POLGNP: A DETAILED MODEL OF POLISH GNP

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• Document Creation Date: December 21, 2016
• Document Release Date: January 6, 2009
• Sequence Number: 6
• Publication Date: April 1, 1984
• Content Type: REPORT

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Jlreetorate of
Intelligence
POLGNP:
A Detailed Model
of Polish GNP
A Technical Intelligence Report
Confidential
Confidential
EUR 84-10046
April 1984
Copy 3
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'?Direcfit gate of Confidential
Intelligence
POLGNP:
A Detailed Model
of Polish GNP
This paper was prepared by
the Office of European Analysis. Comments and
queries are welcome and may be directed to the
author
Confidential
EUR 84-10046
April 1984
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. 1:- Confidential
Summary
Information available
as of 1 January 1984
was used in this report.
POLGNP:
A Detailed Model
of Polish GNP
The international financial crisis has increased the need to assess the
ability of national economies to grow and prosper in the 1980s even if the
key inputs of energy and imports are limited for financial, political, or
military reasons. This research paper describes and provides documenta-
tion on a new model of the Polish economy, POLGNP, that will allow us to
assess Poland's adjustment to resource constraints and the prospects for
economic recovery.
POLGNP is a system of mathematical equations which determines the
Polish economy's requirements for domestic production, hard currency
imports, soft currency imports, and energy in order to achieve particular
goals for consumption, investment, defense, civilian government, and
exports. Dependence on imports and energy adjusts at different rates and
in different directions across economic sectors. Furthermore, energy and
import requirements are very sensitive to the mix of production as well as
its level. Reliable projections of energy and import needs thus require a
high degree of disaggregation. POLGNP starts from given targets for
seven domestic end uses of GNP and 12 categories of exports. To achieve
those targets, POLGNP balances trade-offs between production in 34
domestic sectors, 12 hard currency import categories, and 12 soft currency
import categories. After these have been determined, POLGNP derives
requirements for capital, labor, and energy in the forms of coal, oil, gas,
and hydro/nuclear.
This paper describes the present version of POLGNP. The second section
discusses the structure of the model in general and schematic terms. The
third section reviews the performance properties of the model in historical
simulation and several alternate future simulations. The fourth section
provides an assessment of POLGNP and looks to further development.
Three appendixes provide more detailed information on the model, the
supporting data development, and historical simulation. Appendix tables
also report the result of one simulation over 1982-90 and indicate the
degree of detail POLGNP is designed to provide.
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April 1984
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i,ontiaennal
Applying POLGNP
UNCODED Appendixes
Treatment of Issues and Methodological Innovations 6
Historical Validation of POLGNP, 1971-81 9
Results of the Baseline Simulation, 1982-90
The Importance of the Composition of Demand
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POLGNP:
A Detailed Model
of Polish GNP
Econometric macromodels have been increasingly
used to analyze centrally planned economies during
the past half decade. They provide a convenient
mechanism for examining the interactions of many
factors simultaneously and for studying the potential
impact of policies and economic events on the path an
economy is expected to follow
Standard macromodels, however, have proved defi-
cient in handling several key issues, which have been
particularly important since the late 1970s. Develop-
ments in productivity growth; substitutability between
domestic and imported inputs; and the changing
resource burdens of shifts among-and in the compo-
sition of-consumption, investment, defense, and ex-
ports have been assumed or roughly approximated.
The data and methodology necessary to calculate
these relationships have been either unavailable or
underutilized, and such microeconomic relationships
require a level of detail and sectoral interdependence
present in few macromodels
The primary purpose of POLGNP is to determine the
resource costs and, thus, the feasibility of Polish
economic recovery, especially the ability of the econo-
my to reduce its dependence on hard currency im-
ports. The structure of the model is designed to
accommodate analysis of policy shifts and technologi-
cal adjustments affecting the trade-offs between do-
mestic production, soft currency imports, and hard
currency imports. The model will help to answer the
following specific questions:
? What domestic and imported resources will be
required to fulfill plans for domestic end uses and
exports in the 1980s?
? How successfully is the Polish economy shifting
away from dependence on hard currency imports
and at what cost?
? Are there particular exports or domestic end uses
which can be expanded with a minimal need to
increase hard currency imports?
The model has already demonstrated that the techno-
logical structure of the Polish economy-under the
stress of drastic cutbacks in hard currency imports
because of financing problems-shifted abruptly in
1981 away from dependence on hard currency
POLGNP is the product of a continuing effort to
develop a model to handle these microeconomic rela-
tionships and to relate them to macroeconomic trends
in Eastern Europe. POLGNP depends on the funda-
mental structure of the GNP accounting framework:
the demand side of the GNP accounts consists of
domestic end uses and exports; the supply side consists
of domestic producing sectors and imports. The two
sides must always be equal even when the economy is
in disequilibrium. The demand components of GNP-
consumption, investment, defense, and exports-are
'fed into the model. They are exogenous variables
derived from plan targets or other sources. POLGNP
then calculates the supply components of GNP-
domestic economic activity in each sector and im-
ports-required to achieve those targets. POLGNP
also derives the capital stock, labor input, and energy
consumption necessary to support the demand side of
GNP.
imports.
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Figure 1
Conventional Input-Output Analysis
Receiving sectors
Electric
power
column
^
^
M
Value added by labor
and capital
^^
N
Coal
column
^
0
Gross values
of inputs
Final
demand
columns
Gross
values
of
outputs
Accounting
identity
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Lonrnaenual
Figure 1, continued
A key element in POLGNP is the 1977 Polish input-output table.
The strengths of the input-output table are completeness, consisten-
cy, and detail. It includes every transaction that occurred in the
economy in the year of the table. The input-output table is a
rectangular grid of cells with each sending sector occupying a row
of cells and each receiving sector a column of cells. An individual
cell then reports total shipments for the year from its row sector to
its column sector. For example, the value of coal shipped to electric
generating plants is in the single cell in the coal row and electric
power column. Each transaction in the economy is included in one
cell. The input-output table used for POLGNP has 60 rows and 77
columns for a total of 4,620 cells.
By convention, the right columns in the input-output table are final
demand columns-destinations for outputs that are not sources of
further production. These final demand columns correspond to end
uses in GNP accounts-consumption, investment, government
spending, additions to inventories, and net exports. These columns
(along with some possible extra rows that provide data not used in
conventional input-output analysis) are broken off from the input-
output table to leave a square matrix (number of rows equals
number of columns) consisting only of rows and columns for
producing sectors, that is, sectors which provide inputs as well as
receive outputs. This matrix is often called the transactions or flow
matrix.
Each cell in each column is then divided by the value of total output
of the column sector. For example, if the cell in the coal row and
electricity column has an entry of $12 and the total output of the
electricity sector is valued at $100, the quotient in that cell is 0.12;
that is, for every dollar of electricity output, the coal sector must
deliver 12 cents of coal to the electricity sector. If this division is
performed on every entry in the transactions matrix, the result is a
matrix of direct-input coefficients.
One limitation of the direct-input coefficient matrix is that it does
not represent the total coal requirement for electric power genera-
tion but only the direct requirement. The coal sector uses electric
power to mine the coal to ship to the electricity sector. Further-
more, the timbers in the coal mines were most likely cut in sawmills
run on electricity. To increase electric power output, all the other
sectors need more electricity to produce the inputs they must
deliver to both the electric power sector and to each other. Every
sector in the economy is indirectly dependent in an infinite
backward linkage on every other sector in the economy. Wassily
Leontief received a Nobel prize for discovering a simple formula to
calculate all these linkages and generate a matrix of direct-plus-
indirect coefficients, often called a Leontief matrix.
This matrix is a powerful aid in calculating an economy's resource
and production needs. For example, by reading down the electricity
column of the Leontief matrix, one can determine the additional
output required in each sector to add one more unit to the output of
the electricity sector. Furthermore, multiplication of each cell in a
final demand column such as consumption by its corresponding
element in a row of the Leontief matrix (such as electricity) will
yield the total direct-plus-indirect electricity requirement to satisfy
that level of consumption. This is a typical input-output calculation.
Although quite powerful, conventional input-output analysis must
often be modified to deal with particular analytical problems. In
POLGNP, these problems include:
? Integration of imports into the full input-output analysis rather
than as a column of negative entries under final demand.
? Accounting for changes in the technological relationships reflect-
ed in the input-output coefficients, which for POLGNP are
constant 1977 coefficients, and projecting those changes into the
future.
? Allowing for the probability of unpredicted technological changes
in a reasonable way.
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Figure 2
General Flow Diagram of POLGNP
Leontief(I-A)-t matrix I Modified 1977 Polish input-
4_ output table
Direct input coefficients for
capital, labor, coal, oil, gas,
and primary power
Consumption of food, housing,
and other; investment,
defense, other government,
and inventory change
14- Energy, metals, machinery,
Supply side requirements chemicals, mineral products,
assuming constant technology wood and paper, light industry,
processed foods, misc.
34 domestic sectors industry, agriculture, forestry,
12 hard and soft currency and misc. traded products
import categories
13 aggregates
Impact of weather on
agriculture
Supply side requirements with Supply side requirement with
technology varying according (_.echnology changes reconciled
to past patterns 34 domestic sectors
34 domestic sectors 12 hard and soft currency
12 hard and soft currency import categories
import categories 13 aggregates
13 aggregates
oal, oil, gas, primary power,
total
4'
Capital and labor requirements
with sectoral productivities
varying according to past
atterns
34 domestic sectors
12 hard and soft currency
import categories
13 aggregates
Energy requirements with
technology changes reconciled
34 domestic sectors
12 hard and soft currency
import categories
13 aggregates
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POLGNP is an annual model consisting of 182
equations connecting a like number of endogenous
variables with only 20 exogenous variables. Seventy-
eight of the equations are econometric estimates of
technological change, 75 reconcile conflicting require-
ments patterns, 23 are accounting relationships, and
six are input-output calculations. Most of the input-
output matrix calculations (see figure 1) are per-
formed outside the simulation model because includ-
ing these calculations in the model would make it too
large for the modeling software to handle.
The general structure of the Polish GNP model is
shown in figure 2. Since POLGNP is demand driven,
the model is devoted to describing the supply response
to changes in demand. Each supply response is calcu-
lated through three iterations: first, assuming the
technology reflected in the 1977 Polish input-output
table; second, applying past (1971-81) patterns of
technological responses to demand changes individ-
ually to each supplying component; and third, allow-
ing for technological change because of factors other
than demand changes and reconciling any differences
resulting from projecting past patterns of technologi-
cal change for individual sectors. A smaller portion of
the model then estimates the capital, labor, and
energy required to support these supply responses
using a similar three-step approach.
POLGNP is driven entirely by effective aggregate
demand 2-domestic end uses' and exports-and does
not respond to other factors (except weather's impact
on agriculture). Thus, if a political factor (such as a
regime decision to hold down consumption) or an
economic factor (such as a shortage of hard currency
credits) constrains GNP, this must be reflected in the
assumptions about effective aggregate demand that
feed into the model. POLGNP calculates the require-
ments for capital, labor, energy, and imports needed
See appendix A for a more complete technical discussion. (u)
Effective aggregate demand results in actual expenditure and
receipt of goods and services. Aggregate demand may not be
effective if goods and services are not available
' In GNP accounting, domestic end uses are categories that receive
goods and services, but do not supply goods and services within the
accounting framework. In POLGNP, these domestic end uses are
consumption, investment, government, and additions to inventories.
to satisfy an assumed list of demands; it does not
determine whether those requirements can be met.
