EU accession: a road to fast-track convergence?
Bower, Uwe ; Turrini, Alessandro
INTRODUCTION
The economic growth record of the new member states (NMS) of the
European Union (EU) after the recovery from transition in the early
1990s has been impressive. The region is widely believed to have
benefited from catching-up dynamics as well as economic and
institutional integration with the EU. However, the debate is open
regarding the degree to which EU integration mattered for catching up
and through which channels.
Although the empirical growth literature is extensive, only a few
studies have used growth regressions to analyse the impact of EU
accession on growth. Crespo-Cuaresma et al. (2002) make explicit
reference to EU membership in explaining growth, analysing pre-2004
accessions and finding the length of EU membership to have a
significantly positive effect on economic growth. Schadler et al. (2006)
analyse the growth experience of the NMS and other emerging market
countries and find that income levels, population growth, investment,
openness and institutional quality determine growth. Falcetti et al.
(2006) and Iradian (2007) focus on the growth experience of transition
countries and find a significant impact of institutional factors and
transition reforms, as well as a significant impact of recovery from
transition-related output losses. Cihak and Fonteyne (2009) conduct a
cross-section growth regression augmented by an NMS dummy variable and
find that economic growth in NMS exceeded that of their remaining sample
of developed and developing countries.
We make a step forward compared with the existing literature in two
respects. First, we assess the impact of EU accession on the growth
performance of NMS in a panel analysis after controlling for a series of
institutional factors. This way, we check whether, on top of
facilitating institutional convergence, and therefore growth, the
prospect of EU accession had an additional significant impact per se.
Second, we investigate which factors appear to be associated with
stronger growth-enhancing effects of EU accession, testing in particular
the effects of initial income levels, institutional quality and
financial development in conjunction with the growth experience of the
NMS during the accession stage.
This paper employs a large cross-country data set to provide a
significant control group. The panel data set comprises annual
observations of advanced, emerging and transition economies starting in
1960. In addition to the standard determinants per-capita GDP,
population growth, investment, openness and human capital formation, we
also include variables related to economic transition and EU
integration, namely initial output loss, terms-of-trade growth and
institutional quality of the legal system, freedom of trade, and the
regulatory environment. The role of institutional quality for growth is
stressed, for example, by Acemoglu et al. (2005). Controlling for all
these effects, the additional EU accession impact is measured in a
difference-indifference approach. The interaction of an accession time
dummy with an NMS region dummy permits to assess whether the prospect of
EU accession affected the growth rate of NMS, relative to the
pre-accession period and to the old EU-15 member states (OMS).
The results suggest a significant EU accession effect on top of the
impact of the remaining explanatory variables. Although the NMS growth
rates appear significantly lower than those of the OMS during the
transition period of the early 1990s, the NMS perform significantly
better than the OMS during the EU accession period, as compared to the
1994-1999 reference period. The results are basically robust with
respect to the definition of the sample. Potential endogeneity of
investment as an explanatory variable is addressed by using initial
sub-period values and the relative price of investment in a set of
instrumental variable regressions.
Interacting the 'accession dummy' with various
explanatory variables, it is found that the growth effect during the
accession period was particularly strong for those NMS with relatively
low initial income levels, weak institutional quality and lower degrees
of financial development. EU accession seems to have had a fast-track
convergence effect particularly on the economic laggards among the NMS.
Furthermore, accession is likely to have improved institutional quality,
further supporting the growth in the NMS. By triggering financial
inflows and the reconstruction of the banking system, the prospect of
accession may have fostered growth in those NMS with weak financial
depth.
The remainder of this paper is structured as follows. The next
section presents some stylised facts, highlighting the growth
performance of the NMS over time and investigating signs of convergence
graphically. The subsequent section explains the data, methodology and
results of various growth regression specifications to investigate
growth effects of EU accession. The final section concludes.
STYLISED FACTS
The growth performance of the NMS has been described as a typical
catching-up experience, starting from lower initial per-capita income
levels and characterised by higher average growth rates than the mature
economies of the OMS.
NMS growth rates have been volatile, yet mostly above those of the
OMS and other mature economies. Figures la and b show the growth rates
of the 10 transition NMS. The Baltics as well as Bulgaria and Romania
appear to be strongly affected by the aftermath of the Russian economic
crisis of 1998 but exhibit elevated growth rates between 2000 and 2008.
Growth rates for the remaining NMS were somewhat lower, fluctuating
around 6%-7%. For 2009, the graphs show forecast figures as of autumn
2009, highlighting how growth rates slumped in the wake of the global
financial crisis. The complexity of the determinants and the effects of
the 2009 financial crisis, however, are beyond the scope of this paper
so that the following analysis takes a pre-crisis perspective.
[FIGURE 1 OMITTED]
Catching-up dynamics ('beta convergence') are illustrated
in Figure 2a which shows that the average annual per-capita growth rates
of those EU countries with lower initial-year income levels (1996) tend
to exhibit higher growth rates, indicated by a downward-sloping trend
line. (1) The NMS are clearly concentrated in the top-left quadrant of
the graph, notably the Baltic countries. Some NMS like Slovenia and the
Czech Republic, however, are located not far from OMS countries such as
Portugal and Greece. The graph confirms the widely agreed conclusion
that regards the EU as a 'convergence club' (see Schadler et
al. (2006)).
Sigma convergence is an alternative way of assessing income
convergence, that is, the decrease of cross-country variation of growth
rates over time. The NMS have made considerable progress since the
beginning of the decade. Figure 2b shows the standard deviation of
national per-capita growth rates, in percent of the average. In contrast
to the notion of the EU as a 'convergence club', sigma
convergence mostly stems from developments in the NMS. Although the
cross-country variation of growth rates among the OMS remained largely
stable over time, that of the NMS declined continuously since 2000.
The role of institutional quality is increasingly at the core of
growth theory. Figure 3 shows the average of the Fraser Institute's
indices for the quality of the legal system, freedom of trade and
regulation quality, ranging from 1 for poor to 10 for optimal
institutional quality (see Gwartney and Lawson, 2009). Comparing 1999 to
2005 shows that all NMS except Slovenia clearly improved their
institutional quality. The index of Slovenia has not changed much over
time, indicated by a position very close to the 90 degree line.
