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European Integration and Economic Growth: A Counterfactual Analysis Nauro F Campos Fabrizio Coricelli Luigi Moretti Brunel University Paris School of Economics.

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Presentation on theme: "European Integration and Economic Growth: A Counterfactual Analysis Nauro F Campos Fabrizio Coricelli Luigi Moretti Brunel University Paris School of Economics."— Presentation transcript:

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

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

7 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. What is a SYNTHETIC COUNTERFACTUAL?

8 Original Example: Basque GDP & ETA

9 Assumptions: 1.Z should contain variables that help the approximation of Y 1t 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

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 1.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

12 Main Results

13 Portugal

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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

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19 Statistical significance

20 DID estimates show most results are statistically significant

21 Interpretation

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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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