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Trade Dynamics in the Euro Area: A Disaggregated Approach Peter Wierts a, Henk van Kerkhoff a and Jakob de Haan a,b,c a b De Nederlandsche Bank, the Netherlands University of Groningen, the Netherlands c CESifo, Munich, Germany Draft, October 2011 (work in progress) Abstract We investigate the contribution of export composition to trade imbalances in the euro area. Results show that export composition matters. The elasticity of real exports with respect to the real exchange rate decreases from -1 to -0.3 along the spectrum from low- to hightechnology export. Core EMU countries have a comparatively higher technology intensity of exports and a lower response to the real exchange rate, while the opposite is the case in the southern periphery. Results also underline the role of development of the domestic economy in increasing technology intensity. The views expressed are those of the authors only and do not necessarily reflect the views of De Nederlandsche Bank. 1. Introduction Although it is widely believed that unsustainable fiscal policies are the root cause of the current debt crisis in the euro area, a case can be made that current account imbalances play perhaps a more important role (Mansori, 2011). Although Greece and Portugal had high budget deficits between 2000 and 2007 (average deficit of 5.4 and 3.7% of GDP, respectively), Ireland and Spain had no deficits on average (average surplus of 0.3 and 1.5 percent of GDP, respectively). However, according to the figures provided by Mansori, these four countries take the top positions in the ranking based on the average current account deficit as a percentage of GDP, followed by Italy.1 No matter what the root cause of the crisis, the size and persistence of these current account imbalances of countries in the European Economic and Monetary Union (EMU) warrant further analysis. According to Berger and Nitsch (2010), the size and persistence of bilateral current account imbalances have increased after the introduction of the euro. Countries with less flexible labour and product markets exhibit systematically lower trade surpluses than others. This finding suggests the need for more market flexibility, but Bednarek et al. (2010) find that EMU has had no effect on labour market reform thereby rejecting the popular belief that EMU will create greater labour market flexibility. This belief is based on the argument that in the union, monetary policy is no longer available to individual countries to respond to asymmetric shocks, which increases incentives to undertake structural reform. The present paper focuses on the export performance of countries in the euro area. Exports and imports contribute differently to trade imbalances and are driven by different variables. Whereas imports are mainly demand driven, the ability to export is linked to the capability to compete on European and world markets. Exports also determine the capacity to sustain imports without running down foreign assets or increasing liabilities (for given factor earnings). Moreover, with exports as a dependent variable, the role played by the real exchange rate is highlighted relative to studies that investigate the size of bilateral trade (i.e. measured as exports plus imports, divided by two). In the latter case, changes in the real exchange rate will have offsetting effects (Flam and Nordström, 2003). 1 Ireland had a surplus on its trade account (see Table 1). 1 There is much diversity in export growth rates of countries in the euro area, both across countries and over time. As will be discussed in more detail in section 2, previous research points to high labour costs in explaining a lack of real exchange rate flexibility. Indeed, Figure 1 suggests a negative relationship between unit labour costs and export growth. However, it also becomes clear that the relationship is rather weak. Figure 1. Export growth and growth of unit labour costs, 1996-2010 Unit Labour Costs versus Export Performance of Euro Area Countries Annual growth for 1996-2010 20 15 Export-Performance 10 5 0 -5 -10 -15 -6 -4 -2 Euro Area Countries 0 2 Unit Labour Cost 4 6 8 Southern Periphery Source: AMECO. 2 In this paper we investigate a complementary explanation related to the composition of export. We start from a simple question. Which products and which trade partners explain most of the export dynamics of each country? The hypothesis is that the type of products exported and the growth performance of the trade partner play a role in explaining export growth, a role that may be even more important than price competiveness. For example, German export to China and other emerging markets grew strongly, but exports from several other euro area countries, notably those in the Southern periphery, did not. Anecdotal evidence suggests that the composition of exports may play a role here. In emerging economies, demand for high-quality products from Germany – such as capital goods and cars – was stronger than demand for low-quality products. Investigating disaggregated trade data may help to shed light on such arguments. Our main research question considers the response of different classes of exports to price and income developments. The lower is the technology intensity of exports, the stronger could be international competition, and the more sensitive exports may be to increases in the real