Abstract
One of the greatest challenges of the twenty-first century is to ensure that the world population has reliable access to adequate, affordable and nutritious food sufficient to avoid hunger. Agricultural trade liberalization is often considered a central element of economic strategies aiming at improving food security in developing countries. Many, however, argue that most developing countries may not benefit from freer agricultural trade and that liberalization may accentuate food insecurity. From an empirical perspective, little is known about the effects of trade on food security in developing countries. We estimated the effects of food trade openness on extreme hunger in developing countries using a novel two-step approach. First, we estimated the reverse causal impacts of hunger on food trade openness using rainfall anomalies as instrumental variables to generate exogenous variation in hunger. In a second step, we estimated the effect of food trade openness on hunger using the residual food trade openness that is not driven by hunger as an instrument. We found that a 10% increase in food trade openness would increase the prevalence of undernourishment by about 6%. We also found evidence that developing countries reduce food trade openness as a response to increased hunger, suggesting protectionist policies. A percentage point increase in undernourishment prevalence would decrease food trade openness by 0.9%. Our results suggest that countries may be better off adopting food self-sufficiency for some time, despite such actions clashing with World Trade Organization’s regulations and current agenda.
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Notes
Levine and Rothman (2006) is a notable exception.
Conceptually, we think a linear-log form seems more adequate. Indeed, our specification suggests that as trade openness increases, the gains in terms of hunger reductions become smaller, but remain positive. A quadratic specification would mean that past a certain level, trade openness could result in hunger increases. On a purely statistical basis, we also estimated a model using a linear-linear form and then compared the adjusted R-2 and compared the residuals, finding that the linear-log form performed best. We also tested the inclusion of a squared term in our main model and the coefficient turned out to be statistically insignificant.
The exclusion of climate variables such as rainfall or temperature from the ‘basic’ model is consistent with the classification of climate as an immediate and underlying cause of undernutrition. Similarly, the inclusion of food aid would not respect this principle as food aid is considered to be an underlying factor (Ickes et al. 2015). On a more practical level, the coverage of data on food aid, food consumer price indexes or agricultural foreign direct investment is particularly limited for developing countries and/or the pre-2000s period. For example, the OECD discourages the use of sector aid data (and thus food aid) before 2002 because the coverage of sector disbursements is incomplete.
We originally used temperature anomalies but found that as instruments they do not meet the exclusion restrictions in this setting.
This is implemented using the BACON command in Stata.
FDI data do not give a complete picture of international investment in an economy. Balance of payments data on FDI do not include capital raised locally, an important source of investment financing in some developing countries (World Bank 2018).
This is calculated as (e−0.009 − 1) ≈ 0.89%.
While we follow Brückner and Lederman (2015) who included the instruments in the second step (here shown in column 2), it is important to note that the models in columns 1 and 2 are not exactly the same. We provide a full comparison of models including and excluding the instruments in Appendix 4 Table 12. The differences are minimal.
This is calculated as: \( \frac{10.86}{18.13}\ast \frac{10}{100}\approx 5.99\% \).
An extension of the approach to two potentially endogenous variables can be found in Mary et al. (2017).
We do so for food supply and producer prices.
Full tables of the interaction models can be requested from the author.
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Appendices
Appendix 1
Appendix 2. Test of exclusion restrictions in the first step
Appendix 3. First-stage estimates for causal pathways
Appendix 4. Robustness analyses
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Mary, S. Hungry for free trade? Food trade and extreme hunger in developing countries. Food Sec. 11, 461–477 (2019). https://doi.org/10.1007/s12571-019-00908-z
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DOI: https://doi.org/10.1007/s12571-019-00908-z