Abstract
This paper examines the impact of trade costs on real exchange rate volatility. The relationship is examined by constructing a two-country Ricardian model of trade, based on the work of Dornbusch, Fischer, and Samuelson (1977), which shows that higher trade costs result in a larger nontradables sector, in turn leading to higher real exchange rate volatility. We then construct a remoteness index to proxy for trade costs, and provide empirical evidence supporting the channel.
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Notes
In Bravo-Ortega and di Giovanni (2005), we highlight a different mechanism through which trade costs affect real exchange rate volatility. In this paper, the impact of heterogeneous suppliers of traded goods on real exchange rate volatility is examined using a multicountry model of trade.
In Hau’s paper, the size of the nontraded goods sector is fixed, and there is only one traded good.
Naknoi (2004) has also examined a similar channel in a dynamic general equilibrium framework. However, her work concentrates on short-run dynamics, whereas we argue that endogenous nontradability should be modeled in a long-run context. Furthermore, we provide direct evidence to test the hypothesis drawn from our model.
The results can be obtained using the more general constant elasticity of substitution (CES) function, but the CES function greatly complicates the algebra. Therefore, the more specific function (that is, logarithmic) is used for clarity.
The assumption of independent productivity shocks, that is, Cov(ε, ε*) = 0, may seem strong. However, the assumption does not alter our main result. If there were covariance in the shocks, one extra term would be added.
Note that similar conditions will hold ex post.
For more details, see Obstfeld and Rogoff (1996).
These assumptions allow us to introduce uncertainty in a tractable manner.
Note that, as argued in the previous footnote, the assumption of independent domestic and foreign shocks does not alter our results. Given the setup of the model, the solution for real exchange rate volatility, equation (7), would have the additional term Cov(ε, ε*)(zH − zF). Therefore, volatility will always increase as trade costs increase.
We also experimented with fixed versus floating exchange rate dummies, but our results were robust to the inclusion of these variables.
We include some countries that experience hyperinflation, such as Bolivia (BOL), Uganda (UGA), and the Democratic Republic of the Congo (ZAR), where exchange rate volatility is very high owing to a short period of time. However, if anything, including these countries will bias our estimation away from finding a strong relationship between volatility and remoteness.
Taking the volatility of the log change has two advantages over taking the volatility of the log level: (1) the resulting measure is invariant to the country, and (2) the measure allows us to interpret the coefficients in the regressions essentially as elasticities.
The income grouping is based on the World Bank’s Atlas method. Further information and the country groups can be found at http://www.worldbank.org/data/countryclass/classgroups.htm
Note that in reporting subsample analysis, we only include measures of import and export taxes for robustness checks. However, Remoteness is robust to the inclusion of all the other controls.
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The authors would like to thank Müge Adalet, Barry Eichengreen, Miguel Fuentes, Pierre-Olivier Gourinchas, Gian Maria Milesi-Ferretti, Maurice Obstfeld, Andrew Rose, an anonymous referee, and the Editor, Bob Flood, for comments. We thank Susanne Winkler for research assistance.
Appendix
Appendix
Two-Country Real Exchange Rate Volatility
The variance of the real exchange rate can be expressed as follows:
where we have used the fact that only e and ε* are stochastic and that zF and zH remain fixed after shocks are realized.