This chapter summarizes the past research conducted on analyzing the effect of the EU accession on the economic growth of the member states. Furthermore, the differences in the method and variables used in this thesis compared to the previous research are discussed.
The previous literature on the effect of joining the EU on member countries is still insufficient. While there is a large number of research papers estimating the effect of the euro adoption on the member countries, there is only a limited amount of econometric research papers estimating the monetary benefits of the EU membership on the member countries. Furthermore, most of the previous research focuses on the past enlargements10. Also, no previous research solely focused on the V4 countries and their GDP and its components.
Nonetheless, based on past research, the EU participation mostly led to higher GDP per capita for the member countries (Badinger, 2005; Kutan & Yigit 2007; Crespo Cuaresma, Ritzberger-Grünwald & Silgoner, 2008; Campos, Coricelli & Moretti, 2014). The past literature mostly uses the Solow model or the endogenous growth theory. The Solow model builds on an exogenous growth theory, where the long-term economic growth is achieved by the exogenous rate of technological change (Solow, 1956). This means that either economic policy or integration would lead only to a level effect caused by temporarily higher economic growth rate. The Solow model introduced the diminishing returns to capital, according to which, those countries that reached lower output per capita will experience higher growth of the output level compared to those with higher values and in the long run, they would converge to the richer old member states. On the contrary, the endogenous growth theory accounts the technological change as an endogenous variable by firms investing to research to reach higher technological level (Romer, 1990). For this theory, the economic integration may cause a long-run positive effect on the economic growth of the country. Furthermore, the profits generated by higher technological levels by investing to research and innovation encourages the long-run economic growth, and the long-run economic growth boosts up with the larger size of the economy (Crespo Cuaresma et al., 2008).
10 EU enlargements that took place before year 2004.
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There is not a clear consensus what theory should be implemented, nonetheless, most researchers choose to use the Solow model due to its relative simplicity.
Overall, the results of the previous research are mostly consistent, agreeing on the positive effects of participation in the EU on most of the countries, except for Greece that experienced negative effect. One of the first researches on the membership effect was by Henrekson, Torstensson and Torstensson (1997), the authors found significant positive effect on the economic growth of the member countries. Nonetheless, the authors warned against not completely robust data to change of control variables, and also measurement errors.
Furthermore, Badinger (2005) found positive impacts of the EU membership on the income per capita of fifteen member states, however, the results were not completely robust. Badinger (2005) estimated by panel data regression that the sum of the EU members’ income per capita would be lower by approximately one-fifth without the economic integration. The estimated scale growth effect was only temporary; however, the level effects were sizable (Badinger, 2005). Similar robust results were retrieved by Böwer and Turrini (2010), the authors estimated by using panel regression increased income per capita growth generated after the EU accession.
Kutan and Yigit (2007) also estimated positive results generated by the EU participation on the EU members that occur especially in the long run. Furthermore, the authors estimated positive effect of the EU accession on the output, productivity and convergence levels of the old EU15 (Kutan & Yigit, 2007).
Furthermore, Crespo Cuaresma et al. (2008) used panel regression to estimate the membership effects, and the authors determined that the EU accession has a positive, nonetheless, unbalanced effect on the long-term economic growth of the EU members.
The effect was higher for the relatively poorer countries11, which confirms the convergence theory (Crespo Cuaresma et al., 2008).
While previous papers considered only the old fifteen member states, the following authors were also focusing on the Eastern enlargement. Based on the Solow growth theory, Mann (2015) found EU had small, however, positive medium-run impact on the economic growth of the new member countries led by the trade integration in the single market with substantial benefits that were not measured, such as higher attractiveness for investors and lower risk premium. Molendowski (2015) found that the effects of
11 The authors estimated the results only for the fifteen old member states, therefore considered only enlargements before year 2004.
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joining the EU on the V4 economies slightly differed. While Poland received the highest GDP growth rate during the first ten years of the EU participation, Hungary came out with the worst results (Molendowski, 2015).
European Commission (2001) scenario for the Eastern enlargement was that the EU10 economies can achieve as high as 5.5% growth rate due to the EU accession generated by FDI inflow and higher labor force growth due to the higher labor force participation.
Rapacki and Próchniak (2008) based their study on the Solow model theory and tested the convergence levels of the EU10 and EU15. Rapacki and Próchniak (2008) estimated a significant positive effect from the EU accession on the economic performance of the Eastern enlargement countries encouraged by the FDI inflow, structural reforms and structural funds money inflow. Furthermore, the authors predicted that the convergence between the old EU15 and EU10 can take between eight and thirty-three years (Rapacki & Próchniak, 2008).