exchange rate.2 Moreover, rising incomes in emerging markets may give rise to stronger demand for high-quality goods. We therefore run separate regressions for different export categories, classified according to technology intensity. Our hypothesis is that in hightechnology intense countries, exports may respond less to real exchange rate appreciation and more to increases in income abroad. Our dataset contains exports from euro area countries to their top 20 trade partners. We use the OECD ISIC classification that distinguishes between exports from high-technology, medium-high technology, medium-low technology and low technology industries. The remainder of this paper is organised at follows. Section 2 briefly reviews literature on current account imbalances, adjustment capacity and the effect of EMU on trade. Section 3 takes a first look at our data. Section 4 presents results from our baseline regressions on disaggregated export categories and (groups of) countries. Section 5 presents robustness checks and section 6 offers our conclusions and discusses the policy implications of our findings. 2 Moreover, higher technology intensity may also lead to higher profit margins, so that exchange rate changes may – at least temporary – lead to lower profit margins instead of quantity adjustments. 3 2. Literature review Imbalances The emergence of persistent current account imbalances in the euro area is well documented. Berger and Nitsch (2010) find that bilateral intra-euro area imbalances have become more persistent after the introduction of the euro. Countries with high labour and product market inflexibility and slow movements in the real exchange rate have larger and more persistent deficits. Chen et al. (2010) add that growing current account deficits are also related to asymmetric trade developments vis-à-vis the rest of the world, in particular China and Central and Eastern Europe. Lane (2010) finds that the introduction of the euro did not immediately induce a widening of imbalances. Instead, the dispersion of current account imbalances increased markedly after 2003. The financing of current account deficits was facilitated by increased financial integration following the introduction of the euro. Moreover, financial innovations related to the credit and securitisation boom that started around 2003/4 also contributed. In the same vein, Giavazzi and Spaventa (2010) find that widening imbalances were not driven by a matching increase in the production capacity of traded goods and services in ‘cohesion countries’ (Spain, Ireland, Greece and Portugal). Instead, capital inflows were used to finance a boom in non-tradable consumption and residential construction. Adjustment options therefore go beyond traditional channels of price and wage adjustment, structural reform and fiscal consolidation, but also include a macro-prudential policy perspective on aggregate credit growth in the financial system as a whole (Jaumotte and Sodsriwiboon, 2010). Adjustment capacity The debate on adjustment capacity in EMU can also be seen from a somewhat longer historical perspective. Debated questions are (i) whether the euro area resembles an optimal currency area (OCA) and (ii) if not, how this would affect adjustment to asymmetric shocks. At the start of EMU there was broad consensus among economists that it is not an optimum currency area (OCA). For example, as early as 1993, Bayoumi and Eichengreen distinguished between ‘core’ and ‘periphery’ countries. In their analysis of shocks and adjustment they find that countries around Germany experience relatively small and highly correlated aggregate supply disturbances. In the periphery, supply disturbances are larger and more idiosyncratic. 4 These authors therefore conclude that monetary union with the core countries would be less problematic than for the larger group. After the start of EMU, Artis (2002) uses six criteria3 to distinguish three clusters: ‘the core’ (Germany, France, Austria, Belgium, The Netherlands), and the ‘northern’ (Ireland and Finland) and ‘southern’ (Italy, Spain, Portugal and Greece) peripheries. We use this classification in our empirical analysis. A controversial issue is how a ‘non-optimal’ currency area would function. EMU was argued to lead to long and costly adjustment processes in case of asymmetric shocks, a lack of flexible market adjustment and low labour market mobility. A more optimistic view is that monetary union would increase trade intensity and business cycle synchronization. This would fuel an endogenous process in the direction of an optimum currency area (Frenkel, 1997). The latter argument hinges on the trade effect of EMU, and on its subsequent effect on business cycle synchronization. Initial studies on the trade effect indicated that currency unions should as the EMU may lead to large increase in international trade (Rose, 2000). Since then, estimates of the trade effect of EMU have become progressively smaller over time (see, e.g., Micco et al., 2003, Baldwin, 2006). Berger and Nitsch (2008) even conclude that, after controlling for the trend increase in trade integration, the euro’s impact on trade disappears. Although there is a broad consensus in the literature that higher trade intensity leads to more business cycle synchronization, Inklaar et al. (2008) find that the trade effect on business cycle synchronization is smaller than previously