Equipment investments and growth nexus--evidence from socialist and transition Croatia.
Skare, Marinko ; Sinkovic, Dean
Reference to this paper should be made as follows: Skare, M.;
Sinkovic, D. 2012. Equipment investments and growth nexus--evidence from
socialist and transition Croatia, Technological and Economic Development
of Economy 18(3): 504-528.
JEL Classification: G31, O11, O16, O47, R11, P24.
1. Introduction
Economic growth has become one of the most dynamic fields of
research in economics through which many theories have tried to explain
why some countries grow faster than others. Capital accumulation has
always been the vital factor in GDP growth; however, its role in
economic growth theory has been changing over time.
The recent literature in economic growth argues that allocation
efficiency of investment is more important than the level of investment.
This argument leads to the question--are investments being allocated in
the most productive and efficient way? New growth theories emerged
during 1990s leading to investigation of the link between different
types of investments and GDP growth. This line of thought was first
addressed in the work of Mankiw et al. (1992) with a human capital
augmented Solow model. They pointed out that accumulation of capital,
with an emphasis on human capital, results in higher social rates of
returns than the one explained by the conventional Solow model (Ding,
Knight 2009). De Long and Summers (1991, 1992, 1993, 1994) published a
series of papers investigating the relationship between the different
components of investment and economic growth and concluded that
equipment investments play a more important role in lifting output
growth than structure investments. Their main argument was that
structures cannot be exported and do not produce as strong externalities
as equipment. With no ability to export and fewer abilities to create
externalities, structure investments would have less influence on total
factor productivity. Skare, Sinkovic (2007) use a De Long and Summers
theoretical framework for the Republic of Croatia, for the period
1960-2006, and conclude the relationship between equipment investment
and economic growth is stronger than between structure investments and
growth.
We utilize the well-known standard approach introduced by Mankiw et
al. (1992), and Benhabib and Spiegel (1994). We found a correlation
between different components of investments (equipment, structure and
human capital) and real GDP growth in Republic of Croatia for two
periods--the pre-transition period (1960-1989) and the transition period
(1990-2009). Similar to the work of Temple (1998) this paper improves
previous works by using data on human capital (estimated stock of human
capital) to control for the growth effect of education and technical
structure of investments. Furthermore, to capture evolution and
interdependence between variables, we use the Vector auto regression
model (VAR) and the Vector error correction model (VECM). By means of
quantitative (OLS, VAR and VECM) and qualitative analysis the authors
have come to the following findings:
--The correlation coefficient between equipment investments and
growth appears to be much higher than the one between structure
investments and growth for both pretransition and transition period.
--There is a strong correlation between human capital and growth
for the transition period.
--Equipment investments raise total factor productivity
(correlation coefficient 0.42).
--Extensive growth pattern with high input expansion and low
productivity growth has been the key factor accounting for the slow
economic growth in Croatia.
--Equipment investment influence on TFP is stronger in an unstable
Phillips curve environment than in a stable one (at least for Croatia).
We organize the paper as follows. Section I sets out the
theoretical framework for this research. Section II sets out our
analysis of the nature of growth in the Republic of Croatia. Sections
III, IV and V carry out the data framework set out the model and
interpret the empirical results for the pre-transition (1960-1989) and
transition (1990-2009) periods. Section VI puts forward some concluding
remarks. The formal statistical results are provided in an appendix.
2. Literature review on equipment and growth
From a historical perspective both the theoretical and empirical
literature emphasize capital accumulation as a key factor in determining
long run economic performance. However, mainstream theories associated
with Solow (1957) argue that macroeconomic policy cannot affect growth
rates over the long term. Solow's conclusion was that capital
accumulation would increase the growth rate in the short term, bringing
countries to a higher level of income (transition effect), but would not
generate any long term GDP growth because of the diminishing returns on
capital. In other words capital deepening should be encouraged only
because of the transition effect. Although some economists still accept
this framework of economic growth, most of them find that the extended
Solow version, given by Mankiw et al. (1992), provides more answers. The
extended Solow model suggests that the conventional Solow model could
explain most of the variations in GDP between countries, but it
emphasizes the special role of human capital via education. MRW use the
augmented Solow model with human capital to show that the social
marginal product of human capital and physical capital is somewhat
larger. However, the main focus of their model was on human capital with
no special role for the disaggregated level of physical capital.
The theoretical basis for this research is driven from the findings
of De Long and Summers (1991, 1992, 1993, 1994) and Temple (1998). De
Long and Summers (henceforth DLS) stressed the main grounds on how
equipment investments could be important for GDP growth (De Long,
Summers 1991: 3-4):
--First, from a historical standpoint the application of
capital-intensive technologies has played an important role in those
countries that have grown rapidly in the last 100 years.
--Second, there must be strong positive externalities associated
with equipment investments as technological progress (total factor
productivity growth) is largely embodied in the form of new investment
goods (Greenwood et al. 1996). Ninety-five percent of private-sector
research and development in America is undertaken by the manufacturing
sector, and within that the equipment sector accounts for more than half
of all research and development (Summers 1990). Therefore, investigating
the special role of equipment investments seems to be desirable.
--Third, countries that apply a government-led "developmental
state" approach to structural changes invest more heavily, have
lower equipment prices and enjoy more rapid economic growth (Hendricks
2000).
The aforementioned assumptions imply that more equipment
investments mean faster technological progress generated through
positive externalities when working with modern. As in the Solow model,
the main generator of economic growth is technological progress;
however, the same progress is not generated by 'manna from
heaven' but is driven by the applications of suitable macroeconomic
policies.
Research of De Long and Summers (1991, 1992) emphasizes the
positive and significant correlation between the growth of GDP per
worker and the share of real GDP devoted to equipment investments. The
results of their cross section regression in a sample of 61 countries
show that a 1 percentage point increase in equipment investments is
estimated to increase the average annual GDP per worker by 0.223
percentage point per year. The difference in equipment investment in a
statistical sense accounts for much of the growth performance for the
sample countries. They found that countries with a high share of
equipment investments grew extremely rapidly, even when controlling for
a number of factors and no matter whether equipment investments were a
result of high savings or low relative equipment prices. Japan achieved
a growth rate edge of 2.2% per year from 1960 to 1985 relative to the
average pattern. Conversely, Argentina has suffered a growth deficit of
2.1% per year. More than four fifth of this difference is accounted for
by high or low equipment investments.
Soon after, De Long and Summers released a series of papers
emphasizing how equipment investments yield important external benefits.
Many economists started investigating this approach. Auerbach et al.
