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The level and growth effects in empirical growth models for the Nordic countries: A knowledge economy approach

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We estimate the steady state growth rate for the Nordic countries using a “knowledge economy” approach. An endogenous growth framework is employed, in which total factor productivity is a function of human capital (measured by average years of education), trade openness, research and development, and investment ratio. We attempt to identify the variables which have significant level and growth effects within this framework. We find that education plays an important role in determining the long-run growth rates of Sweden, Norway, and Denmark; and trade openness has growth effects in Sweden, Finland, and Iceland. The investment ratio plays an important role for growth in Finland. In addition to growth effects, education also has level effects in Sweden, Finland, and Iceland. Research and development, has no level or growth effects in any of the Nordic countries. This may be attributable to the fact that research and development are driven by openness and education. Policy measures are identified to improve the long-run growth rates for these countries.
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CENTRE FOR APPLIED MACROECONOMIC ANALYSIS
The Australian National University
_________________________________________________________
CAMA Working Paper Series August, 2012
__________________________________________________________
THE LEVEL AND GROWTH EFFECTS IN EMPIRICAL GROWTH MODELS FOR THE
NORDIC COUNTRIES: A KNOWLEDGE ECONOMY APPROACH
Arusha Cooray
School of Economics, University of Wollongong
Centre for Applied Macroeconomic Analysis, ANU
Antonio Paradiso
Italian National Institute for Statistics (ISTAT), Italy
________________________________________________________
CAMA Working Paper 36/2012
http://cama.anu.edu.au
1
The level and growth effects in empirical growth models for the Nordic
countries: A knowledge economy approach
Antonio Paradiso*
anto_paradiso@hotmail.com
Italian National Institute for Statistics (ISTAT), Rome (Italy)
Arusha Cooray
arusha.@uow.edu.au
School of Economics University of Wollongong, Wollongong (Australia)
Centre for Applied Macroeconomic Analysis, Australian National University (Australia)
Abstract
We estimate the steady state growth rate for the Nordic countries using a “knowledge economy”
approach. An endogenous growth framework is employed, in which total factor productivity is a
function of human capital (measured by average years of education), trade openness, research and
development, and investment ratio. We attempt to identify the variables which have significant
level and growth effects within this framework. We find that education plays an important role in
determining the long-run growth rates of Sweden, Norway, and Denmark; and trade openness has
growth effects in Sweden, Finland, and Iceland. The investment ratio plays an important role for
growth in Finland. In addition to growth effects, education also has level effects in Sweden,
Finland, and Iceland. Research and development, has no level or growth effects in any of the Nordic
countries. This may be attributable to the fact that research and development are driven by openness
and education. Policy measures are identified to improve the long-run growth rates for these
countries.
Keywords: Endogenous growth models, Trade openness, human capital, investment ratio, Steady
state growth rate, Nordic countries
JEL Classification: C22, O52, O40
*The views expressed in the article are those of the author and do not involve the responsibility of the Institute.
Acknowledgements: We wish to thank Jakob Madsen for supplying the R&D data, and Mauro Costantini , Steinar
Holden, Jakob Madsen, Amnon Levy and Peter Sorensen for valuable comments. We thank Bill B. Rao for his
invaluable contribution in a preliminary stage of this paper.
2
1. Introduction
During the second half of the 1990s the Nordic countries (Sweden, Finland, Norway, Denmark, and
Iceland) were among the most successful economies in the OECD. These countries, with the
exclusion of Denmark, exhibited above average GDP growth rates from 1995 to 2010 (Norway
2.5%, Sweden 2.6%, Finland 2.9%, Denmark 1.5%, and Iceland 2.9%), in comparison to an average
growth rate of 1.8% for the 15 European Union countries. The Nordic countries additionally, are
among the top performers in the Knowledge Economic Index (KEI) constructed by the World Bank.
The KEI is based on an average of four sub-indexes the four pillars of the knowldege economy: (1)
economic incentive and institutional regime, (2) innovation and technological adoption, (3)
education and training, and (4) information and communication technologies (ICTs). The Nordic
countries are exemplified by their strong performance in these four pillars. Denmark, Sweden,
Finland and Norway rank within the top 5 in the KEI (see Table 1). Although Iceland comes lower
down the KEI, it has seen the fastest improvement in rankings among the top 20 countries rising 8
spots to 13th place in 2009 from 1995 (World Bank 2012).
Compared to other regions, the Nordic countries are relatively homogenous with respect to human
resources. This is due to the emphasis placed on free public education by the Nordic welfare state.
Education is a vital component of a knowledge based economy as it influences both the demand for,
and supply of innovation. A well educated labour force is a pre-requisite for the adoption of
innovation and investment in Research and Development (R&D). Investment in R&D and
diversification through trade have been equally important for restructuring the Nordic economies
towards knowledge based economies. The performance of these economies in terms of the KEI
suggests that education, investment and trade have played an important role in the emergence of
new knowledge based industries and knowledge spillovers promoting long-term growth in the
Nordic countries.
Many studies have shown evidence of R&D knowledge spillovers through trade as a channel of
total factor productivity (TFP) growth, for example, Coe and Helpman (1995), Engelbrecht (1997),
Lumenga-Neso et al. (2001), Madsen (2007b), Lichtenberg et al. (1998). Increased openness, raises
the intensity of competition through the transfer of technology embodied in traded products, lowers
barriers to trade, reduces the monopoly power of domestic firms, and could facilitate R&D, through
the dynamic competition of firms in a Schumpeterian sense. Studies by Coe and Helpman (1995)
and Nadiri and Kim (1996) have highlighted the role of technological spillovers through trade
3
liberalization, for improving the efficiency of the domestic R&D sector. Similarly, Griffith et al.
(2003) show that R&D promotes innovation, the transfer of technology and R&D supported
absorptive capacity. However, it is also possible that there is an interaction of an economy’s R&D
activity with its stock of human capital, due to the fact that the major input into the R&D process is
highly skilled labour. This is evidenced by the studies of Blackburn et al. (2000) and Bravo-Ortega
and Lederman (2010) who show that economic growth is independent of research activity which is
driven by human capital accumulation1. Similarly, Bils and Klenow (2000) argue that human capital
could accelerate the adoption of technology and is necessary for technology use. The studies of
Welch (1970), Bartel and Lichtenberg (1987) and Foster and Rosenzweig (1996) support the
argument that human capital is important for the adoption of technology while the studies of Doms
et al. (1997), Autor et al. (1998), Berman et al. (1998) support the argument that human capital is
important for technology use.
Hence, given the importance of knowledge spillovers as a channel of Total Factor Productivity
(TFP) growth, we use an endogenous growth framework, in which total factor productivity is
assumed to be a function of human capital (measured by average years of education), trade
openness, investment ratio, and R&D. Within this framework we try to distinguish between
variables which have significant level effects and growth effects in the Nordic countries over the
1960 to 2010 period. This is the first study to our knowledge, which examines level and growth
effects from a knowledge economy perspective for the Nordic countries. Country-specific time
series data technique is used to conduct this study2. Our approach broadly follows the specification
and methodology in Rao (2010), Balassone et al. (2011), Paradiso and Rao (2011), and Casadio et
al. (2012).

