## _wp04154 - References

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---

### I. INTRODUCTION
- EMU presented as a catalyst for economic integration with potential welfare gains and a key potential benefit of fostering further trade integration.
- Mechanisms by which currency unions may promote trade:
  - lower (currency) transactions costs
  - reduced exchange rate uncertainty
  - enhanced competition through greater transparency
- Central empirical questions:
  - Has monetary union fostered greater trade integration among member states, and how large is EMU’s impact on the area’s trade flows?
  - How have the trade effects of EMU evolved through time?
  - Have trade gains been evenly distributed among member states; which countries benefited more, and have cross-country differences widened or narrowed?
  - What policies and preconditions have been important in positioning some euro-area countries to benefit more in terms of trade integration?
- EMU timeline framing:
  - single currency introduced on January 1, 1999
  - physical euro notes and coins introduced on January 1, 2002
- Paper structure preview:
  - literature review; recent developments in external and internal trade flows; econometric framework and variables; panel estimates of EMU trade effects at area-wide and national levels; determinants of cross-country differences; concluding remarks

### II. RELATED LITERATURE
- Key prior findings and debates:
  - Rose (2000): cross-sectional finding that trading partners in a currency union experienced a three-fold increase in bilateral trade relative to other partners.
  - Glick and Rose (2002): panel analysis exploiting entry/exit into currency unions, finding trade roughly doubles (sample does not cover EMU experience).
  - Micco et al (2003): gravity-model regressions suggest EMU fostered further trade integration and the positive effect has been rising over time.
- Methodological tensions:
  - Some literature finds little negative impact of exchange rate volatility on trade; some studies find positive effects of exchange rate volatility (Côté (1994), McKenzie (1999); Clark, et al (2004) underscores traditional findings).
  - Critiques of Rose methodology: Persson (2001), Tenreyro (2001).
- This paper’s methodological contributions:
  - accounts for possible non-stationarity using panel cointegration techniques
  - examines trade effects at individual-country level to assess distribution of relative trade gains and determinants

### III. RECENT EURO AREA TRADE DEVELOPMENTS
- Aggregate trends:
  - Both intra-area and extra-area trade in goods have steadily expanded over the past decade.
  - External trade with all partners has grown relatively faster in recent years, increasing as a share of total trade.
- Prior-to-2004-enlargement note:
  - aggregate flows between EMU member states have risen more sharply than trade between EU members not in the euro area with their EMU trading partners (European Commission (2003)).
- Figure 1 series description: Extra-Area Imports; Intra-Area Imports; Intra-Area Exports; Extra-Area Exports (log index; January 1990 = 0). Source: Eurostat

### IV. MEASURING THE TRADE EFFECTS OF EMU
- Identification strategy:
  - Start from the gravity model of international trade and control for non-EMU factors affecting bilateral trade.
- Panel equation (as presented):
  - ijijttijttijtjtitjtittijijt
    EMUEUFTAyyYYTradeελδαββτγ+++++++=)ln()ln()ln(
    21
- Definitions and controls:
  - Trade_ijt: bilateral trade in goods between trading partners i and j at time t.
  - γ_ij: fixed effect for country-pair i-j (captures time-invariant factors such as distance, area, common language, common border).
  - τ_t: common time effects for year t.
  - Y and y: gross domestic product in level and per capita terms (logarithms of product of GDPs Y_i Y_j and GDPs per capita y_i y_j).
  - FTA_ijt: dummy = 1 if both countries maintain an FTA in year t; 0 otherwise.
  - EU_ijt: dummy = 1 if both partners belong to the European Union in year t; 0 otherwise (EU time effects included).
  - EMU_ijt: key variable; = 1 if both trading partners belong to the euro area in year t; 0 otherwise. Static variant EMU_ij also considered.

