Report

European Integration and Economic Growth: A Counterfactual Analysis Nauro F Campos Brunel University Fabrizio Coricelli Luigi Moretti Paris School of Economics University of Padova Conference on “Transition Economics Meets New Structural Economics” London, SSEES/UCL, June 2013 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 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? 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 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 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 What is a SYNTHETIC COUNTERFACTUAL? More formally: Be Y an outcome variable (eg. GDP per capita). it Yit Yit I C t . T0 where Y itC 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: N 1 ˆit Y w i *Yit for t T.0 I it i2 N 1 The set of weights is W ( w 2 ,..., w N 1 ) with w i and 1 w.i 0 i2 Thus pre-treatment: N 1 w N 1 i * Yit Y1t i2 where Z is a set of covariates/predictors of Y. w i2 i * Z i Z1 Original Example: Basque GDP & ETA SYNTHETIC COUNTERFACTUAL: Assumptions 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 posttreatment 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). What did we do? • Synthetic counterfactuals method • Estimate growth and productivity payoffs • EU membership • All enlargements: 1973, 1980s, 1995, 2004 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 Main Results 15000 10000 5000 rgdpch 20000 Portugal 1970 1980 1990 year PRT 2000 synthetic PRT 2010 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 Statistical significance DID estimates show most results are statistically significant Interpretation 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 Thank you