Abstract
Recently, an investigation of unobserved and time-varying multilateral resistance and omitted trade determinants has assumed a prominent role in order to precisely measure the Euro effects on trade. We implement two methodologies: the factor-based gravity model by Serlenga and Shin (The Euro Effect on Intra-EU Trade: Evidence from the Cross Sectionally Dependent Panel Gravity Models, Mimeo, University of York, 2013) and the spatial-based techniques by Behrens et al. (J Appl Econ 27:773–794, 2012), both of which allow trade flows and error terms to be cross-sectionally correlated. Applying these approaches to the dataset over 1960–2008 for 190 country-pairs of 14 EU and six non-EU OECD countries, we find that the Euro impact estimated by the factor-based model amounts to only 4–5 %, far less than the 20 % estimated by the spatial-based model. The cross-section dependency test results also confirm that the factor-based model is more appropriate in accommodating correlation between regressors, and unobserved individual and time effects. Overall we may conclude that the trade-creating effects of the Euro should be viewed in the proper historical and multilateral perspective rather than in terms of the formation of a monetary union as an isolated event.
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- 1.
In particular, Bun and Klaassen (2007), and Berger and Nitsch (2008) simply introduce time trends with heterogeneous coefficients, and find that the Euro effect on trade falls dramatically. However, Baldwin et al. argue that including time trends in an ad hoc manner is not the satisfactory empirical approach. SS also show that simply introducing heterogeneous time trends is not yet sufficiently effective in capturing any upward trends in omitted trade determinants, which suggests that such diverse measures might be better described by stochastic trending factors (e.g. Herwartz and Weber 2010).
- 2.
The multilateral resistance function and trade costs, both of which affect bilateral trade flows, are not only difficult to measure, but also are likely to vary over time. A number of ad hoc approaches have been proposed in the literature. Simply, fixed time dummies or time trends are added as a proxy for time-varying effects in the gravity equation, e.g. Baldwin and Taglioni (2006), Bun and Klaassen (2007) and Berger and Nitsch (2008). Alternatively, some studies include regional remoteness indices (e.g. Melitz and Ghironi 2007).
- 3.
Bailey et al. (2012) also discuss that the extent of cross-sectional dependence crucially depends on the nature of factor loadings. The degree of cross-sectional dependence will be strong if φ i is bounded away from 0 and the average value of φ is different from zero.
- 4.
We estimate \(\boldsymbol{\theta }_{t}\) consistently using the Bai and Ng (2002) procedure.
- 5.
- 6.
For the factor-based models, d it is consistently estimated by \(\hat{d} _{it} = y_{it} -\boldsymbol{\hat{\beta }}_{CSD}^{{\prime}}\mathbf{x}_{it} -\mathbf{\hat{\boldsymbol{\lambda }}}_{i}^{{\prime}}\mathbf{f}_{t}\), where \(\boldsymbol{\hat{\lambda }}_{i}\) are the OLS estimators of \(\boldsymbol{\lambda }_{i}\) consistently estimated from the regression of \(\big(y_{it} -\boldsymbol{\hat{\beta }}_{CSD}^{{\prime}}\mathbf{x}_{it}\big)\) on \(\big(1,\mathbf{f}_{t}\big)\) for i = 1, …, N. Next, for the spatial-based models, d it is consistently estimated by \(\hat{d} _{it} = y_{it} -\hat{\rho }_{SARAR}y_{it}^{{\ast}}-\boldsymbol{\hat{\beta }}_{SARAR}^{{\prime}}\mathbf{x}_{it}\), where \(\hat{\rho }_{SARAR}\) and \(\boldsymbol{\hat{\beta }}_{SARAR}\) are the ML estimators of ρ and β in (12.5) and (12.6).
- 7.
See Table 12.1 in SS for the key summary figures of EU trade shares and growths.
- 8.
When comparing with the estimation results reported in SS for the smaller dataset with 91 country-pairs among 14 EU countries, we find the following notable difference that the impacts of EMU and CEE increase from 0.21 and 0.14 to 0.39 and 0.31, respectively.
- 9.
