USSR: SEASONALITY IN ENERGY PRODUCTION AND FOREIGN TRADE

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• Document Page Count: 29
• Document Creation Date: December 22, 2016
• Document Release Date: March 16, 2012
• Sequence Number: 2
• Publication Date: February 1, 1981
• Content Type: REPORT

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National
Foreign
W Assessment
Center
USSR: Seasonality in
Energy Production and
Foreign Trade
ER 81-10004
February 1981
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National
Assessment
Center
USSR: Seasonality in
Energy Production and
Foreign Trade
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ER 81-10004
February 1981
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USSR: Seasonality in
Energy Production and
Foreign Trade
Summary Soviet economic activity exhibits a seasonal pattern that reflects a number
of separate influences, the most important being weather and the end-of-
year rush'(storming) to plan fulfillment. These seasonal forces complicate
interpretation of monthly, quarterly, and semiannual data because longer
term trends are confounded with seasonal variations. Most interest focuses
on production trends-how fast is growth? has output fallen? Reliable
inferences about trends often cannot be drawn based directly on the raw
data, however, especially if one is looking for turning points, such as a major
decline in oil production.
Although the breakdown of economic series into trend and seasonal compo-
nents is standard practice in short-term analysis of Western economies, it is
little noted and even less frequently carried out in Soviet literature-
probably because Communist economic theory associates short-term fluc-
tuations with capitalism. The Soviet practice of basing year-to-year
comparisons on the same period within the year does tend to eliminate some
of the seasonal distortion. Not only is this misleading on occasion, it also uses
the available data inefficiently.
This paper focuses on seasonality in several key statistical series for the
Soviet economy during 1960-80, using the Bureau of Census's X-11 seasonal
adjustment method. The series selected for analysis include quarterly data
(1960-80) on the production of electricity, oil and gas condensate, coal,
natural gas, and export and import values.
Our analysis suggests that seasonal influences are a significant determinant
of quarterly fluctuations in Soviet economic activity, explaining between 35
percent and 95 percent of quarterly changes in activity in the areas exam-
ined. The importance of seasonality, however, varies considerably. Elec-
tricity and foreign trade are most sensitive to seasonal influences; oil
production is relatively unaffected.
Because of storage limitations, output of electricity and natural gas is
particularly responsive to short-term fluctuations in demand. Demand peaks
in the first quarter when energy requirements for heating and lighting are
high and again in the fourth quarter when energy needs rise to accommodate
the high level of economic activity associated with yearend storming.
Storage is much less a problem for oil and coal, which can be stockpiled as a
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buffer against demand fluctuations. Short-term production changes, there-
fore, primarily reflect supply factors. In the case of coal and oil, offsetting
seasonal factors tend to dampen production fluctuations.
Seasonal volatility in foreign trade exceeds that in most forms of energy.
Exports are high in the second quarter and peak in the fourth quarter. The
second-quarter surge probably represents a drawdown of stocks that accu-
mulated and could not be delivered during the harsh weather of the first
quarter. Storming and a desire to fulfill calendar year export commitments
may explain the fourth-quarter peak. In contrast, the first two quarters are
most important for imports-a relatively new phenomenon perhaps caused
by the growing reliance on grain imports to sustain livestock herds until the
summer-fall harvest.
As long-term growth slowed in most sectors, seasonal factors assumed a
somewhat larger role in determining quarterly changes in economic activity.
But the volatility of seasonal forces seems to show no general trend.
Fluctuations became less marked in most of the sectors in the early 1960s.
The volatility of natural gas production diminished throughout the period
before leveling off in the last few years. Oil output, which had become more
stable, increased in volatility in the latter 1970s as Siberian oil began to
dominate national output. Fluctuations in coal output have been increasing
rapidly through most of the period as strip mining became more important.
While seasonal fluctuations have become less pronounced for exports, the
seasonality of imports has increased-in part because of the growing impor-
tance of grain purchases.
Studies that deseasonalize basic Soviet data and also that examine this
seasonal pattern are important to understanding the workings of the Soviet
economy. For instance, seasonally adjusted data reveal an unusual decline in
oil production in the winter of 1979 that the unadjusted data are unable to
reveal. Besides locating turning points more accurately and rapidly, season-
ally adjusted data permit identification of the underlying growth rate by
isolating the rebound effect that occurs when a seemingly good year follows
a poor year. This is feasible because seasonal adjustments validate the
comparison of different quarters within the same year; without seasonal
adjustment this would be a hazardous procedure.
