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Impact of exchange rate volatility on the commodity trade between Pakistan and the US

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Abstract

The impact of exchange rate volatility on the trade flows has been intensively studied by researchers after the adoption of flexible exchange rate system. Different studies have used data from different countries at different aggregation level. In this paper we consider the experiences of 57 Pakistan’s exporting to and 52 importing industries from the US over the period 1980–2014. We find that almost 50 % of the industries are affected by exchange rate volatility in the short-run. However, short-run effects last into the long run only in 26 exporting industries and 18 importing industries. Due to such disaggregation we find that all four large exporting and importing industries are affected positively in the long run. The list includes industries coded 656 (made up articles with 32.45 % of exports share), 841 (clothing with 50.7 % exports share), 263 (cotton with 8.64 % import share) and 282 (iron and steel scrap with 2.10 % imports share).

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Notes

  1. https://www.uschamber.com/sites/default/files/legacy/reports/0903_uspakistan_opt.pdf.

  2. See Sauer and Bohara (2001, p. 133). Note that LDCs stands for Less Developed Countries.

  3. Note that due to different sample size, the level of exchange rate volatility was found to be I(1) by Doganlar (2002) who was then able to apply Engle–Granger method which requires all variables in a given model to be I(1). The same method was used by Saqib and Sana (2012) with the same outcome that aggregate exports of Pakistan are adversely affected by exchange rate volatility.

  4. While most studies have concentrated on Pakistan’s exports, Alam and Ahmad (2010) considered Pakistan’s imports. After using cointegration analysis they did not find any significant impact of exchange rate volatility on Pakistan’s total imports.

  5. Hassan (2013) is another study that considered response of total imports and total exports of Pakistan to volatility in exchange rate of major partners such as the US, UK and UAE. Once the export and import demand models were estimated, volatility of rupee–dollar had no significant impact but the other two exchange rate volatility measure had significantly positive impact.

  6. For other applications of this method see For some other applications of this approach see Halicioglu (2007, 2013), Narayan et al. (2007), Tang (2007), Mohammadi et al. (2008), Wong and Tang (2008), De Vita and Kyaw (2008), Payne (2008), Dell’Anno and Halicioglu (2010), Chen and Chen (2012), Shahbaz et al. (2012), Wong (2013), Bahmani-Oskooee and Zhang (2014), Hajilee and Al-Nasser (2014), and Tayebi and Yazdani (2014).

  7. These findings are consistent with other studies which have engaged in analyzing trade flows at commodity level, e.g., Bahmani-Oskooee et al. (2015) who conducted similar analysis for Egypt–US commodity trade flows and Bahmani-Oskooee and Wang (2007) who did it for the US–China commodity trade.

  8. The remaining industries that react adversely to rupee depreciation are 612, 651, 652, 656, 841, 891, 897 and 899.

  9. See Banerjee et al. (1998, Table 1, p. 276).

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

  11. These findings are similar to those of Bahmani-Oskooee and Wang (2007) who considered the case of US–China commodity trade, Bahmani-Oskooee and Hegerty (2009) who did similar analysis for the case of US–Mexico, Bahmani-Oskooee et al. (2015) who did the same for US–Egypt commodity trade and Bahmani-Oskooee and Durmaz (2016) for the case of US–Turkey except that all large industries in this paper are affected positively by exchange rate uncertainty.

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Acknowledgments

Valuable comments of an anonymous referee and those of the editor are greatly appreciated.

Author information

Authors and Affiliations

  1. Department of Economics, The University of Wisconsin-Milwaukee, Milwaukee, WI, USA

    Mohsen Bahmani-Oskooee

  2. School of Economics, Quaid-i-Azam University, Islamabad, Pakistan

    Javed Iqbal & Saqib Ullah Khan

Authors
  1. Mohsen Bahmani-Oskooee
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  2. Javed Iqbal
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  3. Saqib Ullah Khan
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Corresponding author

Correspondence to Mohsen Bahmani-Oskooee.

Appendix: data and sources

Appendix: data and sources

Annual data over the period of 1980–2014 are used to carry out the estimation. They come from the following two sources.

  1. (a)

    The World Bank WITS system (World integrated trade solutions).

  2. (b)

    International financial statistics (IMF data).

1.1 Variables

X i :

Pakistan’s export volume of industry i to the US. Nominal figures come from source a. In the absence of annual price level for each industry, we follow Bahmani-Oskooee and Ardalani (2006) and deflate each industry trade value by the Pakistan’s export unit value. The data of exports unit value come from source b

M i :

Pakistan’s import volume of industry i from the US. Nominal import data for each industry come from source a. In the absence of annual import price level for each industry, again, we follow Bahmani-Oskooee and Ardalani (2006) and deflate each industry import value by the Pakistan’s import unit value index. Pakistan’s import unit value index comes from source b

Y US :

The US real GDP, source b

Y PAK :

Pakistan’s real GDP, source b

REX :

The real bilateral exchange rate between Pakistani rupee and the US dollar. It is defined as (CPIUS × NEX)/CPIPAK, where NEX is the nominal bilateral exchange rate defined as number of rupees per dollar, CPIpak is the price level in Pakistan and CPIUS, the price level in the US. Data on all three variables come from source b

V :

Volatility of the real bilateral exchange rate, REX. Following Bahmani-Oskooee and Hegerty (2009), V is calculated as standard deviation of the 12 monthly real exchange rate within that each year. Monthly CPI data for both countries and the nominal exchange rate data come from source b

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Bahmani-Oskooee, M., Iqbal, J. & Khan, S.U. Impact of exchange rate volatility on the commodity trade between Pakistan and the US. Econ Change Restruct 50, 161–187 (2017). https://doi.org/10.1007/s10644-016-9187-9

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  • DOI: https://doi.org/10.1007/s10644-016-9187-9

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