For the PCCE estimation we consider \(\mathbf{f}_{t} = \left \{\overline{TRADE}_{t},\overline{TGDP}_{t},\overline{SIM}_{t},\overline{RLF}_{t},\overline{CEE}_{t}\right \}^{{\prime}}\) and \(\mathbf{s}_{t} = \left \{t\right \}\) in (12.3), where the bar over variables indicates their cross-sectional average. For the PC estimation, we first extract six common PC factors using the Bai and Ng (2002) procedure, and use them as f t in (12.3) together with \(\mathbf{s}_{t} = \left \{t\right \}\). See SS for more details about a selection of the final specification on the basis of statistical significance and empirical coherence.
- 10.
This result is crucially different from those reported in SS. This may be due to the fact that we now employ a larger number of country-pairs. In particular, the OECD dataset includes large countries such as the US, Japan and Canada, that have recently experienced a steady growth in the intra-industry trade. The presence of those countries might help to better identify the effect of relative factor endowments by fostering intra-industry trade, see OECD (2010).
- 11.
We observe form Table 12.1 in SS that the share of the intra-trade increase from 37.2 % in 1960 to around 60 % from 1990 onwards.
- 12.
Assuming that LAN is the only time invariant variable correlated with individual effects, we use the same instrument variables, \(IV = \left \{RER_{it},RLF_{it}\right \}\). We also consider an additional instrument set, denoted \(IV 1 = \left \{IV,\boldsymbol{\hat{\xi }}_{it}\right \}\), where \(\hat{\xi }_{it} =\hat{\lambda } _{i}\,f_{t}\), and \(\hat{\lambda }_{i}\) are estimated loadings. See SS for more details about a selection of the final set of HT and AM instrument variables.
- 13.
Serlenga (2005) estimates coefficients on GDP h and GDP f , using the triple index model, where h and f indicate home and foreign countries, and finds that the sum of their coefficients are close to the coefficient on TGDP hf obtained from the double index model.
- 14.
We expect ρ to be negative because it measures the multilateral trade resistance. For example, if the trade barriers between country k and country j (k ≠ i and k ≠ j) are reduced, then the trade flow between country j and country k increases while the trade flow between the country i and j decreases. Indeed we find that the autocorrelation coefficient between y and Wy is − 0. 014 for W = trade, − 0. 019 for W = population, − 0. 218 for W = distance, and − 0. 165 for W = border.
- 15.
These contradictory findings can be explained as follows: When we use W = border and distance, the spatial matrices capture the effect of proximity and distance on trade flow, and therefore, a depreciation of the home currency leads to an increase in trade flow, especially as the distance rises. On the other hand, when we employ W = trade and pop, the spatial matrices control for multilateral resistance in which case it would prevent the trade flow (exports) to increase as RER rises.
- 16.
For example, the indirect spillover effects of GDP, SIM, EMU and CEE are all negative and significant. Where indirect effects are positive, they are insignificant or negligible.
- 17.
Close inspection of Figs. 12.1 and 12.2 reveals that here are the following (minor) differences among six different estimation results: The decrease in border and language effects is slightly more pronounced for the PCCE estimator than the PC estimator. Turning to the spatial models, we find that the time-varying patterns for W=Population and W=Distance are similar whereas the spatial models with W=Trade and W=Border produce similar results. Further, the fall in language effect is sharper for W=Distance.
- 18.
On the basis of our most preferred specification with unobserved factors (strong CSD) and endogeneity (AM-IV estimates), we are able to document a negative albeit the lower impact of distance on trade.
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Acknowledgements
We are grateful to the coeditor, Roberto Patuelli, an anonymous referee, Badi Baltagi, Peter Burridge, Matthew Greenwood-Nimmo, George Kapetanios, Minjoo Kim, Hashem Pesaran, Ron Smith, Takashi Yamagata, the seminar participants at Universities of Bari and York, and the conference delegates at the Fifth Italian Congress of Econometrics and Empirical Economics, 2013 and Cross-sectional Dependence in Panel Data Models, May 2013 at Cambridge for their helpful comments. The usual disclaimer applies.
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Mastromarco, C., Serlenga, L., Shin, Y. (2016). Multilateral Resistance and the Euro Effects on Trade Flows. In: Patuelli, R., Arbia, G. (eds) Spatial Econometric Interaction Modelling. Advances in Spatial Science. Springer, Cham. https://doi.org/10.1007/978-3-319-30196-9_12
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