Estimation results

Estimation of models like (16) needs to control for unobserved country-pair-effects that may be correlated with the covariates of the equation. There are at least two approaches that one may pursue towards this end. The first is the so-called fixed effects (FE) estimator (or “within estimator”), the second is the first difference (FD) estimator. Both approaches eliminate all time-invariant, unobserved pair-specific heterogeneity. For T = 2, both estimators are identical, but for T > 2 the relative efficiency of the estimators depends on whether or not the error terms in (16) are serially correlated. If they are, then the FD estimator is more efficient than the FE estimator, as emphasised by Baier and Bergstrand (2007). A further advantage of the FD estimator is that it is not compromised by trade and GDP data that follow near-unit-root processes. This is because, in order to wipe out time-invariant unobserved heterogeneity, the FD estimator relies on differencing with respect to the previous period, while the FE estimator achieves the same goal by subtracting sample means. Weighing all strengths and weaknesses, our preferred estimation employs FD. However, for the sake of comparison with earlier studies and with an eye on robustness, we report FE-estimates in the appendix.

It is important to be clear about the definition of the interaction term in the FD estimation. More specifically, using ∆ to denote the first difference operator, the FD estimation equation is

∆ ln(Xijt) = β1∆ ln(Yijt) + β2∆EUbothijt+ β3∆EA2ijt

+ β₄∆ ln( ¯m_ijt) + ˜β₅[EA2_ijt× ∆ ln( ¯m_ijt)]

+ ˜ξ_it+ ˜µ_jt+ ˜_ijt, (18)

where a tilde denotes suitably transformed fixed effects. Notice that the interaction term in this equation is no straightforward first difference of the interaction term in (16), hence the coefficient ˜β₅which is related to, but not identical to β₅ in (16). More

specifically, for the FD version of the econometric model, our maintained hypothesis implies that changes in the cost discrepancy term ln( ¯m_ijt) lead to different changes in bilateral exports for euro member countries, compared to countries with independent currencies.¹⁷

For both approaches, FE and FD estimation, we proceed in “three × three steps”, featuring three alternative restrictions regarding fixed effects and three different specifications regarding included or excluded covariates, respectively. To facilitate a quick and easy interpretation, tables 2 (in the text) and A.2 (in the appendix) organise our presentations around these steps of estimation. The first set of results allows for country pair and year fixed effects, but collapses the exporter×time and importer×time fixed effects to a simple year fixed effect: ξ_it = µ_jt = δ_t(and accord-ingly for the FD version). These results, presented mainly for reasons of comparison, are found in columns A through C of tables 2 and A.2, respectively. It is important to bear in mind, however, that these estimates are likely to be biased, since they omit the multilateral resistance terms that we have emphasised in Section 4 above.

Therefore, we present a second set of results where we control for multilateral trade resistance, but assuming symmetry as in Anderson and Van Wincoop (2003), mean-ing ξ_it = µ_jt. In other words, these results – found in columns D through F of tables 2 and A.2 – include country×time fixed effects that do not make a distinction between a country’s role as an importer and exporter, respectively. However, this is exactly what our extended gravity model of Section 4 strongly suggests we should do. Hence, a final set of results allows for the most general set of fixed effects, i.e., ξit 6= µjt, and accordingly for the FD version (18). In these results, we thus have separate importer- and exporter×time fixed effects.

Within each set of assumptions regarding the structure of fixed effects, we first report estimates that ignore all nominal cost divergence terms in columns A, D and G. Obviously, this is mainly for comparison with existing literature. In line with the

17Applying plain first differencing to [EA2_ijt× ln( ¯m_ijt)] would imply that we attach meaning to the levels of c(w_i) and c(w_j) behind our observations of ln( ¯m_ijt). We must be careful to avoid this. The reason is that in the OECD data source that we use, the underlying data on c(wi) and c(wj) are defined as indices with base period 2005. Scaling each of the unit-labor cost index to 100 for 2005 negates a meaningful international comparison for any one year. We therefore restrict our analysis to using changes across time. In doing so, we scale our measure such that ¯mij,1999= 1.

Thus, we must avoid ever using information on the absolute levels of c(wi) and c(wj) in our data transformations. It can be shown that first-differencing the interaction term in (16) would imply that we do so. The above definition of the interaction term in (18) guarantees that we do not. It is, thus, consistent with this data limitation. For FE estimation, by complete analogy we first apply the “within transformation” to ln( ¯mijt), and then interact this with EA2ijt. Again, our hypothesis is that β₄should not be different from zero, while ˜β₅ is statistically significant and negative.

goal of our paper, we then include the divergence terms ln( ¯mijt) or ∆ ln( ¯mijt), re-spectively, to see whether or not nominal cost divergence as such makes a difference.

