To examine how changes in relative national prices affect trade flows, this study estimates the impact of changes in industry-specific effective real exchange rates on industry-level trade balances, exports, and imports. We analyze the variations in industry-specific intra-industry trade and vertical specialization, which may both govern the long-run relationship between real exchange rates and trade flows. We employ sample information from 13 manufacturing industries across five Asian countries from 2001 to 2015. Rather than country-level aggregate measures, we use disaggregated industry-specific real exchange rates, which provide better measures of relative national prices and may help to uncover different responses that are masked by aggregate data. Fixed-effect estimations reveal that greater price competitiveness, as measured by depreciation in industry-specific effective real exchange rates, increases industry-level trade balances. We find that the elasticity of industry-level trade balances with respect to industry-specific real exchange rates declines as vertical specialization increases. There is also some limited evidence that this elasticity increases as intra-industry trade increases. Taken together, our findings suggest that global supply chains are more important than intra-industry trade in examining the response of trade balances to real exchange rate changes. Importantly, these heterogeneous impacts imply that policies regarding exchange rate management may be of limited potency and will affect different industries in different ways.
This is a preview of subscription content, access via your institution.
Buy single article
Instant access to the full article PDF.
Tax calculation will be finalised during checkout.
Subscribe to journal
Immediate online access to all issues from 2019. Subscription will auto renew annually.
Tax calculation will be finalised during checkout.
World exports in 2015 were $21.28 trillion. In that year, exports from East Asia and Pacific region were $6.59 trillion, with China’s exports at $2.36 trillion, Japan’s exports at $0.77 trillion, Korea’s exports at $0.63 trillion, Malaysia’s exports at $0.21 trillion, and Indonesia’s exports at $0.18 trillion.
To measure the contribution of this industry to manufacturing exports in the countries in our sample, we rely on the median of time-varying share of Electrical Machinery and Apparatus industry’s exports in total manufacturing exports. Based on the UN Comtrade data (World Bank 2017), this share is 37% for China, 13% for Indonesia, 20% for Japan, 28% for Korea, and 47% for Malaysia. This industry is ranked as the highest contributor to exports in China, Korea, and Malaysia and second and third highest in Japan and Indonesia, respectively.
These findings are consistent with theoretical models in which demand elasticity is declining in firms’ performance (e.g., Melitz and Ottaviano 2008).
This group of exporters are already among the most productive firms with lowest prices, which may lead them to pass on the exchange rate movements more fully.
Hong Kong and Macao’s data are excluded from the industry-level observations for China.
We are grateful to an anonymous referee for raising this point.
For cross-country IIT variations, for example, models that are developed by Markusen and Venables (2000) and Helpman et al. (2004) suggest that trade barriers may create incentives for horizontal FDI in the destination country; in return, horizontal FDI may lower the amount of IIT. Veeramani (2009) offers empirical evidence that support the importance of this interaction for the IIT pattern in India.
See Helpman and Krugman (1985) and Helpman (2011, pp. 79–95) for a theoretical justification of why IIT is expected to decrease when the difference in factor composition increases; Helpman (1987), Clark and Stanley (1999), and Cieslik (2005) offer empirical support for this proposition. Beyond relative factor endowments, there are also some models suggesting that cross-country differences in technology (Falvey et al. 2011) and productivity (Song and Sohn 2012) may determine the IIT patterns.
See Helpman (1984) and Helpman (2011, pp. 142–146) for a theoretical discussion of why differences in factor endowment and factor prices incentivize multinationals to engage in vertical FDI, providing their subsidiaries with intermediate inputs. Also, see Grossman and Rossi-Hansberg (2008) for a detailed discussion of task trade and Antràs and Helpman (2004) for the organizational forms arising from global sourcing.
See Chinn (2006) for a primer on calculating different measures of effective exchange rates at the country level.
