Previous Article in Journal
Policy Alignment Between ECB Unconventional Monetary Policies and China’s Monetary Reforms—A Cross-Region Study
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Economies
Volume 13
Issue 11
10.3390/economies13110326
economies-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Ten Lessons from the EU Accession of Ex-Communist Countries

by
Călin Vâlsan
1,2,*,
Amos M. Rahat
3 and
Elena Druicăa
2
1
William School of Business, Bishop’s University, Sherbrooke, QC J1M 1Z7, Canada
2
Department of Applied Economics and Quantitative Analysis, University of Bucharest, 030018 Bucharest, Romania
3
Doctoral School of Economics and Administrative Sciences, Faculty of Business Administration, University of Bucharest, 030018 Bucharest, Romania
*
Author to whom correspondence should be addressed.
Economies 2025, 13(11), 326; https://doi.org/10.3390/economies13110326
Submission received: 11 October 2025 / Revised: 2 November 2025 / Accepted: 6 November 2025 / Published: 12 November 2025
Versions Notes

Abstract

We investigate the broad economic impact of accession in the case of 13 countries who joined the European Union starting with 2004, by comparing them with both EU and non-EU countries. Using 25 years of data, we document significant post-accession improvements in productivity and/or GDP per capita. Overall, these countries outperformed in terms of growth and productivity the European countries that never joined the EU. The newly admitted countries also out-borrowed non-EU countries in order to finance their transition and their subsequent economic growth. We also document a significant short-term, inverse relationship between the quality of governance and total factor productivity, on the one hand, and the ratio of debt-to-GDP on the other hand. This suggests that increases in the level of indebtedness could be driven by poor political governance and weak productivity. Borrowing appears to represent a compensatory, stop-gap measure rather than the result of sound economic strategy.

1. Introduction

One of the most noteworthy events in recent history is the European Union (EU) accession of the former communist countries of Eastern Europe, who joined as a group in 2004 (with Romania and Bulgaria joining in 2007, and Croatia in 2013). The relatively large number of new members who joined almost at once, and the economic, social, and cultural differences between East and West, turned this event into a rare opportunity for assessing the social and economic outcome of EU enlargement.
Many economists and policymakers had predicted a win-win outcome (Baldwin et al., 1997; Breuss, 2002; Nuroglu & Kurtagić, 2012), while others had pointed out that EU membership is neither a necessary nor a sufficient condition for economic growth, which is driven mainly by liberalization and access to markets (Tupy, 2003).
The studies that follow the 2004–2007 enlargement focus mostly on Gross Domestic Product (GDP henceforth) growth and not surprisingly report a mix of sigma- and beta- convergence (Próchniak & Witkowski, 2013; Dvoroková, 2014; Siljak, 2018). Some of the more obvious limitations of convergence studies aside (Gluschenko, 2012), there is a glaring contrast between the narrow GDP growth focus, and the sweeping goals of European integration. There is no research that combines into a single conceptual framework economic, governance, and cultural dimensions in order to document the long-run dynamic of the 2004–2007 EU enlargement.
This is where our study fills an important knowledge gap. Using data from 1996 to 2023 from the current member states and assimilated, we attempt to determine how GDP, total factor productivity (TFP), total debt to GDP, and six governance indicators co-evolved before and after the accession of former communist countries. If accession to the EU is indeed a win-win proposition, we should be able to uncover supporting evidence.
Our results indicate significant improvements in productivity and/or GDP per capita for the newly admitted former communist countries, when compared to both existing EU member states, and the countries left outside the EU. However, it appears that the quality of governance is averaging out, which might not necessarily represent good news for the older EU member states. There is a differentiation in terms of economic performance among the newly-admitted countries, with Bulgaria and Romania not surprisingly underperforming in some areas the ten countries that first joined the EU in 2004.
We document a significant and inverse relationship between the quality of governance, TFP, and GDP per capita, on the one hand, and the ratio of debt-to-GDP on the other hand. This suggests that increases in the level of indebtedness could be driven (especially in the short-term) by poor political governance and weak productivity. Borrowing appears to represent a compensatory, perhaps populist, stop-gap measure rather than the result of sound economic strategy.
Our paper is structured as follows: In Section 2 we present a brief background and we explore the existing academic literature in order to develop our hypotheses and build our model. In Section 3 we present our data and briefly describe the methodology. In Section 4 we present our econometric results and in Section 5 we discuss and interpret them. Section 6 concludes.