1 Reis and Sequeira (2007) examine the interaction between the technological change and human capital accumulation
and its implications for investment in R&D from a theoretical perspective.
2Country-specific time series studies are important because it is hard to justify the basic assumptions of cross-section
and panel data studies that the forces of economic growth and underlying structural parameters are the same for all
countries and at all times, even if the countries belong to the same region or area. Furthermore, while cross-section and
panel data studies may give some insights into growth enhancing policies, they are not useful to estimate country-
specific steady state growth rates (SSGRs) and identify the effects of policies to improve SSGRs. See Greiner, et al.
(2005) on this point.
4
Our empirical results are consistent with the views of Blackburn et al. (2000) and Bravo-Ortega and
Lederman (2010) in that R&D is not statistically significant as a shift variable (both level and
growth effects) for any of the Nordic countries. This is probably because openness and HKI interact
with R&D, as mentioned above, so that R&D does not provide any additional information already
embodied in trade openness and human capital.
The paper is organized as follows. In Section 2 we illustrate the characteristics of the Scandinavian
model in the light of the knowledge economy framework. Section 3 presents the specification of the
model and implications for the estimates of the long run growth rate, which is the same as the
steady state growth rate (SSGR) in the Solow growth model. Section 4 presents our empirical
results and Section 5 concludes.
2. Scandinavian Countries as Knowledge Economies
In the past few decades where countries have experienced the effects of globalization and technical
innovation, knowledge has become the key driver of competitiveness and economic growth.
Dahlman and Anderson (2000) define a knowledge economy as “one that encourages its
organization and people to acquire, create, disseminate and use (codified and tacit) knowledge more
effectively for greater economic and social development”. Derek et al. (2004) postulated that the
knowledge economy is based on four pillars: (1) educated and skilled workers; (2) effective
innovation system of firms, research centers, universities, and other organizations; (3) modern and
adequate information of infrastructure to facilitate information dissemination; (4) economic and
institutional regimes to provide incentives for the efficient use of knowledge. In essence, these
authors postulate that the amount of knowledge is used as a key determinant of total factor
productivity (TFP). Strengthening the above four pillars will lead to an increase in the pool of
knowledge available for economic production.
The five Nordic countries can be defined as knowledge economies according to these
characteristics. Based on the work of Derek et al. (2004), the World Bank has developed an index
called the Knowledge Economy Index (KEI). The KEI is an economic indicator that measures a
country’s ability to generate, adopt and diffuse knowledge. The KEI summarizes each country’s
performance on 12 variables corresponding to the four knowledge economy pillars introduced
above. Variables are normalized on a scale of 0 (worst) to 10 (best) and the KEI is constructed as
5
the simple average of the normalized values of these indicators. For an overview of the
methodology and the construction of the index see World Bank (2008). In Figure 1, we make an
over-time comparison of the KEI of some countries in terms of their relative performance for two
points in time viz., 1995 and 2009. Countries above the diagonal line have made an improvement
in the KEI in 2009 compared to 1995, whereas countries below the line experienced a decline. As
we can see, Denmark, Finland, Sweden, and Norway rank very high in terms of the KEI, although
Denmark and Finland’s KEI in 2009 is a slightly smaller compared to 1995. Iceland has a KEI
index in line with other Western European countries but higher than some technological countries
such as Japan. Table 1 presents the KEI and its four components for 2009 for the best 5 countries
and Iceland, out of a total of 146 countries. Denmark ranks highest, followed by Finland, and
Sweden; Norway is in fifth position, whereas Iceland is placed 13th. It is interesting to note that
Iceland is penalized for not having a very high innovation system, whereas it is in line with the top
countries for education and economic incentive regimes.
The indicators used in the empirical analysis for estimation of the four components are the
following - Economic and institutional regime: To proxy for the innovation system, we use trade
openness as an indicator of the level of economic and institutional regime operating in the country3.
An open country is a country with (a) low tariff and non-tariff barriers on trade, (b) low barriers to
technology transfers and (c) low power of national monopolies in areas such as
telecommunications, air transport, finance and insurance industries (Houghton and Sheehan 2000)).
Innovation system: We use trade openness and R&D as proxies for innovation in a country. Trade
openness is perceived by many authors to have a positive impact on efficiency and innovation in the
economy. The idea is that international trade leads to faster diffusion of technology, and hence
higher productivity growth. In addition, there are also spillover effects due to “learning by doing”
gains and better management practices triggered by new technology leading firms to the best
practice technology (Krugman 1987)4. R&D is associated with the development of new ideas, new
products, product improvements and new technologies leading to innovation in a system. This is
supported by Griffith et al. (2003) who show that R&D promotes innovation, the transfer of
technology and R&D supported absorptive capacity. Human capital and education: One commonly

3 See for example Jenkins (1995), Baldwin and Gu (2004), Greenway and Kneller (2004), Coe and Helpman (1995),
Engelbrecht (1997), Madsen (2007b), Lumenga-Neso et al. (2001) and Lichtenberg et al. (1998).
4The studies of Jenkins (1995), Baldwin and Gu (2004), Madsen (2007b), Greenway and Kneller (2004), Coe and
Helpman (1995), Engelbrecht (1997), Lumenga-Neso et al. (2001) and Lichtenberg et al. (1998) support the argument
of R&D spillovers through trade as a channel of TFP.
6
used measure of human capital is the average years of schooling of the adult population5. Average
years of schooling is clearly a stock measure and reflects the accumulated educational investment
embodied in the current labour force6. Information infrastructure: Empirical assessments of the
effects of ICTs on aggregate output and economic growth typically entail the use of ICT
investment. However, due to the non availability of this series for a long time span and the
importance of non-ICT investments as well in economic growth, we use the aggregate series of
investment (as a ratio of GDP) in our estimations7.
Figure 1
Knowledge Economic Index by Countries:
1995 versus 2009
SE
NO FI
G7
US
AU
JP
DE
WE
SG
DN
CA
UKNL
ITA
ES
IS
7.5
8
8.5
9
9.5
10
7.588.599.510
2
0
0
9
1995
Source: World Bank-Knowledge Assessment Methodology (KAM), www.worldbank.org/kam. Notes: Countries above
the diagonal line have made an improvement in the KEI compared to 1995, whereas countries below the line
experienced a regression. Legend: DN = Denmark; SE = Sweden; FI = Finland; NL = Netherland; US = U.S.A.; NO =
Norway; IS = Iceland; UK = United Kingdom; CA = Canada; AU = Australia; DE = Germany; G7 = Group of seven
viz., France, Germany, Italy, Japan, United Kingdom, U.S.A., Canada; WE = Western Europe; JP = Japan; SG =
Singapore.

5 The average years of schooling are used by Hanushek and Woessmann (2008) and Krueger and Lindhal (2001) for
example. We use the data constructed by Barro and Lee (2010). This data are available only at five years intervals since
1950. We linearly interpolate the data between the five years. Another frequently used measure in empirical research is
enrollment rates. According to Bergheim (2008) the enrollment rate is not a useful measure of human capital because it
does not include information on years of education. Other measures available are cognitive skills indicators (IQ test and
standardized tests on reading, science, and mathematics) but these measures are not available over a long time span; for
example the OCED Program for International Student Assessment (PISA) has data starting only from 2000.
6Engelbrecht (1997) acknowledges the role of human capital in domestic innovation and knowledge spillovers.
7De Long and Summers (1991) for example, show that equipment investment has a significant effect on economic
growth. Further, Levine and Renelt (1992) and Sala-i-Martin (1997) have shown that the investment share is a robust
variable in explaining economic growth.
7
[Table 1, about here]
3. Specification of the Model
The steady state solution for the level of output in the Solow (1956) growth model is:
1
*
s
y
A
gn




(1)
where *(/)yYL is the steady state level of income per worker, s = the ratio of investment to
income,
= depreciation rate of capital, g = the rate of technical progress, n = the rate of growth of
labour,
A
the stock of knowledge and
the exponent of capital in the Cobb-Douglas production
function with constant returns (see below). This implies that the steady state rate of growth of per
worker output (SSGR), assuming that all other ratios and parameters are constant, is simply TFP
because:
*
ln lnySSGR ATFP  (2)
However, the determinants of TFP are not known and are exogenous in the Solow (1956) growth
model. The new growth theories based on endogenous growth models (ENGM) use an optimization
framework and suggest several potential determinants of TFP. However, to the best of our
knowledge there is no ENGM which rationalizes that TFP depends on more than one or two
selected variables. We make TFP a function of a few of the determinants identified by the ENGMs.
For example, if the findings of Levine and Renelt (1992) are valid, then TFP depends only on the
investment ratio in spite of the findings by Durlauf et al. (2005) and Jones (1995).
Note that the SSGR can be estimated by estimating the production function. The production
function can also be extended by assuming that the stock of knowledge (
A
) depends on some
important variables identified by the ENGMs8. We start with the well-known Cobb-Douglas
production function with constant returns:
1,0 1
tttt
YAKL

(3)

8See Rao (2010), Paradiso and Rao (2011), Casadio et al. (2012).
8
where t
Y is aggregate output, t
A
the stock of knowledge , Kt the stock of physical capital, and Lt
the labour force in period t.
We assume the following general evolution for the stock of knowledge A, where 0
Ais the initial
stock of knowledge,
Z
is a vector which may consist of more than one variable9, whereas Sand
Ware assumed to consist of one variable each and T is time.

