REGIONAL ECONOMIC MODELING IN THE SOVIET
UNION
Daniel L. Bond
International Research and Exchanges Board
The National Council for Eurasian and East European Research
910 17th Street, N.W.
Suite 300
Washington, D.C. 20006
TITLE VIII PROGRAM
FINAL REPORT TO
NATIONAL COUNCIL FOR SOVIET AND EAST EUROPEAN RESEARCH
TITLE:
Regional Economic. Modeling
in the Soviet Union
AUTHOR:
Daniel L. Bond
CONTRACTOR:
International Research & Exchanges Board
PRINCIPAL INVESTIGATOR:
Daniel L. Bond
COUNCIL CONTRACT NUMBER:
620-10
The work leading to this report was supported in whole or in
part from funds provided by the National Council for Soviet
and East European Research.
REGIONAL ECONOMIC MODELING IN THE SOVIET UNION
by Daniel L. Bond
SUMMARY
Because of the size and territorial diversity of the Soviet Union,
regional considerations have always played an important role in Soviet
economics and planning.
In order to study and plan for regional
development Soviet analysts have in recent years increasingly turned to
the use of mathematical-economic models. This has been a part of a
general "modeling" trend in Soviet economics which began in the late
1950s and is now a dominant approach in the discipline.
Economic modeling in the Soviet Union is inextricably a part of
economic planning, from whence it derives its purpose, support, and
approach. The introduction of mathematical modeling in Soviet planning
began, and has been most successful, at the lowest levels of planning
— i n problems more of an engineering than of an economic nature. Here
relationships can be expressed in physical terms, evaluation criteria
are more easily formulated and are less controversial, and the necessary data can be gathered rather easily.
However the methodological
vacuum at the higher levels, where questions of resource allocation on
a society-wide scale are considered, quickly drew efforts to apply the
modeling approach here also. But in every respect the problems multiply rapidly.
In spite of many difficulties, a major effort has been placed on
the development of models for macro-level (i.e., national or regional
level) planning in the Soviet Union.
This may be attributed to the
decision taken by top party leaders to reduce the pressures for
economic reforms of a decentralizing nature by seeking compensating
improvements in increased efficiency of central planning. Not wanting
to lose control of the economy, but needing to increase its efficiency,
Soviet leaders have chosen to emphasize improvements in the planning
process rather than attempt more fundamental reforms.
An effort was begun in the 1960s to develop computer based economic models for use in planning. Two objectives appear to have been of
primary importance: (1) to allow plans to be constructed and modified
more rapidly and with less manpower; and, (2) to improve the efficiency
of resource allocation through the construction of better plans. These
objectives have been pursued, to a great extent, independently of one
another. A massive program to secure the first objective is now well
underway in the creation of "automated systems of plan construction"
at all levels of the planning hierarchy.
The focus here is on the
introduction of automated data management and plan calculation, with
little concern so far given to changing the nature of the decision
making process.
To date most of the models proposed for this system
have simply replicated the same proceedures employed in the past by the
planning bureaucracy.
-i-
A s e c o n d — a n d largely separate—effort has been underway to
develop mathematical-economic models that would not just replicate the
administrative planning process, but would provide planners with
the means for improving the quality of their decisions. It is felt that
for macro-level planning specific types of models can provide useful
information for making what are termed "pre-plan calculations". As a
first step in planning there is a need for extrapolations of trends in
the economy, analysis of the implications of decisions concerning
resource allocation, evaluation of the probable impact of technological
progress, identification of possible imbalances between goals and
resources, etc.. These need to be carried out at a high level of
sectoral, regional and functional aggregation with a time horizon of
five to fifteen years. They are intended to provide planners with the
basic information necessary for making strategic decisions and to serve
as a basis for subsequent physical planning calculations whcih are
carried out in much greater detail. Some Soviet economists and planners feel that one way this information can be provided quickly and at
low cost is through the use of computer based modeling.
One of the difficulties often encountered in formal mathematicaleconomic modeling is that each technique restricts the representation
of the real world in some way.
Input-output models are useful for
understanding inter-sectoral relationships in production, but. they are
not as useful as econometric models based on sectoral production
functions when it comes to the analysis of substitution possibilities
or temporal changes in productivity.
Optimization models are useful
tools for deciding on the, best allocation of resources to reach some
goal, but they are not as helpful in understanding how system behavior
constrains the planners' ability to make these allocations. Many other
examples could be cited, as each formal technique has its limitations
in regard to the nature of the specifications allowed, the type of data
required for Its application, or the form of the solution process
involved.
Both balancing and optimization models have been extensivelystudied by Soviet economists, and they now are also beginning to be
used in the planning process. However an awareness of certain limitations of these models has recently become evident. ' It is being
recognized that the economy is far too complex for any single objective function to effectively reflect the goals of economic planning.
Also, in order to be solvable with available algorithms and computers,
balancing and optimization models must be specified in rather rigid
formats.
The limits this places on the flexibility for modeling
certain economic processes has become increasingly obvious as Soviet
economists have attempted to make their models more comprehensive.
Some of the most interesting experiments of the last decade have
involved models where global optimization has been foregone in order to
introduce greater complexity in models' objective functions and structural specifications. In particular, interest in the use of simulation
models has became widespread during this period.
Simulation models
are designed to answer questions
of the type "if epecificed actions
are taken in period t, then what will be the results In period t+1,
-ii-
t+2, ... t+n?" They may be similiar in structure to balancing models
(that is, they consist of a set of equations which when properly
specified can be solved for the endogenous variables given values of
the exogenous variables), but are qualitatively different in the degree
of closure and flexibility of internal specification.
The difference between simulation and optimization models is even
more significant in that simulation models do not require an overall
objective function for their solution. Instead simulation models are
designed to perform calculations of an "if ... then" nature. That is,
they allow examination of many alternative paths of development, where
each path is assoicated with a particular set of government policies
and external conditions. The choice of a "best" combination of policies and results is left up to the model user. As a result of dropping
the extremal value solution procedure, the flexibility of internal
specification of simulation models is substantially greater, and
computational problems less, than that of optimization models. However,
the dropping of the objective function does raise questions as to the
normative value of the solutions obtained from the use of these
models.
Although simulation models do not automatically provide a
"best" solution (of the type obtained in optimization models) they can
be used to generate information which can then be used in less formal
decision making processes.
Two broad classes of simulation models can be identified based on
the manner in which their parameters used in the models are obtained,
In one group parameters are set on the basis of expert opinion or
planning "norms", in the second they are based on statistical estimation using time series or cross section data.
The latter, know as
econometric models, have shown a very rapid increase in popularity.
One reason given for using the statistical approach to parameterization
is very practical—it is often difficult to obtain from experts or
higher authorities parameter values of precisely the type needed.
Statistical estimation presents a quick and inexpensive substitute.
In contrast to the situation in the West, in the Soviet Union
there is very little basic economic research using statistical techniques designed to test hypotheses. Thus there is no substantial body of
econometric research from which Soviet modelers can draw useful inputs
for their work.
(Although Western techniques can be imitated, it has
been obvious that most Western specifications are not applicable to
the Soviet economy.) Often the specifications used in the econometric
models show little variation from those used in other simulation
models. In only a few instances has the choice of econometric methods
of parameterization been reflective of an experimental approach to
model specification.
This points up another characteristic of many modeling efforts
in the Soviet Union.
Often models are specified on the assumption
that the relationships protrayed are deterministic and known. Thus in
the initial stages of model construction there is no experimentation
with alternative specifications that would require that data collection
be carried out in the early stages of model construction. This leads
to a separation of model building and empirical research which seems
-iii-
strange to Western analysts who normally pursue both tasks simultaneously. Especially in Western econometric model-building, model
specification has followed, and been built upon the results of, many
years of empirical research directed at hypothesis testing. It is an
attribute of mechanistic theories that they are considered to be
obvious, so that there is little need to carry out sophisticated
statistical analysis to verify that the theory correctly explains these
interactions.
Thus, even in present day Soviet econometric modeling,
the objective of most efforts in statistical estimation is to obtain
parameters, not to test specifications.
By Western standards, the structure of Soviet econometric models is
quite simple.
Several reasons for reliance on simple specifications
can be suggested: shortness of the time series available for model
estimation; inexperience in the use of multivariant statistical
estimation techniques; the high multicolinearity of most Soviet time
series statistics which makes it difficult to estimate meaningful
parameters when several explanatory variables are used simultaneously;
a mechanistic view of the operation of the economy which leads researchers to view variables as causally linked in one-to-one relationships; and the reliance upon structural rather than reduced form
equations.
An additional explanation has been offered by Soviet
researchers which may be equally important.
Since Soviet models are
intended for use in the planning process it is essential that the model
builders be able to explain to the model users the meaning of the
estimated coefficients.
These model users are accustomed to dealing
with parameters in the form of "norms" which are usually obtained from
engineering studies or are based on simple economic accounting relationships.
Coefficents obtained from statistical estimations of
equations containing only one or two explanatory variables are often
similar in value and can be interpreted with reference to these norms.
When more explanatory variables are introduced this relationship
between norms and coefficients often breaks down, and it becomes
difficult to explain the meaning of the estimated parameters without
reference to a body of statistical theory unfamiliar to most potential
model users.
Probably the most successful of the simulation models, in terms of
actual application to planning needs, are those that have combined
both econometric, and non-econometric parameterization techniques in
what are called "integrated systems of models". In these models the
economy is protrayed at several levels of disaggregation. Macro-level
relationships are described on the basis of econometrically estimated
equations, while sectoral detail is obtained through the use of
input-output relationships.
In certain instances critical variables
are calculated in alternative ways as a check on their values. Consistency is maintained between levels or approaches either through
sequential disaggregation or adding-up, or through direct adjustments
made by the model user at various check-points in the solution process.
These models allow greater flexibility in specification, and present
fewer problems either in terms of the necessary data base or their
computation requirements, than other forms of models.
Substantial
-iv-
progress has been made in introducing these models into the planning
process in several republican Gosplans, and arguments have been made
that these models are the proper prototype for modeling at the national
level.
In terms of future directions for model development and application, one of the critical areas of improvement will need to be in the
linking of national and regional models and forecasts. At present in
the Soviet Union there is no organized means for integrating the
efforts of the many research centers using models to forecast developments nationally and in individual regions.
A linked system would
provide a means by which forecasts generated for the nation as a whole
could be used as an input into the forecasting of regions (what
is known in the West as a "top down" approach to regional forecasting)
or where regional forecasts could be aggregated into national forecasts
(a "bottom up" approach to national forecasting). Although there are
some Soviet models that combine both national and regional dimensions,
they are not a substitute for a linked system.
The expertise and
involvment in planning of many locally based modeling groups is a
vital ingrediant which cannot be replicated in one center.
Some
regional models already have provision for using forecasted values of
national variables.
However the necessary national forecasts are not
readily available.
Likewise, there is no indication that national
forecasters make any systematic use of the available forecasts of
regional groups.
Three deficiencies in the present organization of forecasting in
the Soviet Union have been pointed out: (1) forecasting groups work in
relative isolation from one another, without the necessary exchange of
information, i.e., there is no hierarchical system of forecasting
organized under a single leadership; (2) because the planning organizations do not take an active enough role in the forecasting effort
there is still insufficient integration of forecasting with the planning process; (3) the industrial ministries and other organizations in
various sectors of the economy do not actively and regularly participate in long-range forecasting, although their inputs are vital to the
success of any such effort.
Some Soviet economists have expressed the view that the recent
(July, 1979) Central Committee resolution on improvements in the
planning system will have a significant impact on the role of forecasting, and thus on the development of economic modeling. The new resolution is a first step toward correcting the deficiences noted above.
It makes mandatory for the first time the preparation of medium and
long term economic and social forecasts. Academy of Science institutes
are charged with the development of twenty years "programs of scientific-technical progress" which are to be delivered regularly to Gosplan.
These are then to be used by Gosplan, working together with the industrial ministries and the republican Councils of Ministers, to prepare
ten year forecasts of the social and economic development. It can be
expected that the demands generated by these new requirements will
serve as a stimulus to the continued evolution of economic modeling in
the directions indicated above.
-v-
REGIONAL ECONOMIC MODELING IN THE SOVIET UNION
by Daniel L. Bond
Introduction
The role of
becoming
regional
increasingly
factors in Soviet economic development is
important
as disparities
between
the
existing
location of production centers and the location of new labor and raw
material supplies become greater.
of these spatial
Two of the major underlying causes
shifts are the disparity between
the rapid rate of
population growth among the Moslems of the Central Asian region and the
very slow population
growth elsewhere, and
the depletion of mineral
resources (especially fossil fuels) in the European regions resulting
in the necessity of developing
Siberia.
new sources in the Far North and
These developments have alreadly caused a significant in-
crease in investment for the eastern regions, and are likely to lead to
other
important
changes in Soviet
regional
planning, especially
as
regards the use of labor.
Because of the importance of spatial considerations in economic
planning, questions of regional analysis have always played an important role in Soviet economics.
An especially active area of research
in recent years has been in the use of mathematical-economic modeling
—-part of a general "modeling" trend in Soviet economics which began in
the late 1950s and is now a dominant approach in the discipline.
The research undertaken for this paper focused on developments in
regional economic modeling that have taken place in Soviet Union during
the 1970's.
The purpose
of this examination was to understand
how
Soviet economists and planners view the structure and functioning of
the regional
economy as this is revealed
in the methods and models
they have developed.
Information for the preparation of this paper came from two main
sources. First, many
books, reports, and
—1 —
articles dealing
with
the
subject of regional modeling have been published in the Soviet Union,
and
a fairly comprehensive
out.
review of these publications was carried
A list of those documents which were found to be most relevant
and informative is provided in the bibliography.
Second, the author
was fortunate to have the opportunity to meet and talk with most of the
leading Soviet practitioners in this field while on extended visits to
the Soviet Union in 1975-76 and 1979.
Although the literature on economic modeling in the Soviet Union
is
extensive, there
Modeling
are
limitations
to this source
of information.
is an empirical science, and this has led to a dilemma for
In 1975-76 the author spent
participant
in
an
exchange
ten months in the Soviet Union as a
program
organized
by
the
International
Research and Exchanges Board (IREX) and the Soviet Ministry of Higher
Education.
For most of this time he studied at the economics faculty
of Leningrad State University.