2. Background

Many studies analyzing long-term economic growth over a broad geographical scale consider sigma- and beta-convergence. The methodology was pioneered largely by (Baumol, 1986; Sala-i-Martin, 1996). Using almost a century of economic data, Baumol (1986) is able to document unprecedented convergence in GDP growth and productivity among developed countries and planned economies, to the exclusion of the third world, who seemed to be lagging behind. A similar finding is reported by Sala-i-Martin (1996), who presents evidence of conditional beta-convergence with a speed to the tune of 2% per year.
The rise of the EU and the dissolution of the communist bloc, in particular, made this type of research both captivating and accessible. It made it captivating because it set out to answer a very profound question with respect to the promise of economic prosperity held by regional integration, the likes of which the world had never seen before. It made it accessible because the context of European integration, the fall of communism, and the subsequent accession of former communist countries to the European Union gave us the closest thing to a large-scale controlled experiment to test our economic theories.
The EU is arguably the most successful supranational institution ever created. Born in the wake of WWII and initially known as the European Economic Community when the Treaty of Rome was signed in 1957, it set out to create a free trading bloc among a small number of Western European nations. As time went on, its objectives and scope became increasingly more ambitious and far reaching. Successive waves of enlargement brought the number of initial countries from six in 1957, to currently 27, after UK chose to exit the bloc in early 2020.
The largest waves of enlargement occurred in 2004–2007 when 12 countries in total, most of them former communist countries, joined the EU. Croatia joined in 2013. As already mentioned earlier, this particularly large group makes it possible to conduct a more rigorous and in-depth analysis to ascertain the extent to which EU membership indeed makes a difference.
Most studies that investigate the accession of these countries attempt to document sigma- and/or beta-convergence. A 2013 study that only considers the former communist countries of Eastern Europe documents beta-convergence at a rate of 1.5 percent to 2 percent per annum (Próchniak & Witkowski, 2013). Absolute beta-convergence is also documented by (Dvoroková, 2014), although sigma convergence seems to suggest a different story. A similar, yet weaker result appears to be reported by (Siljak, 2018) taking into account the 2008 sub-prime meltdown, whose effects mostly interfere with absolute convergence.
Other studies had previously documented convergence among a larger number of countries, oftentimes including European countries, prior to the 2004–2007 enlargement waves (M. Mello & Perrelli, 2003; Liberto & Symons, 2003; De La Fuente, 2003; Giudici & Mollick, 2008; Miller & Upadhyay, 2002; Dobson & Ramlogan, 2002; Nakamura, 2001).
Convergence studies are insightful, yet the interpretation of results is fairly limited. The more common misconception is that growth convergence implies economic gaps are narrowing, which is not necessarily true (Friedman, 1992; Jenkins & Van Kerm, 2011; Nolan et al., 2011; Gluschenko, 2012).
The seminal work of (Acemoglu et al., 2014), is representative of a vigorous line of inquiry which links economic prosperity to the evolution of social democracies and finds the democratic process has indeed a significant impact on growth. This result is echoed by several other studies (Narayan et al., 2011; Eberhardt, 2022; Mathonnat & Minea, 2019; Madsen et al., 2015; Tavares & Wacziarg, 2001; Colagrossi et al., 2020).
Rule of law also has a significant and positive impact on income per capita (Ozpolat et al., 2016; Bhagat, 2020; Zaric & Babic, 2021; Ridley, 2021). Moreover, development and prosperity are contingent on the nature of institutions and the entrenchment of elites (Acemoglu & Robinson, 2008; Mathonnat & Minea, 2019).
To date, no one to our knowledge has incorporated GDP, TFP, indebtedness, and governance into a single framework to study the short- and long-term impact of European enlargement. It is this important knowledge gap we are addressing here. If EU membership is indeed beneficial, we should be able to document improvements on multiple dimensions after enlargement for the newly admitted countries, when compared to both existing member states, and countries that never joined the EU.
We expect that TFP drives GDP growth, yet GDP should also have a significant impact on TFP because a high GDP per capita is the telltale sign of a developed economy holding the potential for significant technological advances and innovation (Gehringer et al., 2016; Siller et al., 2021).
As a country becomes wealthier, more individuals would arguably demand increased political power and representation, which could translate into better governance. Higher expectations of income redistribution and social justice could generate an increasing demand for public borrowing made possible by sustained prosperity.
Debt-to-GDP is thus bridging economic development and governance (Betz & Pond, 2023). A functional government, dedicated to improving income per capita and advancing social development will often borrow to finance infrastructure projects that will boost productivity and economic growth. Good governance and growth increase the credit rating of a nation and lowers its borrowing costs. On the other hand, a large public debt could act as a hard constraint on governance and hence entail improvements. If corporate debt can keep agency costs down and serve as a source of managerial discipline, the debt of a nation could also act as a device for containing the agency costs associated with the governance of the country (Lasfer, 1995; A. S. Mello & Parsons, 1992; Kim & Sorensen, 1986; Harvey et al., 2004; Brockman & Unlu, 2009; Leland, 1998; Kaplan & Thomsson, 2017).
However, stagnant productivity, poor governance and dysfunctional politics can determine policymakers to keep borrowing in order to pay salaries to government employees and deliver privileges to entrenched political clients (Peev & Mueller, 2012). This can happen when the cost of effectively monitoring and disciplining managers, politicians, and bureaucrats become prohibitive (Canarella & Miller, 2022; Imaginário & Guedes, 2020; Streeck, 2014).
Based on all of the above, we hypothesize that economic development, productivity, overall indebtedness, political governance, and cultural characteristics are broad variables that act as a system, with multiple feed-back linkages, constantly influencing each other. Moreover, we hypothesize that after accession, the group of former communist countries show improvements in either GDP per capita, productivity, and governance when compared to the countries who never joined the EU, and even when compared to the countries already members of the EU:
H1. 
GDP per capita, TFP, the governance index, and debt-to-GDP are cointegrated, exhibiting statistically significant long-run correlations.
H2. 
Compared to existing EU countries, ex-communist countries experienced post-accession improvements in some or all of the following variables: GDP per capita, TFP, and the governance index.
H3. 
Compared to non-EU countries, ex-communist countries experienced post-accession improvements in some or all of the following variables: GDP per capita, TFP, and the governance index.
Overall, our analysis is aimed at uncovering meaningful changes associated with the enlargement process. We expect that the 12 countries that joined the EU in 2004–2007, plus Croatia in 2013, show improvements in growth, productivity, and governance when compared to both existing EU members and the remaining countries that never joined the EU. To this objective we turn our attention next.

3. Data and Methods

Data was collected for the entire population of 48 countries defined by the United Nations as part of the European continent (including Cyprus), for the period 1996 to 2023 (years 1996, 1998, 2000, and annually from 2002 to 2023). The variables collected subject to availability include: (i) Gross Domestic Product (GDP) per capita (current US$) (ii) Gross Public Debt, as % of GDP (iii) Total Factor Productivity (TFP—based on Penn World Tables), at current PPP (USA = 1), and the six World Bank Worldwide Governance Indicators (WGI). For brevity, in Table 1, we present the variables used in our analysis.
The Worldwide Governance Indicators (WGI) is a World Bank initiative and is comprised of the following dimensions: (i) Voice & Accountability (ii) Political Stability and absence of Violence/Terrorism (iii) Government Effectiveness (iv) Regulatory Quality (v) Rule of Law and (vi) Control of Corruption.
After excluding four micro-states, the remaining 44 countries1 were initially grouped into the following subgroups for comparative analysis: (a) The 13 EU member states joining the European Union (EU) starting with 2004 (b) The 14 EU member states already members of the EU prior to 2004, supplemented with: Iceland, Norway, Switzerland and the UK, that is, a total of 18 countries. (c) European countries according to the UN definition, which are not part of the European Union.
Our methods consist of Pooled Mean Group (PMG) and Dynamic Panel using the Generalized Method of Moments (GMM).
The six governance variables employed in this study show multi-collinearity, hence we take the first principal component as an index of overall governance. Initial statistical tests reveal most series are non-stationary. The Choi meta-tests reject the null hypothesis that all groups are stationary, and the GLS version of the Augmented Dickey-Fuller test fail to reject the null hypothesis of a unit root in the case of a majority of units and variables. For brevity we do not report the results here. Given that we have a mix of I(1) and I(0), we also perform cointegration tests and find strong evidence in its favor (Table 2).
Kao’s test suggests multiple cointegration relationships among the four variables under investigation, but we have to be cautious because some series might in fact be stationary. Based on the panel-level statistics (nipanel and tpanelpar), Pedroni’s test also suggests cointegration among the first three variables. Not surprisingly, group-level statistics are generally weaker, which is consistent with the literature and reflects heterogeneous dynamics across individual countries. This result narrows our choice of models in the next stages of the analysis.

