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Asymmetric J-curve in the commodity trade between Pakistan and United States: evidence from 41 industries

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Abstract

All previous studies that have assessed the J-curve phenomenon for Pakistan have relied upon methods that assume that the adjustment of the exchange rate follows a linear path. In this paper, we consider the trade balance of 41 industries that trade between Pakistan and one of its major partners, the US, and extend the literature by assuming that the exchange rate adjusts in a nonlinear manner. When we estimated a linear ARDL model for each industry, we found support for the J-curve only in 10 industries. However, when we estimated a nonlinear ARDL model, we found support for the asymmetric J-curve in 19 industries. In 8 industries, the support was due to favorable effects of rupee depreciation, and in 11 industries, it was due to unfavorable effects of rupee appreciation.

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Notes

  1. For details of the J-curve theory, see Magee (1973) and for its first empirical test using a traditional method, see Bahmani-Oskooee (1985, 1989).

  2. The idea initially was introduced by Bahmani-Oskooee and Fariditavana (2016) who used trade data from G7 countries to show asymmetric effects of exchange rate changes on the trade balance.

  3. As discussed by Bahmani-Oskooee (1991) defining the trade balance as a ratio not only allows us to express the model in log form, but also it allow us to use a unit-free measure of the trade balance.

  4. Note that as argued by Bahmani-Oskooee (1986) if increase in domestic output is due to an increase in production of import-substitute goods, then an estimate of β2 could be positive and that of β3 could be negative.

  5. Note that since an increase in RER reflects rupee depreciation, partial sum of positive changes will also reflect rupee depreciation, denoted by DEP.

  6. Indeed, Shin et al. (2014) argue that the critical values of the F test should stay the same for both models at high and conservative level.

  7. For some other applications of these concepts, see Durmaz (2015), Lima et al. (2016), Nusair (2017), Aftab et al. (2017) and Arize et al. (2017).

  8. As mentioned, both models require that no variable must be I(2). We had to confirm this by applying the ADF test and indeed, all 2nd-differenced variables were stationary.

  9. If Pakistan’s import demand is inelastic, rupee depreciation will raise cost of imports, again leading to a decline in the trade balance.

  10. Note that critical values provided by Pesaran et al. (2001) are for large samples. Banerjee et al. (1998) who introduced this test within the Engle-Granger cointegration method tabulate these values for small samples.

  11. As for the long-run effects of economic activity, the U.S. real GDP carries a significant coefficient in 23 cases and that of Pakistan in 21 industries. Note that as mentioned earlier, estimates attached to income could be positive or negative.

  12. Again, like the linear models, real GDP variables carry significant coefficient in almost half of the industries. Furthermore, other diagnostic statistics are similar to those of the linear models in Table 2 and need no repeat here.

  13. Note that there are four more industries in which the RAPP variables carried a significantly positive and meaningful coefficient, placing these four industries among those whose trade balance could be hurt by rupee appreciation. We did not include these in the list of asymmetric J-curve to avoid double-counting since in these industries the RDEP variable also carries a significantly positive and meaningful coefficient. These four industries are 612, 657, 663, and 851. Leaving aside the J-curve concept, a total of 14 industries will be hurt by rupee appreciation.

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Authors and Affiliations

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

    Mohsen Bahmani-Oskooee

  2. Department of Economics, Government College University Faisalabad, Faisalabad, Pakistan

    Ahmed Usman

  3. School of Economics, Quiad-e-Azam University, Islamabad, Pakistan

    Sana Ullah

Authors
  1. Mohsen Bahmani-Oskooee
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  2. Ahmed Usman
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  3. Sana Ullah
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Correspondence to Mohsen Bahmani-Oskooee.

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Sana Ullah: Thanks are to an anonymous reviewer whose comments improved our paper. Any error left in the paper is our own.

Appendix: Data set and variables

Appendix: Data set and variables

Annual data over the period 1978–2017 is used to carry out the empirical exercise. The data comes from the following sources:

  1. (a)

    World Bank (World Integrated Trade System, WITS)

  2. (b)

    International Financial Statistics

1.1 Variables

TBi = trade balance of industry i defined as the ratio of Pakistan’s exports of industry i to the US over the same industry’s imports from the United States. Data for all 41 industries comes from source a.


\(Y^{PAK}\) = Real GDP of Pakistan. Data comes from source b.


\(Y^{US}\) = Real GDP of the US Data comes from source b.


RER = The real bilateral exchange rate between rupee and the dollar defined as RER = (\(CPI^{US}\) * NER/ \(CPI^{PAK}\)) where NER is the nominal exchange rate defined as number of rupees per US dollar, CPIUS is the Consumer Price Index in the US and CPIPAK is the Consumer Price Index in Pakistan. Hence, an increase in RER signifies a real depreciation of the Rupee against the dollar. Data on nominal exchange rate NER and CPI for both countries come from source b.

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Bahmani-Oskooee, M., Usman, A. & Ullah, S. Asymmetric J-curve in the commodity trade between Pakistan and United States: evidence from 41 industries. Eurasian Econ Rev 10, 163–188 (2020). https://doi.org/10.1007/s40822-019-00137-x

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  • DOI: https://doi.org/10.1007/s40822-019-00137-x

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