Further evidence on Orcutt’s hypothesis using Korean–US commodity data

Abstract

Orcutt’s hypothesis in international economics asserts that trade flows respond to exchange rate changes faster than to changes in relative prices. Previous research has provided mixed support by using trade flows of one country with the rest of the world, an aggregate concept. A second group of studies is now emerging which use prices and trade flows at commodity level. We add to this second group by using data from 10 Standard International Trade Classification single-digit industries that trade between Korea and the United States. The hypothesis was supported in small fraction of industries.

Keywords:

• Orcutt’s hypothesis
• Korea–US trade
• industry data

JEL Classification:

• F31
• F41

Notes

¹ Indeed, Orcutt (1950, p. 126) hinted to this point without any empirical support.

² Note that as the Appendix shows, E is defined as number of Korean won per US dollar. Hence, an increase reflects won depreciation or dollar appreciation.

³ Note that under Pesaran et al.’s (2001) approach variables could be I(0) or I(1). Since almost all macro variables are either I(0) or I(1), there is no need for pre unit-root testing.

⁴ For other applications of this approach, see Bahmani-Oskooee et al. (2005), Bahmani-Oskooee and Hegerty (2007), Halicioglu (2007, 2013), Narayan et al. (2007), Tang (2007), Mohammadi et al. (2008), Wong and Tang (2008), De Vita and Kyaw (2008), Payne (2008), Bahmani-Oskooee and Gelan (2009), Dell’Anno and Halicioglu (2010), Chen and Chen (2012), Wong (2013) and Tayebi and Yazdani (2014).

⁵ Some studies have only estimated Equations 1 and 3 to judge the price elasticities, hence the Marshall–Lerner condition. Examples are Houthakker and Magee (1969), Marquez and McNeilly (1988), Bahmani-Oskooee and Niroomand (1998), Caporale and Chui (1999) and Warner and Kreinin (1983).

⁶ The negative income elasticity in industry coded SITC4 implies that as Korean economy grows it produces more of close substitutes for these goods and therefore, imports less of them.

⁷ The critical values are 3.77 at the 10% and 4.35 at the 5% significance level. These are from Pesaran et al. (2001, Table CI(iii) Case III, p. 300).

⁸ For details of these two tests, see Brown et al. (1975); and for a graphical presentation and application within error-correction models, see Bahmani-Oskooee et al. (2005).