In 1979 he returned to the Soviet Union
for eight months, under the auspices of the American Council of Learned
Societies/Soviet Academy of Sciences Exchange of Economists, to work at
the Central
Sciences
Economic Mathematics
in Moscow,
During
Institute of the Soviet Academy of
both visits several
trips were made to
research institutes in other cities, including the Economics Faculty at
Vil'nius State Universtiy, the Institute of Economics of the Lithuanian
Academy of Science, the Research Institute of Planning and Economics of
the
Lithuanian
Gosplan, the
Institute
of
Economics
of
the
Latvian
Academy of Science in Riga, and the Institute of Economics and Organization of Industrial Production of the Siberian branch of the Academy
of Sciences in Novosibirsk.
The author wishes to thank the many Soviet
economists who assisted him in his research, and would like to acknowledge the support and financial assistance that he received from IREX,
the National Council for Soviet and East European Research, the Soviet
Ministry of Higher Education and
-2-
the Soviet Academy of Sciences.
Soviet economists.
Much of the economic data collected
Union is not published in open sources.
in the Soviet
Data not officially released
are considered to be state secrets, and the publications of researchers
using this data are carefully censored.
for researchers working
This is especially restrictive
in areas such as econometric modeling, where
little of significance can be discussed without revealing the nature of
the data used
or supplying
estimation and simulation results.
What
this has meant is that, in at least some cases, researchers who have
been
allowed
access
to unpublished
privelege of publication.
sources
have
had
to
forego
the
This probably applies most to work which is
undertaken in the research institutes and computer centers attached to
the national and regional planning committees
other
places, such as
institutes
(Gosplans). Workers in
of the Academy of
Science or the
universities, have been freer to publish, but in many cases have
had access only to the limited published data.
Thus any review, such
as the present one, which relies heavily on published sources will be
to some extent incomplete.
A second limitation of reliance on published sources is that
only rarely does one find in print any discussions of actual forecasts
or
analyses
carried
out
with
these models.
Most
presentations
models in the Soviet literature focus on methodological issues.
of
There
seems to be strict control on the release of forecasts of any type.
Also,
in only
research
problems.
a few
appeared
instances have
results
obtained
from modeling
in connection with discussions of actual
economic
The opportunity to explore these issues in private discus-
sions with Soviet researchers has been useful in some cases in filling
in
the picture, but Western
their
attempts
Soviet models.
researchers
to review forecasting
and
are clearly handicapped
analytical
in
applications of
The Role of Modeling in a Centrally Planned Economy
Economic modeling in the Soviet Union is inextricably a part of
economic planning.
Modeling derives its purpose, support, and approach
from planning.
The origins
of Soviet interest in modeling are found in the
attempt to introduce objective scientific principles into the planning
process.
Planning
in the past
has
largely
been
an
administrative
activity designed to mobilize the economic resources of the country in
the service of "building
socialism".
Marxist-Leninist-Stalinist
theories of economics have supplied certain general strategic principles
for
planning, but
development
at
the
concrete
level
of drafting
detailed
plans these prinicples fall short of what is needed.
The introduction of mathematical modeling in Soviet planning
began, and has been most successful, at the lowest levels of planning
— i n problems more of an engineering than of an economic nature.
relationships can be expressed
Here
in physical terms, evaluation criteria
are more easily formulated and are less controversial, and the necessary data can be gathered rather easily.
However the methodological
vacuum at the higher levels, where questions of resource allocation on
a society-wide scale are considered, quickly drew efforts to apply the
modeling approach here also.
But in every respect the problems multi-
ply rapidly.
In spite of many difficulties, a major effort has been placed on
the development of models for macro-level (i.e., national or regional
level) planning
in the Soviet Union.
decision
taken by
top party leaders
economic
reforms of
a decentralizing
This may be attributed
to reduce
to the
the pressures
nature by seeking
for
compensating
improvements in increased efficiency of central planning.
Consider-
ation of the problems created by the huge territory of the Soviet Union
may have played a part in this decision.
for instance, in the following statement:
Such thoughts arc expressed,
The great spatial heterogeneity of the Soviet national
economy is a major factor to be taken into account
when working out the structural principles of the planning and control system and state economic policy. For
this reason, not all elements of the economic reforms
carried out in the socialist, countries are acceptable
in Soviet conditions. In particular, combined centralized planning and self-sufficiency, directive planned
targets and means of economic regulation should guarantee accumulation of the state resources for making major
shifts in the location of production forces. Obviously,
this limits self-financing of the expanded reproduction
of individual economic units, both in sectoral (enterprises, production associations) and territorial (union
republics, administrative regions) aspects. (Granberg,
ed. (1976b), p. 6)
Not wanting to lose control of the economy, but needing to increase its efficiency, Soviet leaders have chosen to emphasize improvements
in the
reforms.
planning
process
rather
than attempt more
fundamental
One avenue that has promised such improvement is the greater
application of computers in planning.
And together with this has come
the need to develop methods of economic calcuation which could exploit
the possibilites of this new technology.
An effort was begun in the 1960s to develop computer based economic models for use in planning.
Two objectives appear to have been of
primary importance: (1) to allow plans to be constructed and modified
more rapidly and with less manpower; and, (2) to improve the efficiency
of resource allocation through the construction of better plans.
These
objectives have been pursued, to a great extent, independently of one
another.
A massive program to secure the first objective is now well
underway
in the creation of "automated systems of plan construction"
at all levels of the planning hierarchy.
The focus here is on the
introduction of automated data management and plan calculation, with
little concern
making process.
so far given
to changing
the nature of the decision
To date most of the models proposed for this system
have simply replicated the same, proceedures employed in the past by the
planning
bureaucracy.
(A good
discussion
of this approach, and
the
modeling problems associated with it can be found in Vasiliauskas, et
al. (1975).)
A
second—and
largely
separate—effort
has been underway
to
develop mathematical-economic models that would not just replicate the
administrative
the means
for
planning
improving
process, but would provide
the
quality
planners with
of their decisions.
Based
on
work in the field of optimization models of a programming type, Soviet
economists developed by the 1960's a methodological basis for improved
resource
allocation modeling
there have been
some successes
practice of transportation
planning.
at
the micro
level.
in introducing
In
recent
such models
years
into the
and plant or industrial branch production
But the Soviet attempt to use this same approach for more
comprehensive planning problems has not met with great success.
Around
1970 several proposals were prepared for a system of programming models
which would allow for the "optimal" planning of the national economy at
all levels.
(See Aganbegian, et al. (1972) and Fedorenko, ed. (1972).)
But after more than a decade of research little progress has been made
in the implementation of such a system.
Interest
in comprehensive
model
systems
of the above
sort has
waned in recent years, but there is continuing interest in the development of models of more limited scope.
It is felt that for macro-
level planning specific types of models can provide useful information
for making what are termed
in planning
"pre-plan calculations".
there is a need
As a first step
for extrapolations of trends in the
economy, analysis of the implications of decisions concerning resource
allocation, evaluation of the probable
impact
of technological
pro-
gress, identification of possible imbalances between goals and resources, etc.. These need to be carried out at a high level of sectoral,
regional
and
functional aggregation with
fifteen years. They are intended
a time horizon of five to
to provide planners with
the basic
information necessary for making strategic decisions and to serve as a
basis for subsequent physical planning calculations whcih are carried
out in much greater detail.
Some Soviet economists and planners feel
that one way this information can be provided quickly and at low cost
is through the use of computer based modeling.
—6—
This much more limited
role for models is now being tested in a number of organizations, and
much of
the leading work of this type involves the use of regional
models.
How the Economy is Portrayed in Soviet Regional Models
The way Soviet
in
Marxist
theory,
economists view their economy is largely founded
the
economic history, and
system.
and
experience
of
centralized
planning,
Soviet
the concepts and coverage of their statistical
It is not surprising then that Soviet models contain variables
relations
somewhat different from those found in Western models.
Thus it is useful to describe the Soviet conception of the structure of
their economy, drawing from and generalizing upon the content of these
models as a group.
To aid in this effort, Figures 1 and 2 are provided.
Here the
major variables found in Soviet models are identified and related to
one another.
(A list of variable definitions is provided with these
figures.) For the sake of simplicity, the economy represented has only
two regions, with three sectors identified in each region.
In Figure 1
the variables represent real stocks or flows of goods and services; in
Figure
2,
financial
stocks
and
flows.
(To
distinguish between
the
physical and financial expressions of the same variable a tild has been
placed over the financial variables in Figure 2.
noted
that this differentiation
paper.)
is not used
However, it should be
in the remainder of the
The indicated linkages between variables (denoted by arrows),
are intended
sequential
only as an aid to conceptualization.
flow
transactions.
of
resources,
commodities,
They
services,
or
indicate a
financial
But they do not necessarily represent the sequence of
causal relationships as portrayed in Soviet models.
Such sequencing
will be discussed in following portions of this paper where specific
models are presented.
Variables
have been arranged
-7-
in horizontal
and vertical
panels
Figure 1.
Schematic Diagram of Real Variables
in Soviet Regional Economic Models
Figure 2.
Schematic Diagram of Financial Variables
in Soviet Regional Economic Models
-10-
(indicated by the dotted lines) as a way of identifying various categories of economic activity.
In the left-most vertical panel of Figure
1 are to be found demographic and regional natural resource variables,
while
this panel
income formation.
in Figure 2 contains variables describing
As with most of the variables indicated in these
figures, numerous sub-categories could be identified
these.
personal
for each of
A number of Soviet regional models contain considerable detail
on demographic structure, identifying changes in the population's age
and sex composition, and place of residence (urban or rural).
Regional
natural resources are usually identified by type, the most common being
land, water, mineral resources, and timber.
Although the major cate-
gories of additions to and deductions from personal income are identified in Figure 2, each of these could also be further disaggregated
(wages and other labor income, personal taxes and other payments to the
state
budget,
pensions,
stipends, and
other
income
from
the
state
budget, and interpersonal money transfers between regions.)
In both figures
the central panels are devoted
to variables
describing the production, process in various types of economic sectors.
In keeping with Marxist theory two major subdivisions of production are
identified:
the sphere of material production, represented by sectors
i and j, in which commodity production and services required in this
production are carried out, and the service or "non-production" sphere
which provides social and personal services including
activities
related to education, health, culture, government, scientific research,
defense, housing, public transportation, and every-day personal services.
Within the production sphere a distinction is made between sector
i, the output of which is utilized only within the region where it is
produced, and sector j , which produces commodities for both local use
and export outside the region.
sectors of
type
i would make
In terms of Western regional theory
up the "export
base" of each region.
There is an additional dimension to this distinction in the organization
of
Soviet
economic management
-11-
and
financing.
Activities of
strictly regional significance (both in the production and non-production spheres) are planned and administered at the regional level, and
their financing is secured locally from enterprise funds or the regional budget.
But sectors active
in interregional trade are often
considered to be of national significance, and their administration and
financing are either jointly shared by regional and national bodies, or
they may be almost entirely controlled
from the national level.
It
should be noted that this division of authority leads to constraints on
the independence of republic Gosplans in formualting plans for local
development, since they have little control over the planning of
certain sectors located on their territory.
In Figure 1 the variables within each of these sectors are those
representing production inputs and outputs of goods or services.
Among
r
the inputs to production are labor ( L ) , the services of capital
(Kr ) , and purchases from other sectors (Mr ) .
In Figure 2 a distinction
between gross output (Xr ) and net output (Yr ) is made.
In addition
to subtracting from gross output payments for intermediate purchases,
it is necessary to deduct depreciation payments (Dr ) to arrive at net
output.
(This peculiarity of Soviet accounting practice arises from
the inadequacy of Societ capital accounting.
76.)
Payments made from
net output are
See Becker (1972), p.
shown in Figure 2.
These
include wages and other payments for labor (Wr ) , taxes and other
payments into the state budget and credit system (Tr ) , and the residual
category of profits or subsidies (Pr ) .
The process of capital formation is portrayed in detail, and
involves both additions
to (Vr ) and subtractions from (Sr ) previous
period capital stocks ( K r t - 1 ) .
The flow of investment goods goes either
directly into newly formed capital (ready to be used in production) or
it goes into
(Ur ) .
only
the stock of uncompleted
capital construction projects
Since any particular project is completed and brought into use
after
a
period
of
time,
the
relationahip
between
uncompleted
construction and new additions to utilized capital stock is a lagged
one.
(This largely determines
the dynamic properties of most Soviet
-12-
models.)
Flows of materials between the production sectors, and the corresponding
payments, are also
indicated
in both
figures
( M r ) . Much of
the work of Gosplan is involved with planning these flows and insuring
that the material output and input plans of sectors and enterprises are
consistent.
As might be expected, these interindustry relations also
play an important role in almost all Soviet models.
It should be noted
variables are shown.
that in the non-production sector, no output
This reflects Marxist theory, and Soviet account-
ing practice—the definition of output exludes the results of activities of the non-production sector.
(This means that the Soviet measure
of national income is only about 75-80% of what it would be if measured
by Western accounting methods.)
The effect is that in most models of
the Soviet economy all sectors are accounted for on the input s i d e —
i.e., factor allocation equations identify labor, capital and materials
allocated to each~~but on the output side, no equations are provided
showing any outcome of activities in the non-production sector.
In
effect, non-production sector activities are transferred into a category of final demand.
Variables of the right-hand panels represent the distribution of
regional production (Xr ) and
imports
( E s r ) among final uses
(in
Figure 1) and the sources of financing of these uses (in Figure 2 ) .
Part of the output goes into current consumption.
This is divided into
two parts: private consumption of commodities ( C r ) , and material
inputs going into the non-production sector (G r ) .
Commodity output
r
is also used for accumulation purposes (O ) . The largest portion goes
into the creation of new fixed capital stocks ( I r ) , but some is used
for capital repairs and renovation (KR r ) and for working capital and
inventories
(KWr).
Finally, a portion of the exporting
output is sent to other regions ( E
rs
sector's
).
The financing of consumption and accumulation is somewhat complex.
In each of the production sectors funds for accumulation expenditures
are partly self-financed, coming
-13-
from profits (P r ) and
depreciation
deductions
state
( D r ) , and
budgets
partly
financed
and credit systems
r
(B
by
the
and B) .
regional
and
national
(In the Soviet Union
about one-third of all funds for investment come from the state budget
and
are
not
directly
repaid.)
