ISSN 10757007, Studies on Russian Economic Development, 2014,.docx

ISSN 1075�7007, Studies on Russian Economic Development,
2014, Vol. 25, No. 5, pp. 431–438. © Pleiades Publishing, Ltd.,
2014.
Original Russian Text © M.S. Gusev, 2014.
431
Economic growth forecasting is an integral part of
decision�making support (including strategic deci�
sion�making) on the level of both individual compa�
nies and entire countries. This means that arguments
of economic forecasting are used to justify decisions
and economic policy measures. In other words, eco�
nomic forecasting can be seen as a system of argu�
ments in favor of economic growth trajectory and its
possible variations.
The analysis of economic growth factors in eco�
nomic forecasting and their comparison with the the�
oretical narrative of economic growth1 allows one to
verify the reliability of economic forecasts for the for�
mulation of economic policy.
The effect of the factors of economic growth is
most fully manifested at long intervals; therefore, their
analysis should be based on published long�term fore�
casts. When short�and medium�term forecasts are
elaborated, the explicit (or implicit) hypotheses are made
about the immutability of many factors, most important of
which is existing technology. In general, this assumption
can be justified because the main task of short� and
medium�term forecasting is to assess the inertial scenarios

of development and their possible variations related to the
application of economic policy measures.
This paper analyzes long�term global economic
projections and analytical studies on the long�term
trends of economic growth.
The Description of Factors of Economic growth in the
Forecast and Analytical Materials2. The most popular
1 In this paper, the theoretic narrative of economic growth
refers
to a set of factors of economic growth justified by A. Smith in
[1]. In this paper, the economic growth is understood, first of
all,
as per capita income growth, which is the result of labor
produc�
tivity growth due to the accumulation of capital and labor divi�
sion.
2 In this section, the description of forecasts is given according
to
the publications of the respective organizations.
global economic forecast is the probably the IMF’s
World Economic Outlook. Although the timeline of this
forecast is limited to 5 years, this publication often
contains abstracts that describe and analyze long�term
trends. For example, the latest issue of the World Eco�
nomic Outlook [2] contains the analysis of the eco�
nomic slowdown in BRICS countries in 2013. The
analysis concludes that the economic slowdown in
these countries is due to a cyclical deceleration of eco�
nomic activity and a decreasing growth rate of poten�
tial output3.

According to IMF experts, the cyclical slowdown is
due to the following factors. In the midst of the global
financial crisis, the BRICS countries decided to
launch unprecedented measures to support the econ�
omy. The unfolding global economic recovery led to an
increase in external demand, lower interest rates, and
higher commodity prices. However, the effect of these
factors was exhausted by 2011; the economic stimula�
tion programs were ended, the external demand
slowed down, and the commodity prices stabilized.
The slower growth of the potential output was due
to various factors in different countries. For example,
in India, it was entailed by the problems related to the
expanding offer in mining, energy, telecommunica�
tions, and others sectors. The issuance of permits was
suspended and the approval of new projects slowed
down. Corporate balance sheets were overwhelmed by
debt.
A decreasing long�term balanced economic growth
in China and Russia is due to the exhaustion of
resources, which fuelled the previous models of eco�
nomic growth. The Chinese model relied on the
extensive economic growth, where economic expan�
sion is achieved at the expense of a high savings rate,
the creation of new capacities, and the population’s
3 The concept of potential output is analyzed below.
MACROECONOMIC PROBLEMS
Modeling Economic Growth for Long�Term Global
Economic Forecasting
M. S. Gusev