2
12 1
0tttt
Z
TS S W
t
AAe

 
(4)
Substituting (4) into (3) gives:

2
12 1 1
0tttt
ZT S S W
ttt
YAe KL

 
(5)
Dividing both sides of equation (5) by L yields:

2
12 1
0tttt
ZT S S W
tt
yAe k

 
(6)
where (/)yYLand (/)kKL.
Taking the natural logarithmic transformation of (6) gives,
2
0121
ln ln ln
tttttt
yAZTSSW k

 (7)
Equation (7) captures the actual level of per capita output due to two types of variables viz., factor
accumulation and variables due to factors other than factor accumulation such as , and .
Z
SW
Specification of these other variables that may affect output is an empirical issue. Their effects may
be trended (
Z
), nonlinear (S) or simply linear (W). The variables that should be included in the
vector Z, and in S and W is also an empirical matter. We have experimented with various
alternatives but to conserve space report only the best and plausible results.
Taking first differences of (7) gives:
2
11 2 1
ln ln
tt t t t t t
y
ZT Z S S W k

 
(8)

9 For simplicity we ignore the i subscript.
9
Only trended variables (i.e.,
Z
variables entering the vector multiplied by trend) have a permanent
growth effect. For this reason, the variables in the Z vector are the sole determinants of the long-run
steady state growth rate. The other two variables S and W have only a level effect on output (i.e.,
they can raise the economy’s income level permanently but they have only transitory growth
effects), but with an important difference. S influences the level of output in a non-linear manner,
whereas W affects output in a linear manner.
For equation (8) to make sense 10
and 20
, so that the S variable has its maximum effect
when
12
0.5 /S
 . This variable, prior to reaching its maximum effect, increases at a
decreasing rate. Each additional unit of S contributes less and less to the level of output. Examples
in the empirical growth literature of variables that may influence the output this way are trade
openness and education. Dollar and Kraay (2004) suggest that countries that had greater increases
in trade volumes saw greater increases in growth, but that countries with greater levels of trade
volumes saw lower levels of growth. This would seem to suggest that the effect of trade openness
on growth is such that it takes an inverted U-shaped pattern. In this case there might be an ‘optimal’
level of openness. A country possessing a trade regime more closed than its optimal level would
increase growth by liberalizing; a country possessing a more open trade regime than its optimal
level it would see lower levels of growth (Nye et al., 2002).
Concerning the education variable, several analyses show that the production of human capital
exhibits increasing returns to scale for low levels of education and decreasing returns to scale for
high levels of education. Krueger and Lindahl (2001), Paradiso et al. (2011), Casadio et al. (2012)
find that the best fit of the data is provided by a regression model that considers a quadratic form of
education. In particular, Krueger and Lindahl (2001) find that on average 7.5 average years of
schooling is the maximum level of the inverted U-shaped relation between schooling and output.
Above this level, marginal education has a negative effect, so incremental education is expected to
depress the growth rate. Several empirical studies have found a negative impact of schooling on
economic growth - see Pritchett (2001), Benhabib and Spiegel (1994), Spiegel (1994), Lau et al.
(1991), Jovanovic et al. (1992), Bils and Klenow (2000). Pritchett (2001) advanced three possible
reasons for this: 1) The institutional/governance environment could have been sufficiently perverse
so that the accumulation of educational capital lowered economic growth; 2) The marginal returns
to education could have fallen rapidly as the supply of educated labor expanded while demand
remained stagnant; 3) Educational quality could have been so low that years of schooling created no
10
human capital. The author sustains that the extent and mix of these three phenomena explains the
negative impact of education on growth. It is unlikely that these factors would cause schooling to
have a negative effect in the Nordic countries. In the case of the Nordic countries, the negative
effect of education above a certain level might be better explained by wage compression
(Fredriksson and Topel 2010), high tax rates (Fredriksson and Topel 2010), labour market
segregation (Nordic Co-operation on Gender Equality 2010). Wage compression occurs when wage
structures are not in proportion to professional maturity. This phenomenon has been historically
very high in the Nordic countries. There could be distortionary effects of higher education levels
associated with wage compression when schooling is over a certain level, for example, high skilled
workers have high expectations in terms of wages, and wage compression may discourage the
moral and the effort of high skilled workers pushing down productivity and therefore output.
Furthermore, Bils and Klenow (2000) show that countries with higher enrolment rates do not exhibit faster
human capital growth. This is because countries with high levels of human capital are maintaining these high
levels. Bils and Klenow find that as the years of enrolment increase, the returns to schooling falls.
In steady state, when ln 0k
and all differences go to zero, the Steady State Growth Rate
(SSGR) is equal to the growth rate of the stock of knowledge ( ln
A
)10 :
1t
SSGR Z
(9)
In what follows we try to understand the potential factors influencing the level effects and the
SSGR (i.e., the variables entering in the Z vector) and policy that can improve it.
4. Empirical Estimates
Data from 1960 to 2010 (with the exception of Iceland for which the data sample is from 1970-
2010) are used to estimate the SSGR, which is the long run growth rate. The long run relationship,
equation (2), is estimated using standard time series methods of cointegration. Our selected growth-
enhancing variables are: the ratio of trade openness (TRADE) to GDP, ratio of investment to GDP
(IRAT), ratio of R&D expenditure to GDP (R&D), and human capital (HKI) measured by years of
schooling. Definitions of variables and sources of data are provided in the Appendix. All variables

10The steady state is defined as a situation where all variables grow at a constant, possibly zero, rate (Sala-i-Martin,
1994).
11
are included in the estimation. Some of these variables may not be statistically significant due to
multicollinearity. In particular, we find no role for R&D as a shift variable (either as a level or
growth effect) for all Nordic countries11. This is probably because there is an interaction between
TRADE and HKI and R&D, as explained in Section 1. In the paper, we report only the estimations
showing economically and statistically plausible results.
Three estimations techniques are implemented viz., Fully Modified OLS (FMOLS), Canonical
Cointegrating Regression (CCR) and Dynamic OLS (DOLS). These estimators deal with the
problem of second-order asymptotic bias arising from serial correlation and endogeneity, and they
are asymptotically equivalent and efficient (see Saikkonen (1991) on this last point). The standard
least squares dummy variable estimator is consistent, but suffers from second-order asymptotic bias
that causes test statistics – such as the t-ratio – to diverge asymptotically (Phillips and Hansen,
1990). Therefore, in order to draw inferences, we use FMOLS, CCR, and DOLS estimation
techniques whose t-ratios are asymptotically standard normal12.
Our estimation strategy is as follows. We estimate the long-run relationship with the three methods
stated above (FMOLS, CCR, DOLS) and if all the results are similar and plausible, we verify the
existence of a cointegrating relationship under the Engle-Granger (EG) residual test. If the test
confirms the existence of a long-run relationship, we construct an Error Correction Model (ECM).
Then we study the factor loading and tests for correct specifications i.e., we test for normality,
absence of autocorrelation, and no heteroskedasticity in the residuals.
Dummy variables are added in the long-run estimations and are discussed in the Appendix. For
Finland and Denmark we consider two dummies for the 1960s taking into account important
changes in these two economies (see Appendix for explanation of these events), whereas we include
a dummy variable for the financial crisis for all countries. Two issues have to be discussed
regarding the use of these dummies. There is a debate in the literature on what critical values should
be used to judge the significance of the residual-based ADF test when dummy variables are
included in the cointegrating equations. Ireland and Wren-Lewis (1992) argue that since the dummy
variable is not stochastic, it could be interpreted simply as a modification to the intercept term. This
allows researchers not to regard the dummy variable as an extra variable and use the same critical