We are grateful to an anonymous referee for raising this point. There are two reasons for the correlation between the IERER measures within a given country. First, the IERER measure is a function of the nominal exchange rate (NERf,t(c)), which is country-specific and common for all industries. Second, industry-specific price measures (Pi,t(c)) may respond to common shocks; e.g., monetary expansions or contractions, productivity shocks, factor market shocks, etc. We also expect some correlations between the IERER measures in different countries. For example, industry-specific productivity shocks that are common among leading exporters may cause a correlation between the IERER measures in different countries. Correlation coefficient matrices are available upon request.
We use the amount of national currency per US dollar (period average) as our measure for the nominal exchange rate. The data is from the IMF’s International Financial Statistics (2020b).
The industry-level output data for these countries are not available in the OECD STAN dataset.
For example, for the food industry, ISIC R3 three-digit codes 151, 1520, 153, 154, 155, and 1600 are combined into the two-digit code 15–16, and similarly for other industries. For details on the two- and three-digit codes, see United Nations Statistics Division (2002).
This dataset provides us with the industry-specific output data until 2014 but does not include information for 2015. It also does not include the optical instrument industry. We use the OECD STAN dataset to obtain the output measure for the optical instrument industry in the US.
We assume that the mean of εci, t is equal to zero. We also assume that it is uncorrelated with itself, IERER, the control covariates, and the fixed-effect parameter.
We employ sample information from 13 manufacturing industries across five Asian countries over 15 years, from 2001 to 2015. In an unbalanced panel with no missing information, we would have had access to 975 (=13×5×15) observations. As described in Section 3.1.3, however, we do not have access to some of the information for domestic and US industry-specific output data. Also, as indicated in Section 3.1.1, we have access to VS information only from 2001 to 2011. Thus, our sample size declines further when we include the control for VS and its interaction with IERER.
We obtain similar results when we limit our sample to the observations that are used in the estimations with IIT and VS variations (e.g., Table 4, column VI).
This coefficient is significant at 5% level of significance when we do not cluster our standard errors.
The same pattern emerges if we drop Japan and Korea’s petroleum industries that have relatively high VS values (Fis ,2 and 14). We are grateful to an anonymous referee who motivated this test. Detailed estimation results are available upon request.
The unconditional mean for IIT is equal to 0.62, with standard deviation being equal to 0.27. Deviating one standard deviation from the mean, IIT increases to 0.89.
The unconditional mean for VS is equal to 0.29, with standard deviation being equal to 0.16. Deviating one standard deviation from the mean, VS increases to 0.45.
We are grateful to an anonymous referee for raising this point.
The OECD countries that we use are: the US, Germany, France, the UK, Italy, Canada, Spain, Mexico, the Netherlands, Poland, Sweden, Belgium, Austria, Denmark, Finland, Greece, Portugal, Czech Republic, Hungary, and Slovak Republic.
Across all industry-year pairs, about 36% of OECD industry-specific outputs is made up of the US output.
We obtain similar results when we include an OECD industry output control that excludes US output.
Detailed confidence interval estimations are available upon request.
Using the fixed-effect estimator, we allow for correlation between the unobserved country-industry effect (ηci) and other covariates. However, using the random-effect estimator, we need to assume that ηci is not correlated with other covariates (Wooldridge 2002, pp. 251–252). This assumption appears to be harmless as the obtained random-effect results (column I of Table 8) are in line with fixed-effect results (column VI of Table 4).