Notice, however, that this specification does not yet address our prime concern. Our key hypothesis comes into play only when comparing coefficient estimates obtained upon inclusion of ln( ¯m_ijt) or ∆ ln( ¯m_ijt) alone with those obtained upon including in-teraction terms EA2× ln( ¯m_ijt) or EA2×∆ ln( ¯m_ijt), respectively. Loosely speaking, the hypothesis implies that most of the significance of the cost divergence term gets shifted into the interaction term, if included in the specification.

Our estimation relies on a panel of 20 OECD countries for the years from 1993 through 2006 - a common setup in the euro effect literature.¹⁸ Table 2 presents results obtained using the FD estimator, while the FE estimates are presented in the appendix table A.2. In either case, the orthogonality results that we have derived in the previous subsection nicely come through: Coefficient estimates for log-GDPs, as well as the EU15- and euro-area variables, do not change if we include the cost-divergence variable ln( ¯m_ijt). Also, the results are generally in line with the early literature on the trade effect of the euro, as in Micco et al. (2003). The coefficient for the product of GDPs is rather low, but this is a frequent finding for recent OECD-country data and fixed effects specifications; see Baldwin et al. (2008).

The insignificant coefficient for EU membership may appear surprising at first sight.

But considering that within the range of our sample only three countries have joined (Austria, Finland and Sweden), it becomes obvious that there is little time-series variation left in the data for a fixed-effects-estimation. The insignificant coefficient estimates are thus hardly surprising.¹⁹

The trade cost effect from fixed effects estimation presented in columns A through C of table A.2 are broadly in line with the earlier consensus estimates of the litera-ture: The euro is revealed to increase the level of bilateral trade by about 9 percent.²⁰ But a positive trade cost channel effect is not upheld in the FD estimation of table 2 where coefficients are all insignificant, which corroborates the consensus in more recent contributions to the literature.

18The countries included are: Australia, Austria, Belgium, Canada, Denmark, Finland, France, Germany, Greece, Ireland, Italy, Japan, Korea, Netherlands, Norway, Portugal, Spain, Sweden, United Kingdom, and United States. The time span was chosen with data quality considerations in mind. In particular, Baldwin (2006) discusses weaknesses of pre-1993 trade data for European countries. Trade data are taken from the IMF DoTS and are retrieved via Datastream. The unit labor cost indices, used for construction of the cost-divergence-terms, are provided by the OECD.

19Estimating the effect of EU membership on trade in a setting without fixed effects and over a longer time period usually yields coefficients around 20% (Baldwin (2006)).

20This is calculated in the familiar way as [exp( ˆβ3) − 1] × 100.

Table2:Estimationresults–FirstDifference(FD)Estimator timeeffectscountry×timeeffectsimporter/exporter×timeeffects ABCDEFGH log-productGDPs0.489***0.489***0.489***0.749***0.749***0.749***0.407***0.407*** (10.03)(9.98)(10.03)(6.17)(6.16)(6.16)(4.07)(4.07) bothinEU15-0.001-0.001-0.001-0.043-0.043-0.043-0.043-0.043 (-0.06)(-0.06)(-0.06)(-1.26)(-1.27)(-1.26)(-1.21)(-1.21) bothinEA0.0110.0110.011-0.015-0.015-0.015-0.015-0.015 (0.49)(0.49)(0.49)(-0.39)(-0.39)(-0.39)(-0.40)(-0.40) ∆ln(¯mijt)-0.098-0.055-0.098-0.0550 (-0.99)(-0.52)(-1.10)(-0.58)(.) EA2×∆ln(¯mijt)-0.485***-0.485**-0.714** (-3.06)(-2.37)(-2.07) Observations49404940494049404940494049404940 R2 0.1850.1860.1860.2710.2710.2720.3730.373 Notes:Dependentvariable:lnexports.Periodcovered:1993-2006.Sampleincludespotentially20x19countrypairs.Estimation method:firstdifferences.Allregressionsincludeaconstant(notshown).Robustt-statisticsareshowninparentheses.∗∗∗and∗∗ indicatesignificanceat1%and5%,respectively.

While the FE estimation in table A.2 reveals a significantly negative effect for the level of ln( ¯m_ijt), the same is not true for the FD results in table 2. However, as emphasised above, our key hypothesis requires that we compare estimates for the coefficient on this variable when appearing alone in the regression equation with estimates obtained upon inclusion of the interaction term with euro-area member-ship. The value of ˆβ₄, indicating the negative influence of nominal cost divergence on bilateral exports per se, becomes much smaller if we control for euro area cur-rency misalignment, in line with our theoretical considerations of Sections 2 and 4 above. At the same time, the coefficients β5 and ˜β5, respectively, come out with statistically significant negative estimates in either specification, except for the final column of Table A.2. Thus, our hypothesis receives impressive empirical support.