4. Results

4.1. Pooled Mean Group Analysis

At first, we model the relationship among our four leading variables using the Auto-Regressive Distributed Lag Model (ARDL) in either Pooled Mean Group (PMG) or Mean Group (MG) form. ARDL is in fact an unrestricted variation of the Vector Error Correction Model applied to panel data when dealing with a mix of I(1) and I(0) series. The long-run coefficients are presented in Table 3. Because the results of the Pesaran tests indicate significant cross-sectional correlation (not reported here for the sake of parsimony), we also run a Common Correlated Effects (CCE) specification of the panel model, in order to account for unobserved common factors that might be responsible for cross-sectional dependence.
In the case of the ARDL model, all error correction coefficients are negative and significant at the 1% level, confirming once again the long-term relationships among the four variables. In the long-run, overall governance appears to be associated with higher GDP per capita, increased productivity, and a higher debt-to-GDP ratio. GDP per capita is strengthened by better governance and higher productivity. The relationship between TFP and GDP per capita in the second model is positive and significant as initially expected. Higher levels of indebtedness, however, appear inversely related to GDP per capita. TFP appears strengthened by better governance and weakened by a growing public debt burden, in the same vein as GDP per capita. The inverse relationship between GDP per capita and productivity in the third model is surprising and will be interpreted later, after correcting for short-term persistence in the dependent variables. A better overall governance seems to correlate with more borrowing, while a higher real GDP per capita results in a lower overall indebtedness. We also report an inverse relationship between TFP and Debt-to-GDP.
All Hausman test values are not statistically significant, suggesting the PMG model is preferable in all four instances. MG estimation allows for greater heterogeneity across short-term and long-term coefficients, but the estimates tend to be less efficient when this assumption does not hold. The former is more restrictive because it assumes all long-run coefficient to be similar across all countries and tends to be more efficient when this assumption holds. The results of the CCE panel specification are largely consistent with the PMG approach, albeit the statistical significance of some coefficients is somewhat weaker. Overall, these results confirm H1.
Under the PMG and CCE approach, however, all categorical variables are expunged from the model, hence we cannot perform a Diff-in-Diff analysis. In addition, there is a significant degree of persistence in all four variables considered above. This needs to be addressed before drawing stronger conclusions.

4.2. Dynamic Panel Analysis

The best way to address the issue of persistence in the outcome variables is to use a dynamic panel approach using the two-stage Generalized Method of Moments (GMM henceforth) with collapsed instruments and asymptotic standard errors. We are mindful of the cross-sectional dependence present in our data, but according to Pesaran (2006), our panel data has time and cross-sectional dimensions that are relatively manageable and entail a risk of bias and lack of consistency that is minimal. As it will be shown later, our results appear robust and consistent across various model specifications.
We are segregating the sample into several subsamples to be analyzed into what amounts to an adapted staggered Diff-in-Diff dynamic panel approach. We use the 14 countries already members of the EU, to which we add UK, Norway, Switzerland, and Iceland, as EU assimilated nations, representing the first control group. UK has been an active EU member between 1973 and 2020, hence economically, socially, and politically is very much in step with the other European Union nations. Norway, Switzerland, and Iceland, although never EU members have shadowed EU policies and regulations, and enjoyed a level of economic development and social prosperity comparable to that of EU member countries. Norway has even been admitted to the EU in the early 1970s, but has declined membership, preferring to retain its political and economic autonomy, although otherwise very much aligned with European values and policies. Switzerland is a member of the Schengen area, like the vast majority of EU nations.
The treatment groups are represented by the 10 and respectively 2 countries—mostly ex-communist countries—that joined in 2004 and 2007. In order to avoid severe multicollinearity, we do not stagger the analysis in the case of Croatia, who joined in 2013, and whose GDP represents only 0.4% of the total EU GDP. We thus stagger the post-enlargement periods to start in 2004 and 2007. Another group is made of the remaining European countries that never joined the EU, for which we include the dummy variable NON_EU. We also include two yearly dummy variables: one for the year prior to the 2007–2008 financial crisis and the other one for the year prior to the COVID-19 pandemic. Given that Bulgaria and Romania share a common Eastern Orthodox cultural heritage, and the remaining 11 countries overwhelmingly share a common Catholic cultural heritage, the dummy variables used to differentiate between cohorts also represent proxies for cultural heritage. Results are presented in Table 4.
As expected, all four outcome variables have a very high degree of persistence, and their lagged values are among of the most important and significant predictors in our models.
In the first model, TFP has a positive impact on governance but the coefficient does not appear significant. Debt-to-GDP is also correlated to overall governance, but the relationship is inverse and statistically significant. The dummies for the countries that joined in 2004 and 2007 are not statistically significant. After enlargement, only one interaction shows a negative and statistically significant coefficient, in the case of the two countries that joined in 2007, that is, Bulgaria and Romania. The dummy for the year prior to COVID-19 appears positive and statistically significant, possibly suggesting that during the pandemic that ensued, the overall quality of political governance has declined across the board. The dummy for the non-EU countries is negative and significant at the 10% level.
In the second model, all predictors appear statistically significant, with the exception of GOV_PC1 and the dummy for the year preceding COVID-19. The dummies for the countries who joined the EU in 2004 and 2007 show negative coefficients, yet their corresponding post-accession interactions show positive coefficients. The dummy for the year preceding the 2007–2008 financial and economic crisis is positive and significant. The dummy for the non-EU countries is negative and statistically significant.
In the third model, all predictors are significant at the 1% level. Again, we note the negative relationship between indebtedness and productivity. The dummies for the countries that joined the EU in 2004 and 2007 show negative coefficients. The post accession interaction in the case of the ten countries that joined in 2007 is positive, yet the one associated with the two countries that joined in 2007 is negative. The dummy for the year preceding the 2007–2008 financial and economic crisis is negative, and the one for the year prior to COVID-19 appears positive. The dummy for the non-EU countries is again negative and statistically significant.
In the fourth and last model, all predictors are significant at the 1% level. Only three predictors, that is, the lagged value of debt-to-GDP, GDP per capita, and the post-accession period interaction for the two countries that joined in 2007 show positive coefficients. At this point, we contend that both H2 and H3 are confirmed.
We are using a two-step estimation with collapsed instruments and asymptotic standard errors, and the test-statistic for AR(2) errors largely lack statistical significance in most cases. The Hansen overidentification test is a generalization of the Sargan test, and it is more appropriate for the two-step GMM procedure used here. All test estimates are statistically insignificant, or marginally significant at the 10% level (with one notable exception), indicating that by-and-large the instrumentation of our models is appropriate.

