What Would Korea-US Free Trade Agreement Bring?

ABSTRACT

This paper uses a computable model of trade to forecast the effects of the US–Korea free trade agreement on the manufacturing sector. The model uses the Eaton–Kortum methodology to explain intra-industry trade instead of the usual Armington assumption. It is parameterized using 2005 data for 15 industries and 53 countries. The results show that implementing KORUS would increase the US manufacturing exports to Korea by 56.9% and Korean manufacturing exports to the US by 18.9%. It would also increase manufacturing employment by 26,500 jobs in Korea and 34,200 jobs in the US. In addition, KORUS would lead to significant changes in the patterns of trade and production. The US and Korea would increase their specialization in the industries where they have strong technological comparative advantages. Finally, KORUS would increase welfare in both countries, but only modestly: by 0.27% in Korea and 0.013% in the US.

Keywords:

• Korea–US free trade agreement
• KORUS
• specialization
• comparative advantage
• employment
• computable models
• trade policy

JEL CLASSIFICATIONS:

• F1

Notes

¹Data are from the World Bank's WDI database and the US CIA's The World Factbook.

²Of course, this similarity is aided by the fact that these studies used very similar models. For example, five of the studies used the GTAP model.

³See for example Leamer (1984). Most of this literature focuses on factor endowment differences as the determinants of specialization, whereas this paper focuses on the differences of technology.

⁴The main reason why the Armington-based models significantly underpredicted the effects of NAFTA is that the Armington elasticities they employed (between 2 and 3) were too small. See Ruhl (2008) for an analysis of this issue.

⁵The assumption of tradability of the non-manufacturing output means that the wages w_n in each country are given by the productivity in non-manufacturing and the (numeraire) price of the non-manufacturing good deflated by the price of the bundle of intermediates used in producing this good.

⁶Kortum (1997) and Eaton and Kortum (1999) provide microfoundations for this approach. Parameter T_ij governs the mean of the distribution, while parameter θ, which is common to all countries and industries, governs the variance. The support of the Fréchet distribution is (0, ∞).

⁷To receive $1 of product in country n requires sending d_nij≥1 dollars of product from country i. By definition, domestic transport costs are set to one: d_nnj≡ 1. Trade barriers result in d_nij>1. Note that trade costs are not restricted to be symmetric (d_nij can be different from d_inj). Waugh (2007) studies the effects of the asymmetry of trade costs.

⁸It follows from . The last equality follows from a known statistical result (see Eaton & Kortum, 2002).

⁹Note that parameter T is not the same as total factor productivity (TFP). T is an exogenous parameter of the Fréchet distribution. TFP, on the other hand, is endogenous and represents the average productivity of the firms actually operating in an industry.

¹⁰This is true because conditional on the fact that country i actually supplies a particular good, the distribution of the price of this good is the same regardless of the source i.

¹¹This system of equations is easily solved using numerical methods in Matlab.

¹²A similar procedure is followed by Levchenko and Zhang (2011).

¹³They also obtain a second estimate of 3.6, but 8.28 is their preferred estimate since θ=3.6 results in unreasonably high trade costs.

¹⁴In the data, in addition to intermediate and final goods, there are also investment goods. Since there is no investment in the model, investment goods are treated as intermediate goods.

¹⁵Anderson and van Wincoop (2004) roughly estimate the average international trade cost between rich OECD countries to be around 1.7 (excluding local distribution margins, see Anderson & van Wincoop, 2004, pp. 692–693). This is lower than the (non-weighted) average trade cost of 2.84 estimated in this paper. However, our dataset includes many less-developed countries that have much higher trade costs than the rich OECD countries. If these countries are excluded from the dataset, the average trade cost for the remaining rich OECD countries is 1.76, which is much closer to the number reported in Anderson and van Wincoop (2004).

¹⁶Remember that these international trade cost are measured relative to domestic trade costs, which have also been changing over the decades. Rising trade cost for US imports in this case means that it is rising relative to the US domestic trade costs.

¹⁷This is consistent with previous studies (listed in the Introduction) that forecast the effects of NAFTA.

¹⁸These numbers are greater than what has been generally predicted by the previous studies. This is consistent with the comment made in the Introduction about the current Armington-based models.