thought. A related question concerns the effect of EMU on structural reforms. With monetary policy no longer available to respond to asymmetric shocks, the need for flexibility and incentives to undertake structural reform may increase (Bean, 1998). Alesina et al. (2008) report that the adoption of the euro has been associated with acceleration in the pace of structural reforms, but did not lead to labour market reforms. Likewise, Bednarek et al. (2010) find that EMU has had no effect on labour market reforms that enhance the capacity of an economy to adjust to economic shocks and those that aim to raise long-run equilibrium output. Overall, the available evidence therefore provides limited support at best for an endogenous process towards an optimal currency, in line with the emergence of large and persistent imbalances. 3 These criteria, measured relative to Germany, are: (1) inflation; (2) business cycle cross-correlation; (3) labour market performance; (4) real bilateral exchange rate volatility; (5) trade intensity and (6) monetary policy 5 Our paper in perspective The two papers closest to our study are Chen et al. (2010) and Flam and Nordström (2003). Chen et al. (2010) run separate export and import regressions. They find no evidence that Germany benefits from higher export demand elasticities than other euro area countries. When focusing specifically on export to China, they do find that the export demand elasticities of Greece, Italy, and Spain vis-à-vis China are significantly lower than the euro area average. Contrary to their approach, we start from the hypothesis that differences in export elasticities should be related to the type of export goods and services (using disaggregated data). We also focus on the response of export categories to the real exchange rate (price elasticity) in addition to demand (or income) elasticities as in their approach. Flam and Nordström (2003) show results of export regressions that are disaggregated according to 8 SITC categories. They find mixed results for the euro effect across trade categories. Contrary to our approach, they include 20 industrialised countries and do not include emerging markets. Moreover, they do not classify export categories according to technology intensity. Also, our focus is not on the trade effect of the euro but on adjustment mechanisms, i.e. the price and income elasticities across export categories. 3. A first look at the data Trade imbalances Table 1 shows the development of trade balances of euro area countries since the start of EMU. It becomes clear that the core and northern periphery countries generally have a positive balance, whereas the southern periphery countries generally have a negative balance. What is more, there is no clear tendency that these deficits reduce over time. [Insert Table 1 here] Partner countries As indicated, our disaggregated approach concentrates on partner countries and export composition. The literature on the trade effects of the euro illustrates the relevance of taking into account the trend increase in trade over time (see section 2). A priori, we see no reason correlation. 6 why this trend would only apply to the euro area. In investigating export dynamics, we therefore correct for the trend increases and focus on the share of particular products in total exports. Moreover, we investigate exports as a percentage of GDP, so that we do not lose sight of the size of trade. Table 2 shows the destination of the exports of the various country groups that we distinguish for particular years. Several conclusions can be drawn from this table. First, column (3) of Table 1 shows that since 1990 the share of the core countries in the exports of the southern periphery has declined. So, in contrast to popular belief, European integration has not led to an increase in the share of the core and the northern periphery in the exports of the southern periphery. In fact, over time the exports of countries in the southern periphery became more oriented towards other countries in the southern periphery. Interestingly, also for the countries in the core the share of exports to other countries in the core has declined over time. Second, exports of the core countries are more oriented towards emerging economies than exports of the southern periphery. In fact, the aggregate figures for the southern periphery suggest a rosier picture than is the case for most countries in this region, as exports of Italy have become more oriented towards emerging markets than exports of the other countries in the southern periphery as will be discussed below. Third, when exports are expressed as a share of GDP it become clear that the southern periphery is less oriented towards exports than the core. In the first years of the present century, exports as share of GDP increased in the core, while it was stagnant in the southern periphery. [Insert Table 2 here] Figure 2 shows more detailed information for each country. As said, apart from Italy, the share of emerging countries in exports of the southern periphery has hardly increased over time. In contrast, in several (but not all) countries in the core, emerging economies have become an important export market. For Portugal and Greece, and to a lesser extent Spain, the southern periphery has become more important over time for their exports. Even though the core is still the most important destination