Institute of Economic Forecasting, Russian Academy of
Sciences, Moscow, Russia
e�mail: [email protected]
Received November 13, 2013; in final form December 2, 2013
Abstract—The correspondence between the factors of economic
growth described in long�term economic
forecasts and long�term forecasting modeling tools, as well as
the classical theoretical interpretation of eco�
nomic growth is analyzed.
DOI: 10.1134/S1075700714050049
432
STUDIES ON RUSSIAN ECONOMIC DEVELOPMENT Vol.
25 No. 5 2014
GUSEV
migration from the countryside to the city. By now, the
high savings rate has led to the excessive capacities and
lower returns on capital. At the same time, it is
expected that, starting from 2014, the economically
active population will decline in absolute terms and,
thus, result in a labor shortage by 2020. In Russia, the
economic growth of the 2000s was propelled by the
exploitation of idle capacities and growing global
commodity prices. At the same time, the growth was
originally constrained by the worn infrastructure
(especially, transport and power networks) and the
unfavorable business climate. The effect of the above
mentioned factors, which used to fuel the economic

growth, has by now exhausted in Russia. The situation
in the country is aggravated by unfavorable demo�
graphic trends translated into the declining population
of the working age.
One can distinguish between the following growth
factors in the IMF outlook: business cycle stages,
external demand, government spending, expansion of
production capacity, accumulation rate, increase in
manpower, business climate, and quality of infrastruc�
ture.
The description that accompanies the forecast
refers to the methodology of determining the cyclical
component in the dynamic output. According to this
method, the cyclic component is calculated as the dif�
ference between the actual rate of economic growth
and the growth of potential output. The assessment of
the latter is primarily a result of econometric calcula�
tion based on the use of a set of indicators, such as out�
put, unemployment, and the actual economic growth
rate (this method is discussed in more detail below).
In this context, the process of establishing the
potential output growth rate plays a key role. The fac�
tors that support the achieved growth rate of potential
output and the assessment of business cycle stages are
of secondary, auxiliary importance.
In addition, it should be noted that the actual
description of the economic growth factors in the IMF
outlook is reduced to how fast and with the help of
which economic policies different countries can
regain the potential output growth.
In the Conference Board’s Global Economic Out�

look [3], the main factor that propels the acceleration
of economic growth in the medium term in the United
States and other countries is the restored growth of
output to the potential economic growth, which can
be achieved by increasing workforce capacity and
technological advancement.
In the long�term outlook, the global economic
slowdown will be affected by the slowdown in develop�
ing countries, especially India and China, which will
shift from a growth model based on the accelerated
investment growth to a more balanced model.
The description of the factors of growth contained
in this outlook suggests that in the long run the global
economy and national economies will grow at the rate
equal to the growth rate of potential output affected by
the investment dynamics.
To justify the economic slowdown in developing
countries, the Goldman Sachs BRICS outlook [4]
relies on the hypothesis of the converging growth of
labor productivity in developing and developed coun�
tries. The rest of the forecast provides the description
of results and refers to the shares of specific countries
in the global economy.
The PwC’s World in 2050 outlook [5] lists the fol�
lowing factors of economic growth:
—the dynamics of the working�age population;
—the assessment of human capital growth (calcu�
lated based on the number of years spent on educa�
tion);

—The volume growth of productive assets as a
result of investment dynamics;
—Total factor productivity (the evaluation based
on the hypothesis of a reducing labor productivity gap
with regard to a leading country).
At the same time, the potential of a reducing labor
productivity gap is associated with an increased open�
ness of the economy and the enhanced competition, as
well as improved business climate.
The OECD Looking to 2060 outlook [6] assumes
that the financial crisis of 2007–2008 did not alter the
potential global economic growth. Once the conse�
quences of the global financial crisis have been over�
come, it is expected that the growth rate of global
GDP will reach the target of 3% per year and will
remain at this level for the next 50 years. The GDP
growth will be supported by fiscal reforms (aimed at
stabilizing the share of public debt in GDP) and struc�
tural changes, as well as the growing share of develop�
ing, dynamically growing countries in the global GDP.
If economic policy is not changed, the current
account imbalances of biggest economies could again
drop to the level of 2007–2008 by 2030, which will
result in higher interest rates and lower GDP growth.
Possible impact of the continuing low�end demand
on the potential output growth was not considered in
the forecast. Similarly, possible debt defaults by indi�
vidual countries, violation of trade relations between
two countries, scarcity of natural resources due to the
excessive pressure on the environment (depletion and
the emerging scarcity of natural resources) were