11 R&D is not statistically significant for Sweden, Norway, and Iceland. For Denmark and Finland R&D is statistically
significant but the residual EG test does not reject the null hypothesis of no cointegration.
12 Montalvo (1995) shows that the DOLS estimator has a smaller bias compared to the CCR and FMOLS.
12
values. This approach is followed for example by Bahmani-Oskooee (1995), and more recently in
the long-run growth literature by Rao (2010), Paradiso and Rao (2011), Casadio et al. (2012). The
second issue concerns the nature of the financial crisis dummy inserted in the long-run relation
estimated. This dummy covers the last three observations for Sweden, Finland, and Denmark (2008-
2010), the last four observations for Norway (2007-2010)13, and the last two for Iceland (2009-
2010). For this reason, the dummy could be interpreted such as a structural break occurring at the
end of the sample period. We do not have enough instruments (from an empirical and econometric
point of view) to detect the exact nature of this break, because the dataset stops in 2010 when the
crisis is still in action14. The econometric techniques available, Geregory and Hansen (1996)
cointegration test for example, are not able to detect the break occurring very close to the end of the
sample period. For this reason, we consider this dummy as a temporary and not a shift dummy.
Since the period under investigation is very long (over 40 years) and comprises important economic
changes, we investigate the stability of our estimated ECMs. In doing so, we subject the error
correction equation to the Quandt (1960) and Andrews (1993) structural breakpoint tests. Using
insights from Quandt (1960), Andrews (1993) modified the Chow test to allow for endogenous
breakpoints in the sample for an estimated model. This test is performed at every observation over
the interval [,(1 )]TT
and computes the supremum (Max) of the k
F statistics
([,(1)]
sup supkT Tk
FF


) where
is a trimming parameter. Andrews and Ploberger (1994)
developed two additional test statistics i.e. the average (ave F) and the exponential (exp F). The null
hypothesis of no break is rejected if these test statistics are large. Hansen (1997) derives an
algorithm to compute approximate asymptotic p-values of these tests.
4.1 Sweden
In the model for Sweden, trade openness and average years of schooling enter as long-run growth
determining variables. This is reasonable because Sweden ranks very high in terms of education
according to The Global Competitiveness Report (2011-2012) of the World Economic Forum and
the Barro and Lee (2010) education dataset. Sweden has also, historically supported trade

13 In Norway the financial crisis began in 2007, before the other Nordic countries, as reported by Grytten and Hunnes
(2010) .
14 Bagnai (2006) suggests the same reason for explaining that different studies have found a structural break in the US
twin deficit relation in the 1990s only because they do not have a large data sample, whereas ex-post this was only a
transitory phenomenon.
13
liberalization in the interest of its industrial firms (the access to foreign markets is required for
growth). According to equation (4) we have 12
,,
Z
HKI Z TRADE S HKI
and the equation
that is estimated is:
2
12 1 2
ln . ln
tttttt
y Interc k HKI HKI HKI T TRADE T
 
   
(10)
It is interesting to note that HKI enters as a variable having both a level (in a non-linear way) and
growth effect; openness enters as a shift variable having a growth effect. The results for equation
(10) are reported in Table 2. The estimates for equation (10) are satisfactory in that all of the
coefficients are correctly signed and statistically significant. The EG residual test shows that a
cointegration relationship exists at the 5% level of statistical significance. The ECM shows a
statistically significant factor loading (
) and has the expected negative sign. The diagnostic tests
show that the model is correctly specified. Table 3 (Quandt-Andrews test) shows that the ECM is
stable over the sample period under investigation.
[Tables 2-3, about here]
According to the results in Table 2, HKI as a level shift variable
(2
12 1t
HKI HKI HKI T
 
) has its maximum level effect when it equals a value of 7.9
(average years schooling)15. This implies that further increase in education will have negative
effects on growth. This is illustrated in Figure 2.

15 The HKI pattern for HKI Twas simulated assuming that an added one year of education is obtained after 10 years.
This assumption is in line with data on schooling for Sweden for the period 1960-2010.
14
Figure 2: Level effect of HKI in Sweden
0.4
0.6
0.8
1
1.2
1.4
1.6
1.8
1234567891011
HKI
By the end of the sample period in 2010, HKI reaches a value of 11.57 well above the optimal
value of 7.9. This effect is in line with the results of Krueger and Lindahl (2001). In long-run
steady-state, this level effect is intended to be superseded by a trended component of HKI (and the
other growth enhancing variables such as trade openness). But it is clear that there is a trade-off
between the short-run and long-run effect of HKI on output. A possible reason could be that high
wage compression and taxes in Sweden compared to international standards, may discourage the
productivity of skilled workers in the short-run, while in the long-run, these detrimental effects are
offset by positive effects of higher education linked to the introduction of new ideas and
technological improvements.
The SSGR ( 112 1tt
HKI TRADE

 ) for Sweden is illustrated in Figure 3. Trade openness and
HKI play an important and positive role in TFP growth. HKI contributes to 1.7% of income per
capita growth in the last 10 years, whereas TRADE yields a contribution of 1.3%. Finally, we plot
the per worker GDP growth (DLYL) against SSGR. The SSGR shows a smooth pattern with a
slight upward trend towards 3.3%.
15
Figure 3: SSGR for Sweden
0
0.005
0.01
0.015
0.02
0.025
0.03
0.035
1961
1963
1965
1967
1969
1971
1973
1975
1977
1979
1981
1983
1985
1987
1989
1991
1993
1995
1997
1999
2001
2003
2005
2007
2009
SSGR_HKI SSGR_TRADE SSGR
0.04
0.03
0.02
0.01
0
0.01
0.02
0.03
0.04
0.05
0.06
1961
1963
1965
1967
1969
1971
1973
1975
1977
1979
1981
1983
1985
1987
1989
1991
1993
1995
1997
1999
2001
2003
2005
2007
2009
DLYL SSGR
4.2 Finland
The model for Finland considers trade openness and investment ratio as long-run growth
determining variables. HKI enters only with a non-linear level effect. Investment and trade
openness enter multiplied by trend. That is, according to equation (4) we have
12
,,
Z
TRADE Z IRAT S HKI so that:
2
12 1 2
ln . ln
tttttt
y Interc k HKI HKI TRADE T IRAT T
 
  (11)
The results for equation (11) are reported in Table 4. All the coefficients are statistically significant
and have the expected signs. The EG residual cointegration test confirms the existence of a long-run
relationship. The ECM shows a highly statistically significant factor loading and has the expected
negative sign. The residual diagnostic tests show that the model is correctly specified. Table 5
shows the Quandt-Andrews structural break tests for the ECM. The results are satisfactory because
the ECM does not show a break and it is stable over the period investigated.
[Tables 4-5, about here]
16
Figure 4: Non-linear level effect of HKI in Finland
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
1.1
1.2
1234567891011
HKI
Figure 4 shows the non-linear level effect of average years of schooling. The maximum level effect
is when average years of schooling is equal to 8.3 years. Thereafter the effect is negative. At the end
of the sample period (2010), schooling is 9.97, and additional investment in education may be
detrimental for income. This could also be due to the wage compression structure as in Sweden.
The SSGR ( 1121tt
TRADE IRAT