To summarise, the FD estimation suggests that a significant currency misalignment problem from nominal cost divergence occurs only for euro-area divergence, and it does so with clear statistical significance, as witnessed by the t-statistics for the estimates of ˜β₅. The results displayed in column (H) in table 2 suggest that a 10%

increase in the misalignment index, on average, leads to a 7% decrease in bilateral exports and vice versa. Given that the estimation in this case does not allow us to include both ln( ¯m_ijt) and the interaction term at the same time, the above value gives us the total effect of misalignment for euro area trade flows. This is irrespective of whether misalignment matters for other trade flows or not.

In a similar vein, the FE estimation clearly indicates that it is primarily the use of the common currency that makes cost-divergence an implicit currency misalignment, causing a deviation of trade flows from the “gravity norm” level.

These results hold important implications for the policy discussion, since trade creation through currency misalignment is fundamentally different from trade effects through the conventional trade cost channel, as we have emphasised in Section 2 above. While euro-induced trade cost reductions may be expected to exert a largely symmetric influence on trade across countries and for imports as well as exports, the misalignment channel is of an asymmetric nature, affecting exports and imports for each country differently, and with substantial asymmetry across euro-member countries. Also, while trade-cost-induced trade creation is clearly welfare increasing, the same does not hold true for a misalignment-induced boost in exports.

Trade cost reductions are readily identified as sources of additional gains from trade, accruing to all countries on the same footing. In contrast, euro-induced trade effects from “implicit currency misalignment” are akin to terms of trade effects. Somewhat paradoxically, the welfare effects associated with such trade effects are negative for

countries where the euro boosts exports, and vice versa for countries where the euro predominantly boosts imports.²¹

Our results thus strongly suggest that we look at euro-induced trade effects in a disaggregate manner across member countries. Table 3 depicts the euro effect for each country, as calculated from our coefficient estimates and each country’s average level of misalignment towards other member states since the start of the euro. Given the insignificant coefficient estimates for the conventional trade cost channel of the euro effect, we treat this channel as non-existent. Yet, we stress that the misalignment effect, and therefore the induced heterogeneity of the overall effect, does not depend on the level of the trade cost channel effect. We would always see some countries gaining more than others, but on a different level.²² The implied heterogeneity is considerable. Germany and Austria stick out as the only countries with a sizable trade effect of above 5%. For Belgium, Finland and France the overall effect is still positive, yet close to zero. The Netherlands join the countries of Southern Europe and Ireland with negative effects on their bilateral euro area exports. Thus, the true impact of entering the currency union creates trade gains

Table 3: Country specific euro effects Country overall euro effect on exports in %

Germany 6.26

for some, but not for all countries. For others, the opposite holds true.

21This statement ignores the welfare effects from intertemporal trade. Of course, a trade sur-plus generates additional net foreign assets which represent future consumption possibilities which should not be ignored when considering the welfare implications of euro-induced trade scenarios.

22The values are calculated based on results from column (H) in table 2 with the help of equation (17b) and the values from 1.

We have put our estimates to a number of robustness checks.²³ First, we use alternative unit labor cost measures also provided by the OECD. This does not change the results in any noteworthy way. Second, we shorten the sample length, starting in 1995 and dropping the EU15 indicator variable. Again, the results are robust.

Third, we split the sample into pre-euro years and the years following the euro’s introduction. This allows us to test whether the misalignment effect was already present before 1999 for the euro countries. Reassuringly, we find no misalignment effect for the future euro members before the launch of the common currency. We take this as evidence that the EMS provided enough flexibility for nominal exchange rates to adjust in a way maintaining purchasing power parity. For the years in which the euro is a reality we find the misalignment effect to be negative and significant for the euro members, confirming our above results. Table 4 summarises this robustness check. In the appendix, we list all results in detail.

Table 4: Effect of misalignment before and after the euro’s introduction

1993 - 1998 1999 - 2006

A B C A’ B’ C’

EA2×∆ ln( ¯m_ijt) 0.031 0.031 0.060 -0.676*** -0.676*** -0.759**

(0.14) (0.17) (0.30) (-2.72) (-2.79) (-2.16) Notes: First-difference estimation;

A: time effects only, B: country×time effects, C: importer/exporter×time effects.

Robust t-statistics are shown in parentheses. For the 1993-1999 subsample EA2 identifies pairs among future euro member countries.