for exports (except for Greece that exports a lot to its neighbouring countries), the importance of exports to the core has declined in all countries in the southern periphery. [Insert Figure 2 here] 7 Composition of exports Figure 3 shows the development of the composition of exports of manufactured goods, using four different categories of manufactured goods, ranging from high to low technology goods (see Annex 1 for details). Unfortunately, a similar decomposition for services could not be made. Figure 3 shows that the share of high-tech manufactured goods in most core countries is about 20 percent, while it is much lower in the southern periphery. Except for Greece, the share of high-tech manufactured goods also has not increased in the southern periphery, in contrast to the northern periphery. [Insert Figure 3 here] 4. Baseline results Baseline regression We now investigate econometrically the role of the real exchange rate, the composition of exports and export behaviour of country groups in the euro area. We start from a standard gravity equation for exports, as estimated e.g. by Marquez (1990), Bayoumi (1999), Flam and Nordström (2003) and Chen et al. (2010). The main innovative element is that we run separate regressions for export classified according to technology intensity, as shown in the previous section. We also run regressions for separate groups of countries, to see if results match with the differences in export composition. The baseline specification is: log( Ecijt ) 1 L log Ecijt 2 ( L) log( Rijt ) 3 L log( PI jt ) 4 L log( RI it ) c cij tt cijt Subscripts are for export category c of reporting country i to partner country j, at time t. L is the lag operator. Ecijt is the volume of bilateral exports, of category c, from euro area country i (the reporting country) to partner country j in year t. Rijt is the real bilateral exchange rate between countries i and j at time t, PIj is real income of partner country j, RIi a measure of income/development in reporting country i. c is a constant, cij a country pair fixed effect that picks up the effect of all variables that are (near) constant between trade partners, such as 8 distance and culture. tt is a time effect (dummy for each year) to capture common shocks and cijt a residual. The main variables of interest are the long run elasticities of real exports with respect to the real exchange rate (expected coefficient is negative), real foreign income (expected coefficient is positive) and a measure of domestic development (expected coefficient is positive). The inclusion of the last variable is motivated by new trade theory that stresses increases returns to scale (Bayoumi, 1999). The proxy that Bayoumi (1999) and Flam and Nordström (2003) use is real income of the reporting country. We use real GNI/Capita at PPP basis in order to focus on differences in development.4 Moreover, we instrument this variable, or include it with a lag, to avoid possible reverse causality problems. Data and estimation method The dependent variable captures exports from each euro area country to at least its top 20 trade partners. This leads to the inclusion of 44 partner countries in total. Our sample period is 1988-2009. Export data are in current dollars. We first revert them to euros, and then deflate with the export deflator of each country.5 Our preferred way of calculating bilateral real exchange rates would be to use export prices or ULC. Like other authors, we end up using CPI due to data availability. Finally, GDP data are deflated by the GDP deflator. Our specification contains a lagged dependent variable, while the time dimension is short relative to the large number of country-pair groups. The Arellano-Bond estimator was developed for small-T large-N panels that include endogenous variables; hence this is our preferred estimation method. In addition, the robustness of results across estimation methods is shown.6 4 Regressions with real domestic income give similar patterns in the results. Moreover, excluding this variable has a minor effect on the other coefficients of interest. 5 Several studies mention that deflators for bilateral exports (in dollars) do not exist. They use therefore deflators at country level. It seems that different solutions are implemented. Chen et al. (2010, p. 46) use the reporters country’s export deflators. Micco et al. (2003, p. 327) deflate exports for all countries by US CPI. 6 We also show result from the method proposed by Bruno (2005) that corrects the bias on the lagged dependent variable (i.e. Least Squares Dummy Variables Corrected, or LSDVC). This estimator does not allow for instruments, so that we lag reporter income with a lag; moreover it is intended for panels with a small number of individuals. For comparison, the baseline regression also shows results estimated with LSDV (least squares dummy variables), so that the effects from the dynamic bias can be seen. Finally, we also show results from a fixed effects regression where reporter income is instrumented. 