(1993) found that the link between different components of investments
and growth in the OECD countries is fully consistent with the Solow
model. He stressed two main shortcomings in the approach of De Long and
Summers; first De Long and Summers failed to conduct any statistical
test of the Solow model, and second it fails to survive the test of
robustness. In a 1998 paper Temple investigated the relationship between
equipment investment and growth by using the MRW framework. His research
improved the work of De Long and Summers and Auerbach by using a
well-recognized and accepted theoretical framework, taking a more
rigorous approach to outliers, using data on human capital, taking
unobserved heterogeneity into account and by applying instrumental
variables. He observes three different samples: first the
'non-oil' sample of developing and industrialized countries,
second the 'non-oil' sample excluding OECD countries, and a
third sample which includes the OECD countries. The results showed that
equipment investment is weakly correlated with growth in the OECD sample
but strongly correlated with the large group of developing countries, an
outcome that is pretty much consistent with the De Long and Summers
findings. Even so, a more interesting result was that the magnitude of
the estimated returns on equipment investment was well over 50 percent
and much higher than the estimated return on structure investments.
Furthermore, Temple and Voth (1996) carried out a robust regression and
concluded that the Solow model is strongly rejected for the poorest
countries. Another important paper on this topic is Jones (1994) who
suggested that there is a strong negative correlation between economic
growth and the relative price of machinery. Significant doubt on the
investment growth nexus remains between the positive causality
supporters Barro (1991), De Long and Summers (1991, 1992), Levine and
Renelt (1992) and critics Blomstrom, Lipsey and Zejan (1993). Sufian
(2010) examines the impact of mergers and acquisitions on the technical
efficiency of the banking sector. Dulleck and Foster (2008) show that
link between equipment investment and growth is lower or negative in
countries with low human capital stock. Benhabib and Spiegel (2005)
support the thesis advanced by Dulleck and Foster (2008) finding that
critical level of human capital is needed to boost total productivity
growth. Greenwood and Krusell (2007) argue that to investigate the
investment-growth link quantitative theory should be preferred to the
traditional growth accounting approach. Del Rio (2010) shows that faster
investment--specific disembodied technological progress reduces job
creation and consequently economic growth in the long-run. Field (2007)
points to the absence of evidence supporting the existence of positive
systematic relationship between rates of equipment investments and total
factor productivity growth.
Tridico (2007) investigates the relationship between the human
development process and economic growth in transitional countries,
finding a Granger causality relationship between the two variables.
Tvaronavicius and Tvaronaviciene (2008) find a relationship between
fixed investment and economic growth in Lithuania. Tvaronaviciene,
Grybaite and Korsakiene (2008) investigate the inflow of FDI into
Lithuania as a possible financial source for fixed investments
[stressing out main FDI determinants]. Tvaronaviciene and Grybaite
(2007) also studied particular aspects of development and determinants
of FDI for the Lithuanian economy by investigating how different levels
of penetration of foreign capital into certain economic activities
relate to the country's economic growth. Tvaronaviciene (2006)
investigates the main driving forces of economic growth in Lithuania,
giving special attention to the roles of FDI and international trade.
3. Nature of economic growth in Croatia
Investigation of the key factors of economic growth in Republic of
Croatia should generate valuable insights for policy makers wanting to
establish an efficient macroeconomic framework in order to improve the
economy's productive and competitive capacity in transition or
former transitional countries. This will positively affect GDP,
employment and other important economic variables in the long run.
Skare (2001) analysed the nature of economic growth in Croatia for
the pre-transition period (1950-1990). This research showed that more
than 50 percent of the GDP growth was generated through the use of labor
and 44 percent using capital. The overall growth record for Croatia
represented the classical growth theory framework emphasizing the key
growth sources--physical capital and labor force (Table 1). Human
capital and technological progress generated only a minor impact on
output because of the disinvestment process in R&D and education.
Despite a solid average growth rate of 2.67 percent over 1960-2009, the
speed of the Croatian economy's convergence towards the EU still
remained slow.
Table 1 gives us estimated shares of capital ([alpha]), human
capital ([beta]) and labor ([delta]) in national income. Estimated
factor shares are within expected ranges as suggested by theory (Solow
growth model) and international research studies for development
economies and countries in transition. The results in Table 1 indicate
that the Solow model did not function well for the Croatian economy for
the given period of time. The economic growth in Croatia was intensely
generated by labor, contrary to the Solow doctrine that emphasized a
negative relationship between population and GDP growth rates.
Furthermore, technological progress had a minor impact on the real GDP
growth rate. However, the reason for the second finding might be due to
a poor depreciation policy and as mentioned, disinvested processes in
R&D. Dolenc et al. (2011) show that Croatia is classified among
countries with high unemployment, low employment rate and higher tax
wedge. They find a positive link between the tax wedge at different wage
levels and the unemployment rate.
Table 2 shows that the investment share of GDP was strongly
correlated with the GDP per capita growth rate for the same period of
time. The correlation coefficient was even higher for human capital
indicators (secondary and primary school enrolment) proving the main
arguments from the extended Solow model.
Having in mind that Croatia is a country in transition with a poor
technical structure of investments (investing mainly in structures
(roads, hotels, residential buildings and similar infrastructure)), with
a low level of human capital investment, it is no wonder that the
economic future in Croatia is problematic. The current technical
composition of investment certainly does stimulate GDP, but with no
meaningful economic impact in the long term (Figure 1). Large
investments in equipment and human capital are needed to change the
technical structure of investment and boost growth.
This paper improves on previous work on economic growth and
equipment investment in Croatia (Skare, Sinkovic 2007). First we use
data on human capital stock to control for the growth effect of
education, extending the work of Mankiw, Romer, Weil and Temple and
others, using proxies for human capital or capital stock. Then we carry
on a separate research on the investment-growth nexus in the
pre-transition (planned economy, 1960-1989) and post-transition (market
economy, 1990-2009) periods.
[FIGURE 1 OMITTED]
A huge spike in structure investments in the transition period was
due to large government spending in order to rebuild war-damaged regions
and road infrastructure with the goal of stimulating the tourism sector.
On the other hand, private sector investments were focused on building
residential real estate infrastructure. At the same time, SMEs and
industry sectors, which represent the key engines of equipment
investments, were denied the needed financial support from both
commercial and developmental-government banks.