### Data, sample, and key variables
- Panel details:
  - annual data from 1992-2002 for 22 industrial countries
  - N = 231 country pairs
  - T = 11
  - NT = 2541 observations
- Sample composition statistics:
  - Observations recording a free trade agreement (FTA): 1263 observations or 50 percent
  - Observations that are EU trading partners: 893 observations or 35 percent
  - Observations between formal EMU trading partners (years 1999-2002) excluding Greece: 180 observations or 7 percent
- EMU variable interpretation:
  - coefficient λ, or the set {λt}, represent the effects of EMU on trade between member states relative to their industrial country peers after controlling for size, population, FTAs, EU membership, pair fixed effects, and time effects

### Estimation issues and empirical strategy
- Primary econometric challenges:
  - Possible endogeneity of right-hand side variables
  - Possibility of unit roots and non-stationary data
- Empirical strategy overview:
  - Determine order of integration via panel unit root tests (LLC, IPS, Hadri)
  - Test for cointegration among integrated variables via panel cointegration tests (Pedroni)
  - Use panel dynamic OLS (DOLS) to address non-stationarity, simultaneity bias, and residual serial correlation; DOLS used as robustness check on OLS
- Panel unit root test results (sample period 1980-2002; time and fixed effects included):
  - Ln(tradeij): LLC = -0.07; IPS = -6.21**; Hadri = 13.54**
  - Ln(YiYj): LLC = 4.18; IPS = -1.04; Hadri = 14.41**
  - Ln(yiyj): LLC = 2.14; IPS = -3.86**; Hadri = 14.52**
  - Note: * (**) denotes significance at the 5 (1) percent level
- Panel cointegration tests (Pedroni) — strong evidence of cointegration among bilateral trade, GDP and GDP per capita:
  - Panel v = 7.17** (No time effects) and 4.66** (With time effects)
  - Panel ρ = -4.28** (No time effects) and -5.28** (With time effects)
  - Panel Phillips-Perron = -9.14** and -12.47**
  - Panel ADF = -5.24** and -9.98**
  - Group Phillips-Perron = -7.69** and -13.11**
  - Group ADF = -7.20** and -12.45**

### Main quantitative findings on EMU’s trade effects
- OLS vs DOLS:
  - DOLS point estimates generally lower than OLS counterparts; DOLS corrected standard errors significantly higher
- Average impact of EMU on intra-area trade (panel DOLS):
  - EMU has raised intra-area trade by an average of 7-8 percent relative to other trade among industrial countries (DOLS point estimates; statistically significant at the 5 percent level using adjusted standard errors)
  - Dynamic effects largest and statistically significant in 2001 and 2002
- Selected DOLS coefficient values (adjusted standard errors in parentheses; * (**) indicates significance at the 5 (1) percent level):
  - Ln(YiYj): 1.740** (0.530) and 1.741** (0.533)
  - ln(yiyj): -0.736 (0.608) and -0.703 (0.611)
  - FTA: 0.012 (0.059)
  - EMU (aggregate): 0.073* (0.030) and 0.068** (0.032) depending on specification
  - Year-specific EMU dummies: EMU01 0.120* (0.052); EMU02 0.114* (0.052)
- Robustness and alternative specifications:
  - Replacing real GDP and GDP per capita with nominal counterparts raises EMU trade impact to 12 percent at a significance level of 1 percent based on DOLS; dynamic impact becomes significant earlier (1999 versus 2001)
  - Allowing heterogeneity in gravity coefficients between EMU and non-EMU partners does not overturn positive EMU trade effect
  - Including EMU effects on extra-area trade (dummy = 1 if exactly 1 trading partner is a euro area member) increases the point estimate on intra-area trade to 14 percent
- Trade creation vs trade diversion:
  - Both intra- and extra-area trade have grown faster in relative terms under the euro; evidence suggests monetary union has not had trade-diverting effects

### Cross-country heterogeneity in EMU trade effects
- Country-specific estimation method:
  - Interact country dummies with EMU (e.g., SPAIN*EMU) to isolate trade impact for each euro area economy (Belgium and Luxembourg combined; Greece excluded)
  - Aggregate EMU variable defined as EMU less the specific country when estimating that country’s deviation
- Area-wide aggregate EMU effect and country deviations (DOLS, adjusted standard errors; * (**) indicates significance at the 10 (5) percent level):
  - Aggregate EMU effect typically around 0.068** to 0.095** (standard errors ≈ 0.032)
  - Selected significant country absolute deviations (Table 2 examples):
    - Austria: 0.089* (0.053)
    - Germany: 0.097* (0.054)
    - Netherlands: 0.129** (0.054)
    - Spain: 0.131** (0.054)
    - Finland: -0.005 (0.055) in one specification; relative deviations show Finland -0.098* (0.055)
    - Portugal: -0.018 (0.054) in one specification; deviations table shows Portugal -0.112** (0.054)
    - Ireland: deviations vary; under-performed intra-area but outperformed in extra-area trade
- Distribution and dynamics:
  - Average trade gain by country ranges from 6 to 9½ percent depending on excluded member state (Table 2)
  - Spain and Netherlands record significantly larger trade gains than average; Ireland, Finland, and Portugal underperformed
  - Dynamic estimates show rising but disparate trade gains across member states; cross-sectional standard deviation of country point estimates in 2002 is about twice that in 1999
  - Dispersion of trade effects has not declined over time for smaller euro area economies

