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Exchange rate volatility and commodity trade between the U.S. and the Philippines

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Abstract

ASEAN, the Association of Southeast Asian Nations, was founded in 1967 and the Philippines is one of the founding members. Since it relies upon floating exchange rates, its trade flows could be affected by uncertainty introduced by such rates. Previous studies used aggregate trade flows of the Philippines with the rest of the world and did not find any significant effects of exchange rate uncertainty on its trade flows. Suspecting that those studies suffer from aggregation bias, in this paper we concentrate on its trade flows with its major partner, the U.S., and disaggregate these flows by commodity. We then investigate the sensitivity of 133 U.S. exporting industries to the Philippines and 64 U.S. importing industries from the Philippines to exchange rate uncertainty. We find that almost half of the commodity flows are affected in the short-run. The short-run effects last into the long run in 50 % of the affected industries.

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Notes

  1. This information comes from the official website of ASEAN: http://www.asean.org/ in general and ASEAN Community in Figures (ACIF) 2013) and ASEAN Community Chart Book 2013 and 2014.

  2. For definitions of both the nominal bilateral rate, NEX, and the real bilateral exchange rate, REX, see the Appendix.

  3. Again, the F test must be applied to establish joint significance of lagged level variables as a sign of cointegration.

  4. For other applications of this approach see Wong and Tang (2008), Payne (2008), and Wong (2013).

  5. Note that due to the volume of the results, we only report the short-run estimates for our variable of concern, i.e., the volatility measure of real bilateral exchange rate, VOL.

  6. Note that at the 10 % significance level the lower and upper bound critical values, where we have 40 observations and three exogenous variables are 2.93 and 4.02 respectively. These come from Narayan (2005, Case III, p. 1988). Comparable figures for large samples from Pesaran et al. (2001, Table CI-Case III, p. 300) are 2.72 and 3.77.

  7. Note that while in the industry coded 001, 34 % of adjustment takes place within one year (since data are annual), in the industry coded 042 almost 75 % of the adjustment takes place within 6 months.

  8. For a graphical presentation of the CUSUM and CUSUMSQ tests see Bahmani-Oskooee et al. (2005).

  9. Note that in estimating model (2) we included a dummy variable to account for the Asian Financial Crisis of 1997. Only in 14 % of the cases the dummy was significant. The affected U.S. export industries were: 011, 091, 121, 221, 512, 515, 531, 652, 655, 663, 693, 712, 724 and 851.

  10. Once again, in estimating the import error-correction model (4), a dummy variable was included to account for the financial crisis of 1997. The dummy was significant in industries coded 054, 061, 072, 099, 111, 121, 291, 612, 641, 651, 652, 655, 665, 696, 698,726, 831, 842, 893, 894 and 899.

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Author information

Authors and Affiliations

  1. The Center for Research on International Economics and Department of Economics, The University of Wisconsin-Milwaukee, Milwaukee, WI, 53201, USA

    Mohsen Bahmani-Oskooee

  2. Department of Economics, Pennsylvania State University, Mont Alto, Mont Alto, PA, 17237, USA

    Hanafiah Harvey

Authors
  1. Mohsen Bahmani-Oskooee
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  2. Hanafiah Harvey
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Corresponding author

Correspondence to Mohsen Bahmani-Oskooee.

Additional information

Valuable comments of an anonymous referee are greatly appreciated. Remaining errors, however, are ours.

Appendix data definitions and sources

Appendix data definitions and sources

All data are annual (1973–2012) and come from the following sources:

  1. (a)

    The World Bank.

  2. (b)

    The International Financial Statistics of the IMF.

Variables:

  • X i US = Volume of exports of commodity i by the U.S. to the Philippines. Export value data in dollars for each commodity come from source a. In the absence of the export price for an individual commodity we follow Bahmani-Oskooee et al. (2013) and use the aggregate export price index for the U.S. and deflate the nominal exports of each commodity. The aggregate export price index comes from source b.

  • M i US = Volume of imports of commodity i by the U.S. from the Philippines. Import value data for each commodity comes from source a. Again, in the absence of the import price of an individual commodity we use aggregate import price index of the U.S. and deflate the nominal imports of each commodity. The aggregate import price index comes from source b.

  • Y US = Measure of the United States income. It is proxied by real GDP. The data come from source b.

  • Y PH = The Philippines’s real GDP. Data come from source b.

  • REX = Real bilateral exchange rate between U.S. dollar and the Philippines peso. It is defined as (PUS * NEX/ PPH), where PUS is U.S. CPI, PPH is the Philippines’s CPI, and NEX is the nominal bilateral exchange rate defined as the number of Philippines’ peso per U.S. dollar. Thus, a decline in REX reflects a real depreciation of the U.S. dollar. Data for NEX, PPH, and PUS come from source b.

  • VOL = Volatility measure of the real bilateral exchange rate (REX). Following Bahmani-Oskooee et al. (2013), it is defined as the standard deviation of the 12 monthly real exchange rate (REX) within each year. All monthly data come from source b.

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Bahmani-Oskooee, M., Harvey, H. Exchange rate volatility and commodity trade between the U.S. and the Philippines. Int Econ Econ Policy 14, 263–291 (2017). https://doi.org/10.1007/s10368-016-0336-4

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  • DOI: https://doi.org/10.1007/s10368-016-0336-4

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