in this publication are those of the authors and do not necessarily represent those of IFAD.

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Authors and Affiliations

  1. Research and Impact Assessment Division, IFAD, Rome, Italy

    Aslihan Arslan, Romina Cavatassi & Marup Hossain

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  1. Aslihan Arslan
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  2. Romina Cavatassi
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Correspondence to Aslihan Arslan.

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The authors declared that they have no conflict of interest.

Additional information

This article belongs to the Topical Collection: Food System Transformations for Healthier Diets, Inclusive Livelihoods and Sustainable Environment

Guest Editors: Romina E Cavatassi, Leslie Lipper, Ruerd Ruben, Eric Smaling, Paul Winters

Appendix

Appendix

Table 7 Rural–urban gradient thresholds and average population density
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Table 8 List of variables used in the LASSO specifications
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Table 9 Selected variables and coefficients (low structural transformation sample)
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Table 10 Selected variables and coefficients (high structural transformation sample)
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Table 11 Selected variables and coefficients (low rural transformation sample)
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Table 12 Selected variables and coefficients (high rural transformation sample)
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Standardized coefficients of the post-selected variables from the Lasso liner model are shown in the table. Optimal value of lambda is estimated using a fivefold cross-validation methods.

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Arslan, A., Cavatassi, R. & Hossain, M. Food systems and structural and rural transformation: a quantitative synthesis for low and middle-income countries. Food Sec. 14, 293–320 (2022). https://doi.org/10.1007/s12571-021-01223-2

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