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

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

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

CitY 0Tt

it

N

ii

Iitit YwY

1

2

*̂ 0Tt

),...,( 12 NwwW

1

2

1N

iiw 0iw

1

21*

N

ititi YYw

1

21*

N

iii ZZw

What is a SYNTHETIC COUNTERFACTUAL?

Original Example: Basque GDP & ETA

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

What did we do?

• Synthetic counterfactuals method

• Estimate growth and productivity payoffs

• EU membership

• All enlargements: 1973, 1980s, 1995, 2004

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

Main Results

Portugal50

0010

000

1500

020

000

rgdp

ch

1970 1980 1990 2000 2010year

PRT synthetic PRT

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