5. Discussion

We find that all four variables that stand for economic growth, economic efficiency, indebtedness, and political governance exhibit a very high degree of time persistence. They have significant momentum in the short term. Our results have been impacted by two major crises that occurred in the wake of the 2004–2007 wave of EU enlargement. The first crisis, that is, the subprime meltdown, started as a financial crisis in the fall of 2008, and quickly turned into an economic crisis that stretched over a couple of years (Fligstein & Habinek, 2017). The second major economic crisis was triggered by the COVID-19 pandemic. The 2020 crisis was atypical: It started with an abrupt decline in output, followed by a V-shaped recovery, accompanied by massive deficit spending and inflationary pressures that persist until today (Michie, 2020). Next, we summarize the findings we consider salient.
First, there is a direct two-way relationship between the overall quality of governance and TFP. Good governance makes for a predictable and stable political and social climate which in turn creates the conditions for improved productivity. Good governance enables the emergence of economic efficiency. Improved productivity and efficiency create the economic conditions for further improvements in political governance. This probably owes to the emergence of a social strata with increasing economic power and autonomy, leading to a stronger need for political voice and representation, which in turn results in better governance (Fukuyama, 2014).
Second, growth in GDP per capita is associated with better governance and higher productivity (with one notable exception). Again, this result is robust and consistent with our expectations. It is nonetheless reassuring to confirm that governance indeed matters to economic prosperity.
Third, there is a significant two-way inverse relationship between the degree of indebtedness as measured by the ratio of debt-to-GDP on the one hand, and GDP per capita and TFP on the other hand. This relationship holds over both the short- and long-run, with one notable exception. It appears that over the short-run, higher levels of GDP per capita entail higher levels of indebtedness.
It could be that lower productivity leads to increased borrowing, perhaps in an attempt to compensate for lack of efficiency. At the same time, more debt probably results in higher interest rates depressing the influx of technology enhancing capital. The phenomenon of debt overhang is well documented in the economic and finance literature; (Dawood et al., 2024; Lo & Rogoff, 2015). Moreover, beyond a certain point, the costs of managing an economy on the verge of financial distress outweigh the benefits of borrowing.
Fourth, there is a significant two-way relationship between governance and the debt-to-GDP ratio. Over the long run this relationship appears positive, yet when adjusting for short-term persistence in a dynamic panel framework, it becomes negative and highly significant. Over the long run, improvements in governance might bring predictability, stability, and more responsibility, which in turn allow for a higher degree of indebtedness. The discipline and restrictions entailed by higher levels of indebtedness might lead to improvements in governance.
Over the short run, however, lapses in the quality of overall governance might be associated with reckless borrowing, perhaps in order to pacify the population, make up for reduced efficiency, and reward political cronies (Švarc, 2017; Kopecký & Spirova, 2011; Liargovas & Pilichos, 2022; Buti & Fabbrini, 2023). A higher level of unsustainable borrowing might have a destabilizing political and economic impact. During a political and/or economic crisis the quality of governance has a tendency of getting worse before getting better (Malle, 2009). The crisis that engulfed Greece in 2015 is perhaps one of the best cases in point (Mavroudeas, 2016). The recent large deficits and worsening of financial standing in Romania represent another compelling example (Fitch Rating Report, 2025).
Fifth, over the long run, the PMG analysis reveals higher levels of real GDP per capita to be associated with lower productivity when in principle, stronger growth and the accumulation of wealth should create the conditions for further improvements in productivity. Although the CCE analysis casts doubts over the significance of this result, one might speculate that higher levels of prosperity generate economic surpluses that are subsequently spent on fringe social programs and in rewarding political clienteles. There is a parallel in the Free Cash Flow proposition in corporate finance, where the economic surplus not distributed to shareholders and not invested in value-increasing projects ends up squandered by the management on perks, private benefits of control, and vanity projects (Richardson, 2006; Jensen, 1986; Prescott, 2002; Fernald, 1999).
The sixth important finding is that the ex-communist countries of Eastern Europe are clearly poorer and less efficient than the rest of the EU, but after accession their growth in GDP per capita and productivity appear significant. This is not surprising given that they started from a lower overall level. Bulgaria and Romania who joined in 2007, show a relatively poorer economic performance post accession, that could be due in part to the 2007–2008 crisis, in spite of the fact we have included a control variable for that event.
There was a significant gap at the beginning of the 1990s between the EU and ex-communist countries in terms of GDP per capita and TFP. After enlargement, however, the new member states saw the most rapid increases in wealth and productivity. When combining the various evidence on convergence that has emerged recently with our findings, we can conclude that we have perhaps witnessed a narrowing of the initial wealth and productivity gap existing in the early 1990s (Irandoust, 2021; Kozuń-Cieślak & Markowska-Bzducha, 2021; Tabash et al., 2024).
Our seventh finding shows the countries that joined the EU in 2004–2007 also witnessed significant improvements in per capita GDP and productivity when compared to the countries that remained outside the EU. This is perhaps one of the most telling results, since it settles to a certain extent the question of whether EU membership made a real difference.
Eighth, we have not found clear evidence in favor of improvements in political governance post-accession, for either the entire EU membership or the countries that joined as a group between 2004–2007. It appears as though there are no differences in the quality of governance between the main treatment group and other groups. There is one exception, in the case of Bulgaria and Romania who show a statistically significant negative coefficient during post-accession.
We have reasons to believe, however, that the quality of political governance was significantly lower among the 13 countries that joined in 2004–2007. Our data hints at an overall decline in the quality of governance across the board, yet the results are not statistically significant, with the exception of the COVID-19 pandemic, where we see a clear indication of decline in the quality of governance. There is also enough circumstantial outside evidence showing that since the mid-2000 most nations, including major industrialized nations, did indeed backslide in terms of democracy, rule of law, and/or political accountability (Fukuyama, 2015, 2018; Kapidžić, 2020; Meier et al., 2019; Marsh, 2013; Plattner, 2020; Keck, 2022; Smolka, 2021).
Countries such as the UK, France, Italy, Austria, Slovakia, Poland, Hungary, Romania, and even the Netherlands have either flirted with, or experienced a resurgence of authoritarianism, populism and majoritarianism. UK’s exit from the European Union was fueled by right-wing populist sentiment. Hungary and Slovakia embraced authoritarian political figures and even illiberalism. France and Italy struggled and continue to struggle with the rise of right-wing populism. Romania and Poland struggled and continue to struggle with overt, authoritarian nationalism.
Nineth, the most dramatic borrowing occurred in the wake of the 2008 subprime meltdown, when most nations stepped up to reinforce their faltering financial systems. The ratio of debt-to-GDP has skyrocketed across all European nations to levels never seen since World War II. Massive deficits and more borrowing also occurred in the wake of the COVID-19 crisis, when virtually everyone rushed to stabilize their economies in response to the disruption caused by the pandemic.
Ex-communist countries, with an initial dismal economic efficiency and dubious creditworthiness, still have lower overall levels of indebtedness (Yarashevich, 2013). During transition, borrowing took off dramatically (Campbell, 1995). After accession, ex-communist countries still show lower overall levels of indebtedness, although they borrowed neck-to-neck with the other Western nations. Borrowing has been driven by the needs of structural adjustments, and the need to manage the two major crises mentioned earlier (Paczoski et al., 2019). Among the thirteen countries that joined the EU, Bulgaria and Romania appear to show a dramatic increase in the level of indebtedness.
Membership in the European Union allowed unparalleled access to financial markets and capital for the 13 countries under analysis, something that other nations, such as Russia, Serbia, Albania, or Belarus were not able to enjoy. The countries that joined the EU in 2004, while boasting a higher overall level of indebtedness when compared to non-EU countries, show a lower intercept in Table 4 after accession, most likely due to higher productivity and growth in GDP.
Last, but not least, cultural heritage plays a major role in shaping economic growth, economic efficiency, and political governance. The ten countries that joined in 2007 (and Croatia in 2013) have a predominantly catholic heritage, while Bulgaria and Romania, who joined in 2007 have an Eastern Orthodox cultural heritage. This cultural distinction might be ultimately responsible for explaining the observed differences among the 13 countries, and between them and the other Western European nations.
The cultural traditions of Eastern Orthodoxy and Catholicism combined with communism has resulted in a relatively lower level of per capita GDP, lower economic efficiency, weaker governance, and historically lower levels of indebtedness. After enlargement, GDP per capita and productivity experienced significant improvements, above and beyond those of other EU countries. At the same time, these countries engaged in massive deficit spending and significantly increased their debt-to-GDP burden.
Our research has several important limitations. In spite of controlling for the two major economic shocks, which occurred in 2007–2008 and 2020, we believe that the almost perfect overlapping between the former crisis and the accession of Bulgaria and Romania in 2007 resulted in underestimating the impact of enlargement. We were constrained in our econometric analysis by the need to ensure an optimal model specification, hence we opted for minimizing most known econometric issues. The staggered Diff-in-Diff analysis, in particular, raised several challenges, and we settled for the specification that strikes the best compromise among AR(2) errors, cross-sectional dependence, and multicollinearity.
Another important limitation pertains to the interpretation of some results, which at times becomes speculative. The interpretation of the relationship between indebtedness and the other major variables, in particular, relies on a great deal of circumstantial evidence, and corroboration with the results of other studies. It has become clear that the pursuit of this particular avenue should make the subject of future research, by extending the data set and the number of variables.