Accumulation
in
the
non-production
sector is primarily budget financed. Personal consumption is covered by
personal income ( Z r ) , and withdrawals
consumption
is largely
budget
from savings ( J r ) .
Social
financed, but there are some payments
from personal income.
Considerable transfer of income among sectors and among regions is
achieved by means of the state budgets, which are important instruments
used by the central planners to control the allocation of resources in
the
Soviet Union.
national
In Figure 2 both regional budgets (B r ) and a
budget (B) are indicated.
deductions from
both enterprise
Certain categories of taxes and
funds (T r ) and personal
income
(Q r )
are predesignated to go into one budget or the other, while other types
of
deductions
flexibility
are
in
prorated
between
budgets.
this process, especially
There
is considerable
in the apportionment
of
turnover taxes between budgets, that allows net transfers between the
regions to be adjusted
to correspond
to the dictates of the central
plan.
A special note on the role of price information should be added.
In Figures 1 and 2 prices variables, other than the wage rate ( W r ) ,
do not appear.
This
almost no direct role
is a reflection of the fact that prices play
in Soviet models.
In some models wages are
portrayed as a factor in determining the allocation of labor resources.
However the importance of these prices as an explanatory variable in
such cases is to be doubted.
ty of housing
Demand for specialized labor, availabili-
and cultural facilities, urban location of employment,
etc., seem to be more
important
than the rather limited
differences
among regions in wage rates, and any role for prices and markets as an
allocative mechanism in other realms is rarely even suggested.
are
necessary, of course, for aggregating
Prices
physical units into value
terms, and in some models price series are included for linking current
-14-
and constant price series.
to
search
for
other
But, in general, Soviet modelers have had
stimuli
and
criteria
for
economic
choice, and
prices play only a secondary role in their models.
In order
to understand
the relationship among
this system, it is important
the variables of
to be aware of the following relation-
ships :
(1)
The aggregate
identity
between
gross
output measured
either
in
terms of cost of inputs or sales of product:
(By properly
defining M , this identity also holds for each
sector.)
(2)
The macro
equality
of net
product plus depreciation
and
final
demand (which does not hold for each sector individually):
(3)
The definition of regionally utilized
national income (net pro-
duct) :
(4)
The composition of final demand:
(5)
The definition of accumulation
repair and renovation
and
in terms of
investment, capital
increases in inventories and working
capital:
(6)
The identity of depreciation and
(7) . The
equality
between
the ' value
capital repair and renovation:
of
investment
and increases
in
uncompleted construction and new capital stock put into operation:
(8)
The distinction between
sectors
investment goods:
-15-
producing
and sectors using
(9)
The identity relating current value of capital stock to previous
period stock, new capital stock put into use and capital scrapping
(10) The composition of net product:
Among the remaining, non-identity, relationships among the variables of Figures 1 and 2 the following play the most important roles in
Soviet models.
(11) The relationship between inputs
(both primary and
intermediate)
and output (at either the aggregate or sectoral level).
the many
functions
specifications of
used
Among
the production and factor demand
in Soviet models, the following
are the most
common:
(12) The determination, at the aggregate level, of the share of final
product
going
for
accumulation
purposes, and
the
share of the
latter used for investment in new capital stock formation:
(13) The level of aggregate labor supply:
(14) The distribution of factors of production among sectors:
(15) The relationship between investment and capital formation:
-16-
(16) The rate of capital scrapping:
(17) The level of regional imports and exports by sector:
(18) The aggregate net trade balance, or the relationship between
utilized and produced regional income:
(19) The determination of factor incomes:
(20) The volume of personal consumption:
Other
discussed
relationships, less commonly
found
in Soviet models, are
in the following sections where some of the more prominent
examples of regional models are described in more detail.
None of the models developed
in the Soviet Union include all of
the variables of Figures 1 and 2.
The most comprehensive of the
models are the multiregional, multisectoral models which
include
variables for several regions and sectors, and thus cover all parts of
the figures to some
be
represented
since
they
regional
extent.
The content of single region models could
by a horizontal
contain variables
industrial
sector
band
for
or
across
only one
"branch"
parts of
region.
(otrasl')
these
figures
Finally multimodels, could
be
represented by a vertical band containing variables of a single sector,
but including more than one region.
Examples of each of these types of
models is provided later in this paper.
It is useful to give specific meaning to the terms "regional" and
"spatial" as they might be used to characterize these models.
A model
can be said to be "regional" when the empirical content of the model
-17-
reflects
conditions
production
and
specific
consumption
to
a particular
functions
are
region.
usually
For example,
specified
on
the
basis of some economic theory without reference to conditions of any
particular region.
estimated
using
But if the parameters of these functions have been
historic data
from some region, the estimated func-
tions may be said to be "regional".
"spatial" dimension
In order for the model to have a
it is necessary that it include some representa-
tion of the role of distance in economic relations—this affects both
the
specification
of the model and
its empirical content.
Most
frequently this involves some means for calculating costs of transportation or choice of location and using this infomation for determining
the
level
of
activity
in
regions
and
the level and
composition of
exchanges among them.
A lack of emphasis on what may be considered specifically "spatial"
aspects
models of
of
economic
single regions.
development
Usually
is
fairly
typical
of
Soviet
there is no direct treatment of
transport costs, and thus these models are limited in their ability to
deal with those aspects of regional development which are related to
exchanges among regions.
In general, single region models differ from
national models only in that the parameters used are specific to
conditions of a particular region.
almost
identical
to
national
But in their structure they are
models. They
are
"regional", but
not
"spatial", models.
On the other hand, in multiregional models, there is direct
reflection of spatial economics.
These models may contain detailed
models of the transportation system, or the cost of transporting
products
way.
among
the various
regions may
be reflected
in
some other
Also explicit treatment of the role of land use in location is
usually present, since information on regional natural resources enter
the solution either
through constraints on resource use or inclusion
of resourse costs.
In the various
an integral
branch models transportation
is also treated as
part of the production process, and minimization of all
-18-
costs,
including
transportation
solution process.
for
endogenous
costs, is usually the
basis of the
However since these models usually do not provide
determination
of
aggregate
regional
variables
they
may, as the reverse of the single region models, be called "spatial",
but not really "regional", models.
In this review most attention will be given to models that are
specifically "regional" in nature.
Soviet Approaches to Economic Modeling
Soviet economic models of the 1960s were primarily of two types:
balancing or optimization models.
of equations
Balancing models consist of a set
(usually linear) describing
the interrelationships among
a set of endogenous variables and between these variables and a set of
exogenous variables.
Usually
the parameters of the equation system
are obtained from a set of accounting balances for a given year (using
either historic
data or forecast data).
If the system is properly
specified, with given values for the exogenous variables a unique set
of values can be determined
for the endogenous variables.
The most
widely applied models of this type were input-output models based upon
Leontief's description of production.
Often
the exogenous variables
consist of a set of final demands and the model's solution yields the
necessary
final
levels
demands
of
are defined
latter are solved
expressed
several
in
production
exclusive
demand
for
primary
of accumulation
inputs.
If
items, and
the
for endogenously, this results in the model being
dynamic
periods
and
form,
in order
requiring
to
a
simultaneous
realistically
process of capital formation.
reflect
solution
over
the multi-period
Considerable use has been made of this
type of model for economic analysis and planning in the Soviet Union.
Balancing models are attractive to Soviet planners
because
they
"consistency".
are well
suited
to assist
in solving
in large part
the
problems of
Much of the effort of planners is spent simply insur-
-19-
ing
that
"outputs" produced
and
"inputs" required
by all activities
match one another.
Most Soviet optimization models are built around the framework of
such
static
or
dynamic
balancing
models.
This basically
involves
adding constraints on the use of primary inputs and specification of
an objective function. In macro-models the latter is usually expressed
in terms of maximization of material welfare of the population, and
this is expressed in terms of some pre-specified mix of final consumption, or, in the case where consumption functions are included in the
model, maximization
of
national
income.
Optimization
substantially
reduces the number of exogenous variables for which prior information
must
be supplied, and
gives
the values of endogenous values
(again
usually output levels) which are the "best" in terms of the objective
function.
Both balancing and optimization models have been extensively
studied
by Soviet economists, and
used in the planning process.
tations
of
recognized
these models
they now are also beginning to be
However an awareness of certain limi-
has recently become
evident.
It is being
that the economy is far too complex for any single objec-
tive function to effectively reflect the goals of economic planning.
Also,, in order to be solvable with available algorithms and computers,
balancing
formats.
and
optimization models must be specified
The limits
in rather rigid
this places on the flexibility for modeling
certain economic processes has become increasingly obvious as Soviet
economists
have
attempted
to make
their models more
comprehensive.
Another characteristic of most Soviet optimization models is that
frequently little distinction is made between the. plan as an objective
and the. plan as a program of action.
Most of these models are design-
ed to solve for the optimal structure of the economy at some specific
time, but most give little guidance as to how to achieve this structure.
This is especially evident in that most programming models have
objective functions expressed
In part this is related
only in terms of end state conditions.
to a failure to distinguish between instru-
-20-
ments and
targets, and
in part
it
is a result of an ideology that
accepts no constraints on the ability of the government to reshape the
economy.
But Soviet economists have found that even if they determine
the optimal state of the economy for some future year, they have still
to answer an important question: how do you get from here to there?
In answering this question they are now paying more attention to real
constraints on the ability of the government
the economy.
to redirect the path of
These constraints arise from the forces of inertia, the
limited number and effectiveness of available instruments of economic
policy, and the impact of external and uncontrollable forces.
Although considerable
struction
effort
of optimization models
now being tried.
continues to be spent on the confor planning, other approaches
are
Some of the most interesting experiments of the last
decade have involved models where global optimization has been foregone
in order
functions
and
to
introduce greater complexity
structural
specifications.
in models' objective
In a number
of the newer
types of models extremal solution techniques are still used, but in a
more
flexible
developed
for
way.
combining
different aspects of
Such
models
programming
For example, heuristic
based
separate
procedures
optimization
problems
have
been
describing
the economy into an overall modeling framework.
on
"compositional"
programming
or
are being developed, and are described
"interaction"
later in this
paper. However, it is often rather difficult to control the properties
of both the path and end
state using optimization models, even the
more flexible forms mentioned above.
Solutions to some of these problems have been found in the use of
simulation models, interest
in which became widespread in the Soviet
Union only in the 1970's.
Simulation models are designed to answer
questions
of the type "if specificed actions are taken in period t,
then what will be the results in period t+1 , t+2, ... t+n?"
They may
be similiar in structure to balancing models (that is, they consist of
a
set of
equations which when properly
specified
can be solved for
the endogenous variables given values of the exogenous variables), but
-21-
are qualitatively different in the degree of closure and flexibility
of internal specification.
In contrast
to traditional balancing
models, where the exogenous variables are components of final demand,
the exogenous variables of simulation models usually relate to instruments of government policy and conditions of the external environment.
The difference between simulation and optimization models is even
more significant
in that simulation models do not require an overall
objective function for their solution.
Instead simulation models are
designed to perform calculations of an "if ... then" nature. That is,
they allow examination of many alternative paths of development, where
each path is assoicated with a particular set of government policies
and external conditions. The choice of a "best" combination of policies and results is left up to the model user.
the
extremal
value
solution procedure, the
As a result of dropping
flexibility
of
internal
specification of simulation models is substantially greater, and
computational problems less, than that of optimization models. However,
the dropping of the objective function does raise questions as to the
normative value of
models*
Although
the solutions obtained
simulation models
from
the use of these
do not automatically
provide a
"best" solution (of the type obtained in optimization models) they can
be used to generate information which can then be used in less formal
decision making processes.
Two broad classes of simulation models can be identified based on
the manner in which their parameters used in the models are obtained.
In one group
parameters are
set on
the basis of expert
opinion or
planning "norms", in the second they are based on statistical estimation using
time series or cross section data.
econometric models, have
shown a very rapid
The latter, know as
increase in popularity.
One reason given for using the statistical approach to parameterization
is very practical—it
higher
authorities
Statistical
is often difficult
parameter
estimation
values
presents
of
a quick
to obtain
precisely
and
from experts or
the
inexpensive
type
needed.
substitute.
In contrast to the situation in the West, in the Soviet Union
-22-
there is very little basic economic research using statistical techniques designed to test hypotheses.
Thus there is no substantial body of
econometric research from which Soviet modelers can draw useful inputs
for their work.
been obvious
(Although Western techniques can be imitated, it has
that most Western specifications are not applicable to
the Soviet economy.)
Often the specifications used in the econometric
models show little variation from those used in other simulation
models.
of
In only a few instances has the choice of econometric methods
parameterization
been
reflective
of
an
experimental
approach
to
model specification.
Since most
basis,
regional statistics
Soviet modelers
have
usually less than twenty.
Publication
of
are available only on an annual
available
relatively
few
observations,
The resulting limited degrees of freedom
regional
statistical
handbooks
began
on
a
regular
basis only in the late 1950s or early 1960s for most regions. In
attempting
analysts
to
model
are more
the
regional
fortunate
and multiregional
in respect
economy,
Soviet
to the types of regional
data collected than are most of their Western counterparts.
Regional
social accounts are maintained which are comparable in content to the
national accounts.
(This applies primarily for the fifteen republics—
less complete accounts are prepared for other regional divisions of the
country.)
In contrast
to
the United
States, for example, there is
information on regional net and gross output, investment, profits and
capital stock.
The primary weaknesses of the statistical system are in
the lack of adequate information on interregional trade flows, population migration, and transfers of personal income.
An
important
supplement
to
the
annual
time series data
are
the interindusty accounts that are now compiled on both a national and
regional basis about every five years.
(A review of Soviet regional
input-output studies is provided in Gillula and Bond (1977).)
data are of course
essential
to input-output modeling, and
optimization models employing input-output balances.
-23-
These
also to
means that analysts working with econometric models must be satisfied
with very simple specifications.
Usually only one or two variables are
found on the right hand side of estimated equations.
By Western
standards, the structure of these models is thus quite simple.
Several
other reasons for reliance on simple specifications can be suggested:
inexperience in the use of multivariant
statistical estimation tech-
niques; the high multicolinearity of most Soviet time series statistics
which makes it difficult to estimate meaningful parameters when several
explanatory variables are used simultaneously; a mechanistic view of
the operation of the economy which leads researchers to view variables
as causally linked in one-to-one relationships; and the reliance upon
structural rather than reduced form equations.