Ahmed S, Appendino M, Ruta M (2015) Depreciations without exports? Global value chains and the exchange rate elasticity of exports. In: World Bank. Pol Res Wor Pap 739
Amiti M, Itskhoki O, Konings J (2014) Importers, exporters, and exchange rate disconnect. Am Econ Rev 104(7):1942–1978
Antràs P, Helpman E (2004) Global sourcing. J Polit Econ 112:552–580
Auer RA, Schoenle RS (2016) Market structure and exchange rate pass-through. J Int Econ 98:60–77
Bahmani-Oskooee M, Hegerty SW (2010) The J-and S-curves: a survey of the recent literature. J Econ Stud 37(6):580–596
Berman N, Martin P, Mayer T (2012) How do different exporters react to exchange rate changes? Q J Econ 127(1):437–492
Bussiere M, Gaulier G, Steingress W (2020) Global trade flows: revising the exchange rate elasticities. Open Econ Rev 31:25–78
Chinn MD (2006) A primer on real effective exchange rates: determinants, overvaluation, trade flows and competitive devaluation. Open Econ Rev 17(1):115–143
Cieslik A (2005) Intra-industry trade and relative factor endowments. Rev Int Econ 13:904–926
Clark DP, Stanley DL (1999) Determinants of intra-industry trade between developing countries and the United States. J Econ Dev 24(2):79–95
Demiral M (2016) Currency depreciation, trade balance and intra-industry trade interactions in Turkey’s OECD trade. Int J Econ Financ 8(4):8–22
Falvey R, Greenaway D, Yu Z (2011) Catching up or pulling away: intra-industry trade, productivity gaps and heterogeneous firms. Open Econ Rev 22(1):17–38
Feenstra RC, Gagnon JE, Knetter MM (1996) Market share and exchange rate pass-through in world automobile trade. J Int Econ 40(1–2):187–207
Garetto S (2016) Firms’ heterogeneity, incomplete information, and pass-through. J Int Econ 101:168–179
Grossman GM, Rossi-Hansberg E (2008) Trading tasks: a simple theory of offshoring. Am Econ Rev 98:1978–1997
Helpman E (1984) A simple theory of international trade with multinational corporations. J Polit Econ 92:451–471
Helpman E (1987) Imperfect competition and international trade: evidence from fourteen industrial countries. J Jpn Int Econ 1:62–81
Helpman E (2011) Understanding global trade. Harvard University Press, Cambridge, MA
Helpman E, Krugman PR (1985) Market structure and foreign trade. MIT Press, Cambridge, MA
Helpman E, Melitz MJ, Yeaple SR (2004) Export versus FDI with heterogeneous firms. Am Econ Rev 94(1):300–316
Hummels D, Ishii J, Yi KM (2001) The nature and growth of vertical specialization in world trade. J Int Econ 54(1):75–96
IMF (2020a) Direction of Trade Statistics. Retrieved from: https://data.imf.org/?sk=9D6028D4-F14A-464C-A2F2-59B2CD424B85. Accessed 29 Jul 2020
IMF (2020b) IMF Data: International Financial Statistics. Retrieved from http://www.imf.org/en/Data. Accessed 29 Jul 2020
Jorgenson DW, Ho MS, Samuels JD (2018) Educational attainment and the revival of US economic growth. In: Hulten CR, Ramy VA (eds) Education, skills, and technical change: Implications for future US GDP growth. University of Chicago Press, Chicago, pp 23–60
Kharroubi E (2011) The trade balance and the real exchange rate. BIS Quarterly Review
Krugman PR, Obstfeld M, Melitz MJ (2012). International economics: Theory & Policy. Pearson, London
Leigh MD, Lian W, Poplawski-Ribeiro M, Szymanski R, Tsyrennikov V, Yang H (2017) Exchange rates and trade: A disconnect? In: IMF Wor Pap WP/17/58
Markusen JR, Venables AJ (2000) The theory of endowment, intra-industry and multi-national trade. J Int Econ 52(2):209–234
Melitz MJ, Ottaviano GI (2008) Market size, trade, and productivity. Rev Econ Stud 75(1):295–316
Nguyen TNA, Sato K (2020) Invoice currency choice, nonlinearities and exchange rate pass-through. Appl Econ 52(10):1048–1069
OECD (2010) Measuring globalisation: OECD economic globalisation indicators 2010. Retrieved from https://doi.org/10.1787/9789264084360-en
OECD (2019). Structural Analysis (STAN) Database: Input-Output Database – Input-Output Tables (IOTs) ISIC Rev. 3. Retrieved from https://stats.oecd.org. Accessed 14 Apr 2019