ignored by this particular outlook.
It is argued that the scientific and technological
progress will be the main factor of economic growth.
In various forecasts, the contribution of this factor is
measured by total factor productivity.
It is expected that, for individual countries, the
productivity growth rate will depend on the degree of
openness of the domestic market and the intensity of
domestic competition. The higher these characteris�
tics are, the faster can the productivity grow.
25 No. 5 2014
MODELING ECONOMIC GROWTH FOR LONG�TERM
GLOBAL ECONOMIC FORECASTING 433
In general, the description of the factors of eco�
nomic growth in the OECD forecast is reduced to the
description of those that enable individual economies
to achieve the growth of potential output. The first
group of these factors includes fiscal consolidation
(reduction or stabilization of public debt in relation to
GDP) and the removal of global imbalances (defi�
cit/surplus of the current accounts). The second group
of factors includes demographic trends, participation
of the population in education, the increasing age of
retirement, intensifying liberalization of domestic
markets in developing countries, growing public
spending on social security, and the increasing avail�
ability of consumer credit4.

According to the Long�Term Forecast of the Socio�
economic Development of the Russian Federation for the
Period until 2030, prepared by Ministry of Economic
Development [7], long�term global economic growth
will depend on the pace of technological progress and
the possible use of capital and human resources. Given
the population growth and environmental constraints,
the economic growth in developed countries will be
based on productivity growth stimulated by the scien�
tific and technological advancement. Intensifying glo�
balization will promote opportunities to catch up to
growth in developing countries and will expand the
access to the achievements of global technological
development by promoting susceptibility to techno�
logical innovations and improving the business cli�
mate. However, growing population and natural
resources constraints, as well as the increasing neces�
sity of financial balance will prevent the global econ�
omy from regaining high pre�crisis growth rates (at the
annual level of 4% or higher).
By 2030, the global economic slowdown will be
unfolding under the influence of the following factors:
—Shrinking workforce in leading developed coun�
tries and a slowdown in the labor force growth in
developing countries;
—Gradual decreasing productivity growth in fast�
growing Asian countries, which will accompany the
narrowing gap between the latter and the leading
countries;
—A decreasing rate of accumulated fixed capital,
reduced funding of cutting�edge basic research and
development;

—Intensifying environmental constraints associ�
ated with the rising costs incurred by the need to pre�
serve acceptable habitat and environmental standards
of production and consumption not only in developed
countries but also in developing countries.
As projected by the Russian Ministry of Economic
Development, long�term economic growth in Russia
will be determined by the following:
—changes in the external demand;
4 According to the forecast, the share of consumer credit in
GDP
in all countries will reach the U.S. level of 2% per year.
—productive capital growth;
—an increase in total factor productivity (at the
first stage, by adopting the existing procurement prac�
tices and purchase of advanced equipment and, later,
based on qualitative improvement of education and
domestic scientific research and innovation develop�
ments);
—higher quality of human capital.
Classical theoretical model of economic growth. The
classical economic theory [1] associates the level of
economic income with the size of accumulated capital
and the degree of labor division. Rising income levels
(or economic growth) depend on increasing produc�
tivity. In turn, the level of productivity is achieved due
to the level of division of labor, which is made possible
by the accumulation of a certain amount of capital. A