Taken together, the descriptive evidence suggests that catching-up
dynamics were at work in most NMS. Several key drivers of economic
growth, however, point at important cross-country differences. The
Baltic countries exhibit particularly strong growth rates in the
presence of comparably low initial income levels and large improvements
in institutional quality. The aim of the regression analysis in the
following section is to shed light on the role of EU accession on top of
standard growth determinants and to identify potential channels of this
effect.
[FIGURE 2 OMITTED]
ASSESSING THE EU ACCESSION BOOM
Data and methodology
To carry out panel regressions, a large cross-country data set is
used to provide a significant control group. The data set comprises
annual observations of 62 advanced, emerging and transition economies
from 1960 to 2008. (2) Besides the 27 EU member states and the remaining
11 OECD countries, 24 additional middle-income countries are considered.
(3) Explanatory variables include standard growth determinants, namely
per-capita GDP, population growth, investment, openness, terms-of-trade
growth and human capital formation. (4) This baseline growth regression
specification is augmented to take into account explanatory factors
specific to the growth performance of transition countries and NMS. To
control for the impact of changing terms of trade following
transition-related structural change and developments in world commodity
prices, terms of trade changes are included among the set of explanatory
variables (Iradian, 2007). To account for catching-up effects after the
output break-downs of formerly communist countries in the early 1990s,
an output loss variable is constructed as the ratio of current output to
the average output during 1990-1995 (akin to Falcetti et al., 2006 and
Iradian, 2007). Furthermore, in light of the shaping view that
institutions are key to the development process (eg, Acemoglu et al.,
2005), and in line with recent analogous analyses on growth in
transition economies and NMS, standard specifications of growth
regressions are augmented with the inclusion of various indicators are
employed to proxy for the institutional quality of the legal system,
freedom of trade and the regulatory environment.
[FIGURE 3 OMITTED]
The data on real per-capita GDP in PPP terms, investment and
openness ratios are provided by the Penn World Tables. Population growth
and terms of trade are taken from the World Bank's World
Development Indicators (WDI) whereas the source of the human capital
variable (average years of schooling for the whole population) is Barro
and Lee (2000) and the source of the indices on institutional quality is
the Fraser Institute. (5)
Following standard practice in the estimation of growth
regressions, annual observations are converted into averages over
5-year, non-overlapping sub-periods, in order to avoid that short-term
disturbances affect results. (6) Dummy variables capture the
idiosyncratic effects of time periods and of geographic regions. The
interaction between time and geographical effects permits to assess
whether a particular group of countries performed above the control
country group and time period in a particular period of time. Although
accession of the EU-10 was formally completed as of 1 May 2004 (that of
Bulgaria and Romania as of 1 January 2007), there is agreement that much
of the accession-related growth effects took place already before the
official dates, in light of the economic and institutional restructuring
associated with the achievement of the 'acquis communautaire',
EU transfers related to accession, and sizable investment, FDI, and
technology transfer in anticipation of EU accession (eg, Schadler et
al., 2006). Hence, the interactions of the 2000-2004 and the post-2005
dummies with an NMS dummy are used to assess whether accession affected
the growth rate of the NMS on top of the impact of the remaining
explanatory variables above that of the control group with respect to
the reference period. (7)
Conceptually, the question of the exogeneity of EU accession may
arise. (8) Although this study proposes that EU accession is likely to
have promoted growth rates in the NMS, it could be argued that a strong
growth performance may have influenced the process of becoming an EU
accession country. For the following reasons, we believe that the former
mechanism dominates the latter. Firstly, the EU accession criteria are
not geared towards high growth rates. Instead, they require a
'functioning market economy and the capacity to cope with
competitive pressure'. (9) This requirement served as an incentive
for structural reform for all potential accession countries which mostly
induced lower growth rates in the short term and stronger growth in the
medium-to-long term, therefore rather supporting the causal link from
envisaged accession to growth. Secondly, only European countries are
eligible for EU membership so that geography determines an exogenous
preselection. Thirdly, our econometric strategy seeks to minimise the
influence of potential endogeneity. Regressions are specified as a
difference-in-difference approach which implies that the difference in
growth rates between the accession period and the preceding reference
period, compared to the reference country group, matters, not the level
of growth rates before accession. Moreover, by using control variables
linked to accession and growth, such as the measures of institutional
quality, the accession dummies pick up the growth effects of accession
on top of these control variables.
Regression results
Baseline results
Basic specifications provide a satisfactory performance, presented
in Table 1. Specification (1) includes standard growth regression
variables used to assess conditional convergence in large cross-country
data sets. Per-capita GDP growth is regressed on the initial sub-period
values of the log of per-capita GDP as well as on population growth,
investment ratios, openness and the human capital proxy variable.
The coefficients are significant and show the expected signs, with
the exception of population growth, the significance level of which
falls below the 10% threshold. Human capital variables, however, are
either not available for most of the NMS (Barro and Lee (2000) data), or
available for only some NMS, and few years (WDI). Mixing data from
different sources based on different definitions does not seem conducive
to creating a consistent variable. Hence, to keep a sufficiently large
amount of data on NMS, the baseline regressions to assess the impact of
accession exclude human capital variables. Of course, as a result of the
exclusion of a largely significant explanatory variable, an omitted
variable bias issue arises. However, as shown in specification (2),
which is based on the same sample as (1) but excludes the schooling
variable, it appears that the bulk of the bias is found in the
coefficient of initial income per capita (omitting the human capital
variable leads to an underestimation of the speed of convergence), while
the performance of the remaining explanatory factors is fairly robust.
The baseline specification is augmented to take into account
NMS-specific growth determinants and institutional factors.