More results were presented by Breuss (2001) that estimated the EU10 will gain from the EU membership approximately ten times higher increase of the real GDP per capita compared to the old EU15. Breuss (2001) estimated that Poland and Hungary will increase their real GDP the most, approximately by 8 to 9% a year, nonetheless the Czech Republic will gain less, approximately 5 to 6% a year.
Furthermore, Maliszewska (2009) estimated the benefits for the Eastern enlargement countries from joining the single market, the author expected increase of wages in the new EU countries and in terms of GDP, Poland is expected to generate 3.4% growth of GDP and for Hungary the gain is estimated to be 7%. The benefits are assumed to the trade liberalization, work mobility and elimination of the trade barriers (Maliszewska, 2009).
By using the SCM, Campos et al. (2014) found that joining the EU had a positive effect on the member countries and furthermore, the authors estimated positive effects of joining the EU on most of the old fifteen member countries, except for Greece. The researchers estimated that if the member states never joined the EU, on average, their level of GDP per capita would be lower by 12% (Campos et al., 2014). Nonetheless, when estimating the effects for the Eastern enlargement, Campos et al. (2014) did not reach unanimous results. For year 2004 as a treatment, the authors found positive effects of the EU entry for the Czech Republic and opposite results were retrieved for the rest of the V4 countries. When the authors used 1998 as a treatment year, the effects turned
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positive for all treated countries, but Slovakia. However, by changing the treatment date, the authors were left with relatively short pre-intervention period, which may have affected the SCM results12. Overall, the authors found positive effect gained by joining the EU on the output levels of the member countries. Nonetheless, the authors did not discuss the results regarding the economic environment of the treated countries, which is an important factor that needs to be considered for the results interpretation.
Most of the previous research warns against the credibility of the results13. Using SCM may prevent some of the difficulties with estimating the EU membership effect, therefore should present more accurate results than other methods, such as regression or Difference in Differences. By using the SCM, values that sum up to 1 are assigned to each of the countries, based on the similarities of the chosen predictors.
The thesis complements the current research of the EU membership by estimating the results on the GDP per capita and the GDP components of four EU countries. The selected covariates are based on the previous research, but, complemented by several different variables. Furthermore, the choice of the predictors is constant for each of the countries and outcome variables to ensure clear results driven by the same covariates.
12 See Abadie and Gardeazabal (2003).
13 The researchers mainly warn against the results credibility due to lack of robustness of the results, heterogeneity, possible measurement errors and spill-over effects.
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This chapter presents the dataset and the sample used for the estimation. The thesis aims to analyze the impact of joining the EU on the V4 countries and therefore, the macroeconomic dataset is composed of standard economic growth predictors. Two data sources were used, World Bank’s Databank and United Nations Development Programme’s Human Development Report. Further explanation of the variables and the sources of the variables is attached in Appendix A: Data description.
The selection of the outcome variables and predictors was based on Abadie, Diamond and Hainmueller (2015) complemented by several new predictors. The country-level data are collected for the period 1991-2017, depending on the country’s data availability14. The approximately decade long pre-treatment period should retrieve satisfactory results. In the previous research were used two possible years as a treatment.
The year of accession, 2004, or the year of signing the accession documents, 1998 or 1999. The latter option provides too short pre-treatment period, which may lower the credibility of the results, therefore year 2004 was used as a treatment. The countries share the same donor pool and predictors to maintain the same conditions and environment for each of the countries15.
The credibility of the results is checked by three robustness tests16. First robustness check is conducted by adding Iceland, Norway and Switzerland to the dataset. Those countries were firstly omitted from the donor pool as having signed trade agreements with the EU. The second check was conducted by leaving-one-out re-analysis of the sample, based on Abadie (2019). For each of the SCM estimates, the country with highest value assigned was dropped from the dataset. The third robustness check tests the robustness of the estimates on larger donor pool that consists of 37 countries17. The results of the robustness checks can be found at the end of each country’s results section18.
14 For the Czech Republic and Hungary, the data are retrieved from 1991, for Slovakia from 1992 and for Poland from 1994. This gives 13 years, 12 years and 9 years long pre-treatment panel, respectively.
15 The pre-intervention period length differs for the treated countries due to different data availability for each of the countries.
16 A robustness check proposed by Abadie (2019) with backdating the treatment was not used as the pre-treatment period for this check is not long enough.
17 The added countries for this robustness check are United States, Guatemala, Indonesia, Mexico, Mongolia, South Africa, Iceland, Norway and Switzerland.
18 In the graphs, robustness check of leaving one country out of the dataset is called "leave-one-out", for adding the three countries with EU trade agreements, the variable is called "add" and for larger donor pool, the robustness check is called "different donor pool".