Given a list of available resources, POLGNP cannot
tell what domestic end use and export targets policy-
makers will try to achieve with them. Calculation of
the input requirements necessary to sustain a growth
target, however, provides a unique capability to assess
the feasibility of the target. Moreover, the shares of
GNP devoted to consumption, investment, and trade
can change dramatically. This framework allows us to
examine the implications of changes in the composi-
tion as well as the magnitude of GNP.
Model Variables
All projections from a model are conditioned by
assumptions regarding the exogenous variables. The
exogenous variables in POLGNP fall into three
groups.'
Demand Side Domestic Targets. These variables in-
clude seven end uses of GNP: personal consumption of
food, housing, and other goods and services; invest-
ment; civilian and military government expenditures;
and changes in inventories.
Demand Side Export Targets. These variables include
exports divided into 12 commodity categories: energy,
metals, machinery and construction, chemicals, min-
eral products, wood and paper products, light industri-
al products, processed foods, miscellaneous industrial
products, agricultural products, forestry products, and
miscellaneous traded goods and services.
Weather. This variable affects the supply response of
the sources of agricultural products.
From the three groups of exogenous variables, the
model is able to project the endogenous variables
Aggregate Supply Side Variables. Each of these 13
variables indicates the supply side response from a
commodity/ service category regardless of source-
domestic value added or gross value of imports. The
zlotys unless otherwise noted
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aggregates match the commodity/service groups list-
ed under exports with the addition of a category for
nontraded goods and services
Domestic Supply Side Variables. Value added is
projected for each of 34 producing sectors and then
added to obtain GNP
Import Supply Side Variables. Imports are projected
for each of the 12 commodity/service categories listed
and are separated by origin into imports from hard
currency and soft currency trading partners, resulting
in 24 import categories
Energy Requirements. Domestic energy requirements
are calculated in barrels per day oil equivalent for
coal, oil, gas, and primary electric power (hydro and
nuclear)
Labor and Capital. Labor and capital requirements
are calculated in full-time equivalent employees and
constant zlotys, respectively
Treatment of Issues and
Methodological Innovations
POLGNP has been designed specifically to account
for the changing substitutability between domestic
production, hard currency imports, and soft currency
imports. Disaggregation is required since substitut-
ability differs dramatically from sector to sector. For
example, there is little physical difference between a
barrel of Soviet oil and one imported for hard curren-
cy,6 but machinery imported for hard currency is
often very different technically from domestically
produced or CEMA-origin machinery. POLGNP
takes these differences in substitutability into account
by first treating each of 13 product groups separately.
Each group includes value added in one or more
domestic production sectors, gross value of hard cur-
rency imports, and gross value of soft currency im-
ports. After substitution among the product groups
'Appendix A provides a more complete description of the analytical
model
'I he question o subsidies is not relevant here. Oil imports have
been reevaluated in 1977 domestic zlotys regardless of country of
origin. Subsidy is a financial issue and does not affect technological
has been treated, POLGNP calculates the effects of
substitutions on disaggregated domestic production,
hard currency imports, and soft currency imports
within each product group
POLGNP disaggregates the problems of predicting
the supply responses of the Polish economy into
component problems for the various product groups
and sectors and departs from standard practice in
order to handle each of the three component problems
as follows:
? The problem of the supply response of each product
group and sector to changes in the level and compo-
sition of aggregate demand with technology held
constant was solved by applying standard input-
output techniques to a specially constructed Polish
input-output table with a unique treatment of
imports.
? The problem of supply response with technological
change predicted in response to changes in demand
was handled by applying standard econometric re-
gression techniques to equations relating actual
sectoral supply responses to the sector supply re-
sponses as predicted from the input-output
calculations.'
? The problem of supply response taking into account
both technological change predicted in response to
changes in demand and the likelihood of unpredict-
ed technological change was handled by adjusting
the sectoral supply responses so that the GNP
accounting constraint is obeyed with domestic value
added plus imports equal to domestic end uses plus
exports
Adjustments to reconcile sources and uses of GNP are
often made proportionally so that much of the adjust-
ment is imposed on larger sectors. In POLGNP,
' This is quite different from conventional means used to handle
technological change in input-output analysis which require pro-
jecting changes in all the input-output coefficients. The 58-by-58
transactions matrix underlying POLGNP has 3,364 such coeffi-
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Model Ancestry and Relatives
POLGNP has ancestors in the analytical literature
for both centrally planned and Western market econ-
omies.
POLGNP also has many relatives. The input-output-
based linkage in one form or another is central to
most macromodels in which supply side sector detail
is prominent. Two examples are the Wharton Econo-
metrics and the Data Resources, Inc annual models
POLGNP differs from both its ancestors and relatives
in its full integration of imports into domestic eco-
nomic activity, its treatment'of technological changes
and their impacts on the economy, and its approach
to the issue of hard currency dependence. These
unique features make POLGNP a possible paradigm
for analyzing other medium- and small-size trade-
dependent economies, both market oriented and cen-
of the US economy.
trally planned.
POLGNP is also different from SOVMOD, SOVSIM,
and other supply-drive models of centrally planned
economies. Those models start with available sup-
plies of capital, labor, and energy; allocate those
supplies across sectors; and then allocate the prod-
ucts of the sectors to domestic uses and exports..
POLGNP starts with exogenous targets for domestic
uses and exports and then determines in great detail
the domestic production and imports required to
meet those targets.
ture from common practice.
however, this would mean that most of the adjust-
ments to the supply response would occur in domestic
as opposed to import sectors only because the domes-
tic sectors are bigger. The adjustments, however,
should be proportionate not to sector size but to the
relative likelihood of unpredicted technological
change affecting the supply responses of the sectors.
This variability can be measured by the standard
errors e of the regressions used to handle the second
component problem. This use of the standard errors to
adjust proportionately to the likelihood of unpredicted
technological change in POLGNP is another depar-
economy to demands placed on it
Once the problems of supply response are solved and
adjustments are made to reconcile sources and uses of
GNP, POLGNP sums the results to yield a detailed
picture of the most likely response of the Polish
Standard errors are statistical measures of the degree to which
equations err in predicting the values of their dependent variables
over historical periods. 25X1
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Historical Validation of POLGNP, 1971-81
Validation is the simulation' of an equation system
over a historical period with comparison of the simu-
lation to history.' POLGNP has been validated over
the period 1971-81, twice as long as the period over
which POLGNP would normally be simulated and
including years of substantial disruption in the Polish
economy (see inset, "The Polish Economy, 1970-81").
The results of the validation exercise 10 were very
encouraging (see detail in appendix C), but there is as
yet no standard against which to compare the results,
since, to the best of our knowledge, POLGNP is
unique. Rather than serve as a test of success or
failure of POLGNP, the validation exercise indicates
which sectors in the domestic economy and which
import commodity groups are amenable to forecasting
and the relative degree of confidence appropriate to
those forecasts. Figures 2 and 3 plot the actual and
simulated values of key aggregate variables. The
' Although validation is essential in assessing an equation system, it
involves potential pitfalls and requires careful assessment. Low
errors do not ensure absence of problems, nor do high errors
necessarily imply difficulties. Low errors can be achieved by tying a
model closely to the circumstances peculiar to the validation period
and limiting the flexibility of the model. The model will then track
history well but will be unable to forecast well if the economic
environment changes. On the other hand, high errors may be
expected if the model is-validated over a turbulent period as
POLGNP has been. Validation assumes knowledge of exogenous
variables-in POLGNP, the seven domestic end uses of GNP, the
12 categories of exports, and the severity of weather conditions
10 The results are reported for levels rather than average growth
rates because average growth rates allow the ups and downs to
cancel out. For example, the average annual growth rate of hard
currency imports from 1971 to 1981 was 6.3 percent. Over that
period, however, the growth in individual years ranged from a high
following table summarizes the performance of the
key aggregates and their components:
Root mean
squared percent-
age errors a
Gross national product
1
Average for 34 component sectors
4
Hard currency imports
11
Average for 12 component categories
57
Soft currency imports
9
Average for 12 component categories
20
Capital stock
3
Employment
1
Apparent energy consumption
5
Average for coal, oil, and gas
5
a Method of calculation: (1) calculate the percentage error for each
of the 11 years simulated; (2) square the percentage errors; (3)
compute the mean or average value of the squared errors; and (4)
take the square root of this mean or average. This is the most
demanding error statistic because plus-and-minus errors cannot
average out over time and large errors receive greater weight.
The relatively high. errors for the import categories
were examined further. Most of the high errors for
imports occurred in 1981 and were concentrated in
imports from hard currency trading partners. This
suggests an important conclusion-the decline in Po-
land's hard currency imports in 1981 was much
greater than expected, given (1) the drop in GNP, (2)
the changing composition of its domestic end uses and
exports, and (3) past import dependence. We conclude
that the technological structure of the Polish econo-
my-under the stress of drastic cutbacks in hard
currency imports because of financing problems-
shifted in 1981 abruptly away from dependence on
hard currency imports. We do not yet know if this is a
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The Polish Economy, 1970-81
POLGNP was validated over a period of turbulent
change in the Polish economy. The Polish economy
was subjected to several shocks in the 1970s. The
decade opened with the workers'revolt in December
1970, which brought Edward Gierek to power. The
new regime soon implemented a development strategy
based on extensive modernization and growth of the
capital stock. The enlarged and improved capital
stock was to combine with foreign technology and
material inputs to increase productivity and support
rising real incomes. By 1973 the Polish economy had
developed significant momentum: (1) rapid economic
growth was exceeded only by expectations for the
future, (2) trade links with the rest of the world
expanded dramatically, and (3) energy policy shifted
toward the substitution of relatively clean and effi-
cient oil for coal in domestic energy consumption.
The rise of OPEC drastically altered the economic
environment. Once cheap and plentiful oil became
scarce and expensive. Moreover, Soviet willingness to
supply oil below world prices only postponed the need
to switch back to coal. Poland's planners also faced
recession in the West and stiff competition for export
markets from aggressive newly industrializing coun-
tries. Polish determination to continue expansionary
policies virtually guaranteed that hard currency im-
ports would outrun exports. As the economy became
increasingly dependent on imports and failed to im-
prove its export competitiveness, the growing hard
currency trade deficit wasfinanced by increased
borrowing.
Economic discipline was continually sacrificed to
political expediency. Belatedly in July 1980, the
regime attempted to impose discipline by sharply
raising consumer prices. The move sparked strikes
and demonstrations and eventually the formation of
Solidarity. In early 1981 Poland suspended payments
on servicing its large foreign debt. Serious financing
problems required the regime to cut imports drasti-
cally. This shock to the economy contributed to a
9 -percent decline in GNP during 1979-81.
permanent shift or if it might be due to hard currency
imports in the pipeline to final users
The payoff from the sector detail in POLGNP is the
minimal size of errors for key aggregate variables.
