European Integration and Economic Growth: A Counterfactual Analysis
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European Integration and Economic Growth:
A Counterfactual AnalysisNauro F Campos Fabrizio Coricelli Luigi Moretti
Brunel University Paris School of Economics University of Padova
Conference on “Transition Economics Meets New Structural Economics”London, SSEES/UCL, June 2013
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Motivation• Are the countries that joined the European Integration
project better-off?
• Direct costs of EU membership (ok), indirect costs (???), and benefits (??)
• Voluminous literature on effects of single market, Euro, enlargements, trade and growth
• Range of estimates from Eichengreen-Boltho to Badinger: without Integration, pci Europe 5-20% lower
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Counterfactuals are key
• Counterfactuals and causality
• Wide use of counterfactuals: “EU average” and “compared to France” (“75% of EU average”)
• Can we improve upon these counterfactuals?
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Research Question and Method
• What would have been the growth rates of per capita GDP and productivity in EU countries if they had not become full-fledged EU members?
• Synthetic control methods for causal inference in comparative case studies or
“synthetic counterfactuals”
• Abadie et al: AER 2003, JASA 2009, mimeo 2012
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Method: Synthetic counterfactuals
• A recent development in econometrics of program evaluation (Imbens and Wooldridge JEL 2009)
• “artificial control group” (JEL 2009, p. 79)
• It estimates the effect of a given intervention by comparing the evolution of an aggregate outcome variable for a country “treated” to its evolution for a synthetic control group
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Synthetic counterfactuals (con’t)
• Researcher specifies: (1) treatment (what and when), (2) matching covariates, and (3) “donor pool” (to synthetic/artificial control group)
• Method minimizes the pre-treatment distance (mean squared error of pre-treatment outcomes) between the vector of treated country’s characteristics and the vector of potential synthetic control characteristics
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More formally:
Be Y an outcome variable (eg. GDP per capita).
where is unknow for .
Given N+1 the observed countries, with i=1 the treated country and i =2,…, N+1 the control/donor countries, Abadie et al. (AER 2003, JASA 2010) show that:
for .
The set of weights is with and .
Thus pre-treatment:
where Z is a set of covariates/predictors of Y.
Cit
Iitit YY
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ititi YYw
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What is a SYNTHETIC COUNTERFACTUAL?
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Original Example: Basque GDP & ETA
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Assumptions:1. Z should contain variables that help the approximation of Y1t pre-treatment, but
should not include variables which anticipate the effect.2. Donor countries (i=2,…,N+1) should not be affected by the treatment.If assumptions (1) and (2) do not hold, it's likely that the estimation of the post-
treatment effect is downward biased.
Advantages:• It allows the study of the dynamic effects.• It is designed for case-study, so it can allow the evaluation of treatment
independently from: i) the number of treated units; ii) the number of control units; iii) the timing of the treatment.
Disadvantages:• It does not allow the assessment the significance of the results using standard
(large-sample) inferential techniques: only permutation tests on the donor sample (placebo experiment).
SYNTHETIC COUNTERFACTUAL: Assumptions
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What did we do?
• Synthetic counterfactuals method
• Estimate growth and productivity payoffs
• EU membership
• All enlargements: 1973, 1980s, 1995, 2004
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Three key issues1. Year treatment starts (EU membership)
– 1973: IRL, DK, UK; 1980s: Greece, SP, Port; 1995: Austria, Fin, Sweden; 2004: Poland CZ etc
2. Matching over which covariates? – Similar to Abadie AER 2003: investment, labour force,
population, share of agriculture in GDP, level of secondary and tertiary education, etc
3. Donor pool: used a range from whole world to neighbours, but report upper middle income
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Main Results
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Portugal50
0010
000
1500
020
000
rgdp
ch
1970 1980 1990 2000 2010year
PRT synthetic PRT
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Main Sensitivity analysis:2004 Enlargement and Anticipation
Not shown today: different GDP measures, of labour productivity, changes in
covariate sets, regional evidence, Full range of placebo tests
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Statistical significance
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DID estimates show most results are statistically significant
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Interpretation
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Summary and main findings• Strong tendency for the growth and productivity
effects from EU membership to be positive• Yet considerable heterogeneity across countries• GDP/productivity significantly increase:
Denmark, Ireland, UK, Portugal, Spain, Austria, Finland, Estonia, Poland, Latvia and Lithuania
• Growth effects tend to be smaller: Sweden, Czech Republic, Slovakia, Slovenia and Hungary
• Greece is the only exception • Magnitude of aggregate, average effect: 10
percent
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Thank you
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