9 Results Table 3 shows results for our baseline regression for all euro area countries following a general to specific strategy. We start from including three lags for all variables, and remove those that are statistically insignificant. The lagged dependent variable is highly statistically significant. Excluding it leads to autocorrelation in the residuals.7 Moreover, two lags of the real exchange rate need to be included, and one lag of real partner income. Our interest is in the long-run elasticities. Table 4 therefore reports them directly, based on the results of Table 3. The long-run coefficients are calculated by removing the time dimension, adding up the different lags, and dividing them by 1 minus the coefficient of the lagged dependent variable. Coefficients have the expected sign, and are mostly highly significant. A 1% appreciation in the real exchange rate leads to a 0.6-0.8% decrease in real exports. This finding is in line with the results of Marquez (1990, p. 74), who finds elasticities between -0.5 and -1.1. It is also close to the result reported by Flam and Nordström (2003) who find a coefficient of approximately 1. The elasticity of real exports with respect to real partner income is about -1. This is slightly slower than results reported in other studies which find coefficients just above 1 (Flam and Nordstrom, 2003) or elasticities between 1 and 2 (Marquez, 1990). Our results for reporter capita per GNI/capita also show elasticities around 1 for most estimation methods. If we use real reporter income instead, we find elasticities in the range of 0.5-0.7. This finding of a higher elasticity for partner income than for own income is in line with other studies (Bayoumi, 1999; Marquez, 1990) [Insert Tables 3 and 4 here] We now move to the regressions for the composition of exports. To save space, Table 5 only reports the long-term elasticities (calculated as in Table 4). Results are always highly significant, unless stated otherwise in the table. In line with our hypothesis, we find that price elasticities differ considerably among export categories. Moving from low-technology to high-technology industries decreases the elasticity from -0.9 to -0.3. This is in line with the idea that price competition is stronger in low-technology industries. 7 Of the studies mentioned, only Marquez (1990) includes a lagged dependent variable, and finds it to be 10 Moving to the demand side – i.e. the effect of real partner income – we find mixed results. Exports from low-technology industries indeed show the lowest response to partner income. At the same time, demand elasticities decrease when we move from medium-low to hightechnology industries. In a related approach, Chen et al. (2010, p. 15) do not find that Germany (which has a relatively high share of high technology export) benefits from higher export demand elasticities than other euro area countries. Perhaps the most surprising results concern the role of own income. Moving along the spectrum from low to high-technology exports, the coefficient increases strongly, and becomes highly statistically significant.8 This suggests that structural development of the domestic economy coincides with higher technology intensity of exports, presumably through development of human and physical capital. As a consequence, it lessens the impact of real exchange rate movements on exports. As a first sensitivity test, Table 6 excludes The Netherlands and Belgium from the sample. Due to their geographical location, these countries have a large and growing share of reexports, of over 50% of total exports for The Netherlands. Re-exports are generally of a high quality, but production costs incurred in the re-exporting cover only a very small proportion of total costs. Prices are for 90% determined by import prices (Mellens et al., 2007, p. 24). These effects may potentially influence our results. Indeed, the results in Table 6 show that dropping these countries increases the difference in responses to the real exchange. The elasticity of real export to the real exchange rate now decreases from -1 for low technology industries to -0.3 for high technology industries. [Insert Tables 5 and 6 here] Table 7 shows the results for different country groups in the euro area. We expect a stronger response to the real exchange rate in the southern periphery, since the technology intensity of export is generally lower (see section 3). Indeed, we find that real exports in core EMU statistically significant. 8 We find a similar pattern when we use real own income (instrumented) instead. In this case, the variable is always statistically significant, negative for low-technology industries, and positive and strongly increasing when 11 countries are less sensitive to real exchange rate movements than countries from the southern periphery (elasticity of -0.46 versus -0.70). This provides additional insight in the causes of persistent current account imbalances. The contribution of real exchange rate appreciations to persistent current account imbalances is well known. Our results suggest that differences in export composition may have contributed as well. [Insert Table 7 here] 5. Robustness checks (still to be done) We could add additional control variables: 1. Price effects from 3rd countries and competition from China See previous note for how price competition from 3rd countries has been measured. In addition, there is interest in the effect of increasing exports from China and other emerging markets. 