) is presented in Figure 5. TRADE and IRAT play a positive
and significant role in determining the SSGR. The average contributions of TRADE and IRAT to
SSGR are very similar: 0.5% and 0.6%, respectively.
Figure 5: SSGR for Finland
0
0.002
0.004
0.006
0.008
0.01
0.012
0.014
0.016
0.018
1961
1963
1965
1967
1969
1971
1973
1975
1977
1979
1981
1983
1985
1987
1989
1991
1993
1995
1997
1999
2001
2003
2005
2007
2009
SSGR_IRAT SSGR_TRADE SSGR
0.06
0.04
0.02
0
0.02
0.04
0.06
0.08
0.1
1961
1963
1965
1967
1969
1971
1973
1975
1977
1979
1981
1983
1985
1987
1989
1991
1993
1995
1997
1999
2001
2003
2005
2007
2009
DLYL SSGR
4.3 Norway
Norway has a historically higher number of years of education according to the Barro-Lee (2010)
dataset. According to the Global competitiveness report (2011-2012) Norway has evolved into a
very open economy, measured by the share of GDP and gross trade flows (exports and imports of
17
goods and services are higher than in most other countries). Norway’s long-run growth is
determined only by the average years of schooling. Trade openness enters as a variable having a
linear level effect only. Accordingly, we assume that, and ,
Z
HKI W TRADE
so that:
1
ln . ln
tttt
yInterc k HKIT TRADE

  (12)
Estimates of this equation are reported in Table 6. All results appear satisfactory in terms of the
statistical significance of coefficients, the EG residual test, ECM, and residual diagnostic tests. The
Quandt Andrews test conducted in Table 7 shows that the estimated ECM is stable.
[Tables 6-7, about here]
In the case of Norway, 11t
SSGR HKI
and the contribution to SSGR is trivial (it only
determined by HKI). Figure 6 shows the pattern of SSGR together with the per capita output
growth dynamic (DLYL). SSGR shows a slight upward pattern toward 1% at the end of the sample.
Figure 6: SSGR and DLYL for Norway
0.03
0.02
0.01
0
0.01
0.02
0.03
0.04
0.05
0.06
1961
1963
1965
1967
1969
1971
1973
1975
1977
1979
1981
1983
1985
1987
1989
1991
1993
1995
1997
1999
2001
2003
2005
2007
2009
DLYL SSGR
4.4 Denmark
In the Denmark model, the average years of schooling is the sole variable explaining long-run
growth. This result is not unexpected. According to the education index16, published by the United

16 The education index is one of three indices - the other two are the income index and life expectancy index on which
the human development index is built. It is based on the adult literacy rate and the combined gross enrollment ratio for
primary, secondary and tertiary education.
18
Nations’ Human Development Index17 in 2009, based on data up to 2007, Denmark has an index of
0.993, amongst the highest in the world, in line with Australia, Finland and Belgium. Literacy in
Denmark is approximately 99% for both men and women. Accordingly, we assume that
Z
HKI,
so that:
1
ln . ln
ttt
yInterc k HKIT
 
(13)
The results appear satisfactory with regard to coefficient signs, the EG residual test, ECM, and
diagnostic tests on the ECM. These results are reported in Table 8 below. The stability test
conducted using the Quandt Andrews test (Table 9) shows that the ECM is stable over the period
1960-2010.
[Tables 8-9, about here]
The SSGR is small because the average years of schooling is the only variable entering long-run
growth. In this case 11t
SSGR HKI
is plotted in Figure 7 together with output growth
(DLYL). The SSGR shows a similar pattern to Norway’s SSGR, a slight upward trend but slightly
higher (1.2% at the end of the sample).
Figure 7: SSGR and DLYL for Denmark
0.08
0.06
0.04
0.02
0
0.02
0.04
0.06
0.08
0.1
1961
1963
1965
1967
1969
1971
1973
1975
1977
1979
1981
1983
1985
1987
1989
1991
1993
1995
1997
1999
2001
2003
2005
2007
2009
DLYL SSGR

17The Human Development Index (HDI) is a comparative measure of life expectancy, literacy, education, and standards
of living for countries worldwide published by United Nations. It is a standard means of measuring well-being. It is
used to distinguish whether the country is a developed, a developing or an under-developed country.
19
4.5 Iceland
For Iceland, the long-run growth model is determined by trade openness. Average years of
schooling enters as a level effect variable. The importance of openness for growth is not surprising
since the benefit of the trade openness, as maintained by Alesina et al. (2005), is larger for small
countries. In this case, we have ,
Z
TRADE S HKIand accordingly equation (7) becomes:
2
12 1
ln . ln
ttttt
y Interc k HKI HKI TRADE T
 
  (14)
The results of the cointegrating estimations are reported in Table 10. The results appear satisfactory
in terms of coefficients signs, the residual cointegration test (EG test), ECM, and diagnostic tests on
ECM residuals. Table 11 reports the Quandt-Andrews test for stability of the ECM. The result show
that the ECM is stable over the period 1970-2010.
[Tables 10-11, about here]
In Figure 8 we report the nonlinear level effect of HKI. The maximum level effect is reached at 8.45
years of education.
Figure 8: Non-linear level effect of HKI in Iceland
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
1.8
2
2.2
1234567891011
HKI
In the case of Iceland the SSGR is trivial ( 11t
TRADE
). Figure 9 illustrates the SSGR against
per capita output growth (DLYL). The SSGR reaches a value of 2% toward the end of 2000.
20
Figure 9: SSGR and DLYL for Iceland
0.06
0.04
0.02
0
0.02
0.04
0.06
0.08
0.1
1970
1972
1974
1976
1978
1980
1982
1984
1986
1988
1990
1992
1994
1996
1998
2000
2002
2004
2006
2008
2010
DLYL SSGR
5. Conclusions
We use a knowledge economy approach to identify the variables which have level and growth
effects in the Nordic countries, where TFP is assumed to be a function of human capital, trade,
investment and R&D. Trade openness, human capital (proxied by years of education) and the ratio
of investment to GDP play key roles in determining their productivity and the long run growth rate
(SSGR). We show that education plays an important role in determining the long-run growth rates
of Sweden, Norway, and Denmark. Trade openness has growth effects in Sweden, Finland, and
Iceland. The investment ratio plays an important role in influencing the growth rate in Finland. In
addition to growth effects, education also has level effects in Sweden, Finland, and Iceland. Our
results show no role for R&D however, either as a level or growth enhancing variable. This result is
in line with studies maintaining that openness and education may influence R&D patterns (Coe and
Helpman (1995), Nadiri and Kim (1996), Blackburn et al. (2000), and Bravo-Ortega and Lederman
(2010)), so that incorporating R&D does not provide any additional information. Another argument
put forward by Moen (2001), are the high implementation costs of new innovations which he
attributes to the finding of a negative relationship between R&D expenditure and economic growth
for the Nordic countries.
A noteworthy feature of our estimates is the non-linear level effects of years of education (HKI) in
Sweden, Finland and Iceland. Evidence shows that wage compression and taxes have affected
decisions to work and invest in human capital in Sweden (Fredriksson and Topel (2010)). For
example, Fredriksson and Topel (2010) state that the combined effect of income, payroll and value
added taxes led to a fall in the take home wage to 21% of pre-tax wages in Sweden which adversely
21
affected capital formation and economic growth. Similarly, wage flexibility has been low in Finland
also due to centralized wage bargaining systems (OECD 2010).Therefore the same could be said to
apply to Finland which has similar labour market conditions to Sweden. Iceland however, has
relatively flexible labour market conditions compared to Sweden and Finland. Therefore, the non-
linear level effects of education here might be explained by labour market segregation (Barro 1998,
Kalaitzidakis et al. 2001). Evidence shows that higher educational levels have not been translated
into higher wage levels for females compared to males in Iceland (Nordic Co-operation on Gender
Equality 2010). This is partially due to preference of females for certain occupations leading to a
gender segregated labour market. In Denmark and Norway on the contrary, the results of the present
study show that human capital has linear level effects and is thus not constrained from contributing
to growth by assisting in the absorption of new technologies.
The challenge for Sweden and Finland are the strain of highly taxed labour in an environment of
global mobility in factors of production. Therefore the policy implications stemming from this
study are the need for greater labour market flexibility in the case of Sweden and Finland, and
greater labour market integration in the case of Iceland to further maximize the effects of human
capital on the absorption of new technologies to promote growth.
22
Appendix
Data Appendix
Y = Real GDP; L = Employment (Total economy); CAP = Real Capital Stock; HKI = Human
Capital measured as average years of education; IRAT = Ratio of investment to GDP; TRADE =
Ratio of imports plus exports to GDP; R&D = ratio of total research and development expenditure
to GDP. All data, excluding HKI, are taken and constructed from the AMECO-EUROSTAT
database with the exception of data for Iceland for which Y and IRAT are taken from the World
Bank, L from the OECD Statistics Portal, and TRADE from the Penn World Tables (PWT) 7.0
(Heston et al., 2011). HKI is taken from the Barro-Lee (2010) database for all countries. R&D are
from Madsen (2007a) who uses R&D data from the OECD, Main Science and Technology
Indicators; OECD, Paris, OECD Archive (OECD-DSTI/EAS); National Science Foundation,
Statistics Netherlands, and UN Statistical Yearbook.
The real capital stock for Iceland is constructed through the perpetual inventory method (PIM)
using the gross fixed capital formation available from World Bank database. The PIM formula is:
11
tt t
KK I

