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
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 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

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, %.
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
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

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
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

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 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)

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”
>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 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

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 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

28. 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%

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