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The Potential Capability of the New Endogenous Growth Theory (NGT) in Explaining
Growth: Review of Theoretical and Empirical Literature
Adrino Mazenda
PhD Candidate, Department of Economics, University of Fort Hare
Email: [email protected]
Abstract
The paper discusses the new endogenous growth theory and its empirics in explaining the growth
process. The compilation will show the theory’s perspective of technological change as a driver
of economic improvement. It will also depict how this growth can be persistent in the long run.
The causes of technological improvement and the practical measures that have to be undertaken
as triggers of a successful implementation of the model in reality are also discussed. Finally
empirical evidence on envisaged growth components will be reviewed.
Keywords: Technology, Innovation, R&D, New Growth Theory, Economic Growth
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1.1 Introduction
The godfathers of economics such as Thomas Malthus, Adam Smith and David Ricardo provided
many of the basic ingredients that appear in modern theories of economic growth. These ideas
include the basic approaches of competitive behaviour and equilibrium dynamics, the role of
diminishing returns and its relation to the accumulation of physical and human capital, the
interplay between per capita income, technology growth and economic growth (Muradzikwa,
Smith and de Villiers, 2006: 415). The Endogenous Growth Model otherwise known as the New
Growth Model was developed in the 1980s. Its emergence was a direct consequence of the
criticism of neoclassical failures. In the neoclassical model, various principles failed to be
explained:
i. Savings rate and technological progress
ii. Why do growth patterns for some countries show sustained increase, despite the fact
that they display a rising capital to labour ratio?
iii. Why has the neoclassical prediction of per capita income convergence failed?
iv. Why is there an increase in the economic gap between rich and poor nations?
v. What lies behind medium to long term acceleration and deceleration in growth?
Paul Romer, in 1986, primarily led efforts to overcome the above mentioned anomalies by
including endogenous factors of production, production functions and technological innovations.
1.2 Assumptions
The endogenous model assumes constant marginal product of capital at the aggregate level or at
least that the limit of marginal product of capital does not tend toward zero. In other word the
theory explains marginal returns to capital to be constant or increasing such that total product
will be continuously increasing but would not likely diminish (Mankiw and Romer, 1991). This
does not imply, though, that large firms are more productive than small ones because at firm
level the marginal product of capital may still be diminishing.
Perfect competition assumption, which is a pillar of the neoclassical theory, is relaxed and some
degree of monopoly power is thought to exist. Under this assumption, there is a dissection of all
economic sectors into the production sector which is responsible for the manufacture of final
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output and the research and development that is responsible for the development of ideas
continuously. The research and development sector is assumed to make monopoly profits by
selling ideas to production firms. Technology, synonymous with ‘knowledge’, is neither an
ordinary good (being rival and excludable), nor a public good (being non-rival and nonexcludable), but non-rival and partial excludable good (Mankiw and Romer, 1991).
1.3 Theory
According to Mankiw and Romer (1991), the real value of the endogenous growth model will
emerge from its attempt to model endogenous component of technological progress as an
integral part of the theory of economic growth. In this articulation, the implication is that the
current technology is insufficient to be a driver of long term economic growth. Thus for any
economy, the status quo alluding to knowledge is both unfavourable and undesirable for the
occurrence of growth.
The endogenous growth theory and its proponents believe improvements in productivity can be
linked to a faster pace of innovation and extra investment in human capital. This is in the
presence of industrialization, resulting in economy-wide increasing returns to scale (Todaro and
Smith, 2013). Each industry produce with perfect returns to scale and each firm’s capital stock
include its knowledge which is a public good and spill-over to other firms on the economy.
The theoretical notation of the theory is formally presented as:
Q = ALαKβ
Where:
Q = Total production (the monetary value of all goods produced in a year)
.L = Labour input
K=Capital input
A= Total factor productivity
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α and β are output elasticity for labour and capital, respectively. These values are constants
determined by available technology. Output elasticity measures the responsiveness of output to a
change in levels of either labour or capital used in production (Todaro and Smith, 2013).
For example if α = 0.15, a 1% increase in labour would lead to approximately a 0.15% increase
in output. If α + β = 1, the production function has constant returns to scale. That is, if L and K
are each increased by 10%, Y increases by 10%. Thus α + β < 1 implies that returns to scale are
decreasing. If α + β > 1 returns to scale are increasing (Todaro and Smith, 2013).
Romer (1993:81) asserts that, if it is assumed that the number of researchers producing
knowledge is constant, the model will predict that all growth is due to technological progress.
That is to say the capital-labour (K/L) ratio, the stock of knowledge and output, all grow at a
constant rate. Without technical progress, there will be no growth.
Proponents of the new endogenous growth theory argued that there is a need to nurture
innovativeness and provide a basis for inventiveness. The new growth model predicts positive
externalities and spill-over effects from the development of a high valued knowledge economy.
