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De Facto exchange rate arrangement tightness and bilateral trade flows
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Economics Letters 99 (2008) 228 – 232 www.elsevier.com/locate/econbase
De Facto exchange rate arrangement tightness and bilateral trade flows Peter Egger ⁎,1 Ifo Institute, University of Munich, CESifo, and GEP at the University of Nottingham, Germany Ifo Institute for Economic Research, Poschingerstrasse 5, 81679 Munich, Germany Received 24 October 2006; received in revised form 4 April 2007; accepted 8 May 2007 Available online 18 May 2007
Abstract This paper estimates the effect of switching into and out of 14 different modes of exchange rate arrangements on bilateral exports between 1948 and 2001 in the world economy. Increasing the exchange rate arrangement tightness by one degree in the classification of Reinhart and Rogoff [Reinhart, Carmen M. and Kenneth S. Rogoff (2004), The Modern History of Exchange Rate Arrangements: A Reinterpretation, Quarterly Journal of Economics 119, 1–48.] increases bilateral trade flows by about 2–4% as compared to their initial level. © 2007 Published by Elsevier B.V. Keywords: Exchange rate arrangements; Bilateral trade flows
1. Introduction The first paper on the impact of common currencies on trade flows by Rose (2000, 2001), initiated a vivid research activity on that nexus. A key finding in Rose's piece was that, for the average country-pair, a common currency leads to a higher bilateral trade volume. While there is some debate about the magnitude of that effect, it seems quite robust in qualitative terms (see Glick and Rose, 2002; Rose and Stanley, 2005). This paper aims at contributing to this literature on exchange rate arrangements and trade, yet its scope differs from previous work. The main difference lies in the departure from three commonly adopted assumptions: first, that exchange rate arrangements notified to the International Monetary Fund are identical to ones that countries actually apply; second, that the effect of a particular currency arrangement on bilateral trade can be estimated by comparing the level or change in trade flows of the ‘treated’ country-pairs (e.g., ones with a currency union or a currency board in place) to all pairs, where this particular arrangement was not in place, ignoring that the trade flows among these pairs might have been affected by other, less tight modes of exchange rate arrangements at the same time; third, that ‘multilateral’ effects of ‘bilateral’ arrangements can be ⁎ Tel.: +49 89 9224 1238. E-mail address: [email protected]. 1 Helpful comments by an anonymous referee are gratefully acknowledged. 0165-1765/$ - see front matter © 2007 Published by Elsevier B.V. doi:10.1016/j.econlet.2007.05.021
ignored (see Anderson and van Wincoop, 2003, for a rigorous theoretical and empirical treatment of multilateral effects of trade impediments on bilateral trade in general and Rose and van Wincoop, 2001, for an application in the context of estimating currency union effects on trade). A recent paper by Reinhart and Rogoff (2004) re-writes the history of exchange rate arrangements and identifies significant discrepancies between actual and notified exchange rate arrangements. They introduce a new classification that differentiates among 14 modes of arrangements. I will rely on their classification to abandon the first one of the above stated assumptions in previous work. For convenience, I provide the classification in the Appendix. This allows exploiting the full range of variation in exchange rate arrangements to estimate the average impact from switching among two arbitrary arrangements on bilateral trade, thereby abandoning the second of the mentioned assumptions in previous work. Finally, I rely on a Taylor-approximated version of Anderson and van Wincoop's (2003) model with multilateral resistance (i.e., multilateral effects on bilateral trade) as derived by Baier and Bergstrand (2006). An explicit modeling of multilateral resistance – rather than an implicit one by including fixed exporter-by-year and importer-by-year effects in a panel data model – is required for inference about the quantitative effects of exchange rate arrangements on bilateral trade. Here, we additionally account for the influence of all possible time-invariant determinants by using a fixed country-pair effects estimator.