An additional explana-
tion has been offered by Soviet researchers which may be equally
important.
Since Soviet models are intended for use in the planning
process it is essential that the model builders be able to explain to
the model users the meaning of the estimated coefficients.
These model
users are accustomed to dealing with parameters in the form of "norms"
which are usually obtained
from engineering
simple economic
relationships.
accounting
studies or are based
Coefficents obtained
on
from
statistical estimations of equations containing only one or two explanatory variables are often similar in value and can be interpreted with
reference to these norms.
duced
When more explanatory variables are intro-
this relationship between norms
and
coefficients often
breaks
down, and it becomes difficult to explain the meaning of the estimated
parameters without reference to a body of statistical theory unfamiliar
to most potential model users.
Probably the most successful of the simulation models, in terms of
actual
both
application
econometric
what are called
to
and
planning
needs, are
non-econometric
"integrated
those that
parameterization
have
combined
techniques
in
systems of models". In these models the
economy is protrayed at several levels of disaggregation.
Macro-level
relationships are described on the basis of econometrically estimated
equations, while
sectoral detail is obtained
-24-
through
the use of
input-output
relationships.
In certain instances critical variables
are calculated in alternative ways as a check on their values.
sistency
is maintained
between
levels
or
approaches
either
Con-
through
sequential disaggregation or adding-up, or through direct adjustments
made by the model user at various check-points in the solution process.
These models allow greater flexibility in specification, and
present fewer problems either in terms of the necessary data base or
their computation requirements, than other forms of models.
tial progress has been made in introducing
planning
process
in several
Substan-
these models into the
republican Gosplans, and
arguments
have
been made that these models are the proper prototype for modeling at
the national
level.
(See Dadaian
(1975) and Dadaian
and Rayatskas
(1976).)
The Organization of Soviet Research on Regional Modeling
To carry out research on modeling
a highly organized
system of
research has evolved. Much of this research is carried out in institutes directly connected to the national and republican State Planning
Commissions (Gosplans).
Other centers are found within the national
and republican Academies of Science or in the universities.
Although
institutes in this second group are not administratively controlled by
the Gosplans, their research efforts are usually coordinated to serve
the needs of the planning system.
There seems to be good communications among researchers at these
institutes, and sufficient direct personal contact to allow transfer of
new modeling
techniques from one group to another.
In some cases a
particular model developed in one region has been tried by researchers
in other regions of the country.
been
constrained
by
excessive
Also research does not seem to have
centralization,
varieties of models that have been developed.
as
witness
the many
But there does appear to
be difficulties in the coordination of the efforts of the many centers
-25-
when it comes to carrying out the task of forecasting.
This will be
discussed in the conculusion.
A list of some of the major research groups active in regional
modeling research in the Soviet Union during the past decade is provided in Table 1.
An indication of the types of regional modeling
undertaken in each institute, and a list of some of their principle
investigators, is also provided.
(The latter information is intended
primarily to help the reader connect references in the bibliography to
the work of particular research centers.)
of the major institutes is provided
A brief review of the work
in the following sections of this
paper.
By focusing on the work of a few institutes it is possible to lose a
sense of the extent of modeling work going on all across the Soviet
Union.
This applies especially to work in republican Gosplan insti-
tutes and universities.
A indication of the extent of such activities
can be gathered form reports on modeling conferences such as Kol'tsov
(1975) and Kol'tsov and Stavchikov (1976).
-26-
Table 1.
Type of Organization
Research organizations under
the State Committee of Planning
(Gosplan) of the USSR
Research organizations under
the Gosplans of Union
Republics
Institutes of the Academy of
Science
Universities
Major Organizations Involved in Regional Modeling
Organization
Type of modeling
Principle investigators
Council for the Study of Productive
Forces (SOPS) of the USSR Gosplan
multiregional industrial branch
models; multiregional multisectoral optimization models
N.N. Nekrasov, S.A. NIkolaev,
M.M. Albegov, V.P. Evstigneev
Ukrainian Branch of the Scientific
Research Institute of Planning and
Norms of the USSR Gosplan
regional macroeconometric models
F.I. Kushnirskii, A.S. Emel'ianov
Central Scientific Research
Institute of Economics of the
RSFSR Gosplan
macro and multisectoral simulation
models
L.A. Kozlov, G.B. Rozanov
Scientific Research Institute of
Economics & Planning the National
Economy of the Lituanian Gosplan
macro & multisectoral regional models
R.L. Rayatskas, S. Zhemaitaitite
0. Bal'sis
Central Economics-Mathematics
Institute (TsEMI) of the
Soviet Academy of Sciences
regional & multiregional macro &
multisectoral models involving both
input-output & econometric approaches;
multiregional sectoral models
A.G. Fedorenko, V.S. Dadaian
E.F.Baranov, B. Breev, I. Matlin
B.L. Isaev, A. Kol'tsov
M. Bedenkova, N. Basalaeva
Institute of Economics and
Industrial Organization (IEOPP)
of the Siberian branch of the
Soviet Academy of Sciences
multiregional multisectoral inputoutput models; territorial production
complex models; multiregional sectoral
models
A.G. Aganbegian, A.G. Granberg,
M.K. Bandman, A.G. Rubinshtein
A.M. Alekseev, R.I. Shniper
Institute of Economics of the
Lithuanian Academy of Sciences
macro & multisectoral regional models
involving both Input-output and econometric techniques
L.M. Satunovskii, A.I. Burachas,
V.M. Rutkauskas
Institute of Economics of the
Estonian Academy of Sciences
regional macroeconometric models
V. Semenov
Institute of Economics of the
Latvian Academy of Sciences
regional macroeconometric models
I.G. Adirim, la.A. Ianov,
R.Ia. Pochs, I.M. Shteinbuk
Institute of Economics of the
Belorussian Academy of Sciences
regional macroeconometric models
F.S. Martinkevich
Economics Faculty of Vil'nius
State University
regional macro and multisectoal
models involving combined inputoutput & econometric approaches
R.L. Rayatskas, S.A. Zhemaitaitite
M.V. Bel'kindas, 0. Bal'sis
Leningrad State University
regional sectoral models
G.V. Shalabin
Leningrad Financial-Economic
Institute
macro & multisectoral regional models
V.Ia. Feodoritov
The Council for the Study of Productive Forces
At
the national
level
the Council
for
the Study of
Productive
Forces (SOPS) under Gosplan USSR has been active for many years in the
development of long range (fifteen
to twenty year) plans for the
overall
Soviet
regional
greatest
development
of
the
economy.
efforts have been to develop and apply models
the location of new plant
out
Sciences.
in conjunction with major
for planning
(This work has been
institutes
of
the Academy of
See Aganbegian and Fedorenko, eds. (1978).)
One of
the earlier
(1971) and consisted
SOPS models was developed
of
the
prespecified
Nikolaev
regional
This model was designed to help solve the
distribution
national production plan.
applied, where
by S.A.
of a five region model for 25 groups of inter-
regionally traded products.
problem
Council's
capacity for particular industrial branches,
such as the coal and ferrous metals industries.
carried
The
of production starting
from a
An optimization approach was
the criteria was the minimization of total production
and transportation costs at fixed prices for all inputs.
All capital
expenditures were also given exogenously, so the model was completely
static.
Since the model did not cover all sectors of production, and
did not depict non-production activities, it was necessary to provide
estimates
of
total
regional
and national
demand
for
the
output
of
each sector modeled.
A similar approach was used in a model proposed by M. M. Albegov
(1975),
except
here
the
ties
between
sectoral
production
plans are
linked via their joint demand for local labor and natural resources.
Again the solution focuses on the regional distribution of production
needed
assumed
to realize an exogenously given total demand.
that the model is linked
Also it is
to a system of branch optimization
models since the objective function is specified as the minimization
of increased cost of deviations from the optimal branch model solutions (which are added to the costs of regional resource use).
Since
the SOPS models
focus primarily on spatial rather
than
regional features of the economy, they will not be considered in
greater detail here.
-28-
The Institute of Economics and Organization of Production
The
first multiregional model
of
the Soviet Union actually to
be put into use was developed by A. G. Granberg at the Institute
of Economics
and Industrial Production in Novosibirsk.
solutions were first run in 1967.)
(Experimental
This is a linear programming model
which is designed to find levels of regional output in each of a number
of sectors
which maximize
the value of national
(C+G) in the final year of the planning period.
consumption
Each region is repre-
sented by a set of input-output and interregional trade equations, an
upper limit on total labor use
and maximal
A constraint
(max
levels
on
of
total
I ) , obtained
output
and constraints on minimal
in certain
sectors
capital expenditures
for all regions
together
from the solution of a macro model of the Soviet
Union, is also included.
(Dynamic input-output models of the national
economy, which can be used
constructed at IEOPP.
to generate such information, have been
See Aganbegian and Val'tukh, eds. (1975).) The
key set of equations in the model are those that express the requirement that for each sector of production in each region output be
greater
than
exports
intermediate
and
minus impor
final
consumption
plus
The regional distribution and
sectoral structure of non-productive consumption are fixed as proportions of total consumption.
Each of the other components of demand are
determined by multiplying values of gross output by fixed coefficients.
Requirements
for labor
and
investment
are similarly
tied
to the level of production. (See Figure 3.)
Although the model is fairly simple in its basic structure, its
application presents some difficult computational problems and requires
a massive amount of data.
A number of versions of this model have
A detailed description of this model is available in English in
Granberg (1975) .
-29-
Figure 3.
Structure of the IEOPP Multiregional Model
been
tested
(see
Granberg
and
Chernyshev
(1970),
Granberg
(1973),
Chapter 3, and Granberg, ed. (1975)), the most recent ones involving
a model with 16 sectors and ten regions.
model
with
approximately
105
sectors.
Plans call for a 24 region
However
this would
involve
more than 2.5 thousand equations, and computational difficulties are an
impediment
to this further disaggregation.
at IEOPP is being
devoted
to overcoming
(A good deal of work
this computational
problem.
See Astanina and Volkonskii (1975) and Bakhtin and Berliand
(1977).)
Computational difficulties
account for one of the weaknesses of
the model-—the lack of realism in the specification of capital formation.
Granberg has noted that operative use of dynamic models does not
seem possible for extended-period
plify calculations, investment
forecasts.
Thus, in order to sim-
is related directly to change in the
level of production and no lags are involved.
A primary data requirement for the system is the set of regional
input-output and trade coefficients, and the compilation and analysis
of historic data of this type has been a second major research task
carried out at IEOPP. (See Granberg, ed. (1975) and Shiper and Denisova, ed. (1974)/)
So far the coefficients used in the model have come
directly from ex post accounts or from projected input-output accounts
prepared by experts.
There has been no attempt to endogenize changes
in the structure of intersectoral or interregional exchange. Information on the capacity of regions to support production in certain
industries is also required to set the upper and production limits used
in the model.
Regional resource base studies are necessary for this
purpose.
A promising
new modifiction to the basic model has
recently
been proposed by one of Granberg's co-workers, Alexander Rubinshtein.
(See Rubinshtein (1976).)
In the model proposed by Rubinshtein, which
is termed a model of regional interaction (vzaimodeistvie), the specification of each region's economic
structure is very similar to the
regional models in Granberg's system, but the optimization criteria is
radically different.
Instead of an overall objective funtion for the
-31-
system
of
regions, separate
regional
objective
functions
(Maximization of regional consumption is typical of these.)
are
used.
The model
is based on a compositional approach to mathematical programming, which
means that ". . . local optimization criteria reflect internal interests of each subsystem, and are not derived
nor do
they
(Granberg
necessarily
(1978,
p.
71)
reduce
from a global criterion,
to the global optimality criterion."
The
solution
algorithm
models consists of a set of rules describing
for
compositional
the interaction between
sub-systems, in this case interregional trade.
Trade between regions is carried out under common exchange prices,
and there are constraints on the import-export balances. It is this set
of prices which determines the solution to the overall system.
central "coordinating" task is to find sets of equilibriating
and
evaluate
the total effect
and
interregional
income
The
prices
distribution
effects of the regional solutions formed on their basis.
In the case where the regions are conceived as autonomous economic
units ("such as might be the case in trade among non-aligned nations) it
is reasonable to require (in the static version of this model) that the
export-import
balance for each region be zero at the prevailing ex-
change prices.
resulting
sense.
As an outcome of this the distribution of effect;
from a set of equilibrium prices is optimal in the Pareto
But as Rubinshtein points out, " . . .
individual regions can
have objectively different conditions of the use of labor . . . .
If
for some reason a region is placed in more favorable conditions, the
effect of interregional exchange occuring
mostly, for that region.
from this is realized,
So equal inputs of labor in different regions
may produce considerably differing results. In contrast, to ensure that
the principle of equal pay for equal labor be implemented, all regions
in
a
socialist
country must
application conditions."
be
reduced
to
objectively
equal
labor
In order to realize this latter principle the
regional model constraints can be modified so as to allow export-import
imbalances for individual regions. (The sum over all regions of these
imbalances,
of course, remains zero.)
-32-
The imbalances
represent
either subsidies or deductions from the income of regions, and
thus
resemble rent payments designed to correct for the effect of differing
regional
conditions
of labor.
The determination of the appropriate
level of imbalances must be determined
model.
As Rubinstein notes, the
outside
practical
the framework of the
definition of
transfers is a basic difficulty of the model's application.
these net
However,
by making explicit the role of interregional income redistribution, the
model
brings
to the forefront
one
of the critical
issues of multi-
regional planning.
Two
important
determinants
development—interregional
of
the
pattern of
multiregional
trade ties and the distribution of invest-
ment funds-—are represented in the IEOPP models in a workable manner.
In other areas, however, the models are less viable.
For example, the
lack of any endogenous depiction of labor migration and the very
simplified treatment of the transport sector are problems. (In Granberg
(3.973) ways are suggested for strengthening the model in both of the
latter areas, but these improvements do not seem to have been implemented as yet.)
A greater difficulty arises from the incongruence
between the methodological approach and the level of aggregation of the
models.
The programming approach, in order to give realistic results,
requires a level of sectoral detail that is probably not obtainable due
to computational
and data limitations.
As the model now exists the
initial limits placed on the upper and lower levels of output in each
sector and region dominate the solution, as in most cases the solution
values will be pushed to either one of the two extremes.
A number of application studies have been carried out using the
IEOPP multiregional models.
Most
Siberian
the results
development—with
have been focused
presented
on the role of
supporting
more rapid development of this region. (See Granberg (1979).)
the
The fact
that the necessary data have been gathered to make these models operational
is one of
IEOPP's main achievements.
It has been proposed
that the multiregional model be used in conjunction with national and
industrial
branch and
regional
territorial
-33-
complex models which
are
being developed by other groups at IEOPP.
These other models are also
optimization
structure
models, with
much
sectoral balances. (See Alekseev
of
their
based
on inter-
(1978) and Bandman (1976).)
If an
integrated system can be achieved then the solution from the sectoral
models may be used to set initial limits on levels of output in the
multiregional models.
And as was mentioned above, the multiregional
model alreadly has provision for obtaining certain control totals from
the national model.
Another
approach
to multiregional
modeling
which
is now
being
tried at IEOPP is to convert the multiregional optimization model into
a
system
of
regional models linked
by a transportation model.
The
primary change in structure would be that in the regional models only
net
exports
determined
(1975).)
would
be
solved
for, and
interregional
in the transportation model.
flows would
be
(See Astanina and Volkoskii
The resulting system is very similar in concept to the one
being developed at the Central Economic-Mathematics Institute, which is
described in the next section of this paper.
The Central Economic-Mathematics Institute
The Central Economic-Mathematics Institute (TsEMI) of the Academy
of Sciences of the USSR has, since the early 1960s, been one the major
centers of research on regional modeling, and through its organizing
role in the Academy, TsEMI has guided the research activities of many
other groups throughout the Soviet Union.
Much of the initial research
on regional input-output analysis and modeling was done here, and in
recent years TsEMI has been the leader in developing ways for combining
regional with national and sectoral planning models.
Over the past decade a group within TsEMI, headed by E.F. Baranov,
has focused its efforts on an attempt to develop an integrated national
/regional/sectoral model.
The most comprehensive description of the
overall system is provided in an article by Baranov and Matlin (1976),
-34-
which describes an experimental version of the model which was begun in
1972.
Within this system the major components are a set of regional
models, a set of sectoral models, and a national level model.
Each of
these components is specified as a separate optimization problem, and
the overall system is solved by a formal technique for coordinating the
solutions of the components—using a so-called "compositional programming" process.
In addition to these main components there are a number
of auxiliary models which are used to forecast interregional migration,
calculate transportation costs, and check the consistency of solutions
of the separate components.
A schematic diagram of the basic structure
of the regional component of the model is provided in Figure 4.
A
brief summary of the system and its solution process follows.
The process of solution begins with the set of regional models.
Here a two step proceedure is used.
programming
problem determines
products in 24 regions.
In the first step a simple linear
ouput levels
for each of 98
(The choice of products and regions corres-
ponds to those used in the regional input-output accounts.)
The.
optimization criteria for each regional model is maximization (over a
ten year plan period) of total income of workers in that region
(The relationship between output and worker income for each product and
and region is simply an exogenously given multiplier.)
The constraints
placed on the solution are as follows: (a) natural resource utilization
(for each of seven types of resources) can not exceed specified limits
in
each region
; (b) requirements
for
total
labor
and
skilled
labor (of thirty three categories) can not exceed regional availabilities
; (c) output of each product in each region can not exceed
certain maximum levels
are given
exogenously--for
for the first iteration these levels
succeeding
iterations
they come
from
the solution of the sectoral models.)
After the solution of this step has been achieved, a second set of
calculations are made to determine
each region.
total demand
In the first step, final consumption
for each product in
is determined.
On the basis of the solution of the regional models, total income in
-35-
Figure 4. Structure of the TsEMI National/Regional/Sectoral Model
each region is determined, and income of state workers and collective
formers
separately
identified.
Total retail sales are derived
from
these values, using equations specifying the share of their income that
each of the two income groups spends in state stores.
for
each of 78 types of goods
Then consumption
is estimated as a function of total
retail sales and a time variable. (A number of different specifications
were
tried for each product, and
the one giving the best fit for
historic time series data was used. The values obtained from individual
forecasts of product sales are normalized
sales.)
to correspond
to total
Finally these forecasts of sales are aggregated to correspond
to the 98 product classification of the input-output system.
(Exoge-
nously given estimates of personal consumption not satisfied
through
the state run stores are also added.)
Consumption of products in production processes
culated
by means of input-output multipliers, and
capital
is calformation
by means of a set of dynamic capital stock formation equations in which investment lags are portrayed.
key variables are the output forecasts obtained
In the latter the
in the first step of
the regional model solutions, and the key paramenters are exogenously
given
capital-to-output
coefficients
and
capital
construction
lag
coefficients.
Total
casts
for
demand
in the region is obtained
consumption, intermediate
input
by summing
the fore-
requirements, and
current
period investment.
After regional output and consumption forecasts have been made,
this information is passed on to the
two-step
estimated
solution
process
is
transportation model.
involved.
on the basis of historic
First
regression
Here, a
equation,
data, are used to calculate
the
volume of regional imports and exports for each product. (See Bedenkova
(1978))
On the basis of these estimates, volumes of freight dispatch-
ing and receiving are calculated, with within-region and cross-regional
flows accounted
for separately.
These latter calculations are made
only for nine types of heavy freight carried by railroads and reported
-37-
in annual
transport
statistics.
But these represent 80% of freight
turnover and about half of total
types.
ton-kiloineters of transport of all
The remaining share of shipments are represented by weighting
coefficients calculated from the regional input-output accounts.
forecasts are used
tion
programming
patterns
and
in the second
model
which
transport
costs
These
step as inputs into a transporta-
determines
for use
optimal
interregional
flow
in the sectoral models.
(See
Kovshov (1977) for a detailed description of the transportation model.)
In
the conceptualized
system
a number
of
functional models of
regional socio-economic processes are linked with
block.
that
the above regional
In the experimental version only one such model was tested,
for
interregional
migration
of
the
population
Drawing on the findings of Matlin (1975), regional migration flows are
determined
forecasting
by a
two-step
process which
results. First, each
was
found
region-to-region
to
give
flow
adequate
is
forecast
simply as a function of time, using results of historic period timeseries regressions. Then deviations
from these initial estimates are
forecast using multi-factor regression equations.
The factors included
are the relative levels (between region of origin and region of destination)
of
capita, and
final
the
consumption,
rate
per
of growth
capita
of
fixed
income, retail
productive
sales
capital
per
stock.
Values for each of the latter factors are obtained from the solution of
the regional models.
These migration forecasts are then used to adjust
the regional constraints on labor suply used in the regional programming models. Research in the area of forecasting demographic
and
intersectoral
labor
migration
is
also
being
carried
out
changes
at
at
TsEMI. (See Staroverov (1979) and Breev and Kriukov (1974).)
The following phase of the solution process involves examination
of the regional production plans from the view-point of the optimality
of their combined results.
which
is specified
This is carried out in the macro-model,
as a linear
programming
problem.
The objective
function is to maximize the effect of the utilization of natural and
labor resources over the planning period (subtracting labor income from
-38-
this value) , V7here the instrumental variable is the ratio between the
output levels received from the regional models and levels of output
which would maximize the value of the objective function.
The calcula-
tion of effect of resource utilization is based on natural and labor
resource
shadow
prices
obtained
from
the
solution of
the
regional
models.
The constraints on the solution are: (a) sufficiency of
production in each sector to cover all consumption needs (at this stage
certain additional final demand
items are added, such as public con-
sumption, which
includes
capital,
(b) requirements
etc.);
defense
expenditures, increases
that
investment
in working
in each sector be
less than exogenously specified limits; and, (c) a required increase in
the level of per capita income each year of the plan period.
(When
the instrumental variable indicates a change in the level of sectoral
output as received
from the regional models, this same change is
reflected in each of the inequality constraints.)
The next set of calculations serves to adjust the constraints to
be used in the block of sectoral models.
First a determination is made
of any difficiencies in product outputs relative to product demands on
the basis of aggregated regional model solutions.
Then new limits are
set for the sectoral model representing the minimal levels of sectoral
output needed to insure adequate supplies of these products or limits
on
the demand
block,
the
for
limits
these products by
on
capital
other sectors.
investment
Also, in this
by sector are
recalculated
based on the direction of
adjustment
in outputs indicated
in the solution of the macro-model.
In the block of sectoral models a quadratic programming problem is
used to seek levels of regional output (in each of the eighteen sectors) which would maximize profits in each sector over the plan period.
Profits are calculated by subtracting from the value of output the cost
of
intermediate
inputs,
transportation
costs, natural
labor costs, and expenditures on capital.
resource
and
Constraining the solution
are the minimum level of output and maximum use of deficit resources,
plus capital stock and investment balances.
-39-
The prices
used
to value output and inputs in the objective
function are determined endogenously in this block.
fied
so
that
These are speci-
the price of a product goes down as the level of its
production increases or up as the level of its consumption increases.
For natural and labor resources, their implicit prices go up as more is
consumed.
tially
It is reported that the use of such relationships substan-
increases
the speed
of convergence of
the model's
solution.
(These functions were specified heuristically as a result of experimental
solutions
of
the model.)
The choice
of a quadratic
objective
function was also made in order to speed convergence.
The results of the sectoral models are then used for setting
product
output
iterations.
limits
in the regional models for the next cycle of
(Since a 18-sector classification is used in the sectoral
models, a disaggregation
to the 98-product level must first be made
before these results are used in the regional models.-)
This cycle of calculations is repeated until the optimal solutions
of the regional, national and sectoral models are in agreement.
This
stage is reached when in the solution of the macro-model the indicated
modifications of output levels (received
from the regional block) is
less than some specified minimum amount.
Baranov and Matlin (1976, p.
647) report that in the experimental
tests of this model monotonous
convergence occurred, and the results, even with the use of hypothetical data, showed a ". . . sufficient degree of trust-worthiness."
No
numerical results have been published, however.
In the words of the designers of the model "the most important
peculiarity of this system consists in the following.
In the sectoral
and regional models the evaluation of the efficiency of possible
variants in the distribution of production is carried out from different points of view.
The sectors are oriented to the choice of the most
efficient variant from the. point of view of the commensurability of
sectoral expenditures and results.
The regions evaluate each variant
from the position of the best utilization of local--labor and naturalresources
for achieving maximal welfare of the population living in
-40-
their territory." (Baranov and Matlin (1976, p. 628)
The
final choice
of a development plan comes through
adjustment in the sectoral and regional plan, based
iterative
on exchanges of
information between the two sets of model which results in redefinitions of the set of possible solutions for each side, until there is
coincidence of their solution.
In the process the role of the central
model is to keep the search on the bounds of the production possibilities frontier. The usefulness of such an approach, in lieu of a
solution
process
based
on global
optimization
(which has been more
traditional in Soviet mathematical-economic modeling), has been debated
in Soviet
literature recently. (See Danilov-Danil'ian
and Zavel'skii
(1975) and the articles listed in their bibliography.)
Although there
is general agreement to the effect that theoretically there is a single
overall objective function in a socialist economy, usually expressed as
the maximization of social welfare of the populations
several econo-
mists have begun to argue that for planning and modeling purposes such
a concept can not be made operational in any effective way.
have
appeared
a
number
of
alternative
approaches—one
of
Thus there
the most
prominent being the TsEMI approach in which differences in objectives
between functional groups are explicitly recognized and allowed for in
the process of overall systems modeling.
Recent
research
on
further
development
of
the TsEMI
system of
models has been primarily directed at improving the realism of each of
the three major components of the system.
A number of recent publica-
tions describe this work.
An article by Matlin (1978) on the central model explains how a
new aggregate macro-model has been linked to the central model.
This
macro model is an 18-sector dynamic input-output model which is especially designed to allow the user (who need not be a modeling specialist) to easily and quickly determine the general implications of
alternative development goals. Once a desirable set of macro-objectives
has been determined
these are then translated
into specific require-
ments of final demand as used in the previously described central model
-41-
In a book edited by Aganbegian and Fedorenko (1978) recent progress in the development of sectoral models is described.
(This work
is being carried out as a joint effort of TsEMI, IEOPP, and SOPS.)
classses of
sectoral models
are being
developed.
Two
First there is a
proto-type model which has been designed to be applied to most industrial branches not having specific spatial development characteristics.
The second group of models are individuallly designed for those sectors
whose growth is shaped by the distribution of natural resources and the
development of transportation sector.
Included are special models for
the mining, energy, and agricultural sectors.
A highly varied range of activities are being pursued relative to
the regional components.
A recent survey of this work is provided in a
volume edited by Baranov and Kol'tsov (1978).
described
are: (1) the use of detailed
Some of the major areas
regional input-output tables
with intersectoral flows represented in physical terms; (2) methods for
forecasting
input-output
relations;
(3) improvements
in balances of
inter-regional trade flows; (4) improving the comparability of regional
input-output
tables;
(5). problems
of
input-output
tables;
(6) improvements
representing
in
the
price changes
representation
dynamics of capital formation in input-output models.
these
areas, the
use
of
physical
flows
especially important advance in making
modeling
input-output
of
in
the
The first of
tables, is
the results obtained
an
from
exercises more directly applicable to the planning process,
where most indicators are treated in physical terms.
Although
the research at TsEMI is perhaps the most
intensively
supported work of its type in the Soviet Union, and substantial results
have been achieved, there seem to be some major problems in terms of
application
of
this research.
One of the problems may be that the
effort has simply been extremely ambitious in its attempts to create a
model that is both comprehensive and detailed. Soviet planning requires
detail—but
by introducing great detail into their models, TsEMI
researchers have faced problems when it comes to obtaining the data
necessary
to
implement
their
models.
-42-
Surprisingly,
the
national/
regional/ sectoral model developed under Baranov, even after a decade
of research effort, has still to be
tested
calculations
on a set of
so
far
have
been
based
using "real" data.
economic
All
accounts
compiled for only one year and containing many numbers which are simply
"best guesses".
could
be
Although much of the information that would be needed
obtained
from
the
Central
Statistical
Administration, the
necessary cooperation between the two organizations has not been
realized.
This situation points up a characteristic of many modeling efforts
in the Soviet
Union.