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Summary iii
Introduction 1
The Literature on Soviet Seasonality
Techniques of Seasonal Adjustment 3
Analysis of Seasonal Influences in Energy Production and Foreign Trade 3
The Data 3
Components of Change in Activity Levels 4
Annual Activity Profiles 5
Seasonality Patterns 5
Trends in Seasonal Influences 9
The Deseasonalized Results
12
USSR: Quarterly Statistics on Energy Production and Foreign Trade
16
1.
Relative Importance of Three Components in Explaining Variations
in Energy and Foreign Trade
5
Relative Importance of Seasonal Factors in Explaining Variation in
Activity Over Selected Periods
10
A-1.
Electricity Production
17
A-4.
Oil and Gas Condensate Production
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1. Activity Profile for Energy Production, 1960-79
2. Activity Profile for Foreign Trade, 1960-79
3. Seasonal Indexes for Energy Production, 1979
4. Seasonal Indexes for Foreign Trade, 1979
5. Annual Standard Deviations of Indexes of Seasonality 10
for Energy Production
6. Annual Standard Deviations of Indexes of Seasonality
for Foreign Trade
7. USSR: Electricity Generation
8. USSR: Natural Gas Production
9. USSR: Oil and Gas Condensate Production
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USSR: Seasonality in
Energy Production and
Foreign Trade
Much economic analysis relies on monthly or quarterly
data to draw conclusions about economic trends.
Empirical research on short-term movements in West-
ern economies generally uses data that have been
adjusted to remove seasonal patterns in order to isolate
the underlying economic trends and cyclical compo-
nents. Yet relatively little effort has been devoted to
doing the same for socialist economies. Since the Sovi-
ets report quarterly data only on a cumulative basis for
that calendar year, Western and Soviet analysts usu-
ally compare the cumulative performance through a
given quarter of one year only with the corresponding
figure in another year. This is an inefficient and some-
times misleading procedure for removing seasonal in-
fluences. It also fails to isolate the seasonal pattern,
which itself could be a source of useful information
about the economy.
Removing seasonal influences from quarterly eco-
nomic statistics should be an important step in the
analysis of Soviet economic activity. The adjusted data
provide a more reliable basis for making short-term
forecasts and assessing the timing of a turning point in
a certain indicator. Conversely, raw quarterly data
could give a distorted picture because of seasonal fac-
tors. Annual data will not confirm the existence of a
turning point until the end of the year, or even later. In
addition, the seasonal pattern isolated in the adjust-
ment process yields insights on the production rhythm
within a year and shows the impact of such variables as
weather and the planning cycle on economic
performance.
This paper discusses case studies in which the
deseasonalization of time series data is used to deter-
mine both the underlying trend and the role of seasonal
influences in several key areas of Soviet economic
activity-the various energy-producing sectors and
foreign trade. The first part of the paper briefly surveys
the literature on seasonal influences in the USSR and
summarizes the theory and techniques of seasonal
adjustment. Then, we analyze seasonality in energy
production and foreign trade. In each area, we examine
the importance of seasonal influences in explaining
changes in levels of activity, explore the causes behind
changes over the course of a year, and determine if
those forces have become more or less significant over
time. Finally, the deseasonalized activity series are
presented, and examples are given to show how they
can be used to improve short-term analysis.
The Literature on Soviet Seasonality
Relatively little research has focused on seasonality in
Soviet economic activity. Raymond Hutchings pio-
neered Western analysis of seasonal influences on
Soviet industry, although his work has been primarily
qualitative.' He devoted much attention to cataloging
production patterns in terms of such measures as the
relative activity of each quarter and the frequency with
which certain annual patterns occur.
His chief quantitative contribution is the development
of a measure of seasonal variability-the degree to
which economic activity over the year deviates from a
steady path. Hutchings's index is computed for a given
year by first expressing each quarter's activity as a
ratio of the previous quarter. These ratios are then
adjusted by the average quarterly variation over the
year to eliminate long-run trends. The sum of the
logarithms of the four adjusted ratios is used as a
measure of seasonal variation independent of both
trend and scale effects. He uses this index to perform
both cross-sectional comparisons of the volatility of
different economic aggregates and intertemporal
comparisons of trends in the relative importance of
seasonality.
