2.1 Setup

Our theoretical model follows Caliendo and Parro (2015), who provide a multi-sector version of Eaton and Kortum (2002) with input-output linkages. We briefly derive the gravity equation to be estimated and describe how we simulate counterfactual equilibria. Details are relegated to the Appendix.

There are N countries indexed by i and n, as well as J sectors indexed by j and k. Sectoral goods are either used as inputs in production or consumed, with the representative consumer having Cobb-Douglas preferences over consumption Cnj of sectoral final goods with expenditure shares αnj∈0,1 and ∑jαnj=1.

Labor is the only production factor and labor markets clear. The labor force Ln is mobile across sectors such that Ln=∑j=1JLnj, but not between countries. In each sector j, there is a continuum of intermediate goods producers indexed ωj∈[0,1] who combine labor and composite intermediate input and who differ with respect to their productivity zijωj. Intermediate goods are aggregated into sectoral composites using CES production functions with elasticity ηj. In all markets, there is perfect competition.

A firm in country i can supply its output at price

The minimum cost of an input bundle is cnj, where Υnj is a constant, wn is the wage rate in country n, pnk is the price of a composite intermediate good from sector k, βnj≥0 is the value added share in sector j in country n and γnk,j denotes the cost share of source sector k in sector j’s intermediate costs, with ∑k=1Jγnk,j=1. κinj denotes trade costs of delivering sector j goods from country i to country n such that

where tinj≥0 denotes ad-valorem tariffs, Din is bilateral distance, and Zin is a vector collecting trade cost shifters (such as FTAs or other trade policies).

Productivity of intermediate goods producers follows a Fréchet distribution with a location parameter λnj≥0 that varies by country and sector (a measure of absolute advantage) and shape parameter θj that varies by sector (and captures comparative advantage). ^[2]

Producers of sectoral composites in country n search for the supplier with the lowest cost such that

Caliendo and Parro (2015) show that it is possible to derive a closed form solution of composite intermediate goods price

where Aj=Γ1+θj(1−ηj)11−ηj is a constant.

2.2 Gravity

Given this structure, one can show that a country n’s expenditure share πinj for source country i’s goods in sector j is

which forms the core of a gravity equation.

Log-linearizing eq. (5) and making use of eq. (2), one obtains the following gravity equation:

Min,tj is the value of imports of country n from partner country i in sector j at time t. The interesting parameters are the sectoral tariff elasticities θ and shifters of sectoral trade costs δ. The vector €in,t takes the value of one if two countries i,n share the Euro at time t, and zero otherwise, where we allow for different parameters between different country groups and also with respect to directionality.

In the baseline gravity model, €in,t in eq. (6) contains only one single binary variable which switches to one if two countries are both members of EMU. In further specifications the vector €in,t contains binary variables that specifically control for trade flows between Germany and the ‘old’ and ‘new’ EMU members.^[3] In a symmetric gravity specification, the directional effects - whether Germany is the exporter or importer - are ignored, while this distinction is made in the asymmetric gravity specification. The following sub-chapters explain the vector €in,t in more detail.

To identify causal treatment effects the panel nature of the data is exploited. Given the nature of the underlying theoretical model, these estimates can be translated into changes in ad valorem tariff equivalents of non-tariff trade costs. 1+τin,tj depicts the ad valorem tariff, with the trade elasticity 1/θj>0. Since we can observe the data for these ad valorem tariffs for all bilateral pairs across sectors, the trade cost elasticity can be correctly identified and then later be used for the CGE simulations. Second, unbiased estimates of δljθj are needed, where l≡[1,2].

Identifying variation stems from the membership accessions between 1995 and 2011, which is our available time frame. The Euro was officially launched on January 1, 1999 in 12 EU countries.^[4] Between 2002 and 2015, the remaining members joined.^[5] The vector Zin,t contains dummy variables accounting for membership in the EU, the Schengen Area or other regional trade agreements.