2. Institutional dummies Additional to country pair dummies: EMU dummy for bilateral pairs of 11 Euro area countries (1999-now) and Greece (2001-now); Possibly: dummy for individual countries joining the EU after start sample period (this dummy would capture country pairs within the EU; note however that it would show strong overlap with the EMU dummy): Greece-1981; Portugal, Spain-both 1986; Austria, Finland, Sweden-1995; Cyprus, Czech Republic, Estonia, Hungary, Latvia, Lithuania, Malta, Poland, Slovakia, Slovenia-all 2004; Bulgaria, Romania-2007. 6. Conclusion and policy implications Countries in the southern periphery of the euro area (Greece, Italy, Portugal and Spain) have large and persistent current account imbalances. An important policy issue is how these imbalances can be redressed. Whereas policymakers so far have focused on the lack of real exchange rate flexibility, we examine the impact of the composition of exports and, related to we move towards high-technology industries. 12 that, the destination of the exports. Our analysis shows that in contrast to popular belief, European integration has not led to an increase in the share of the core and the northern periphery in the exports of the southern periphery. In fact, over time the exports of countries in the southern periphery became more oriented towards other countries in the southern periphery. Exports of the core countries are more oriented towards emerging economies than exports of the southern periphery. No doubt, this reflects the composition of exports. Whereas the share of high-tech manufactured goods in most core countries is about 20 percent, it is much lower in the southern periphery and generally has also not increased since the start of EMU. This is the bad news coming from our analysis for the southern periphery. Policies aiming at increasing the share of high-technology exports take time and will not have much impact in the short run. The goods news coming from our analysis for the southern periphery is that the elasticity of real exports with respect to the real exchange rate decreases from -1 to -0.3 along the spectrum from low-technology to high-technology export. This implies that if countries in the southern periphery are able to reduce their real exchange rate, i.e. increase their price competitiveness, they will be able to substantially increase their exports, thereby redressing their current account imbalances. 13 References Alesina, A., S. Ardegna and V. Galasso (2008), ‘The Euro and Structural Reforms’, NBER Working Paper, No. 14479. Artis, M.J. (2002), ‘Reflections on the Optimal Currency Area (OCA) Criteria in the Light of EMU’, Central Bank of Chile Working Papers, No. 193. Baldwin, R. 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Sodsriwiboon (2010), ‘Current Account Imbalances in the Southern Euro Area’, IMF Working Papers No. 139. Lane, P., ‘International Financial Integration and the External Positions of Euro Area Countries’, OECD Economics Department Working Papers, No. 830. Mansori, K. (2011), ‘What Really Caused the Eurozone Crisis?’, http://streetlightblog.blogspot.com/2011/09/what-really-caused-eurozone-crisis-part.html. Marquez, J. (1990), ‘Bilateral Trade Elasticities’, The Review of Economics and Statistics, 72(1): 70-77. 14 Mellens, M.C., H.G.A. Noordman and J.P. Verbruggen (2007), Re-exports: international comparisons and implications for performance indicators, CPB document No. 149. Micco, A., E. Stein and G. Ordoñez (2003), ‘The Currency Union Effect on Trade: Early Evidence from EMU’, Economic Policy 18(37): 315-356 Rose, A.K. (2000), ‘One Money, One Market: the Effect of Common Currencies on Trade’, Economic Policy, 15(30): 7-45. Appendix Data Sources The numerators for Table 1 were extracted in September 2011 from Eurostat. Link to the table: http://epp.eurostat.ec.europa.eu/tgm/table.do?tab=table&init=1&plugin=1&language=en&pcode=tet00002 The nominal GDP denominators were taken from Thomson-Datastream. The variables for Figure 1 were taken from the spring-2011 release of the AMECO-database of the European Commission. The data for Table 2 and Figure 2 were taken from the OECD ITCS-database in December 2010. Our selection of partner-countries is based on a top-20 ranking of export destinations for the 12 euro area member countries for each of the years: 1980-2009. We studied the 1digit SITC-2 classification. We used this older classification for availability of longer timeseries. In this paper we only used the export totals rather than the 1-digit subtotals. The nominal GDP denominators were taken from Thomson-Datastream. For Figure 3 and the estimation of our regression-models we used the OECD STAN Bilateral Trade database. Our decision to use this source rather than the mentioned ITCS-data was based on the availability in this source of an export quality indication, which proved more successful in modelling than the 1-digit SITC-2 classification. In addition to this the ISIC-3 classification that forms the basis for the export quality indicators, captures more of the technological developments than the older SITC-2. Drawbacks of this choice are: shorter time-series and the loss of some of the partner-countries from our top-20 based list. In addition to the data shown we used extra variables for our regression models: - - Deflators of the exports of goods for the 12 euro area member countries from the AMECO-database (spring-2011 release). CPI-series for all 12 euro area and partner-countries from the IMF IFS-database. For some countries (China, Belarus, United Arab Emirates and Ukraine) supplemented from Thomson-Datastream. Real-GDP series for all 12 euro-area and