Technological change is no longer seen as an “exogenous black box” factor as was implied by
the neoclassical models (Muradzikwa et al, 2006: 415).
In the new growth theory, the presence of imperfect competition or monopolistic markets is
crucial in driving economic growth. It enables a circumstance of increasing returns to factor
inputs, thus explains why economic growth has not converged to a steady state as advocated by
the neoclassical model. It also explains the existence of divergence and cross country growth
comparisons (Lucas, 1990: 92). This is further explained by industrial nations today, where the
stock of new capital is increasing at levels far greater than the labour force. Evident in
developing countries is that population increases do not lead to increased economic growth rates
but actually in developed economies where increased investment has escalated economic growth.
The new growth theory refute former notions that large populations are a generator of economic
growth (Romer, 1991 in Muradzikwa et al, 2006); instead, technology as an endogenous factor in
the production function is the prima facie motor of economic growth. Much in the same vein as
this, the neoclassical theory implies that poor nations are supposed to grow at faster rates than
rich ones as new investment is far from reaching its level of diminishing marginal returns and the
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labour force is still experiencing positive growth. This implies an eventual convergence towards
world -wide steady state. Empirical evidence, on the other hand, shows the opposite that is a
widening gap between rich and poor countries (Lucas, 1990: 93).
In a nutshell, the addition of knowledge as factor of production raises the return on investment,
which made Romer (1993) to conclude that there are five basic facts that can be attributed to
economic growth:
i. There are many firms in a market economy, thus there is essentially competition.
ii. Discoveries differ from other inputs that many people can use at the same time. This fact
means that information or knowledge is a non-rival good; every firm or agent can utilize
knowledge at a given time.
iii. Physical attributes can be replicated. In competitive market economy, the production
function has homogeneity of degree one.
iv. Technological advances require people and their effort. Technological change does not
occur solely with the advance of time but through human activities and discoveries.
v. Many individuals and firms have power and earn monopoly rents on ideas and
knowledge. This information can be excludable, at least temporarily. This allows firms a
certain monopoly power.
2. Implications of the New Endogenous Growth Theory
2.1 Human capital.
New growth models suggest that investment in human capital may have significant positive
externalities. Not only does education allow individuals to adapt faster to new technologies, it
also allows for higher specialization in higher occupations and thus higher returns (Helpman,
Elhanan and Rangel, 1999). Furthermore, it facilitates the accumulation of knowledge through
learning by doing. Knowledge therefore provides a strong rationale for investment in education
and training.
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2.2 Public infrastructure.
Investment in public infrastructure may raise the productivity of private capital. This includes
both materials such as roads and railways and immaterial such as education and property rights
(Helpman and Rangel, 1999).
2.3 Research and development.
Research and development allows for new and better products. Success of research and
development is dependent on the level of human capital and structures that provide incentives to
innovate and invent; thus engage in research. High levels of human capital encourage a faster
diffusion of knowledge and technology. Well educated people learn faster. Incentives such as
patents allow for return on research and development (Helpman, Elhanan and Rangel, 1999).
2.4 Trade reform
International trade increased the size of the market and in turn the extent of competition in the
goods market. Increased market size allows for larger amounts of devotement to research and
development and for greater returns to this investment. It may also allow for the exploitation of
economies of scale. Increased competition forces firms and individuals to continuously innovate
and stay ahead (Muradzikwa et al, 2006: 417).
3. Empirical Evidence
Empirical analysis on the NGT and the growth process is made subject to an assessment of
growth determinants.
3.1 Investment
Using cross country regression for five industrialised countries for the period 1870-1987 and in
twenty –four OECD countries for the period 1950-1992, Xu (2000) finds a positive long- run
effect of investment on growth for four industrialized countries and fourteen OECD countries.
Bond et al (2004) utilised time series annual data in 98 countries for the period 1960-1998 in
prediction of investment and output growth rate per worker. In the short run, an increase in the
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share of investment yields a higher growth rate of output per worker in the steady state. In the
long-run investment was found to be statistically significant in explaining growth.
Bosworth and Collins (2003) concluded on the usefulness of capital stock to investment rate in
explaining output growth. R2 for capital stock was found to be high in the regression analysis
results, as compared to a very little correlation on mean investment rate.
3.2 Trade Openness
Theoretical literature on endogenous growth provides three mechanisms through which trade
openness may affect growth, namely the domestic rate of innovation, amount of technology that
can be transferred and the adoption of technologies from more advanced countries (Cameron,
Proudman and Redding, 1998).
Sachs and Warner (1995) investigated on the importance of trade openness as a major
determinant of cross country growth. Economic indicators where used to distinguish between
closed and open economies. Results of the study suggested that openness is important because it
allows poor countries to catch up with the rich, whereas being closed to trade results in
stagnation at the lower income level.