Specifications (3) and (4) employ the maximum available samples and
supplement the regression with relevant additional control variables to
test the impact of accession on the NMS. In line with expectations, the
NMS perform significantly worse during the 1990-1994 period and
significantly better in 2000-2004 and post-2005, relative to the omitted
reference period 1995-1999 and the control group of OMS. The size and
significance level of the coefficients for the 2000-2004 period are both
larger than in the post-2005 period, indicating that the bulk of the
accession effect could have materialised already in the run up to the
official date of accession. (10)
Specification (4) includes in addition the output loss variable as
well as the three institutional indicators, measuring the quality of the
legal system, freedom of trade and the quality of regulation in product,
labour and financial markets. The coefficient of output loss is positive
but not significant. The three institutional variables are positively
associated with growth although only freedom of trade is significant at
the 5% level. As a result of the inclusion of the institutional
variables, the impact of accession shrinks somewhat in magnitude,
suggesting that improvements in institutional quality themselves were
associated with the accession process. The coefficients of population
growth and terms of trade growth turn significant on the 10% level with
the inclusion of the institutional quality variables.
For some countries actual growth rates diverged quite considerably
from the prediction of the empirical model as illustrated by Figure 4.
The graph plots the actual and predicted average growth rates over the
three sub-periods since 1995 for the transition NMS based on
specification (4) of Table 1. The actual growth rates exceed
model-predicted rates in several countries, most notably in Estonia,
Latvia and Lithuania and during the accession period (2000-2004). This
evidence points to signs of overheating in these economies and suggests
that these countries were growing in excess of what seems justified by
economic fundamentals and compared to benchmark countries, potentially
driven by excessive capital inflows and exceeded 'speed
limits' of healthy growth. A detailed analysis of the factors
driving growth in the Baltics is provided by the European Commission
(2009b) and Lendvai and Roeger (2009).
[FIGURE 4 OMITTED]
Regression results appear to be robust with respect to the
definition of the sample. Specification (5) focuses on the post-1990
period, yielding a more balanced panel. The emerging picture is broadly
similar to that of the baseline specification. Terms of trade growth
seems less relevant as a driver of growth whereas the positive accession
impact for the NMS during the 2000-04 and the post-2005 periods are
still significant.
Finally, specification (6) repeats the specification in (5) but
restricting the sample to transition economies. In spite of the limited
number of observations, this check permits to obtain a further control
for transition-related factors. Not surprisingly, the coefficient of
initial per-capita GDP is larger than in the full country sample,
because of stronger catching-up effects in transition economies.
However, the explanatory power of investment ratios is lower, a
phenomenon common to previous studies, which reflects
over-capitalisation of previously planned economies and capital
scrapping during transition. Institutional quality appears to have
played a more prominent role, highlighted by the significance of now two
out of three institutional indices, namely legal system quality and
freedom of trade. Also the size of the coefficients is larger, pointing
at stronger growth effects of good institutions in transition economies.
The NMS dummies are not significant, except when interacted with the
1990-1994 period. A possible interpretation of this result is that,
during the reference period (1995-1999), growth in the NMS was higher
than in other transition economies at that time which may have been more
affected by the Russian crisis of the late 1990s. (11) In the early
2000s, however, growth was strong in both country groups. For
comparison, regression specification (7) employs the period 1990-1994 as
baseline, showing a large and significant positive coefficient of the
NMS*(1995-94) interaction term. Compared to the transition recession
period in the early 1990s, the NMS grew significantly faster in the late
1990s than the other transition countries. During the period of
anticipated accession in the early 2000s, the NMS interaction term is
borderline significant, indicating that growth in the NMS was stronger
as compared to the other transition countries, relative to the early
1990s.
An important robustness issue in growth regressions pertains to the
possible endogeneity of explanatory variables. Most notably the
investment ratio is likely to be subject to endogeneity, given that
investment not only favours growth but growth also tends to boost
savings and thus investment (see eg Carroll and Weil, 1994). One popular
strategy to account for simultaneity of investment suggests using
initial values of sub-periods (see Temple, 1999). Alternatively, it has
been suggested to use the relative price of investment goods as proxy
and instrumental variables. (12) Table 2 presents specifications aimed
at checking robustness of the baseline regression specifications (4) and
(5) in Table 1 with respect to possible endogeneity of the investment
variable.
The first two columns of Table 2 use the initial sub-period values
of the investment ratio rather than the average. Columns (3) and (4)
replace the investment ratio by the relative price of investment as a
proxy variable. The last two columns report instrumental variable
regression results, using initial sub-period investment as well as the
relative investment price to instrument the investment ratio. All three
approaches yield very similar results and underscore the robustness of
the estimates. The relative price of investment delivers the expected
negative, significant coefficients. Population growth and output loss
turn significant in most specifications. The first-stage coefficients of
the IV estimations are highly significant and the F test statistics are
above the critical values, indicating no weak instrument problem. Table
A1 in the Appendix reports further robustness results, using initial
values in OLS and IV regressions also for the other explanatory
variables (akin to Rousseau and Wachtel, 2009). The results are largely
confirmed.
What did contribute to growth effects of EU accession?
To shed light on the channels through which the accession effect
may have influenced economic growth, Table 3 presents regression results
with interaction terms. The accession dummy, that is, the dummy for the
NMS during the period 2000-2004, is interacted with three alternative
variables for the full sample as well as the transition-country
sub-sample. (13) For the full sample, the IV specification (5) of Table
2 is applied whereas, for the transition countries, the small number of
observation restricts the method to OLS. Specification (1) tests the
combined impact of initial per-capita GDP and accession, finding a
strongly significant and negative coefficient. The result is very
similar when the post-1990 sample is applied, see specification (2).
Hence, the catching-up effect is even stronger for the NMS during the
accession period than otherwise. EU accession appears to have helped in
speeding up the catching-up process, possibly via technology transfer
through increased trade and FDI inflows. Indeed, technological growth as
reflected by total factor productivity has been shown to be a major
driving force in the NMS (Cihak and Fonteyne, 2009). Increased labour
mobility and migration may also have played a role in speeding up the
convergence process, boosting capital-labour ratios and supporting
aggregate demand through remittances (International Monetary Fund,
2008).
Specifications (3) and (4) interact the enlargement dummy with an
institutional index, namely the quality of the legal system.
Coefficients for both samples indicate a significant negative impact of
the interaction term, suggesting that a weaker legal system quality is
associated with a larger growth gain of accession. A possible
interpretation is that EU accession led to institutional catching up and
then to increasing growth which is not captured by the Fraser indices of
institutional quality. The positive incentive effect of EU membership on
institutional development has been demonstrated empirically by Belke et
al. (2009) and Di Tommaso et al. (2007).