partner-countries from the IMF WEOdatabase. GNI per capita series in PPP terms for all 12 euro area and partner-countries from the World Bank Global Development Indicators. US-Dollar exchange rates for all 12 euro area and partner-countries from the IMF IFSdatabase. Fixed exchange rates for the euro versus the old national currencies for the 12 euro area member states from the ECB. 15 Table 1. Trade Imbalance for Euro-Area Countries and Regions External trade by declaring country; Trade balance as a percentage of GDP 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 Core: Germany * France Austria Belgium Luxembourg Netherlands Total Core 3.2 0.7 -2.5 5.6 -14.2 3.0 2.9 -0.9 -2.4 4.7 -14.3 3.9 4.5 -0.4 -2.0 5.0 -12.7 5.5 6.2 0.2 0.2 7.0 -10.7 5.6 6.0 -0.4 -0.9 6.6 -9.8 5.8 7.1 -0.9 -0.5 5.9 -11.1 6.2 6.9 -1.9 -0.7 4.2 -8.2 6.7 6.9 -2.0 -0.1 3.8 -8.6 6.9 8.0 -2.8 0.2 4.2 -10.1 7.4 7.2 -3.5 -0.7 1.1 -10.7 6.5 5.8 -2.9 -1.6 3.8 -6.4 6.9 6.1 -3.3 -1.7 4.7 -7.9 7.2 2.2 1.5 2.6 3.8 3.5 3.8 3.3 3.2 3.5 2.6 2.4 2.4 Northern Periphery: Ireland Finland 25.4 7.8 27.2 9.5 30.8 8.5 29.1 8.0 24.5 6.5 23.1 5.3 20.3 3.4 16.0 3.8 14.5 3.4 15.8 1.7 23.9 0.8 27.8 0.5 Total Northern Periphery 15.3 17.4 18.7 18.1 15.3 14.1 12.0 10.1 9.1 8.6 11.9 13.1 -12.2 1.2 -14.5 -5.0 -13.3 0.2 -17.3 -7.0 -12.8 0.7 -16.4 -6.2 -10.8 0.6 -14.3 -5.7 -9.5 0.1 -16.1 -5.9 -10.4 -0.1 -16.3 -7.2 -13.2 -0.7 -15.3 -8.5 -12.9 -1.4 -16.2 -9.3 -12.8 -0.6 -17.7 -9.4 -14.7 -0.8 -19.0 -8.7 -11.6 -0.4 -14.9 -4.5 -11.7 -1.8 -13.7 -4.9 -2.4 -4.0 -3.4 -3.1 -3.5 -4.2 -5.1 -5.9 -5.7 -5.8 -3.6 -4.3 Southern Periphery: Portugal Italy Greece Spain Total Southern Periphery * Including former GDR from 1991. 16 Table 2 Exports of Goods by and between Euro-Area Regions As a Percentage of Total Trade Northern Periphery Southern Periphery 41.8 1.1 11.9 24.5 0.6 4.5 40.4 0.7 6.0 22.6 5.0 17.6 100.0 46.6 13.2 10.7 100.0 40.6 1.2 14.5 As a Percentage of GDP Northern Periphery Southern Periphery 9.5 0.2 2.7 7.5 0.2 1.4 5.7 0.1 0.8 20.1 4.1 28.7 100.0 5.2 1.1 4.0 22.8 14.2 4.0 3.2 30.5 2.8 0.6 4.1 14.1 29.2 0.6 7.1 43.8 1.0 11.4 10.2 0.3 3.6 7.9 0.1 1.9 5.9 0.1 1.5 26.5 4.1 13.1 100.0 46.4 8.7 8.1 100.0 25.0 4.0 14.8 100.0 6.7 1.0 3.3 25.1 12.5 2.3 2.2 26.9 3.4 0.5 2.0 13.5 34.1 1.5 14.3 27.9 0.5 7.5 36.6 1.2 13.4 10.8 0.5 4.5 15.6 0.3 4.2 7.4 0.2 2.7 27.5 8.6 14.0 100.0 45.4 6.5 12.3 100.0 23.7 7.0 18.1 100.0 8.7 2.7 4.4 31.6 25.4 3.6 6.9 55.9 4.8 1.4 3.7 20.3 33.9 1.4 12.7 26.7 0.4 7.8 32.2 0.9 13.6 13.2 0.6 5.0 11.2 0.2 3.3 6.4 0.2 2.7 22.6 13.9 15.4 100.0 39.7 12.0 13.3 100.0 19.0 10.8 23.5 100.0 8.8 5.4 6.0 38.9 16.6 5.0 5.6 41.8 3.8 2.2 4.7 20.1 Core Core 1980 Core Northern Periphery Southern Periphery Rest Industrialised World Emerging M arkets Rest of the World World 1990 Core Northern Periphery Southern Periphery Rest Industrialised World Emerging M arkets Rest of the World World 2000 Core Northern Periphery Southern Periphery Rest Industrialised World Emerging M arkets Rest of the World World 2008 * Core Northern Periphery Southern Periphery Rest Industrialised World Emerging M arkets Rest of the World World Core: Austria, Belgium, France, Germany, Luxembourg, Netherlands. Northern Periphery: Finland, Ireland. Southern Periphery: Greece, Italy, Portugal, Spain. Rest Industrialised World: Australia, Canada, Denmark, Japan, Korea, New Zealand, Norway, Sweden, Switzerland, UK, US. Emerging M arkets: Brazil, China, Czech Rep.**, Hong Kong, Hungary, India, Poland, Russian Federation ***, Slovak Rep.**, Slovenia. * 2008 is used because of missing trade-data for Spain in later years. ** Before 2003: Former Czechslovakia. *** Before 2002: Former USSR. 17 Table 3. Total exports, all euro area countries GMM Stata command: Lagged dep. var. Log RER Log RER L1 Log RER L2 Log partner real income Log partner real income L1 Log Reporter (GNI/Capita, PPP) xtabond 0.68 (0.056)*** -0.64 (0.065)*** 0.30 (0.050)*** 0.10 (0.051)** 2.13 (0.17)*** -1.81 (0.22)*** -0.15 (0.16) Log Reporter (GNI/Capita, PPP) L1 Time dummies Pairwise fixed effects Number of observations Number of groups Number of instruments R-squared (overall) Standard errors Yes Yes 7768 440 101 n.a. robust Dependent variable: log of real exports Fixed effects Fixed effects Fixed effects, IV Corrected xtlsdvc xtreg xtivreg 0.79 0.69 0.69 (0.012)*** (0.019)*** (0.0073)*** -0.56 -0.59 -0.59 (0.19)*** (0.042)*** (0.027)*** 0.31 0.25 0.25 (0.19)*** (0.047)*** (0.036)*** 0.13 0.10 0.11 (0.097) (0.041)** (0.023)*** 1.97 2.08 2.08 (0.35)*** (0.13)*** (0.085)*** -1.75 -1.76 -1.76 (0.42)*** (-13.10)*** (0.081)*** 0.40 (0.045)*** 0.22 0.36 (0.12)* (0.064)*** Yes Yes Yes Yes Yes Yes 8208 8208 8208 440 440 440 n.a. bootstrapped 0.92 robust 0.91 conventional Tabel 4. Long-run coefficients GMM Dependent variable: log of real export Fixed effects Fixed effects Fixed effects, IV Corrected (LSDV) (LSDVIV) (LSDVC) -0.59 -0.76 -0.75 1.01 1.02 1.02 1.04 1.17 1.30 -0.76 1.01 -0.48 (insignificant) Notes: the estimation Arellano-Bond dynamic panel-data estimation. GMM type instruments: L(2/3).log(export volume); L(2/3)log reporter income. Standard errors are adjusted for clustering on panel groups. Log RER Log partner real income Log Reporter (GNI/Capita, PPP) 18 Table 5. Long-run coefficients by exports categories. Total Dependent variable: log of export LowMedium-low Medium-high technology technology technology industries industries industries -0.93 -0.89 -0.80 0.91 