P. Egger / Economics Letters 99 (2008) 228–232
2. The empirical model With cross-sectional data Anderson and van Wincoop's model motivates an empirical specification based on log nominal bilateral exports ( ln Xij) as the dependent variable and the following set of explanatory variables: a constant (α); log exporter and importer GDP as two separate variables (ln Yi, ln Yj ; hypothesizing unitary parameters for both of them); a set of trade friction variables in logs – for dummy variables such as regional trade agreements, exchange rate arrangements, adjacency, etc., the impact of the k'th dummy variable on trade is (∂ln Xij / ∂ln eβk dk,ij); and multilateral resistance terms for both the exporter and the importer (Pi, Pj), which are also known as consumer price index terms with models of monopolistic competition. Anderson and van PN Wincoop make use of two crucial insights, namely that j¼1 Xij ¼ Yi – hence, total exports of a country sum up to GDP is local sales are included as well – and that the multilateral resistance terms are non-linear known functions of trade frictions (e.g., the aforementioned k different dummy variables reflecting regional trade agreements, exchange rate arrangements, adjacency, etc.). Based on that, they derive an empirical log-non-linear estimation framework to estimate the impact of trade frictions on trade. Baier and Bergstrand approximate this system to obtain a log-linear model that also accounts for multilateral resistance but is easier to implement. They illustrate that their approximation obtains estimates that are very close to the ones of the Anderson and van Wincoop model in typical empirical applications. Let me
229
assume without loss of generality that there are only tradefriction-related dummy variables involved (in practice, the most important continuous friction variable would be bilateral distance, yet in the model applied here all time-invariant frictions are captured by the fixed country-pair effects). Taylor-approximating the multilateral resistance terms around an equilibrium with positive but symmetric trade frictions across country-pairs, Baier and Bergstrand derive the following linear regression model: ln Xij ¼ a þ
K X
bk d˜k;ij þ Yi þ Yj þ uij
ð1Þ
k¼1
N N N X N 1X 1X 1 X d˜k;ij ¼ dk;ij dk;ij dk;ij þ 2 dk;ij N i¼1 N j¼1 N i¼1 j¼1 ð1Þ
Where K is the number of trade friction dummy variables, N denotes the number of countries in the data, and uij is an error term. With panel data this model becomes: ln Xijt ¼ at þ
K X
bk d˜k;ijt þ Yit þ Yjt þ uijt
k¼1
Nt Nt Nt X Nt 1X 1X 1 X d˜k;ijt ¼ dk;ijt dk;ijt dk;ijt þ 2 dk;ijt Nt it¼1 Nt jt¼1 Nt it¼1 jt¼1
ð2Þ where Nt is the number of countries covered in year t, and uijt = μij + νijt with μij denoting fixed country-pair effects that may
Table 1 The impact of exchange rate arrangements on bilateral exports (1948–2001) Explanatory variable
No multilateral resistance Model A
Arrangement 1 Arrangement 2 Arrangement 3 Arrangement 4 Arrangement 5 Arrangement 6 Arrangement 7 Arrangement 8 Arrangement 9 Arrangement 10 Arrangement 11 Arrangement 12 Arrangement 13 Arrangement 14 Regional trade agreement Log exporter GDP Log importer GDP Observations Country-pairs Total R2 Within R2 F-tests (F-statistics): Fixed country-pairs Fixed annual effects
With multilateral resistance Model B
Model C
Model D
(Contemporaneous effects)
(Contemporaneous effects)
(Including lagged effects)
(Poisson, fixed effects)
Coefficient
t-statistics
Coefficient
t-statistics
Coefficient
F-statistics
Coefficient
z-statistics
0.507 0.199 0.337 0.127 0.297 0.123 0.160 0.086 0.301 0.021 0.233 0.066 0.074 −0.048 0.484 0.539 0.891 238,159 14,360 0.849 0.357
13.99⁎⁎⁎ 13.00 ⁎⁎⁎ 3.30 ⁎⁎⁎ 5.89 ⁎⁎⁎ 2.58 ⁎⁎⁎ 2.24 ⁎⁎ 6.31 ⁎⁎⁎ 6.73 ⁎⁎⁎ 5.20 ⁎⁎⁎ 1.31 5.11 ⁎⁎⁎ 4.80 ⁎⁎⁎ 5.05 ⁎⁎⁎ −4.33 ⁎⁎⁎ 33.26 ⁎⁎⁎ 53.68 ⁎⁎⁎ 81.67 ⁎⁎⁎
0.564 0.206 0.284 0.149 0.343 0.147 0.136 0.116 0.376 0.019 0.185 0.103 0.034 − 0.074 0.374 0.550 0.905 238,159 14,360 0.849 0.355
12.81 ⁎⁎⁎ 11.82 ⁎⁎⁎ 2.34 ⁎⁎ 6.12 ⁎⁎⁎ 2.73 ⁎⁎⁎ 2.41 ⁎⁎ 4.81 ⁎⁎⁎ 8.04 ⁎⁎⁎ 5.28 ⁎⁎⁎ 1.01 3.71 ⁎⁎⁎ 6.39 ⁎⁎⁎ 1.51 − 2.06 ⁎⁎ 23.42 ⁎⁎⁎ 55.44 ⁎⁎⁎ 83.80 ⁎⁎⁎
0.937 0.246 0.388 0.160 0.327 0.085 0.138 0.142 0.609 0.036 0.296 0.127 0.029 − 0.134 0.442 0.506 0.955 229,727 14,328 0.852 0.336
57.89 ⁎⁎⁎ 72.62 ⁎⁎⁎ 2.41 ⁎ 13.45 ⁎⁎⁎ 4.42 ⁎⁎ 1.96 7.38 ⁎⁎⁎ 35.49 ⁎⁎⁎ 15.35 ⁎⁎⁎ 3.02 ⁎⁎ 14.66 ⁎⁎⁎ 18.95 ⁎⁎⁎ 0.68 3.95 ⁎⁎ 305.84 ⁎⁎⁎ 1348.83 ⁎⁎⁎ 3266.92 ⁎⁎⁎
0.351 0.056 0.126 0.020 0.000 − 0.007 0.004 0.033 0.129 − 0.132 0.008 0.041 − 0.030 − 0.049 0.389 0.614 0.613 525,602 15,739 – –
324.53 ⁎⁎⁎ 47.65 ⁎⁎⁎ 39.51 ⁎⁎⁎ 23.78 ⁎⁎⁎ 0.04 − 4.01 ⁎⁎⁎ 4.37 ⁎⁎⁎ 46.18 ⁎⁎⁎ 46.95 ⁎⁎⁎ − 99.93 ⁎⁎⁎ 2.91 ⁎⁎⁎ 64.71 ⁎⁎⁎ − 41.60 ⁎⁎⁎ − 18.63 ⁎⁎⁎ 585.79 ⁎⁎⁎ 1026.44 ⁎⁎⁎ 1071.53 ⁎⁎⁎
31.52 ⁎⁎⁎ 32.69 ⁎⁎⁎
31.31 ⁎⁎⁎ 41.67 ⁎⁎⁎
30.58 ⁎⁎⁎ 41.32 ⁎⁎⁎
– –
Notes: Lower arrangement class values indicate tighter arrangements. The Poisson model accounts for both multilateral resistance and fixed country-pair effects. ⁎⁎⁎, ⁎⁎, and ⁎ indicate significance at 1%, 5%, and 10%, respectively.
