Download Macroeconomic stability and growth in Eastern Europe

Macroeconomic stability and growth in Eastern Europe
Valeriano Martínez
Blanca Sanchez-Robles*
Department of Economics
Universidad de Cantabria
Abstract
This paper examines, from an empirical approach, the process of growth of some
countries in Eastern Europe over the last decades. More specifically, we carry over a
panel data analysis of 13 countries over the period 1992-2008. We specifically want to
test for the impact on growth of two main categories of variables: measures of
investment (both domestic and foreign), and indicators of macroeconomic stability.
Basic results suggest that domestic investment has played a key role in the growth
performance of this group of nations; macroeconomic stability, as captured by low
levels of inflation and public deficits, has also been beneficial for growth according to
our estimations. Although there are some features of growth which are distinctive of the
area, our analysis suggests that the economic performance of the region has also been
influenced by classical factors which have been documented as key drive engines of
growth for other groups of countries.
1
Introduction
In 1989, and after months of social and political turmoil, the Berlin Wall collapsed.
Although some changes had started to take place sometime in advance in several
particular countries, the episode may be regarded as the official starting point of a
complex and long progression of deep transformations in Eastern Europe (EE) and the
Former Soviet Union (FSU). These nations, usually known as transition economies1,
began to experience deep political, social and economic transformations in order to
mutate from socialist, centrally planned entities, to market economies and democratic
regimes. The process has been particularly complex and difficult since it has required
the contemporaneous transformation of the economy, the political system and the
institutional framework.
Such a multifaceted phenomenon soon attracted the interest of many economists,
international organizations and practitioners, and the study of transition economies
gained momentum as a field of economics. Due to the interplay of many different
aspects, the analysis of the countries in Eastern Europe is also very illuminating for
those interested in economic growth, both at the theoretical and the empirical level.
Indeed, the performance of GDP per capita in the area in the last decades has not been
homogeneous among countries or over time. Output has followed a particular pattern in
each nation, usually characterized by a recession (of different magnitude and length) at
the beginning of the transition. Figure 1 displays per capita GDP growth in some
1
The term transition economies usually refers to CEE and FSU countries although it can also encompass
all countries, not necessarily in Europe, that evolve from a situation of centrally planned economies to
market structures. In this sense, countries as China, Mongolia, Vietnam, Cambodia and Laos may be
regarded as transition economies, as some researchers do.
2
Eastern European countries, grouped in three categories2, and two Former Soviet Union
Republics, Russia and Ukraine, in 1992-2008. As shown in the figure, growth has
neither been uniform across countries nor over time. Along the period considered GDP
per capita more than doubled for the Baltic republics and the five CEE countries. Russia
and the SEE also increased noticeably their level of GDP per capita, despite the severe
recession of the early stages of transition. Ukraine, instead, has registered a less
dynamic process of growth.
Figure 1. Growth in the CEE and FSU, 1992-2008
7000
6000
GDp per capita (constant 2000 US$)
5000
4000
3000
2000
1000
0
1992
1993
1994
1995
1996
Central Eastern Europe
1997
1998
1999
South Eastern Europe
2
2000
2001
2002
The Baltic Republics
2003
2004
Russia
2005
2006
2007
2008
Ukraine
The groups are the following:
CEE (Central and Eastern Europe): Czech Republic, Hungary, Poland, Slovak Republic and Slovenia.
SEE (Southern and Eastern Europe): Bulgaria, Croatia and Romania.
BALT (The Baltics): Estonia, Latvia and Lithuania.
Our sample is made up of these countries plus Russia and Ukraine.
3
In this paper we want to devote some time to the consideration of growth in this group
of nations, for several reasons; in the first place, from a historical, cultural and
geographical view they are an important part of Europe, and their performance may be
crucial for the future evolution of all European countries. Second, they constitute a
remarkable example of a transformation from a scenario of slow growth and sparse
productivity to another characterized by higher levels of efficiency and GDP growth,
with the subsequent improvement in the quality of life of the citizens of the area. Third,
interesting lessons may be drawn from the different performance of output in the
various countries.
In this regard, we carry out a panel data analysis of a sample of 13 countries in the area
over the period 1992-2008. We want to pay special attention to some kinds of factors
which have been identified by recent literature on growth as being crucial for economic
performance; these factors may be summarized in two categories: capital accumulation
and macroeconomic stability.