1.59 1.23 Hightechnology industries -0.29 1.19 -0.76 Log RER 1.01 Log partner real income -0.48 -0.61 -0.17 1.23 2.25 Log Reporter (insignificant) (insignificant) (insignificant) (GNI/Capita, PPP) Number of 7768 7768 7759 7766 7731 observations Number of groups 440 440 440 440 440 Number of 101 101 101 101 instruments Notes: the estimation Arellano-Bond dynamic panel-data estimation. GMM type instruments: L(2/3).log(export volume); L(2/3)log reporter income. Standard errors are adjusted for clustering on panel groups. Table 6. Exports categories, excluding NL and BE, long-run coefficients Total export Dependent variable: log of export of Low-technology Medium-low Mediumindustries technology high industries technology industries -1.00 -0.88 -0.83 0.97 1.53 1.31 Hightechnology industries -0.84 -0.30 Log RER 1.10 1.18 Log partner real income 0.19 -0.08 0.28 1.54 2.86 Log Reporter (insignificant) (insignificant) (GNI/Capita, (insignificant) PPP) Number of 6968 6968 6959 6966 6931 observations Number of 396 396 396 396 396 groups Number of 101 101 101 101 101 instruments Notes: the estimation Arellano-Bond dynamic panel-data estimation. GMM type instruments: L(2/3).log(export volume); L(2/3)log reporter income. Standard errors are adjusted for clustering on panel groups. 19 Table 7. Total exports, country groups, long run coefficients Dependent variable: log of export of Log RER All euro area Core Southern periphery Northern periphery -0.76 -0.46 -0.70 -1.03 1.01 1.16 1.37 1.22 Log partner real income -0.48 1.03 1.09 2.77 Log Reporter (GNI/Capita, (insignificant) PPP) Number of 7768 2972 3196 800 observations Number of 440 176 176 44 groups Number of 101 98 101 63 instruments Notes: the estimation Arellano-Bond dynamic panel-data estimation. GMM type instruments: L(2/3).log(export volume); L(2/3)log reporter income. Standard errors are adjusted for clustering on panel groups. 20 Figure 2 Figure 2A Exports of Goods From Euro-Area Countries by Destination: Core Countries As a Percentage of Total Exports of Goods Austria As a Percentage of GDP 100% 50 80% 40 60% 30 40% 20 20% 10 0% 0 80 85 90 95 00 05 France 100% 80 85 90 95 00 05 80 85 90 95 00 05 80 85 90 95 00 05 80 85 90 95 00 05 100 80 60 40 50 200 100% 0% 80% 80 85 90 95 00 05 40 60% 30 40% 20 20% 10 0% 0 80 85 90 95 00 05 Germany 100% 50 80% 40 60% 30 40% 20 20% 10 0% 0 80 85 90 95 00 05 Belgium-Luxembourg 100% 100 80% 80 60% 60 40% 40 20% 20 0% 0 80 85 90 95 00 05 80 85 90 95 00 05 80 85 90 95 00 05 Netherlands 100% 100 80% 80 60% 60 40% 40 20% 20 0% 0 80 85 90 95 00 Emerging M arkets Core Northern Periphery Southern Periphery Other Industrialised Countries Rest of the World 05 Source: OECD ITCS-Database. 21 Figure 2B Exports of Goods From Euro-Area Countries by Destination: Peripheral Countries As a Percentage of Total Exports of Goods Finland As a Percentage of GDP 100% 100 80% 80 60% 60 40% 40 20% 20 0% 0 80 85 90 95 00 05 80 85 90 95 00 05 Ireland 100% 100 80% 80 60% 60 40% 40 20% 20 0% 0 80 85 90 95 00 05 80 85 90 95 00 05 80 85 90 95 00 05 80 85 90 95 00 05 80 85 90 95 00 05 80 85 90 95 00 05 Greece 100% 50 80% 40 60% 30 40% 20 20% 10 0% 0 80 85 90 95 00 05 Italy 100% 50 80% 40 60% 30 40% 20 20% 10 0% 0 80 85 90 95 00 05 Portugal 100% 50 80% 40 60% 30 40% 20 20% 10 0% 0 80 85 90 95 00 05 Spain 100% 50 80% 40 60% 30 40% 20 20% 10 0 0% 80 85 90 95 00 Emerging M arkets Core Northern Periphery Southern Periphery Other Industrialised Countries Rest of the World 05 22 Figure 3 Figure 3A Exports of Goods From Euro-Area Countries by Quality: Core Countries As a Percentage of Total Exports of Goods Austria As a Percentage of GDP 100% 50 80% 40 60% 30 40% 20 20% 10 0% 0 88 90 92 94 96 98 00 02 04 06 08 88 90 92 94 96 98 00 02 04 06 08 88 90 92 94 96 98 00 02 04 06 08 88 90 92 94 96 98 00 02 04 06 08 France 100% 50 80% 40 60% 30 40% 20 20% 10 0% 0 88 90 92 94 96 98 00 02 04 06 08 Germany 100% 50 80% 40 60% 30 40% 20 20% 10 0% 0 88 90 92 94 96 98 00 02 04 06 08 Belgium-Luxembourg 100% 100 80% 80 60% 60 40% 40 20% 20 0% 0 88 90 92 94 96 98 00 02 04 06 08 88 90 92 94 96 98 00 02 04 06 08 88 90 92 94 96 98 00 02 04 06 08 Netherlands 100% 100 80% 80 60% 60 40% 40 20% 20 0 0% 88 90 92 94 96 98 00 02 04 06 08 Non-manufactured Goods Low Technology M anufactures M edium-low Technology M anufactures M edium-high Technology M anufactures High Technology M anufactures Source: OECD STAN Bilateral Trade Database. 23 Figure 3B Exports of Goods From Euro-Area Countries by Quality: Peripheral Countries As a Percentage of Total Exports of Goods Finland As a Percentage of GDP 100% 100 80% 80 60% 60 40% 40 20% 20 0% 0 88 90 92 94 96 98 00 02 04 06 08 88 90 92 94 96 98 00 02 04 06 08 88 90 92 94 96 98 00 02 04 06 08 Ireland 100% 100 80% 80 60% 60 40% 40 20% 20 0% 0 88 90 92 94 96 98 00 02 04 06 08 Greece 100% 50 80% 40 60% 30 40% 20 20% 10 0% 0 88 90 92 94 96 98 00 02 04 06 08 88 90 92 94 96 98 00 02 04 06 08 88 90 92 94 96 98 00 02 04 06 08 88 90 92 94 96 98 00 02 04 06 08 88 90 92 94 96 98 00 02 04 06 08 Italy 100% 50 80% 40 60% 30 40% 20 20% 10 0% 0 88 90 92 94 96 98 00 02 04 06 08 Portugal 100% 50 80% 40 60% 30 40% 20 20% 10 0% 0 88 90 92 94 96 98 00 02 04 06 08 Spain 100% 50 80% 40 60% 30 40% 20 20% 10 0 0% 88 90 92 94 96 98 00 02 04 06 08 Non-manufactured Goods Low Technology M anufactures M edium-low Technology M anufactures M edium-high Technology M anufactures High Technology M anufactures 24