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P. Egger / Economics Letters 99 (2008) 228–232
be correlated with the explanatory variables and νijt being a stochastic, time-variant error term that is assumed to be uncorrelated with the observables in the model. 3. Data and application A key element to our analysis are the indicators for exchange rate arrangement tightness based on the work of Reinhart and Rogoff (2003a,b, 2004). For the period 1948–2001 and the country-pairs with positive (positive or zero) nominal export
flows in US dollars, we have 97,390 (261,555) observations, where country-pairs do not use the same reference currency. We particularly exploit information on those country-pairs that at least once switch into/out of one of the 14 exchange rate arrangement tightness classifications covered in Reinhart and Rogoff (2004). Overall there are 53,938 such switching countrypairs with positive export values (60,510 pairs with zero or positive bilateral exports) for whom we estimate the impact of a change in exchange rate arrangement tightness on bilateral exports. Table A.1 in the Appendix provides an overview of the
Fig. 1. Exchange rate arrangement tightness and log bilateral exports (Lower arrangement index values indicates tighter exchange rate arrangements).
P. Egger / Economics Letters 99 (2008) 228–232
numbers of country-pair-year observations for each type of arrangement and the corresponding switchers in the data. Here, I focus on the empirical analysis. Table 1 summarizes the results of three alternative regression models. All of them include fixed country-pair effects and fixed year-specific effects. Model A ignores multilateral resistance and focuses on the impact of contemporaneous effects of switching from one exchange rate arrangement toPanother. Hence, the results rely on the regression ln Xijt ¼ at þ Kk¼1 bk dk;ijt þ Yit þ Yjt þ uijt ; where d˜k;ijt in Eq. (2) is simply replaced by dk,ijt. Models B and C include multilateral resistance terms. While Model B is identical to the one in Eq. (2), Model C also includes triple-lagged explanatory variables (neither once-lagged nor twice-lagged effects are separately or jointly significant but triple-lagged ones are). Hence, Model C accounts for potential dynamic effects of regional trade agreements or exchange rate arrangements while Model B does not. Finally, in Model D I follow Santos Silva and Tenreyro (2006) in estimating a Poisson model of the level of exports on the dummy variables as in the previous models to account for the numerous zeros in the data.2 All standard errors and test statistics are based upon heteroskedasticity-consistent estimates of the variance–covariance matrix. In short, the performance of the models is very good in comparison to previous work (e.g., the within R2 of the models are several times higher than those in Glick and Rose, 2002). Neither fixed country-pair effects nor fixed time effects should be excluded from the model. Multilateral resistance matters, but its omission does not change the parameter estimates too much. Similarly, it does not make a big difference for the “marginal” effect of switching among exchange rate arrangements (note P that the impactP of a single Nt Nt 1 1 country-pair's switching on d þ k;ijt it¼1 jt¼1 dk;ijt Nt Nt P P N N t t 1 d is quite small, rendering the adjustment 2 k;ijt it¼1 jt¼1 Nt factor of dk,ijt in Eq. (2) small as well. Instead, there is sluggish adjustment to the long-run impact of arrangement switching. In the data at hand, the accommodation of the full effect on trade over a period of four years. Accounting for sluggish adjustment in Model C obtains estimates that are closer (but not identical) to the ones of a cross-sectional model. The latter finding highlights the fact that omitted dynamics may be one reason for the difference between within (here, fixed country-pair) and between (crosssectional) models as is well-known in the econometrics literature (see Baltagi, 2005). Finally, accounting for the zeros in the annual export matrices in a fixed country-pair-effects Poisson model (Model D) leads to lower point estimates of exchange rate arrangement tightness on exports than in Models A–C. While I have allowed for an arrangement-specific impact on bilateral trade, I am ultimately interested in whether adopting a tighter arrangement regime (indicate by a lower classification
2 Santos Silva and Tenreyro (2006) suggest using a Poisson pseudomaximum-likelihood estimator for bilateral trade flow analysis. It is easy to control for fixed effects, since there is no incidental parameters problem with the Poisson model unlike with other non-linear models. The Poisson model is based on the assumption that the variance is proportional to the mean. The latter is likely violated. Therefore, I follow Santos Silva and Tenreyro's suggestion to use an Eicker-White robust covariance estimator for inference.