' See his Seasonal Influences in Soviet Industry, Oxford University
Press, 1971, and "Recent Trends of Seasonality in Soviet Industry
and Foreign Trade," in Jahrbuch der Wirtschaft Osteuropas, Vol-
ume 8, 1979, pp. 247-285.
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Hutchings's indexes are a useful step in isolating sea-
sonal factors in Soviet economic data but have some
inherent conceptual problems:
? Extracting trends based on a single year's growth
rate leaves the procedure unnecessarily susceptible to
distortions from an atypical quarter.
? Irregularities that develop within the course of a year
because of extraneous nonseasonal variables are not
considered; rather such aperiodic events are counted
as seasonal fluctuations in his indexes.
? The procedure, moreover, does not extract the pat-
tern of variability over the year; because it merely
measures the average degree of deviation from
smooth growth, insights into the ebb and flow of
yearly economic activity are quite limited.
Soviet research in this area has been sparse. A theoreti-
cal article published a few years ago, typical of Soviet
technical discussions on seasonality, simply reviewed
the statistical properties of several alternative mea-
sures of seasonal volatility.' The article indicated that
at least one research institute used a simple index
based on the coefficient of variation to analyze short-
term activity. The lack of more sophisticated ap-
proaches undoubtedly lies in the very severe constraint
on computer access at most research facilities and on
the low priority given to seasonality studies.
Soviet references to seasonality are usually political
rhetoric calling for a smoother rhythm of production.
This theme has been expressed at the highest levels, for
example, President Brezhnev declared at the 25th Par-
ty Congress that "such shortcomings as losses of work-
ing time, idling, and lack of regularity in work are
especially intolerable."' What Brezhnev and others
have foremost in mind are the losses in efficiency and
production caused by the rush (storming) at the end of
planning periods to earn bonuses or escape penalties.
3 A. I. Tarasov, "Indicators of Seasonal Unevenness," Ekonomika i
matematicheskiye melody, No. 4, 1975 (translated in JPRS
1820/72, April 1975).
' N. Fontalin, "Smoothness-A Condition of Efficiency,"
Promyshlennost' Belorussii, No. 12, 1977 (translated in JPRS
70809, March 1978).
Soviet literature generally does not distinguish be-
tween this storming and seasonal phenomena, which
are natural and not necessarily counterproductive.
Soviet empirical work on the rhythm of production has
generally concentrated on patterns within a month.'
These articles usually divide the month into halves or
thirds and compare output in the different periods.
While this research on very short-term variations in
economic activity could be useful, only average break-
downs of the rhythm within a month are usually given,
limiting the usefulness of the data for examining vari-
ations in seasonality over the full year.
Soviet research on seasonality that is useful to Western
scholars for the most part is found in occasional arti-
cles in technical journals that report on industry case
studies in sectors such as electricity generation and
meat production.' But even these analyses are gen-
erally confined to questions of the average share of
annual output accounted for by a given month or
quarter.
The best Soviet study of seasonality seems to be
Zorkal'tsev's.6 He computed average monthly de-
viations of production from trend for a large number of
industrial products for the period 1965-72 and stressed
the need for more research on seasonal phenomena,
Examples are: P. Galkin, "Rhythm in Production,"
Ekonomicheskaya gazeta, No. 1, 1976; R. Gareyev, "In the Strug-
gle for Rhythm of Production," Ekonomicheskaya gazeta, No. 39,
1976; V. Virkunen, "A Clear Rhythm Through Tens of Days and
Days," Ekonomicheskaya gazeta, No. 4, 1978; Ya. Arloff, "Heavy
Industry and Consumer Goods," Ekonomika i organizatsiya
promyshlennogo proizvodstvo, No. 5, 1978; Fontalin, op. cit., and
Ya. Kvasha, "Methods of Cost Computation in Setting Up Produc-
tion," Voprosy ekonomiki, No. 1, 1976 (translated in Problems of
Economics, August 1977).
'S. P. Gladkova, "Seasonal and Annual Fluctuations in Electric
Power Output Analyzed," Gidrotekhnicheskoye stroitel'stvo, No.
