U.S.-Africa trade balance and the J-curve: An asymmetry analysis

ABSTRACT

For the first time in the body of literature, we consider bilateral trade balance models of the US with each of her 20 trading partners from Africa and try to assess the J-curve phenomenon. After applying the linear and nonlinear ARDL approaches, we find support for the J-curve effect in three partners from the linear models. However, support rises to eight partners when we shift to nonlinear models. Furthermore, we find short-run asymmetric effects of exchange rate changes in almost all models and significant long-run asymmetric effects in half of the partners.

KEYWORDS:

• Africa
• asymmetry cointegration
• bilateral J-curve
• US

Disclosure statement

No potential conflict of interest was reported by the authors.

Supplementary material

Supplemental data for this article can be accessed here.

Notes

¹ Bahmani-Oskooee and Ratha (2004) and Bahmani-Oskooee and Hegerty (2010) are good review articles on the J-curve.

² It should be noted that the new definition of the J-curve using a cointegration method has also applied to models that use aggregate trade data between one country and the rest of the world. A few examples are: Lal and Lowinger (2002), Hacker and Hatemi-J (2003), Moura and Da Silva (2005), and Halicioglu (2007, 2008). As for Africa, Kulkarni (1996) found support for the J-curve effect in Egypt and Ghana, and Kulkarni and Bhatia (1997) in Kenya. But the most comprehensive study is by Bahmani-Oskooee and Gelan (2012), who considered aggregate trade balances of nine African countries (i.e., Burundi, Egypt, Kenya, Mauritius, Morocco, Nigeria, Sierra Leone, South Africa, and Tanzania) with the rest of the world and found no support for the J-curve effect. Following Bahmani-Oskooee and Fariditavana (2015), future research should also apply asymmetry analysis to aggregate models.

³ The Appendix is available online at www.tandfonline.com/uitj.

⁴ This section closely follows Bahmani-Oskooee and Fariditavana (2016), who used such methods to estimate bilateral trade balance models between the US and six of its partners from the industrial world.

⁵ Note that an estimate of b could also be negative and that of c positive if economic growth is due to the production of import-substitute goods (Bahmani-Oskooee 1986).

⁶ However, by applying the ADF test, we had to confirm that there is no I(2) variable. Also note that Pesaran, Shin, and Smith’s (2001) critical values are for large samples. Narayan (2005) tabulates critical values for small samples.

⁷ For the formulation of the POS and NEG variables, see Bahmani-Oskooee and Fariditavana (2016). Intuitively, the partial sum of the positive (negative) values of a variable at time t is the cumulative sum of the positive (negative) values after replacing the negative (positive) values by zeros.

⁸ See Shin, Yu, and Greenwood-Nimmo (2014, 291) for details.

⁹ For some other applications of these methods, see Verheyen (2013), Nusair (2017), Tayebi and Yazdani (2014), Hajilee and Al-Nasser (2014), Durmaz (2015), and Aftab et al. (2017).

¹⁰ See Table A in the online Appendix for the list of all 20 partners.

¹¹ At the asymptotic level, critical values from both sources are the same. However, since Pesaran, Shin, and Smith’s (2001, 303) study does not produce the critical values for small samples such as ours, we will rely upon Banerjee, Dolado, and Mestre’s (1998) values.