### Trade creation versus trade diversion at the country level
- Average estimates since 1999:
  - Average gains to intra-area trade: 14 percent
  - Average gains to extra-area trade: 8 percent
- Cross-country dispersion examples:
  - Ireland: under-performed on intra-area gains but outperformed on extra-area gains
  - Portugal and Finland: lagged in both intra- and extra-area trade gains

### Candidate determinants of cross-country differences
- Factors examined in bi-variate regressions (limited observations; reported R2 and slope coefficients):
  - Trade openness (initial share of EU exports, 1998): R2 = 0.00; β = 0.00
  - Trade patterns (initial share of intra-industry trade, 1997): R2 = 0.26; β = 0.19
  - Exchange rate volatility (NEER volatility, 1990-1998): R2 = 0.01; β = 0.86 (variance of log index)
  - Product market reforms (Internal Market implementation deficit, 1998): R2 = 0.55; β = 3.04
- Intra-industry trade (Grubel-Lloyd index, 1997, percent share of intra-industry trade with EU partners):
  - Finland 50; Ireland 53; Portugal 55; Italy 60; Netherlands 61; Spain 72; Austria 75; Germany 80; Belgium 81; France 87
  - Observed pattern: higher initial intra-industry trade shares tended to be associated with larger EMU trade gains (accounts for about one-third of variation)
- Market flexibility and Internal Market implementation deficit (1998, percent):
  - France -5.6; Portugal -4.9; Ireland -4.4; Italy -3.9; Austria -3.7; Belgium -3.5; Germany -2.9; Netherlands -2.8; Spain -2.2; Finland -1.7
  - Lower implementation deficit (better compliance) correlates with higher trade gains; Finland is an outlier

### Policy implications and conclusions
- Main conclusions:
  - EMU has had a positive impact on intra-area trade; panel evidence indicates monetary union has boosted trade among member states by roughly 10 percent during the euro’s existence (aggregate measure; dynamic and specification-dependent estimates typically point to 7-14 percent depending on definition and controls)
  - Gains in intra-area trade do not appear to come at the expense of extra-area trade; EMU appears trade-creating rather than trade-diverting
  - Dynamic effects of EMU on trade integration appear to be rising over time
  - Trade gains from the single currency have not been evenly distributed among member states; dispersion persists (and has not narrowed) for smaller euro area economies
  - Countries that outpaced the average: Spain, Netherlands, Austria, and more recently Belgium; countries that lagged: Portugal, Finland, and Ireland (Ireland’s extra-area performance is stronger)
  - Accounting for country disparities likely rests on a confluence of factors including policies that enhance market flexibility and initial trade patterns (notably the share of intra-industry trade)
- Policy-relevant recommendations:
  - Policies that facilitate factor and firm mobility and ease entry/exit costs can help economies adapt and realize trade benefits from the common currency
  - Improving readiness to capture potential benefits—including reforms that enhance market flexibility and deepen intra-industry linkages—should be a focus given persistent cross-country dispersion

*Source: _wp04154 - References*

### References..............................................................................................................

### _wp04154 - References..............................................................................................................

### I. INTRODUCTION
- EMU (Economic and Monetary Union) is presented as a catalyst for economic integration with potential welfare gains for member states; fostering further trade integration is a key potential benefit.
- Mechanisms by which currency unions may promote trade:
  - lower (currency) transactions costs
  - reduced exchange rate uncertainty
  - enhanced competition through greater transparency
- Central empirical questions posed:
  - Has monetary union fostered greater trade integration among member states, and how large is EMU’s impact on the area’s trade flows?
  - How have the trade effects of EMU evolved through time?
  - Have trade gains been evenly distributed among member states; which countries benefited more, and have cross-country differences widened or narrowed?
  - What policies and preconditions have been important in positioning some euro-area countries to benefit more in terms of trade integration?
- EMU is characterized as a process: single currency introduced on January 1, 1999; physical euro notes and coins introduced on January 1, 2002. The analysis is framed as a “progress report” based on evidence to date.
- Paper structure preview:
  - literature review
  - recent developments in external and internal trade flows
  - econometric framework and variables
  - panel estimates of EMU trade effects at area-wide and national levels
  - determinants of cross-country differences
  - concluding remarks