In order to account for multilateral resistance, importer- and exporter-specific year fixed effects, νi,tj and νn,tj, are included. These terms are generally unobserved and fully control for all exporter- and importer-specific time-varying determinants of trade (such as production or consumption). Effectively, they also control for nominal and real exchange rates movements relative to a third currency, and in combination (through triangle arbitrage) between countries i and n. νinj are bilateral country-pair fixed effects, which absorb all time-invariant bilateral trade frictions. The fixed effects may account for potential endogeneity issues of the EMU dummy if two countries that decide to join a currency union have traditionally traded a lot with each other (see e. g. Micco et al. 2003). This fixed effect may also prevent potential selection bias. The selection of country pairs into plurilateral agreements may not be completely random, but is also not a purely bilateral decision. We further believe that reverse causality is not a major issue. Apart to potential endogeneity, this also addresses omitted variable bias in integration agreements (see, e. g. Baier/Bergstrand 2007). εin,tj is the random error term. As recommended by Santos Silva and Tenreyro (2006), we estimate the model using Poisson Pseudo Maximum Likelihood (PPML) methods to address the OLS inconsistency and sample selection bias. We cluster standard errors at the country-pair level.

Following the common practice (see Baier/Bergstrand 2007), we exploit variation within country-pairs and sectors over time to then identify the effects of policy changes. Thus, econometric identification relies on countries joining an agreement and the EMU in the period 1995–2011.

2.3 Comparative statics

We wish to answer the question: How does welfare (real per capita income) in the observed baseline 2014 differ from a counterfactual situation in which the Euro did not exist. To answer this question, we need to close the model introduced in Section 2 above. We do this be requiring that in all countries, accounting for trade surpluses, income equals expenditure, and that for all sectors, goods markets clear. Appendix A.1 provides the essential equations.

We are interested in the effects of the decrease in transaction costs due to the membership of the EMU on income, trade, and value added. As shown by Dekle et al. (2008), the model can be solved in changes. Let z denote the initial level of a variable and z′ its counterfactual level. The Appendix A.2 provides more detail. The transaction cost shocks are then given by κˆinj=1+tinj′1+tinjeδj(Zin′−Zin) and the change in real income (our measure for welfare) is

Solving the model in changes has several important advantages. First, certain constant parameters which would be difficult to estimate such as the level of absolute advantage, the level of non-tariff trade barriers, or the elasticity of substitution drop out from the analysis. This should reduce measurement error. Second, the procedure has computational advantages as one does not need to solve for the baseline and the counterfactual equilibria separately.

2.4 Construction of confidence intervals

We simulate confidence intervals for all endogenous outcome variables. More specifically, we use the variance-covariance matrix of the sectoral gravity regressions and, assuming joint normality, we draw a thousand different parameter sets for each sector. We use these to calibrate a thousand simulation exercises, obtaining a distribution of changes in outcome variables. We report the 5th and the 95th percentiles of these distributions (the 90 % confidence interval) together with the mean. This allows for a sound treatment of statistically insignificant gravity coefficients and for a proper quantification of parameter uncertainty.

3.1 Data

The main data base is the World Input-Output Database (WIOD). It is described in detail by Timmer et al. (2015). It provides information on the expenditure shares α, the cost shares β and γ, as well as data on bilateral trade shares π, bilateral trade in final and intermediate goods in producer and consumer prices detailed by sector, countries’ total value added wnLn, values of production, and trade surpluses S.

There are two waves of WIOD data. The first wave includes data for 40 countries, 16 goods sectors and 19 services sectors for the years 1995 until 2011. The second wave, which was published in 2016 includes information about 43 countries, a rest-of-the-world aggregate and 56 sectors for the years 2000 to 2014. Unfortunately, no official concordance between the two waves exists, and any mapping of sectors is likely to contaminate the crucial time variance in the data required for proper estimation. For this paper, we use the first WIOD wave to be able to cover the first Euro accessions by Germany, Italy, Belgium, Finland, France, Ireland, Luxembourg, Netherlands, Portugal, and Spain in 1999. One disadvantage of the first WIOD wave is the fact that we cannot take account of the most recent Euro accessions of Lithuania and Latvia in 2014 and 2015. Single Market and Customs Union effects are identified through the enlargement of the EU between 1995 and 2011 and thus do not cover the most recent accessions by Croatia. We thus cover almost all Euro and EU accessions, which leaves us confident to correctly proxy the Euro effects.