higher level of division of labor provides a higher level
of manpower productivity (specialization at various
stages of production), helps reduce production costs
(including by increasing the scale of production), and
frees up resources for their subsequent investment in
development and improvement of productive assets.
The enhanced division of labor also results in the
increasing accumulation of knowledge and human
capital in the society. Furthermore, the division of
labor stimulates the further accumulation of capital,
which is associated not only with the quantitative
expansion, but also with qualitative changes reflected
in the emergence of new industries, products, technol�
ogies, and manufacturing capacities.
The reinforced division of labor and specialization
entails an increase in the number of linkages between
individual production stages and raw material pro�
cessing stages. When more steps are included in the
production of finished goods and services, manufac�
turers are more highly specialized, the relationships
between producers are greater, the manufacturing of
the final product is more effective, and the level of pro�
ductivity and economic income are higher. However,
specialization growth has its limitations. The rein�
forced division of labor is constrained by the market
size. In other words, it becomes unprofitable if the
obtained additional amount of product cannot be sold
or exchanged for other products.
Since the second half of the 20th century, the pro�
cesses of the enhanced division of labor and capital
export, which was reflected in the system of foreign
economic relations and the structure of world trade,
have played important roles in expanding opportuni�

ties for economic growth in both developed and devel�
oping countries. Meanwhile, according to the above
economic forecasts, no source describes the changes
in the most significant qualitative components of eco�
nomic growth, such as intensifying specialization and
closer linkages between producers. The discrepancy
between the description of the factors of economic
growth in the classical theory of economic growth and
the quantitative long�term forecasts designed based on
434
25 No. 5 2014
GUSEV
economic models is so great that one can argue that
the currently developed long�term forecasts do not
provide a sufficient explanation of the published esti�
mates of economic growth. Specifically, as shown
above, no source considers the division of labor and
specialization as a factor in economic growth.
The current narrative practices of economic growth
and their difference from the theoretical postulates
might be due to certain characteristics and ideology of
econometric models applied for designing economic
forecasts.
Modeling tools of long�term forecasting. The World
Economic Outlook is based on the aggregation of fore�
casts obtained for individual countries [8]. The fore�
cast for each country is developed by a separate group

of experts; therefore, there is no single methodology
for forecast estimates.
However, based on the description of factors
underpinning the economic dynamics in BRICS
countries, one can conclude that the analysis of eco�
nomic growth largely relies on the concept of potential
output. This concept is based on Okun’s law, which
links output growth to unemployment, and the Phil�
lips curve, which relates inflation to unemployment.
Essentially, the concept of potential output is based
on the search for the production volume that can be
obtained at full employment without accelerating
inflation. The employment rate, which has no effect
on the acceleration of inflation (or the level of natural
unemployment), is determined based on the following
equation [9]:
(1)
where Pe is the level of expected inflation, P is the fac�
tual inflation, U* is the level of natural unemploy�
ment, is the factual level of unemployment, and α is
the coefficient.
In other words, the natural rate of unemployment
is the level at which inflation expectations coincide
with actual inflation. If it is roughly assumed that
inflation expectations are equal to the inflation in the
previous period, it is possible to assess the level of nat�
ural unemployment based on the above equation.
Then, relation (2) [10] allows us to estimate the
potential output at the natural rate of unemployment
as follows:

(2)
where Y* is the potential output growth rate and is
the factual output growth rate.
A comparison of the actual growth rate and the
estimated potential output growth rate allows one to
assess the relevance of various economic policy mea�
sures applied to stimulate economic growth or, con�
versely, to cap it. The slowdown is due to a downward
wave of the business cycle where the actual output
growth rate is below the potential one. Alternatively, it
is due to a decreasing long�term growth rate of poten�
( ),
e
P P U U= + α −*
U
[( )/ ],U U Y Y Y− = α −* * *
Y
tial output. Similarly, one can explain the acceleration
of economic growth.
The long�term growth rate of potential output is
primarily defined by two methods: either based on a
trend extracted from the GDP time series by the
Hedrick�Prescott filter, or by estimating the maximum
possible production with the help of the production
function for a given natural rate of unemployment.