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label) increases a country-pair's bilateral trade on average or not. To shed light on this issue, I provide plots of the parameter point estimates of Models B–D in Fig. 1a–c. In each of the panels, I include a linear trend (the slope of which is similar to the outcome of a regression that uses the arrangement index instead of a dummy variable for each arrangement class). The figures suggest that, depending on the model estimated, switching into the next tighter arrangement on average increase a country-pair's nominal exports by about 2–4%, all else equal. This outcome indicates that it may be harmful to pool countrypairs in the control group without accounting for their arrangement characteristics when inferring the consequences of a particular arrangement on trade flows. However, the findings also point to a positive impact of arrangement tightness on bilateral trade beyond common currencies. The latter finding is in line with results by Klein and Shambaugh (2006), who find that pegging exchange rates increases international versus intranational trade by up to 35%. This result is based on an empirical analysis which deploys Shambaugh's (2004) and, alternatively, a highly aggregated version of Reinhart and Rogoff's de facto arrangement classification (collapsing it into two categories only). Appendix A. Classification of exchange rate arrangements [1] No separate legal tender; [2] Pre-announced peg or currency board arrangement; [3] Pre-announced horizontal band that is narrower than or equal to +/− 2%; [4] De facto peg; [5] Pre- announced crawling peg; [6] Pre-announced crawling band that is narrower than or equal to +/− 2%; [7] De factor crawling peg; [8] De facto crawling band that is narrower than or equal to +/− 2%; [9] Pre-announced crawling band that is wider than or equal to +/− 2%; [10] De facto crawling band that is narrower than or equal to +/−5%; [11] Moving band that is narrower than or equal to+/−2%; [12] Managed floating; [13] Freely floating; [14] Freely falling. Table A.1 Exchange rate arrangements in periods t versus t + 1 in the data Arrangement in period t
Countries with positive exports
All countries covered
Observations
Observations
Changers
Changers
1 592 117 648 136 2 15,153 1476 35,403 2977 3 144 55 207 78 4 3709 747 8087 1568 5 197 79 481 185 6 535 169 860 272 7 4155 797 8071 1670 8 18,308 2494 34,029 5320 9 391 239 625 369 10 12,929 2056 28,833 4916 11 1182 118 1592 204 12 15,869 3327 28,657 6018 13 18,855 2380 27,004 4157 14 36,393 7317 89,550 19,144 Source: Data constructed according to Reinhart and Rogoff (2003a,b, 2004). Period covered is 1948–2001. Note: Observations that do not have the same reference currency enter the model as the base category.