Where
= depreciation rate and I = is real investment. The PIM requires data on I, a value of
,
and a value of the initial capital stock 0
K.
The initial capital stock is chosen so the capital-output ratio in the initial period equals the average
capital-output ratio over the period 1960-1970:
1970
1960
1960
1960
1
11t
t
t
KK
YY
The depreciation rate is chosen such that the average ratio of depreciation to GDP using the
constructed capital stock series matches the average ratio of depreciation to GDP in the data over
the calibration period. The World Bank database reports depreciation as “consumption of fixed
capital”.
The choice of depreciation rate
matches the average ratio of depreciation to GDP in the data over
the calibration period 1970-2010:
23
2010
1970
10.13
36t
t
t
K
Y
The above three equations (PIM, capital-output ratio, and the depreciation-GDP ratio) form a
system used to solve for the initial capital stock 0
K, the depreciation rate
, and the capital stock
series t
K.
Dummy variables in the long-run relation
The dummy variables are inserted after an inspection conducted on the residuals of the
cointegrating regression. If we detect large departures in the mean-reverting behavior of the
cointegrating residuals in some periods, we insert dummy variables in the long-run relationship.
The departures correspond to important social and economic events described below for each
country.
Sweden. One dummy is added for the 2008-2010 financial crisis.
Finland. A first dummy for years 1966-1968 is added in the estimation. This period was
characterized by some important policy changes: income policies limiting wage increases to growth
in productivity, abolition of all index clauses, a market devaluation by 24% in 1967 (Kouri 1975). A
second dummy is inserted taking into account the 2008-2010 financial crisis.
Norway. Two dummy variables are added in the estimation. One dummy for the period 1989-1991.
(Nordic crises; see Honkapohja (2009)), and the other for the 2007-2010 financial crisis (see
Grytten and Hunnes (2010) for a chronology of financial crises in Norway).
Denmark. Two dummies are added in the estimated equations. One dummy for the years 1961-
1963 (evolution in the Danish industrial structure, see Marcussen (1997)), and the other for the
2007-2010 financial crisis.
Iceland. A dummy is added for the financial crisis 2009-2010.
24
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30
Table 1: KEI and its Four Component Values for the Best Countries (2009)
Rank Country KEI Economic Incentive
Regime
Innovation Education ICT
1 Denmark 9.52 9.61 9.49 9.78 9.21
2 Sweden 9.51 9.33 9.76 9.29 9.66
3 Finland 9.37 9.31 9.67 9.77 8.73
4 Netherlands 9.35 9.22 9.45 9.21 9.52
5 Norway 9.31 9.47 9.06 9.6 9.10
.
.
.
.
.
.
.
.
.
.
.
.
.
.
13 Iceland 8.95 9.54 8.07 9.41 8.80
Source: World Bank-Knowledge Assessment Methodology (KAM), www.worldbank.org/kam.
31
Table 2: Results for Sweden: 1960-2010
12 1 2
2
ln . ln
ttt ttt
y Interc k HKI HKI T TRADE THKI
 
   
FMOLS DOLS CCR
I
ntercept -2.862
(0.435)
[6.582]***
-3.283
(0.333)
[9.869]***
-2.868
(0.442)
[6.487]***
lnk 0.682
(0.085)
[8.027]***
0.532
(0.091)
[5.860]***
0.696
(0.082)
[8.472]***
TRADE T 0.015
(0.002)
[8.192]***
0.015
(0.002)
[7.827]***
0.015
(0.002)
[7.699]***
HKI T 0.001
(0.000)
[3.052]***
0.001
(0.001)
[1.350]
0.002
(0.000)
[3.237]***
HKI 0.543
(0.103)
[5.284]***
0.521
(0.188)
[2.771]***
0.553
(0.102)
[5.434]***
2
HKI -0.043
(0.007)
[6.031]***
-0.046
(0.015)
[3.081]***
-0.044
(0.007)
[6.392]***
-0.314
(0.098)
[3.222]***
EG residual test -5.221**
LM(1) test (p-value) 0.321
LM(2) test (p-value) 0.434
LM(4) test (p-value) 0.504
JB test (p-value) 0.484
BPG test (p-value) 0.776
Notes: Standard errors are reported in ( ) brackets, whereas t-statistics in [ ] brackets. *, **, *** denotes significance at
10%, 5%, and 1%, respectively. FMOLS = Fully Modified Ordinary Least Squares; DOLS = Dynamic Ordinary Least
Squares; CCR = Canonical Cointegrating Relationship. EG = Engle-Granger t-test for cointegration.
= factor loading
in the ECM; BPG = Breusch-Pagan-Godfrey heteroskedasticiy test; JB = Jarque-Bera normality test; LM = Bresuch-
Godfrey serial correlation LM test. FMOLS and CCR use Newey-West automatic bandwidth selection in computing the
long-run variance matrix. In the DOLS leads and lags are selected according to SIC criteria. The standard errors for the
DOLS estimation are calculated using the Newey-West correction. A dummy for 2008-2010 (financial crisis) and for
2004 (peak in the GDP growth (+4.2%)) are added in the ECM formulation.
32
Table 3: Quandt-Andrews structural break tests for Sweden ECM, 1960-2010
Statistics Value Break Probability
Max LR F-stat 2.245 1996 1.000
Max Wald F-stat 13.228 1996 0.373
Exp LR F-stat 0.720 - 1.000
Exp Wald F-stat 4.988 - 0.211
Ave LR F-stat 1.388 - 1.000
Ave Wald F-stat 8.326 - 0.145
Note: Probabilities calculated using Hansen's (1997) method.
33
Table 4: Results for Finland: 1960-2010
2
12 1 2
ln . ln
tttttt
y Interc k HKI HKI TRADE T IRAT T
 