However, to pin down the baseline, we have constructed a concordance between the two waves and work with the year 2014, the most recent one available. We use WIOD data on sectoral outputs, bilateral aggregated intermediate and final trade shares final expenditure and intermediate cost shares. Moreover, we match the cross-section of tariffs in 2014.^[6] Data on bilateral preferential and MFN tariffs stem from Felbermayr et al. (2018b). Sectoral trade cost elasticities θ and the trade cost changes δ are identified through structural state-of-the-art gravity estimation. Data on tariffs and on trade from WIOD are used to estimate trade elasticities for the 16 manufacturing and agricultural sectors – jointly with the ad-valorem equivalent changes in NTBs associated with the different steps of European and trade integration in general.^[7] We use data on RTA membership from the WTO.^[8] Data on membership in the EU, the Eurozone and the successive accession of countries to the Schengen Agreement stem from the European Commission. Information about the EU membership and RTA membership is taken the website of the European Commission.

3.2 Gravity analysis of average effects

The first baseline gravity model estimates the average trade effect of bilateral country pairs being members of the EMU at time t. So, €in,t in eq. (6) is not a vector, but rather contains only one single binary variable which switches to one if two countries are both members of EMU. Further, control variables, such as being a member of the European Union, the Schengen Area, a customs union or a trade agreement are also included (Zin,tj). We start with this simple specification to make our results comparable to earlier literature. The first line in Table 1 shows the estimates for aggregate goods and services trade.

On average, becoming a EMU member increased imports of goods by 7.8 % and is statistically significant. This average result is in line with literature, (see e. g. Felbermayr et al. 2018a; Larch et al. 2017); the authors find rather small, but positive effects, although lacking significance in the latter example. Interestingly, the effect on services trade is small and statistically not significant.

The rest of Table 1 shows the gravity estimation results for all 16 goods sectors. The EMU has heterogeneous effects across the sectors, but with the only exception of the textiles sector, effects are positive. Many coefficients have large standard errors. As a consequence, we expect sizeable confidence intervals in our simulation exercise. In the area of services industries, sales and repair of vehicles, or accommodation (hotels) have strongly benefitted. Again, most estimated effects are positive but standard errors are large.

3.3 Singling out Germany and allowing for directionality

Table 2 goes one step further and singles out Germany from the other EMU members. Moreover, it distinguishes between ‘old’ and ‘new’ EMU members.^[9] However, effects are still symmetric in the sense that German exports and imports are affected similarly. Dropping time indices to avoid clutter, the vector €in in eq. (6) becomes

Columns (1) and (2) show that especially trade between Germany and the other ‘old’ EMU members was enhanced due to EMU. On average trade in goods increased by 13.8 % and trade in services by 7.2 %, with both being statistically significant. Trade between Germany and the ‘new’ member states significantly decreased by 11.5 % in manufacturing sectors, and by 10.5 % in services sectors (Column (2) and (5)). Next, columns (3) and (4) (broad goods), and column six (broad services) differentiate between Germany being an exporter and an importer. So, we have

which allows Germany’s Euro-effects to be asymmetric. To save degrees of freedom, in this specification, we do not decompose the effect for the remaining Euro zone members. Estimation results suggest that German exports of goods towards the old members increased by 18.2 % (see column (4), line asym.\euro{}DEU,old) and goods’ imports from old EMU members increased by 7.5 % (see column (4), line asym.\euro{}old,DEU). German services exports towards old EMU members increased by 16.3 % (see column (6)). But, as for imports, the effect is not distinguishable from zero. In contrast, exports and imports of goods from and to Germany to and from the new members even decreased by 11.2 % (see column (4), line asym.\euro{}DEU,new) and 11.8 % (see column (4), line asym.\euro{}new,DEU). The trade effects for the German service industry are even more pronounced: German exports in services to the new members decreased by 16.9 %, which is also significant, whereas German services imports decreased by 4.7 %. But this result is not significant.

To sum up, the effect on German exports and imports varies substantially. Further it also differs across the trading partners. Trade with old EMU members expanded, whereas, trade with the new members decreased, both across services and goods and for German exports and imports. Note that this is not due to a ‘wrong’ initial exchange rate between Germany and the new members, as initial conditions are accounted for by country-year fixed effects. The effects can also not be explained by different paths of prices (i. e. inflation) or even nominal exchange rates, which are effectively dealt with by fixed effects. Also, trade diversion cannot be blamed, because it is taken into account by the inclusion of fixed effects (which proxy for multilateral resistance terms). Note, that the welfare effects of the EMU do not depend on whether outward trade costs have gone down; of course, inward trade costs are at least of equal importance for welfare gains.