6. Conclusions

We are able to document a credible post-accession improvement in per capita real GDP and/or productivity for the newly admitted countries who joined the EU in 2004–2007—all of them with a Catholic or Eastern Orthodox cultural heritage—when compared to both the EU average, and the countries that remained outside the EU.
The most dramatic result, however, pertains to the level of indebtedness, as measured by the ratio of debt-to-GDP. Virtually all countries in Europe embraced deficit spending and saw their debt burden take off, in some cases, to levels never seen since WWII. The former communist countries of Eastern Europe started off with relatively low debt-to-GDP ratios, which translated into a significant unused borrowing capacity as soon as growth and productivity improved significantly. This borrowing capacity has been subsequently used to the fullest in order to address the structural adjustment needs associated with the transition process and the coping needs triggered by the two major crises discussed earlier.
It appears as though borrowing is not the result of a well-thought-out national growth strategy; rather, it is used as a stop gap measure, possibly with a populist flavor, in order to compensate for a lack of economic efficiency and administrative effectiveness.
Recent years have witnessed the rise of a strong anti-EU sentiment among the citizenry of member states, who embrace a populist narrative replete with globalist conspiracies. The European Union is no doubt facing many shortcomings and challenges, and arguably needs to reform its overbearing and byzantine bureaucratic system. But there is still at least one good news story to be told. This current research brings credible evidence in support of the contention that at least until now, joining the European Union has on balance resulted in positive economic outcomes for the former communist nations of Eastern Europe.

Author Contributions

Conceptualization, C.V. and A.M.R.; methodology, C.V.; software, C.V.; formal analysis, C.V. and E.D.; data curation, A.M.R.; writing—original draft preparation, C.V.; writing—review and editing, C.V., A.M.R. and E.D. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The data used in this study were derived from the following resources available in the public domain: WGI https://www.worldbank.org/en/publication/worldwide-governance-indicators; GDP https://data.worldbank.org/indicator/NY.GDP.PCAP.CD?end=2023locations=ATstart=1996view=chart; Debt-to-GDP https://www.imf.org/external/datamapper/d@FPP/USA/FRA/JPN/GBR/SWE/ESP/ITA/ZAF/IND; Total Factor Productivity https://www.rug.nl/ggdc/productivity/pwt/pwt-releases/pwt1001?lang=en.

Conflicts of Interest

The authors declare no conflicts of interest.