The root mean squared percentage errors for gross
domestic product-Poland's reliance on domestic pro-
duction rather than imports-is only 1 percent." The
same statistic for total imports is only 3 percent. The
mean percentage errors-which allow overestimates
and underestimates to cancel-for GNP and imports
are zero indicating a very accurate long-run picture of
trade-offs in Poland between domestic and imported
goods and services. The split in imports between
capitalist and socialist sources is less accurate with
root mean squared percentage errors of 11 and 9
percent, respectively. Some imports such as oil differ
little or not at all between hard currency and soft
currency sources. Hence the decision to import from
one source or another will depend on availability,
price, or even political considerations. Since these
factors are not considered in POLGNP, the errors are
higher in determining hard and soft currency imports
than in determining total imports
Finally, the performance of the equation system in
predicting domestic use of energy, capital, and labor
is excellent-root mean squared percentage errors of
1 to 5 percent
Baseline Simulation, 1982-90 12
POLGNP projections depend on assumptions regard-
ing the exogenous variables of the model. These
variables define the demands placed on the Polish
economy for domestic uses-consumption, invest-
ment, government spending, and inventory accumula-
tion-and for exports. In addition, weather conditions
affect agriculture, and the rest of the economy must
adjust to agricultural performance. Except for weath-
er, these exogenous variables are to some degree
" Projecting GNP is more difficult the larger the share of foreign
trade. The Polish economy meets about four-fifths of the demands
placed on it with domestic production rather than imports.
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Figure 3
Historical Validation of POLGNP
- Actual
- Simulated
1.6 1971 72 73 74 75 76 77 78 79 80 81
Hard currency imports
Billion 1977 domestic zlotys
I I I I I I 11 I
1.6 1971 72 73 74 75 76 77 78 79 80 81
Soft currency imports
Billion 1977 domestic zlotys
50 I I I I I I I I I I I 50 I I I I I I I I I I
0 1971 72 73 74 75 76 77 78 79 80 81
Capital stock
Trillion zlotys of 1 Jan 1977
0 1971 72 73 74 75 76 77 78 79 80 81
Employment
Million workers
15.5
I I I I I I I I I I
4 1971 72 73 74 75 76 77 78 79 80 81 15.0 1971 72 73 74 75 76 77 78 79 80 81
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controlled by Polish policymakers. The degree of Table 1
control varies from government expenditures, which Key Assumptions for 1982
ll
d
are contro
e
, to exports, which can be reduced by
fiat but not increased unless foreign markets can be
found. Domestic and foreign policies or significant
economic events imply various combinations of these
variables. The reaction of POLGNP to hypothetical
policy changes and especially to external events de-
scribed by shifts in particular variables can be ex-
tremely useful in determining the path of the econo-
my's adjustment to such changes as well as in further
evaluating the model itself. Such projections are
All domestic end uses -10.5
Personal consumption, food -7.2
Personal consumption, housing 4.0
Personal consumption, other -19.1
Investment -18.5
The potential impact of particular events or policy
changes is usually assessed by comparing two model
projections, a reference case and a case incorporating
the assumed changes in terms of shifts in parameters
or exogenous variables. As a reference case, we
developed a baseline projection of demands placed on
the Polish economy from 1982 to 1990. The key
assumptions for 1982-are shown in table 1.
Results of the Baseline
Simulation, 1982-90
1982. This was a year of both dramatic decline in
aggregate demand and shift in its composition away
from domestic end uses and toward exports. The
assumed decline in domestic end uses of 10.5 percent
and the rise in exports of 9.4 percent resulted in a
drop in GNP of only 6.8 percent; total imports decline
9.1 percent due to a drop in imports from socialist
countries of 9.8 percent and from hard currency
trading partners of 8.3 percent.
Increases in hard currency imports are concentrated
on energy (92 percent), chemicals (17 percent), wood
and paper products (1,606 percent), light industrial
products (23 percent), and miscellaneous industrial
products (37 percent). POLGNP reflects a rebound in
the Polish economy's needs for these hard currency
imports after sharp reductions in 1980 and 1981. Soft
currency imports in 1982 also register some increases:
mineral products, miscellaneous industrial products,
and agricultural products. The following domestic
sectors also gain despite the overall decline in GNP:
coal, oil, machinery, precision instruments, livestock
products, housing, and government.
vwcI IIIIMILL, NvW4L
Government, defense
Additions to inventories
Exports
4.0
5.4
-20.5
9.4
1982 Export Share in 1981 Assumed Share in
Commodity Groups a Total Exports 1982 Total Exports
Machinery and 55.2 53.8
construction
Metals 7.7 6.8
Chemicals 8.3 7.9
Wood and paper 2.6 2.0
Light industry 8.7 7.4
Processed foods 5.5 6.4
Other categories No change from
1981
Assumptions 1983-85. All domestic end use and export categories
are assumed to hold constant at their 1982 levels.
Assumptions 1986-90. All domestic end use and export categories
are assumed to grow 1 percent per year.
Weather. Normal weather is assumed throughout the period 1982-
a Although based on the best available data, these assumptions may
not reflect what actually occurred in 1982. The need to convert all
data to 1977 domestic zlotys with provisional deflators and conver-
sion factors increases the likelihood of revisions once formal data
are available.
Capital stock in 1982 registers an increase of 4.3
percent despite the decline in GNP, an occurrence
with historical precedent in Poland in 1979-8 1. The
requirement for labor, on the other hand, falls, but
only slightly. Energy use declines even more than
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GNP, 10.5 versus 6.8 percent, due to dramatic de-
creases in the need for both coal and oil and because
the most energy intensive components of demand fell
more than the less energy-intensive components. In
terms of domestic uses, the largest declines were in
investment (- 18 percent) and other personal
consumption, including durables (-19 percent). Food
consumption only fell 7 percent while housing
consumption increased 4 percent. The big losers in
terms of total exports were machinery, metals, and
chemicals.
1983-90. The exogenous variables are assumed to be
stable through 1990. No changes are assumed in
1983-85, and all 12 export categories and seven
domestic end uses grow at 1 percent per year during
1986-90. These assumptions allow POLGNP to settle
down and reflect undercurrents of technological
change without further shocks.
The first major conclusion is that, with constant
demand, GNP declines by 0.2 percent average per
year as the economy substitutes imports for domestic
value added. Furthermore, when demand grows by 1
percent per year, GNP grows by 0.73 percent.
POLGNP reflects the historical tendency of the Pol-
ish economy to meet increases in demand with an
import response (unless constrained by hard currency
availability) rather than domestic production and
indicates that this tendency changes slowly. The
sectors in which value added declines the most with
stagnant demand are:
Domestic Sector
Percentage Range of
Annual Decline
Coal
-4.2 to -2.0
Electricity
- 3.5 to - 2.5
Nonferrous metals
-4.1 to -2.0
Wood products
- 7.0 to - 2.5
Miscellaneous material
products and services
-2.2 to -2.0
These domestic sectors would lose domestic markets
to imported substitutes without financial constraints
on imports. Imported oil and gas, for example, would
substitute for domestic coal and electricity. The fol-
lowing domestic sectors, however, would grow appre-
ciably by substituting their outputs for competing
imports under stagnant demand conditions:
Percent Range of
Annual Growth
Chemicals a
-0.7 to 3.8
Paper
0.4 to 1.7
Textiles
0.6 to 2.4
Clothing
0.6 to 2.8
Leather products
0.2 to 1.9
Agriculture
- 1.5 to 3.8
a The performance of the domestic chemicals industry, in particu-
lar, is interesting. It is able to resist loss of domestic markets to
imports in periods with great demand fluctuations (see appendix C)
and gains market share against imports in periods of steady
Even with no change in the level and composition of
aggregate demand, imports rise. While soft currency
imports rise by about 1 percent per year during 1983-
85, hard currency imports decline 1.2 percent in 1983,
then rebound with a 4.6-percent increase in 1984 and
a smaller 0.4-percent increase in 1985. Most of this
growth is due to growth in energy and machinery hard
currency imports.
Capital stock in POLGNP continues to increase 5.4
percent per year even in a stagnant economy-a
continuation of the past tendency to accumulate
capital regardless of economic conditions. Labor re-
quirements decline with GNP, but at one-half to two-
thirds the rate. Energy consumption, on the other
hand, drops by up to 3 percent each year during 1983-
85, reflecting both conservation and substitution of
gas for coal and oil
When demand growth picks up to 1 percent per year
in 1986-90, GNP begins to grow but only three-
fourths as fast as demand. Some sectors-coal, elec-
tricity generation, machinery, and electrical equip-
ment-continue to contract moderately as they
continue to lose domestic customers to imported sub-
stitutes. Total imports increase an average 2.2 percent
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per year bolstered by a 3.6-percent jump in hard
currency imports in 1986. Hard currency import
growth slows to 2 percent in 1987, recovers slightly to
2.3 percent in 1988, and then subsides to 1.8 percent
in 1989 and 1990. This variation in growth occurs
even when demand growth is steady at 1 percent per
year. POLGNP has picked up a rhythm in Polish
hard currency imports: their growth picks up in 1984,
1986, and 1988 and slows somewhat in the interven-
ing years. Soft currency imports, on the other hand,
tend to grow more slowly and steadily. This behavior
apparently reflects reliance on hard currency imports
as a quick response to increases in demand, and then a
corresponding slowdown in the following year, with a
similar rebound in growth in the third year. Over
time, this cyclical pattern in hard currency imports
continues but diminishes. This minicycle in the
growth of hard currency imports has historically been
overwhelmed by the normal fluctuations in the level
and composition of demand in the Polish economy.
The minicycle only becomes apparent when distur-
bances to steady growth have been removed.F__-]
With 1-percent growth in aggregate demand during
1986-90, the stock of capital increases on average by
5.6 percent per year, required employment by less
than 0.2 percent per year, and energy use by less than
0.1 percent per year. The low growth rate for energy
use displays an interesting time pattern, with energy
use actually declining in 1986 and 1987 and turning
slightly positive in 1988-90. This pattern results from
the substitution of gas for coal, which accumulates to.
150,000 barrels per day oil equivalent between 1985
and 1990.
The Importance of the Composition of Demand
To demonstrate the importance of the composition of
demand, POLGNP has been resimulated over the
1982-90 period after changing the underlying as-
sumptions. The new assumptions are given in the two
following scenarios:
? 1970 Demand Composition Scenario. The shares of
the 19 components of aggregate demand during
1983-90 are set at their 1970 shares. Over the
period 1970-81, 1970 had the lowest hard currency
imports/GNP ratio (0.06).
? 1976 Demand Composition Scenario. The shares of
the 19 components of aggregate demand during
1983-90 are set at their 1976 values. Over the
period 1970-81, 1976 had the highest hard currency
imports/GNP ratio (0.159).
Two key assumptions, however, were not changed:
? In 1982 baseline values were used for the exogenous
variables-12 export categories, seven domestic end
uses, and weather conditions. Thus, for 1982 the
baseline and two alternative scenarios are identical.
? In 1983-90 the baseline value for aggregate de-
mand-total exports plus total domestic end uses-
was used. Thus, differences between the scenarios
and the baseline stem only from differences in the
composition of aggregate demand. Besides demon-
strating the use of POLGNP, these scenarios also
help gauge the importance of shifts in composition
of aggregate demand with the level held constant in
determining Poland's import needs.
The assumptions for the two scenarios above may be 25X1
compared with each other and the baseline assump-
tions in table 2. The impact of changes in the
composition of aggregate demand on annual growth
rates of key variables-and the variability of those
growth rates over time-are shown in figure 4. Sum-
mary results are given in table 3. This table and figure
3 make the following important points:
? First, the growth rates of key variables are sensitive
to the composition of aggregate demand as well as
its growth rate, and the composition is critical in
determining resource requirements. The average
annual rate of growth of GNP differs by 0.6
percentage point across the scenarios; that for hard
currency imports by 0.5 point; the rate for soft
currency imports by 3.3 points; and that for energy
consumption by 0.6 point.
