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

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)

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!

Costs and Benefits of Joining a Monetary Union 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

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

Measuring Trade Effects (2) Newer (Export) Gravity Models Much work on “theory-consistent” gravity estimation Use Least Squares on exports with time-varying country Dummy Variables to control for multilateral resistance, other general equilibrium effects: ln(Exportsijt) = CUijt + Zijt+ {λit} + {ψjt} + ijt Xijt = nominal value 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

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) …

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)

Forest Plot of (45) Literature EMU Estimates

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

Meta-Estimates of EMU Trade/Export Effect Estimator Sample Point Est. 95% Confidence Interval P-value, no Hetero. Lower Upper Fixed All (45) .085 .078 .091 .000 Random .116 .084 .147 Export (27) .140 .092 .189 Dyadic (35) .126 .088 .164 Monadic (9) .132 -.027 .291

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!

Publication Bias

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

EMU Effect and Sample Size

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

Meta Regressions of EMU Trade Effect Weight Std. Err. Obs-1 GSCites-1 Log Countries .16 (.06) .15 (.05) .20 .11 (.04)   Log Years .14 .13 .09 Obs -.05 -.03 (.03) .01 Time-Vary Country FE -.04 (.07) -.07 -.00 Exports not Trade .07 .04 .05 Dyadic FE .03 .02 -.01 P(value) .63 .81 .78 Adjust R2 .26 .27 .47 .29

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 γ even 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)

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

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

(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

Gravity Estimates of EMU Effect Varying end dates and country samples 2001 2003 2005 2007 2009 2011 2013 All .08 (.05) .12 (.04) .17 (.03) .19 .25 (.02) .36 .43 Rich .00 -.01 -.02 -.04 .07 .11 EU -.33 (.06) -.36 -.32 -.28 -.26 (.07) -.25 -.24

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

Graphical Estimates of EMU Export Effect

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

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

… and Practice: Estimates of Multilateral Resistance shows Bias 2001 2003 2005 2007 2009 2011 2013 All .08 (.05) .12 (.04) .17 (.03) .19 .25 (.02) .36 .43 Rich .00 -.01 -.02 -.04 .07 .11 EU -.33 (.06) -.36 -.32 -.28 -.26 (.07) -.25 -.24 Observations 597,565 642,571 688,519 735,025 782,047 829,708 877,736 42,673 46,851 51,824 57,317 62,764 68,428 75,096 22,887 24,341 25,788 27,350 28,891 30,434 31,982 Average of Exporter/Importer-Year Fixed Effects, {λit}, {ψjt} for rich-country Observations Exporter (All) .94 .95 .97 .98 1.00 Importer (All) 1.18 1.17 1.16 1.15 1.14 1.12 1.10 Average of Exporter/Importer-Year Fixed Effects, {λit}, {ψjt} for other non-rich Observations -.07 -.08 -.09 -.10 Average difference between Full and Rich sample estimates for EMU observations Exporter .65 .64 .62 .66 .67 Importer 1.06 .99 .86 .77 .68

Conclusion/Summary: Why do Estimates of 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%

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