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References Anderson, James E., van Wincoop, Eric, 2003. Gravity with gravitas: a solution to the border puzzle. American Economic Review 93, 170–192. Baier, Scott L., Bergstrand, Jeffrey H., 2006. Bonus Vetus OLS: A Simple Approach of Addressing the Border Puzzle and Other Gravity Equation Issues, unpublished manuscript, University of Notre Dame. Baltagi, Badi H., 2005. Panel Data Econometrics, 3rd edition. Wiley and Sons, New York. Glick, Reuven, Rose, Andrew K., 2002. Does a currency union affect trade? The time-series evidence. European Economic Review 46, 1125–1151. Klein, Michael W., Shambaugh, Jay C., 2006. Fixed exchange rates and trade. Journal of International Economics 70, 359–383. Reinhart, Carmen M., Rogoff, Kenneth S., 2003a. Part I. The Country Chronologies and Chartbook Background Material to A Modern History of Exchange Rate Arrangements: A Reinterpretation, unpublished manuscript, The International Monetary Fund. Reinhart, Carmen M., Rogoff, Kenneth S., 2003b. Part II. Parallel Markets and Dual and Multiple Exchange Rate Practices Background Material to A Modern
History of Exchange Rate Arrangements: A Reinterpretation, unpublished manuscript, The International Monetary Fund. Reinhart, Carmen M., Rogoff, Kenneth S., 2004. The modern history of exchange rate arrangements: a reinterpretation. Quarterly Journal of Economics 119, 1–48. Rose, Andrew K., 2000. One money, one market: the effect of common currencies on trade. Economic Policy 30, 7–45. Rose, Andrew K., 2001. Currency unions and trade: the effect is large. Economic Policy 33, 449–461. Rose, Andrew K., Stanley, T.D., 2005. A meta-analysis of the effect of common currencies on international trade. Journal of Economic Surveys 19, 347–365. Rose, Andrew K., van Wincoop, Eric, 2001. A meta-analysis of the effect of common currencies on international trade. American Economic Review Papers and Proceedings 91, 386–390. Santos Silva, João M.C., Tenreyro, Sylvana, 2006. The Log of Gravity. Review of Economics and Statistics 88, 641–658. Shambaugh, Jay C., 2004. The effects of fixed exchange rates on monetary policy. Quarterly Journal of Economics 119, 301–352.
Economics Letters 99 (2008) 228 – 232 www.elsevier.com/locate/econbase
De Facto exchange rate arrangement tightness and bilateral trade flows Peter Egger ⁎,1 Ifo Institute, University of Munich, CESifo, and GEP at the University of Nottingham, Germany Ifo Institute for Economic Research, Poschingerstrasse 5, 81679 Munich, Germany Received 24 October 2006; received in revised form 4 April 2007; accepted 8 May 2007 Available online 18 May 2007
Abstract This paper estimates the effect of switching into and out of 14 different modes of exchange rate arrangements on bilateral exports between 1948 and 2001 in the world economy. Increasing the exchange rate arrangement tightness by one degree in the classification of Reinhart and Rogoff [Reinhart, Carmen M. and Kenneth S. Rogoff (2004), The Modern History of Exchange Rate Arrangements: A Reinterpretation, Quarterly Journal of Economics 119, 1–48.] increases bilateral trade flows by about 2–4% as compared to their initial level. © 2007 Published by Elsevier B.V. Keywords: Exchange rate arrangements; Bilateral trade flows
1. Introduction The first paper on the impact of common currencies on trade flows by Rose (2000, 2001), initiated a vivid research activity on that nexus. A key finding in Rose's piece was that, for the average country-pair, a common currency leads to a higher bilateral trade volume. While there is some debate about the magnitude of that effect, it seems quite robust in qualitative terms (see Glick and Rose, 2002; Rose and Stanley, 2005). This paper aims at contributing to this literature on exchange rate arrangements and trade, yet its scope differs from previous work. The main difference lies in the departure from three commonly adopted assumptions: first, that exchange rate arrangements notified to the International Monetary Fund are identical to ones that countries actually apply; second, that the effect of a particular currency arrangement on bilateral trade can be estimated by comparing the level or change in trade flows of the ‘treated’ country-pairs (e.g., ones with a currency union or a currency board in place) to all pairs, where this particular arrangement was not in place, ignoring that the trade flows among these pairs might have been affected by other, less tight modes of exchange rate arrangements at the same time; third, that ‘multilateral’ effects of ‘bilateral’ arrangements can be ⁎ Tel.: +49 89 9224 1238. E-mail address: [email protected]. 1 Helpful comments by an anonymous referee are gratefully acknowledged. 0165-1765/$ - see front matter © 2007 Published by Elsevier B.V. doi:10.1016/j.econlet.2007.05.021
ignored (see Anderson and van Wincoop, 2003, for a rigorous theoretical and empirical treatment of multilateral effects of trade impediments on bilateral trade in general and Rose and van Wincoop, 2001, for an application in the context of estimating currency union effects on trade). A recent paper by Reinhart and Rogoff (2004) re-writes the history of exchange rate arrangements and identifies significant discrepancies between actual and notified exchange rate arrangements. They introduce a new classification that differentiates among 14 modes of arrangements. I will rely on their classification to abandon the first one of the above stated assumptions in previous work. For convenience, I provide the classification in the Appendix. This allows exploiting the full range of variation in exchange rate arrangements to estimate the average impact from switching among two arbitrary arrangements on bilateral trade, thereby abandoning the second of the mentioned assumptions in previous work. Finally, I rely on a Taylor-approximated version of Anderson and van Wincoop's (2003) model with multilateral resistance (i.e., multilateral effects on bilateral trade) as derived by Baier and Bergstrand (2006). An explicit modeling of multilateral resistance – rather than an implicit one by including fixed exporter-by-year and importer-by-year effects in a panel data model – is required for inference about the quantitative effects of exchange rate arrangements on bilateral trade. Here, we additionally account for the influence of all possible time-invariant determinants by using a fixed country-pair effects estimator.
