The Effect of European Monetary Union on Trade

1. The Effect of European MonetaryUnion on Trade Andrew K. Rose UC Berkeley-Haas, ABFER, CEPR, NBER Berkeley, March 2019 (for Eichengreen’s Ec191)

2. Currency Unions • Bilateral Currency Unions (“Dollarization”) • British £: Bahamas (-1965), NZ (-1966), India (-1966), Ireland (-1978) …. • US $: Panama, Bahamas (1966-), Ecuador (2000-), El Salvador (2001-), Zimbabwe (2009-) …. • Fr Franc: Morocco (-1957), Algeria (-1968) … • Multilateral Currency Unions • CFA Franc Zones • Eastern Caribbean Currency Union • Common (Rand) Monetary Area • European Economic and Monetary Union, EMU (1999-)

3. Costs and Benefits of Joining a Monetary Union Key Costs • Loss of nominal exchange rate as policy tool • Loss of national monetary policy control • EMU: these costs are high! Potential (Economic) Benefits • Greater transparency of prices encourages greater competition and efficiency • Reduced currency risk encourages more trade and investment • Is there actually any substantive benefit in the data?

4. Debate in Literature on Magnitude of Trade Effect of CUs • Big, 90-100%. • e.g. Glick and Rose (2002), Frankel (2010) • Moderate, 40-50% • e.g. Eicher and Henn (2011) • Small, 0-20% • e.g. Micco et al (2003), Bun and Klaasen (2002, 2007), de Nardis and Vicarelli (2003), Flam and Nordstrom (2007), Berger and Nitsch (2008), Camarero et al (2013) • Negative? • e.g. Baldwin and Taglioni (2007)

5. Specific Motivation • Glick-Rose (2002) used panel approach to investigate effect of currency unions on trade, using data for 1948-1997 before establishment of EMU • Found currency unions increase trade by ≈90% • Current paper uses data for 1948-2013 and asks • Is EMU similar to other currency unions? • Is there symmetry between currency union exit and entry? • Assumed symmetry before. Couldn’t test because had only 16 entries, 130 exits in 1948-1997 sample • Can test now with EMU entries • Do advances in methodology matter?

6. Preview of Findings • EMU different from other CUs, increases trade among EMU countries by ~50% • Find symmetry • Econometric methodology matters a lot • Sample matters a lot as well

7. Measuring Trade Effects“Old” Methodology: Gravity Equation ln(Tradeijt) = CUijt + Zijt+ {δt} +ijt • Tradeijt = average nominal value of bilateral trade between i and j at time t, • CU = 1 if i and j use the same currency at time t and 0 otherwise, • Z = gravity control variables, usual suspects: e.g. GDP, distance, common language, border, regional RTA, colonial history, etc. … • {δt} = year-specific effects

8. Methodological Issues in Estimating • Simultaneity (persists) • Omitted variables • Effects of CU between i and j on other countries through “multilateral resistance” effects • General equilibrium effects on spending and output for all countries • Homogeneity implicit in treating all currency unions alike

9. Data Set • IMF DoTS trade: >200 “countries” 1948-2013 (with gaps) • Population, real GDP: WDI > PWT > IFS • Country Characteristics: World Factbook • Regional Trade Agreements (RTAs): WTO • Currency Unions: Glick-Rose updated • 1:1 par for extended period of time (not just hard fixes) • Transitive: x-y and y-z imply x-z

10. Full Results

11. Gravity Estimates for Trade e1.3 ~3.7x e.92 ~2.5x nil Note: Pooled OLS estimates. Other gravity regressors and year dummies included, but not reported. Robust standard errors in parentheses.

12. Prefer (Within) Fixed Pair Effect Estimator • Exploits variation over time, answers the policy question of interest, i.e. the (time series) question • “What is the trade effect of a country joining (or leaving) a currency union?” • Controls for unobserved pair effects, including potential endogeneity of currency union ln(Tradeijt) = CUijt + Zijt+ {δt} + {θij} +ijt

13. Gravity  Estimates for Trade with Pair Fixed Effects e.41 -1 ~ 51% Note: Pooled OLS estimates. Other gravity regressors and year dummies included, but not reported. Robust standard errors in parentheses.

14. How Does this Compare with Literature? • Easiest to graph (large) literature

15. Forest Plot of (45) Literature EMU Estimates

16. Meta-Estimate • Random effects estimator delivers estimate of (exp(.116)-1≈) 12.3% • Economically non-trivial • Statistically significant • Robust to reasonable sub-samples

17. Meta-Estimates of EMU Trade/Export Effect

18. Publication Bias • Over twenty (of 45) papers unpublished • Still, can investigate easily with standard techniques • Funnel plots of estimate against precision indicates weak right skew • Many estimates outside 95% confidence interval! • Results in Figure 2 • Conclude: little evidence of publication bias • But worrying dispersion!

