Why do Estimates of the EMU Effect on Trade Vary So Much?

1. Why do Estimates of the EMU Effect on Trade Vary So Much? Andrew K. Rose Berkeley-Haas ABFER, CEPR, NBER

2. Motivating Questions (with answers) • Why do estimates of the EMU trade effect vary so much in the literature? • Larger data sets – more countries, more time – deliver larger estimates • Consistent with meta-analysis, data, theory • Given this, what actually is the EMU trade effect? • Around 50% (much larger than most literature)

3. Methodological Wrinkle • Use meta-analysis to motivate data/theory work • Leads to conclusion that most literature irrelevant • But for understandable reasons • Literature used as motivation for analysis that refutes (most of the) literature!

4. Costs and Benefits of Joining a Monetary Union Costs • Loss of nominal exchange rate as policy tool • Loss of national monetary policy control Benefits • Greater transparency of prices encourages greater competition and efficiency • Reduced currency risk encourages more trade and investment

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

6. Measuring Trade Effects (2)Newer (Export) Gravity Models • Much work on “theory-consistent” gravity estimation • Use Least Squares on exportswith time-varying country Dummy Variables to control for multilateral resistance, other general equilibrium effects: ln(Exportsijt) = CUijt + Zijt+ {λit} + {ψjt} + ijt • Xijt= nominalvalue of bilateral exports from i to j at time t, • {λit} = set of time-varying exporter dummy variables, • {ψjt} = set of time-varying importer dummy variables

7. Examples of Currency Unions • Multilateral Currency Unions • European Economic and Monetary Union “EMU” (1999-) • By far the most interesting and relevant • Different: involves a) rich; b) large countries; and c) targets inflation • CFA Franc Zones • Eastern Caribbean Currency Union • Common (Rand) Monetary Area … • Anchor Currency Unions • British £: Bahamas (-1965), NZ (-1966), Ireland (-1978) … • US $: Panama, Bahamas (1966-), Ecuador (2000-), • Fr Franc: Morocco (-1957), Algeria (-1968) …

8. Debate in Literature on Magnitude of Trade Effect of CUs • Ridiculous, 200% • Rose (2000) • Big, 90-100%. • Glick and Rose (2002), Frankel (2010) • Moderate, 40-50% • Eicher and Henn (2011) • Small, 0-20% • 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? • Baldwin and Taglioni (2007)

9. Forest Plot of (45) Literature EMU Estimates

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

11. Meta-Estimates of EMU Trade/Export Effect

12. 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!

13. Publication Bias

14. 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

15. EMU Effect and Sample Size

16. Confirmation via Meta-Regression • Want to check ocular evidence of Figure 3: • Strong positive effect of #countries • Strong positive effect of #years • Other effects? • Check via Meta Regression Analysis (Table 2) • Check for sensitivity to weighting • Check for other determinants

17. Meta Regressions of EMU Trade Effect

18. 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)

19. 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

20. Confirmation Technique (Intentionally Prosaic) Data • IMF DoTS trade: >200 “countries” 1948-2013 • >875,000 observations! • Country Characteristics: World Factbook • Regional Trade Agreements (RTAs): WTO • Currency Unions: Glick-Rose updated • 1:1 for extended period of time (not hard fixes); transitive Estimating Equation ln(Exportsijt) = CUijt + Zijt+ {λit} + {ψjt} + ijt

21. (Why We Want a Large Data Set) A large data set – spanning both countries and time (increasing importance): • Many degrees of freedom • Direct comparison of individual CUs/RTAs • Check sensitivity wrt span of years: important in meta-analysis! • Ditto wrt country span (ditto) • Better multilateral resistance estimates? • (Now!) feasible to include 22k country-time (+32k dyadic) FE

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

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

24. Graphical Estimates of EMU Export Effect

25. 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!) decreases EMU export effect

26. Why (the Differences)? Theory … • 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 (λ, Ψ FE) biases γ down. • Multilateral trade resistance is a function of all bilateral trade barriers, so all trade partners should be included • Just a (large number of) fixed effects • Can check if fewer countries/years lead to smaller FE

27. … and Practice: Estimates ofMultilateral Resistance shows Bias

28. 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 (2005): 47%

29. 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 • But difficult to use in big panels like ours • Interaction of effects of joining CUs and other forms of economic integration, such as regional trade arrangements • Many countries joined EMU in years prior to joined EMU