? Second, the baseline scenario with the smallest
share of demand allocated to investment has the
highest growth rate of GNP. This contrasts with
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Lonttaentlal
Table 2
Component Shares of Total Aggregate
Demand Assumed for Three Scenarios,
1983-90
Percent Table 3
Average Annual Percentage Growth
Rates, 1983-90
1970
Demand
Composition
Scenario
1976
Demand
Composition
Scenario
1982
Demand
Composition
Scenario
(Baseline)
Export share in
aggregate demand
15.0
16.5
19.6
Share in total exports
100.0
100.0
100.0
Energy
20.9
17.7
11.8
Metals
8.0
6.2
6.8
Machinery
31.8
41.4
53.8
Chemicals
7.8
8.6
7.9
Minerals
0.8
0.8
0.9
Wood and paper
4.4
2.6
2.0
Light industry
8.1
9.2
7.4
Processed foods
11.6
9.4
6.4
Other industry
0.7
0.5
0.6
Agricultural products
5.3
2.9
1.7
Forest products
0.6
0.5
0.6
Other products and
services
0.1
0
0.2
Domestic end use share 85.0
in aggregate demand
Share in total domestic100.0
end uses
Of which:
Food
23.1
Housing
11.1
Other
23.5
Investment
23.0
Government
Of which:
Civilian
9.3
Defense
4.7
Additions to inven-
1970
Demand
Composition
1976
Demand
Composition
1982
Demand
Composition
(Baseline)
GNP
0.0
-0.2
0.4
Hard currency imports
2.4
2.3
1.9
Soft currency imports
2.7
5.0
1.7
Capital stock
5.5
5.5
5.6
0.0
-0.1
0.1
-1.0
-0.4
-0.9
Moreover, Polish investment relies heavily on im-
ported, as opposed to domestic, machinery and
construction. Thus, increasing investment at the
expense of other end uses, such as consumption,
increases imports at the expense of domestic produc-
tion. This reduces GNP.
83.5
? Third, neither capital stock nor employment shows
100.0
any sensitivity to changes in the composition of
demand; if capital utilization and effective labor
,
however
could be measured and simulated
we
,
,
believe, they would show more variability.
21.6
24.9
8.8
13.1
? Finally, the baseline simulation with its aggregate
22.7
21.3
demand composition approximating the 1982 actual
30.2
20.4
composition is the scenario that involves the lowest
growth in hard currency imports
It shows the
.
highest GNP growth with minimum import growth
7.3
10.6
and is even more suitable for the realities of the
3
3
4
6
.
.
1980s than the output mix of 1970, the year with
the lowest historical hard currency import/GNP
ratio.
supply driven models in which investment increases
capital stock, which in turn increases GNP. In
Poland, however, lags in commissioning new capital
and variable retirement rates have broken the close
connection between investment and capital stock.
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Figure 4
Comparison of Three Scenarios: Annual Growth Rates
1982 composition (baseline)
1976 composition
-8 1982 83 84 85 86 87 88 89 90
-8 1 1 1 1 1 1 1 1 1
-10 1982 83 84 85 86 87 88 89 90
-1.5
4.0 1982 83 84 85 86 87 88 89 90 -2.0 1982 83 84 85 86 87 88 89 90
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-12 1982 83 84 85 86 87 88 89 90
-10 1982 83 84 85 86 87 88 89 90
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Applying POLGNP
The simulations done with POLGNP indicate that an
econometric model of this kind, which links sectoral
GNP with foreign trade detail, is a reliable and useful
tool in studying how an economy adjusts to a chang-
ing economic environment. POLGNP provides a con-
sistent framework for investigating the effects of
alternate levels and compositions of aggregate de-
mand and for examining the linkages between these
demands and the economy's supply responses. The
few scenarios reported here indicate that significant
adjustments have taken place in the Polish economy
in the late 1970s and particularly in 1981. Any
projection for the future must take these adjustments
into account.
Applications of POLGNP to growth studies will help
to analyze the long-term prospects for Polish econom
is recovery. The model can be applied to alternate
demand scenarios to indicate the differences in capi-
tal, labor, and energy requirements; the shift among
hard currency and soft currency imports and domestic
supplies of goods and services; and their impacts on
Polish recovery and growth potential in the 1980s.
These studies based on applications of POLGNP will
serve as a comprehensive description of the range of
Polish economic options, Polish flexibility in the face
of shifts in resource availability (particularly with
respect to oil and hard currency imports), and other
economic problems facing Polish policymakers.l
In the long run, the usefulness of POLGNP can be
enhanced by further developments, especially in four
specific areas: data, specification, historical study,
and comparable models for other countries. First, the
data on which POLGNP is based are detailed GNP
and foreign trade accounts converted to constant
domestic zlotys. Neither the domestic GNP nor the
foreign trade accounts used in this paper are provided
by Polish statistical offices; both are the results of
groundbreaking efforts to generate these accounts.
While this work was done as carefully and thoroughly
as feasible, given time and resource constraints, a
second data development effort building on the initial
one is likely to improve the quality of the data
substantially. Moreover, the Polish economy is being
forced to undergo some dramatic technological trans-
formations. While POLGNP is designed to be sensi-
tive to changing technological relationships, an econo-
metric model estimated on historical data cannot
project economic relationships that have no historical
precedent. In order to model the Polish economy
accurately, each additional year of data is important
and could improve the model's performance.
Second, further historical study of the Polish economy
is essential. While several published assessments of
the Polish economy in the 1970s are available, none
benefited from this study's use of input-output data
and detailed GNP accounts with fully integrated and
consistent domestic economic and foreign trade rela- 25X1
tionships. Historical study using this data will shed
considerable light on the ability of the Polish economy
to undergo technological transformation.
Third, specification of the equations in POLGNP is
extremely important. The "workhorse" equation esti-
mates the supply response of each sector as a function
of demand for that sector's output as derived from the
input-output table and a single-lag autoregressive
correction term. This specification has served quite
well, but others might serve better. One prime candi-
date is a first-difference equation without the auto-
regressive correction term. Nothing is yet known
about the effects of this and other possible specifica-
tions when embedded in a model such as POLGNP in
which endogenous variables are adjusted relative to
the standard errors of their estimating equations to
force compliance with accounting constraints.
Fourth, construction of comparable models for other
countries will help us better understand both the
technological transformations occurring in these
countries and the internal workings of detailed GNP
models of the POLGNP type. Hungary, with its
reputation for managerial flexibility and technological
innovation, would be particularly interesting for com-
parison purposes
Finally, in POLGNP, questions of the level and
composition of aggregate demand-domestic end uses
and exports-are handled outside the model, but they
obviously have a strong bearing on the character of
any analysis conducted with the model. We need to
improve our understanding of the determinants of
these variables in order to upgrade our analysis of the
Polish economy and its prospects.
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Appendix C
Historical Validation
of POLGNP, 1971-81
POLGNP has been validated over the period
1971-81.19 This 11-year period is twice as long as the
period over which POLGNP will normally be simulat-
ed and includes years of substantial disruption and
change in the Polish economy. The longer the period
of simulation, the more likely that any instabilities in
the model will become obvious. The substantial dis-
ruption and change over the validation period test
POLGNP's capacity to identify turning points.
POLGNP's performance can be assessed by examin-
ing several error statistics. The mean or average error
and the mean percentage error allow overestimates in
some years to cancel out underestimates in other
years. This gives an indication of how well the
variable is tracked over the long term despite errors
which cancel each other over intervening years. The
mean error allows, comparison of relative importance
of errors across variables. The mean percentage error
indicates the magnitude of each error relative to the
magnitude of the true value of the variable. The most
rigorous error measure is the root mean squared
percentage error.20 It magnifies the effect of particu-
larly large errors by squaring them. Thus, we concen-
trate on the root mean squared percentage errors in
our evaluation. (See table 4.) Note first that the errors
for the 13 major product and service aggregates are
quite small, 3 or 4 percent except for processed foods
(9 percent), miscellaneous traded, nonindustrial prod-
ucts and services (7 percent), and miscellaneous indus-
trial products (5 percent). The Polish economy, like
other developed economies, has little ability to substi-
tute among these major aggregates. Processed foods
might well be categorized under agriculture as part of
1' One change in POLGNP was required to simulate over 1971-81.
The balancing mechanism for miscellaneous nonindustrial traded
goods and services was simplified to prevent POLGNP from
generating negative gross imports of this small, volatile, hodge-
podge category after eight years of simulation. The impact of this
temporary specification on the rest of the model was barely
21 Method of calculation: (1) calculate the percentage error for each
of the 11 years simulated; (2) square the percentage errors; (3)
compute the mean or average value of the squared errors; (4) take
the square root of this mean or average. This is the most demanding
the food delivery system of the economy, with explicit
recognition of the trade-offs between unprocessed
foods from the agriculture sector and processed foods
from industry.
For the 34 producing sectors of GNP, the root mean
squared percentage errors average about 4 percent.
GNP originating in oil production registers a high 22
percent. The Polish oil industry is extremely small and
produces at its maximum regardless of changes in oil
demand; hence, large errors are to be expected from a
demand-driven forecast. The other standout root
mean squared percentage error appears for miscella-
neous nonindustrial material products and services,
one of the residual domestic sectors for which demand
is difficult to estimate
The largest root mean squared percentage errors
occur for imports: an average 57 percent for imports
from capitalist countries and 20 percent for imports
from socialist countries. In general, imports from
capitalist countries in each category are less than
imports from socialist countries and will have larger
percentage errors. But the major cause of the higher
errors is the limited ability of Poland to control the
supply response to changes in demand for imported
goods. Much of the error in imports from capitalist
countries for each category occurs in 1981 when hard
currency constraints forced a much sharper drop in
those imports than would have been predicted simply
from the drop in domestic end uses and exports. For
example, the root mean squared percentage error for
capitalist-originating imports of wood and paper prod-
ucts is 368 percent. If we calculate the same statistic
for 1971-80 (omitting 1981), the root mean squared
percentage error drops to 23 percent. The plunge in
imports of wood and paper products from capitalist
countries was made up by a large upsurge in imports
of those products from socialist countries, and 1981
registers the highest error for wood and paper product 25X1
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Table 4
Simulation Errors of Endogenous Variables,
1971-81
Mean Error
Mean Percentage
Root Mean Squared Largest Percentage
Error
Percentage Error
Error in Any Year
Energy
1,461
1
3
6
Domestic value added
Coal
1,576
3
9
16
Oil
57
7
22
40
Gas
58
0
5
-10
Capitalist imports
602
24
66
206
Socialist imports
-923
-3
9
-18
Metals
-2,220
-1
3
6
Domestic value added
Ferrous metals
-198
-1
4
-7
Nonferrous metals
-59
-1
3
-6
Metalworking
-16
-0
2
-3
Capitalist imports
-1,917
3
24
61
Socialist imports
-30
-0
3
5
Machinery
-5,941
-1
2
-4
Domestic value added
Machinery
-768
-1
3
-7
Precision instruments
-150
-1
5
-9
Transport equipment
-1,998
-3
7
-13
Electrical equipment
-310
-1
4
-7
Construction
-408
-0
3
7
Capitalist imports
-21,903
-19
27
-38
Socialist imports
19,595
19
23
50
Chemicals
160
0
2
-4
Chemicals
-703
-1
3
-6
Capitalist imports
-231
-0
6
-10
Socialist imports
1,094
5
6
12
Minerals
-342
-1
2
-4
Construction materials
-370
-1
3
-7
Glass and ceramics
24
0
5
9
Capitalist imports
43
5
20
43
Socialist imports
-39
1
14
38
Wood and paper
142
0
3
5
Domestic Value Added
Capitalist imports
-563
94
368
1,220
Socialist imports
3
2
12
-32
Light industry
-227
-0
3
9
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Table 4 (continued)
Mean Error
Mean Percentage
Root Mean Squared Largest Percentage
Error
Percentage Error
Error in Any Year
Leather and shoes
-3
0
2
-6
Capitalist imports
28
4
20
47
Socialist imports
23
1
11
-18
Processed foods
2,906
4
9
- 20
Domestic value added
Processed foods
-3,283
-4
8
-18
Capitalist imports
8,500
37
43
67
Socialist imports
-2,300
-19
46
-104
Other industry
14
0
5 ~
8
Domestic value added
Other industry
-11
-0
3
-7
Capitalist imports
34
6
26
60
Socialist imports
-9
2
17
53
Capitalist imports
117
2
13
34
Socialist imports
62
12
40
86
Forestry
23
0
4
-9
Transport and communications
343
0
2
4
Domestic trade and distribution
222
0
2
4
Housing
-115
-0
1
-2
29 Confidential
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Table 4
Simulation Errors of Endogenous Variables,
1971-81 (continued)
Mean Percentage
Root Mean Squared Largest Percentage
Error
Percentage Error
Error in Any Year
Other traded products and services
3,646
Domestic value added
Other material products and services
4,518
7
11
19
Financial and other
nonmaterial services
-861
-3
6
-13
Capitalist imports
-1
6
36
73
Socialist imports
-10
-16
44
75
Gross domestic product
-2,172
-0
1
-1
Total imports
2,161
0
3
7
From capitalist countries
-15,296
-4
11
18
From socialist countries
17,457
7
9
19
Domestic energy consumption a
39,370
2
5
7
Coal a
28,753
1
4
7
Oil a
3,822
1
5
-9
Gas a
5,240
3
6
10
Primary electricity a
924
168
530
1,755
Domestic capital stock b
145
2
3
6
Employment c
-12
-0
1
-3
a In thousand barrels per day oil equivalent.