Finally, the last two columns of Table 3 include the ratio of
private credit to GDP as a measure of financial development and interact
this variable with the accession dummy. The interaction coefficients
turn out to be significant and negative in both samples, implying that a
country with a low degree of financial development benefited more from
EU accession in terms of economic growth. (14) In the NMS, financial
sector development went hand in hand with overall economic transition
and eventual EU accession. Initiated by extensive bank restructuring and
privatisation as well as sizable FDI inflows, financial depth in the NMS
increased markedly since the mid-1990s (European Commission, 2009a).
Notably the Baltic countries as well as Bulgaria and Romania, having
started with less advanced financial development and experienced
boosting capital inflow and credit ratios thereafter, also exhibited the
highest growth rates of real per-capita GDP during the EU accession
period. Recent financial market turbulences have, however, revealed the
substantial risks entailed in this development.
Financial development and advances in institutional quality have
also been found to be intertwined. Kose et al. (2006) argue that the
growth effects of financial development and integration are partly, if
not mainly, influenced by institutional quality. In turn, financial
liberalisation can impose discipline on macroeconomic policies and
thereby lead to an improved institutional environment.
CONCLUSION
This paper investigates the growth performance of the NMS in the
context of the EU accession boom. Based on a large cross-country data
set, panel regressions test for standard growth determinants and
accession-related variables. The analysis finds that, on average, the
accession period was characterised by an overall positive growth
experience for the NMS, on top of the effects of other explanatory
variables. Interestingly, this positive effect remains significant even
after controlling for institutional factors that are possibly related to
accession, such as freedom of trade and the quality of the legal and
regulatory system. This suggests that TFP growth improvements associated
with accession-related factors, like FDI and technology transfer,
improved resource allocation and governance associated with financial
integration, could have played a relevant role. Growth in the Baltic
countries, among others, was particularly strong and exceeds model
predictions for the early 2000s but falls short in the second half of
the decade. It appears that, in these countries, excessive capital
inflows and economic overheating may have resulted in exceeded
'speed limits' and contributed to below-average performance
once the economic crisis started to hit. Hence, the strong growth
performance of some countries in the 2000s may be partly based on
structural factors and institutional improvement and partly because of
transitory factors reflected in economic overheating. The European
Commission (2009b) as well as Lendvai and Roeger (2009) provide detailed
analysis on the experience of the Baltics.
The regression results are robust to changes in the sample and
estimation method. Restricting the sample to post-1990 observations
delivers very similar result. Comparing the NMS to the other transition
countries in the sample indicates a positive, borderline-significant
growth effect during the accession period for the NMS when the early
1990s are used as reference period.
To investigate potential channels of the growth impact of EU
accession, various variables are interacted with the accession dummy. It
is shown that countries with lower initial income levels, weaker
institutional quality and less advanced financial development benefited
more strongly from EU accession in terms of economic growth. As
expected, accession seems to have sped up the catching-up process and
improved the institutions in the laggards among the NMS. By triggering
capital inflows, the prospect of EU accession is also likely to have
improved economic growth for those NMS with lower degrees of financial
depth.
The present analysis is a first step to understand the mechanisms
underlying the positive factors of economic growth effects during the EU
accession process. For future research it would be useful employing also
micro-level evidence to further investigate the driving forces of
accession-related growth effects in the NMS. Moreover, the complexity of
the determinants and effects of the financial and economic crisis which
started to unfold in 2008 and hit some of the NMS particularly hard
would deserve more in-depth analysis.
APPENDIX
Details on data sources and variable definitions
* Growth in real GDP per capita (%). Source: World Development
Indicators.
* Initial real GDP per capita (PPP): value recorded in the first
year of each 5-year periods. Source: Penn World Tables.
* Population growth (%). Source: Would Development Indicators
* Openness: sum of imports and exports on GDP (%). Source: Penn
World Tables.
* Years of schooling: average years of schooling across whole
population. Source: Barro and Lee.
* Terms of trade growth (%). Source: World Development Indicators.
* Quality of legal system: index computed by Fraser Institute
summarising elements of legal system and property rights protection.
* Freedom of trade: index computed by Fraser Institute summarising
information on tariff and non-tariff barriers and capital movement
controls.
* Quality of regulation: index computed by Fraser Institute
summarising elements (including the extent of public versus private
ownership) of regulations affecting labour, product and financial
markets.
