Exchange Rates and Trade Balances: Effects of Intra-Industry Trade and Vertical Specialization

Abstract

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.

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Notes

  1. 1.

    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.

  2. 2.

    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.

  3. 3.

    These findings are consistent with theoretical models in which demand elasticity is declining in firms’ performance (e.g., Melitz and Ottaviano 2008).

  4. 4.

    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.

  5. 5.

    Hong Kong and Macao’s data are excluded from the industry-level observations for China.

  6. 6.

    Figures A3 and A4 in the appendix show the histogram of IIT and VS variations across all country-industry pairs. Figure A5 shows the scatterplot for industry-specific medians of time-varying IIT and VS, by country.

  7. 7.

    We are grateful to an anonymous referee for raising this point.

  8. 8.

    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.

  9. 9.

    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.

  10. 10.

    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.

  11. 11.

    Industry-specific exchange rate measures are increasingly used in empirical research in international economics. Recent examples include Tabrizy (2020) and Nguyen and Sato (2020).

  12. 12.

    See Chinn (2006) for a primer on calculating different measures of effective exchange rates at the country level.

  13. 13.

    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.

  14. 14.

    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).

  15. 15.

    The industry-level output data for these countries are not available in the OECD STAN dataset.

  16. 16.

    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).

  17. 17.

    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.

  18. 18.

    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.

  19. 19.

    Berman et al. (2012) make use of similar interaction terms, exploring the effect of heterogeneities that stem from firm- and sector-level productivity. Ahmed et al. (2015) also use similar interactions in exploring the effect of global value chains.

  20. 20.

    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.

  21. 21.

    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).

  22. 22.

    This coefficient is significant at 5% level of significance when we do not cluster our standard errors.

  23. 23.

    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.

  24. 24.

    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.

  25. 25.

    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.

  26. 26.

    See Figs. A3 and A4 for the histogram of variations in IIT and VS measures among the above observations.

  27. 27.

    We are grateful to an anonymous referee for raising this point.

  28. 28.

    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.

  29. 29.

    Across all industry-year pairs, about 36% of OECD industry-specific outputs is made up of the US output.

  30. 30.

    We obtain similar results when we include an OECD industry output control that excludes US output.

  31. 31.

    Detailed confidence interval estimations are available upon request.

  32. 32.

    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).

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Acknowledgements

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.

Funding

The dataset in use is in part acquired through the University of Oklahoma Libraries Data Grant.

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Correspondence to Saleh S. Tabrizy.

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The authors have no conflicts of interest or competing interests.

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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.

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The authors provide the estimation codes upon request.

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Appendix 1

Appendix 1

Table A1 Pairwise Correlation Coefficients for IIT and VS
Fig. A1
figure10

Industry-specific Trade Balances

Fig. A2
figure11

Histogram of Variations in Trade Balances

Fig. A3
figure12

Histogram of variations in IIT among the observations that are included in Table 4, column VI

Fig. A4
figure13

Histogram of variations in VS among the observations that are included in Table 4, column VI

Fig. A5
figure14

Industry-specific Median IIT and VS

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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

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Keywords

  • Trade balance
  • Exchange rate
  • Intra-industry trade
  • Vertical specialization

JEL Classification Codes

  • F14
  • F31
  • F41