1. Has the European Monetary Union Increased Trade?
Andrew K. Rose
UC Berkeley-Haas, CEPR, NBER
Heidelberg, Nov 2016
(based on research joint with Reuven Glick, FRBSF)

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, %.
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 before establishment of EMU
Found currency unions increase trade by ≈90%
Current paper uses data for 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 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
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. Gravity  Estimates for Trade
 Table 1
EER 2002 
New 
With non-EMU and EMU CUs dis-aggregated
All CUs
1.30 (.13)
.92 (.09)
All Non-EMU CUs
1.12 (.11)
All EMU
.02 (.08)
Sample period
#Obs.
219,558
426,953
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.

11. 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

12. Gravity  Estimates for Trade with Pair Fixed Effects
 Table 2
EER 2002 
New
With non-EMU and EMU CUs dis-aggregated
All CUs
.65 (.05)
.63 (.07)
All Non-EMU CUs
.75 (.10)
EMU
.41 (.05)
Sample period
#Obs.
219,55
426,953
#Pair FE
11,178
14,801
e ~ 51%
Note: Pooled OLS estimates. Other gravity regressors and year dummies included, but not reported. Robust standard errors in parentheses.

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

14. Forest Plot of (45) Literature EMU Estimates

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

16. 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

17. 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!

18. Publication Bias

19. 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

20. EMU Effect and Sample Size

21. 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

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. 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)

24. 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

25. 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 of time-varying exporter dummy variables,
{ψjt} = set of time-varying importer dummy variables
{θij} = set of time-invariant pair-specific dummy variables

26. With non-EMU and EMU CUs
Gravity  Estimates for Exports with country-year effects for exporter & importer & country pair FE
 Table 5
Aggregate
With non-EMU and EMU CUs
dis-aggregated
All CUs
.34 (.02)
All Non-EMU CUs
.30 (.03)
EMU
.43 (.02)
Sample period
#Obs.
879,794
#Country-year effects
22,438
#Pair FE
33,886
e ~ 54%
Note: Other gravity regressors and year dummies included, but not reported. Robust standard errors in parentheses.

27. 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

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

29. Symmetry Tests, Exports with country-year and pair FE
Table 6
F-stat (p value)
After CU Entry = - After CU Exit?
.8 (.71)
Before CU Entry = - Before CU Exit?
.8 (.68)
Both
1.0 (.49)
After non-EMU CU Entry = After EMU Entry?
1.3 (.17)
Before non-EMU CU Entry = Before EMU Entry?
1.4 (.16)
2.8 (.00)
After non-EMU CU Exit = - After EMU Entry?
.9(.51)
Can’t reject
Can’t reject
Table reports F-test statistic for Ho of identical slopes Σkθk Σkφk for given CU pairs and time periods

30. Sensitivity Analysis of  Estimates : Dis-aggregating Other CUs 
EMU
.43** (.02)
.43** (.02)
Other CUs
.30** (.03)
(.06)
CFA Franc
.58** (.10)
ECCU $
1.64** (.11)
Aussie $
(.20)
Brit. £
.55** (.03)
French Franc
.87** (.08)
Indian Rupee
.52** (.11)
US $
(.06)
Note: Other gravity regressors, country-year and pair dummies included, but not reported. 879,794 annual observations,

31. Sensitivity Analysis of  EMU Estimates: Varying Country and Sample Period
  for EMU
All Countries
.43** (.02)
[879,794]
.47** (.03)
[424,230]
.18** (.03)
[691,074]
[386,653]
.18** (.04)
[235,510]
Upper Income Countries
(GDP p/c>$12,736)
.11** (.03)
[75,468]
.16** (.03)
[45,401]
-.02 (.04)
[52,103]
-.01 (.04)
[35,865]
-.09* (.04)
[22,036]
Industrial
Countries + Present/future EU
-.01 (.02)
[73,253]
.04 (.02)
[26,763]
-.09** (.03)
[61,939]
-.16**(.03)
[27,570]
-.07 (.04)
[15,449]
Present/future EU
-.27** (.02)
[30,731]
-.04 (.02)
[13,337]
-.31** (.04)
[25,115]
-.29**(.03)
[12,230]
-.10** (.03)
[7,721]
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.

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

33. 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

34. Graphical Estimates of EMU Export Effect

35. 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

36. 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

37. Estimates of Multilateral Resistance: Evidence of 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

38. Summary Glick-Rose (2002) concluded Based on:
“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, , 15 EMU years

39. Conclusions Symmetry holds between currency union entry and exits
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

40. 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 survey (2005): 47%

41. 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