An empirical study of growth through trade in Nigeria for the period from 1975-2012 was
carried by Ibraheem et al (2013). OLS regression analysis was used as an estimation technique.
Results of the study indicate that total trade, FDI flow and exchange rate contributed positively
to growth, while degree of openness of the economy was found to have contributed negatively to
growth.
3.3 Institutional Quality (Human Capital)
La Porta, et al (2004) examined the different patterns of growth for North and South Korea. The
debate was based on whether or not political institutions cause growth, and was made in the line
of institutional view against development view. They argued that majority indexed of
institutional quality were unsuitable to test the institutional growth- nexus and the instrumental
variable techniques used to control for endogeneity were conducive to flawed regressions. In the
conclusion, education (human capital accumulation) and wealth where found to result in
institutional evolution. Poor countries where pitied for dodging the poverty traps in the quest of
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alluding policies meant to promote human capital accumulation and pro-market mechanisms
devoted to assure property rights.
Arabi and Abdalla (2013) investigated on the impact of human capital on economic growth in
Sudan for the period 1982-2009. A simultaneous model which employed the three -stage least
squares technique was utilized. The model envisaged human capital with a proxy of school
attainment, investment in education and health to economic growth, total productivity, foreign
direct investment and human development index. Empirical results of the study showed that
quality of education and health quality factor has a determinant role on economic growth. Total
factor productivity which represented state of technology and human development were found to
have a negative effect on economic growth. Obsolescence of technology was incriminated for the
adverse effect.
3.4 Research and Development
For the period 1971-1990, Coe and Helpman (1995) investigated on the effect of R&D on
domestic and foreign economy for 21 OECD countries. They constructed for every country of
their sample, a stock of domestic knowledge based on R&D expenditure and a foreign R&D
capital stock. Results of the study showed that smaller countries benefit from foreign R&D more
than large countries. Belgium was the largest beneficial followed by Ireland, Netherlands and
Israel. R&D expenditure was also found to have a major impact in raising productivity in foreign
countries and in the domestic economy.
Coe et al, 1997 in support of his previous work on international R&D spill-overs, provided on
quantitative estimates of international spill-overs for a group of 77 countries by examining the
extent to which less developed countries with low R&D benefit from R&D performed in
industrial countries. The period of study was from 1971-1990. The model was specified
distinctively from Coe and Helpman (1995). The specification of the regression equation
included a proxy for human capital, foreign R&D only is considered and the trade openness is
connoted with the ratio of imports of machinery and equipment from industrial countries to
GDP. Results of the study suggest that all the factors in the model specification are responsible
for growth of developing countries.
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Using theoretical and empirical evidence for the period 1975-2000 for US manufacturing
industry data, Minniti and Venturini (2014) investigated on R&D policy in promoting innovation
and economic growth for the US (Schumpeterian Growth). Results of the study indicated that
R&D policy has a persistent impact on the rate of economic growth and the US economy rapidly
adjusted to policy changes. The impact of R&D tax credits on economic growth appears to be
long lasting and statically robust. R&D subsidies were associated with an increase in economic
growth in the short run indicating that the policy instrument had a temporary effect.
4 Conclusions
Different economic growth determinants have been reviewed to highlight on the suitability of the
NGT in explaining growth. The conclusions are commonly coherent at the global and regional
level and less contested than during the Solow era. Regardless of this phenomenon, econometric
results are still object to criticism. New challenges on NGT emanate on growth determinants,
significance, how to cope with model uncertainty, availability of data and non-linearity in growth
econometrics. Continuous improvement of data quality aspects on R&D, trade and financial
indicators is necessary for successful continuous application of the model in economic growth.
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5 References
Arabi, K.A.M, Abdalla, S.Z.S. (2013). The Impact of Human Capital on Economic Growth:
Empirical Evidence from Sudan, Research in World Economy, 4 (2), 1-20.
Bond, R, Hoeffler, A., Temple, J. (2001). GMM Estimation of Empirical Growth Models: CEPR
Discussion Paper, No. 3048, November.
Bosworth, B, Collins, SM. (2003). The Empirics of Growth: An Update, Brookings Papers on
Economic Activity, 2, 113-206.
Cameron, G, Proudman, J, Redding, S. (1998). Productivity Convergence and International
Openness, Bank of England Working Paper 77.
Coe, D, Helpman, E. (1995). International R&D Spill-Overs, European Economic Review, 39,
840-888.
Coe, D, Helpman, E, Hoffmaister, A.W. (1997). North and South R&D Spill-Overs, Economic
Journal, 107, 130-149.
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Minniti, A, Venturini, F. (2014). R&D Policy and Schumpeterian Growth: Theory and Evidence,
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