Note

1
We excluded 4 microstates, that is, Andorra, San Marino, Lichtenstein, and Monaco.

References

  1. Acemoglu, D., Naidu, S., Restrepo, P., & Robinson, J. (2014). Democracy does cause growth (w20004; p. w20004). National Bureau of Economic Research. [Google Scholar] [CrossRef]
  2. Acemoglu, D., & Robinson, J. A. (2008). Persistence of power, elites, and institutions. American Economic Review, 98(1), 267–293. [Google Scholar] [CrossRef]
  3. Baldwin, R. E., Francois, J. F., & Portes, R. (1997). The costs and benefits of eastern enlargement: The impact on the EU and central Europe. Economic Policy, 12(24), 125–176. [Google Scholar] [CrossRef]
  4. Baumol, W. J. (1986). Productivity growth, convergence, and welfare: What the long-run data show. The American Economic Review, 76(5), 1072–1085. [Google Scholar] [CrossRef]
  5. Betz, T., & Pond, A. (2023). Democratic institutions and regulatory privileges for government debt. European Journal of Political Economy, 79, 102438. [Google Scholar] [CrossRef]
  6. Bhagat, S. (2020). Economic growth, income inequality, and the rule of law. Available online: https://leeds-faculty.colorado.edu/bhagat/RuleLaw.pdf (accessed on 10 October 2025). [CrossRef]
  7. Breuss, F. (2002). Benefits and dangers of EU enlargement. Empirica, 29(3), 245–274. [Google Scholar] [CrossRef]
  8. Brockman, P., & Unlu, E. (2009). Dividend policy, creditor rights, and the agency costs of debt. Journal of Financial Economics, 92(2), 276–299. [Google Scholar] [CrossRef]
  9. Buti, M., & Fabbrini, S. (2023). The political determinants of fiscal governance in the EU: Towards a new equilibrium. Politics and Governance, 11(4), 112–121. [Google Scholar] [CrossRef]
  10. Campbell, J. L. (1995). State building and postcommunist budget deficits. American Behavioral Scientist, 38(5), 760–787. [Google Scholar] [CrossRef]
  11. Canarella, G., & Miller, S. M. (2022). Firm size, corporate debt, R&D activity, and agency costs: Exploring dynamic and non-linear effects. The Journal of Economic Asymmetries, 25, e00233. [Google Scholar] [CrossRef]
  12. Colagrossi, M., Rossignoli, D., & Maggioni, M. A. (2020). Does democracy cause growth? A meta-analysis (of 2000 regressions). European Journal of Political Economy, 61, 101824. [Google Scholar] [CrossRef]
  13. Dawood, M., Feng, Z. R., Ilyas, M., & Abbas, G. (2024). External debt, transmission channels, and economic growth: Evidence of debt overhang and crowding-out effect. Sage Open, 14(3), 21582440241263626. [Google Scholar] [CrossRef]
  14. De La Fuente, A. (2003). Convergence equations and income dynamics: The sources of OECD convergence, 1970–1995. Economica, 70(280), 655–671. [Google Scholar] [CrossRef]
  15. Dobson, S., & Ramlogan, C. (2002). Economic growth and convergence in Latin America. Journal of Development Studies, 38(6), 83–104. [Google Scholar] [CrossRef]
  16. Dvoroková, K. (2014). Sigma versus beta-convergence in EU28: Do they lead to different results? Mathematical methods in finance and business administration, 33, 701–721. [Google Scholar]
  17. Eberhardt, M. (2022). Democracy, growth, heterogeneity, and robustness. European Economic Review, 147, 104173. [Google Scholar] [CrossRef]
  18. Fernald, J. G. (1999). Roads to prosperity? Assessing the link between public capital and productivity. The American Economic Review, 89(3), 619–638. [Google Scholar] [CrossRef]
  19. Fitch Rating Report. (2025). Romanian deficit reduction, debt stabilisation remain key for sovereign rating. Available online: https://www.fitchratings.com/research/sovereigns/romanian-deficit-reduction-debt-stabilisation-remain-key-for-sovereign-rating-11-06-2025 (accessed on 3 November 2025).
  20. Fligstein, N., & Habinek, J. (2017). How the American financial meltdown of 2008 caused the global financial crisis. The Routledge Companion to Banking Regulation and Reform, 21, 373. [Google Scholar]
  21. Friedman, M. (1992). Do old fallacies ever die. Journal of Economic Literature, 2129–2132. Available online: https://www.jstor.org/stable/2727976 (accessed on 10 October 2025).
  22. Fukuyama, F. (2014). Political order and political decay: From the industrial revolution to the globalization of democracy. Macmillan. [Google Scholar]
  23. Fukuyama, F. (2015). Why is democracy performing so poorly? Journal of Democracy, 26(1), 11–20. [Google Scholar] [CrossRef]
  24. Fukuyama, F. (2018). Against identity politics: The new tribalism and the crisis of democracy. Foreign Affairs, 97, 90. [Google Scholar]
  25. Gehringer, A., Martínez-Zarzoso, I., & Nowak-Lehmann Danzinger, F. (2016). What are the drivers of total factor productivity in the European Union? Economics of Innovation and New Technology, 25(4), 406–434. [Google Scholar] [CrossRef]
  26. Giudici, E., & Mollick, A. V. (2008). Convergence in the Eastern Caribbean States. The Annals of Regional Science, 42(4), 893–909. [Google Scholar] [CrossRef]
  27. Gluschenko, K. (2012). Myths about Beta-Convergence. Available online: https://www.researchgate.net/publication/256041289_Myths_about_Beta-Convergence (accessed on 10 October 2025). [CrossRef]
  28. Harvey, C. R., Lins, K. V., & Roper, A. H. (2004). The effect of capital structure when expected agency costs are extreme. Journal of Financial Economics, 74(1), 3–30. [Google Scholar] [CrossRef]
  29. Imaginário, J., & Guedes, M. J. (2020). Governance and government debt. Risk Governance and Control: Financial Markets and Institutions, 10(3), 34–49. [Google Scholar] [CrossRef]
  30. Irandoust, M. (2021). FDI and financial development: Evidence from eight post-communist countries. Studies in Economics and Econometrics, 45(2), 102–116. [Google Scholar] [CrossRef]
  31. Jenkins, S. P., & Van Kerm, P. (2011). The measurement of economic inequality. Oxford University Press. [Google Scholar] [CrossRef]
  32. Jensen, M. C. (1986). Agency costs of free cash flow, corporate finance, and takeovers. The American Economic Review, 76(2), 323–329. [Google Scholar]
  33. Kapidžić, D. (2020). The rise of illiberal politics in Southeast Europe. Southeast European and Black Sea Studies, 20(1), 1–17. [Google Scholar] [CrossRef]
  34. Kaplan, S. B., & Thomsson, K. (2017). The political economy of sovereign debt: Global finance and electoral cycles. The Journal of Politics, 79(2), 605–623. [Google Scholar] [CrossRef]
