Register  |  Login  |  Inquiries  |  Sitemap
Advanced Search
Journal of Economic Integration 2013 December;28(4) :533-550.
DOI: https://doi.org/10.11130/jei.2013.28.4.533
Do Fixed Exchange Rates Cause Greater Integration?
Amr Sadek Hosny 
University of Wisconsin-Milwaukee, Milwaukee, U. S. A.
Corresponding Author: Amr Sadek Hosny ,Tel: +1 4142294375, Fax: +1 4142293860, Email: amrsadek@uwm.edu
Copyright ©2013 Journal of Economic Integration
ABSTRACT
A classic argument in favor of a fixed exchange rate regime (ERR) has been the promotion of international trade between the pegging country and its base country. Results from previous literature point to a significant and highly positive effect of adopting a fixed ERR on bilateral trade between any given country pair. In this paper, it is argued that these results should not be interpreted as causal effects, since countries do not typically choose their ERR independently of their trade flows. This source of selection bias can be greatly reduced by using the matching approach in estimating treatment effects. Estimates of the effect of fixed ERR using this procedure are close to the ordinary least squares estimates reported in the literature, suggesting that there is little bias in these conventional estimates. These findings are robust to using different propensity score matching methods.

JEL Classification
C21: Cross Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions
F31: Foreign Exchange
F41: Open Economy Macroeconomics
Keywords: Exchange Rate Regimes | International Trade | Causality | Matching Estimators | Treatment Effect | Gravity Equation
 
REFERENCE
1. Abadie, A., D. Drukker, J. Herr, and G. Imbens. 2004. Implementing matching estimators for average treatment effects in Stata. The STATA Journal 4 (3): pp. 290-311.
2. Abadie, A. and G. Imbens. 2002. Simple and bias-corrected matching estimators. Technical Report, Department of Economics, University of California, Berkley.
3. Adam, C. and D. Cobham. 2007. Exchange rate regimes and trade. The Manchester School 75 (Supplement1): pp. 44-63.
4. Baranga, T. 2010. Estimating the effects of fixed exchange rate regimes on trade: Evidence from the formation of the Euro. Working Paper.
5. Becker, S. and A. Ichino. 2002. Estimation of average treatment effects based on propensity scores. The STATA Journal 2 (4): pp. 358-377.
6. Bergin, P. and Ching-Yi Lin. 2008. Exchange rate regimes and the extensive margin of trade. NBER Working Paper no. 14126. National Bureau of Economic Research, Cambridge: MA.
7. Caliendo, M. and S. Kopeinig. 2008. Some practical guidance for the implementation of propensity score matching. Journal of Economic Surveys 22 (1): pp. 31-72.
8. Cochran, W. 1968. The effectiveness of adjustment by subclassification in removing bias in observational studies. Biometrics 24 (2): pp. 295-314.
9. Feenstra, Robert C., James R. Markusen, and Andrew K. Rose. 2001. Using the gravity equation to differentiate among alternative theories of trade. Canadian Journal of Economics 34 (2): pp. 430-47. Glick, R. and A. Rose. 2002. Does a currency union affect trade? The time series evidence. European Economic Review 46 (2002): pp. 1125-1151.
10. Heckman, J. and R. Robb. 1985. Alternative models for evaluating the impact of interventions. In J. Heckman and B. Singer (eds), Longitudinal Analysis of Labor Market Data (pp. 156–245). Cambridge: Cambridge University Press.
11. Heckman, J., H. Ichimura and P. Todd. 1997. Matching as an econometric evaluation estimator: Evidence from evaluating a job market programme. Review of Economic Studies 64 (4): pp. 605-654. Heckman, J., H. Ichimura and P. Todd. 1998. Matching as an econometric evaluation estimator. Review of Economic Studies 65: pp. 261-294.
12. Holland, P. W. 1986. Statistics and causal inference. Journal of the American Statistical Association 81: pp. 945-970.
13. Klein, M. and J. Shambaugh. 2006. Fixed exchange rates and trade. Journal of International Economics 70 (2006): pp. 359-383.
14. Lechner, M. 1999. Earnings and employment effects of continuous off-the-job training in East Germany after unification. Journal of Business & Economic Statistics 17 (1): pp.74–90.
15. Morgan, S. and D. Harding. 2006. Matching estimators of causal effects: Prospects and pitfalls in theory and practice. Sociological Methods & Research 35 (1): pp. 3-60.
16. Nichols, A. 2007. Causal inference with observational data. The STATA Journal 7 (4): pp. 507-541.
17. Rose, A. 2000. One money, one market: The effect of common currencies on trade. Economic Policy 15 (30): pp. 9-45.
18. Rose, A. 2001. Currency unions and trade: The effect is large. Economic Policy 16 (33): pp. 449-461.
19. Rose, A. 2004. Do we really know that the WTO increases trade? The American Economic Review 94 (1): pp. 98-114.
20. Rose, A. and M. Spiegel. 2011. The Olypic effect. The Economic Journal 121 (553): pp. 652–677.
21. Rosenbaum, P. and D. Rubin. 1983. The central role of the propensity score in observational studies for causal effects. Biometrika 70 (1): pp.41–50.
22. Rubin, D. 1973a. Matching to remove bias in observational studies. Biometrics 29 (1): pp. 159-183.
23. Rubin, D. 1973b. The use of matching sampling and regression adjustment to remove bias in observational studies. Biometrics 29 (1): pp. 185-203.
24. Shambaugh, J. 2004. The effect of fixed exchange rates on monetary policy. The Quarterly Journal of Economics 119 (1): pp. 301-352.
25. Smith, J. and P. Todd. 2005. Does matching overcome LaLonde’s critique of nonexperimental estimators? Journal of Econometrics 125: pp. 305-353.
Editorial Office
Center for Economic Integration, Sejong Institution, Sejong University, 209, Neungdong-Ro, Gwangjin-Gu,
Seoul, 05006, Korea
TEL : +82-2-3408-3338    FAX : +82-2-3408-3338   E-mail : jei@sejong.ac.kr
Browse Articles |  Current Issue |  For Authors and Reviewers |  About
Copyright© by Center for Economic Integration. All right reserved.