One of the reasons for such a scenario might be the
'successful' privatization of the Croatian banking sector that
took place soon after the end of the war. Since 2000 most of the
transitional countries experienced large foreign investments in the
banking sector that resulted in foreign bank ownership of most bank
assets in all of the countries. By the end of 2000 almost 85 percent of
the Croatian banking sector assets were controlled by foreign investors
who indeed vastly improved the quality and performance of the financial
services. However, when choosing to channel financial resources between
a relatively risky SME sector and profitable and low risk household
sector, Croatian private commercial banks have chosen the second. Rising
from very low levels in 1999, household loans grew almost six times by
2008, reaching 35 percent of GDP--the highest level in all the
transitional countries. In the same period, enterprise credit grew
almost three times (see Table 3). Dolenc (2010) finds no important
macroeconomic effects of privatization in the former transition country
of Slovenia.
The ignorance toward the SMEs and industry sectors, the two sectors
that represent the main driving forces of long term economic growth and
development (innovations, technology transfer and new employment),
created some serious socio-economic problems. Since Croatia's
scarce financial resources have been channelled in a very poor manner,
it resulted in inappropriate composition of investments. Yilmaz and
Koyuncu (2010) show empirical evidence on negative relationship between
foreign bank concentration and banking crises for transition countries.
Perhaps the reason for banks' reluctance to lend to the Croatian
industry sector might be a poor privatization and enterprise transition
process. Croatian private banks decided to promote consumption spending
instead of investment spending. Overall, huge lending boom in Croatia
generated the following problems:
--Highly indebted households in which income has been nose diving
ever since 2008 due to rising unemployment coming from the mix of poor
economic policies and the global financial crisis.
--Poor composition of investments.
At the same time, the Croatian government channelled most of its
resources toward building highways and other road infrastructure. Since
most of those investments were financed by external borrowings, it
represents another burden for Croatian economic performance. Therefore,
Croatian economic growth was fueled by consumption spending (household
loans spent mostly on real estate and imported goods) and government
spending (structure investments) that might have very dangerous
implications for the economy in the long run. In a situation when
external debt has reached almost 100 percent of GDP and with inadequate
levels of human capital and equipment investments, Croatia could face
serious problems soon.
4. Data and methodology
In the study, we use several different approaches to estimate the
equipment investment--growth nexus in Croatia. In the first part of the
analysis we use a Cobb-Douglas production function to identify growth
sources and total factor productivity (TFP) we need for further
analysis. We use the Cobb-Douglas production function with capital,
human capital and labor inputs.
Y = [A.sub.t][K.sup.[alpha].sub.t][H.sup.[beta].sub.t][([L.sub.t]).sup.[delta]] (1)
after taking logs and differencing,
[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (2)
with: [K.sub.t] = Capital stock; [H.sub.t] = Human capital stock;
[L.sub.t] = labor force and assuming [alpha] + [beta] + [delta] < 1
(decreasing returns to all capital as in Mankiw, Romer and Weil with no
steady states). Rather than imposing the constant return to scale
hypothesis (CRS), we tested the model under CRS assumption, and the
results reject the CRS hypothesis, backing up the assumption of
decreasing returns to scale.
A modified De Long and Summers (1991) model of the form
Y = c + [[beta].sub.E] [i.sub.E] + [[beta].sub.S] [i.sub.S] +
[delta]GAP + [gamma]L + [epsilon] (3)
with: Y = real GDP growth rate; [i.sub.E] = investment share/GDP;
[i.sub.S] = structure share/GDP; GAP = GDP gap; L = labor force growth
rate is used to analyze investment-specific productivity change to
output growth in Croatia. Following the work of Benhabib and Spiegel
(1994) we use the augmented Solow model to investigate the relationship
between equipment investments and output in Croatia. The model as in
Benhabib and Spiegel (1994) is presented in the following form:
[DELTA]log Y = [DELTA]log A + [alpha][DELTA]log E +
[gamma][DELTA]log S + [beta][DELTA]log H + [theta][DELTA]log L +
[[epsilon].sub.t] (4)
with: Y = Croatian gross domestic product (expressed in
Geary-Khamis 1990 international prices $, millions), (Geary 1958; Khamis
1972); A = technical progress (efficiency parameter); E = investments in
equipment (expressed in Geary-Khamis 1990 international prices $,
millions), (Geary 1958; Khamis 1972); S = investments in structures
(expressed in Geary-Khamis 1990 international prices $, millions),
(Geary 1958; Khamis 1972); H = human capital (expressed in
Geary-Khamis' 1990 international prices $, millions), (Geary 1958;
Khamis 1972); Labor = labor force; [[epsilon].sub.t] = stationary
disturbance term.
In his research, Temple (1998) uses a Cobb-Douglas production
function similar to the one used in Mankiw, Romer and Weil (1992) in the
form
[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (5)
with standard notation.
Using Cobb-Douglas labor augmenting form
Y = [E.sup.[alpha]] [S.sup.[gamma]] [H.sup.[beta]]
[(AL).sup.1-[alpha]-[gamma]-[beta]] (6) or in terms of labor
productivity
[y.sub.t] = [e.sup.[alpha].sub.t][s.sup.[gamma].sub.t][h.sup.[beta].sub.t][a.sup.1-[alpha]-[gamma]-[beta].sub.t] (7)
with: [e.sub.t] = E (stock of equipment)/L; [S.sub.t] = S (stock of
structures)/L; [h.sub.t] = H (human capital stock)/L and [y.sub.t] = Y/L
we derive the growth accounting equation (in terms of log differences)
and productivity growth expressed as
[g.sub.y] = [alpha][g.sub.e] + [gamma][g.sub.s] + [beta][g.sub.h] +
(1 - [alpha] - [gamma] - [beta])[g.sub.a]. (8)
Because of the expected heterogeneity in the data we use also time
series VAR and VECM approach to investigate the relationship between
equipment investments and growth. To investigate the relationship
between economic growth and physical capital at a disaggregated level
(equipment and structures), labor and human capital, we use the time
series approach by using the Vector autoregressive models (VARs). That
way we want to examine the nature of the statistical causality between
labor, human capital, equipment, structure investments and output with a
particular insight on the equipment investments and output causality.
[DELTA][y.sub.t] = [a.sub.0] + [GAMMA](L)[DELTA][y.sub.t-1] +
[PI][xy.sub.t-k] + [[mu].sub.t] (9)
with: [y.sub.t] a vector of K observable endogenous variables (GDP,
Equipment stock, Structure stock, Human capital stock, Labor force) and
matrix of coefficient [GAMMA]; lag operator (L); [PI] matrix of
cointegrating vector and [mu] white noise process.
We use the macroeconomic time series data that often involve the
unit root problem that could compromise the results of the Granger
causality test. Therefore, we test the data for the existence of
cointegration in order to see if the conventional granger form test has
to be reparametrized. First we test how investments in Croatia affect
output (do investments cause output, INV [right arrow] GDP) and if a
bilateral causality between investments and output exists, i.e. does
output cause investments GDP [right arrow] INV.
Before proceeding with the Granger causality test, we have to test
the series for the non-stationarity (existence of unit root) with a
standard stationarity test (results are reported in the appendix). To
test for the stationarity and the impact of equipment investments on
Croatian GDP we split the observation period in two: first one (1)
denoting the pre-transition time (former socialist republic of Croatia)
oriented toward a more planned economy, and second (2) the transition
period after 1990 with Croatia moving toward a free market economy. The
transition period was tested for the structural breaks in time series
because of the war (1991-1995) and the need to (or not) introduce a
dummy variable for the war. Tests performed on the data series suggest
that the level data are non-stationary time series. Unit root tests show
the existence of a unit root process proving non-stationarity between
time series used in the model (see appendix). Appropriate transformation
of the series before entering the OLS models was applied.
Data were obtained from the Statistical Yearbooks of Yugoslavia
(1960-1990) and the Statistical Yearbooks of Croatia (1991-2009), and we
calculated the series in 1985 dollars. For the methodology of human
capital measurement in Croatia see Skare (2001). Additional data was
obtained from Maddison (2001, 2003).
To uncover the truth we must go under the surface of the raw data
and search for the hidden signals in the data. We use spectral density
analysis to trace out possible quantitative relationships that may
occur. First, we filter the "raw" data to detrend and smooth
the original series by Fourier's transformation. Fourier's
transformation procedure can be used only on the stationary data. To
eliminate the possibility of the presence of non-periodic components in
the series, we transform each of the input series (tapering, mean
subtraction and detrending to get smoother spectrums and correct for the
leakage problem) so we are cleared to proceed with the Fourier
transformation.
U(k) = [1/N] [N-1.summation over (j=1)] u(j)[e.sup.-i2[pi]jk/N],
(10)
U(j) = [[N-1/2].summation over (k=-[N/2])] U(k)[e.sup.-i2[pi]jk/N].
(11)
Spectral density estimation aloud to "scan" the general
variance in the time series in terms of cycles that correspond to each
signal frequency to identify sine and cosine functions over different
frequencies that are more strongly correlated to the time series data.
Extension of the single spectrum analysis is the cross-spectrum analysis
for uncovering the correlation between two time series data at different
frequencies. We are looking for correlated cyclical behaviour for
investment/output. To decompose covariance of the time series data in
frequency components (correlation analysis) we use several standard
procedures (1).
[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (12)
[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (13)
[C.sub.K] = [P.summation over (j=-P)] [w.sub.j] [(RC).sub.K+j]
Cospectral density estimate (14)
[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (15)
[K.sub.K] = [A.sup.2]K/[S.sup.x.sub.K] [S.sup.y.sub.K] Squared
coherency spectrum ([R.sup.2]) (16)
[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (17)
with Tukey-Hamming window (5) of the form
[W.sub.k] = 0.54[(D).sub.p] (2[pi][f.sub.k]) + 0.23[D.sub.p]
(2[pi][f.sub.k] + [[pi]/p]) + 0.23[D.sub.p] (2[pi][f.sub.k] - [[pi]/p])
(18)
for k = 0,..., p.
Since we filter all the time series data we refer to the data used
in the spectral analysis as detrended time series.
5. Equipment investment growth nexus in socialist Croatia
(1960-1989)
Much theoretical and empirical research on the investment and
long-term economic growth nexus has emerged since 1950. The Croatian
experience could be valuable evidence for both theoretical and empirical
economic knowledge. Our results can be of practical importance for
countries similar to Croatia but also for all other economies. Our
attempt to reveal the role of gross equipment investments for long-term
economic growth in Croatia can provide useful knowledge on the
investment--growth debate. Here we explore the role of equipment
investment for future output growth in Croatia during the socialist and
transition eras. Results can bring us closer to the answer about the
causality between equipment investment and growth not just for Croatia.
Comparing the results from both periods, we can assess the link between
investments and growth for small, closed, socialist countries but also
for small, open, market economies. In addition, we can search for the
constraints that might arise in a state-planned economy and the
importance of economy structure on the causality we wish to explore.
This section introduces a new way of exploring the equipment
investments--economic growth nexus by means of spectral density
analysis. Causality between investment and growth is captured in the
coherence spectrum, with statistical significance of a potential
relationship offered through the square coherency spectrum.
Figure 2 displays a bivariate spectral analysis for equipment
investment and output. The cospectral density plot (top left panel) is
significant in the long run (after six years). Bilateral periodicity in
the short run for equipment investment and output is not present.
Movement between the two series in the long run is significant. In the
phase spectrum plot (bottom right panel) we notice a most significant
influence of fixed investment on output concentrated in the band (8, 30)
years), supporting investment as a means of promoting growth in the long
run. The chart points to a constant and significant long run
relationship, while in the short run a volatile relationship appears.
The coherence and squared coherence plot (top right panel) registers a
maximum frequency domain correlation at 30 years, pointing out where to
find a significant relation between investment and output (over the (8,
30) year's band). A strong correlation (ranging from 0.30-0.98)
with the [R.sup.2] equal to an average value of 0.64 to a maximum of
0.98 is found in the observed band but also out of the band, suggesting
that output can be expressed as a linear function of fixed investments.
[FIGURE 2 OMITTED]
Notice that phase differences and amplitude ratios stay constant
over a long frequency domain. A significant relationship between output
and investment is also validated in average correlation parameters of
Gain values (bottom left panel), supporting evidence of fixed investment
output with a maximum gain value around the 30 years over the (0, 30)
band period. The maximum gain value reaches 0.5 during the thirty-year
period, suggesting a stronger impact of investment on output in the long
run. The average gain value (regression coefficient) is 0.32 with the
expected return (0.32$) on fixed investments (1$) supporting the
importance of investment and capital accumulation in promoting economic
growth, particularly in the long run. The estimated share attributed to
equipment is much higher than expected by the traditional Solow model
(0.04), showing that investments in equipment were an important source
of growth in socialist Croatia. Results support the traditional view of
the Solow model and the role of investments in capital stock
accumulation. Investments in equipment seem to have particular
importance for economic growth in socialist economies with high social
returns to equipment investments (much more than predicted by the Solow
model). The estimated share attributable to equipment matches the one
identified by Temple and Voth (1996) for developing countries, which
Croatia, in fact, was during the period 1960-1989, supporting his
conclusion that investments in equipment are strongly correlated with
growth for developing economies. We conclude that positive and important
two-way causality runs between equipment investments and growth where a
1$ increase in output leads to a 2.10$ increase in capital stock. Using
standard econometric methods, we examine the nexus between investment
and growth once again and compare the results with the one obtained by
cross-spectral analysis.
Table A1 show the test statistics for the Augmented Dickey Fuller
and Phillip Perron unit root tests. The data series in level data thus
demonstrates non-stationarity with the presence of the unit root. Both
ADF and PP tests failed to reject the null hypothesis of
non-stationarity, except for capital in ADF without trend and intercept.
Both tests reveal that data series in the first difference are
stationary with the unit root hypothesis rejected for the data in a
first difference at the standard significance level. After ADF and PP
tests rejected the unit root hypothesis pointing to non-stationarity in
data, we tested the data for cointegration using the Johansen
cointegration test (maximum eigenvalue and trace test). The plotted data
of the series shows that all series are trend stochastic, so we use the
Johansen cointegration test on series under a linear trend assumption
and only intercept in the cointegration equations. Results of the
cointegration trace test are presented in Table A2. The null hypothesis
of no cointegration had to be rejected at the 5% confidence level tested
against the H1 hypothesis of one cointegrating vector (r = 1). The data
in the table show that trace test indicates one cointegrating vector
with rank test (max eigenvalues) showing zero cointegrating relations
(vectors). Following Engle and Granger (1987) after cointegration
between data of order has been identified, we construct a Granger
causality test for level VARs to test statistical causality between
output, investments (in equipment and structure), labor and human
capital. According to Engle and Granger, if two series are found to be
integrated of order (tested with ADF and PP test) and cointegrated (we
found one cointegrating vector), the standard Granger causality test
applies.
Before testing for bi-directional feedback (Granger causality test)
we first choose the optimal lag length by estimating VAR (2a-2e) model
including all variables in levels (non-differentiated data). To choose
the optimal lag length we use AIC and SBC and FPE criteria (see Table A3
in appendix). The optimal lag selection results differ for various lag
order tests. AIC, FPE and HQ test suggests four lag models while LR and
SC suggest one lag model. Using BIC instead AIC we choose one lag model
as optimal (according to SIC criteria in the appendix). For safety
reasons, we checked the residual graph and residual autocorrelation, and
both support the one lag model. To test for short run causality between
dependent and independent variables, we run the Granger causality test.
Results of the Granger causality test are presented in Table 4. We
explore the possible existence of a systematic relationship between past
values of equipment investment, structure investment, human capital,
labor, output and future values of output. The test results support
previous findings on the importance of equipment investments on economic
growth in Croatia. From Table 4 we can see that the null hypothesis of
no Granger causality between output and equipment investment is rejected
at the 5 percent significance level. Data suggest a uni-directional
relationship between output and equipment investments.
Causality test results show that investment-specific technological
change is important for production in Croatia. Purchasing more equipment
and machines is likely to boost economic growth. Causality analysis
shows the existence of investment-specific technological change but then
again, only for equipment (machines) and not structures (building). To
support the findings from the causality analysis we provide data on the
prices for machines and buildings relative to the price of consumption
non-durables in Figure 3.
We can see that investment-specific technological change was indeed
present in the production of machines during the observed period since
the relative price of machines in comparison to the price of consumer
nondurables significantly and persistently fell. This was not the case
for the construction industry, where the relative price of buildings to
consumer nondurables rose over the entire period. The observed relative
price of buildings follows the causality results (negative relationship
to output) that infer no investment-specific technological change for
structures as inputs into the production function. Another important
aspect should be mentioned here, price volatility or inflation. Figure 4
show that strong price volatility was present over the full period.
[FIGURE 3 OMITTED]
Strong price volatility visible from Figure 4 was negatively
correlated with investment in structures (-0.25) inferring that in a
small, closed and socialist economy such as Croatia, price volatility
crowded out possible investment-specific technological change for
structures (increase in the marginal product of investments in
structures). Similar correlation holds for the equipment-price relation
with much lower intensity (-0.08). Our conclusion is that
investment-specific technological change for investment in structures is
inversely related to strong price volatility, such as was present in
Croatia. In the following, we present the results of the estimated
models.
Our main findings from estimating the model (1) confirm the
importance of equipment investment for growth in Croatia. As visible in
Table 5, a percentage point increase in equipment investment is expected
to boost output by 1.476 percentage points per year. Notice that
investment in structures is also important, but the return on investment
in equipment is three times the return on investment in structures.
Other findings are similar to the one identified by De Long and Summers
with negative impact of labor force growth to output level (as in the
Solow model).
Equipment investments (gross fixed capital formation for equipment)
include all expenses for fitting, transportation, customs duties and
insurance. Structure investments (gross fixed capital formation for
construction works) include the value of equipment built into
constructions (lifts, central heating installations, etc.) and related
projects. Results from the model (2) support the hypothesis that
equipment investment is important for economic growth. The equipment
investment-growth link is once again proven but with a much lower return
on equipment investment than before. Findings from (2) show that a
percentage growth in equipment investment is expected to increase
production by 0.11 percentage points. The difference between shares of
equipment and structure investments in this model is negligible (and
much lower in comparison to the modified De Long and Summers
regression). Although the technical structure of gross investments is
not important model (1), equipment investment's significance for
growth is supported. Human capital and labor coefficients are not
statistically significant with an expected sign for labor force growth
in Solow tradition but unexpected for human capital pointing to the
possible simultaneity bias problem. Residuals and model diagnostics back
up the estimated model. Our results do not support the Solow and
augmented Solow models as in Mankiw et al. (1992) and Temple (1998) due
to low [R.sup.2]s and the unit root problem. Model (3) poorly fits the
data with [R.sup.2] = 0.09 and low statistical significance of
individual variables. The implied capital share in income ([alpha] =
0.06-0.09) is below values found by MRW. Structures' share (implied
([beta] = 0.03-0.10) is still smaller than the values identified by MRW,
as is human capital's share [delta] = 0.03.
[FIGURE 4 OMITTED]
In model (4) investment in equipment's effect on production is
estimated at 0.10 at the 10% significance level. Other variables in the
model are not statistically significant with low [R.sup.2] = 0.06. To
account for possible discrepancies from different OLS model
specification and techniques, we estimate a VECM model (5) with
Johansen's procedure. The estimated reintegrated vector shows that
variables of equipment, structure and human capital stock enter the
model significantly at the 5 and 10% levels. The estimated long-run
equipment coefficient is around the values identified by Temple (1998)
for developing and OECD countries. The estimated value is within the
window suggested by previous models (0.07-0.12). The human capital
coefficient is significant at the 10% level with a positive impact of
0.08 on the production level and within expected boundaries for Croatia.
The main findings from the VECM model are the importance of equipment
investment for growth and the negative impact of investment in
structures on output (as in Temple 1998 for OECD countries). Equipment
investment registers a stronger positive impact on growth in relation to
capital accumulation when used as a variable in the model. Labor's
share in output is lower than expected and not statistically
significant. The technical structure of investments in all models proved
to be an important determinant of growth.
6. Equipment investment growth nexus in transitional Croatia
(1990-2009)
We use the same set of equations and methods (except the VECM
model) to estimate the relationship between equipment investments and
output in Croatia during the transition period (1990-2009). It is
important to mention that Croatia is still a country in transition on
the path to the EU. Here we show just the results for the transition
period. Estimation results are presented in Table 6.
In the transition period, we see a change in the technical
structure of investments, with massive investments in structures such as
highways, bridges, schools, and universities, particularly after 2000.
As expected, the impact on growth from equipment investment remains
high, with a rising share of structure investments in national income.
Also, the impact of investment in structures on output growth is
positive (as in the previous period) but now stronger, inferring a
possible investment-specific technological change for structures. One
possible explanation could be a sharp increase in the demand for housing
and apartments, price stabilization and a strong rise of the
construction industry. Model (1) was extremely difficult to fit on
transition period data because of a strong serial correlation and
heteroscedasticy problem regarding equipment and investment share in
national income as well the impact of war on output. As a consequence,
low statistical significance for equipment and structures is present.
Model (2) fits the data very well. Equipment investment once again
is proving important for output growth even in the transition period,
with the share of equipment investment in output similar to values we
find for the pre-transition period. Accumulation of human capital was
also an important source of growth during transition, particularly
because of Bologna reforms in higher education. Model (3) shows
different results, to some extent, with a statistically significant
influence of structure stock on output, with a higher share in contrast
to equipment stock's share in output during the transition period
and specifically the one we find in the pre-transition period. Equipment
stock's influence on output remains positive and significant but
now lower relative to structure's share in output. The last model
(4) gives us a more balanced picture of equipment and structure
stock's shares in output. Both equipment and structure stocks were
important for output growth in the transition period accompanied by
human capital with the highest impact on output. This result confirms
the importance of equipment investment for growth in Croatia during the
transition. Moreover, it shows the increasing importance of structure
investments and human capital accumulation after 1990.
7. Conclusions
In this paper, we suggest that the augmented Solow model with human
capital, equipment and structure stocks can explain output growth in
Croatia. Results of the study prove the importance of equipment
investment for economic growth in Croatia both in pre-transition and
transition period. Our major finding is that technical structure of
investments is important in explaining growth and should be accounted
for in growth models. The same applies to human capital, which should be
measured differently from standard procedures (years of schooling,
enrolment rates and ect.) as suggested by Skare (2001). We believe that
growth models could do a better job in international comparisons if
investment in technical structure and estimated human capital stock are
included because they fit the data more accurately. As for Croatia, we
find evidence of an extensive growth pattern as in other former
socialist countries. Growth was generated mainly through input
accumulation and marginally through TFP growth. This changed, to some
extent, after 1990, still the diminishing returns to scale remain
persistent. Besides the war damages, this is the explanation for slow
Croatia's convergence to EU growth. Another important aspect of the
study is that the importance of capital for growth changes significantly
if investment's share of GDP is used as a proxy for capital. We
believe a better fit of data relating to capital's share in income
assessment can be obtained by using estimated capital stock or real data
on capital stock. Diminishing returns to capital and human capital back
up our hypothesis that the structure of investment is more important
than how much money is invested. Croatia's case shows that poor
technical structure of investments (large investments in structure) and
in university school campuses will result in diminishing return to scale
unless accompanied by more investments in equipment and knowledge
(libraries, computer labs, distance learning and other forms of learning
equipment).
The empirical results of the relationships between disaggregated
components of investment and economic growth carried out by De Long and
Summers (1991, 1992, 1993, 1994) and Temple (1998) find a strong basis
in the evidence of Croatia. This paper's results suggest that
equipment investment has a special role in boosting GDP growth, since
the equipment investments-growth link appears to be stronger than the
structure investment-growth link for both pre-transition and transition
periods. We also found a strong positive relationship between human
capital and GDP growth for the transition period that is consistent with
previous work of Skare (2001). Various tests confirm the consistency and
robustness of the regression results. Bearing in mind that Croatia is a
country in transition whose main investment focus for many years was on
structures (highways, tourism sector infrastructure, residential
buildings and so on), alongside insufficient human capital investment,
it is no wonder that the economic future in Croatia looks problematic.
Furthermore, since 2000 Croatia went through a huge lending boom in
which Croatian private commercial banks mostly promoted household
consumption spending instead of investment spending. Rising from very
low levels in 1999, household loans grew almost six times by 2008
reaching 35 percent of the GDP--the highest level in all the
transitional countries. Poor allocation of the local savings did not
ensure channelling adequate level of the financial resources to the SMEs
and industrial sectors, the two main engines of innovation, technology
transfer and economic growth. The current composition of investments
certainly does stimulate GDP levels, but only in the short term with no
meaningful long-term economic impact. The fact that most of those
investments, especially the government investments, are being financed
by external borrowings could actually swamp most foreseeable benefits
arising for the Croatian economy. This suggests that additional research
on this topic is urgently required. This study is our humble
contribution to the international studies on growth differences and
nature of growth in transitional countries.
APPENDIX
Table A1. Augmented Dickey-Fuller and Phillips-Perron Unit Root Test
Level First
differences
Variables Intercept Intercept None Intercept
and trend
GDP -1.214 -0.258 3.968 -4.666 ***
Capital 2.7344 -2.914 10.947 -4.356 ***
Human capital -1.953 -2.393 0.327 -5.704 ***
Labor -0.145 -1.129 5.965 -3.903 ***
Equipment -1.457 -0.563 -0.200 -5.696 ***
investments
Structures -1.8594 -1.234 -0.555 -3.431 ***
Phillips-Perron Level First differences
Variables Intercept Intercept Intercept Intercept
and trend and trend
GDP -1.170 -0.511 -4.775 *** -6.116 ***
Capital 2.089 -2.821 -4.537 *** -5.381 ***
Human capital -1.935 -2.308 -5.852 *** -6.731 ***
Labor -0.267 -1.548 -3.934 *** -3.817 **
Equipment -1.536 -0.644 -5.694 *** -6.301 ***
investments
Structures -1.664 -0.608 -3.446 *** -3.705***
First differences
Variables Intercept and None
trend
GDP -5.998 *** -2.905 ***
Capital -5.375 *** -1.437
Human capital -6.807 *** -4.936 ***
Labor -3.777 *** -3.073 ***
Equipment -6.270 *** -5.736 ***
investments
Structures -3.739 *** -3.513 ***
Phillips-Perron Lag selection
Variables
GDP 1
Capital 1
Human capital 1
Labor 1
Equipment 1
investments
Structures 1
Note: *, **, *** denote statistical significance at the 10%, 5%
and 1% levels
Source: Author's calculation
Table A2. Johansen test statistics for cointegration between
log (Y), log (E), log (S), log (Labor), log (H)
Selected (0.05 level *)
Number of Cointegrating
Relations by Model
Data Trend: None None Linear
Test Type No Intercept Intercept Intercept
No Trend No Trend No Trend
Trace 3 3 1
Max-Eig 2 0 0
Data Trend: Linear Quadratic
Test Type Intercept Intercept
Trend Trend
Trace 1 1
Max-Eig 0 0
Source: Author's calculation
Table A3. VAR Optimal Lag Selection
VAR Lag Order Selection Criteria
Endogenous variables: GDP EQUIPMENT STRUCTURES HUMAN_CAPITAL LABOR
Exogenous variables: C
Date: 04/01/10 Time: 23:25
Sample: 1960 1989
Included observations: 26
Lag LogL LR FPE
0 -1161.376 NA 6.36e+32
1 -1022.616 213.4759 * 1.05e+29
2 -996.2516 30.42093 1.20e+29
3 -968.2978 21.50292 2.00e+29
4 -902.7942 25.19370 7.30e+28 *
Lag AIC SC HQ
0 89.72121 89.96315 89.79088
1 80.97049 82.42214 * 81.38851
2 80.86551 83.52686 81.63188
3 80.63829 84.50936 81.75302
4 77.52263 * 82.60340 78.98571 *
* indicates lag order selected by the criterion
LR: sequential modified LR test statistic (each test at 5% level)
FPE: Final prediction error
AIC: Akaike information criterion
SC: Schwarz information criterion
HQ: Hannan-Quinn information criterion
Source: Author's calculation
DOI: 10.3846/20294913.2012.705253
Acknowledgment
We are grateful two anonoymous referees for their helpful comments
and suggestions in efforts to improve the article.
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(1) For technical detail and references see Bloomfield, P. 1976.
Fourier analysis of time series. New York: John Wiley and Sons and
Iacobucci, A., Spectral Analysis for Economic Time Series, OFCE Working
Paper, N. 2003-07.
Marinko Skare (1), Dean Sinkovic (2)
Department for Economics and Tourism "Dr. Mijo Mirkovic",
Juraj Dobrila University of Pula, Preradoviceva 1/1, 52100 Pula, Croatia
E-mails: (1)
[email protected] (corresponding author); (2)
[email protected]
Received 27 October 2011; accepted 12 May 2012
Marinko SKARE. Professor of Economics, Economic Research Journal
Editor in Chief, Member of Editorial Board of several international
journals, Department Economics and Tourism "Dr Mijo Mirkovic"
in Pula, Juraj Dobrila University of Pula. He served as Assistant Dean
for Education, Faculty of Economics & Tourism, Pula, Assistant Dean
for International Cooperation, Faculty of Economics & Tourism, Pula,
Main and Team Researcher on several scientific projects, Former Dean of
the Faculty of Economics & Tourism, Pula and Former Vice President
for International Cooperation, Juraj Dobrila University of Pula. He has
published several books and a large number of scientific papers on the
subject of economic growth, welfare economics and poverty, human
capital, economics in transition, economic philosophy and monetary
economics. He is member of the American Economic Association, Royal
Economic Society, Economic History Association, Economic History
Society, and Association for Comparative Economic Studies.
Dean SINKOVIC is Senior Assistant at Juraj Dobrila University of
Pula (Department of Economics and Tourism) and Visiting Professor of
Marketing Management at Albstadt-Singmaringern Hochshule (Germany). He
holds an MBA degree in marketing and finance from University of Illinois
at Chicago, Master of Science and PhD in Economics from University of
Pula. His academic background is in teaching and research on economic
growth, investments as well as financial and policy development. He is
an author and co-author of several research papers and books and has
been actively involved in many local development projects in Istria
County. He was an A + student as an undergraduate student. In 2003 he
was awarded a membership to honour society to collegiate schools of
business Beta Gamma Sigma in recognition for high scholastic
achievements (University of Illinois). He is also a member of Who's
Who Historical Society of Professional Management since 2003 (University
of Illinois).
Table 1. Sources of growth in Republic of Croatia (1960-2009)
Percentage Average
distribution annual
with capital growth
stock rate
Real output growth 100.0 2.67
Factors contributions:
Capital (stock 21.7 5.27
measured)
([alpha] = 0.11)
Labor ([delta] = 0.40) 23.5 1.57
Human capital 15.7 4.65
([beta] = 0.09)
Technological progress 39.1 -
Percentage
distribution
with investment
(I/GDP)
Real output growth 100.0 Real output growth
Factors contributions: Factor contributions:
Capital (stock 49.3 ([alpha] = 0.25) Capital
measured) (I/GDP as proxy)
([alpha] = 0.11)
Labor ([delta] = 0.40) 21.2 ([delta] = 0.36) Labor
Human capital 13.9 ([beta] = 0.08) Human
([beta] = 0.09) capital
Technological progress 14.2 Technological progress
Source: Author's calculation
Table 2. Growth characteristics for Croatia, 1960-1989
Characteristics Overall Correlation
average with GDP
growth rate
Real per capita GDP growth 1960-1989 4.2 100,0
Investment share of GDP 18.3 0.45
Government consumption share of GDP 14 0.15
Inflation rate 43.6 -0.20
Exports as a share of GDP 19.3 0.12
Imports as a share of GDP 25.9 0.27
Secondary school enrolment rates 1960 34 0.83
Primary school enrolment rates 1960 78 0.50
Population growth 0.4 0.24
Real per Capita GDP in 1960 2.324$ 0.20
Source: Author's calculation
Table 3. Loan Portfolio of the commercial banking sector in Croatia
(1999-2008), in millions of KN
1999 2000 2001 2002 2003
Government 2,990 4,102 4,100 6,700 8,547
Financial 1,286 1,116 1,190 2,156 3,057
Institutions
Enterprises 24,889 24,986 32,393 41,695 45,269
Households 17,925 21,570 28,464 41,111 52,587
Other 0,700 0,582 0,514 0,788 0,734
TOTAL 47,790 52,356 66,661 92,450 110,194
2004 2005 2006 2007 2008
Government 9,031 12,758 14,517 14,316 21,495
Financial 3,289 3,867 4,035 6,950 5,796
Institutions
Enterprises 49,590 58,670 75,070 83,324 94,114
Households 62,652 75,713 92,682 109,545 122,742
Other 0,703 0.948 1,470 2,134 2,510
TOTAL 125,265 151,010 187,774 216,269 246,657
TOTAL
Government 98,557
Financial 32,742
Institutions
Enterprises 530,001
Households 624,991
Other 10,135
TOTAL 1296,43
Source: Croatian National Bank (2009)
Table 4. Level VAR Granger-causality test
Equation [X.sub.1] [X.sub.2] [X.sub.3]
GDP Equipment Structures
stock stock
([X.sub.1]) 0.911325 0.973709 -0.284528
[0.0000] *** [0.0044] ** [0.4857]
([X.sub.2]) 0.083291 0.888803 -0.144126
[0.1871] [0.0002] *** [0.3165]
([X.sub.3]) -0.041607 0.836397 0.542956
[0.3832] [0.0000] *** [0.0000] ***
([X.sub.4]) 0.086491 0.101602 0.006314
[0.1558] [0.6017] [0.0289] **
([X.sub.5]) 4.529422 37.02875 -10.82519
[0.015] ** [0.0023] *** [0.1685]
Equation [X.sub.4] [X.sub.5] [R.sup.2]
Human capital Labor force
stock
([X.sub.1]) 0.092280 0.000304 0.9928
[0.0358] ** [0.0763] *
([X.sub.2]) -0.014284 -0.002549 0.8605
[0.9481]] [0.1132]
([X.sub.3]) -0.306724 0.000651 0.9168
[0.0775] [0.5874]
([X.sub.4]) 0.359804 -0.001666 0.8448
[0.0986] * [0.2746]
([X.sub.5]) -28.71189 0.872207 0.9968
[0.0226] *** [0.0000] ***
Source: Author's calculation
Table 5. Regression results 1960-1989 (all models)
Dependent variable Model (1) Model (2) Model (3)
Real GDP Log Log
growth difference difference
GDP GDP/L
constant -13.10235 *** 0.033624 *** 0.015273
(3.652937) (0.008952) (0.0362334)
[i.sub.E] 1.4760087 ***
(0.381355)
[i.sub.S] 0.470544 ***
(0.261833)
GAP 0.384638
(0.172593)
L -0.367155
(0.254436)
[DELTA] log E 0.116051 ***
(0.033512)
[DELTA] log S 0.089611 *
(0.046630)
[DELTA] log H -0.039974
(0.047864)
[DELTA] log L 0.148453
(0.260015)
Equipment 0.104807 *
(0.0533127)
Structures 0.0381736
(0.0398570)
Human capital -0.000188
(0.0455601)
(n + g + [delta]) 0.00048
(0.0138264)
[DELTA] log E/L
[DELTA] log S/L
[DELTA] log H/L
[DELTA] log GDP(-1)
[DELTA] log E(-1)
[DELTA] log S(-1)
[DELTA] log H(-1)
[DELTA] log L(-1)
Trend
[R.sup.2] 0.425639 0.404708 0.223978
Normality 0.455997 0.973759 0.88452
RESET 0.2449 0.3757 0.1834
Restriction 0.001438 0.002142 0.036741
Implied [alpha] 0.09
Implied [beta] 0.03
Implied [delta] 0.00
Dependent variable Model (4) Model (5)
Log Log
difference difference
GDP/L GDP
constant 0.012362 * -0.082282
(0.0620458)
[i.sub.E]
[i.sub.S]
GAP
L
[DELTA] log E
[DELTA] log S
[DELTA] log H
[DELTA] log L
Equipment
Structures
Human capital
(n + g + [delta])
[DELTA] log E/L 0.100710 *
(0.0493213)
[DELTA] log S/L 0.0261644
(0.048975)
[DELTA] log H/L 0.0162612
(0.0372036)
[DELTA] log GDP(-1)
[DELTA] log E(-1) 0.095685 **
(0.04273)
[DELTA] log S(-1) -0.217014 **
(0.05707)
[DELTA] log H(-1) 0.086821 *
(0.07431)
[DELTA] log L(-1) 0.346976
(0.23703)
Trend 0.001870 ***
(0.00055)
[R.sup.2] 0.206847 0.380609
Normality 0.89507
RESET 0.2023
Restriction 0.032196
Implied [alpha]
Implied [beta]
Implied [delta]
Notes: Figures in parenthesis are standard errors. 'Restriction'
is the p-value for the F-test restriction (slope coefficients
equal zero). 'Normality' is the p-value for the residual
normality test. 'RESET' is the p-value for the Ramsey RESET test.
*** Statistically significant a 1% level, ** at 5%, * at 10% level
Table 6. Regression results 1990-2009 (all models)
Dependent Model (1) Model (2) Model (3) Model (4)
variable
Real GDP Log Log Log
growth Difference difference difference
GDP GDP/L GDP/L
constant 3.419554 *** 0.018924 * 0.120271 0.018240
(0.382337) (0.010206) (0.0660924) (0.010232)
[i.sub.E] -0.336493
(0.429490)
[i.sub.S] 0.134813
(0.236803)
GAP 1.058956 ***
(0.088521)
L -0.077899
(0.121731)
[DELTA] 0.144762 **
log E (0.053352)
[DELTA] 0.079931
log S (0.055100)
[DELTA] 0.146765 ***
log H (0.045356)
[DELTA] 0.210345
log L (0.405237)
Equipment 0.0702635
***
(0.0207935)
Structures 0.132713
***
(0.0324306)
Human 0.0386942
capital (0.0514983)
(n + g + 0.0341171
[delta]) (0.0233145)
[DELTA] 0.124763
log E/L **
(0.050126)
[DELTA] 0.109262
log S/L **
(0.047847)
[DELTA] 0.131345
log H/L ***
(0.043152)
War (dummy) -5.06478 *** -0.032664 -0.0102026 -0.015064
(0.808503) (0.025633) (0.0229206) (0.019601)
[R.sup.2] 0.9352770 0.820785 0.905555 0.750758
Normality 0.831143 0.804101 0.531993 0.259686
RESET 0.0876 0.2165 0.8760 0.9166
Restriction 0.00000 0.000051 0.000035 0.000131
Implied 0.06
[alpha]
Implied 0.10
[beta]
Implied 0.03
[delta]
Notes: Figures in parenthesis are standard errors. 'Restriction' is
the p-value for the F-test restriction (slope coefficients equal
zero). 'Normality' is the p-value for the residual normality test.
'RESET' is the p-value for the Ramsey RESET test.
*** Statistically significant a 1% level, ** at 5%, * at 10% level