  35. Keck, T. M. (2022). Erosion, backsliding, or abuse: Three metaphors for democratic decline. Law & Social Inquiry, 48(1), 314–339. [Google Scholar] [CrossRef]
  36. Kim, W. S., & Sorensen, E. H. (1986). Evidence on the impact of the agency costs of debt on corporate debt policy. The Journal of Financial and Quantitative Analysis, 21(2), 131. [Google Scholar] [CrossRef]
  37. Kopecký, P., & Spirova, M. (2011). Jobs for the boys? Patterns of party patronage in post-communist Europe. West European Politics, 34(5), 897–921. [Google Scholar] [CrossRef]
  38. Kozuń-Cieślak, G., & Markowska-Bzducha, E. (2021). Foreign direct investment in the post-communist member states of the European Union: Who are the leaders? Central European Review of Economics & Finance, 33(2), 23–34. [Google Scholar] [CrossRef]
  39. Lasfer, M. A. (1995). Agency costs, taxes and debt: The UK evidence. European Financial Management, 1(3), 265–285. [Google Scholar] [CrossRef]
  40. Leland, H. E. (1998). Agency Costs, Risk Management, and Capital Structure. The Journal of Finance, 53(4), 1213–1243. [Google Scholar] [CrossRef]
  41. Liargovas, P., & Pilichos, V. (2022). Is EU fiscal governance effective? A case study for the period 1999–2019. Economies, 10(8), 187. [Google Scholar] [CrossRef]
  42. Liberto, A. D., & Symons, J. (2003). Some Econometric Issues in Convergence Regressions. The Manchester School, 71(3), 293–307. [Google Scholar] [CrossRef]
  43. Lo, S., & Rogoff, K. (2015). Secular stagnation, debt overhang and other rationales for sluggish growth, six years on. Working paper No. 482. Available online: https://ssrn.com/abstract=2552574 (accessed on 10 October 2025).
  44. Madsen, J. B., Raschky, P. A., & Skali, A. (2015). Does democracy drive income in the world, 1500–2000? European Economic Review, 78, 175–195. [Google Scholar] [CrossRef]
  45. Malle, S. (2009). Soviet legacies in post-Soviet Russia: Insights from crisis management. Post-Communist Economies, 21(3), 249–282. [Google Scholar] [CrossRef]
  46. Marsh, I. (2013). The decline of democratic governance: An analysis and a modest proposal. The Political Quarterly, 84(2), 228–237. [Google Scholar] [CrossRef]
  47. Mathonnat, C., & Minea, A. (2019). Forms of democracy and economic growth volatility. Economic Modelling, 81, 594–603. [Google Scholar] [CrossRef]
  48. Mavroudeas, S. D. (2016). The Greek crisis: Structural or conjunctural? In The great financial meltdown (pp. 307–327). Edward Elgar Publishing. [Google Scholar] [CrossRef]
  49. Meier, K. J., Compton, M., Polga-Hecimovich, J., Song, M., & Wimpy, C. (2019). Bureaucracy and the failure of politics: Challenges to democratic governance. Administration & Society, 51(10), 1576–1605. [Google Scholar] [CrossRef]
  50. Mello, A. S., & Parsons, J. E. (1992). Measuring the agency cost of debt. The Journal of Finance, 47(5), 1887–1904. [Google Scholar] [CrossRef]
  51. Mello, M., & Perrelli, R. (2003). Growth equations: A quantile regression exploration. The Quarterly Review of Economics and Finance, 43(4), 643–667. [Google Scholar] [CrossRef]
  52. Michie, J. (2020). The COVID-19 crisis—And the future of the economy and economics. International Review of Applied Economics, 34(3), 301–303. [Google Scholar] [CrossRef]
  53. Miller, S. M., & Upadhyay, M. P. (2002). Total factor productivity and the convergence hypothesis. Journal of Macroeconomics, 24(2), 267–286. [Google Scholar] [CrossRef]
  54. Nakamura, H. (2001). An empirical reexamination of the Solow growth model. Journal of the Japanese and International Economies, 15(3), 323–340. [Google Scholar] [CrossRef]
  55. Narayan, P. K., Narayan, S., & Smyth, R. (2011). Does democracy facilitate economic growth or does economic growth facilitate democracy? An empirical study of Sub-Saharan Africa. Economic Modelling, 28(3), 900–910. [Google Scholar] [CrossRef]
  56. Nolan, B., Salverda, W., & Smeeding, T. M. (2011). The Oxford handbook of economic inequality. Oxford University Press. [Google Scholar] [CrossRef]
  57. Nuroglu, E., & Kurtagić, H. (2012). Costs and benefits of the EU enlargement: The impact on the EU and SEE Countries. Journal of Economic and Social Studies, 2(2), 41–65. [Google Scholar] [CrossRef]
  58. Ozpolat, A., Guven, G. G., Ozsoy, F. N., & Bahar, A. (2016). Does rule of law affect economic growth positively? Research in World Economy, 7(1), 107. [Google Scholar] [CrossRef]
  59. Paczoski, A., Abebe, S. T., & Cirella, G. T. (2019). Debt and deficit growth rate reporting for post-communist European Union member states. Social Sciences, 8(6), 173. [Google Scholar] [CrossRef]
  60. Peev, E., & Mueller, D. C. (2012). Democracy, economic freedom and growth in transition economies. Kyklos, 65(3), 371–407. [Google Scholar] [CrossRef]
  61. Pesaran, M. H. (2006). Estimation and inference in large heterogeneous panels with a multifactor error structure. Econometrica, 74(4), 967–1012. [Google Scholar] [CrossRef]
  62. Plattner, M. F. (2020). Democracy embattled. Journal of Democracy, 31(1), 5–10. [Google Scholar] [CrossRef]
  63. Prescott, E. C. (2002). Prosperity and Depression. American Economic Review, 92(2), 1–15. [Google Scholar] [CrossRef]
  64. Próchniak, M., & Witkowski, B. (2013). Real β-convergence of transition countries: Robust approach. Eastern European Economics, 51(3), 6–26. [Google Scholar] [CrossRef]
  65. Richardson, S. (2006). Over-investment of free cash flow. Review of Accounting Studies, 11(2–3), 159–189. [Google Scholar] [CrossRef]
  66. Ridley, D. (2021). Capitalism/democracy/rule of law interactions and implications for entrepreneurship and per capita real gross domestic product adjusted for purchasing power parity. Journal of the Knowledge Economy, 12(1), 384–411. [Google Scholar] [CrossRef]
  67. Sala-i-Martin, X. X. (1996). The Classical approach to convergence analysis. The Economic Journal, 106(437), 1019. [Google Scholar] [CrossRef]
  68. Siljak, D. (2018). Beta convergence among former socialist countries. South East European Journal of Economics and Business, 13(2), 72–83. [Google Scholar] [CrossRef]
  69. Siller, M., Schatzer, T., Walde, J., & Tappeiner, G. (2021). What drives total factor productivity growth? An examination of spillover effects. Regional Studies, 55(6), 1129–1139. [Google Scholar] [CrossRef]
