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Natural Resources and Undernourishment in Developing Countries? Is There a Curse?

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Abstract

Food security is a crucial issue for developing countries, with many populations suffering from undernourishment. While numerous factors contribute to this issue, the role of natural resources has been neglected. This paper, therefore, examines for the first time how natural resource dependence affects the prevalence of undernourishment in developing countries. Accounting for the effects of total rents and point resources, the results show that natural resource dependence explains the prevalence of undernourishment (including stunting and low birth weight) ceteris paribus. Appraising the natural resources-undernourishment nexus by geographical location suggests that the effect is more pronounced in sub-Saharan Africa, South Asia, and low- and lower-middle-income countries. Among the mechanisms to explain this result, we identify control of corruption, democracy, internal conflicts, income inequality, and agricultural investments as potential transmission channels through which natural resources influence undernourishment.

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Fig. 1

Source: authors’ construction from World Bank (2023)

Fig. 2

Source: authors’ construction from World Bank (2023)

Fig. 3

Source: authors’ construction

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Notes

  1. Based on a meta-analysis of 605 estimates, Havranek et al. (2016) show that about 40% of empirical studies find a negative effect of resource dependence on long-term growth, 40% find no effect, and 20% find a positive effect, showing uncertainty about the empirical relevance of the resource curse. Similarly, Dauvin and Guerreiro (2017), in a meta-analysis of 1,419 estimates, show that while the resource curse is present in developing countries, natural resources do not harm growth in developed countries. They conclude that the quality of institutions is crucial to mitigating the resource curse.

  2. See Mousavi and Clark (2021) for a systematic literature review.

  3. The most famous cases are the diamond-funded rebellions in Sierra Leone and Angola. Oil also offers many financing opportunities for rebels, including extortion raids against oil companies.

  4. See Sebri and Dachraoui (2021) for a meta-analysis.

  5. As suggested by Smith and Haddad (2015), stunting has replaced low birth weight as the preferred measure of child undernourishment for setting and monitoring international targets. However, for a better appreciation of the robustness of the results, we use both indicators.

  6. Our dependent variable the prevalence of undernourishment is persistent as their correlation with their first lags is substantially higher (i.e., 0.992) than the rule of thumb threshold value of 0.8 considered for establishing persistence.

  7. This estimator is asymptotically more efficient than the difference GMM (Roodman 2009a).

  8. All explanatory variables are considered as potentially endogenous.

  9. We take lags of orders 2 through 4. We performed the estimations using the Stata module xtabond2 following Roodman (2009b). The choice of lags was based on the results of Arellano and Bond’s residual autocorrelation tests. The latter reports an autocorrelation of order 1 for \({\xi }_{i,t}\), which leads to considering the lags from t-1 onwards.

  10. The standardised coefficients are calculated according to the following formula: \({\beta }_{x}={\alpha }_{x}\frac{{\lambda }_{x}}{{\lambda }_{y}}\) with \({\beta }_{x}\), \({\alpha }_{x}\),\({\lambda }_{x}\),\({\lambda }_{y}\) corresponding to the standardised coefficient, the initial estimated coefficient, the standard deviation of the resource rent, and the standard deviation of the prevalence of undernourishment, respectively.

  11. We use agricultural gross fixed capital formation as a proxy for agricultural investment. It is defined as the fixed assets of the economy and net changes in the level of inventories. This measure represents a good proxy for investment in agriculture because this measure the annual flows of physical investment in the agricultural sector. This measure is regularly used by the FAO to assess investment trends in agriculture (see FAO (2023)).

  12. This system of equations is estimated by two-step GMM. However, in Eq. (3), where Zi,t is a dichotomous indicator, we use a logit regression with robust standard errors. Studies on the impact of natural resources on the occurrence of conflict address endogeneity issues by delaying the explanatory variables so that the values of the explanatory variables in the previous year explain the onset of conflict in a year (Bodea et al. 2016). We adopt this approach by using lagged values for the explanatory variables.

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Acknowledgements

The authors are grateful to the Editor and two anonymous reviewers of this journal for very insightful comments and suggestions of great benefit to the paper. The usual disclaimer applies.

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  1. LAREFA, Faculty of Economics and Management, University of Dschang, Dschang, Cameroon

    Henri Njangang & Sosson Tadadjeu

  2. Development Research Department at African Development Bank, Abidjan, Côte d’Ivoire

    Sosson Tadadjeu

  3. CEREG, Department of Economics, University of Yaounde II, Yaounde, Cameroon

    Joseph Keneck-Massil

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Appendix

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Table 12 Definitions of the study variables
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Table 13 Assessing the consistency of system GMM estimators
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Njangang, H., Tadadjeu, S. & Keneck-Massil, J. Natural Resources and Undernourishment in Developing Countries? Is There a Curse?. Environ Resource Econ (2024). https://doi.org/10.1007/s10640-024-00877-8

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