In addition to the World Economic Outlook, the
IMF also elaborates global economic forecasting
models, e.g., The Global Projections Model (GPM)
[11], which are designed based on a single methodol�
ogy. These models are based on the assessment of the
potential growth rate. Possible deviations from this
rate and the speed at which this rate is regained are
described by the system of equations for individual
macroeconomic indicators.
The methodology of the Conference Board’s Long�
Term Global Economic Outlook is based on the con�
struction of production functions for each country,
which is included in the forecast. Production func�
tions allow one to decompose the contribution of indi�
vidual components made to the economic growth,
including labor; capital; and total factor productivity,
which is estimated as the residue in the historical
period.
The specification of the applied production func�
tion is described in detail in [12, p. 4]. The difference
between this specification and the Cobb–Douglas
production function consists of introducing an addi�
tional factor of economic growth, which is associated
with a qualitative change in the labor force and is cal�
culated as a measure of total wage weighted by the level
of qualification and the number of hours worked.
Economic growth forecasting requires one to assess
the dynamics of individual components of the produc�
tion function. As a result, GDP growth forecast is
based on lagged values of the model parameters and
exogenously defined indicators, such as population
dynamics, taking the age composition, life expectancy,
inflation, the share of manufacturing and services sec�

tors in GDP, the level of education, and trade and
financial openness into account.
Goldman Sachs’s Long�Term Outlook [13] is based
on an assessment of the standard production function
where GDP growth depends on changing labor
resources, accumulated productive capital stock, and
technological advancement. The labor force dynamics
is set according to the UN projections [14]. The accu�
mulation of productive assets is determined by their
deterioration and investment dynamics. The techno�
logical advancement of individual countries is set
based on a hypothesis of converging levels of produc�
tivity in a given country and the leading country
(United States). The approach velocity is defined
using the Growth Environment Score, which allows
one to assess the ability of the economy to grow,
25 No. 5 2014
depending on a variety of political, social, and eco�
nomic factors.
PwC’s outlook [5] calculates the economic growth
rates for each country based on the production func�
tion, which, in addition to the factors of capital, labor,
and technological progress, includes components
such as the quality of the labor force, which reflects the
level of education. Volume capital growth is based on
hypotheses of dynamic capital intensity and the accu�

mulation rate. The labor force dynamics is set accord�
ing to the UN projections. The quality of labor is
determined by the number of years spent on educa�
tion. The pace of technological progress is defined
according to a gap between a given country and the
leading country (United States). The larger the gap,
the faster the speed of technological progress.
The OECD’s Long�Term Outlook is based on the
forecast of potential output, which is assed using a
modified Cobb–Douglas production function with
constant returns on scale. This function includes the
following factors: labor, fixed assets, human capital,
and labor�saving technological advancement. The
extrapolation of factor values (with the exception of
accumulated capital) allows one to obtain a long�term
forecast of potential output5.
The OECD long�term global economic model is
the most developed one compared with the others.
This model consists of the following blocks: the use of
GDP, income generation, the population’s income
and expenditure, prices, public finances (income,
expenditure, and public debt), foreign trade, foreign
direct investment and foreign exchange reserves, and
the monetary assets and liabilities of institutional sec�
tors (households, government, private sector, and the
external sector).
One of the features of the OECD model is the
interdependence of calculations for individual coun�
tries, which is achieved by taking estimated trade
flows, foreign direct investment, and balance sheets of
the external sector into account.
The structural saturation of the model is used to

describe the deviation of the current values of the eco�
nomic growth from the potential values.
Unfortunately, the Ministry of Economic Develop�
ment does not substantiate the methodology applied
for long�term forecasting of global and Russian econ�
omies. However, given the forecast emphasis on
human capital, workforce dynamics, physical volume
of capital, hypotheses about labor productivity and
total factor productivity, one can assume that the long�
term forecast was developed using the production
function.
This thesis is indirectly confirmed by the transcript
of the report prepared by the experts of the Federal
Budget Research Organization at the Institute of
Macroeconomic Research [16], which specifically
5 The long�term model is described in more detail in [15].
refers to the fact that one of the key methodological
features of long�term forecasting consists of “using
factor models to generate a potential trajectory of
growth with the embedded parameters of factor effi�
ciency depending on hypotheses of the technological
advancement linked to structural solutions in the field
of innovation and infrastructural progress.” At the
same time, one of the complex tasks of long�term fore�
casting is associated with the “macrobalancing of sce�
nario conditions and the elaboration of a potential
trend of economic development, with the enlarged
macrostructure of production and use.”
Macroeconomic models and models based on the
intersector balance can also be applied as modeling
methods of long�term forecasting independent of the