Our main conclusions are in line with, and complement, other contributions. Our
results, which are still preliminary and need to be re-examined in further research,
suggest that capital accumulation has been an important part of the process of
development. Macroeconomic stability, as proxied by fiscal and price stability, has also
collaborated to GDP growth.
2. A brief survey of the literature
As it was said above, the research on transition countries has been relatively abundant
and has been carried out from different, complementary approaches.
4
a) Research on the early years of transition
There is already a relatively large number of papers which analyze the first stages of the
economic and political transformation which started in the early 90s in EE and FSU.
Some issues which have received particular attention in this regard are the causes of the
initial recession, the optimal speed of transition – more specifically, the advantages and
drawbacks of gradual adjustment versus shocks therapies - and the role played by
liberalization, democratization and the institutional background (see Campos and
Coricelli, 2002, and Popov, 2007, for reviews of the literature). These contributions
have a short or medium run perspective, different from the long run analysis we intend
in this paper. Nonetheless, they provide some useful insights about the economic
scenario in these countries.
Although there are some papers relating the initial recession to a weak consumption
demand, (Lipton and Sachs, 1990; Blanchard et al., 19913) nowadays most experts
attribute the fall in output to supply factors:
the collapse of the previous central
planning system entailed a change in relative prices and in trade patterns, which in turn
implied a massive reallocation of resources (Popov, 2007).
Within this category linking the recession to supply factors, some specific explanations
stand out. One particular hypothesis focuses on the absence of a developed credit
market capable of efficiently connecting saving and investment and providing firms
with financial resources. The lack of proper financial mechanisms, in turn, entailed inter
firms arrears, barter, or other harmful or inefficient practices (Calvo and Coricelli, 1992,
3
See also Frydman et al., 1991; Berg and Sachs, 1992.
5
1993, 1996; Gaddy and Ickes, 1998; Marin and Schnitzer,1999; Alesina and La Ferrara,
2000).
Other papers focused in the disorganization brought about by the disappearance of the
prevailing economic relationships, linkages and networks, established during the central
planning era. In the previous situation each company was often linked to a small
number of other firms which acted as its clients or suppliers. Information needs in this
setup were limited, and monopoly rents arose. Once central planning mechanisms
disappeared, it was not obvious to firms how to negotiate with potential buyers or
sellers, how to get and transmit the information which is usually conveyed by price
adjustments in decentralized economies, or how to search for the best partners
(Blanchard and Kremer, 1997; Roland and Verdier, 1997). This phenomenon was
especially acute in FSU, and less dramatic in countries where some market mechanisms
have already been prompted by incipient reforms.
Moreover, further pieces of research focused on firm and investment dynamics over the
transition; sometimes the process was described along the lines of a Schumpeterian
creative destruction, or a complex course of interaction between old and new, more
productive firms (Aghion and Blanchard, 1994; Chadha and Coricelli, 1995; Hernandez
Cata, 1997; Castanheira and Roland, 2000).
b) Investigation about the determinants of growth
A second broad category of papers has analyzed the connection between particular
variables or aspects of the economic and social organization and output growth.
In this regard, De Melo et al., (1996) launched an important line of research which
examined the links between liberalization and growth (Sachs, 1996; Fischer et al.,
6
1996a, 1996b; Selowsky and Martin, 1997; Fidrmuc, 2003). These papers documented a
positive impact of liberalization on growth. Subsequent pieces of research have tried to
circumvent the econometric problems related to liberalization indicators (for instance,
endogeneity) or have stressed the equal or even larger importance of initial conditions,
such as the geographical proximity to Western Europe or the recent historical
background (Aslund et al., 1996; Krueger and Ciolko, 1998; Heybey and Murrel, 1999;
Popov; 2000; De Melo, 2001)4.
There is yet another relevant category of papers which focused on the role of institutions
and stressed their importance for growth, especially of those capturing the rule of law
and other legal aspects (Havrylyshyn and van Roden, 2000; Raiser et al., 2001; Godoy
and Stiglitz, 2006; Beck and Laeven, 2006; Popov, 2007)5. These papers provide useful
insights but have some econometric limitations: they usually perform cross country
instead of panel analyses, sometimes because of the lack of available data for all years
and countries considered in the sample.
Efendic et al. (2008) try to overcome the limitations of cross country analysis and to
correct the potential endogeneity bias and dynamic panel bias underlying static
specifications by means of estimating a system GMM model. They report a positive
impact of institutions on growth, but also document that this impact is not robust to the
proxy of institutions included and to the dependent variable chosen in the model (level
of GDP versus rate of growth). Furthermore, according to their results, institutions do
4
Efendic et al., (2008), however, question the relevancy of some of these proxies for initial conditions
Kostevc et al., (2007) have suggested that institutions are also relevant in order for a country to be able to
attract inflows of Foreign Direct Investment.
5
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not seem to impact growth contemporaneously, but rather over longer periods, possibly
of five years.
Schadler et al. (2006) and Iradian (2009) have also extended and complemented some of
the static models described above by performing panel data analyses along the lines of
the recent empirical research on economic growth.
Our research here is closer in spirit to Schadler et al., (2006),Efendic et al., (2008) and
Iradian (2009). We intend to exploit the time series and cross sectional dimension of the
data by means of static and dynamic panel data techniques. In this setup, furthermore, is
easier to tackle some endogeneity issues. The estimations we performed and report here
differ from those presented in the aforementioned two contributions in several aspects:
the country sample we employ, the time horizon covered by the data and the variables
included. In particular, we want to pay special attention to foreign direct investment and
macroeconomic policies.
c) Other potential drivers of growth: FDI
The research described in this paper is also related to other sets of contributions, first, to
those focusing on the macroeconomic impact of FDI.
The important role played by inflows of foreign capital in the nations of Central and
Eastern Europe has already been pointed out in the literature (see, for example,
Bijsterbosch and Kolasa, 2010). As argued within the literature on growth, FDI may be
regarded as a crucial mechanism of technological diffusion: it provides access to state of
the art technology, practices and knowhow to countries which are not capable yet of
innovating and generating technological progress but may benefit, instead, of advances
made elsewhere and transmitted via foreign investment. Among others, this idea has
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been justified at the theoretical level by Romer, (1993), Barro and Sala i Martin (1997)
and Borenstein et al., (1998).
FDI was especially relevant for CEE and FSU nations during the transition from central
planning to free market: these countries, which enjoyed relatively high levels of human
capital, lacked instead the ability to successfully develop new products and services that
could be delivered through market mechanisms; they did not have, either, updated
marketing and organizational knowhow. Socialist countries were obsolete not only in
terms of technology per se but also of management techniques, design of personnel
incentives, marketing strategies, financial planning and other activities along the value
chain.
This fact is a consequence of their having been distant from the usual channels of
transmission and pervasion of new discoveries and advances – for example,
international trade based upon market mechanisms - for several decades. In such a
scenario, the entrance of new firms which could help disseminate not only discoveries
and advances in terms of new products, services or processes, but also marketing and
organizational techniques, might have been really beneficial.
At the empirical level, there are some papers that show the positive impact of FDI on
productivity and growth for alternative sample of countries (Benoga and SanchezRobles, 2003). Bijsterbosch and Kolasa, (2010) document a positive correlation of FDI
inflows and productivity for 8 transition countries over the period 1995-2005. However,
they also report that the interaction of FDI with other variables (human capital or
institutional environment) is not always clear at the empirical level. They attribute this
effect to the fact that FDI, in order to be capable of generating externalities and
9
impacting productivity, needs a certain degree of absorptive capacities in the domestic
firms. These capacities may only show up when a certain level of consolidation has
been achieved by the economy.
d) Macroeconomic stability and growth
The view about the role that the State should play in the process of growth of a country
has evolved over time, partly because of the different paradigms and schools of thought
that have dominated the field in the various moments. In the neoclassical Solow (1956)
model, for example, economy policies could not alter the rate of growth in the steady
state, and thus the effective role that could be played by the state was very small. In the
framework of some subsequent endogenous growth models, however, (Romer, 1986;
1990; Lucas, 1988, Barro, 1990; Rebelo, 1991) the public sector could conceivable play
a more active part, via the provision of public capital or the correction of externalities
associated with knowledge or technology. Generally speaking, nonetheless, there is a
number of authors suggesting that, as far as growth is concerned, one of the main tasks
of the State should be providing the adequate framework for the activity of private
agents. In this regard, the public powers should warrant the necessary conditions in the
economy in order for crucial inputs (human or physical capital, knowledge, technology)
to accumulate. This implies ensuring that the economy has the desired degree of
macroeconomic stability to warrant the confidence of investors, provide incentives for
the most productive destination of the inputs and rend the accumulation of inputs
feasible and profitable at reasonable rates of risks. In contrast, an economy marked by
macroeconomic instability will present an excessive degree of uncertainty, that in turn
will deter agents from investing or will cause them make wrong decisions regarding the
allocation of resources to alternative projects.
10
It is not straightforward to define or even describe macroeconomic stability. We may,
however, get closer to this notion by means of selecting some indicators. The World
Bank (1990) defines as stable a macroeconomic framework with these features: low and
predictable rate of inflation, appropriate real interest rate, stable and sustainable fiscal
policies, competitive and predictable exchange rates and viable current account. In
accord with this description, Fischer (1993) argues that macroeconomic, price and fiscal
stability are deeply related.
There are some theories explaining why the macroeconomic instability which manifests
in high or unpredictable inflation is detrimental for growth. Inflation reduces the rate of
return of investment, for example via rigidities in the tax system (Bruno, 1993; Jones
and Manuelli, 1993). Inflation may harm the productivity of inputs, by distorting prices
and affecting the capacity of efficiently allocate resources (Smyth, 1994). It can be a
signal of lack of competition in relevant markets or of lack of control of fiscal and
monetary policy. It may foster agents to devote resources to unproductive tasks such as
hedging (De Gregorio, 1993). Furthermore, high rates of price variation increase the
risk premium of interest rate and impede the good functioning of financial markets.
On empirical grounds, there are some papers that find a negative correlation between
inflation and growth (Kormendi and Meguire, 1985; Grier and Tullock, 1989; Fischer
1993; Barro, 1995). Moreover, Fischer et al., (1996), Efendic et al, (2008) and Iradian
(2009) document a negative correlation between inflation and growth for the case of
transition economies.
As far as fiscal stability is concerned, and on a priori grounds, the potential impact of
deficits may be related to the nature of the expenditures which cause it. Outlays in
11
infrastructure impact the economy differently than government consumption. As a
matter of fact, public investment may foster economic growth (Barro, 1990)6. Although
this consideration is true, it is now apparent that in many cases public deficits exert a
crowing out effect on private investment, increase risks premia in financial markets, and
may be a signal of an excessive size of the public sector, that in turn foster activities as
rent seeking or corruption, instead of directing individuals to more productive
activities7. On empirical grounds, the negative correlation between public deficits and
growth for wide samples of countries has been suggested by Grier and Tullock, (1989)
and Barro, (1991)8. The positive impact of public surplus on growth has been unveiled
by Easterly and Rebelo (1993), Fischer, (1993) and Garrison and Lee, (1995). Evidence
concerning transition countries is mixed at this point. On the one hand, Iradian, (2009)
finds a negative correlation between fiscal deficits and growth, whereas Efendic et al.
(2008) report a positive relationship.
3. Empirical analysis
a) Variables
In order to get the rationale behind our empirical work, it may be useful to consider this
basic framework for a growth model:
Y = AK
(1)
K& = Y − C − δK
(2 )
6
This effect may also be caused by transfers via increases in the productivity of human capital (Cashin,
1995).
7
See Murphy, Shleifer and Vishny, (1991).
8
Sanchez-Robles (1998) provides some evidence suggesting a negative correlation between public
deficits and growth for the case of Spain.
12
Where Y is output, K is capital, A is technological change, C is consumption, δ is the
depreciation rate and a dot over a variable represents its derivative with respect to time.
A encompasses, broadly speaking, all the factors that can enhance the productivity of
capital. K may be thought of as an aggregate made up of public and private capital (as in
Barro, 1990), human capital and physical capital (Rebelo, 1991; Lucas, 1988) or
domestic and foreign capital (Bengoa and Sanchez-Robles, 2005). Alternatively, Y and
K may be considered in per capita terms.
From equations (1) and (2) above it is apparent that the rate of growth of output per
capita will depend on two main types of factors: first, on the accumulation of capital in
its broadest sense; second, on all variables increasing the productivity of capital. As an
important part of the second sort we shall consider here the macroeconomic stability of
the country.
Following this distinction, we have included in our basic specification two main kinds
of variables. The first one encompasses all the broad category of capital accumulation:
both domestic and foreign.
The second one will be made up of different variables intended to proxy for
macroeconomic stability. A stylized indicator in this direction is the budget surplus or
deficit. The expected sign of the budget balance is positive, (i.e. the impact of deficits
will be negative) since we are no longer in the framework of short term macroeconomic
policies of the Keynesian style, where public deficits may have an expansionary effect
on the economy.
Another important indicator is the inflation rate. The expected sign is negative as well.
Moreover, we have included as control variables in some equations measures of other
13
aspects which have been reported by the literature as being relevant for the performance
of transition countries: the quality of the institutional setting and the lag of the
dependent variable.
b) Sample, data and methodology
The following 13 countries encompass our sample: Bulgaria, Croatia, Czech Republic,
Estonia, Hungary, Latvia, Lithuania, Poland, Romania, Russia, Slovakia, Slovenia,
Ukraine9.
All these countries were socialist economies in the past and started to transit to market
economies after 1989. Although they share common features, there is also some
heterogeneity among them.
The time horizon is 1992 to 2008. We chose as the starting point the year 1992 because
of two reasons; the first one is historical - these countries started the transition to market
economy around 1990 - and the second is methodological: prior to this date available
data were scarce and in most cases not reliable.
We took as the final point the last year for which data for all countries and variables
were available.
Within our econometric strategy we decided to perform a panel data analysis. Panel
data, first of all, widens notably the possibilities of the study: it allows us to explore the
implicit information in the time dimension of data, provides more variability, permits to
9
We did not include in the sample the following countries: Albania, Armenia, Azerbaijan, Belarus,
Georgia, Kazakhstan, Kyrgyzstan, Moldova and Uzbekistan because all data employed in the estimations
were not available in those cases.
14
reduce multicollinearity problems among variables and increases the size of the sample
and thus the degrees of freedom.
Secondly, it is probable that the countries considered have structural differences among
them due to technology or agents preferences. Panel data techniques allow to control for
this type of unobservable heterogeneity (Arellano and Bond, 1990).
Finally, and as already pointed out by some of the literature10, it is possible that some of
the variables included in the estimations pose problems of endogeneity that should be
managed using instrumental variables. GMM procedures help solve this problem since
we can use lagged variables as instruments and obtain, therefore, more consistent and
efficient estimations.
The baseline specification is a model of the form:
growthi ,t = α i + βX it + ε it
Where growth is the rate of growth of real GDP in per capita terms (in Purchasing
Parity Power, PPP), Xit is a set of variables that could influence growth, α and β are the
estimation parameters and εit is the error term. i represents the 13 countries and t
captures time.
The strategy was to perform the estimation with static panel techniques (fixed and
random effects) and with dynamic panel procedures (GMM). In addition, we ran
10
For expample Efendic et al. (2008).
15
alternative versions of equation, as it is common in empirical growth literature. This
procedure helps reduce multicollinearity.
c) Results
The results of this analysis are shown in Tables 1 and 2.
Columns 1-4 in Table 1 show the results from a static panel data estimation with
random effects. We also performed a fixed effects estimation; according to the
Hausman test, however, we cannot reject the null hypothesis of the random effects
estimation being a preferable representation of the data generation process.
Domestic investment as a percentage of GDP is positively and significantly correlated
with per capita growth. The point estimate is rather large and quite stable among
estimations. It is also in accord with the results documented by Iradian (2009). Human
capital indicators (secondary and tertiary school enrolments) are also positive and
significant, although the impact of secondary enrolment seems more relevant, as is
usually the case in these countries.
As far as the indicators of macroeconomic stability are concerned, inflation displays a
negative sign and is also significant at the 99% level. The point estimate is stable as
well. The budget surplus has a positive correlation with growth, and is also significant.
According to our results the impact of this variable on growth is remarkable, varying
between 0.35 and almost 0.6. These figures are also similar to those reported by Iradian
(2009).
Finally, we have included as control variables two proxies of the degree of institutional
development of the country, the Index Free from Corruption, from the Heritage
16
Foundation, and the measure of the country risk provided by The Institutional Investor.
These indexes vary from 0 to 100; higher levels represent a smaller level of corruption
and of risk. They are also positively correlated with growth and are significant at
conventional levels.
The only variable which does not display the expected sign is FDI as a percentage of
GDP. We attribute this counterintuitive result to a problem related to the construction of
the indicator, which is capturing annual inflows but not cumulative levels of foreign
investment. Another possible explanation for this result is along the lines of the
absorptive capacities hypothesis stated by Bijsterbosch and Kolasa, (2010). Perhaps our
estimations do not find a positive impact since our data encompass also the beginning of
the process, when some countries might not had surpassed yet the threshold necessary
in order to benefit from technological diffusion. This is a puzzling issue, however, and
one that deserves further consideration in future versions of this paper.
Columns 5-8 in Table 1 display the results obtained when estimating our baseline
equation by GMM.
The first lag of the rate of growth is included in these equations. It captures the potential
inertia or persistence present in the growth performance. It is positively and
significantly correlated to growth, pointing to the existence of inertia. In other words,
the current economic behaviour of the countries in the sample seems to be influenced by
their past economic performance.
As far as model diagnosis is concerned, the more important tests in GMM models is the
test of autocorrelation of second order. The null hypothesis is no autocorrelation. The
relatively high p values reported mean that the null hypothesis cannot be rejected. The
17
Hansen test is directed at over identification and validity of instruments. The null here is
that instruments are valid and there is not over identification. The p value corresponding
to this test is again high, and thus we cannot reject the null hypothesis of the
identification being correct.
Table 2 displays the results from slightly different versions of our baseline specification.
Now the equations include the first lag of real GDP per capita, in order to test for the
presence of convergence. The point estimate is negative and significant, suggesting the
existence of some mechanisms towards convergence in the sample.
We have performed as well some estimations taking as the dependent variable the level
of GDP per capita, instead of its rate of growth. Generally speaking the results
(available upon request) are similar to those obtained when regressing the growth rate of
GDP. However, in some of these equations FDI has a positive sign. It is a puzzling
result, that deserves further attention, why the sign of the correlation between FDI and
GDP is not robust to the dependent variable employed.
Sunmming up, and according to our results, macroeconomic stability, as proxied by low
inflation and budget stability, seems to have been beneficial for growth in the countries
of our sample. This correlation is present when alternative techniques are employed.
Concluding remarks
The countries in Eastern Europe have undergone a long and difficult process of
transformation from centrally planned to market economies.
The challenge was remarkable, since these countries not only had to carry out the
economic reforms that allow them to grow and compete with the rest of countries in the
18
world markets, but also the political reforms that spur the transformation of the obsolete
existing institutions into more democratic and market oriented. All macroeconomists,
and specially those interested in growth, may draw insights and lessons from the
attentive consideration and study of these processes. These facts have implied a
particular pattern of growth in the last two decades, with their own features.
In this paper we have carried out an empirical analysis of potentially important factors
of growth in Eastern Europe countries. Results point to the importance of domestic
investment and macroeconomic stability - as reflected by low levels of inflation and a
balanced budget - as key drivers of growth.
These results seem reasonable. In a long term growth framework, deficits are
detrimental to growth since they are associated to corruption, rent seeking, an excessive
size of government and distortions in the resource allocation. A low rate of inflation
suggests an effective degree of market liberalization. Furthermore, in a heavily
competitive environment characterized by strong rivalry because of the arrival of
foreign firms, price stability is key.
Domestic investment also seems to have been an important driver of growth. Evidence
concerning FDI is puzzling, however. The correlation with GDP growth is negative,
whereas the sign is positive when GDP in levels is taken as the dependent variable. This
is, no doubt, something that needs to be assessed more carefully.
Our analysis suggests that, although this group of countries has followed a particular
process, with specific features, the key factors explaining growth for other samples of
countries are also relevant in this case.
