8
An alternative, could be taking K as the source of learning by doing process as in the case
of Arrow (1962) or Romer (1986). In these models, learning process which is created by
physical capital investments, dissemination among the producers leading to an overall gain of the
economy (positive externalities), thus, altering the decreasing returns process of physical capital
to a constant returns stage. In this respect, physical capital becomes the engine of growth. In the
Solow growth model, investments do not have economic growth effect. The structuralized
nature of learning in endogenous growth models have been developed by Romer (1986).
Production function then will look like;
(5)
Y
AK
L
=
+
−
α β
α
1
Different from
neoclassical growth models the function has a
β
coefficient as an exponential
value for capital. This constant term reflects the spreading pace of knowledge accumulation to
the rest of the economy. The rare case
α
+
β
=1 shows the case where capital output ratios leads to
steady growth. This Ak type, is similar to savings driven economic growth process as in the case
of endogenous economic growth models. The contribution of physical capital on economic
growth has been tested empirically by, Romer (1987), De Long and Summers (1991, 1992). In
this empirical study, explanatory power of share of investment in GDP has been tested against
the economic growth rate. Barro and Lee (1994), in their empirical study found that % 1 increase
in investment leads to a %0.12 increase in economic growth rate. On the contrary to Barro and
Lee, Levine and Renelt (1992), asserts the weaknesses of the explanatory power of the above
cited regression results.
If the regressed economies can be examined under the assumption of steady state
equilibrium, the functional relationship will be inconsistent with the
neoclassical economic
growth assumptions. Reason being, economic growth effect of saving and investment can be only
realized if the economy is not under steady state equilibrium.. These growth rates, after the non-
existence of convergence, which is the phase where steady equilibrium has been reached
(Mankiw, Romer and Weil, 1992). Grossman and Helpman (1991), ties the investment growth
interaction to technology creation in the R&D sector. Thus, their endogenous economic growth
model will have the following form.
(6)
!
K
Y
r
Y
Y
=
+
αγ
γ
Here
γ
Y
, shows the national income growth as a result of resources devoted to R&D;
α
shows
capital-national income elasticity and;
r shows the discount factor. The model shows the
contribution of physical capital to long term economic growth.
9
Human capital factor is another key factor frequently discussed effecting the economic
growth process. Lucas (1988, 1990), argues the importance of human capital with respect to
physical capital. He asserts that, investment to the education sector creates positive externalities
which enables increasing returns. Under steady state equilibrium, Per capita income increase
should lead to equal to Per capita human capital increases. Romer (1990), makes the distinction
between rival and nonrival environments for inputs of production. Production function with
these two inputs if written in the following form (
F D X
( , )
), copying argument, states that
doubling of nonrival inputs will be increasing output in larger amounts.. This argument relies on
the assumption that, X is a rival and reproducible input while D is nonrival and having no
replacement input cost. D being a productive input, F function can not be written in a concave
form. In other words,
F
D X
F D X
(
,
)
( , )
λ λ
λ
>
. This argument is not very new in economic growth
literature. Solow (1956) accepts the case of externalities; Arrow accepts the case as learning by
doing (1962); Lucas accepts the case as (1988) nonrival and non-excludable goods..
To sum up within the recent economic literature, Romer (1990) argues that increasing
returns stems from the externalities in R&D sector. For him,
endogenous economic growth
models can be examined under two alternatives. The first one, asserts that existing knowledge is
the source of human capital which disappears by death. The second one is, basic technology
knowledge that is passed over generations which shows continuity in itself. At an empirical level
we see that years of schooling is taken as a measure for human capital (Barro, 1991; Barro and
Lee, 1993; Tallman and Wang, 1994).
3. The Formal Model
As hypothesized above in the introduction section, the slow GDP growth rates in the
developed countries could be verified by the spread between technology and human capital
existence. To test our hypothesis, we will use vector auto regression (VAR) technique in
assessing whether innovations in K/H (external shocks to K/H) towards long-run GDP growth
short phase divergences from the average which verifies that K/H is a dominant criteria
explaining GDP growth in developed countries. In this respect, we also would like to test
whether neoclassical (Solow, 1956; Swan, 1956; Cass, 1965; Koopmans, 1965; Arrow et al.,
1961) and endogenous (Romer, 1986 and 1990; Lucas, 1988; Rebelo, 1991; Mankiw, Romer
Weil, 1992) growth theories factor substitutability holds contradicting with the above depicted
hypothesis. Impulse response functions estimated via VAR technique will show us that whether
K/H shows complementarity or substitutability towards GDP growth rate for developed
countries. Impulse response functions explosive or declining nature will support factor