P. Egger / Economics Letters 99 (2008) 228–232
2. The empirical model With cross-sectional data Anderson and van Wincoop's model motivates an empirical specification based on log nominal bilateral exports ( ln Xij) as the dependent variable and the following set of explanatory variables: a constant (α); log exporter and importer GDP as two separate variables (ln Yi, ln Yj ; hypothesizing unitary parameters for both of them); a set of trade friction variables in logs – for dummy variables such as regional trade agreements, exchange rate arrangements, adjacency, etc., the impact of the k'th dummy variable on trade is (∂ln Xij / ∂ln eβk dk,ij); and multilateral resistance terms for both the exporter and the importer (Pi, Pj), which are also known as consumer price index terms with models of monopolistic competition. Anderson and van PN Wincoop make use of two crucial insights, namely that j¼1 Xij ¼ Yi – hence, total exports of a country sum up to GDP is local sales are included as well – and that the multilateral resistance terms are non-linear known functions of trade frictions (e.g., the aforementioned k different dummy variables reflecting regional trade agreements, exchange rate arrangements, adjacency, etc.). Based on that, they derive an empirical log-non-linear estimation framework to estimate the impact of trade frictions on trade. Baier and Bergstrand approximate this system to obtain a log-linear model that also accounts for multilateral resistance but is easier to implement. They illustrate that their approximation obtains estimates that are very close to the ones of the Anderson and van Wincoop model in typical empirical applications. Let me
229
assume without loss of generality that there are only tradefriction-related dummy variables involved (in practice, the most important continuous friction variable would be bilateral distance, yet in the model applied here all time-invariant frictions are captured by the fixed country-pair effects). Taylor-approximating the multilateral resistance terms around an equilibrium with positive but symmetric trade frictions across country-pairs, Baier and Bergstrand derive the following linear regression model: ln Xij ¼ a þ
K X
bk d˜k;ij þ Yi þ Yj þ uij
ð1Þ
k¼1
N N N X N 1X 1X 1 X d˜k;ij ¼ dk;ij dk;ij dk;ij þ 2 dk;ij N i¼1 N j¼1 N i¼1 j¼1 ð1Þ
Where K is the number of trade friction dummy variables, N denotes the number of countries in the data, and uij is an error term. With panel data this model becomes: ln Xijt ¼ at þ
K X
bk d˜k;ijt þ Yit þ Yjt þ uijt
k¼1
Nt Nt Nt X Nt 1X 1X 1 X d˜k;ijt ¼ dk;ijt dk;ijt dk;ijt þ 2 dk;ijt Nt it¼1 Nt jt¼1 Nt it¼1 jt¼1
ð2Þ where Nt is the number of countries covered in year t, and uijt = μij + νijt with μij denoting fixed country-pair effects that may
Table 1 The impact of exchange rate arrangements on bilateral exports (1948–2001) Explanatory variable
No multilateral resistance Model A
Arrangement 1 Arrangement 2 Arrangement 3 Arrangement 4 Arrangement 5 Arrangement 6 Arrangement 7 Arrangement 8 Arrangement 9 Arrangement 10 Arrangement 11 Arrangement 12 Arrangement 13 Arrangement 14 Regional trade agreement Log exporter GDP Log importer GDP Observations Country-pairs Total R2 Within R2 F-tests (F-statistics): Fixed country-pairs Fixed annual effects
