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European Integration and Economic Growth: A Counterfactual Analysis

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1 European Integration and Economic Growth: A Counterfactual Analysis
Nauro 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

2 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

3 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?

4 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

5 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 Imbens, G. and J. Wooldridge (2009), “Recent Developments in the Econometrics of Program Evaluation,” Journal of Economic Literature, 47:1, 5–86.

6 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

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.

8 Original Example: Basque GDP & ETA

Z should contain variables that help the approximation of Y1t pre-treatment, but should not include variables which anticipate the effect. 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).

10 What did we do? Synthetic counterfactuals method
Estimate growth and productivity payoffs EU membership All enlargements: 1973, 1980s, 1995, 2004

11 Three key issues Year treatment starts (EU membership)
1973: IRL, DK, UK; 1980s: Greece, SP, Port; 1995: Austria, Fin, Sweden; 2004: Poland CZ etc 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 Donor pool: used a range from whole world to neighbours, but report upper middle income

12 Main Results

13 Portugal




17 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


19 Statistical significance

20 DID estimates show most results are statistically significant

21 Interpretation


23 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

24 Thank you



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