19. Publication Bias

20. Why do EMU Estimates Vary Across Studies? • Rising with (log) observations • Small (positive) effect of years in EMU • Positive (big) effect of span in years • Positive (big) effect of number countries • Histograms, scatterplots, regressions all provided in Figure 3 • Note paucity of observations • Special note: usually very few countries in sample

21. EMU Effect and Sample Size

22. Confirmation via Meta-Regression • Want to check ocular evidence • Strong positive effect of #countries • Strong positive effect of #years • Other effects? • Check via Meta Regression Analysis • Check for sensitivity to weighting • Check for other determinants

23. Meta Regressions of EMU Trade Effect

24. Quick Summary • In literature: longer, wider spans of data over both time and countries systematically associated with higher estimate of EMU trade effect • Curious … extra data increases γeven if extra observations not directly relevant to EMU! • (Explains why these observations – e.g., small/poor countries – often omitted from studies; natural to include only relevant observations when estimating EMU trade effect – encompassing)

25. Caveat • But … only 7 papers in literature use preferred methodology (exports, dyadic and time-varying country fixed effects) … and most papers use few countries (median 22), years (median 20) • So, seems wise to check meta-results with actual data, plain-vanilla methodology

26. What’s Trustworthy? Measuring Trade Effects Newer (Export) Gravity Models • Much work on “theory-consistent” gravity estimation • Use Least Squares with time-varying country Dummy Variables (LSDV) (+ dyadic FE) to control for multilateral resistance, other general equilibrium effects: ln(Exportsijt) = CUijt + Zijt+ {λit} + {ψjt} + {θij} + ijt • Xijt = nominal bilateral exports from i to j at time t, • {λit} = set oftime-varyingexporter dummy variables, • {ψjt} = set of time-varying importer dummy variables • {θij} = set of time-invariant pair-specific dummy variables

27. Gravity  Estimates for Exportswith country-year effects for exporter & importer& country pair FE e.43 -1 ~ 54% Note: Other gravity regressors and year dummies included, but not reported. Robust standard errors in parentheses.

28. Tangent: Allow for Dynamic Effects • Add (14) leads and lags around currency union exit/entry • i.e. Add ΣkθkCUENTRYijt-k + ΣkφkCUEXITijt-k to gravity equation • Distinguish effects between EMU/non-EMU exit and entries • Estimate with pair FE • Test for Symmetry (post-entry = - post-exit) • Find symmetry holds well

29. Allowing Dynamic Effects, CU exit lowers exports, entry raises exports Figure 2

30. Symmetry Tests, Exports with country-year and pair FE Can’t reject Can’t reject Table reports F-test statistic for Ho of identical slopes ΣkθkΣkφk for given CU pairs and time periods

31. Sensitivity Analysis of  Estimates : Dis-aggregating Other CUs Note: Other gravity regressors, country-year and pair dummies included, but not reported. 879,794 annual observations, 1948-2013.

32. Sensitivity Analysis of  EMU Estimates: Varying Country and Sample Period Note: dependent variable is log exports. Other gravity regressors, country-year and pair dummies included, but not reported. Robust standard errors in parentheses; no. of obs. in brackets.

33. Dimensionality Effects • Adding more years increases γ! • Adding more countries increases γ • Consistent with meta-regressions!

34. Gravity Estimates of EMU EffectVarying end dates and country samples

35. Graphical Estimates of EMU Export Effect

36. Conclusions from Meta-Regression-cum-Regression Analysis • Throwing away data easily allows one to estimate small/negative EMU export effect • Adding years of data in EMU (relevant!) increases EMU export effect • Adding countries outside EMU (seemingly irrelevant!) increases EMU export effect

37. Why the Differences? • Anderson and van Wincoop (2003, p 176); multilateral trade resistance depends positively on trade barriers with all trading partners • Dropping small and/or poor countries (likely to have systematically different trade resistance) leads to biased estimates of multilateral trade resistance; higher multilateral resistance leads to more trade. • Downward-biased estimates of multilateral resistance biases γ down. • Multilateral trade resistance is a function of all bilateral trade barriers, so all trade partners should be included

38. Estimates of Multilateral Resistance:Evidence of Bias

39. Summary • Glick-Rose (2002) concluded “a pair of countries which joined/left a currency union experienced a near-doubling/halving of bilateral trade.” • Based on: • Assumption of symmetry between currency union exits and entries • Caveat: EMU might be different from other currency unions • Our results insensitive to precise econometric methodology • Here, re-estimate using variety of models, annual panel >200 countries, 1948-2013, 15 EMU years

40. Conclusions • Methodology and sample matter • Preferred methodology is panel with country-pair fixed effects • Preferred sample includes all countries, all periods of time • Symmetry holds between currency union entry and exits • EMU is different • EMU boosts trade by 50% • Other currency unions have different effects on trade

41. Conclusion/Summary: Why do Estimatesof EMU Trade Effect Vary so Much? • Varying sample sizes by time and (especially) country • More Data is Better! • Established via meta-analysis and regressions • Truncating sample (omitting small/poor countries) biases downward EMU trade effect in a) theory, b) data, and c) literature • Including entire post-war sample of countries/years delivers large estimate of EMU export effect of γ≈.43 or (exp(.43-1≈) 54%! • Economically large (may grow) • Statistically significant (robust t-statistic>20) • Quite consistent with Rose-Stanley survey (2005): 47%

42. Future Research • Handling zero and missing trade observations • LS estimates may be biased because of: • Heteroskedasticity, and/or • Discarded observations of zero/missing trade • Santos Silva and Tenreyro propose Poisson pseudo-maximum likelihood to handle both • Used to be difficult in big panels • Now Done by Larch, Wanner, Yotov and Zylkin, others • Interaction of effects of joining CUs and other forms of economic integration, such as regional trade arrangements • Countries joined EU shortly before EMU