calculations based on the production function.
Other possible methods of calculating long�term
forecasts of economic growth. Calculations based on
macroeconomic models are substantially similar to the
calculations based on the production function, where
the components related to the use of GDP account are
perceived as the primary drivers of GDP growth,
rather than labor, capital, and technological advance�
ment. Usually, an equation is constructed for each
component of the use of GDP account; the obtained
equations are later combined into a system, while the
forecast calculations are based on the principles of
iterative account. These models can vary significantly
by structural content from the simplest options to the
options with multiple blocks and the interactions
between the blocks in the form of forward and back�
ward communications.
Calculations based on modern intersector models
usually represent a synthesis of intersector and macro�
economic calculations. Their main difference from
the macroeconomic models consists of the use of the
sectoral dimension and the relationship between the
value dynamics demonstrated by an individual indus�
try and the dynamics of indicators of all other sectors
and elements of final demand.
Cross�sectoral and macroeconomic models allow
one to build a forecast based on more complex scenar�
ios and to consider a greater number of economic pol�
icy parameters and economic relationships. However,
cross�sectoral and macroeconomic models describe
the economic growth at the same quality level as the
models based on the production function, i.e., as a
weighted sum of growth (reduction) of the individual

output components (GDP).
CONCLUSIONS
The analysis of the described long�term global eco�
nomic forecasts and their modeling tool shows that in
most cases such forecasts are based on the model of
aggregate production function. The superstructure of
additional units to the production function is mainly
used to assess the rate at which an individual country
436
25 No. 5 2014
GUSEV
can reach the growth rate of potential output calcu�
lated based on the production function.
It should be noted that, despite criticism against
the use of the production function as a tool for analysis
and projection [17], it is still widely used to elaborate
long�term growth forecasts6.
Similar to the production function, cross�sectoral
and macroeconomic models reduce the explanation of
economic growth to the quantitative estimates of the
contribution made by individual factors, which are
usually similar to the components of the use of GDP
account.
The approach to the assessment of economic

growth in the economic forecasts based on the con�
struction of a production function, as well as macro�
economic and intersectoral models, can be described
as quantitative.
At the same time, the economic theory explains
economic growth through qualitative changes associ�
ated with the enhanced division of labor and special�
ization, which are not considered in the quantitative
estimates of economic growth. Therefore, macroeco�
nomic arguments in favor of certain strategic decisions
and policy measures designed to achieve long�term
results, which are not based on the quantitative
approach, have at least no less weight than the argu�
ments based on quantitative assessments. Moreover,
arguments that are not based on quantitative princi�
ples eventually determine the quantitative results of
the elaborated long�term forecasts.
Possible ways to improve modeling tools. The ana�
lyzed global economic models are designed according
to the inevitable logic of economic growth. In other
words, it is assumed that all countries will continue to
experience economic growth in the long�term out�
look. Perhaps, these results are largely due to the exog�
enous prediction of the technological development
pace.
The applied forecasting tools suggest that underde�
veloped countries will inevitably improve their stan�
dards of living. In accordance with the assumptions of
the model structures, poverty in some countries is due
to low capital. This circumstance suggests a high rate
of return on invested capital, which should stimulate
the growth rate of accumulation and the acceleration
of economic growth (maintaining economic growth at

