Export Product Concentration and Poverty Volatility in Developing Countries

ABSTRACT

The present article has examined the poverty volatility effect of export product concentration using a panel dataset of 120 developing countries over the period of 1980 to 2014. Results, based on the feasible generalized least squares estimator, suggest that export product concentration tends to induce greater poverty volatility in low-income countries but reduces poverty volatility in relatively advanced developing countries. These outcomes reflect the fact that export product concentration reduces poverty volatility in countries that improve their manufactured export performance. Finally, the greater the level of export product diversification (or economic complexity), the higher the degree of poverty volatility reduction.

KEYWORDS:

• Export product concentration
• poverty volatility

Acknowledgments

This article represents the personal opinions of individual staff members of the WTO and is not meant to represent the position or opinions of the WTO or its Members, nor the official position of any staff members. Any errors or omissions are the fault of the author.

Disclosure statement

No potential conflict of interest was reported by the author.

Supplemental Material

Supplemental data for this article can be accessed online at https://doi.org/10.1080/08853908.2023.2192016

Notes

¹ Studies include, for example, Bonaglia and Fukasaku (2003); Cadot, Carrère, and Strauss-Kahn (2011); Carmignani and Mandeville (2014); Demetriades, Al-Jebory, and Kamperis (1993); Elhiraika, Aboubakar, and Muhammad (2014); Hausmann, Hwang, and Rodrik (2007); Herzer, Nowak‐lehmann, and Siliverstovs (2006); Hu and Tan (2016); Hummels and Klenow (2005); Lin and Monga (2011); Naudé, Bosker, and Matthee (2010); Razmi (2007); Rodrik (2007); Sachs and Warner (2001); Sannassee, Seetanah, and Lamport (2014); Sheridan (2014); Strobl (2005); and Szirmai (2008, 2011).

² This view has been held by classical and neoclassical economists including Adam Smith (18^th century), David Ricardo (19th century), and Heckscher-Ohlin and Samuelson (20th century).

³ Such shocks include, for example, commodity prices shocks, export demand fluctuations, sudden surges or reversals of capital flows, natural disasters, and diseases (for example, the current COVID-19 pandemic) (e.g., Guillaumont 2009, 2017; Álvarez, García-Marín, and Ilabaca 2021).

⁴ See, for example, di Giovanni and Levchenko (2009); di Giovanni, Levchenko, and Méjean (2014); Felix (2012); Juvenal and Monteiro (2013); Krishna and Levchenko (2013); Maggioni, Turco, and Gallegati (2016); and Vannoorenberghe, Wang, and Yu (2016).

⁵ As noted by Santos-Paulino (2017), the agricultural sector has (compared to the primary commodity sector) potential spillover effects and backward linkages with other sectors in the economy.

⁶ All appendices are available online as a supplemental document.

⁷ Note that results presented later do not change when the variable “ECI” is considered in Model (1) without the natural logarithm.

⁸ Note that as the institutional and governance quality may help reduce the volatility of poverty rates, we have estimated a variant of Model (1) that included (at year t-4 - to avoid endogeneity concerns) the indicator of the level of democracy, which acts as a proxy for the institutional and governance quality. It appeared that the coefficient of this variable is not significant at the conventional significance levels, i.e., it did not alter qualitatively or quantitatively the coefficient of the variable capturing export product concentration.

⁹ The list of LDCs is regularly updated by the United Nations using a number of criteria. Both the list of LDCs and the information concerning this set of countries could be found online at: https://www.un.org/ohrlls/content/least-developed-countries.

¹⁰ Note that the numbers “1.341135” and “1.755382” are obtained from the Stata software when constructing the graph in Figure 3.