Table A1: Further robustness checks
(1) (2)
Estimation method OLS OLS
Log initial GDP per capita -2.20 *** -2.15 ***
(-6.91) (-4.78)
Population growth (initial values) -0.38 * -0.46 *
(-1.93) (-1.82)
Investment ratio (initial values) 0.087 *** 0.066 **
(4.29) (2.49)
Openness ratio (initial values) 0.0083 *** 0.0065 *
(2.98) (1.96)
Terms of trade growth (initial values) 3.96 ** 7.74 ***
(2.21) (2.90)
Output loss (initial values) 0.41 0.83
(0.44) (0.98)
Population growth
Investment ratio
Openness ratio
Terms of trade growth
Output loss
Legal system quality index 0.19 0.39 **
(1.47) (2.22)
Freedom of trade index 0.25 ** 0.45 **
(2.13) (2.33)
Regulation quality index 0.034 -0.29
(0.16) (-1.01)
NMS (dummy) -1.08 -1.16
(-1.20) (-1.21)
NMS during 1990-1994 (dummy) 1.07 1.66
(0.49) (0.69)
NMS during 2000-2004 (dummy) 3.02 *** 3.34 ***
(2.87) (3.10)
NMS after 2005 (dummy) 2.15 ** 2.53 **
(2.27) (2.58)
Maximum time period 1960-2009 1990-2009
Observations 340 200
Adjusted [R.sup.2] 0.446 0.448
First-stage IV estimation
Relative price of investment
Investment ratio (initial values)
Openness ratio (initial values)
Terms of trade growth (initial values)
Output loss (initial values)
Kleinbergen-Paap F statistic
(3) (4)
Estimation method IV IV
Log initial GDP per capita -2.15 *** -2.21 ***
(-7.64) (-5.64)
Population growth (initial values)
Investment ratio (initial values)
Openness ratio (initial values)
Terms of trade growth (initial values)
Output loss (initial values)
Population growth -0.42 * -0.52 *
(-1.79) (-1.67)
Investment ratio 0.100 *** 0.073 **
(4.50) (2.54)
Openness ratio 0.0087 *** (0.0073 **
(3.29) (2.28)
Terms of trade growth 14.8 ** 22.1 ***
(2.22) (2.72)
Output loss 0.30 0.58
(0.34) (0.70)
Legal system quality index 0.20 * 0.37 **
(1.83) (2.41)
Freedom of trade index 0.18 * 0.46 **
(1.66) (2.51)
Regulation quality index 0.071 -0.26
0.36) (-0.97)
NMS (dummy) -1.12 -1.33
(-1.34) (-1.44)
NMS during 1990-1994 (dummy) 1.08 1.86
(0.51) (0.79)
NMS during 2000-2004 (dummy) 2.88 *** 3.20 ***
(3.03) (3.23)
NMS after 2005 (dummy) 2.24 ** 2.54 ***
(2.56) (2.75)
Maximum time period 1960-2009 1990-2009
Observations 341 201
Adjusted [R.sup.2] 0.474 0.436
First-stage IV estimation
Relative price of investment -0.02 *** -0.01 **
(-4.32) (-2.33)
Investment ratio (initial values) 0.83 *** 0.86 ***
(28.47) (21.07)
Openness ratio (initial values) 0.99 *** 0.99 ***
(78.55) (65.79)
Terms of trade growth (initial values) 0.27 *** 0.34 ***
(6.93) (6.90)
Output loss (initial values) 1.07 *** 1.06 ***
(25.10) (24.29)
Kleinbergen-Paap F statistic 9.94 9.93
Notes: OLS (columns 1-2), IV (columns
3-4). See also notes to Table 1.
REFERENCES
Acemoglu, D, Johnson, S and Robinson, J. 2005: Institutions as the
fundamental cause of long-run growth. In: Aghion, P and Dnrlauf, S
(eds). Handbook of Economic Growth. Elsevier: Amsterdam.
Barro, R and Lee, J-W. 2000: International data on educational
attainment: Updates and implications. CID Working paper no. 42, Center
for International Development, Harvard University. Barro, R and
Salad-Martin, X. 2004: Economic growth. MIT Press: London.
Belke, A, Bordon, I, Melnykovska, I and Schweickert, R. 2009:
Prospective NATO or EU membership and institutional change in transition
countries. IZA Discussion Paper 4483, Institut zur Zukunft der Arbeit:
Bonn.
Caselli, F and Tenreyro, S. 2005: Is Poland the next Spain? NBER
Working paper 11045.
Carroll, C and Weil, D. 1994: Saving and growth: A
reinterpretation. Carnegie-Rochester Conference Series Public Policy 40:
133-192.
Cihak, M and Fonteyne, W. 2009: Five years after: EU membership and
macro-financial stability in the new member states. IMF Working paper
09/68, International Monetary Fund: Washington DC.
Crespo-Cuaresma, J, Dimitz, MA and Ritzberger-Grunwald, D. 2002:
Growth, convergence, and EU membership. OeNB Working paper no. 62,
Oesterreichische Nationalbank: Vienna.
Di Tommaso, M, Raiser, M and Weeks, M. 2007: Home grown or
imported? Initial conditions, external anchors and the determinants of
institutional reform in the transition economies. Economic Journal 117:
858-881.
European Commission. 2009a: Five years of an enlarged EU. Economic
achievements and challenges. European Economy 1/2009, European
Commission, DG Economic and Financial Affairs: Brussels.
European Commission. 2009b: Cross country study: Economic policy
and challenges in the Baltics. Rebalancing in an uncertain environment.
European Commission, DG Economic and Financial Affairs, Brussels.
Falcetti, E, Kysenko, Tand Sanfey, P. 2006: Reforms and growth in
transition. Journal of Comparative Economics 34: 421-455.
Gwartney, J and Lawson, R. 2009: Economic freedom of the world.
Annual Report 2009. Economic Freedom Network, www.fraserinstitute.org.
International Monetary Fund. 2008: Regional economic outlook,
Europe: Dealing with shocks. International Monetary Fund: Washington,
DC.
Iradian, G. 2007: Rapid growth in the transition economies: Panel
regression approach. IMF Working paper 07/170, International Monetary
Fund: Washington DC.
Kose, M, Prasad, E, Rogoff, K and Wei, S-J. 2006: Financial
globalisation: A reappraisal. IMF Working paper 06/189, International
Monetary Fund: Washington DC.
Lendvai, J and Roeger, W. 2009: External deficits in the Baltics
1995-2007: Catching up or imbalances? European Economy Economic Paper
394/2009, European Commission, DG Economic and Financial Affairs:
Brussels.
Levine, R and Renelt, D. 1992: A sensitivity analysis of
cross-country growth regressions. American Economic Review 82: 942-963.
Rousseau, P and Wachtel, P. 2009: What is happening to the impact
of financial deepening on economic growth? Vanderbilt University Working
paper 0915.
Schadler, S, Mody, A, Abiad, A and Leigh, D. 2006: Growth in the
central and in eastern European countries of the European Union. IMF
Occasional paper no. 252, International Monetary Fund: Washington D.C.
Temple, J. 1999: The new growth evidence. Journal of Economic
Literature 37: 112-156.
(1) The concept of catching-up convergence stems from the
convergence hypothesis of the neoclassical growth literature. A
Solow-type production function with non-increasing returns to scale
typically implies that the long-term behaviour of the economy will be
independent of the initial conditions. Because of the concavity of the
production function in the capital stock, capital-poor countries will
grow sufficiently faster, that is, catch up to the capital-rich
countries to offset the initial differences. Catching up is subject to
alternative possible factors, including structural transformation,
endogenous growth and gains from trade (see Caselli and Tenreyro, 2005).
(2) Because of uncertainty about data accuracy, observations of
formerly communist countries prior to 1990 are excluded.