a high level). However, the analysis of economic
6 This fact can be explained by the simplicity with which the
pro�
duction function model allows one to decompose the economic
growth into single components and to explain the contribution
value of single factors. At the same time, the remaining
component,
which assumingly, reflects the contribution of scientific and
technolog�
ical progress, remains unexplained in practically all works on
long�
term forecasting. As noted in [18, p. 260], “Rather than
explaining the
peers and the society that the theory (theoretical justification of
the
production function – author’s note) does not clarify anything
based
on the observed productivity growth, the empirical studies
reported on
their “finding” that 80% (or 85 or 75%) of the observed
productivity
growth were due to technological changes.”
dynamics in less developed countries shows that the
level of per capita GDP may remain at a low level for
decades.
Therefore, it is far from being clear that the
enhanced specialization should last forever. As shown
in some theoretical studies (e.g., [18–20]), it is possi�
ble that due to low capital intensity in the economy no
further enhancement of division of labor can take
place and no capital accumulation will occur as a
result of the initial specialization in the production of
goods within a narrow intermediate production range.

Accordingly, economic growth will be limited by the
population growth.
In turn, the premises underlying the classical pro�
duction function are only applicable to the economy if
all firms produce identical goods (or the same set of
goods). This means that economic growth is actually
included in the forward�looking construction as a
hypothesis, rather than a simulated phenomenon.
Given that it is possible to construct a successful
approximation of the output dynamics using the pro�
duction function, it does not follow that this model
adequately takes into account the real mechanisms of
economic growth. After all, in the long�term outlook,
it is possible to identify a trend model for any economy
with an observable economic growth based exclusively
on the use of time series that will reproduce the histor�
ical output dynamics with no less accuracy than the
production function.
In the form in which the production function is
used for long�term global economic forecasting, the
following weaknesses can be identified in this model in
terms of a complete set of factors of economic growth:
—Explaining changes in production specialization
depth;
—Taking the impact of specialization on efficiency
into account;
—Taking the impact of constraints on the depth of
specialization into account;
—Taking crowding forward and backward com�

munications between producers (especially between
producers of intermediate goods) into account.
In the applied models used to develop long�term
economic growth forecasts, the factor of specializa�
tion can be considered by taking into account the
product diversity. In other words, macroeconomic
models should include the sector and product dimen�
sions. The distinction of individual sectors is already
present in the models designed based on the intersec�
tor balance. In addition, models of the two� (three�)
sector economy where individual sectors are described
by production functions are well known. These models
are inherently fairly close to the intersector balance
model.
In general, the introduction of the sector dimen�
sion in the model does not entail any difficulties (with
the exception of a magnified problems of calculation);
25 No. 5 2014
however, the product range of individual sectors is not
considered under forecast constructions anyhow.
Regardless of the extent to which sectoral and product
diversity can be taken into account, the key issue is
related to the consideration of new emerging indus�
tries and products. In the long term, economic growth
is always accompanied by the creation of new prod�
ucts, as well as the emergence of new industries and

markets, and their strong growth. Eventually, one pos�
sible solution to this problem is the enrichment of sce�
nario forecasts with the hypotheses about the emer�
gence of new industries based on the available infor�
mation about promising technologies.
Theoretical studies, which analyze the construc�
tion of economic models that take the impact of spe�
cialization on economic growth into account,
approach the product diversity rather formally. Specif�
ically, they postulate the existence of a certain number
of industries (products), and the specification of the
production function is designed in such a way that the
production volume, along with the technological
progress factor, depends on the diversity of produced
(consumed) products as an additional factor.
Similarly to the theoretical studies, in practical cal�
culations, the diversity (range) factor of products out�
put (consumption) could be introduced into the pro�
duction function by analogy with other factors. Add�
ing the diversity factor of produced (consumed)
products creates new demands with regard to the sta�
tistical data used in calculations.
One strategy to finalize the production function is
associated with the introduction of the intermediate
product factor and the intermediate product diversity
factor. The more diverse is the offer of intermediate
goods, the lower are the costs of the final products
[20]. A limited variety with incomplete interchange�
ability of intermediate products will result in higher
costs incurred by additional processing of intermedi�
ate products.
In turn, the intermediate products can also be