b Million domestic zlotys of 1 January 1977.
c Thousand full-time worker equivalents.
imports from socialist countries and is largely respon-
sible for the 12-percent root mean squared percentage
error in that category in the table.
This analysis applies to almost all of the other catego-
ries. The errors for imports are higher than those for
domestic value added, especially in 1981, and are
largely attributable to unprecedented substitutions
away from imports from capitalist countries and
toward imports from socialist countries. Significant
by its omission from the list of product categories to
which this analysis applies is chemicals. Evidently
there are few substitution possibilities among chemi-
cals produced at home, those imported from capitalist
countries, and those imported from socialist countries.
The supply of chemical inputs from each of these
three sources must go up and down closely with the
technically determined demand for them.
The relatively high root mean squared percentage
Irrors for imported inputs are troublesome since they
indicate the measure of our knowledge and ignorance
about the hard currency import dependence of the
Polish economy. Nevertheless, the source of those
errors points to a very important conclusion. Because
of the international financial crisis, the decline in
Poland's hard currency imports in 1981 was much
greater than would be expected given: (1) its drop in
economic activity in 1981, (2) the changing composi-
tion of its domestic end uses and exports, and (3) past
trends in import dependence. Hence, we believe the
technological structure of the Polish economy shifted
abruptly in 1981 away from dependence on hard
currency imports. We do not know how permanent the
shift is or the extent to which it might be due to hard
currency imports still in the pipeline to final users.
25X1
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The payoff to modeling the sector detail in POLGNP
is indicated in the last 11 lines of the table, where the
errors for key aggregate variables are reported. The
root mean squared percentage error for gross domes-
tic product-Poland's reliance on domestic production
rather than imports-is only 1 percent. The same
statistic for overall import dependence is only 3
percent. The mean percentage errors-which allow
overestimates and underestimates to cancel-for
GDP and imports are 0 percent indicating a very
accurate long-run picture of trade-offs in Poland
between domestic and imported goods and services.
The split of imports between capitalist and socialist
sources is less accurate with root mean squared
percentage errors of 11 and 9 percent, respectively.
The performance of the equation system in predicting
domestic usage of energy, capital, and labor is good
(root mean squared percentage errors of 1 to 5
percent). The exception is hydroelectric power (530
percent), which depends on water levels rather than
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Appendix D
Baseline Simulation
The following tables demonstrate the major strength
of POLGNP-modeling a fully consistent, highly
detailed set of GNP and foreign trade accounts.
Assumptions about domestic end uses and exports
indicate the degree of flexibility and detail which
POLGNP can handle in specifying demands placed
on the economy. The tables on domestic value added;
hard currency imports; soft currency imports; and
capital, labor, and energy requirements show in great
detail the supply response necessary to fulfill these
demands. By carefully comparing these needed supply
responses to expected actual availabilities, potential
bottlenecks can be identified-bottlenecks which
would most likely be missed using more aggregated
models. Because of rounding, components may not
add to the totals shown.
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Table 5
Baseline Simulation for
End-Use Components of Polish GNP
Total end-use components
2,110,324.000
1,889,118.000
1,889,118.000
1,889,118.000
1,889,118.000
Percent change
-10.482
0.000
0.000
0.000
Share
1.000
1.000
1.000
1.000
1.000
Personal consumption, food
507,618.300
471,254.000
471,254.000
471,254.000
471,254.000
Percent change
-7.164
0.000
0.000
0.000
Share
0.241
0.249
0.249
0.249
0.249
Personal consumption, housing
238,274.000
247,805.000
247,805.000
247,805.000
247,805.000
Percent change
4.000
0.000
0.000
0.000
Share
0.113
0.131
0.131
0.131
0.131
Personal consumption, other
497,252.800
402,477.000
402,477.000
402,477.000
402,477.000
Percent change
-19.060
0.000
0.000
0.000
Share
0.236
0.213
0.213
0.213
0.213
Gross fixed capital formation
472,851.900
385,510.000
385,510.000
385,510.000
385,510.000
Percent change
-18.471
0.000
0.000
0.000
Share
0.224
0.204
0.204
0.204
0.204
Government, civilian
192,421.000
200,079.000
200,079.000
200,079.000
200,079.000
Percent change
3.980
0.000
0.000
0.000
Share
0.091
0.106
0.106
0.106
0.106
Government, defense
83,151.630
87,627.000
87,627.000
87,627.000
87,627.000
Percent change
5.382
0.000
0.000
0.000
Share
0.039
0.046
0.046
0.046
0.046
Additions to inventories
118,756.100
94,366.000
94,366.000
94,366.000
94,366.000
Percent change
-20.538
0.000
0.000
0.000
Share
0.056
0.050
0.050
0.050
0.050
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Table 5 (continued)
Million 1977 domestic zlotys
Total end-use components 1,908,010.000
1,927,094.000
1,946,369.000
1,965,836.000
1,985,503.000
Percent change 1.000
1.000
1.000
1.000'
1.000
Share 1.000
1.000
1.000
1.000
1.000
Personal consumption, food 475,966.600
480,726.400
485,533.700
490,389.100
495,293.000
Percent change 1.000
1.000
1.000
1.000
1.000
Share 0.249
0.249
0.249
0.249,
0.249
Personal consumption, housing 250,286.900
252,793.600
255,325.400
257,882.600
260,466.000
Percent change 1.001
1.001
1.001
1.001;
1.002
Share 0.131
0.131
0.131
0.131;
0.131
Personal consumption, other 406,501.400
410,566.100
414,671.400
418,817.800
423,007.000
Percent change 1.000
1.000
1.000
1.000
1.000
Share 0.213
0.213
0.213
0.213
0.213
Gross fixed capital formation 389,365.200
393,258.900
397,191.500
401,163.500
405,175.000
Percent change 1.000
1.000
1.000
1.000
1.000
Share 0.204
0.204
0.204
0.204
0.204
Government, civilian 202,079.800
204,100.600
206,141.600
208,203.100
210,285.000
Percent change 1.000
1.000
1.000
1.000
1.000
Share 0.106
0.106
0.106
0.106
0.106
Government, defense 88,503.250
89,388.250
90,282.100
91,184.900
92,097.000
Percent change 1.000
1.000
1.000
1.000
1.000
Share 0.046
0.046
0.046
0.046
0.046
Additions to inventories 95,309.800
96,262.900
97,225.600
98,197.900
99,180.000
Percent change 1.000
1.000
1.000
1.000
1.000
Share 0.050
0.050
0.050
0.050
0.050
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Table 6
Baseline Simulation for Polish Exports
Total exports
420,534.900
460,080.000
460,080.000
460,080.000
460,080.000
Percent change
9.404
0.000
0.000
0.000
Export/GNP ratio
0.202
0.237
0.237
0.238
0.239
Share
1.000
1.000
1.000
1.000
1.000
Energy
33,548.490
54,510.000
54,510.000
54,510.000
54,510.000
Share
0.080
0.118
0.118
0.118
0.118
Metals
32,422.280
31,110.000
31,110.000
31,110.000
31,110.000
Share
0.077
0.068
0.068
0.068
0.068
Machinery
232,259.100
247,380.000
247,380.000
247,380.000
247,380.000
Share
0.552
0.538
0.538
0.538
0.538
Chemicals
34,886.380
36,170.000
36,170.000
36,170.000
36,170.000
Share
0.083
0.079
0.079
0.079
0.079
Mineral products
3,765.000
4,150.000
4,150.000
4,150.000
4,150.000
Share
0.009
0.009
0.009
0.009
0.009
Wood and paper products
10,737.000
9,080.000
9,080.000
9,080.000
9,080.000
Share
0.026
0.020
0.020
0.020
0.020
Light industry
36,630.000
33,890.000
33,890.000
33,890.000
33,890.000
Share
0.087
0.074
0.074
0.074
0.074
Processed foods
23,089.080
29,240.000
29,240.000
29,240.000
29,240.000
Share
0.055
0.064
0.064
0.064
0.064
Other industry
2,682.699
2,960.000
2,960.000
2,960.000
2,960.000
Share
0.006
0.006
0.006
0.006
0.006
Agricultural products
7,280.000
8,030.000
8,030.000
8,030.000
8,030.000
Share
0.017
0.017
0.017
0.017
0.017
Forest products
2,553.199
2,810.000
2,810.000
2,810.000
2,810.000
Share
0.006
0.006
0.006
0.006
0.006
Other products and services
681.800
750.000
750.000
750.000
750.000
Share
0.002
0.002
0.002
0.002
0.002
25X1
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Table 6 (continued)
Total exports
464,680.700
469,327.700
474,021.200
478,761.600
483,550.000
Percent change
1.000
1.000
1.000
1.000
1.000
Export/GNP ratio
0.240
0.240
0.241
0.241
0.242
Share
1.000
1.000
1.000
1.000
1.000
Energy
55,055.160
55,605.780
56,161.900
56,723.580
57,291.000
Share
0.118
0.118
0.118
0.118
0.118
Metals
31,421.110
31,735.320
32,052.680
32,373.210
32,697.000
Share
0.068
0.068
0.068
0.068
0.068
Machinery
249,853.800
252,352.400
254,875.900
257,424.800
259,999.000
Share
0.538
0.538
0.538
0.538
0.538
Chemicals
36,531.670
36,896.960
37,265.900
37,638.530