11, 1975, pp. 18-21 (translated in JPRS 66544, January 1976), and
L. B. Dekel'man, R. G. Tumanova, and V. D. Filippov, "Methods of
Studying the Seasonal Nature of Production," Myasnaya industriya
SSSR, No. 1, 1978, pp. 5-9 (translated in JPRS 70828, March
1978).
6 V. I. Zorkal'tsev, "Seasonal Impact on Industrial Production,"
Izvestiya sibirskogo otdeleniya akademii nauk SSSR, seriya
obshchestvennykh nauk, No. 6, 1978 (translated in JPRS 72434,
December 1978).
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arguing that they are significant determinants of
short-term output variations and must be considered in
designing policies for reserve capacity, inventory con-
trol, and product interchangeability.'
Techniques of Seasonal Adjustment
The seasonal adjustment techniques now in use include
the calculation of quarterly means, link relatives, and
ratios-to-moving averages. With many issues of
deseasonalization still unresolved, spectral analysis
and regression techniques are also being employed to
deseasonalize data.'
Almost all of these techniques assume that economic
activity in any period is the combined result of random
events, seasonal influences, cyclical fluctuations, and
secular trends. Statistical series are deseasonalized by
filtering out seasonal influences to derive a time series
that reflects primarily cyclical and trend components.'
The various techniques differ in how they accomplish
the filtration. In particular, before any technique can
be applied, a critical assumption must be made. Do the
seasonal factors have an additive or multiplicative
effect on economic processes? 10
Of the many methods for deseasonalizing data, we
have selected the X-11 approach developed by the
Bureau of the Census. This is the most commonly used
method, primarily because of its great flexibility. It
In another article Zorkal'tsev urges the use of regression techniques
that account for seasonal influences as well as trend during the plan
formation process. See "The Basis of Choice of a Regression Model
for the Analysis and Forecasting of Processes of Fuel Supply With a
Seasonal Component," Izvestiya akademii nauk SSSR, energetika i
transport, No. 3, 1978, pp. 135-143.
See Harry M. Rosenblatt, "A New Look Into and Beyond Tra-
ditional Methods of Seasonal Adjustment," X-II Information for
the User, 1969. Also see the papers in Seasonal Analysis of Eco-
nomic Time Series, US Department of Commerce, Economic Re-
search Report, ER-1, 1978. An example of the complex modeling
techniques currently under investigation can be found in Charles I.
Plosser, "The Analysis of Seasonal Economic Models," Journal of
Econometrics, October 1979.
In seasonal adjustment terminology, that part of fluctuation
explainable by the business cycle and secular trend is combined into
a single component known as the trend-cycle. Because the Soviet
Union is not exposed to a Western-style business cycle, henceforth,
we refer to this component purely as trend.
10 The choice itself is an oversimplification as the considerable
controversy on this issue demonstrates. Seasonal factors could im-
pact in combined additive and multiplicative forms in some
nonlinear fashion.
provides an analysis of variance to determine the
shares of variation in output over different timespans
that can be attributed to random events, trend, and
seasonal factors. It also permits changes in the seasonal
pattern over time rather than holding the pattern fixed
as most other methods require. In addition, the X-11
method allows for either additive or multiplicative
seasonal influence assumptions. Finally, one of the
chief advantages of the X-11 is its flexibility in treating
extreme observations so that the deseasonalization pro-
cess is not distorted."
Analysis of Seasonal Influences in Energy Production
and Foreign Trade
The Data
We analyzed quarterly data on production of coal, oil
(including gas condensate), natural gas, and elec-
tricity, and commodity exports and imports to identify
seasonal behavior. (The raw data are presented in the
appendix.) We compiled the energy data from
Ekonomicheskaya gazeta from first-quarter 1960 to
second-quarter 1980; approximately a month after the
end of each quarter, the Central Statistical Agency in
Moscow issues reports on the cumulative energy
production through that quarter. Quarterly production
data are derived by subtracting from each quarter's
cumulative output the previous total, except for the
first quarter, which is given directly. Since published
Soviet statistics are often highly rounded, some error
may be introduced into a particular quarterly total
derived in this manner.
Development of the series for electricity generation is
complicated by the reporting practices employed be-
fore third-quarter 1971. As Hutchings has pointed out,
"All of the X-11 calculations in this paper were based on the
multiplicative assumption regarding seasonal influences. Experi-
ments were also conducted using the alternate assumption of ad-
ditive seasonality. This gave less stable seasonal patterns and was
therefore rejected in favor of the multiplicative adjustment. The
influence of extreme observations on the seasonality calculations was
moderated by discarding data points that were more than 2.5 stand-
ard deviations beyond a moving average mean and reducing the
weight given to points within the band defined by 1.5 and 2.5
standard deviations from the mean.
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power generation output before 1971 was only reported
for the first and third quarters as output "of electric
power stations of general utilization and block sta-
tions" while the second- and fourth-quarter cumulative
figures presumably represented the national total." To
overcome this difficulty, we have used regression tech-
niques to determine historical relationships between
quarterly and cumulative semiannual totals. With the
help of these relationships, we interpolated values for
first- and third-quarter national totals before 1971.
This procedure obviously introduces some additional
error into the pre-1971 data and, therefore, into the
estimates of seasonality for electricity in this period.
The foreign trade data in current prices are obtained
from several sources. Hutchings constructed quarterly
data for imports and exports for the period 1960-67
and 1968-74 in his studies." Post-1974 data were ob-
tained in cumulative form from the journal,
Vneshnyaya torgovlya, current through second-quar-
ter 1980. We derive the quarterly figures by the same
procedure used for energy.
Since these trade figures are ruble values in current
prices, they do not measure the actual volume of trade.
Soviet official data permit a crude adjustment of these
data to a constant price base by the publication of an
annual index series of the volume of exports and im-
ports in comparable prices. By comparing these in-
dexes with the annual trade figures in current prices,
implicit price deflators for the year are derived. Be-
cause we have no information about how prices change
during the course of a year, the same price deflator is
applied to every quarter within a given year.14 The
result is that the X-11 procedure will mix together the
seasonal effects that result from both trade volume and
price fluctuation.
'Z Hutchings, "Recent Trends," op. cit., p. 259. The designation of
block electric power stations probably excludes captive power sta-
tions belonging to specific production facilities.
" Hutchings, Seasonal Influences, pp. 309-310, and "Recent
Trends," pp. 247-285.
" If prices rise uninterrupted during the year, this procedure will
understate the values of the earlier quarters and overstate the values
of the latter quarters. If prices rise and fall during a year, the
direction and degree of bias are indeterminate.
Components of Change in Activity Levels
The deseasonalization process decomposes the change
in a particular time series into shares that may be
attributed to random events, trend growth, and sea-
sonal factors. In a seasonally stable series, the relative
importance of each component depends on the unit of
time used to measure change. Table 1 summarizes the
results for each of the six series examined; we cal-
culated change from period to period, where the period
varied from one to four quarters.
As expected, in all cases the dominance of the seasonal
component diminishes as the unit of time increases.
Seasonal factors are short-term phenomena whose in-
fluence essentially vanishes when year-to-year
comparisons are made. Conversely, the significance of
the trend factor increases as the unit of time lengthens;
in no case does the trend factor account for less than 86
percent of the annual variation in the series examined.
Except for coal, the importance of the random compo-
nent for the energy products either drifts downward
over longer time periods or remains steady.
Seasonal volatility seems most important in explaining
short-term fluctuations in electricity generation and
least important for oil and gas condensate. Coal and
natural gas fall in an intermediate position. The
growth trend is relatively unimportant in explaining
quarter-to-quarter changes in electricity, coal, and
natural gas, but it is very important for oil. Coal is the
only fuel that has a consistently high irregular compo-
nent (from 8 to 18 percent), suggesting that variations
in coal output are to a large extent the result of factors
having nothing to do with trend or seasonal factors. For
example, irregular deliveries of equipment, abrupt
changes in manpower availability, and extreme
weather in certain years could be more important in
coal than in the other sectors.
Foreign trade is much like electricity in that a heavy
seasonal component dominates short-term trade move-
ments. Indeed, the growth trend is not important in
determining changes for periods of less than a year.
Seasonal factors seem to have equally important roles
in both imports and exports, while the influence of
random events is small in both.
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Relative Importance of Three Components in
Explaining Variation in Energy Production
and Foreign Trade a
Periods b
5.1
57.6
37.3
0.9
76.2
22.9
0.6
92.6
6.8
0.4
99.6
NEGL
1
13.8
18.9
67.2
2
3.0
28.5
68.6
3
4.4
70.3
25.3
4
4.0
95.9
NEGL
Coal
18.4
8.3
73.3
2
8.2
27.2
64.6
3
9.1
39.8
51.1
4
13.1
86.8
0.2
Electricity
0.6
4.3
95.1
2
0.1
5.9
94.0
3
0.3
31.8
67.9
4
0.4
99.6
0.1
Exports
1
3.9
3.4
92.8
2
4.4
18.7
76.9
3
2.1
18.1
79.8
4
7.6
92.3
0.2
1
6.4
4.4
89.2
2
7.9
25.3
66.8
3
3.2
20.2
76.7
4
13.0
86.3
0.6
a Because of rounding, components may not add to the totals shown.
b Period length is three months.
Annual Activity Profiles
An activity profile shows how economic activity varies
from quarter to quarter during an average year. It is
not necessarily the pattern recorded in any particular
year because changes in trend and extraneous events
can also affect the distribution of activity within a
given year. Seasonal influences can also change over
time for a host of reasons-changes in production
technology, output mix, or the location of production,
for example.
Activity profiles can be computed from a series of link
relatives: activity in each quarter is expressed as a ratio
of the previous quarter. The mean link relative for a
given quarter is the average of all of the link relatives
for that quarter. Activity profiles are created by chain
linking the quarterly mean link relatives, treating the
first quarter as the base.
The quarterly activity profiles for energy (figure 1)
and foreign trade (figure 2) show the average activity
variations over the year. Because they are not trend
adjusted and because activity has increased over time,
the data show some net growth on a first-quarter to
first-quarter basis. The quarterly patterns, however,
are by no means identical. Generation of electricity
falls in the second quarter, remains low in the third
quarter, and approaches a peak in the fourth and first
quarters. Extraction of oil and gas condensate rises
throughout the year before falling somewhat in the
first quarter. Production of natural gas follows a pat-
tern similar to that of electricity except that it does not
fall by nearly as much in the second and third quarters
and rises by more in the fourth and first quarters-
reflecting the high growth trend in gas extraction. Coal
dips in the second quarter and gradually climbs back
until it peaks in the following first quarter.
Exports and imports generally move together. Values
for the second quarter are nearly 20 percent above
first-quarter values; exports and imports then fall
slightly in the third quarter, although the decline is
steeper for imports. Both series peak in the fourth
quarter with exports climbing most. Finally, both se-
ries fall back to about the same relative level in the
following first quarter-about 10 percent more than
the same quarter a year earlier.
Seasonality Patterns
While activity indexes are useful general measures of
the time patterns of production or trade, they can
distort seasonal influences because growth trends and
random disturbances are mixed in with seasonal fac-
tors. In the Soviet economy, where most indicators of
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Figure 1
Activity Profile for Energy Production, 1960-79
Index: 1st qtr.=100
I I I I I
1 St qtr. 2nd qtr. 3rd qtr. 4th qtr. 1St qtr.
economic activity grow over time, we must especially
remove the trend elements to discern the true seasonal
pattern.
Seasonal indexes computed by the X-11 procedure
convey a clearer impression of the annual pattern of
activity independent of trend and irregular disturb-
ances. If there were no seasonal rhythm, these indexes
would record a value of 100 in every quarter. An index
greater than the base indicates that the quarter in
question is more active than average and a value less
than 100 means just the reverse. Since the X-11
deseasonalization procedure generates different sea-
sonal indexes over time, we will focus on those for
1979. (We defer discussion of historical changes in
seasonality to a later section.)
Seasonal indexes for energy for 1979 (figure 3) show a
slightly different pattern than link relatives by remov-
ing trend. Because electricity cannot be stored, the
Figure 2
Activity Profile for Foreign Trade, 1960-79
Index: 1st qtr.=100
I I I I I
1St qtr. 2nd qtr. 3rd qtr. 4th qtr. 1St qtr.
seasonal pattern of electricity generation reflects de-
mand considerations much more than supply. Based on
Zorkal'tsev's finding that "the amplitudes of the sea-
sonal fluctuations are greater for fuel consumption
than for production," we would expect that electricity
generation would be the most volatile of energy
forms." Indeed, a comparison of the seasonal indexes
for different kinds of energy shows this to be true.