Tables 3 and 4 take the gravity specification, which accounts for directional trade between Germany and the new and old EMU members to a more disaggregated sectoral level. This specification informs the general equilibrium simulations. The respective tables solely show the results for the effects of the Euro between Germany, the old and new members, and the average effects for the remaining EMU members. Estimates of coefficients on additional control variables can be retrieved from the Tables 11 and 12 in the Appendix.

Exports of German agricultural products to old EMU members went up, while the respective effect on imports is less pronounced. Trade between Germany and new EMU members did not experience a decrease in transaction costs. German trade with old members solely increased in the manufacturing industries, except for textile and leather products. German exports towards the new members decreased for almost all manufacturing products, except Coke, Refinery, Printing, Paper Services. Trade with new EMU members decreased through Euro membership. Only a few services sectors could profit.

4.1 Real income changes

Table 5 shows the changes in real income for all members of EMU and the remaining non-EMU members available in the data. Note that high levels of trade with the EMU member states prior to the introduction of the Euro magnify the positive effects because resource savings due to lower transaction costs are larger. Therefore, we do not expect that EMU has benefitted member states symmetrically. This is the reason why small and more central countries such as Luxembourg or the Netherlands belong to the countries that benefited the most in terms of real income gains. Similarly, the Baltic countries and particularly Estonia also experienced an increase in their real incomes through the lowering of transaction costs. The real income effect for Germany is comparable to the average effect across EMU members. Our simulations suggest that Italy and Greece benefited slightly less from the currency union than the other EMU members. One should keep in mind that, in principle, the model could also lead to negative welfare effects for countries inside and outside of the EMU. The reason for this is that terms of trade can move against countries and offset the direct transaction cost savings. However, the analysis suggests that this is not the case for any of the EMU members. All average real income changes are statistically significant at the 10 %-level. Also, European Union members, which are not part of the EMU (such as the UK or Sweden), also indirectly profited from the Eurozone, often because they benefit from an increased level of economic activity in the Eurozone and the associated boost in demand for imported inputs. This is even true for some non-EU and non-EMU countries, such as Australia, who profited from the creation of the Eurozone.

4.2 Effects on international trade

Table 6 shows the effects on overall trade, i. e. across all trade partners for Germany, the remaining EMU members and the non-EMU members across the three sector categories and an aggregate (total). Across all sector categories, Germany sees its overall exports and imports increase; compared to the change in real income, trade increases more, which indicates that the openness of the German economy, measured as total trade over GDP, increases substantially. The same is evident for the remaining EMU members. Non-EMU members, on the other hand, are confronted with overall decreases in exports and imports. Only exports of services expand (statistically significant at the 10 %- level).^[12]

The positive change in exports and imports of the EMU members, including Germany, can be explained through trade creation effects among the EMU members and, possibly, by trade diversion effects with non-members. Table 7 reports the changes in bilateral trade flows for Germany, the remaining EMU members (Rest of EMU) and the rest of the world (ROW). Trade flows are disaggregated into broad categories (agriculture, manufacturing, services). The bold values denote the mean effects which are statistically different from zero at the 10 % level. The trade flows change because of the trade shocks triggered by the formation of the EMU. They are influenced by changing trade costs and changes in total revenue and expenditure and by multilateral resistance forces.

Our simulations suggest that the introduction of the EMU has led to a significant increase in trade among EMU members. Especially agricultural and manufacturing trade could be expanded, while trade in services seems to be less affected. In relative terms, imports from the EMU members towards Germany increased to a higher extent than vice versa. Trade diversion effects are more pronounced in the agricultural and services sectors. EMU members substitute initial agricultural and services trade with non-EMU members with trade among each other, while manufacturing exports of EMU members towards non-EMU and among each other increased. The formation of EMU strengthened the region in terms of purchasing power, which led to an increase of imports from the non-EMU members. Former trade among non-EMU is now substituted with trade towards the Eurozone.

4.3 Effects on value added

Table 8 shows the changes in sectoral value added for Germany. Typically, comparative advantage sectors benefit while those with comparative disadvantage lose. The effects on sectoral value added are heavily influenced by inter- and intranational input-output linkages. This way, while partial equilibrium estimates often fail to yield large positive direct effects for services, many sectors in this area still benefit from the Euro because of their important role in value added networks of manufacturing industries.

Effects are, however, relatively small. According to the estimates, the biggest winners are the chemicals and agri-food. Textiles tend to lose out.^[13] Almost all services sectors win, with effects relatively similar to the aggregate GDP effects, implying that the allocation of production factors changes relatively little.