 
FMOLS DOLS CCR
I
ntercept -3.382
(0.148)
[22.843]***
-3.032
(0.202)
[15.041]***
-3.372
(0.149)
[22.692]***
lnk 0.574
(0.015)
[38.879]***
0.606
(0.015)
[39.729]***
0.576
(0.016)
[36.614]***
IRAT T 0.028
(0.002)
[13.220]***
0.030
(0.004)
[7.559]***
0.028
(0.002)
[11.040]***
TRADE T 0.010
(0.000)
[25.367]***
0.009
(0.000)
[15.246]***
0.010
(0.000)
[20.440]***
HKI 0.260
(0.030)
[8.573]***
0.196
(0.040)
[4.934]***
0.259
(0.031)
[8.436]***
2
H
KI -0.016
(0.002)
[8.477]***
-0.012
(0.002)
[5.252]***
-0.016
(0.002)
[8.282]***
-0.529
(0.157)
[3.374]***
EG residual test -6.069***
LM(1) test (p-value) 0.669
LM(2) test (p-value) 0.908
LM(4) test (p-value) 0.989
JB test (p-value) 0.789
BPG test (p-value) 0.573
Notes: Standard errors are reported in ( ) brackets, whereas t-statistics in [ ] brackets. *, **, *** denotes significance at
10%, 5%, and 1%, respectively. FMOLS = Fully Modified Ordinary Least Squares; DOLS = Dynamic Ordinary Least
Squares; CCR = Canonical Cointegrating Relationship. EG = Engle-Granger t-test for cointegration.
= factor loading in
the ECM; BPG = Breusch-Pagan-Godfrey heteroskedasticiy test; JB = Jarque-Bera normality test; LM = Bresuch-Godfrey
serial correlation LM test. FMOLS and CCR use Newey-West automatic bandwidth selection in computing the long-run
variance matrix. In the DOLS leads and lags are selected according to SIC criteria. The standard errors for the DOLS
estimation are calculated using the Newey-West correction. A dummy for 2008-2009 financial crisis is added in the ECM
formulation.
34
Table 5: Quandt-Andrews structural break tests for Finland ECM (Model 1), 1960-2010
Statistics Value Break Probability
Max LR F-stat 2.915 1976 0.987
Max Wald F-stat 8.744 1976 0.319
Exp LR F-stat 0.596 - 0.974
Exp Wald F-stat 2.196 - 0.327
Ave LR F-stat 1.088 - 0.970
Ave Wald F-stat 3.264 - 0.343
Note: Probabilities calculated using Hansen's (1997) method.
35
Table 6: Results for Norway: 1960-2010
1
ln . ln
tttt
yInterc k HKIT TRADE

 
FMOLS DOLS CCR
I
ntercept -1.561
(0.031)
[49.806]***
-1.617
(0.045)
[2.623]***
-1.557
(0.031)
[49.402]***
lnk 0.586
(0.019)
[31.062]***
0.559
(0.015)
[36.806]***
0.591
(0.018)
[32.737]***
TRADE 0.644
(0.060)
[10.744]***
0.758
(0.075)
[10.149]***
0.639
(0.060)
[10.586]***
HKI T 0.001
(0.000)
[15.689]***
0.001
(0.000)
[7.456] ***
0.001
(0.000)
[16.032]***
-0.47
(0.156)
[2.236]**
EG residual test -6.337***
LM(1) test (p-value) 0.437
LM(2) test (p-value) 0.259
LM(4) test (p-value) 0.447
JB test (p-value) 0.856
BPG test (p-value) 0.220
Notes: Standard errors are reported in ( ) brackets, whereas t-statistics in [ ] brackets. *, **, *** denotes significance at
10%, 5%, and 1%, respectively. FMOLS = Fully Modified Ordinary Least Squares; DOLS = Dynamic Ordinary Least
Squares; CCR = Canonical Cointegrating Relationship. EG = Engle-Granger t-test for cointegration.
= factor loading
in the ECM; BPG = Breusch-Pagan-Godfrey heteroskedasticiy test; JB = Jarque-Bera normality test; LM = Bresuch-
Godfrey serial correlation LM test. FMOLS and CCR uses Newey-West automatic bandwidth selection in computing
the long-run variance matrix. In the DOLS leads and lags are selected according to SIC criteria. The standard errors for
the DOLS estimation are calculated using the Newey-West correction.
Table 7: Quandt-Andrews structural break tests for Norway ECM, 1960-2010
Statistics Value Break Probabilit
y
Max LR F-stat 2.616 2002 1.000
Max Wald F-stat 13.078 2002 0.252
Exp LR F-stat 0.873 - 1.000
Exp Wald F-stat 4.948 - 0.123
Ave LR F-stat 1.687 - 0.997
Ave Wald F-stat 8.433 - 0.068
Note: Probabilities calculated using Hansen's (1997) method.
36
Table 8: Results for Denmark: 1960-2010
1
ln . ln
ttt
yInterc k HKIT
 
FMOLS DOLS CCR
I
ntercept -0.301
(0.267)
[1.129]
-0.332
(0.337)
[0.987]
-0.356
(0.274)
[1.313]
lnk 0.449
(0.111)
[4.045]***
0.428
(0.142)
[3.022]**
0.424
(0.114)
[3.733]***
HKI T 0.001
(0.000)
[7.255]***
0.001
(0.000)
[6.376] ***
0.001
(0.000)
[7.209]***
-0.196
(0.088)
[2.216] **
EG residual test -6.172***
LM(1) test (p-value) 0.470
LM(2) test (p-value) 0.673
LM(4) test (p-value) 0.938
JB test (p-value) 0.748
BPG test (p-value) 0.720
Notes: Standard errors are reported in ( ) brackets, whereas t-statistics in [ ] brackets. *, **, *** denotes significance at
10%, 5%, and 1%, respectively. FMOLS = Fully Modified Ordinary Least Squares; DOLS = Dynamic Ordinary Least
Squares; CCR = Canonical Cointegrating Relationship. EG = Engle-Granger t-test for cointegration.
= factor loading
in the ECM; BPG = Breusch-Pagan-Godfrey heteroskedasticiy test; JB = Jarque-Bera normality test; LM = Bresuch-
Godfrey serial correlation LM test. FMOLS use Newey-West automatic bandwidth selection in computing the long-run
variance matrix. In the DOLS leads and lags are selected according to SIC criteria. The standard errors for the DOLS
estimation are calculated using the Newey-West correction. A spike dummy for 1964 (innovation in Danish pension
system with the introduction of earning-related pension supplement scheme) and one for the financial crisis (2008-
2010) are added in the ECM formulation.
Table 9: Quandt-Andrews structural break tests for Denmark ECM, 1960-2010
Statistics Value Break Probability
Max LR F-stat 1.749 2001 1.000
Max Wald F-stat 8.745 2001 0.688
Exp LR F-stat 0.544 - 1.000
Exp Wald F-stat 2.979 - 0.490
Ave LR F-sta
t
1.066 - 1.000
Ave Wald F-stat 5.332 - 0.367
Note: Probabilities calculated using Hansen's (1997) method.
37
Table 10: Results for Iceland: 1970-2010 2
12 1
ln . ln
ttttt
y Interc k HKI HKI TRADE T
 
 
FMOLS DOLS CCR
I
ntercept 9.523
(1.280)
[7.439]***
9.066
(1.903)
[4.763]***
9.351
(1.218)
[7.679]***
lnk 0.339
(0.067)
[5.049]***
0.343
(0.086)
[3.984]***
0.347
(0.069)
[5.049]***
HKI 0.449
(0.101)
[4.435]***
0.462
(0.162)
[2.844] ***
0.457
(0.111)
[4.112]***
2
HKI -0.030
(0.007)
[3.979]***
-0.027
(0.013)
[2.047]**
-0.030
(0.008)
[3.629]***
TRADE T 0.024
(0.005)
[4.557]***
0.027
(0.008)
[3.305]***
0.024
(0.006)
[4.155]***
-0.637
(0.175)
[3.643] ***
EG residual test -5.059**
LM(1) test (p-value) 0.756
LM(2) test (p-value) 0.942
LM(4) test (p-value) 0.954
JB test (p-value) 0.706
BPG test (p-value) 0.776
Notes: Standard errors are reported in ( ) brackets, whereas t-statistics in [ ] brackets. *, **, *** denotes significance at
10%, 5%, and 1%, respectively. FMOLS = Fully Modified Ordinary Least Squares; DOLS = Dynamic Ordinary Least
Squares; CCR = Canonical Cointegrating Relationship. EG = Engle-Granger t-test for cointegration.
= factor loading
in the ECM; BPG = Breusch-Pagan-Godfrey heteroskedasticiy test; JB = Jarque-Bera normality test; LM = Bresuch-
Godfrey serial correlation LM test. FMOLS use Newey-West automatic bandwidth selection in computing the long-run
variance matrix. In the DOLS leads and lags are selected according to SIC criteria. The standard errors for the DOLS
estimation are calculated using the Newey-West correction.
Table 11: Quandt-Andrews structural break tests for Iceland ECM, 1970-2010
Statistics Value Break Probability
Max LR F-stat 2.714 2004 0.999
Max Wald F-stat 10.856 2004 0.298
Exp LR F-stat 0.611 - 1.000
Exp Wald F-stat 3.556 - 0.198
Ave LR F-stat 1.056 - 1.000
Ave Wald F-stat 4.223 - 0.369
Note: Probabilities calculated using Hansen's (1997) method.
... Poorfaraj et al. (2011) found a similar result for 16 developing countries during 2000-2008 that three knowledge pillars, which are innovation, ICT infrastructure and human capital, were positively correlated with economic growth through producing new technological progress, improving connectivity and supply chains and developing a well-educated workforce. Nevertheless, Cooray et al. (2013) showed different results for Nordic countries during 1996-2010, as the study found that education and economic incentives were positively correlated with economic growth; however, R&D had no significant effect on growth because R&D might be generated through education and openness. Another recent study by Boursin (2020) on the impact of knowledge pillars (education and ICT infrastructure) on Saudi Arabia's growth during 1992-2018 revealed that education was the most important factor that has a crucial impact on growth, while ICT showed an insignificant impact on growth. ...
Article
Full-text available
Purpose This paper aims to examine the relationship between knowledge-economy and economic growth in 16 Asia-Pacific (AP) countries during the period 2011–2018. The study also aims to investigate a diversity of knowledge-economy pillars, including tertiary education, domestic innovation, foreign innovation, economic incentives and institutional regime and information and communications technologies (ICTs) and their relation to economic growth. Design/methodology/approach The study applies a comparative empirical analysis using pooled ordinary least squares (OLS), one-step difference generalised methods of moments (GMM) and bias-corrected least-squares dummy variables (LSDVc) estimators to test this relationship. Findings Pooled OLS estimators deemed suboptimal to the panel data under study, while GMM results reveal a significant relationship between tertiary education, domestic and foreign innovation, government expenditure and investments with economic growth. Of these results, domestic innovation, investments and government consumption are positively correlated with economic growth, whereas tertiary education and foreign innovation show a negative relation. Meanwhile, institutions and ICT have insignificant relationships with economic growth. LSDVc results coincide with GMM results with respect to tertiary education, whereas institutions is the only additional significant and negatively correlated variable with economic growth. Research limitations/implications The main limitation of this research lies in the unavailability of proxy data for knowledge economy pillars in monetary terms, and hence, the paper relies on indices. Originality/value The novelty of the study lies in its aim to investigate economic growth in the AP region that is enhanced by domestic innovation, foreign innovation or both – an area which is empirically understudied in the knowledge-economy context. Further, the paper’s novelty lies in its application of a comparative empirical analysis between the most popular dynamic panel estimators – dynamic GMM and bias-corrected LSDVc for AP countries.
Chapter
In this citation Ton Notermans is dealing with a general consensus amongst policy authorities in the entire OECD area which elsewhere both has been termed The Conservative Revolution 3 and Le Tournant Néo-Libéral en Europe 4. The consensus being that macro-economic policies should have but one goal — the fight against inflation5. To that tenet of consensual macro-economic orthodoxy has been added that government budget deficits are detrimental to the economy, since they absorb national savings and raise interest rates and that government intervention in product, financial and capital markets is economically inefficient6. Epstein and Gintis go on to argue that “the widespread acceptance of this policy orthodoxy in the 80s was based not on empirical evidence of its effectiveness, but rather was a violent reaction against previous Keynesian and Social Democratic orthodoxies that held sway during the stretch of vigorous world economic growth in the period from the conclusion of the second world war to the “stagflationary” era of the mid 1970s”7.
Book
This book explores how to set up an empirical model that helps with forecasting long-term economic growth in a large number of countries. It offers a systematic approach to models of potential GDP that can also be used for forecasts of more than a decade. It is an attempt to fill the wide gap between the high demand for such models by commercial banks, international organizations, central banks and governments on the one hand and the limited supply on the other hand. Frequent forecast failures in the past (e.g. Japan 1990, Asia 1997) and the heavy economic losses they produced motivated the work. The book assesses the large number of different theories of economic growth, the drivers of economic growth, the available datasets and the empirical methods on offer. A preference is shown for evolutionary models and an augmented Kaldor model. The book uses non-stationary panel techniques to find pair-wise cointegration among GDP per capita and its main correlates such as physical capital, human capital and openness. GDP forecasts for the years 2006 to 2020 for 40 countries are derived in a transparent way. The author works for a commercial bank and has been the lead researcher in the bank's project called "Global Growth Centres 2020". © 2008 Springer-Verlag Berlin Heidelberg. All rights are reserved.
Article
The original Center for Business and Policy Studies (SNS) in Sweden and the National Bureau of Economic Research (NBER) in the United States (SNS-NBER) study of the Swedish labor market was written in the midst of the economic crisis of the early 1990s. This chapter is a sequel to that study and focuses on the postcrisis performance of the labor market, emphasizing institutional and other changes that have affected wage determination, inequality, and employment. Since the crisis, wage formation has become more decentralized. Centralized bargaining continues to set minimum wages in different sectors, but firms and unions bargain above the minimum and decide on specifics in local bargaining. Decentralization contributed to rising wage dispersion as wage outcomes were more likely to reflect market valuations for particular skills. Some of the 1990s increase in wage dispersion in Sweden presumably reflected catching up with market forces, but the catch-up does not seem complete, given the changing economic environment. Employment outcomes are worse for low-skilled persons and non-OECD (Organization for Economic Cooperation and Development) immigrants than for other workers. However, in the 1990s, the minimum wage increased in hotels and restaurants, which disproportionately employ the less skilled, presumably contributing to the low share of these sectors in the economy.
Article
Numerical approximations to the asymptotic distributions of recently proposed tests for structural change are presented. This enables easy yet accurate calculation of asymptotic p values. A GAUSS program is available to perform the computations.
Article
I. Introduction, 65. — II. A model of long-run growth, 66. — III. Possible growth patterns, 68. — IV. Examples, 73. — V. Behavior of interest and wage rates, 78. — VI. Extensions, 85. — VII. Qualifications, 91.
Article
In this paper we estimate the growth effects of human capital with country-specific time series data for Australia. Previous empirical studies, based on international data, have been inconclusive, in terms of the extent of the contribution of human capital to growth. We extend the Solow (1956) growth model by using educational attainment as a measure of human capital, as developed by Barro and Lee (2010). The extended Solow (1956) model performs well after allowing for the presence of structural changes. Our results, based on alternative time series methods, show that educational attainment has a small and significant permanent effect on the growth rate of per worker output in Australia. Alternative measures of human capital are also utilized to ensure robustness of results.