38,015.000
Share
0.079
0.079
0.079
0.079
0.079
Mineral products
4,191.559
4,233.531
4,275.926
4,318.746
4,362.000
Share
0.009
0.009
0.009
0.009
0.009
Wood and paper products
9,170.770
9,262.440
9,355.030
9,448.540
9,543.000
Share
0.020
0.020
0.020
0.020
0.020
Light industry
34,228.940
34,571.270
34,917.020
35,266.230
35,619.000
Share
0.074
0.074
0.074
0.074
0.074
Processed foods
29,532.490
29,827.900
30,126.270
30,427.630
30,732.000
Share
0.064
0.064
0.064
0.064
0.064
Other industry
2,989.601
3,019.497
3,049.693
3,080.190
3,111.000
Share
0.006
0.006
0.006
0.006
0.006
Agricultural products
8,110.367
8,191.539
8,273.523
8,356.328
8,440.000
Share
0.017
0.017
0.017
0.017
0.017
Forest products
2,838.033
2,866.347
2,894.942
2,923.823
2,953.000
Share
0.006
0.006
0.006
0.006
0.006
Other products and services
757.450
764.974
772.573
780.247
788.000
Share
0.002
0.002
0.002
0.002
0.002
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Table 7
Baseline Simulation for the Sector-of-Origin
Components of Polish GNP
Total GNP
2,081,086.000
1,940,560.000
1,940,399.000
1,930,418.000
1,927,413.000
Percent change
-6.753
-0.008
-0.514
-0.156
Share
1.000
1.000
1.000
1.000
1.000
Coal
57,956.410
64,094.180
61,372.860
59,922.300
58,751.230
Percent change
10.590
-4.246
-2.364
-1.954
Share
0.028
0.033
0.032
0.031
0.030
Oil
1,083.425
1,114.707
1,117.888
1,127.367
1,130.129
Percent change
2.887
0.285
0.848
0.245
Share
0.001
0.001
0.001
0.001
0.001
Gas
14,456.440
12,682.860
12,484.730
12,458.280
12,407.700
Percent change
-12.268
-1.562
-0.212
-0.406
Share
0.007
0.007
0.006
0.006
0.006
Electricity
38,893.180
35,429.730
34,175.950
33,316.420
32,474.630
Percent change
-8.905
-3.539
-2.515
-2.527
Share
0.019
0.018
0.018
0.017
0.017
Ferrous metals
38,562.440
37,600.000
37,530.460
37,630.790
37,468.270
Percent change
-2.496
-0.185
0.267
-0.432
Share
0.019
0.019
0.019
0.019
0.019
Nonferrous metals
18,846.220
16,916.760
16,217.590
15,846.310
15,533.480
Percent change
-10.238
-4.133
-2.289
-1.974
Share
0.009
0.009
0.008
0.008
0.008
Metalworking
35,842.350
34,473.420
34,368.350
34,436.570
34,283.500
Percent change
-3.819
-0.305
0.198
-0.445
Share
0.017
0.018
0.018
0.018
0.018
Machinery
63,369.260
64,403.140
63,726.290
63,102.270
62,568.810
Percent change
1.631
-1.051
-0.979
-0.845
Share
0.030
0.033
0.033
0.033
0.032
Precision instruments
8,394.961
8,437.898
8,368.793
8,301.152
8,241.656
Percent change
0.511
-0.819
-0.808
-0.717
Share
0.004
0.004
0.004
0.004
0.004
Transport equipment
44,075.530
-43,044.720
42,971.950
42,805.700
42,603.650
Percent change
-2.339
-0.169
-0.387
-0.472
Share
0.021
0.022
0.022
0.022
0.022
Electric equipment
25,819.400
25,173.810
25,006.500
24,807.920
24,605.700
Percent change
-2.500
-0.665
-0.794
-0.815
Share
0.012
0.013
0.013
0.013
0.013
Chemicals
51,137.360
47,271.350
49,066.460
48,719.780
49,603.090
Percent change
-7.560
3.797
-0.707
1.816
Share
0.025
0.024
0.025
0.025
0.026
Construction materials
21,892.040
19,626.910
19,434.310
19,441.600
19,463.850
Percent change
-10.347
-0.981
0.037
0.114
Share
0.011
0.010
0.010
0.010
0.010
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..,umruenaal
Table 7 (continued)
Total GNP
1,940,057.000
1,955,097.000
1,969,375.000
1,984,260.000
1,999,003.000
Percent change
0.656
0.775
0.730
0.756
0.743
Share
1.000
1.000
1.000
1.000
1.000
Coal.
58,416.210
58,087.140
57,749.870
57,351.180
56,872.200
Percent change
-0.570
-0.563
-0.581
-0.690
-0.835
Share
0.030
0.030
0.029
0.029
0.028
Oil
1,141.465
1,147.946
1,151.057
1,150.895
1,147.882
Percent change
1.003
0.568
0.271
-0.014
-0.262
Share
0.001
0.001
0.001
0.001
0.001
12,519.140
12,617.800
12,706.670
12,777.670
12,830.000
Percent change
0.898
0.788
0.704
0.559
0.410
Share
0.006
0.006
0.006
0.006
0.006
Electricity
32,120.340
31,813.400
31,540.690
31,267.300
30.972.620
Percent change
-1.091
-0.956
-0.857
-0.867
-0.942
Share
0.017
0.016
0.016
0.016
0.015
Ferrous metals
37,733.980
37,928.390
38,132.200
38,341.100
38,569.660
Percent change
0.709
0.515
0.537
0.548
0.596
Share
0.019
0.019
0.019
0.019
0.019
Nonferrous metals
15,540.020
15,609.720
15,735.050
15,896.910
16,090.100
Percent change
0.042
0.449
0.803
1.029
1.215
Share
0.008
0.008
0.008
0.008
0.008
Metalworking
34,525.980
34,708.490
34,897.250
35,090.290
35,301.310
Percent change
0.707
0.529
0.544
0.553
0.601
Share
0.018
0.018
0.018
0.018
0.018
Machinery.
2,628.770
62,632.240
62,606.840
62,556.290
62,482.500
Percent change
0.096
0.006
-0.041
-0.081
-0.118
Share
0.032
0.032-
0.032
0.032
0.031
Precision instruments
8,268.566
8,293.797
8,319.254
8,344.172
8,367.938
Percent change
0.326
0.305
0.307
0.299
0.285
Share
0.004
0.004
0.004
0.004
0.004
Transport equipment
42,800.640
42,954.490
43,081.320
43,183.260
43,261.550
Percent change
0.462
0.359
0.295
0.237
0.181
Share
0.022
0.022
0.022
0.022
0.022
Electric equipment
24,610.210
24,569.420
24,495.960
24,394.160
24,267.360
Percent change
0.018
-0.166
-0.299
-0.416
-0.520
Share
0.013
0.013
0.012
0.012
0.012
Chemicals
49,790.250
50,691.840
51,062.950
51,680.140
52,142.670
Percent change
0.377
1.811
0.732
1.209
0.895
Share
0.026
0.026
0.026
0.026
0.026
Construction materials
19,721.730
19,976.170 ?
20,228.050
20,467.870
20,694.930
Percent change
1.325
1.290
1,261
1.186
1.109
Share
0.010
0.010
0.010
0.010
0.010
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Table 7
Baseline Simulation for the Sector-of-Origin
Components of Polish GNP (continued)
Glass and ceramics
8,884.004
7,877.762
7,807.227
7,846.473
7,867.496
Percent change
-11.326
-0.895
0.503
0.268
Share
0.004
0.004
0.004
0.004
0.004
Wood products
24,914.880
19,688.980
18,319.890
17,649.340
17,215.780
Percent change
-20.975
-6.954
-3.660
-2.457
Share
0.012
0.010
0.009.
0.009
0.009
Paper
7,192.477
6,357.477
6,383.992
6,549.477
6,662.535
Percent change
-11.609
0.417
2.592
1.726
Share
0.003
0.003
0.003
0.003
0.003
Textiles
48,076.080
44,236.710
45,288.750
45,969.770
46,228.590
Percent change
-7.986
2.378
1.504
0.563
Share
0.023
0.023
0.023
0.024
0.024
Clothing
.17,345.370
16,030.8 50
16,473.690
16,738.030
16,836.140
Percent change
-7.579
2.762
1.605
0.586
Share
0.008
0.008
0.008
0.009
0.009
Leather products
14, 528.710
13,327.990
13,581.530
13,721.230
13,742.070
Percent change
-8.264
1.902
1.029
0.152
Share
0.007
0.007
0.007
0.007
0.007
Processed foods
70,250.380
70,074.690
65,432.600
65,101.030
64,538.320
Percent change
-0.250
-6.624
-0.507
-0.864
Share
0.034
0.036
0.034
0.034
0.033
Other industry
15,428.780
13,997.800
13,561.950
13,491.800
13,429.140
Percent change
-9.275
-3.114
-0.517
-0.464
Share
0.007
0.007
0.007
0.007
0.007
Construction
118,004.400
99,048.700
96,375.600
97,126.300
96,677.700
Percent change
-16.064
-2.699
0.779
-0.462
Share
0.057
0.051
0.050
0.050
0.050
Agriculture, crops
593,380.600
513,535.100
533,206.800
525,467.800
528,378.300
Percent change
-13.456
3.831
-1.451
0.554
Share
0.285
0.265
0.275
0.272
0.274
Agriculture, animal products
7,526.496
7,891.168
8,099.754
8,098.426
8,107.383
Percent change
4.845
2.643
-0.016
0.111
Share
0.004
0.004
0.004
0.004
0.004
Agriculture, services
4,947.520
4,865.379
4,936.297
4,936.848
4,940359
Percent change
-1.660
1.458
0.011
0.075
Share
0.002
0.003
0.003
0.003
0.003
Forestry
13,988.260
12,325.040
11,868.330
11,946.420
11,856.930
Percent change
-11.890
-3.706
0.658
-0.749
Share
0.007
0.006
0.006
0.006
0.006
Transport and communications
155,858.000
146,607.400
143,493.400-
143,382.600
142,756.700
Percent change
-5.935
-2.124
-0.077
-0.437
Share
0.076
0.074
0.074
0.074
Approved For Release 2009/01/06: CIA-RDP85SO0316R000100010006-4
 Approved For Release 2009/01/06: CIA-RDP85SOO316ROO0100010006-4
wuiiucuual
Table 7 (continued)
Glass and ceramics
7,965.480
8,047.875
8,124.824
8,196.121
8,264.543
Percent change
1.245
1.034
0.956
0.877
0.835
Share
0.004
0.004
0.004
0.004
0.004
Wood products
17,239.410
17,409.730
17,679.790
18,007.920
18,374.290
Percent change
0.137
0.988
1.551
1,856
2.034
Share
0.009
0.009
0.009
0.009
0.009
Paper
6,837.629
6,976.555
7,090.059
7,181.477
7,258.551
Percent change
2.628
2.032
1.627
1.289
1.073
Share
0.004
0.004
0.004
0.004
0.004
Textiles
46,753.980
47,146.560
47,498.950
47,828.140
48,151.050
Percent change
1.136
0.840
0.747
0.693
0.675
Share
0.024
0.024
0.024
0.024
0.024
Clothing
17,017.550
17,146.450
17,259.190
17,363.160
17,464.710
Percent change
1.077
0.757
0.657
0.602
0.585
Share
0.009
0.009
0.009
0.009
0.009
Leather products
13,851.250
13,930.930
14,009.050
14,085.900
14,164.200
Percent change
0.795
0.575
0.561
0.548
0.556
Share
0.007
0.007
0.007
0.007
0.007
Processed foods
65,181.260
65,697.560
66,332.810
66,959.940
67,609.250
Percent change
0.996
0.792
0.967
0.945
0.970
Share
0.034
0.034
0.034
0.034
0.034
Other industry
13,557.860
13,687.770
13,831.040
13,976.410
14,125.140
Percent change
0.958
0.958
1.047
1.051
1.064
Share
0.007
0.007