### II. RELATED LITERATURE
- Rose (2000): cross-sectional finding that trading partners in a currency union experienced a three-fold increase in bilateral trade relative to other partners, controlling for GDP and distance.
- Tension with literature finding little negative impact of exchange rate volatility on trade; some studies find positive effects of exchange rate volatility on trade (references: Côté (1994), McKenzie (1999); Clark, et al (2004) underscores traditional findings).
- Critiques of Rose methodology: Persson (2001), Teneryo (2001).
- Glick and Rose (2002): panel analysis exploiting entry/exit into currency unions, finding trade roughly doubles; their sample does not cover EMU experience.
- Micco et al (2003): directly examine dynamic impact of EMU on trade in panel of 22 industrial countries and smaller panel of 15 EU members; their gravity-model regressions suggest EMU fostered further trade integration and the positive effect has been rising over time.
- This paper’s contributions relative to prior work:
  - accounts for possible non-stationarity using panel cointegration techniques to provide more reliable point estimates and interpretable standard errors in presence of unit roots
  - examines trade effects at individual-country level to assess distribution of relative trade gains among EMU membership, convergence/divergence, and country factors underpinning relative performance

### III. RECENT EURO AREA TRADE DEVELOPMENTS
- Aggregate trends summarized:
  - Both intra-area and extra-area trade in goods have steadily expanded over the past decade (see Figure 1).
  - External trade with all partners (not only industrial partners) has grown relatively faster in recent years, increasing as a share of total trade.
- Note: Within the EU prior to 2004 enlargement, aggregate flows between EMU member states have risen more sharply than trade between EU members not in the euro area with their EMU trading partners (see European Commission (2003)).
- Figure 1 description (log index; January 1990 = 0):
  - Series shown: Extra-Area Imports, Intra-Area Imports, Intra-Area Exports, Extra-Area Exports
  - Source: Eurostat

### IV. MEASURING THE TRADE EFFECTS OF EMU
- Identification strategy: start from the gravity model of international trade to control for non-EMU factors affecting bilateral trade.
- Panel equation (as presented):
  - ijijttijttijtjtitjtittijijt
    EMUEUFTAyyYYTradeελδαββτγ+++++++=)ln()ln()ln(
    21
    , where ln denotes the natural logarithm.
- Definitions of variables and terms in equation (1):
  - Trade_ijt: bilateral trade in goods between trading partners i and j at time t.
  - γ_ij: fixed effect in trade between partner countries i and j (captures time-invariant factors such as geographical distance, area, common language, common border, etc.).
  - τ_t: common time effects for a particular year (captures common time developments such as a linear time trend in trade shares/increasing global integration).
  - Y and y: gross domestic product in level and per capita terms (logarithms of product of GDPs Y_i Y_j and GDPs per capita y_i y_j enter the regression).
  - FTA_ijt: dummy variable for free trade agreements; equals one in years both countries maintain an FTA and zero otherwise (captures FTAs formed/dissolved during sample period).
  - EU_ijt: dummy variable equal to one if both partners belong to the European Union in year t and zero otherwise (intended to control for the trade impact of the EU's Single Market Program distinct from EMU effects). The specification generalizes Micco et al (2003) by including EU “time effects” (time-specific coefficients on EU_ijt).
  - EMU_ijt: key variable of interest; equals one if both trading partners belong to the euro area in year t and zero otherwise. A static variant EMU_ij (without t subscript) is also considered to measure the common average effect of adopting the single currency between 1999 and [text truncated at source].
- Estimation approach rationale:
  - Fixed effects control for omitted time-invariant bilateral factors at expense of isolating individual contributions (e.g., distance).
  - Time effects capture common temporal developments across trading partners.
  - Inclusion of EU controls aims to isolate the impact of the single currency above and beyond the Single Market Program.
  - Panel cointegration techniques are applied to address non-stationarity and provide more reliable inference relative to standard least squares.