  70. Smolka, T. (2021). Decline of democracy—The European Union at a crossroad. Zeitschrift Für Vergleichende Politikwissenschaft, 15(1), 81–105. [Google Scholar] [CrossRef]
  71. Streeck, W. (2014). The politics of public debt: Neoliberalism, capitalist development, and the restructuring of the state. German Economic Review, 15(1), 143–165. [Google Scholar] [CrossRef]
  72. Švarc, J. (2017). A socio-political approach to exploring the innovation culture in post-socialist countries: The case of Croatia. Post-Communist Economies, 29(3), 359–374. [Google Scholar] [CrossRef]
  73. Tabash, M. I., AsadUllah, M., Siddiq, Q., Mansour, M., Daniel, L. N., & Al-Absy, M. S. M. (2024). Symmetries or asymmetries: How MSCI index advanced European markets’ exchange rates respond to macro-economic fundamentals. Economies, 12(12), 326. [Google Scholar] [CrossRef]
  74. Tavares, J., & Wacziarg, R. (2001). How democracy affects growth. European Economic Review, 45(8), 1341–1378. [Google Scholar] [CrossRef]
  75. Tupy, M. L. (2003). EU enlargement costs, benefits, and strategies for central and eastern European countries (vol. 489). Cato Institute. [Google Scholar]
  76. Yarashevich, V. (2013). External debt of post-communist countries. Communist and Post-Communist Studies, 46(2), 203–216. [Google Scholar] [CrossRef]
  77. Zaric, S., & Babic, V. (2021). Is the rule of law essential for economic growth? Evidence from European countries. Journal Transition Studies Review, 28(2), 3–18. [Google Scholar] [CrossRef]
Table 1. Description of variables.
Table 1. Description of variables.
VariableDescription
GOV_PCIThe first principal component of six governance indicators
log(GDP)The logarithm of GDP per capita
TFPTotal Factor Productivity
DEBT_GDPThe ratio of total debt to total GDP
XC_TENDummy = 1 for the 10 countries that joined the EU in 2004
XC_TWODummy = 1 for the 2 countries that joined the EU in 2007
POST_2004Dummy = 1 for the years 2004–2007
POST_2007Dummy = 1 for the years 2008–2023
D_2006Dummy = 1 for the year prior to the 2007–2008 financial and economic crisis
D_2019Dummy = 1 for the year prior to the COVID-19 pandemic
NON_EUDummy = 1 for the countries that never joined the EU
Table 2. Cointegration test results.
Table 2. Cointegration test results.
Panel A. Results of Kao’s Cointegration Test.
GOV_PCIlog(GDP)TFPDEBT_GDP
CONSTANT−5.256 ***10.638 ***1.246 ***54.085 **
GOVERNANCE_PCI-0.220 ***0.050 ***−7.407 ***
log(GDP)0.364 ***-−0.034 ***−71.712 ***
TFP2.540 ***−1.034 ***-6.447 ***
DEBT_GDP−0.007 ***0.004 ***−0.001 ***-
Residuals0.681 ***0.850 ***0.706 ***0.835 ***
Test for differing group intercepts136.699 ***14.011 ***76.635 ***74.695 ***
Panel B. Results of Pedroni’s cointegration tests.
StatisticGOV_PC1l_GDPTFPDEBT_IMF
nipanel−7.0227 ***−7.0792 ***−7.0825 ***53.4337 ***
rhopanel6.64156.44736.31085.6207
tpanelnonpar9.31937.16018.95165.1986
tpanelpar−683.702 ***−1353.114 ***−22483.556 ***16.7759
rhogroup9.50648.72988.58468.1397
tgroupnonpar12.137210.085410.17037.7270
tgrouppar12.562811.006211.26908.1013
***: p < 0.01; **: p < 0.05.
Table 3. Results of the panel ARDL and CCE specification models.
Table 3. Results of the panel ARDL and CCE specification models.
Panel A: Pooled Mean Group Regression Results Using the Pesaran-Shin-Smith Convention.
GOVERNANCE_PCIlog(GDP)TFPDEBT_GDP
GOVERNANCE_PCI-0.289 **0.0227 **13.131 ***
log(GDP)0.322 ***-−0.177 ***−6.000 **
TFP6.479 ***4.296 ***-−39.541 ***
DEBT_GDP0.017 ***−0.014 ***−0.001 ***-
Error correction coefficient−0.376 ***−0.096 ***−0.253 ***−0.167 ***
Hausman test on long-run coeff.5.2030.9863.3111.456
Panel B: Mean Group regression results using the Pesaran-Shin-Smith convention.
GOV_PCIlog(GDP)TFPDEBT_GDP
GOV_PCIn/a0.8230.03923.12 *
log(GDP)0.126n/a−0.063−61.334
TFP0.6133.872n/a−181.051
DEBT_GDP0.000−0.160−1.33n/a
Error correction coefficient−0.674 ***−0.39 ***−0.67 ***−0.38 ***
Panel C: Common Correlated Effects panel regression.
GOV_PCI_CCElog(GDP)_CCETFP_CCEDEBT_GDP_CCE
const−4.786 **10.408 ***−0.0980.567
GOV_PC1_CCEn/a0.074 **0.0051.005
log(GDP)_CCE0.627 ***n/a0.090 *−37.4116 ***
TFP_CCE0.131 *0.253 *n/a−7.924 ***
DEBT_GDP_CCE0.001−0.002 ***−0.001n/a
ADJ-Rsquared0.050.160.030.1
F-value3.95 ***18.97 ***0.8510.161 ***
***: p < 0.01; **: p < 0.05; *: p < 0.1.
Table 4. Results of the dynamic panel analysis using a two-stage GMM with collapsed instruments.
Table 4. Results of the dynamic panel analysis using a two-stage GMM with collapsed instruments.
GOV_PCIlog(GDP)TFPDEBT_GDP
INTERCEPT0.9580.875 ***0.108 ***1.305
GOV_PCIn/a0.0040.001 ***−2.541 ***
GOV_PCI_10.951 ***n/an/an/a
log(GDP)−0.000n/a0.014 ***1.644 ***
log(GDP)_1n/a0.913 ***n/an/a
TFP0.1010.144 ***n/a−4.360 ***
TFP_1n/an/a0.998 ***n/a
DEBT_GDP−0001 **−0.001 ***−0.001 ***n/a
DEBT_GDP_1n/an/an/a0.864 ***
XC_TEN−0.049−0.053 ***−0.006 ***−5.608 ***
XC_TWO−0.115−0.069 ***−0.025 ***−14.495 ***
XC_TEN × POST_2004−0.010.079 ***0.028 ***−2.815 ***
XC_TWO × XC_POST_2007−0.0045 **0.049 ***−0.022 ***1.649 ***
D_2006−0.0260.005 **−0.031 ***−2.269 ***
D_20190.044 ***−0.01390.005 ***−2.549 ***
NON_EU−0.248 *−0.117 ***−0.121 ***−16.271 ***
Test for AR(2) errors−0.349−5.23 ***0.185−0.845
Hansen over-ident. Chi-square24.61330.917 *28.54 *30.69 *
Wald (joint) Chi-square681,658 ***864,705 ***28,456 ***86,312 ***
***: p < 0.01; **: p < 0.05; *: p < 0.1.
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Vâlsan, C.; Rahat, A.M.; Druicăa, E. Ten Lessons from the EU Accession of Ex-Communist Countries. Economies 2025, 13, 326. https://doi.org/10.3390/economies13110326

AMA Style

Vâlsan C, Rahat AM, Druicăa E. Ten Lessons from the EU Accession of Ex-Communist Countries. Economies. 2025; 13(11):326. https://doi.org/10.3390/economies13110326

Chicago/Turabian Style

Vâlsan, Călin, Amos M. Rahat, and Elena Druicăa. 2025. "Ten Lessons from the EU Accession of Ex-Communist Countries" Economies 13, no. 11: 326. https://doi.org/10.3390/economies13110326

APA Style

Vâlsan, C., Rahat, A. M., & Druicăa, E. (2025). Ten Lessons from the EU Accession of Ex-Communist Countries. Economies, 13(11), 326. https://doi.org/10.3390/economies13110326

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop