Download international competitivenss as a catalyst of productivity

International Review of Business Research Papers
Vol. 11. No. 2. September 2015 Issue. Pp. 120 – 131
International Competitiveness as the Catalyst of Productivity
Hanna G. Adamkiewicz* and Stanislaw Maciej Kot**
In this paper, international competitiveness (IC) is defined axiomatically
as a catalyst of productivity. IC enhances national productivity but it is
not consumed when interacting with production factors. In literature, the
distinction between production factors and catalysts has not been yet
analysed. We have modified the Solow model in such a way that it can
account for the interactions of IC with physical capital or human capital.
Therefore, IC, as a business school product, reaches a solid economic
foundations. The panel data from WEF’s The General Competitiveness
Reports and Penn World Tables PWT8.0, for the years 2007-2011 and
for 134 countries, have been used for estimating the theoretical model.
We have found that IC enhances labour productivity when interacting
catalytically with human capital only.
JEL Codes: O11, O19, O43, O47
1. Introduction
The aim of this paper is to propose a theoretical economic background for the
pragmatically-originated concept of international competitiveness (IC). Our hypothesis is
that IC acts as a catalyst of productivity.
We distinguish between “factors” and “catalysts” as the determinants of the economic
growth. In chemistry, a catalyst is a substance that speeds up a chemical reaction but is
not consumed by the reaction. A catalyst works by providing an alternative mechanism
involving a different reaction pathway to the reaction product [McNaught, Wilkinson, 2006].
Our hypothesis states that IC, as a catalyst, is not a new production factor which
complements traditional factors, i.e.
physical capital, human capital, labour and
technological progress. IC enhances national productivity trough interactions with
production factors, while itself remaining unchanged.
In order to account for IC as a catalyst of productivity, we enlarge the classical Solow
(1956) model by adding interactions between IC and production factors. We estimate the
enlarged model using panel data from Penn World Table PWT8.0 [Feenstra et al., 2013]
and The Global Competitiveness Reports of the World Economic Forum (WEF), for the
years 2007-2011.
This paper is motivated by the fact that IC and competitiveness indices, as a business
school product, are based on weak or nonexistent economic foundations [Lall, 2001].
Consensus regarding a definition of IC has not yet been reached. IC is used by
governments and is an important economic policy issue. Although many definitions of IC
*
Department of Economic Sciences, Gdansk University of Technology, Narutowicza St., 11/12, 80-233
Gdansk, Poland, e-mail: [email protected]
**
Department of Economic Sciences, Gdansk University of Technology, Narutowicza St., 11/12, 80-233
Gdansk, Poland, e-mail: [email protected]
Adamkiewicz & Kot
have been proposed, until now they have generally been too vague [Aiginger, 2006]. Our
paper aims to fill the gap between the practical concept of IC and economic theory.
The rest of this paper is organised as follows. Section 2 presents the theoretical
background of IC. Here, we discuss selected definitions of IC and their shortcomings.
Section 3 formulates the theoretical model of the production function with catalysts, and
defines the statistical data used for its estimation. Section 4 presents the empirical results.
Finally, Section 5 offers the main conclusions and recommendations for further research.
2. Literature Review
Michael Porter‟s (1990) idea of the competitiveness of nations, or international
competitiveness (IC) remains influential, especially among practitioners. However,
scholars‟‟ reactions to this idea have not been as enthusiastic as those of policy makers.
According to Krugman (1994), IC is a potentially misleading paradigm and its use as a
basis for policy recommendations is wrongheaded or even dangerous. He asserts that the
concept of IC is elusive or meaningless when applied to national economies since there is
no well-defined bottom line, like going out of business. For economies with little
international trade, competitiveness is, in his opinion, a ridiculous way of saying
“productivity‟‟.
There are many definitions of IC [e.g. Aiginger, 2006, Siggel, 2006]. One can observe that
almost all those definitions link IC with such terms as productivity, economic prosperity,
welfare, well-being or standards of living. Porter (1990, p. 6f) maintains that “…the only
meaningful concept of competitiveness at the national level is national productivity‟‟.
Kohler (2006) also favours the productivity approach to IC. He relates productivity to
comparative advantage in trade theory and to total factor productivity in modern growth
theory. “A country‟s welfare is determined by its absolute level of productivity and not by
some international competitiveness rankings (...). In a trading world, productivity is
magnified, in terms of its welfare potential by international exchange (...) adding the terms
of trade as a second principle determinant of domestic welfare.‟‟
Aiginger (2006) defines competitiveness as the ability of a country or a location to create
welfare. Grilo and Koopman (2006) argue that standards of living are a meaningful
measure of competitiveness and that improving competitiveness could be equated with
enhancing welfare. This is in line with the definition used in the European Competitiveness
Reports: “Competitiveness is understood to mean high and rising standards of living in a
nation (or group of nations) with the lowest possible level of involuntary unemployment, on
a sustainable basis.‟‟ In a similar way, Huggins and Davies, (2006, p.1) define IC as “…the
capability of an economy to maintain increasing standards of living for those who
participate in it, by attracting and maintaining firms with stable or rising market shares in an
activity”.
Kohler (2006) asks the important question of defining the exact meaning of the productivity
of a country. He argues that the answer can be inferred from the role that a country plays
in providing institutions which ensure fruitful interaction between individual workers‟
abilities, and between different firms. Of particular importance in the present context, this
includes financial markets where savings are channelled into productive investment and
capital accumulation. Moreover, it relates to education and human capital formation, i.e., to
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the formation and enhancement of individual abilities. And finally, one might add the role of
„Schumpeterian‟ institutions that duly foster creative destruction and innovation”.
Kohler‟s reasoning harmonizes to some extent with the World Economic Forum‟s definition
of IC: “We define competitiveness as the set of institutions, policies, and factors that
determine the level of productivity of a country. The level of productivity, in turn, sets the
level of prosperity that can be reached by an economy” [The Global Competitiveness
Report 2013–2014, p.4].
The main shortcoming of the abovementioned definitions of IC is the lack of solid
economic foundations [Lall, 2001]. Our definition of IC as a catalyst of productivity fills the
existent research gap.
3. The Methodology and Model
3.1 The Macroeconomic Catalyst of a Nation’s Productivity
From the macroeconomic perspective, the environment of autonomic production
processes can be treated as a catalyst of a nation‟s productivity. Many economists
observe that the more favourable an environment, the more efficient the combination of
capital and labour and thus, the higher the productivity [Acemoglu et al., 2003, 2004, de
Soto, 2000, chapter 3, Kaufman, 2005, pp. 81-98]. This means that interactions between
production factors and environmental components exist. On the other hand, some of
these components, e.g. institutions, infrastructure, legal system, etc, seem to be stable, at
least in the short-run. This means that the components are not consumed during the
aforementioned interactions. Therefore, it justifies treating some environmental
components as catalysts of productivity, but not as additional factors of production.
We propose the following five axioms which a given component of environment
(component, for short) should satisfy to be the macroeconomic catalyst of national
productivity.
Axiom 1. National level. A component operates on the national level.
Axiom 2. Controllability. A component is under the control of a government or other
national institutions, either public or private.
Axiom 3. Disjointedness. Environmental components and production factors are disjointed.
Axiom 4. Interactiveness. A given component enhances national productivity through
interaction with a particular production factor.
Axiom 5. Indestructibility. An interaction between a given component and a production
factor does not use up the component in any way.
Institutions, as defined by North (1990, p.3), can serve as an example of a productivity
catalyst, provided they satisfy our axioms. North and Thomas (1973, p. 2) maintain that
factor accumulation and innovation are only proximate causes of growth. The fundamental
explanation of comparative growth is difference in institutions. According to Acemoglu et
al. (2005), institutions, particularly economic institutions, are fundamental causes of longrun growth. Our concept of the macroeconomic catalyst offers a theoretical mechanism
that can describe such a causal relationship.
The concept of the productivity catalyst can also explain the meaning of IC. The definitions
of IC, presented in Section 2 correspond, to some extent, to our concept. For instance,
The World Economic Forum defines IC as “…the set of institutions, policies, and factors
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that determine the level of productivity of a country.” [The Global Competitiveness Report
2013–2014, p.4]. If we exclude the term “factors‟ from this definition, IC will satisfy all five
axioms. IC enhances productivity, while itself remaining unchanged.
3.2 The Economic Mechanism of IC as a Catalyst of Productivity
We assume that economic development is described by the production function, which
combines physical capital K, labour L and human capital H with the productivity level b in
order to produce output Y, i.e. Y=bf(K,L,H). We assume that the production function is of
the Cobb-Douglas form
(1)
Y  bK  (LH )1
The parameter α is the output elasticity of capital. The production function (1) is first-order
homogeneous, which implies constant returns to scale. When markets are fully
competitive, α is the physical capital share in the product, whereas 1- α is the labour
share in the product [Solow, 1956]. In fact, equation (1) presents an augmented version of
the original Solow model because it contains human capital H as an additional explanatory
variable [Mankiw, Romer, Weil, 1992].
In economics, the term „productivity‟ means the ratio of output and input. By dividing both
sides of (10) by L, we obtain the productivity of labour

Y  K  1
  H
L L
(2)
IC as a catalyst can interact either with physical capital K or with human capital H. We will
omit a catalytic interaction of IC with labour L because L is driven by demographic factors
which seem to be beyond a government‟s control. In order to account for the
abovementioned interactions, we propose two enlarged versions of model (1) which can
be expressed in the following logarithmic form:
logY  log b   log K  (1   ) log H  (1   ) log L
(3)
By denoting c as a proxy for IC, the first version of (3) accounts for the interaction between
IC and K:
logY  log b   log K    c  log K  (1   ) log HL ,
(4)
or equivalently
logY  log b  (    c )  log K  (1   ) log HL ,
(4a)
Y  bK  ( LH )1 K c
(4b)
Hence
and the productivity of labour is

Y   K  1  c
 b  H  K
L   L 

(5)
One can see that the catalytic interaction of IC with physical capital enhances labour
productivity (2) (in brackets) by the factor of Kγ·c.
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The second version of (2) accounts for the interaction between IC and H:
logY  log b   log K  (1   ) log H    c  log H  (1   ) log L (6)
or equivalently
logY  log b   log K  (1      c )  log H  (1   ) log L
(6a)
Hence
Y  bK  ( LH )1 H c
(6b)
and the productivity of labour is

Y   K  1  c
 b  H  H
L   L 

(7)
(7) shows that the catalytic interaction between IC and human capital enhances labour
productivity (2) (in brackets) by the factor of Hδ·c.
We will estimate the unknown parameters b, α, γ, and δ using panel data linear models.
For this purpose the following logarithmic forms of the models (1), (4b), and (6b) will be
used, respectively:
log y  log b   log k
(8)
log y  log b   log k  ck
(9)
log y  log b   log k  ch
(10)
where y=Y/LH, k=K/LH, ck=log c·log K, and ch=log c·log H.
Now, we can explicitly formulate our hypothesis: “IC is a catalyst of productivity.” If this
hypothesis is true, the parameter γ or δ should differ statistically from zero, and fit data
better than model (8) without catalysts. Otherwise, the hypothesis should be rejected.
4. Statistical Data and Estimating Methods
In this paper we use Penn Word Tables PWT8.0 [Feenstra et al., July 2013] as the source
of panel data on GDP, capital, labour and human capital. PWT8.0 offers some novelties in
the measurement of real GDP, physical capital and human capital. In previous versions of
PWT, the measurement of real GDP was based on the prices of final goods. Hence it was
a measure of real GDP that reflected the standard of living in an economy rather than the
production possibilities. Feenstra et al (2009) refer to this concept as “real GDP on the
expenditure side,” or real GDPe .
Beside traditional GDPe,TWP8.0 offers “real GDP on the output-side” (GDPo), which is
intended to measure the production possibilities of an economy. The prices of final goods,
exports and imports are used to calculate GDPo. After adjustments for PPP and for
reference prices over time (with 2005 as the benchmark), the resulting real GDPo reflects a
country‟s productivity, which is comparable across countries and over time [Feenstra et al.,
2013].
PWT8.0 also offers a new measure of capital input that differs from a standard approach. It
breaks down total investment into individual assets rather than assuming investment as a
single homogeneous asset. This implies a varying depreciation rate across countries and
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over time rather than having to be assumed as a constant. Moreover, PPP used to
compare the capital stock across countries is also different from the investment PPP that
is used in the standard approach [Inklar and Timmer, 2013]. In PWT8.0, this new
measurement of capital stock is denoted by “rkna” [at constant 2005 national prices, in mil.
US$].
PWT8.0 also offers a new measure of human capital. The index of human capital
combines a country‟s average years of schooling from Barro and Lee (2012) and an
assumed rate of return for completing different sets of years of education based on
Psacharopoulos (1994). In PWT8.0, the variable „”hc” is the index of human capital per
person. PWT8.0 measures employment in the usual way. The “emp” variable is the
number [in mil.] of persons engaged.
For IC measurement we construct several indices base on selected sub-pillars published
annually by the World Economic Forum in Global Competitiveness Reports. Our five
axioms are the basis of the selection. Table 1 presents the sub-pillars and the indices,
which are an arithmetic means of aggregating individual variables within a category.
Table 1
Selected sub-pillars as potential catalysts of productivity for the years 2007-2011
WEF
symbol
5.03
5.04
5.05
5.06
1.02
5.07
7.08
12.06
2.01
2.02
2.03
2.04
2.05
2.07
6.11
6.12
9.03
Description
Quality of the educational system
Quality of math and science
education
Quality of management schools
Internet access in schools
Intellectual property protection
Local availability of specialized
research
and training services
Brain drain
Availability of scientists and
engineers
Quality of overall infrastructure
Quality of roads
Quality of railroad infrastructure
Quality of port infrastructure
Quality of air transport infrastructure
Quality of electricity supply
Prevalence of foreign ownership
Business impact of rules on FDI
FDI and technology transfer
The catalyst interacting with:
Education
(ch1)
Human
capital H
(ch)
Science
(ch2)
The overall
catalyst of
productivity
(c)
Infrastructure
(ck1)
Foreign
capital
(ck2)
Physical
capital K
(ck)
Protection of minority shareholders‟
interests
Finance
(ck3)
8.03
Affordability of financial services
8.04
Ease of access to loans
8.05
Venture capital availability
1.01
Property rights
Technology
9.01
Availability of latest technologies
(ck4)
12.05
Government procurement of
advanced
technology products
Note: the symbols of indices of catalysts in parentheses.
Source: Authors‟ proposal based on The Global Competitiveness Reports 2007-2011, WEF.
1.20
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Our analysis covers the period 2007-2011 because of the availability of comparable panel
data in both Global Competitiveness Reports and pWT8.0 databases. Although the first
Global Competitiveness Report was already published in 1979, comparable sub-pillars did
not appear until 2007. On the other hand, PWT8.0 data end in the year 2011. Because of
missing data in some years, the number of countries in our sample varies from 116 to131
In this paper, we use panel data, i.e., cross-sectional time series data of the form yit,,
where the i subscript denotes the cross-section dimension, whereas t denotes the timeseries dimension. In our case, i denotes a country, t denotes the year. The following panel
data regression is used for modeling such data:
(11)
yit    X it'   uit , i=1,…,N, t=1,…,Ti
where α is a scalar, β is Mx1 and Xit is the ith observation on M explanatory variables
[Baltagi, 2005, p. 11]. In our case, N is the number of countries, and Ti is the number of
years. We apply a one-way error component model for the disturbances with
uit  i   it
(12)
where µi denotes the unobservable individual-specific effect and vit denotes the remaining
disturbances. We should note that µi is time-invariant and accounts for a county‟s specific
effect which is not included in the regression.
Let us perform some algebra on (11). If (11) is true, it must be also true that
y i    xi    i   i
where y i 
y
t
it
(13)
Ti , xi  t xit Ti and  i  t it Ti .
If we subtract (13) from (11), it must be equally true that
( yit  yi )  ( xit  xi )   ( it   i )
(14)
When µi are fixed parameters to be estimated, (14) describes the so-called fixed effects
model or within model. For random µi, (11) describes the so-called random effects model.
(13) describes the between model.

With  and  estimates of α and β we can assess the goodness of fit with respect to
(11), (13) and (14), using R2 statistics. We use the STATA symbols r2_o, r2_b and r2_w
and call them overall R2, between R2 and within R2 with respect to (11), (13) and (14).
In our case, the fixed effects model seems to be a better specification than the random
effects model because countries in our datasets have not been randomly drawn from the
general population of all countries in the World. The dataset contains only countries for
which statistical data were available.
5. Results
We estimate several versions of the fixed-effects linear (logarithmic) models (8), (9), and
(10) using STATA12. In subsequent tables 2-4, we present estimates of the basic model
(8) and models (9) and (10) where the indices from Table 1 appear from the most general
c index to the most disaggregated indices ch1, ch2, and ck1-ck4. In all tables, the symbol
“ln” denotes natural logarithm. For the sake of convenience, estimated basic model (8) will
be quoted in every table.
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In all tables presented below we use the following STATA symbols: N is the number of
observations, N_g is the number of countries, r2_w is R2 for within model, r2_b is R2 for
between model, r2_o is R2 for overall model. Because of lack of space, we do not present
such statistics as corr –correlation between u and the set of explanatory variables sigma_u
– panel-level standard deviation, sigma_e – standard deviation of εit, rho – ui fraction of
variance, F – statistic test that the coefficients of the regressors are all jointly zero, F_f –
statistic test that all µi are all jointly zero. These omitted statistics are available from
authors by request.
Table 2 presents the estimates of parameters of the model (8) and the enlarged model (9)
where c, ck, and ch interacts either with H or K.
Table 2
Estimated models with interactions between H, K and c, ck, ch
ln k
(1)
ln y
**
0.47581
(0.06662)
lnc·lnK
(2)
ln y
**
0.49125
(0.07020)
0.00548
(0.00848)
lnc·lnH
(3)
ln y
**
0.48631
(0.06993)
(4)
ln y
**
0.49548
(0.06997)
+
0.18648
(0.10883)
lnck·lnK
0.00433
(0.00813)
lnch·lnH
_cons
N
N_g
rho
r2_w
r2_b
r2_o
(5)
ln y
**
0.47265
(0.07117)
**
4.15638
(0.66400)
610
122
0.97894
0.09482
0.85744
0.85291
**
3.92630
(0.70657)
574
119
0.97609
0.10044
0.84952
0.84754
**
3.82119
(0.70540)
574
119
0.97315
0.10541
0.85958
0.85604
**
3.90461
(0.71674)
573
119
0.97604
0.10036
0.84908
0.84729
0.14480
(0.09020)
**
4.01982
(0.69961)
574
119
0.97504
0.10471
0.85928
0.85584
Standard errors in parentheses
+
*
**
p < 0.10, p < 0.05, p < 0.01
Source: Authors‟ calculations.
The first model in Table 2 describes production function (8) without catalysts. The
estimates of two parameters, _cons=ln b and α, are statistically significant. Under perfect
competition in goods and factors markets, α is the share of GDP that is not earned by
labour, whereas 1-α is a labour share. In our case, a physical capital share is 47.58%
which differs significantly from the 30% value commonly assumed [Gollin, 2002]. Our
estimate of a labour share of 52.42% is almost the same as the 52% obtained by Inklaar
and Timmer (2013, Table 7). It strongly contradicts Gollin‟s (2002) conclusion that a labour
share of 70% is a suitable number for all countries.
Overall goodness of fit of the first model is satisfied quite well (r2_o = 0.85). The between
model explains 86 per cent of the variance of the dependent variable. However,
r2_w=0.09 means that the within model fits the data worse than the between model. We
will use the quoted values of goodness of fit as references when comparing models with
catalysts.
All models from (2) to (5) in Table 2 have statistically significant estimates of α and _cons.
However, only model (3) has a statistically significant coefficient of the lnc·lnH regressor
(10% significance level). The goodness of fit of this model is higher than in reference
model 1. This means that IC, being measured by the overall competitiveness index c as a
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Adamkiewicz & Kot
catalyst, enhances national productivity through an interaction with human capital only. Let
us notice that neither ck nor ch, i.e. the more disaggregated indices than c, exhibit a
significant impact on the dependent variable.
Table 3 presents the estimates of the models with the most disaggregated indices of IC as
catalysts.
Table 3.
Estimated models with interactions between H, K and ch1, ch2, ck1-ck4
lnk
(1)
ln y
**
0.47581
(0.06662)
lnch1 lnH
(2)
ln y
**
0.47708
(0.07304)
0.06666
(0.07487)
lnch2·lnH
(3)
ln y
**
0.48990
(0.06959)
(4)
ln y
**
0.46018
(0.07831)
(5)
ln y
**
0.51952
(0.07333)
(6)
ln y
**
0.46855
(0.07714)
*
0.21227
(0.09138)
lnck1·lnK
0.00555
(0.00555)
lnck2·lnK
0.00705
(0.00650)
lnck3·lnK
-0.00274
(0.00331)
lnck4·lnK
_cons
N
N_g
corr
sigma_u
sigma_e
rho
r2_w
r2_b
r2_o
F
F_f
(7)
ln y
**
0.45068
(0.07294)
**
4.15638
(0.66400)
610
122
0.71612
0.56286
0.08255
0.97894
0.09482
0.85744
0.85291
51.016
113.25
**
4.07729
(0.71004)
573
119
0.69350
0.53298
0.08271
0.97648
0.10130
0.85457
0.85171
25.47
100.26
**
3.76075
(0.70369)
574
119
0.63251
0.48652
0.08221
0.97224
0.11021
0.86050
0.85691
28.06
101.46
**
4.23743
(0.74557)
573
119
0.70719
0.54844
0.08264
0.97780
0.10178
0.85118
0.84923
25.61
105.35
**
3.60192
(0.78378)
573
119
0.63620
0.50989
0.08263
0.97441
0.10213
0.84674
0.84505
25.71
108.62
**
4.29730
(0.79735)
573
119
0.71680
0.56540
0.08267
0.97907
0.10115
0.84608
0.84427
25.43
107.15
*
0.01448
(0.00698)
**
4.16248
(0.70368)
573
119
0.68474
0.54192
0.08235
0.97743
0.10827
0.84592
0.84377
27.44
109.08
Standard errors in parentheses
*
**
p < 0.10, p < 0.05, p < 0.01
Source: Authors‟ calculations.
+
In Table 3, only model 3 and 7 have statistically significant estimates of all parameters.
However, model 3 fits statistical data a little better than reference model 1 and model 7.
This means that the catalyst ch2, called “Science”, enhances national productivity through
an interaction with human capital H. Nevertheless, the index ck4, called “Technology”,
could be also be considered as a catalyst of productivity through interaction with physical
capital K.
In order to check multi-catalyst interactions, we estimate models with compositions of
possible catalysts. The results are presented in Table 4.
One can see in Table 4 that none of the models from 2 to 5 is satisfactory. Models 2, 3,
and 4 convince us that productivity cannot be enhanced by catalytic interactions with
physical capital K and human capital H at the same time. Although all the parameters in
model 2 are statistically significant, it fits data slightly worse than reference model 1
(without catalysts).
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In turn, models 5 and 6 in Table 4 show that two or more catalysts which interact with the
same production factor do not enhance national productivity. In model 5 only one
parameter in question is significant. Although model 6 has two significant parameters, it
cannot be accepted because of the negative value of one of the parameters and a
goodness of fit that is slightly worse than that of reference model 1.
Table 4
Estimated models with interaction between H, K and combinations of catalysts
lnk
(1)
ln y
**
0.47581
(0.06662)
lnc·lnK
lnc·lnH
(2)
ln y
**
0.49655
(0.06953)
**
-0.06604
(0.02399)
**
0.98237
(0.30864)
lnch·lnH
(3)
ln y
**
0.46788
(0.07201)
(4)
ln y
**
0.46848
(0.07410)
(5)
ln y
**
0.51289
(0.07431)
0.15223
(0.11465)
-0.08672
(0.09956)
*
0.28415
(0.12235)
0.17649
(0.11160)
-0.00499
(0.01003)
lnck·lnK
lnch1·lnH
lnch2·lnH
lnck1·lnK
lnck2·lnK
lnck3·lnK
lnck4·lnK
_cons
(6)
ln y
**
0.42527
(0.08436)
**
**
4.15638
3.82070
(0.66400) (0.70034)
N
610
574
N_g
122
119
corr
0.71612
0.35734
sigma_u
0.56286
0.49734
sigma_e
0.08255
0.08184
rho
0.97894
0.97364
0.09482
0.12016
r2_w
0.85744
0.79042
r2_b
0.85291
0.78356
r2_o
F
51.02
20.58
F_f
113.25
102.86
Standard errors in parentheses
+
*
**
p < 0.10, p < 0.05, p < 0.01
Note: see Table 1
Source: Author‟s calculations.
**
4.11719
(0.72807)
573
119
0.69016
0.52198
0.08257
0.97559
0.10532
0.85990
0.85627
17.70
99.91
0.00748
(0.00875)
**
3.91562
(0.72725)
573
119
0.65103
0.50225
0.08228
0.97387
0.11175
0.85704
0.85389
18.91
100.28
**
3.55134
(0.74202)
573
119
0.60349
0.47493
0.08231
0.97084
0.11192
0.85895
0.85567
18.95
100.92
-0.00466
(0.00736)
0.01256
(0.00844)
*
-0.00883
(0.00426)
+
0.01812
(0.00972)
**
4.32678
(0.84351)
573
119
0.69672
0.56882
0.08220
0.97954
0.11728
0.83578
0.83351
11.93
97.701
Our empirical findings confirm the hypothesis that IC is the catalyst of national productivity.
IC behaves as a catalyst when interacting with human capital. More specifically, the
scientific component of the production processes environment seems to be such a
catalyst.
129
Adamkiewicz & Kot
6. Conclusions
Our distinction between productivity factors and catalysts opens new research
perspectives for theoretical studies of economic growth as well as for empirical ones.
Introducing catalysts into traditional Solow model seems to have important theoretical
implications. Our theoretical model offers a convincing mechanism of explaining
interactions between catalysts and traditional factors.
The abovementioned distinction also enables to put some empirical findings in order. Until
now, a huge number of growth determinants have been proposed [Durlauf et al. 2005,
Appendix B]. One can suspect that some of those determinants are catalysts rather than
factors.
Our empirical results corroborate the hypothesis that IC is a catalyst of productivity.
Therefore, the pragmatic concept of IC has been placed on a solid theoretical foundations.
However, the generality of this conclusion is limited because it bases on the WEF
quantitative indices of IC. These indices have serious shortcomings as Lall (2001) points
out. The challenge is to develop more reliable indices of IC than the WEF indices.
The concept of IC as a catalyst raises many new questions. In this paper, we have
analysed some problems only from a global perspective. However, the relationships
between IC and national productivity could be different when analysed on a regional scale.
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