(3) The countries included in the sample were as follows: Albania,
Argentina, Australia, Austria, Belgium, Belarus, Brazil, Bulgaria,
Canada, Chile, China, P.R.:Hong Kong, China, P.R.: Mainland, Colombia,
Croatia, Cyprus, Czech Republic, Denmark, Egypt, Estonia, Finland,
France, Germany, Greece, Hungary, Indonesia, Iceland, Ireland, Israel,
Italy, Japan, Korea, Latvia, Lithuania, Luxembourg, Morocco, Mexico,
Macedonia: FYR, Malta, Malaysia, Netherlands, Norway, New Zealand,
Philippines, Poland, Portugal, Romania, Russia, Singapore, Slovak
Republic, Slovenia, Spain, Sweden, Switzerland, Thailand, Tunisia,
Turkey, Ukraine, United Kingdom and Uruguay. Besides data availability,
the choice of emerging market countries is guided by the attempt to
create a control group as similar to the NMS as possible. For this
reason developing and least developed countries as well as countries
with very large shares of oil exports are not included.
(4) See, for example, Barro and Sala-i-Martin (2004), Levine and
Renelt (1992), and Temple (1999), for an overview of explanatory
variables in empirical growth analysis.
(5) These indicators permit to capture major transition-related and
accession-related elements, including change in ownership of financial
and non-financial firms and protection and enforcement of property
rights. Compared with the EBRD transition indicators (used, for
instance, in Falcetti et al., 2006), they are available also for
non-transition countries. Compared with the World Bank Governance
Indicators (used, eg, in Iradian, 2007), they are available for a longer
time period.
(6) Because of missing data for several variables for the latest
years in the post-2005 period, that last sub-period is averaged over the
maximum available data points.
(7) In all regressions, the omitted regional dummy is that for the
OMS, the omitted period dummy is the 1995-1999 period. Hence, the
non-omitted region and time dummies represent the difference to the OMS
and with respect to the 1995-1999 period.
(8) We thank an anonymous referee for pointing out this issue.
(9) For the detailed EU accession criteria, see
http://europa.eu/scadplus/glossary/accession_criteria_copenhague_en.htm.
(10) The difference-in-difference approach quantifies the effect of
the difference in time periods and country groups. The accession dummy
'NMS during 2000-2004' therefore refers to the difference
between the accession period (2000-2004) and the reference period
(1994-1999) for the NMS, minus the same difference for the OMS,
controlling for other factors. The actual average growth rates were as
follows.
1990-1994 1995-1999 2000-2004 post-2005
Average growth rates of real per capita GDP, in %
NMS 3.4 3.6 4.9 2.7
OMS 1.3 3.1 2.1 0.3
(11) Indeed, actual growth rates of real per-capita GDP were larger
in the NMS in 1995-1999 than in the other transition countries.
1990-1994 1995-1999 2000-2004 post-2005
Average growth rates of real per capita GDP, in %
New member states 3.4 3.6 4.9 2.7
Other transition economies 4.5 2.5 7 5.5
(12) See Schadler et al. (2006). Barro and Sala-i-Martin (2004)
show that the relative price of investment is a more robust and less
endogenous determinant of growth than the investment ratio.
(13) Each of the three variables is standardised to mean zero and
standard deviation of one before creating the interaction terms to
facilitate the interpretation of coefficients.
(14) Employing the ratio of liquid liabilities (M3) to GDP as an
alternative measure of financial development leads to very similar
results. Rousseau and Wachtel (2009) examine the impact of financial
development on growth in more depth.
UWE BOWER [1] & ALESSANDRO TURRINI [1,2]
[1] DG Economic and Financial Affairs, European Commission, Rue de
La Lot 200, Brussels 1049, Belgium.
[2] Centre for Economic Policy Research (CERP), 53-56 Great Sutton
Street, London ECIV ODG, United Kingdom.
Table 1: Baseline results
(1) (2)
Sample Full Full
Log initial GDP per capita -1.87 *** -1.62 ***
(-7.03) (-8.43)
Population growth -0.22 -0.25 *
(-1.44) (-1.66)
Investment (in % of GDP) 0.15 *** 0.14 ***
(9.26) (9.14)
Openness (in % of GDP) 0.0041 ** 0.0038 *
(2.09) (1.90)
Years of schooling 0.099 *
(1.66)
Terms of trade growth
Output loss
Legal system quality index
Freedom of trade index
Regulation quality index
NMS (dummy)
NMS during 1990-1994 (dummy)
NMS during 1995-1999 (dummy)
NMS during 2000-2004 (dummy)
NMS after 2005 (dummy)
Observations 406 406
Adjusted [R.sup.2] 0.557 0.555
(3) (4)
Sample Full Full
Log initial GDP per capita -1.62 *** -2.03 ***
(-6.24) (-6.92)
Population growth 0.029 -0.38 *
(0.12) (-1.65)
Investment (in % of GDP) 0.13 *** 0.13 ***
(5.67) (5.63)
Openness (in % of GDP) 0.010 *** 0.0073 ***
(4.08) (2.83)
Years of schooling
Terms of trade growth 5.26 6.41*
(1.16) (1.92)
Output loss 0.68
(0.85)
Legal system quality index 0.12
(1.07)
Freedom of trade index 0.23 **
(2.19)
Regulation quality index 0.11
(0.56)
NMS (dummy) -0.54 -0.59
(-0.63) (-0.73)
NMS during 1990-1994 (dummy) -2.97 ** -1.13
(-2.27) (-0.70)
NMS during 1995-1999 (dummy)
NMS during 2000-2004 (dummy) 2.82 *** 2.65 ***
(2.72) (2.71)
NMS after 2005 (dummy) 2.15 ** 1.78 **
(2.44) (2.07)
Observations 455 351
Adjusted [R.sup.2] 0.457 0.493
(5) (6)
Sample Post 1990 Transition
Log initial GDP per capita -1.96 *** -3.54 ***
(-4.61) (-3.13)
Population growth -0.43 0.074
(-1.44) (0.082)
Investment (in % of GDP) 0.10 *** 0.034
(3.39) (0.43)
Openness (in % of GDP) 0.0056 * -0.025
(1.84) (-1.08)
Years of schooling
Terms of trade growth 9.13 22.4
(1.62) (1.29)
Output loss 0.97 0.037
(1.24) (0.017)
Legal system quality index 0.26 1.21 *
(1.44) (1.88)
Freedom of trade index 0.45 ** 1.77 **
(2.41) (2.32)
Regulation quality index -0.13 0.16
(-0.49) (0.22)
NMS (dummy) -0.65 -0.50
(-0.72) (-0.23)
NMS during 1990-1994 (dummy) -0.88 -4.90 **
(-0.50) (-2.23)
NMS during 1995-1999 (dummy)
NMS during 2000-2004 (dummy) 2.95 *** -0.72
(2.90) (-0.34)
NMS after 2005 (dummy) 2.01 ** -0.69
(2.23) (-0.34)
Observations 208 51
Adjusted [R.sup.2] 0.464 0.595
(7)
Sample Transition
Log initial GDP per capita -3.54 ***
(-3.13)
Population growth 0.074
(0.082)
Investment (in % of GDP) 0.034
(0.43)
Openness (in % of GDP) -0.025
(-1.08)
Years of schooling
Terms of trade growth 22.4
(1.29)
Output loss 0.037
(0.017)
Legal system quality index 1.21 *
(1.88)
Freedom of trade index 1.77 **
(2.32)
Regulation quality index 0.16
(0.22)
NMS (dummy) -5.41 **
(-2.40)
NMS during 1990-1994 (dummy)
NMS during 1995-1999 (dummy) 4.90 **
(2.23)
NMS during 2000-2004 (dummy) 4.19
(1.60)
NMS after 2005 (dummy) 4.21
(1.50)
Observations 51
Adjusted [R.sup.2] 0.595
Notes: Estimation method: OLS. t statistics are reported in
parentheses. The panel structure employs non-overlapping 5-year
periods. *, **, *** denote statistical significance at 10%, 5% and
1% level, using robust standard errors. Column (1) displays standard
textbook specification, column (2) repeats the same regression
excluding the schooling variable but using the same sample as (1).
All specifications include world region dummies, time period dummies
(1995-1999 period omitted) and the interaction between the two sets
of dummies. World regions are defined as follows: OMS (omitted), NMS,
non-EU OECD, non-EU non-OECD; in column (6), the reference group to
the NMS dummy are the remaining transition economies. In column (7),
the 1990-1994 period is omitted.
Table 2: Addressing the possible endogeneity of the investment variable
(1) (2)
Maximum time period Full Post1990
Estimation method OLS OLS
Log initial GDP per capita -2.11 *** -1.94 ***
(-7.02) (-4.51)
Population growth -0.54 ** -0.65 **
(-2.25) (-2.11)
Initial investment (in % of GDP) 0.072 *** 0.044 *
(3.66) (1.74)
Relative price of investment
Investment (instrumented)
Openness (in % of GDP) 0.0088 *** 0.0069 **
(3.23) (2.17)
Terms of trade growth 5.97 * 7.53
(1.75) (1.39)
Output loss 1.42 * 1.80 **
(1.80) (2.36)
Legal system quality index 0.18 0.33 *
(1.44) (1.80)
Freedom of trade index 0.22 ** 0.40 **
(1.99) (2.19)
Regulation quality index 0.086 -0.18
(0.43) (-0.65)
NMS (dummy) -0.93 -0.98
(-1.06) (-1.03)
NMS during 1990-1994 (dummy) -0.59 -0.25
(-0.31) (-0.12)
NMS during 2000-2004 (dummy) 2.71 *** 3.04 ***
(2.68) (2.92)
NMS after 2005 (dummy) 1.72 * 2.00 **
(1.87) (2.09)
Observations 350 207
Adjusted [R.sup.2] 0.437 0.425
First-stage IV estimation
Relative price of investment
Initial investment (in % of GDP)
Kleinbergen-Paap F statistic
Stock-Yogo 10% critical value
(3) (4)
Maximum time period Full Post1990
Estimation method OLS OLS
Log initial GDP per capita -2.27 *** -2.17 ***
(-7.32) (-4.80)
Population growth -0.58 ** -0.63 **
(-2.44) (-2.11)
Initial investment (in % of GDP)
Relative price of investment -0.013 *** -0.012 **
(-3.27) (-2.29)
Investment (instrumented)
Openness (in % of GDP) 0.011 *** 0.0090 ***
(4.12) (2.92)
Terms of trade growth 5.27 9.47
(1.38) (1.63)
Output loss 1.90 ** 1.95 ***
(2.41) (2.61)
Legal system quality index 0.18 0.30
(1.35) (1.55)
Freedom of trade index 0.13 0.31
(1.15) (1.65)
Regulation quality index 0.072 -0.17
(0.37) (-0.67
NMS (dummy) -0.87 -0.89
(-1.05) (-0.99)
NMS during 1990-1994 (dummy) -1.27 -1.00
(-0.75) (-0.54)
NMS during 2000-2004 (dummy) 2.65 *** 2.94 ***
(2.77) (2.94)
NMS after 2005 (dummy) 1.85 ** 2.12 **
(2.09) (2.31)
Observations 350 207
Adjusted [R.sup.2] 0.433 0.436
First-stage IV estimation
Relative price of investment
Initial investment (in % of GDP)
Kleinbergen-Paap F statistic
Stock-Yogo 10% critical value
(5) (6)
Maximum time period Full Post1990
Estimation method IV IV
Log initial GDP per capita -2.05 *** -1.94 ***
(-7.53) (-4.88)
Population growth -0.53 ** -0.64 **
(-2.41) (-2.23)
Initial investment (in % of GDP)
Relative price of investment
Investment (instrumented) 0.084 *** 0.051 *
(4.04) (1.93)
Openness (in % of GDP) 0.0085 *** 0.0069 **
(3.40) (2.33)
Terms of trade growth 5.53 * 7.25
(1.78) (1.45)
Output loss 1.31 * 1.72 **
(1.80) (2.45)
Legal system quality index 0.17 0.32 *
(1.49) (1.90)
Freedom of trade index 0.20 ** 0.40 **
(2.02) (2.37)
Regulation quality index 0.083 -0.17
(0.45) (-0.67)
NMS (dummy) -0.93 -1.00
(-1.18) (-1.15)
NMS during 1990-1994 (dummy) -0.51 -0.19
(-0.29) (-0.10)
NMS during 2000-2004 (dummy) 2.71 *** 3.03 ***
(2.98) (3.20)
NMS after 2005 (dummy) 1.92 ** 2.15 **
(2.29) (2.44)
Observations 349 206
Adjusted [R.sup.2] 0.480 0.450
First-stage IV estimation
Relative price of investment -0.016 *** -0.013 ***
(-4.40) (-2.68)
Initial investment (in % of GDP) 0.82 *** 0.84 ***
(27.50) (19.71)
Kleinbergen-Paap F statistic 661.38 357.62
Stock-Yogo 10% critical value 19.93 19.93
Notes: OLS (columns 1-4), IV (columns 5-6). See also notes to Table 1.