*Source: _wp04154 - References..............................................................................................................................*

### 2002. In the former specification, some years prior to 1999 are also included to examine

### EMU’s Impact on Trade

### Data, sample, and key variables
- Panel consists of annual data from 1992-2002 for 22 industrial countries.  
- Number of country pairs (cross-section dimension): N = 231.  
- Time-series dimension: T = 11.  
- Total number of observations: NT = 2541.  
- Observations recording a free trade agreement (FTA): 1263 observations or 50 percent.  
- Observations that are EU trading partners: 893 observations or 35 percent.  
- Observations between formal EMU trading partners (years 1999-2002) excluding Greece: 180 observations or 7 percent.  
- EMU variable interpretation: coefficient λ, or the set {λt}, represent the effects of EMU on trade between member states relative to their industrial country peers (including extra-area trade), after controlling for economic size and population, free trade agreements, membership in European Union, individual country-pair fixed effects, and common time effects.

### Estimation issues and empirical strategy
- Primary econometric challenges:
  - Possible endogeneity of right-hand side variables.
  - Possibility of unit roots and non-stationary data in the panel.
- Empirical strategy (overview from appendix):
  - Determine order of integration via panel unit root tests (LLC, IPS, Hadri).
  - Test for cointegration among integrated variables via panel cointegration tests (Pedroni).
  - Use panel dynamic OLS (DOLS) estimation to address non-stationarity, simultaneity bias, and residual serial correlation; DOLS results serve as a robustness check on OLS.
- Panel unit root test results (sample period 1980-2002; time and fixed effects included; lag length data dependent):
  - Ln(tradeij): LLC = -0.07; IPS = -6.21**; Hadri = 13.54**.  
  - Ln(YiYj): LLC = 4.18; IPS = -1.04; Hadri = 14.41**.  
  - Ln(yiyj): LLC = 2.14; IPS = -3.86**; Hadri = 14.52**.  
  - Note: an asterisk * (**) denotes significance at the 5 (1) percent level.
- Panel cointegration tests (Pedroni) strongly suggest cointegration among bilateral trade, GDP and GDP per capita:
  - Panel v = 7.17** (No time effects) and 4.66** (With time effects).
  - Panel ρ = -4.28** (No time effects) and -5.28** (With time effects).
  - Panel Phillips-Perron = -9.14** and -12.47**.
  - Panel ADF = -5.24** and -9.98**.
  - Group Phillips-Perron = -7.69** and -13.11**.
  - Group ADF = -7.20** and -12.45**.

### Main quantitative findings on EMU’s trade effects
- OLS vs DOLS summary:
  - DOLS point estimates are generally lower than OLS counterparts and corrected standard errors are significantly higher; OLS standard errors are only indicative under non-stationarity.
- Average impact of EMU on intra-area trade (panel DOLS):
  - EMU has raised intra-area trade by an average of 7-8 percent relative to other trade among industrial countries (DOLS point estimates; statistically significant at the 5 percent level using adjusted standard errors).
  - Dynamic effects indicate the trade impact has been largest and statistically significant in 2001 and 2002.
- Table 1 selected coefficient values (DOLS, adjusted standard errors in parentheses; A * (**) indicates significance at the 5 (1) percent level):
  - Ln(YiYj): 1.740** (0.530) and 1.741** (0.533).
  - ln(yiyj): -0.736 (0.608) and -0.703 (0.611).
  - FTA: 0.012 (0.059).
  - EMU (aggregate): 0.073* (0.030) and 0.068** (0.032) depending on specification.
  - Year-specific EMU dummies (EMU01, EMU02) show elevated and statistically significant effects in later years: EMU01 0.120* (0.052); EMU02 0.114* (0.052).
- Robustness and alternative specifications:
  - Replacing real GDP and GDP per capita with nominal counterparts raises the EMU trade impact to 12 percent at a significance level of 1 percent based on DOLS; dynamic impact becomes significant earlier (1999 versus 2001).
  - Allowing heterogeneity in gravity variable coefficients between EMU and non-EMU partners does not overturn positive EMU trade effect.
  - Including EMU effects on extra-area trade (dummy = 1 if exactly 1 trading partner is a euro area member) increases the point estimate on intra-area trade to 14 percent.
- Trade creation vs trade diversion:
  - Both intra- and extra-area trade have grown faster in relative terms under the euro; evidence suggests monetary union has not had trade-diverting effects.