Often models are specified
on the assumption
that the relationships protrayed are deterministic and known.
Thus in
the initial stages of model construction there is no experimentation
with alternative specifications that would require that data collection
be carried out in the early stages of model construction.
This leads
to a separation of model building and empirical research which seems
strange
to Western
analysts who normally pursue both tasks simulta-
neously. Especially
in Western econometric model-building, model
specification has followed, and been built upon the results of, many
years of empirical research directed at hypothesis testing.
attribute of mechanistic
It is an
theories that they are considered
obvious, so that there is little need
to carry out
to be
sophisticated
statistical analysis to verify that the theory correctly explains these
interactions.
Thus, even in present day Soviet econometric modeling,
the objective of most efforts in statistical estimation, is to obtain
parameters, not to test specifications.
The Institute of Economics of the Lithuanian Academy of Sciences
Many of the basic features of recent Soviet efforts to model
single
the
regions
Institute
are
of
illustrated
Economics
of
in
the
the work
of
L.M.
Satunovskii of
Lithuanian Academy
of
Sciences.
(This particular approach to modeling was developed by V.S. Dadaian at
-A3-
TsEMI in the early 1970s and was tried
in a number of regions.
Arakelian and Dadaian (1970) and Dadaian (1972 & 1974).)
divided into a macro component and sectoral components.
tested
at
models
for
The model is
In the version
the Institute of Economics, the latter component included
both
branches of
Figure 5.
See
the
four
industry.
major
sectors of
The stucture
the
economy and
fifteen
of the model is illustrated
in
(This is a simplified rendition of the model presented in
Satunovskii (1977), Chapter VI.
Here the multi-period structure of the
original is ignored since it does not change the essential nature of
the model.)
The sequence of calculations is as follows.
gross
output
, after
subtracting
, gives final output
the
. Material
value
At the macro level
of material
inputs
inputs are calculated by
multiplying output by a coefficient of material useage, a. The share of
final demand used for investment purposes
using a fixed coefficient, b.
stock formation
is then found by again
The link between investment and capital
is very simple, the ratio of newly formed capital
stock to current
being given by
the parameter f, and
the rate of capital scrapping being given by g.
(It is argued that the
difficulties of
investment
using
and
estimating distributed
lag models to link
investment and new capital formation warrant the simplier approach of
linking
only
current
period
investment
and
The change in capital stock and labor supply
mine the level of labor productivity, p.
new
capital
formation.)
are used to deter-
This relationship is defined
by the parameter 1, which is the coefficient of elasticity between the
change in capital to labor and output to labor ratios.*
Gross output
then is determined by the level of current labor supply and its productivity.
Thus, given exogenous projections of the growth of the labor
supply, and values for the paramenters a, b, f, g, and 1, the level of
ouput, final demand, investment, and capital stock are determined at
the aggregate level.
-44-
Figure 5. Basic Structure of the Satunovskii Model
This information is then used in the next level of the system to
forecast growth of four sectors of the economy (industry, agriculture,
construction, and a residual sector of material production).
and
capital
given
stock
formation
the necessary
in each
parameters
for
sector is determined
each
sector
Output
as above,
.
The
distribution of total labor among sectors is exogenously given, while
the distribution
of investment among
sectors
is found
by solving
a
linear programming problem where the objective function is to maximize
total output
sectors
tion
be
subject
to two constraints: (1) output in each of the
sufficient
(defined
simply
to supply
as
final
a prespecified
demand
minus
pattern of consump-
investment,
) , and
(2) that the sum of sectoral output equals total output as determined
by the macro-level model. For the first set of constraints it is
necessary
to have exogenously given values for regional input-output
coefficients
investment
in
, coefficients
each
sector
for the sectoral composition of
, and
the
sectoral
distribution
of
total (non-investment) final demand
The solution to this problem gives a distribution of investment
that
insures
satisfying
consistency
in
regional
interbranch
a given structure of final demand
gross and net
output obtained
from
relations,
while
and the limitations on
the one-sector model.
Also the
nature of the optimization criteria insures that the difference between
the sum of sectoral
investment and capital
*
The linear programming problem is:
maximize
s.t.c.
-46-
stocks determined at the
gross and net output obtained
from
the one-sector model.
Also the
nature of the optimization criteria insures that the difference between
the sum of sectoral
investment and capital stocks determined at the
sectoral level, and
the aggregate values determined at the aggregate
level will be minimized.
between
the given
aggregate
consistency between
and
sectoral
parameter values.
Since
the aggregate labor productivity parameter p and
its sectoral values
between
This is subject, of course, to consistency
is insured
the sum of sectoral
and
by the requirement of equality
total labor
and output, it is the
consistency of the parametners 1, f, b, and a which should be verified.
The major empirical content of this model is in the values given
to various
values
exogenous parameters.
for forecasting
series data.
The method
purposes
used
for seting
is expert analysis of historic
Formal statistical estimation is not used.
this can be given for the parameter,
these
time
An example of
, the coefficient of elasti-
city between the capital to labor ratio and the output to labor ratio.
(See Satunovskii (1977) pp. 161-172.)
This is the key parameter in a
linear form of the production function which focuses on the change in
labor
productivity.
Lithuanian
data
for
the period
1960
to 1975,
indicates that for the economy as a whole the value of 1, fluctuated
with the range 0.748 to 1.732 with the exception of 1962 when it shoot
up to 4.632.
The opinion is expressed that in the absence of a consis-
tent trend in 1, it is best to take an average historic value and to
make alternative forecasts using values within a rather narrow bracket
around this average.
The forecasting
of values
for
, which describe
the branch
structure of net final demand (final demand minus investment in the
sectors of material prodution) presents special problems in a regional
model as compared to a national model.
As is pointed out in Satunovski
(1977, pp. 92-95) these parameters cannot be considered
simply as a
function of total final demand, as would be appropriate if they primarily described
changes in consumption patterns.
Trade with other
regions is a very significant portion of regional final demand, and
-47-
thus must be considered
considered
in projecting
to be a forecasting
model.
Approximate
treating
external
the values of
calculations
can
be made, it
trade of each sector, and
of
can
output growth rates.
then
be
This is
task that is best handled outside the
based
on
is
suggested, by
thus final demand, as a
function of the level of output of that sector.
projections
.
Simple orientating
extrapolations
of
sectoral
However, since this, in essense, requries that
the solution of the model be used to solve the model, it contributes
very little in the way of explaining
the future pattern of regional
growth.
Projection of the material input coefficients,
ed as a separate task, requiring
conflicting
cient.
, is also treat-
informal consideration of the many
tendencies in technological change affecting each coeffi-
Alternative values of these, reflecting
both increasing and
declining efficiency of material utilization, were used in forecasting
for the Lithuanian economy.
Although simple in structure, this model illustrates the use of
multiple
levels
in regional
macro and sectoral models.
for determining
sectoral
among
the
are
the combining
of
The macro level calculations are essential
rate of accumulation
calculations
sectors, and
simulation models via
needed
to account
to
in
the economy, while
reflect
for differences
the
the
interrelationships
in sectoral rates of
productivity growth and efficiency of capital formation.
Work is continuing
in the Lithuanian Institute of Economics
in the direction of greater elaboration of this model and its application
in forecasting
studies.
(See
Burachas in Belkin, et al (1978).)
-48-
the articles by Rutkauskas
and
Ukrainian Branch of the Scientific Research Institute
of Planning and Norms of the USSR Gosplan
Research on the f i r s t
time-series
of
the
under
Soviet regional econometric model (based on
regression analysis)
Scientific
the U.S.S.R.
Research
was begun at the Ukrainian
Institute
of
Planning
Gosplan in Kiev during
and Norms (SRIPN)
1970-1971.
developer of this model was F.I. Kushnirskii. In i t s
called UKR-1, the model consisted of fifteen
macro-level
relations.
second version,
(See
Emel'ianov
affiliate
The principle
i n i t i a l version,
equations describing only
and
Kushnirskii
UKR-2, contained both macro-level
(1970).)
and sectoral
A
level
variables in a system of simultaneous and recursive links described in
89 equations.
(See Emel'ianov and Kushnirskii
(1972, 1974a).)
Both
models were estimated using annual data for the Ukrainian republic for
the period
1959-1969.
(A similiar
version of the earlier model was
also estimated using national data for the same period. See Emel'ianov
and Kushnirskii (1974b),)
Only the model UKR-2 will be described here.
The major v a r i a b l e s and t h e i r
linkages are depicted in Figure 6.
Although UKR-2 i s composed of seven blocks of
sectoral
blocks
communications,
production)
taneously.
(industry,
agriculture,
trade and distribution,
construction,
transport
and
and other sectors of material
plus a macro-level block—the system must be sovled simulThe primary
function
of
the macro-level
determine the value of the accumulation fund
net output
equations—six
of
the branches
of material
block i s
to
on the basis of total
production
.
The l a t t e r
value i s calculated as the sum of outputs determined in the sectoral
blocks.
Thus the solution of the macro-level depends upon information
form the
sectoral
investment
level
in each
solutions.
sector
determined in the macro model.
ment, c a p i t a l
is
On the other hand, the
a function
of
total
level of
accumulation
A sequence of equations linking invest-
stock formation
and output
at the
sectoral level then closes this circle of simultaneity.
Before
distributing
produced
-49-
net
regional
product
among the two
Figure 6. Basic Structure of the UKR-2 Model
major end uses, accumulation and consumption
made
for the net
, a correction is
trade balance with other
regions
•
The
relationship between this value of utilized regional income and produced national
income is expressed
in the macro-block
in the following
form:
With estimated values being
=
indicates that the ratio of utilized
0.3346 and
=
0.0996, this
to produced regional income for
the Ukrainian republic rose substantially during the 1960s.
It should
be noted that this is the only reflection of regional ties made in this
model.
All the factor allocation calculations are made at both macro and
sectoral levels.
production
Since at the sectoral level only sectors of material
are represented,
investment (and
this means
that estimates of
labor and
the corresponding capital formation variables) going
into the non-production (social services) sectors can be calculated as
the
difference
between
the aggregate
and
the
sum
of
the
estimated
sectoral values.
Investment is given as a function not only of total accumulation,
but also of depreciation
represent
the
depreciation
financing
payments
of
capital
payments which are made
capital stock.
of each sector.
repairs
and
The latter
renovations
on the basis
out
of
of the value of
Capial stock formation is portrayed by first linking
new capital formation
to investment in the current and two pre-
vious years, and then by adding this value to the previous period stock
with an adjustment for capital scrapping.
The first part is represent-
ed by equations of the following type:
These were estimated with the constraint that
Since
there is not any explicit variable for capital scrapping in the model,
the equation for total stock is estimated as:
-51-
so that
provides an adjustment to allow for scrapping that is tied
to the quantity of newly formed capital.
For each sector of material production gross output is calculated
using a linear production function with number of workers and value of
capital
sotck as
the arguments.
These were estimated
under a non-
negativity constraint on the coefficients of the explanatory variables.
Total material inputs into production are calculated as a linear
function of gross output, then net output is calculated as the difference between the two previous values.
In the determination of total labor supply and the allocation of
labor among sectors, the value of total wage payments
is portrayed
as a regulating factor supplementing the impact of growth in the size
of the population.
These functions are of the form:
Since it was assumed that an increase in the wage fund would be associated with an increase in labor supply, these equations were estimated
with a prior constraint that
Since the wage fund
is a function of net output, there is a
subsystem of simultaneous equations within each sectoral block linking
wage fund, labor supply, gross and net output.
Profits
included
and, like wages, are
in
the
sectoral
blocks
of
the model
are also
expressed as a linear function of net: output.
Although
their
the UKR models were not adopted
planning,
they
have
played
an
by Gosplan for use in
important
role
in
stimulating
the development of econometric modeling throughout the Soviet Union and
in other East European countries.
Research
Institute
in
Kiev,
working
Researchers at the Scientific
together
with
economists
from
Belorussia, Latvia and Georgia, produced the first econometric models
of these republics, and helped to initiate independent research efforts
in each.
-52-
The Research Institute of Planning and Economics
of the Lithuanian Gosplan and Vil'nius State University
A group of economists and planners working under the leadership of
R.L. Rayatskas at the Research Institute of Planning and Economics of
the Lithuanian State Planning Commission and Vil'nius State University
has, over the past decade, developed and introduced into the republican
planning process a sophisticated
casting and planning.
system of models for economic fore-
This system of models is designed
to meet the
needs at the republican level of the "automated system of plan calculation", and the work of Rayastskas's group is an almost unique example
of how the two recent trends in use of computer based modeling discussed above could be joined and be made mutually
supportive.
The overall
system has been developed so as to provide models for the automization
of data management, the forecasting of "scientific-technical trends" in
the economy (i.e., rates of productivity growth, changes in the mix of
material inputs used by various sectors, etc.), and the simulation of
regional development.
Only the last of these will be described here.
The simulation model, which the group calls RAISA (based on the
first names of the model's builders), is used for calculating future
values of 132 variables given 96 exogenous norms and variables.
(It is
the role of the forecasting block to provide values for the exogenous
variables
used
methods—for
in
the
simulation
model. Both
formal
extrapolation
the most part based on statistical analysis of historic
time series data—and informal methods—-various approaches to compiling
and
evaluating
Since most
of
expert
the
opinion—are
endogenous
used
for
this forecasting
variables are disaggregated
task.)
either by
demographic group or sector of production the model is quite large.
(In
the version described
there are 400 demographic groups—100 age
groups, broken down further by sex and urban or rural residence, and 33
branches of production.)
The model is organized into eleven sub-blocks
by functional category, but the model is solved as a recursive system
with feedbacks requiring user intervention. (See Rayatskas and Zhemaitaitite (1972) and Rayatskas (1976).)
-53-
The general structure of the model is protrayed in Figure 7.
Although
the model
employs
sectoral
production
functions
to
relate
labor, capital stock, and ouput on the supply side, the main sequence
of calculations
is carried out starting
rather detailed
demographic
model
from the demand side.
is used
to project
changes
population by age and sex, and by urban or rural residence
information is then used to determine
labor
force
figures
total
.
On the basis of both population and labor
personal income
— i s calculated.
in
This
the size and structure of the
—arising
from labor
various types of income from the state budget
transfers
.
A
force
income
,
, and interregional
This income is then distributed among
various types of expenditures—consumption
, payments into the state budget
of goods and
, and savings
services
.
these calculations are made using matrices of fixed
All of
coefficients
relating the various groups of variables.
Personal consumption expenditures are then distributed by sectors
of the economy supplying the relavent goods or services
.
These
calculations are made using regression equations with per capita income
and exogenously given price data as the explanatory variables. To these
estimates
services
are added
sectors
purchases of
and
vector of final demand
goods
and
for accumulation
.
services
by
purposes
the
social
to form a
Here final demand is defined as excluding
the import-export balance
Total output is obtained by using this demand vector in a system
of
input-output
equations.
From
forecasted
output,
and
projected
changes in capital productivity, estimates of the necessary additions
to capital stock
years.
are made for each sector for a series of future
Also, the output calculations are the. basis for determining
employment by sector
.
Then by adjusting
for capital scrapping
, and accounting for the lag in capital construction, a forecast
of investment required for securing this growth is obtained.
Since the
causal sequence is from capital stock to investment, future values of
the former must be obtained by solving the model for as many future
periods as are included in the lag structure.
-54-
Figure 7. Basic Structure of the RAISA Model
This primary sequence of calculations is supplemented by several
important channels of feedback.
investment
expenditures
among
First, there is the distribution of
the
capital-goods
supplying
which then feeds back into the final demand vector.
sectors,
The initial
estimates of investment come from calculations of changes in capital
stock made using exogenously given coefficients of capital stock
formation.
After
this first iteration, however, changes in capital
stock are derived on the basis of output forecasts.
At this stage an
exogenous forecast of both labor and capital productivity changes is
used, so that:
where z is the capital to output ratio and h is the share of output
accounted for by projected increases in producitvity without additional
investment.
The rate of capital scrapping as a share of average yearly
capital stock is given by the coefficient s.
Investment is determined
as:
where the values for n are the shares of newly created capital stock of
each year accounted for by investment in the year t.
tions are carried out only for
Investment
in
the
social
These calcula-
the sectors of material production.
service
sectors
is estimated
simply
as a
multiple of the latter aggregate.
A second channel of feedback attempts to more closely relate
changes in labor, capital, and output by sector, and does so by the
introduction
of
sectoral
production
function
calculations.
First,
using initial output and capital stock values, estimates are made of
required labor supplies.
These are then compared with the previously
calculated availabilites, and if total demand exceeds supply, adjustments are made.
pp.
128-129)
One method suggested for this (in Rayatskas (1976),
involves
using
a
programming
technique
which
finds a
balance allocation of resources minimizing a weighted sum of deviations
-56-
from the initial solution.
(The weights would represent the priority
of each sector in obtaining scarce labor.)
After obtaining a balanced
distribution of labor supplies, the relationship between output,
capital, and
labor
is
then
reexamined
to determine
if
the
initial
assumptions as to factor productivities were valid within an acceptable
range of
tolerance.
If not, then adjustments are necessary
in the
model's exogenous information. These are made by the model user in a
non-formalized manner.
Initial assumptions as to rates of productivity
change can be altered, levels of demand and trade adjusted, rates of
capital construction varied, etc..
A third feedback process involves checking the reasonableness of
the
macro-aggregate
division
of
consumption and accumulation.
regionally
utilized
income
between
In order to go from produced regional
income to utilized regional income, the difference in regional exports
over imports must be subtracted.
lated
in
the model
Both imports and exports are calcu-
simultaneously
with
the calculation
of output.
Imports are forecast as a fixed share of total regional supply (that is
output plus imports).
of
these
Exports are divided into two parts.
is given
exogenously
and
ments established at the national level.
mentary
exports made
possible by
represents
export
The first
require-
The second represents supple-
regional
specialization
and is calculated as a fixed share of total regional production.
,
The
coefficients used in calculated imports and supplementary exports are
defined are follows:
and are introduced into the simulation model as exogenously determined
parameters.
Although the export-import balance is not included direct-
ly into the vector of final demands, it is a component of demand and
thus must be included in the calculation of output.
the following matrix operation:
-57-
This is done using
Here X and F are the vectors of changes in gross output and final
demand,
I is
the
identity
matrix, A
is the matrix of
input-output
coefficients, and m and w are diagonal matrices of the sectoral coefficient
is the vector of obligatory exports.
From estimated gross output are subtracted intermediate purchases
and depreciation payments
tion of capital stocks.
income.
, the latter calculated as a func-
This yields an estimate of produced regional
From this the value of net trade is subtracted to give utiliz-
ed regional income.
The estimated value of utilized income is compared with estimates
of accumulation expenditures calculated in two ways.
One estimate is
obtained by adding investment in fixed capital (in sectors of material
production and the social services sector) to expenditures on changes
in working capital and inventories
difference
between
capital
, and finally subtracting the
repairs
and depreciation payments.
The second estimate is made by adding net increases in capital stocks
to net changes in uncompleted construction, then again subtracting the
difference between capital repair and depreciation payments.
values of the accumulation norm (the ratio
from
these estimates
Since change
The two
) calculated
is compared with an exogenously projected norm.
in this norm is expected
to be very gradual over time,
this serves as a check on the investment and capital stock projections.
If either estimated value diverges significantly from the expected norm
then adjustments in parameters or exogenously given values affecting
the relevant estimates are made by the model user.
(See Rayatskas and
Zhemaitaitite (1972), pp. 100-101.)
One type of feedback that is not present in this model is that
between
the projected
distribution
calculation of labor income.
of employment
by sector and
the
In the calculation of labor income only
the distribution of workers by sex and urban/rural residence is considered.
This is considered
variability in labor income.
sufficient
to account for most of the
Thus there is no feedback from the rest
of the model affecting the original calculations of income and consumption.
-58-
A particular feature of the RAISA system
that enhances its use-
fulness for planning purposes is the identification of various indicators by ministerial
subdivisions as well as by sectoral subdivisions.
For example, if firms of one of the economic sectors identified in the
model
are
under
the
control
of
both
republican
and
union-republic
ministries, then indicators for this sector are disaggreated into these
two parts in certain equations of the model. This helps to make the
results coming out of the modeling more easily "addressable" to the
actual organizational units with which that planners have to communicate.
(See Rayatskas and Zhemaitaitite (1972) pp. 93- 97.)
The RAISA model has undergone considerable testing in the Lithuanian Gosplan, and work on its further development continues.
particularly
interesting
application has
been
One
the use of a version
of the model to calculate a turnpike growth path for the Lithuanian
economy.
(See
Zhemaitaitite
(1975).)
Also
extensive
econometric
studies of the characteristics of regional production function
have
recently been carried out by Rayatskas and Bal'sis (1979). The successfulness of this particular model has led to it being suggested for use
in other regions, and even as a basis for national forecasting.
(See
Dadaian (1975) and Dadaian and Rayatskas (1976).)
The Institute of Economics of the Latvian Academy of Sciences
Among the most recent work in Soviet regional modeling perhaps the
most promising new approach has been tried by a group at the Institute
of Economics
Adirim.
of the Latvian Academy of Science headed
by Dr. I. G.
Here many of the ideas tried in other models have been brought
together into a system of models which combines econometric, balancing,
and optimization techniques to depict both real and financial relations
in the Latvian economy.
One of the difficulties often encountered In formal mathematicaleconomic modeling is that each technique restricts the representation
-59-
'
of
Lhe real
world in some way.
Input-output
models are useful
for
understanding inter-sectoral relationships in production, but they are
not as useful
functions
or
as econometric models based on s e c t o r a l
when i t
temporal
comes to the. analysis of substitution
changes in productivity.
possibilities
Optimization models are
tools for deciding on the best allocation of
goal, but they are not as helpful
production
useful
resources to reach some
in understanding how system behavior
constrains the planners' ability to make these allocations.
Many other
examples could be cited, as each formal technique has i t s
limitations
in regard to the nature of the specifications allowed, the type of data
required
for
its
application,
or
the
form
of
the
solution
process
involved. What the group in Latvia has done i s to try to overcome this
problem by modeling the economy using various methods, and than combining these models into a unified
system using both formal and informal
techniques of feedack between the various components.
The first group of models developed by this group consisted of a
series of macro and sectoral econometric models (LAT-1, LAT-2, LAT-3
and LAT-4) linked
MOB-2).
to a set of
s t a t i c input-output models (MOB-1 and
This system was designed
separately
so as to f a c i l i t a t e
questions.
But
they
are
also
to allow each model to be used
and simplify
used
in
feedback links between individual models.
analysis of
specific
combination with direct
and
The basic structure of this
system is represented in Figure 8.
The LAT models are similar
to the UKR models, but with an addi-
tional level of sectoral disaggregation and some new variables.
aggregate models
(LAT-1 and LAT-2)
the
of
determination
utilized
regional
allocation between consumption
formation
accounting
labor supply
.
the most
(involving
important
steps
are
and
its
income
and a c c u m u l a t i o n i
),
In the
capital
and calculation of
Each i s handled in much the same way as in the
UKR models, except, that Cobb-Douglas production functions (expressed in
growth rate form) are used instead of linear functions and labor supply
i s dependent
upon t o t a l
population and the level of wage income.
-60-
Figure 8.
Basic
S t r u c t u r e of
the LAT and MOB Models
Aggregate econometric models
(LAT-1, LAT-2)
Demand driven input-output model
(MOB-2)
M u l t i - s e c t o r econometric
(LAT-3, LAT-4)
Supply driven input-output
(MOB-3)
models
model
Capital
stock
previous
formation
(three)
is
periods,
tied
and
to investment
investment
in
in the current
fixed
capital
stock
and
is
linked to the amount of regional income going into accumulation funds.
Here a new set of equations is added to calculate increases in working
capital
and
inventories
,
which
is
portrayed
as an
additional
claimant for accumulation funds.
Starting form the estimated value of
produced
final
adding
national
income,
depreciation
total fixed
tion of
payments
capital
product
which
are
stock. Then gross output
, and the
material
total
increase
purchases
.
in
based
by
on
of
the
value
is calculated as a func-
used to explain
The l a t t e r
is calculated
the increase
variable in turn explains
in
the
increase in
At the next
level
(in LAT-3) these labor
supply and
Investment
estimates obtained from aggregate model calculations are disaggregated
by five major sectors
and
commucations,
capital
and
formation
calculated
using
(industry,
a
agriculture,
residual
material
construction,
production
transport
sector),
the
process modeled, and then net ouput in each sector
Cobb-Douglas
technical progress).
production
functions
(with
disembodied
This process is repeated for separate branches of
industry in the LAT-4 model, except gross output measures are substituted
for
net
available.
sectoral
output
since
time-series
data for
the l a t t e r
are not
The estimated output and capital variables obtained at the
and branch levels can then be used to correct the aggregate
estimates made at the macro level.
The MOB models are designed to insure the inter-sectoral
tency of the forecasted
growth path on the basis of exogenous
cients
supply and final
of
carried
interindustry
out
using
calculations.
two
approaches
standard
coeffiThis
input-output
is
balance
In one. approach (MOB-2) gross ouput i s calculated on the
basis of
final
forecasts
of aggregate consumption
exports
to
demand structure.
consis-
demand v a l u e s .
The l a t t e r are obtained
from the macro-level
, accumulation
, and net
of the LAT system and
buting these demands by sector using exogenous coefficients.
•62-
by taking
distri-
Once gross
outputs
are
obtained
intermediate
calculations
purchases
are calculated
for
Values obtained
,
required
depreciation
each sector
in these
of
using
capital
stocks
and value
exogenously given
calculations
,
added
coefficients.
can then be compared with
the
results of the econometric model simulations.
The second approach (MOB-3) starts with forecasts of sectoral and
branch gross output
obtained
from the multi-sector models LAT-3
and LAT-4. These, together with the exogenous coefficients
industry
supply,
Exports
(using
are used to estimate
by sector
coefficients
These
export
are estimated
estimated
estimates,
from
combined
total
final
with
series
inter-
demand by sector.
as a function
time
of
of gross output
regression
the estimates
of
studies).
total
final
demand and the previously described sectoral distributions of consumption
and accumulation make
it
exports and imports by sector
possible
to
then
as r e s i d u a l s .
calculate
both
This information
net
is
used to correct the distribution of net exports obtained from the MOB-2
calculations.
To achieve consistency between the results of the two input-output
models i t may be necessary for the the model user to make adjustments
in the initial solutions.
This can be done, for example, by changing
the allocation of investment and labor among sectors in the LAT-3 and
LAT--4 models.
The parameters for these models are estimated by time series regression analysis, calculated from various economic accounts, set according
to official
norms, or based on a combination of these approaches.
the LAT--3 model the following examples of each can be noted.
Ianov,
Pochs
(1975),
pp. 102-107)
In
the equations
for
In
(Adirim,
allocating
investment and labor among sectors, and in those for linking
sectoral
gross
and net
ordinary
least
squares regression
linking
output,
total capital
all
parameters
were
on time s e r i e s
estimated
data.
using
For the equations
stock to the previous period's
stock and newly
created capital,
accounting data were used to determine the share of
the l a t t e r
that
value
is
usually required
-63-
to cover capital
scrapping
and also to account for differences in form of measurement between the
two series.
The production
mixture of two methods.
function
coefficients are obtained by a
First the output elasticities of capital and
labor are assigned values equal to the average shares of labor income
and non-labor
income in value added by sector. The two remaining
coefficients — f o r
trend—are
the scale factor and
estimated
by
regression
the technical progress time
analysis
after
constraining
production function for the effect of the output elasticities.
the
Like-
wise, the lag structures used for the capital formation equations are
based on norms provided by the construction ministry, but the equations
are then fit to historic data using regression analysis.
The above described system of models has the advantage of being
decomposable
into
its
separate
components
for
individual
solution.
This reduces the requirements for computer capacity and increases the
flexibility of its use and development.
advantages to formally integrating
However, there are also
the various levels into a single
model. • This not only speeds the process of model solution, but it also
allows each variable to be solved for at its most appropriate level of
disaggregation.
Three such "integrated" models developed at the
Institute of Economics of the Latvian Academy of Sciences are described
in Adirim
(1977, Chapter
IV).
Each of
these models
encompassed
all three major aspects of the above system of models (macro, sectoral
and
intersectoral) but at differing levels of detail and structure.
In the first two of these models, INT-1 and INT-2, there are a set
of equations for determining labor and investment in total and for the
production sectors, a set of sectoral labor and investment allocation
equations, capital formation equations, and production functions.
also
have
input-output
equations
to
determine
gross
output,
They
final
product, and material purchases by sector from net output obtained from
the solution of the production function equations. (INT-2 differs from
INT-1 primarily in the inclusion of a block of equations for industrial
branches as well as major sectors, and a more detailed breakdown of
final demand categories in the intersectoral block.)
•64-
The macro and
sectoral levels are fully integrated
1
net output, capital
in that in the former total and
stock, and depreciation are obtained by summation
of estimates obtained
from
the latter.
The intersectoral
component
fulfills only the simplest of functions, serving to calculate certain
identities using exogenously given values of input-output coefficients.
There
is no guarantee, however, that the calculated values of
final
product obtained here will equal the sum of utilized income and depreciation obtained in the macro-level.
be called
for.
Thus informal user adjustment may
These models use the same initial data, and can be
solved for the same set of endogenous variables, as the combined LAT
and MOB models.
The final integrated model, INT-3, differs from the other two in
that it determines investment starting
utilized income.
from final demand and not
The key change is the modification of the balancing
equations to specifically include the requirements on branch output of
investment in the productive sectors.
set
of
exogenously
forecasted
This is done by introducing a
coefficients
structure of sectoral investment.
describing
the balancing
component
material
Thus the model takes on the major
characteristics of a dynamic input-output model.
role of
the
This also changes the
in the solution
process—it
is now
solved simultaneously with the other two components.
Either the combined LAT and MOD models, or the INT models, can be
used to provide forecasts containing basically the same set of variables
as
is
typical
of
dynamic
input-output
models~-that
is, time
series of current and capital accounts encompassing balances of production, income, consumption, and
accumulation.
In the
traditional
input-output model this is done by starting with an exogenously given
forecast of final demand and its structure (including net trade), and
working back from this to determine the required output levels and then
the level of
factor inputs.
In the Latvian models the direction of
calculation is the opposite, since it starts from foreasts of factor
availability
and
distribution, proceeds
through the determination of
output levels, and ends with income and final demand.
-65-
Although
the Institute of Economics
is part
of the Academy of
Sciences, Adirim's group has established
a working relationship with
the Latvian
a testing
models.
earlier
Gosplan which
has provided
ground
for
their
This experience has made them aware of certain limitations of
specifications, and
interesting special models.
led
to the development
of some new and
One of these was constructed specifically
to reflect the constraint placed on regional growth by the capacity of
the republic's
construction
industry.
Since construction is a non-
tradable activity, the regional supply of construction and installation
work is limited by local capacity.
level of investment
This in turn limits the effective
since construction usually make up two-thirds of
the value of investment, and is needed in rather fixed proportion to
investment in machinery and equipment.
This link between development
of the construction sector and the ability to carry out regional
investment plans became obvious to the Latvian economists when in the
early 1970's investment in the republic was constrained by a slowly
growing construction sector in a way that their earlier models could
not reflect.
The model they developed, which is called the PMM (for production
macroeconomic model) model, corresponds in level of aggregaton to the
LAT-2 model, except
identified
sector
that
the construction
of production.
indicated in Figure 9.
industry
is a
separately
Key variables and their
ties are
The major modification from the earlier models
is in the equation for total investment
,
which is here dependent
on the volume of construction-installation work
.
The latter
is determined as a function of capital and labor used in the construction sector, and the level of wages for construction workers
(Although an equation is supplied which links construction wages to the
general wage level
, this variable
is obviously
intended
to be
treated as a policy instrument.) The model also provides for a consistency check between this value of investment derived from the physical
supply
of
investment
goods and
aggregate income balances.
a second
estimate
derived
from
the
The latter are obtained as shares of
-66-
Figure 9.
Basic Structure of the PMM Model
aggregate model
model of the construction sector
regionally
utilized
income, predicted
on
In this model a link is provided
the basis of past
between national
and
trends.
regional
variables by the inclusion of national net output (Y) in the equation
for
regional
mated
output
(expressed
here as final demand—
) . The esti-
parameters indicate an increase in regional output as national
output increases, which is added to the output arising from increases
in regional capital and labor inputs.
Total accumulation
capital put
and
is obtained
into use
increases
by summing
the value of new
, changes in uncompleted
in working
construction
capital, inventories, and
reserves
Accumulation is subtracted from utilized national income
to
yield
total
consumption
fund
.
Since
produced
national
income is a function of labor and capital allocated to the production
sectors, there
years.
is a positive feedback
from accumulation in previous
But there is a negative feedback effect from increased capital
stock through the resulting higher depreciation deductions
Similar
positive
and
negative
feedback
cycles
operate
interaction of capital stock, output, and wages
.
through
the
An increase in
investment and capital leads to higher output and wages.
But at the
same time the increase in capital leads to higher depreciation payments
which lowers produced income and then wages.
A second type of special model was developed in order to reflect
the role of financial relations in regional development, and to aid in
financial
planning.*
These
models, the
so-called
PFM
(production-
*
Research
is also
modeling at TsEMI.
ial
financial
being
pursued
in
the area
of
regional
financial
Under the direction of B. L. Isaev, summary mater-
balalnces
have
been
constructed
(using
data
for
the
Estonian republic) which depcit the flow of financial resources among
the production sectors, the population, the banking and credit system
and the state budgets.
Also the methodological basis for using these
balances in modeling financial flows has been developed.
and Terushkin (1978)).
-68-
(See Isaev
financial models), are designed primarily to simulate the financing of
investment, receipts and outlays of the state budget, and income and
expenditures of the population.
These financial variables are supple-
mented by selected real variables as needed to complete the major macro
balance, and
thus make the models self-contained
(although it is
intended that the PFM models be used in conjunction with the previously
described models.)
As Soviet
socialist
researchers working
economic
on these models point out, in
theory material flows of goods and services have
traditionally been treated as the primary reflection of economic
turnover, while financial flows are viewed only as a means of accounting
for these
transactions.
But
in actual practice, especially
when viewed from the perspective of the participants in these transactions, the
actions
securing
is of
of financial means for covering physical trans-
primary, and
not
secondary, importance.
Also, since
control of financial flows can be used as an instrument of economic
policy, this too increases the need for their more explicit consideration.
The introduction of financial relations into regional modeling
should be of great benefit in understanding and planning the processes
by which income is redistributed within the Soviet Union.
The first
containing
of the two PFM models is a macroeconometric
fifty
equations.
depicted in Figure 10.
The basic
structure
of
model
this model
is
The major difference between this model and
previously described models is in the financial determination of
regional investment and the inclusion of financial flows into and out
of the state budget.
Instead of going directly from the level of regional income
to investment
in the model.
financing.
The
, the
intermediate financing
These are "decentralized" financing and
former
comes largely
from
enterprise
while the latter comes from the state budget.
cation used
in
links are identified
the model
gives
centrally
profits
,
(However, the specifi-
financed
function of the level of decentralized financing.)
-69-
"centralized"
investment
as a
Figure 10. Basic Structure of the PFM-1 Model
Total
expenditures
from
the
state
budget
based on the level of produced regional income
tion
.
Total budget revenues
expenditure
figure.
income
is regularly
of
state
the
(The
estimated
and public consump-
are then linked
estimated
a little more
budget
are
equation
indicates
that
than expenditures.)
are made
to this
budget
Revenues
up of a portion of enterprise
profits, both a personal income tax and a special income tax on unmarried
persons
, and a residual category.
The only identified
component of budget expenditures is that going into investment, and it
is given as a function of centrally financed investment.
Other financial flows include components of personal income and
the depreciation payments.
payments
Personal income
, income of collective
category, while
is made up from wage
farm workers
expenditures are broken into
paid into the budget and a residual category.
and
a residual
the above income taxes
Depreciation is handled
in much the same manner as in the LAT models.
The second financial model, PFM-2, provides considerably greater
detail
PFM-2
on
traces
stages:
the
financial
transactions
the flow of
and
contains
financial resources
over
250 equations.
through
the
following
(1) production, (2) redistribution (among produtive sectors,
social
sectors, the
population, the financial system, the
state
budget, and the credit system), (3) consumption, (4) accumulation, and
(5) external trade. Particular attention is given to the financing of
*
The variables
included
in this model are very similar to those of
the combined material-financial balance models of developed by Isaev at
TsEMI.
But most parameters in the Latvian model are obtained by means
of statistical regressions rather than calculated
balances.
from
financial
This greatly reduces the initial data requriements of the
model in that strict comparability
series is not necessary.
in coverage or valuation of data
Adirim cites this as one of the major advan-
tages of this approach.
•71-
investment and consumption, the role of prices and wages in financing,
the
calculation
of
the
major
regional
financial
balances, and
redistributive role of the financial-credit system.
region and
the nation are represented
not only
the
Links between the
through
exports and
imports of products, but also by inter-budgetary financial tranfers and
personal money holdings.
The model is designed to be used both pas-
sively (to predict the effect on material financial balance of existing
policies an trends) and actively (to determine the required change in
policies to achieve desired results).
In the modeling of financial relationships institutional, rather
than behavioral
or technological regularities are of key importance.
Thus existing administrative and budgetary procedures are reflected in
the specifications of many equations in these models.
the
level
of
variable
aggregation
and
However, due to
inconsistencies
in
variable
measurement, the relationships between variables are typically quantified on the basis of time series analysis rather than administrative
norms.
The treatment of turnover taxes serves as a good example of how
these relationships are handled in the model. These taxes are part of
produced income that is channeled into the state budget by means of tax
payments on industrial goods (primarily consumer goods of the textile
and apparel and food processing industries).
First the value of taxes
collected, realized from the sale of taxed goods, is calculated.
For
each such group of products identified in the model, turnover tax is
calculated as a function of value of retail trade (if the product is
produced in the region and mostly consumed locally, value of output),
the previous year's tax and a time trend.
The various product taxes
are then summed to form a total of collected taxes.
the division of the collected
state budgets.
tax between
Here the turnover
instrument of government policy.
the budget are allocated
The next step is
the regional
and
central
tax plays a special role as an
The majority of other payments into
on a fixed basis.
For example, half of
personal income tax goes into each budget, deductions from profits of
•72-
enterprises under all-union supervision go entirely to the all-union
budget and those of the remaining enterprises go into the republican
budget.
These flows can be considered "guaranteed" and are correspond-
ingly represented
in the model. However, other budget payments, the
most important being
the turnover tax, are apportioned each year
between budgets at the discretion of central authorities.
as
a central
instrument
for
securing
the
They serve
financial requirements of
planned regional development through interregional transfers.
Reflect-
ing this, the sequence of calculations taken in the PFM model is to
first estimate total regional budget expenditures necessary to finance
economic and social development, then project the amount of "guaranteed"
budget
difference
income
between
at
these
the
two
regional level, and
to
estimate
finally to use
the necessary
the
division of
regional turnover tax collections between the republican and all-union
budgets.
-73-
Conclusion
Regional modeling in the Soviet Union has now gone through several
stages of development in terms of technique and in terms of its role in
the planning process.
to simulation
The transition from an emphasis on optimization
in modeling
reflects a shift
in the role
provided
for models in planning.
Limitations to formal modeling serving as a
means
have been
for plan drafting
macro-modeling
has
emerged—medium
recognized, while a new role for
and
long-range
forecasting
as an
input into pre-planning calculations.
In terms of future directions for model development and application, one of the critical areas of improvement will need to be in the
linking of national and regional models and forecasts.
At present in
the Soviet Union there is no organized means for integrating
the
efforts of the many research centers using models to forecast developments nationally and
in individual regions.
A linked
system
would
provide a means by which forecasts generated for the nation as a whole
could
be used as an input into
the forecasting of regions
(what
is known in the West as a "top down" approach to regional forecasting)
or where regional forecasts could be aggregated into national forecasts
(a "bottom up" approach to national forecasting).
of
IEOPP
and
TsEMI
they are not a
involvment
combine
substitute
in planning
both
national
for a linked
of many
locally
and
regional dimensions,
system.
based
vital ingrediant which cannot be replicated
The expertise
modeling
is a
in one center.
Some
at the Institute of
the Latvian Academy of Sciences, already have provision
for using forecasted values Of national variables.
necessary
and
groups
regional models, for example those developed
Economics of
Although the models
national
forecasts are
not
readily
available.
However the
Likewise,
there is no indication that national forecasters make any systematic
use of the available forecasts of regional groups.
L.M. Satunovskii has pointed out three deficiencies in the present
organization of forecasting in the Soviet Union: (1) forecasting groups
-74-
work
in
exchange
relative
of
isolation
information,
forecasting
organized
from
i.e.,
one another, without
there
is
no
the
hierarchical
necessary
system
of
under a single leadership; (2) because the
planning organizations do not take an active enough role in the forecasting effort there is still insufficient integration of forecasting
with
the planning
process;
(3) the industrial
ministries
and
other
organizations in various sectors of the economy do not actively and
regularly participate in long-range forecasting, although their inputs
are vital to the success of any such effort.
(See Satunovskii (1977),
Chapter 1.)
Some
Soviet
economists
Central Committee
the development
first
step
toward
expressed
the view that the recent
resolution on improvements in the planning
will have a significant
on
have
system
impact on the role of forecasting, and
of economic modeling.
correcting
the
thus
The new resolution is a
deficiences
noted
by
Satunovskii.
It makes mandatory for the first time the preparation of medium and
long term economic and social forecasts.
Academy of Science institutes
are charged with the development of twenty years "programs of scientific-technical progress" which are to be delivered regularly to Gosplan.
These are then to be used by Gosplan, working together with the industrial ministries and the republican Councils of Ministers, to prepare
ten year forecasts of the social and economic development.
expected
that
the demands
generated
by
these new
It can be
requirements will
serve as a stimulus to the continued evolution of economic modeling in
the directions indicated above.
"Ob uluchshenii planirovaniia i usilenii vozdeistviia khoziaistvennogo
mekhanizma na povyshenie effektiv'nosti proizvodstva i kachestva raboty"
(Concerning
influence
the improvement
of
of planning and the strengthening of the
economic mechanisms
for
heightening
the
efficiency
of
production and the quality of labor), Resolution of the Central Committee of the Communist Party and the Council of Ministers of the USSR,
July 12, 1979.
— 75 —
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