divided into two or more levels. For example, the first
level, which is closest to the final product, is the level
of components; the second level is formed by the raw
material for the production of components. Accord�
ingly, it would be fair to assume that, in case of inter�
mediate products, a greater variety of intermediate
products of the lower level entails the more efficient
production of intermediate goods of the upper level.
Ultimately, this may mean that the existence of a rich
and diverse resource base is indispensable for
enhanced specialization [20].
The inclusion of the industry dimension and the
range of manufactured products into the model can
help solve the second and the fourth problems speci�
fied in the above list.
The most problematic issue is related to the formal�
ization of changes in the degree of specialization. A
general tendency of enhanced division of labor con�
sists in the displacement of manual labor by mecha�
nized work or a complete replacement of manual work
by equipment, where equipment of a new generation
replaces more outdated equipment. The replacement
of human labor by equipment and the emergence of
new types of technology lead to longer production
processes and an increasing contribution of the labor
factor to the final product in indirect form. The mass
introduction of new technologies that contribute to
longer production methods requires the expansion of
markets in order to ensure return on investment.
This means that long�term forecasting models of
economic growth should describe the technological
progress, including the following elements: R&D

funding, the amount of accumulated knowledge,
human potential in research and development, sus�
ceptibility of producers to the emergence of new tech�
nologies, and return on new technologies. The payoff
criterion of new technologies that get into mass distri�
bution allows one to formalize the third problem. If
the new technology does not sufficiently reduce the
production costs of the entire economy so that this
particular technology is distributed on a mass scale (in
other words, there is a sufficient demand for products
manufactured by using new technology), further spe�
cialization in this direction does not take place.
This approach does not allow one to directly for�
malize the processes of enhanced specialization; nev�
ertheless, this formalization of a link between the tech�
nological progress (e.g., in the form of indirect mea�
sures of material consumption) and the changing size
of markets distributing the products manufactured
with the help of new technologies makes it possible to
take indirectly the changes in the level of specializa�
tion into account.
* * *
The use of economic models is not unjustifiably
becoming more common in decision�making at vari�
ous levels. It is possible that the main advantage of the
rich structural economic models that take the existing
economic relations into account as much as possible
lies in their ability to balance proposed development
scenarios and targets against the existing resource con�
straints. At the same time, the analysis of long�term
forecasts and models applied for long�term global eco�
nomic forecasting shows that the factors that, accord�
ing to the classical economic theory, directly form the

economic growth, are not accounted for in long�term
forecasting. The functional relationship between out�
put and production factors or elements of final
demand and gross industry benchmarks in the models
can help explain the economic growth as a result of the
growing supply of factors, the intensity of their use or
an increase in final demand. In turn, these compo�
nents can be reduced to the dynamics of labor produc�
tivity and technological progress, which are either
specified exogenously or under an iterative solution
438
25 No. 5 2014
GUSEV
method show a positive trend due to the values of the
equation parameters.
This means that the results of economic modeling
and, above all, the economic dynamics are implicitly
or explicitly set by the forecast scenario conditions. In
other words, the results of calculations are of a subor�
dinate character in relation to the arguments and the
logic underlying the development of the forecast sce�
nario, which may depend on subjective reasons.
One could achieve more rigor in the validity of eco�
nomic modeling results by taking into account the fac�
tors of product diversity and enhanced specialization,
as well as their relation to the technical progress and
the size of markets in the designed models.

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Gusev, Mikhail Sergeevich,
Cand. Sci. (Ecom.), Head of Laboratory
Translated by V. Kupriyanova�Ashina
Reproduced with permission of the copyright owner. Further
reproduction prohibited without
permission.

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