### Cross-country heterogeneity in EMU trade effects
- Country-specific estimation approach:
  - Interact country dummies with the EMU variable (e.g., SPAIN*EMU) to isolate trade impact for each euro area economy (Belgium and Luxembourg combined; Greece excluded).
  - Define aggregate EMU variable as EMU less the specific country when estimating that country’s deviation.
- Area-wide (aggregate) EMU effect and country deviations (DOLS, adjusted standard errors; A * (**) indicates significance at the 10 (5) percent level):
  - Aggregate EMU effect reported across specifications: typically around 0.068** to 0.095** (standard errors ≈ 0.032).
  - Country deviations (Table 2, absolute effects) – selected significant deviations:
    - Austria: 0.089* (0.053) in one specification.
    - Germany: 0.097* (0.054) in one specification.
    - Netherlands: 0.129** (0.054) in one specification.
    - Spain: 0.131** (0.054) in one specification.
    - Finland: -0.005 (0.055) (not significant) in one specification; in relative deviations table Finland shows -0.098* (0.055).
    - Portugal: -0.018 (0.054) in one specification; in deviations table Portugal shows -0.112** (0.054).
    - Ireland: deviations vary; Ireland under-performed intra-area but outperformed in extra-area trade.
- Distribution and dynamics:
  - Average trade gain by country ranges from 6 to 9½ percent depending on excluded member state (Table 2).
  - Spain and Netherlands record significantly larger trade gains than average; Ireland, Finland, and Portugal underperformed.
  - Dynamic estimates show rising but disparate trade gains across member states; cross-sectional standard deviation of country point estimates in 2002 is about twice that in 1999.
  - Dispersion of trade effects has not declined over time for smaller euro area economies.

### Trade creation versus trade diversion at the country level
- Average estimates since 1999:
  - Average gains to intra-area trade: 14 percent.
  - Average gains to extra-area trade: 8 percent.
- Cross-country dispersion:
  - Ireland: under-performed on intra-area gains but outperformed on extra-area gains.
  - Portugal and Finland: lagged in both intra- and extra-area trade gains.

### Candidate determinants of cross-country differences
- Potential explanatory factors considered (bi-variate regressions; limited observations; figures report R2 and slope coefficients):
  - Trade openness (initial share of EU exports, 1998): R2 = 0.00; β = 0.00.
  - Trade patterns (initial share of intra-industry trade, 1997): R2 = 0.26; β = 0.19.
  - Exchange rate volatility (NEER volatility, 1990-1998): R2 = 0.01; β = 0.86 (variance of log index).
  - Product market reforms (Internal Market implementation deficit, 1998): R2 = 0.55; β = 3.04.
- Intra-industry trade:
  - Table 3 (Grubel-Lloyd index, 1997, percent share of intra-industry trade with EU partners):
    - Finland 50; Ireland 53; Portugal 55; Italy 60; Netherlands 61; Spain 72; Austria 75; Germany 80; Belgium 81; France 87.
  - Observed pattern: countries with higher initial intra-industry trade shares tended to have larger EMU trade gains (notably Austria, Germany, Spain, Belgium).
  - Ireland, Finland, and Portugal had the lowest initial intra-industry shares and correspondingly low trade gains.
  - Regression suggests about one-third of variation in trade gains accounted for by initial trade patterns.
- Market flexibility and implementation of Internal Market program:
  - Table 4 (Internal Market scoreboard, 1998 implementation deficit in percent):
    - France -5.6; Portugal -4.9; Ireland -4.4; Italy -3.9; Austria -3.7; Belgium -3.5; Germany -2.9; Netherlands -2.8; Spain -2.2; Finland -1.7.
  - Lower implementation deficit (better compliance) correlates with higher trade gains (Figure 5d), though Finland is an outlier.