19
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22
Table1:
Dependent variable: real GDP per capita growth (in percentage)
1
2
3
4
5
6
7
8
Random
Random
Random
Random
GMM
GMM
GMM
GMM
Real GDPpc growth
0,186 **
0,203 **
0,109 **
0,109 **
(Lag 1) (%)
(2.22)
(2.68)
(2.08)
(2.01)
FDI (% GDP)
GKF (%GDP)
-0,016
-0,017
-0,030
-0,025
-0,011
-0,009
-0,034
-0,029
(-1.52)
(-1.48)
(-1.33)
(-1.08)
(-1.22)
(-1.13)
(-1.32)
(-1.22)
0,229 ***
0,235 ***
0,215 **
0,190 **
0,168 ***
0,161 ***
0,221 ***
0,194 **
(4.76)
(4.78)
(2.04)
(2.10)
(3.46)
(3.74)
(2.84)
(2.46)
Secondary school
0,121 ***
0,093 **
0,106 **
0,073 ***
enrollment (%)
(2.72)
(2.51)
(2.40)
(2.76)
Tertiary school
0,026 **
0,038 *
0,017 **
0,036 **
enrollment (%)
(1.98)
(1.92)
(1.33)
(1.98)
Inflation (%)
-0,009 ***
-0,009 ***
-0,009 ***
-0,009 ***
-0,008 ***
-0,076 ***
-0,018 ***
-0,018 ***
(-3.70)
(-3.59)
(-3.05)
(-2.89)
(-4.31)
(-3.36)
(-4.33)
(-4.18)
Deficit (-)/Surplus (+)
0,401 ***
0,361 ***
0,495 ***
0,556 ***
0,313 ***
0,271 ***
0,535 ***
0,590 ***
(% GDP)
(5.56)
(4.70)
(3.68)
(3.77)
(3.16)
(4.60)
(5.41)
(5.44)
Free from corruption
0,862 ***
0,713 **
0,861 ***
0,578 ***
(The Heritage Foundation)
(3.07)
(2.54
(4.92)
(2.66)
Country risk
0,098 **
0,038 **
0,131 **
0,163 **
(Institutional Investor)
(2.16)
(1.97)
(2.20)
(2.14)
166
166
# Observations
155
155
Hansen test
0,652
0,605
0,436
0,394
First order serial correlation
0,043
0,04
0,064
0,034
Second order serial correlation
0,111
0,164
0,703
0,723
2
Adjusted R
Hausman test
155
155
169
169
0,427
0,399
0,384
0,324
2,02
3,15
5,09
4,05
Notes: t-statistics are in parenthesis under the coefficients. *, **, *** denote significance at 10, 5 and 1 per cent levels. Results are reported for random effects with robust standard
errors and two-step System GMM with robust standard errors. Hausman test: H0 (Random effects): CHISQ(k) < CHISQ(k) at critical level. The figures reported for Hansen test and
Arellano-Bond correlation tests are p-values.
23
Table 2:
Dependent variable: real GDP per capita growth (in percentage)
1
2
3
4
GMM
GMM
GMM
GMM
Ln Real GDPpc
-0,016 ***
-0,014 **
-0,024 **
-0,021 **
(Lag1)
(-3.65)
(-3.11)
(-2.31)
(-2.05)
FDI(%GDP)
GKF (%GDP)
-0,012
-0,014
-0,035
-0,033
(-1.34)
(-1.40)
(-1.33)
(-1.61)
0,254 ***
0,245 ***
0,270 ***
0,222 ***
(6.79)
(6.51)
(3.30)
(2.66)
Secondary school
0,174 ***
0,124 **
enrollment (%)
(5.36)
(2.21)
Tertiary school
0,047 ***
0,037 ***
enrollment (%)
(3.76)
(3.05)
Inflation (%)
-0,011 ***
-0,010 ***
-0,009 ***
-0,009 ***
(-5.30)
(-5.11)
(-3.63)
(-3.90)
Deficit (-)/Surplus(+)
0,355 ***
0,237 ***
0,456 ***
0,504 ***
(% GDP)
(6.79)
(4.13)
(4.34)
(4.27)
Free from corruption
0,734 **
0,362 **
(The Heritage Foundation)
(2.10)
(2.03)
Country risk
0,041 **
0,027 **
(Institutional Investor)
(2.03)
(2.11)
168
168
# Observations
155
155
Hansen test
0,705
0,648
0,489
0,404
First order serial correlation
0,068
0,095
0,298
0,137
Second order serial correlation
0,113
0,148
0,657
0,596
Notes: t-statistics are in parenthesis under the coefficients. *, **, *** denote significance at 10, 5 and 1 per
cent levels. Results are reported for random effects with robust standard errors and two-step System GMM
with robust standard errors. Hausman test: H0 (Random effects): CHISQ(k) < CHISQ(k) at critical level. The
figures reported for Hansen test and Arellano-Bond correlation tests are p-values.
24