0.007
0.007
0.007
Construction
97,676.400
98,446.700
99,329.600
100,171.800
101,029.600
Percent change
1.033
0.788
0.897
0.848
0.856
Share
0.050
0.050
0.050
0.050
0.051
Agriculture, crops
528,998.600
532,351.100
534,669.900
537,390.500
539,951.300
Percent change
0.117
0.634
0.436
0.509
0.476
Share
0.273
0.272
0.271
0.271
0.270
Agriculture, animal products
8,153.617
8,188.055
8,215.590
8,242.242
8,266.074
Percent change
0.570
0.422
0.336
0.324
0.289
Share
0.004
0.004
0.004
0.004
0.004
Agriculture, services
4,971.000
4,994.797
5,015.234
5,035.082
5,053.801
Percent change
0.616
0.479
0.409
0.396
0.372
Share
0.003
0.003
0.003
0.003
0.003
Forestry
11,931.390
11,985.110
12,063.090
12,138.080
12,217.610
Percent change
0.628
0.450
0.651
0.622
0.655
Share
0.006
0.006
0.006
0.006
0.006
Transport and communications
145,221.300
147,872.300
150,814.300
153,866.500
157,027.700
Percent change
1.726
1.826
1.989
2.024
2.054
Share
0.075
0.076
0.077
0.078
0.079
Approved For Release 2009/01/06: CIA-RDP85SOO316ROO0100010006-4
 Approved For Release 2009/01/06: CIA-RDP85SO0316R000100010006-4
Table 7
Baseline Simulation for the Sector-of-Origin
Components of Polish GNP (continued)
Trade and distribution
125,514.000
112,059.600
110,652.200
110,896.600
110,689.000
Percent change
-10.719
-1.256
0.221
-0.187
Share
0.060
0.058
0.057
0.057
0.057
Other material products and services
69,126.690
65,021.000
63,743.920
62,406.510
61,009.640
Percent change
-5.939
-1.964
-2.098
-2.238
Share
0.033
0.034
0.033
0.032
0.032
Housing
203,017.400
213,605.300
213,145.600
213,992.000
214,029.900
Percent change
5.215
-0.215
0.397
0.018
Share
0.098
0.110
0.110
0.111
0.111
Other nonmaterial services
27,628.090
24,203.950
23,378.080
23,608.590
23,785.430
Percent change
-12.394
-3.412
0.986
0.749
Share
0.013
0.012
0.012
0.012
0.012
Government, human investment
61,265.240
62,390.660
62,197.750
62,414.960
62,420.500
Percent change
1.837
-0.309
0.349
0.009
Share
0.029
0.032
0.032
0.032
0.032
Government, health and human services
39,081.130
39,745.120
39,771.760
40,000.090
40,055.460
Percent change
1.699
0.067
0.574
0.138
Share
0.019
0.020
0.020
0.021
0.021
Government, administration and
military
34,802.320
37,406.240
36,844.010
37,163.980
37,057.000
Approved For Release 2009/01/06: CIA-RDP85SO0316R000100010006-4
 Approved For Release 2009/01/06: CIA-RDP85SO0316R000100010006-4
Confidential
Table 7 (continued)
Trade and distribution
112,431.400
114,162.900
115,996.700
117,849.400
119,738.100
Percent change
1.574
1.540
1.606
1.597
1.603
Share
0.058
0.058
0.059
0.059
0.060
Other material products and services
60,574.900
60,344.140
60,315.580
60,452.910
60,737.520
Percent change
-0.713
-0.381
-0.047
0.228
0.471
Share
0.031
0.031
0.031
0.030
0.030
Housing
216,545.600
218,727.500
220,863.900
222,936.900
225,004.900
Percent change
1.175
1.008
0.977
0.939
0.928
Share
0.112
0.112
0.112
0.112
0.113
Other nonmaterial services
24,182.980
24,473.500
24,738.090
24.955.000
25,137.860
Percent change
1.671
1.201
1.081
0.877
0.733
Share
0.012
0.033
0.013
0.013
0.013
Government, human investment
63,090.390
63,632.100
64,138.600
64,612.510
65,075.480
Percent change
1.073
0.859
0.796
0.739
0.716
Share
0.033
0.033
0.033
0.033
0.033
Government, health and human services
40,666.820
41,270.010
41,893.140
42,521.140
43,158.540
Percent change
1.526
1.483
1.510
1.499
1.499
Share
0.021
0.021
0.021
0.021
0.022
Government, administration and
military
37,397.020
37,579.690
37,793.270
37,993.380
38,199.630
0.918
0.488
0.568
0.529 -
0.543
Approved For Release 2009/01/06: CIA-RDP85SO0316R000100010006-4
 Approved For Release 2009/01/06: CIA-RDP85SOO316ROO0100010006-4
Table 8
Baseline Simulation for Polish Imports, Total
Total imports
449,774.800
408,633.800
408,795.300
418,776.300
421,778.900
Percent change
-9.147
0.039
2.442
0.717
Import/GNP ratio
0.216
0.211
0.211
0.217
0.219
Share
1.000
1.000
1.000
1.000
1.000
Energy
59,388.190
57,526.920
60,437.530
62,417.220
63,668.880
Share
0.132
0.141
0.148
0.149
0.151
Metals
45,052.930
44,334.080
43,968.520
44,546.110
44,759.780
Share
0.100
0.108
0.108
0.106
0.106
Machinery
145,153.400
134,883.100
139,254.400
143,021.600
146,326.300
Share
0.323
0.330
0.341
0.342
0.347
Chemicals
53,937.790
53,505.140
48,994.860
50,807.310
49,149.500
Share
0.120
0.131
0.120
0.121
0.117
Mineral products
5,795.922
6,490.547
6,895.496
7,239.078
7,382.105
Share
0.013
0.016
0.017
0.017
0.018
Wood and paper products
9,394.620
8,602.293
8,481.641
8,624.785
8,669.781
Share
0.021
0.021
0.021
0.021
0.021
Light industry
18,969.240
20,561.660
20,153.840
20,408.660
20,178.850
Share
0.042
0.050
0.049
0.049
0.048
Processed foods
59,253.570
37,290.260
34,233.210
35,374.790
35,177.740
Share
0.132
0.091
0.084
0.084
0.083
Other industry
4,337.590
6,068.848
6,410.453
6,664.465
6,682.715
Share
0.010
0.015
0.016
0.016
0.016
Agricultural products
48,015.220
38,781.180
39,338.220
38,980.830
39,073.490
Share
0.107
0.095
0.096
0.093
0.093
Forest products
353.500
516.577
565.697
633.851
653.262
Share
0.001
0.001
0.001
0.002
0.002
Other products and services
123.408
73.828
62.050
58.241
57.118
Share
0.000
0.000
0.000
0.000
0.000
Approved For Release 2009/01/06: CIA-RDP85SOO316ROO0100010006-4
 Approved For Release 2009/01/06: CIA-RDP85SO0316R000100010006-4
wuuucuual
Table 8 (continued)
Total imports
432,624.400
441,314.800
451,006.300
460,328.900
470,044.800
Percent change
2.571
2.009
2.196
2.067
2.111
Import/GNP ratio
0.223
0.226
0.229
0.232
0.235
Share
1.000
1.000
1.000
1.000
1.000
Energy
65,534.680
67,542.310
69,862.000
72,556.810
75,698.500
Share
0.151
0.153
0.155
0.158
0.161
Metals
45,294.710
45,604.910
45,806.340
45,856.240
45,773.710
Share
0.105
0.103
0.102
0.100
0.097
Machinery
150,826.800
155,053.400
159,061.500
162,866.100
166,476.700
Share
0.349
0.351
0.353
0.354
0.354
Chemicals
50,872.450
51,180.130
52,367.520
53,193.720
54,253.190
Share
0.118
0.116
0.116
0.116
0.115
Mineral products
7,542.238
7,631.961
7,696.508
7,737.996
7,767.031
Share
0.017
0.017
0.017
0.017
0.017
Wood and paper products
8,821.121
8,924.004
9,021.060
9,104.340
9,183.300
Share
0.020
0.020
0.020
0.020
0.020
Light industry
20,345.620
20,450.380
20,623.960
20,800.140
20,991.240
Share
0.047
0.046
0.046
0.045
0.045
Processed foods
35,744.330
36,072.350
36,507.060
36,904.750
37,320.610
Share
0.083
0.082
0.081
0.080
0.079
Other industry
6,736.980
6,737.641
6,740.289
6,736.465
6,733.137
Share
0.016
0.015
0.015
0.015
0.014
Agricultural products
40,180.450
41,392.250
42,596.520
43,853.880
45,134.950
Share
0.093
0.094
0.094
0.095
0.096
Forest products
666.543
664.108
658.580
649.316
638.936
Share
0.002
0.002
0.001
0.001
0.001
Other products and services
59.004
61.918
65.501
69.548
74.004
Share
0.000
0.000
0.000
0.000
0.000
Approved For Release 2009/01/06: CIA-RDP85SO0316R000100010006-4
 Approved For Release 2009/01/06: CIA-RDP85SOO316ROO0100010006-4
Table 9
Baseline Simulation for Polish Hard Currency Imports
Total hard currency imports
182,907.100
167,801.300
165,718.300
173,263.400
173,945.200
Percent change
-8.259
-1.241
4.553
0.393
Import/GNP ratio
0.088
0.086
0.085
0.090
0.090
Share
1.000
1.000
1.000
1.000
1.000
Energy
3,756.869
7,197.977
9,678.240
10,809.390
11,204.490
Share
0.021
0.043
0.058
0.062
0.064
Metals
10,866.290
10,823.310
11,199.120
11,634.530
12,030.410
Share
0.059
0.065
0.068
0.067
0.069
Machinery
38,205.630
37,436.040
39,947.060
42,009.000
43,692.480
Share
0.209
0.223
0.241
0.242
0.251
Chemicals
25,874.830
30,386.780
26,422.490
29,261.630
27,639.050
Share
0.141
0.181
0.159
0.169
0.159
Mineral products
2,672.552
2,618.724
2,725.794
2,884.714
2,973.376
Share
0.015
0.016
0.016
0.017
0.017
Wood and paper products
210.210
3,585.945
2,936.222
3,050.312
3,062.786
Share
0.001
0.021
0.018
0.018
0.018
Light industry
9,290.150
11,430.660
11,076.900
11,249.970
11,085.730
Share
0.051
0.068
0.067
0.065
0.064
Processed foods
45,925.980
27,502.850
25,294.250
26,155.890
26,004.050
Share
0.251
0.164
0.153
0.151
0.149
Other industry
2,688.868
3,673.498
3,978.199
4,205.082
4,252.348
Share
0.015
0.022
0.024
0.024
0.024
Agricultural products
43,209.520
32,831.500
32,103.550
31,591.500
31,567.340
Share
0.236
0.196
0.194
0.182
0.181
Forest products
171.990
284.104
326.715
381.548
402.987
Share
0.001
0.002
0.002
0.002
0.002
Other products and services
34.528
30.228
30.020
30.182
30.479
Share
0.000
0.000
0.000
0.000
0.000
Confidential 46
Approved For Release 2009/01/06: CIA-RDP85SOO316ROO0100010006-4
 Approved For Release 2009/01/06: CIA-RDP85SO0316R000100010006-4
Confidential
Table 9 (continued)
Total hard currency. imports
180,125.800
183,662.200
187,949.100
191,334.900
194,709.100
Percent change
3.553
1.963
2.334
1.801
1.763
Import/GNP ratio
0.093
0.094
0.095
0.096
0.097
Share
1.000
1.000
1.000
1.000
1.000
Energy
11,327.430
11,280.610
11,172.310
11,032.840
10,876.570
Share
0.063.