OECD (2020) Structural Analysis (STAN) Database: STAN Database for Structural Analysis (ISIC Rev. 4 SNA08). Retrieved from https://stats.oecd.org. Accessed 12 Aug 2020
Oguro Y (2011) The sensitivity of export quantities to exchange rates in the context of intra-industry trade. Retrieved from http://hermes-ir.lib.hit-u.ac.jp/rs/bitstream/10086/18924/1/gd10-167.pdf. Accessed 2 Apr 2019
OPEC (2020) Annual Statistical Bulletin, Table 3.1: World proven crude oil reserves by country. Retrieved from https://asb.opec.org/data/ASB_Data.php. Accessed 12 Aug 2020
Patel N, Wang Z, Wei S-J (2019) Global value chains and effective exchange rates at the country-sector level. J Money, Credit, Bank 51(S1):7–42
Research Institute of Economy, Trade, and Industry. (2017) Industry-specific Nominal and Real Effective Exchange Rates of 18 Countries Worldwide. Retrieved from http://www.rieti.go.jp/users/eeri/en/. Accessed 10 Jan 2017
Sato K, Shimizu J, Shrestha N, Zhang S (2015) Industry-specific real effective exchange rates in Asia. RIETI Discussion Papers
Song EY, Sohn CH (2012) Intra-industry trade and industry distribution of productivity: a Cournot–Ricardo approach. World Econ 35(4):461–482
Tabrizy S (2020) Industrial Research and Development and real exchange rate depreciation in a small open economy. World Econ 43(9):2490–2523
UNIDO (United Nations Industrial Development Organization) (2017) Industrial Statistics Database: INDSTAT4–2016 Edition at the 3- and 4-digit Level of ISIC Revision 3 and ISIC Revision 4. Retrieved from https://stat.unido.org. Accessed 20 Jan 2017
United Nations Statistics Division. (2002) International standard industrial classification of all economic activities, Rev. 3. Retrieved from https://unstats.un.org/unsd/statcom/doc02/isic.pdf. Accessed 20 Jan 2017
Veeramani C (2009) Trade barriers, multinational involvement and intra-industry trade: panel data evidence from India. Appl Econ 41(20):2541–2553
Wooldridge JM (2002) Econometric analysis of cross section and panel data. MIT press, Cambridge
World Bank (2017) World Integrated Trade Solution. Retrieved from http://wits.worldbank.org. Accessed 20 Jan 2017
World Bank Data (2019a) Exports of Goods and Services (current US$), World Bank National Account Data and OECD National Account Data Files. Retrieved from https://data.worldbank.org/indicator/NE.EXP.GNFS.CD. Accessed 2 Apr 2019
World Bank Data. (2019b) GDP (constant 2010 US$) World Bank National Account Data and OECD National Account Data Files. Retrieved from https://data.worldbank.org/indicator/NY.GDP.MKTP.KD. Accessed 2 Apr 2019
Yoshida Y (2013) Market share and exchange rate pass-through competition among exporters of the same nationality. In: RIETI Disc. Pap. Ser. 13-E-084
We would like to thank the editor and three anonymous referees for their advice and comments. Also, the dataset in use is in part acquired through the University of Oklahoma Libraries Data Grant – we are grateful for the grant. Lastly, we would like to thank Darron Arnold, Shyh Yao Chea, Robert Press, Amir Tayebi, and Zhengag Xu for helpful research assistance.
The dataset in use is in part acquired through the University of Oklahoma Libraries Data Grant.
Conflicts of Interest/Competing Interests
The authors have no conflicts of interest or competing interests.
Availability of Data and Material
The data in use are organized by third parties including the United Nations Industrial Development Organization, Organization for Economic Co-operation and Development, Research Institute of Economy, Trade, and Industry, World Bank, and International Monetary Fund.
The authors provide the estimation codes upon request.
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
About this article
Cite this article
Neumann, R., Tabrizy, S.S. Exchange Rates and Trade Balances: Effects of Intra-Industry Trade and Vertical Specialization. Open Econ Rev (2021). https://doi.org/10.1007/s11079-020-09612-4
- Trade balance
- Exchange rate
- Intra-industry trade
- Vertical specialization
JEL Classification Codes