With multilateral resistance Model B
Model C
Model D
(Contemporaneous effects)
(Contemporaneous effects)
(Including lagged effects)
(Poisson, fixed effects)
Coefficient
t-statistics
Coefficient
t-statistics
Coefficient
F-statistics
Coefficient
z-statistics
0.507 0.199 0.337 0.127 0.297 0.123 0.160 0.086 0.301 0.021 0.233 0.066 0.074 −0.048 0.484 0.539 0.891 238,159 14,360 0.849 0.357
13.99⁎⁎⁎ 13.00 ⁎⁎⁎ 3.30 ⁎⁎⁎ 5.89 ⁎⁎⁎ 2.58 ⁎⁎⁎ 2.24 ⁎⁎ 6.31 ⁎⁎⁎ 6.73 ⁎⁎⁎ 5.20 ⁎⁎⁎ 1.31 5.11 ⁎⁎⁎ 4.80 ⁎⁎⁎ 5.05 ⁎⁎⁎ −4.33 ⁎⁎⁎ 33.26 ⁎⁎⁎ 53.68 ⁎⁎⁎ 81.67 ⁎⁎⁎
0.564 0.206 0.284 0.149 0.343 0.147 0.136 0.116 0.376 0.019 0.185 0.103 0.034 − 0.074 0.374 0.550 0.905 238,159 14,360 0.849 0.355
12.81 ⁎⁎⁎ 11.82 ⁎⁎⁎ 2.34 ⁎⁎ 6.12 ⁎⁎⁎ 2.73 ⁎⁎⁎ 2.41 ⁎⁎ 4.81 ⁎⁎⁎ 8.04 ⁎⁎⁎ 5.28 ⁎⁎⁎ 1.01 3.71 ⁎⁎⁎ 6.39 ⁎⁎⁎ 1.51 − 2.06 ⁎⁎ 23.42 ⁎⁎⁎ 55.44 ⁎⁎⁎ 83.80 ⁎⁎⁎
0.937 0.246 0.388 0.160 0.327 0.085 0.138 0.142 0.609 0.036 0.296 0.127 0.029 − 0.134 0.442 0.506 0.955 229,727 14,328 0.852 0.336
57.89 ⁎⁎⁎ 72.62 ⁎⁎⁎ 2.41 ⁎ 13.45 ⁎⁎⁎ 4.42 ⁎⁎ 1.96 7.38 ⁎⁎⁎ 35.49 ⁎⁎⁎ 15.35 ⁎⁎⁎ 3.02 ⁎⁎ 14.66 ⁎⁎⁎ 18.95 ⁎⁎⁎ 0.68 3.95 ⁎⁎ 305.84 ⁎⁎⁎ 1348.83 ⁎⁎⁎ 3266.92 ⁎⁎⁎
0.351 0.056 0.126 0.020 0.000 − 0.007 0.004 0.033 0.129 − 0.132 0.008 0.041 − 0.030 − 0.049 0.389 0.614 0.613 525,602 15,739 – –
324.53 ⁎⁎⁎ 47.65 ⁎⁎⁎ 39.51 ⁎⁎⁎ 23.78 ⁎⁎⁎ 0.04 − 4.01 ⁎⁎⁎ 4.37 ⁎⁎⁎ 46.18 ⁎⁎⁎ 46.95 ⁎⁎⁎ − 99.93 ⁎⁎⁎ 2.91 ⁎⁎⁎ 64.71 ⁎⁎⁎ − 41.60 ⁎⁎⁎ − 18.63 ⁎⁎⁎ 585.79 ⁎⁎⁎ 1026.44 ⁎⁎⁎ 1071.53 ⁎⁎⁎
31.52 ⁎⁎⁎ 32.69 ⁎⁎⁎
31.31 ⁎⁎⁎ 41.67 ⁎⁎⁎
30.58 ⁎⁎⁎ 41.32 ⁎⁎⁎
– –
Notes: Lower arrangement class values indicate tighter arrangements. The Poisson model accounts for both multilateral resistance and fixed country-pair effects. ⁎⁎⁎, ⁎⁎, and ⁎ indicate significance at 1%, 5%, and 10%, respectively.
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be correlated with the explanatory variables and νijt being a stochastic, time-variant error term that is assumed to be uncorrelated with the observables in the model. 3. Data and application A key element to our analysis are the indicators for exchange rate arrangement tightness based on the work of Reinhart and Rogoff (2003a,b, 2004). For the period 1948–2001 and the country-pairs with positive (positive or zero) nominal export
flows in US dollars, we have 97,390 (261,555) observations, where country-pairs do not use the same reference currency. We particularly exploit information on those country-pairs that at least once switch into/out of one of the 14 exchange rate arrangement tightness classifications covered in Reinhart and Rogoff (2004). Overall there are 53,938 such switching countrypairs with positive export values (60,510 pairs with zero or positive bilateral exports) for whom we estimate the impact of a change in exchange rate arrangement tightness on bilateral exports. Table A.1 in the Appendix provides an overview of the
Fig. 1. Exchange rate arrangement tightness and log bilateral exports (Lower arrangement index values indicates tighter exchange rate arrangements).