### Policy implications and conclusions (summary)
- Main conclusions:
  - EMU has had a positive impact on intra-area trade; panel evidence indicates monetary union has boosted trade among member states by roughly 10 percent during the euro’s existence (aggregate measure; dynamic and specification-dependent estimates typically point to 7-14 percent depending on definition and controls).
  - Gains in intra-area trade do not appear to come at the expense of extra-area trade; EMU appears trade-creating rather than trade-diverting.
  - Dynamic effects of EMU on trade integration appear to be rising over time; reported estimates reflect trade-creating effects thus far.
  - Trade gains from the single currency have not been evenly distributed among member states; dispersion persists (and has not narrowed) for smaller euro area economies.
  - Countries that have outpaced the average: Spain, Netherlands, Austria, and, more recently, Belgium. Countries that lagged: Portugal, Finland, and Ireland (though Ireland’s extra-area performance is stronger).
  - Accounting for country disparities likely rests on a confluence of factors including policies that enhance market flexibility and initial trade patterns (notably the share of intra-industry trade).
- Policy-relevant points:
  - Policies that facilitate factor and firm mobility and ease entry/exit costs can help economies adapt and realize trade benefits from the common currency.
  - Initial conditions—such as a higher share of intra-industry trade—appear to enhance the ability to reap trade gains from monetary union.
  - Given persistent cross-country dispersion, policymakers should not take trade gains from currency union for granted but should focus on reforms and conditions that improve readiness to capture potential benefits.

*Source: _wp04154 - 2002*

### REFERENCES

### REFERENCES

### Literature on the Gravity Equation and Trade Flows
- Anderson, J., 1979, “A Theoretical Foundation for the Gravity Equation,” American Economic Review, Vol. 69, No. 1, pp. 106–16.
- Bergstrand, J., 1985, “The Gravity Equation in International Trade: Some Microeconomics Foundations and Empirical Evidence,” Review of Economics and Statistics, Vol. 67, No. 3, pp. 474–81.
- Deardorff, A., 1984. “Testing Trade Theories and Predicting Trade Flows,” in Handbook of International Economics, Vol. I, edited by R. Jones and P. Kenen (Amsterdam: Elsevier Science Publishers), pp. 467-517 .
- ———, 1998, “Determinants of Bilateral Trade: Does Gravity Work in a Classical World?”, in The Regionalization of the World Economy, edited by J. Frankel (Chicago: University of Chicago Press), pp. 7-22.
- Linnemann, H., 1966, An Econometric Study of International Trade Flows, (Amsterdam: North-Holland Publishers).
- Rose, A.K., 2000, “One Money, One Market: Estimating the Effect of Common Currencies on Trade,” Economic Policy, Vol. 30, pp. 7–45.
- Rose, A.K., 2004, “A Meta-Analysis of the Effect of Common Currencies on International Trade,” NBER Working Paper 10373. (Cambridge, Massachusetts: National Bureau of Economic Research).

### Currency Unions, EMU, and Trade Effects
- Bayoumi, T., and B. Eichengreen, 1995, “Is Regionalism Simply A Diversion? Evidence from the Evolution of the EC and EFTA,” NBER Working Paper 5283 (Cambridge, Massachusetts: National Bureau of Economic Research).
- Frankel, J., and A. Rose, 1997, “Is EMU More Justifiable Ex Post than Ex Ante?” European Economic Review, Vol. 41, Nos. 3–5, pp. 753–60.
- ———, 1998, “The Endogeneity of the Optimum Currency Area Criteria,” The Economic Journal, Vol. 108, No. 449, pp. 1009–25.
- ———, 2000, “An Estimate of the Effect of Currency Unions on Trade and Growth,” NBER Working Paper 7857 (Cambridge, Massachusetts: National Bureau of Economic Research).
- Fontagne, L., and M. Freudenberg, 1999, “Endogenous Symmetry of Shocks in a Monetary Union,” Open Economies Review, Vol. 10, pp. 263–87.
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### Exchange Rate Volatility and Trade
- Côté, A., 1994, “Exchange Rate Volatility and Trade: A Survey,” International Department, Bank of Canada Working Paper 94-5.
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### Panel Data, Cointegration, and Time Series Methods
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- Levin, A., C.-F. Lin, and C.-S. Chu, 2002, “Unit Root Tests in Panel Data: Asymptotic and Finite Sample Properties,” Journal of Econometrics, Vol. 108, pp. 1–24.
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- Stock J. and M.Watson, 1988, Testing for Common Trends,” Journal of the American Statistical Association, Vol. 83, pp. 1097–1107.
- Westerlund, J., 2003, “On the Feasibility of Panel Dynamic Estimators in Cointegrated Panels”, (unpublished; Lund, Sweden: Lund University).

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