0.061
0.059
0.058
0.056
Metals
12,442.890
12,711.320
12,835.540
12,808.830
12,634.960
Share
0.069
0.069
0.068
0.067
0.065
Machinery
45,505.560
47,037.590
48,330,990
49,414.180
50,313.130
Share
0.253
0.256
0.257
0.258
0.258
Chemicals
29,540.020
29,527.540
30,553.150
31,052.950
31,824.820
Share
0.164
0.161
0.163
0.162
0.163
Mineral products
3,068.656
3,127.688
3,169.273
3,196.248
3,214.564
Share
0.017
0.017
0.017
0.017
0.017
Wood and paper products
3,111.186
3,143.035
3,171.303
3,194.588
3,215.865
Share
0.017
0.017
0.017
0.017
0.017
Light industry
11,198.740
11,269.480
11,389.170
11,509.090
11,639.020
Share
0.062
0.061
0.061
0.060
0.060
Processed foods
26,497.940
26,815.860
27,215.670
27,588.660
27,976.710
Share
0.147
0.146
0.145
0.144
0.144
Other industry
4,301.563
4;304.551
4,302.363
4,293.570
4,283.992
Share
0.024
0.023
0.023
0.022
0.022
Agricultural products
32,684.910
33,997.550
35,366.520
36,809.060
38,304.190
Share
0.181
0.185
0.188
0.192
0.197
Forest products
416.208
416.051
411.692
403.585
393.983
Share
0.002
0.002
0.002
0.002
0.002
Other products and services
30.959
31.258
31.454
31.552
31.569
Share
0.000
0.000
0.000
0.000
0.000
Approved For Release 2009/01/06: CIA-RDP85SO0316R000100010006-4
 Approved For Release 2009/01/06: CIA-RDP85SOO316ROO0100010006-4
Table 10
Baseline Simulation for Polish Soft Currency Imports
Total soft currency imports
266,867.800
240,832.600
243,077.100
245,512.800
247,833.800
Percent change
-9.756
0.932
1.002
0.945
Import/GNP ratio
0.128
0.124
0.125
0.127
0.129
Share
1.000
1.000
1.000
1.000
1.000
Energy
55,631.320
50,328.950
50,759.290
51,607.830
52,464.390
Share
0.208
0.209
0.209
0.210
0.212
Metals
34,186.640
33,510.770
32,769.400
32,911.580
32,729.380
Share
0.128
0.139
0.135
0.134
0.132
Machinery
106,947.800
97,447.100
99,307.300
101,012.600
102,633.900
Share
0.401
0.405
0.409
0.411
0.414
Chemicals
28,062.960
23,118.360
22,572.370
21,545.680
21,510.450
Share
0.105
0.096
0.093
0.088
0.087
Mineral products
3,123.370
3,871.824
4,169.703
4,354.367
4,408.730
Share
0.012
0.016
0.017
0.018
0.018
Wood and paper products
9,184.410
5,016.352
5,545.422
5,574.477
5,606.996
Share
0.034
0.021
0.023
0.023
0.023
Light industry
9,679.090
9,131.000
9,076.940
9,158.690
9,093.110
Share
0.036
0.038
0.037
0.037
0.037
Processed foods
13,327.590
9,787.410
8,938.969
9,218.910
9,173.690
Share
0.050
0.041
0.037
0.038
0.037
Other industry
1,648.724
2,395.352
2,432.257
2,459.386
2,430.368
Share
0.006
0.010
0.010
0.010
0.010
Agricultural products
4,805.699
5,949.688
7,234.668
7,389.324
7,506.148
Share
0.018
0.025
0.030
0.030
0.030
Forest products
181.510
232.473
238.982
252.303
250.275
Share
0.001
0.001
0.001
0.001
0.001
Other products and services
88.880
43.599
32.030
28.059
26.639
Share
0.000
0.000
0.000
0.000
0.000
Approved For Release 2009/01/06: CIA-RDP85SOO316ROO0100010006-4
 Approved For Release 2009/01/06: CIA-RDP85SOO316ROO0100010006-4
Confidential
Table 10 (continued)
Total soft currency imports
252,498.600
257,652.600
263,057.100
268,994.000
275,335.700
Percent change
1.882
2.041
2.098
2.257
2.357
Import/GNP ratio
0.130
0.132
0.134
0.136
0.138
Share
1.000
1.000
1.000
1.000
1.000
Energy
54,207.260
56,261.730
58,689.740
61,524.020
64,821.950
Share
0.215
0.218
0.223
0.229
0.235
Metals
32,851.830
32,893.580
32,970.800
33,047.410
33,138.740
Share
0.130
0.128
0.125
0.123
0.120
Machinery
105,321.300
108,015.900
110,7 30.600
113,452.000
116,163.600
Share
0.417
0.419
0.421
0.422
0.422
Chemicals
21,332.420
21,652.590
21,814.360
22,140.780
22,428.370
Share
0.084
0.084
0.083
0.082
0.081
Mineral products
4,473.586
4,504.273
4,527.234
4,541.750
4,552.469
Share
0.018
0.017
0.017
0.017
0.017
Wood and paper products
5,709.938
5,780.973
5,849.758
5,909.750
5,967.438
Share
0.023
0.022
0.022
0.022
0.022
Light industry
9,146.880
9,180.890
9,234.790
9,291.050
9,352.220
Share
0.036
0.036
0.035
0.035
0.034
Processed foods
9,246.390
9,256.490
9,291.390
9,316.090
9,343.890
Share
0.037
0.036
0.035
0.035
0.034
Other industry
2,435.419
2,433.091
2,437.928
2,442.896
2,449.147
Share
0.010
0.009
0.009
0.009
0.009
Agricultural products
7,495.539
7,394.699
7,229.996
7,044.816
6,830.762
Share
0.030
0.029
0.027
0.026
0.025
Forest products
250.335
248.057
246.888
245.731
244.953
Share
0.001
0.001
0.001
0.001
0.001
Other products and services
28.045
30.660
34.047
37.996
42.435
Share
0.000
0.000
0.000
0.000
0.000
Approved For Release 2009/01/06: CIA-RDP85SOO316ROO0100010006-4
 Approved For Release 2009/01/06: CIA-RDP85SO0316R000100010006-4
Table 11
Baseline Simulation for Capital, Labor, and Energy
Requirements To Support Polish GNP'Targets a
Capital stock
9,138.700
9,531.490
10,049.570
10,596.440
11,176.910
Percent change
4.298
5.435
5.442
5.478
GNP/capital ratio
227.722
203.595
193.083
182.176
172.446
Percent change
-10.595
-5.163
-5.649
-5.341
Capital/GNP elasticity
-0.637
-655.103
-10.579
-35.189
Labor
16, 574.300
16,362.110
16,313.960
16,307.230
16,297.540
Percent change
-1.280
-0.294
-0.041
-0.059
GNP/labor ratio
125.561
118.601
118.941
118.378
118.264
Percent change
-5.543
0.287
-0.473
-0.096
Labor/GNP elasticity
0.190
35.464
0.080
0.381
Energy
2,342,998.000
2,098,165.000
2,032,251.000
1,985,959.000
1,943,681.000
Percent change
-10.450
-3.142
-2.278
-2.129
GNP/energy ratio
0.888
0.925
0.955
0.972
0.992
Percent change
4.128
3.235
1.805
2.016
Energy/GNP elasticity
1.548
378.634
4.428
13.675
Coal
1,796,998.000
1,605,921.000
1,538,146.000
1,480,746.000
1,423,403.000
Percent change
-10.633
-4.220
-3.732
-3.873
Share
0.767
0.765
0.757
0.746
0.732
340,000.000
293,665.500
279,595.500
271,168.300
264,731.800
Percent change
-13.628
-4.791
-3.014
-2.374
Share
0.145
0.140
0.138
0.137
0.136
188,000.000
187,391.900
203,751.100
223,366.600
244,932.200
Percent change
-0.323
8.730
9.627
9.655
Share
0.080
0.089
0.100
0.112
0.126
Hydro/nuclear
18,000.000
11,188.240
10,759.860
10,679.720
10,615.940
Percent change
-37.843
-3.829
-0.745
-0.597
Share
0.008
0.005
0.005
0.005
0.005
Capital stock in billion zlotys of 1 January 1977. Labor in
thousand workers. Energy in barrels per day oil equivalent. GNP in
million 1977 domestic zlotys.
Approved For Release 2009/01/06: CIA-RDP85SO0316R000100010006-4
 Approved For Release 2009/01/06: CIA-RDP85SO0316R000100010006-4
Confidential
Table 11 (continued)
Capital stock
11,804.570
12,469.520
13,170.770
13,912.070
14,694.590
Percent change
5.616
5.633
5.624
5.628
5.625
GNP/capital ratio
164.348
156.790
149.526
142.629
136.037
Percent change
-4.696
-4.599
-4.633
-4.613
-4.622
Capital/GNP elasticity
8.561
7.267
7.701
7.447
7.571
Labor
16,320.710
16,348.570
16,376.320
16,404.150
16,431.820
Percent change
0.142
0.171
0.170
0.170
0.169
GNP/labor ratio
118.871
119.588
120.258
120.961
121.654
Percent change
0.513
0.603
0.560
0.585
0.573
Labor/GNP elasticity
0.217
0.220
0.232
0.225
0.227
Energy
1,931,808.000
1,927,984.000
1,930,147.000
1,938,385.000 '
1,951,421.000
Percent change
-0.611
-0.198
0.112
0.427
0.672
GNP/energy ratio
1.004
1.014
1.020
1.024
1.024
Percent change
1.275
0.975
0.617
0.328
0.070
Energy/GNP elasticity
-0.931
-0.255
0.154
0.565
0.905
-2.593
-2.358
-2.184
-1.997
-1.864
0.718
0.702
0.686
0.670
0.653
263,517.800
263,861.800
264,984.100
266,740.700
268,786.100
-0.459
0.130
0.425
0.663
0.767
Hydro/nuclear
10,643.130
10,67 5.050
10,706.780
10,742.240
10,777.640
Percent change
0.256
0.300
0.297
0.331
0.330
Share
0.006
0.006
0.006
0.006
0.006
a Capital stock in billion zlotys of 1 January 1977. Labor in
thousand workers. Energy in barrels per day oil equivalent. GNP in
million 1977 domestic zlotys.
Approved For Release 2009/01/06: CIA-RDP85SO0316R000100010006-4
 Confidential
Confidential
Approved For Release 2009/01/06: CIA-RDP85SO0316R000100010006-4
Approved For Release 2009/01/06: CIA-RDP85SO0316R000100010006-4