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numbers of country-pair-year observations for each type of arrangement and the corresponding switchers in the data. Here, I focus on the empirical analysis. Table 1 summarizes the results of three alternative regression models. All of them include fixed country-pair effects and fixed year-specific effects. Model A ignores multilateral resistance and focuses on the impact of contemporaneous effects of switching from one exchange rate arrangement toPanother. Hence, the results rely on the regression ln Xijt ¼ at þ Kk¼1 bk dk;ijt þ Yit þ Yjt þ uijt ; where d˜k;ijt in Eq. (2) is simply replaced by dk,ijt. Models B and C include multilateral resistance terms. While Model B is identical to the one in Eq. (2), Model C also includes triple-lagged explanatory variables (neither once-lagged nor twice-lagged effects are separately or jointly significant but triple-lagged ones are). Hence, Model C accounts for potential dynamic effects of regional trade agreements or exchange rate arrangements while Model B does not. Finally, in Model D I follow Santos Silva and Tenreyro (2006) in estimating a Poisson model of the level of exports on the dummy variables as in the previous models to account for the numerous zeros in the data.2 All standard errors and test statistics are based upon heteroskedasticity-consistent estimates of the variance–covariance matrix. In short, the performance of the models is very good in comparison to previous work (e.g., the within R2 of the models are several times higher than those in Glick and Rose, 2002). Neither fixed country-pair effects nor fixed time effects should be excluded from the model. Multilateral resistance matters, but its omission does not change the parameter estimates too much. Similarly, it does not make a big difference for the “marginal” effect of switching among exchange rate arrangements (note P that the impactP of a single Nt Nt 1 1 country-pair's switching on d þ k;ijt it¼1 jt¼1 dk;ijt Nt Nt P P N N t t 1 d is quite small, rendering the adjustment 2 k;ijt it¼1 jt¼1 Nt factor of dk,ijt in Eq. (2) small as well. Instead, there is sluggish adjustment to the long-run impact of arrangement switching. In the data at hand, the accommodation of the full effect on trade over a period of four years. Accounting for sluggish adjustment in Model C obtains estimates that are closer (but not identical) to the ones of a cross-sectional model. The latter finding highlights the fact that omitted dynamics may be one reason for the difference between within (here, fixed country-pair) and between (crosssectional) models as is well-known in the econometrics literature (see Baltagi, 2005). Finally, accounting for the zeros in the annual export matrices in a fixed country-pair-effects Poisson model (Model D) leads to lower point estimates of exchange rate arrangement tightness on exports than in Models A–C. While I have allowed for an arrangement-specific impact on bilateral trade, I am ultimately interested in whether adopting a tighter arrangement regime (indicate by a lower classification
2 Santos Silva and Tenreyro (2006) suggest using a Poisson pseudomaximum-likelihood estimator for bilateral trade flow analysis. It is easy to control for fixed effects, since there is no incidental parameters problem with the Poisson model unlike with other non-linear models. The Poisson model is based on the assumption that the variance is proportional to the mean. The latter is likely violated. Therefore, I follow Santos Silva and Tenreyro's suggestion to use an Eicker-White robust covariance estimator for inference.
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label) increases a country-pair's bilateral trade on average or not. To shed light on this issue, I provide plots of the parameter point estimates of Models B–D in Fig. 1a–c. In each of the panels, I include a linear trend (the slope of which is similar to the outcome of a regression that uses the arrangement index instead of a dummy variable for each arrangement class). The figures suggest that, depending on the model estimated, switching into the next tighter arrangement on average increase a country-pair's nominal exports by about 2–4%, all else equal. This outcome indicates that it may be harmful to pool countrypairs in the control group without accounting for their arrangement characteristics when inferring the consequences of a particular arrangement on trade flows. However, the findings also point to a positive impact of arrangement tightness on bilateral trade beyond common currencies. The latter finding is in line with results by Klein and Shambaugh (2006), who find that pegging exchange rates increases international versus intranational trade by up to 35%. This result is based on an empirical analysis which deploys Shambaugh's (2004) and, alternatively, a highly aggregated version of Reinhart and Rogoff's de facto arrangement classification (collapsing it into two categories only). Appendix A. Classification of exchange rate arrangements [1] No separate legal tender; [2] Pre-announced peg or currency board arrangement; [3] Pre-announced horizontal band that is narrower than or equal to +/− 2%; [4] De facto peg; [5] Pre- announced crawling peg; [6] Pre-announced crawling band that is narrower than or equal to +/− 2%; [7] De factor crawling peg; [8] De facto crawling band that is narrower than or equal to +/− 2%; [9] Pre-announced crawling band that is wider than or equal to +/− 2%; [10] De facto crawling band that is narrower than or equal to +/−5%; [11] Moving band that is narrower than or equal to+/−2%; [12] Managed floating; [13] Freely floating; [14] Freely falling. Table A.1 Exchange rate arrangements in periods t versus t + 1 in the data Arrangement in period t
Countries with positive exports
All countries covered
Observations
Observations
Changers
Changers
1 592 117 648 136 2 15,153 1476 35,403 2977 3 144 55 207 78 4 3709 747 8087 1568 5 197 79 481 185 6 535 169 860 272 7 4155 797 8071 1670 8 18,308 2494 34,029 5320 9 391 239 625 369 10 12,929 2056 28,833 4916 11 1182 118 1592 204 12 15,869 3327 28,657 6018 13 18,855 2380 27,004 4157 14 36,393 7317 89,550 19,144 Source: Data constructed according to Reinhart and Rogoff (2003a,b, 2004). Period covered is 1948–2001. Note: Observations that do not have the same reference currency enter the model as the base category.
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