Table 3: IV regressions with interaction terms
(1) (2)
Sample Full Transition
Estimation method IV OLS
Log initial GDP per capita -1.67 *** -2.34 **
(-7.18) (-2.43)
Population growth -0.47 ** 0.36
(-2.12) (0.40)
Investment (in % of GDP, 0.089 *** 0.076
instrumented in case of IV) (4.23) (0.95)
Openness (in % of GDP) 0.0083 *** -0.023
(3.31) (-1.11)
Terms of trade growth 5.43 * 19.6
(1.75) (1.14)
Output loss 1.30 * -0.63
(1.80) (-0.29)
Private credit (in % of GDP)
Legal system quality index 0.18 1.51 **
(1.56) (2.46)
Freedom of trade index 0.22 ** 2.03 ***
(2.15) (2.78)
Regulation quality index 0.045 -0.28
(0.25) (-0.38)
NMS (dummy) -0.80 -1.27
(-1.01) (-0.59)
NMS during 1990-1994 (dummy) -0.56 -4.65 **
(-0.33) (-2.26)
NMS during 2000-2004 (dummy) 2.78 *** -0.72
(3.32) (-0.35)
NMS after 2005 (dummy) 1.94 ** 0.45
(2.31) (0.22)
(NMS 2000-2004) * -2.89 *** -4.28 **
(log initial GDP per capita) (-4.00) (-2.44)
(NMS 2000-2004) x
(legal system quality)
(NMS 2000-2004) x
(private credit ratio)
Observations 349 51
Adjusted [R.sup.2] 0.488 0.632
First-stage IV estimation
Relative price of investment -0.02 ***
(-4.40)
Initial investment (in % of GDP) 0.82 ***
(27.05)
Kleinbergen-Paap F statistic 642.47
Stock-Yogo 10% critical value 19.93
(3) (4)
Sample Full Transition
Estimation method IV OLS
Log initial GDP per capita -2.02 *** -3.28 ***
(-7.38) (-2.86)
Population growth -0.51 ** 0.31
(-2.27) (0.36)
Investment (in % of GDP, 0.086 *** 0.057
instrumented in case of IV) (4.09) (0.70)
Openness (in % of GDP) 0.0084 *** -0.024
(3.36) (-1.05)
Terms of trade growth 5.45 * 19.8
(1.76) (1.12)
Output loss 1.31 * -0.50
(1.81) (-0.22)
Private credit (in % of GDP)
Legal system quality index 0.30 2.59 **
(1.54) (2.32)
Freedom of trade index 0.21 ** 2.02 **
(2.08) (2.68)
Regulation quality index 0.073 -0.0084
(0.39) (-0.012)
NMS (dummy) -0.87 -1.19
(-1.10) (-0.55)
NMS during 1990-1994 (dummy) -0.53 -4.26 **
(-0.31) (-2.08)
NMS during 2000-2004 (dummy) 2.16 ** -1.75
(2.18) (-0.77)
NMS after 2005 (dummy) 1.92 ** -0.57
(2.29) (-0.28)
(NMS 2000-2004) *
(log initial GDP per capita)
(NMS 2000-2004) x -1.74 * -3.24 *
(legal system quality) (-1.71) (-1.85)
(NMS 2000-2004) x
(private credit ratio)
Observations 349 51
Adjusted [R.sup.2] 0.481 0.608
First-stage IV estimation
Relative price of investment -0.02 ***
(-4.39)
Initial investment (in % of GDP) 0.82 ***
(27.08)
Kleinbergen-Paap F statistic 647.44
Stock-Yogo 10% critical value 19.93
(5) (6)
Sample Full Transition
Estimation method IV OLS
Log initial GDP per capita -1.81 *** -1.88
(-6.57) (-1.22)
Population growth -0.37 * 0.068
(-1.85) (0.052)
Investment (in % of GDP, 0.085 *** 0.094
instrumented in case of IV) (4.15) (0.93)
Openness (in % of GDP) 0.0090 *** -0.0068
(4.24) (-0.27)
Terms of trade growth 8.80 ** 40.4 **
(2.37) (2.41)
Output loss 0.46 0.48
(0.47) (0.12)
Private credit (in % of GDP) -0.19 * 1.02
(-1.91) (0.63)
Legal system quality index 0.18 0.61
(1.58) (0.91)
Freedom of trade index 0.17 1.46
(1.57) (1.53)
Regulation quality index 0.025 -0.25
(0.15) (-0.46)
NMS (dummy) -0.59 -0.94
(-0.76) (-0.48)
NMS during 1990-1994 (dummy) 0.14
(0.083)
NMS during 2000-2004 (dummy) -1.44 -4.04 *
(-1.63) (-2.08)
NMS after 2005 (dummy) 1.31 1.08
(1.50) (0.67)
(NMS 2000-2004) *
(log initial GDP per capita)
(NMS 2000-2004) x
(legal system quality)
(NMS 2000-2004) x -5.72 *** -7.76 **
(private credit ratio) (-6.14) (-2.76)
Observations 320 37
Adjusted [R.sup.2] 0.478 0.614
First-stage IV estimation
Relative price of investment -0.02 ***
(-3.99)
Initial investment (in % of GDP) 0.82 ***
(23.11)
Kleinbergen-Paap F statistic 552.36
Stock-Yogo 10% critical value 19.93
Notes: OLS (columns 1, 3 and 5), IV (columns
2, 4 and 6). See also notes to Table 1.