Electricity generation peaks in the first and fourth
quarters when we would anticipate demand to be high-
est. In the first or winter quarter, household and
municipal demand for lighting, ventilation, hot water,
and heating purposes would be at a maximum.'b Since
Robert Campbell has shown that household and
municipal sources consumed more than 16 percent of
" Zorkal'tsev, "Seasonal Impact." The next few paragraphs borrow
from this article to help explain the seasonal phenomena we have
witnessed.
" S. L. Pruzner, A. N. Zlatonol'skiy, and A. M. Nekrasov,
Ekonomika energetki SSSR (Moscow: Vyshaya Shkola, 1978) pp.
26-27.
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Figure 3
Seasonal Indexes for Energy Production, 1979
Index: 1979 average=100
Electricity
Natural
gas
on
Coal
1St qtr. 2nd qtr. 3rd qtr. 4th qtr.
electricity available for distribution during 1975, we
would expect the first-quarter share to be substantially
higher than average." With the well-known phenom-
enon of end-of-year storming by industry, the chief
consumer of electricity, the shorter days of fall, and the
advent of cold weather, we would also expect fourth-
quarter demand for electricity to be relatively high.
Peak periods of electricity demand, however, do not
coincide with peaks in hydroelectricity generation.
Hydro potential is at its maximum in the second and,
perhaps, third quarters periods of rather low elec-
tricity demand. Hydroelectricity generation cannot be
used to augment seasonal peak loads; rather it serves
the function of expanding the underlying capacity of
the entire network. This inability to rely on
hydroelectricity in periods of peak seasonal demand
" Robert Campbell, Soviet Energy Balances (Santa Monica: Rand
R-2257-DOE, Part II, December 1978). Most of the energy
consumption statistics in this section are taken from Campbell's
work.
must force increased reliance on thermoelectric sta-
tions and increase the demand for fuel during the
winter.18
Seasonality is next strongest in natural gas production.
While production does not follow current demand as
closely in gas as in electricity, the amount of gas that
can be stored at any one time is limited. Thus, gas
production also peaks in the fall (fourth quarter) and
winter (first quarter). Space heating requirements and
heat losses in industrial processes peak in those quar-
ters. For example, in urban locations, where private
and communal consumption is a large share of local
fuel consumption, the use of fuel in January is three to
five times higher than the monthly average for the
year.19 Moreover, the energy required for heat in indus-
trial processes in the winter is often 40 to 100 percent
more than in the summer.20 Storming in industry,
which accounts for about half of domestic gas
consumption, undoubtedly contributes to the fourth-
quarter surge in gas demand. Despite the increased
production during cold weather, gas shortages occur
because demand rises more than production. To com-
pensate, the shortages are rationed by drawing on
underground storage, by converting to alternative
fuels, and disconnecting some users.21
The seasonal profile for coal production is similar to
that for natural gas except that it has a smaller am-
plitude. Because coal can be stored more readily than
electricity or gas, we would expect supply consider-
ations to dominate the seasonal rhythm in coal output.
One reason for the relatively stable seasonal pattern is
the operation of offsetting supply influences. Winter
weather impedes strip mining and summer weather
hinders shaft mining.22 Summer vacations and the
tendency to do repair work then both serve to hold
down third-quarter output.23
" V. D. Bel'kin and A. F. Tret'yakova, "Optimization of the Branch
Complexes in the System of Interbranch Balance, Calculating Prices
and Rents," I. Ya. Birman, ed., Optimalniy plan otrasli (Moscow:
Ekonomika, 1970) p. 93.
" Ibid., 93.
Ye. N. Il'ina and L. D. Utkina, "Unevenness of Gas Consump-
tion," Ekonomika gasovoy promyshlennosti, No. 9, 1978, pp. 3-15.
Ibid.
=Z B. Pichugin, "Prepare Coal During Summer," Sotsialisticheskaya
industriya, 17 July 1980, p. 3.
21 Zorkal'tsev, "The Basis of Choice," p. 135.
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Demand does play some role in coal's seasonality,
albeit a reduced one. Coal production is at a peak in the
first and fourth quarters when energy demand is also
highest. Undoubtedly, this extra coal output is used for
the generation of electricity in thermal stations and
power and heat in cogeneration facilities, which to-
gether accounted for nearly two-thirds of noncoking
coal consumed in the economy in 1975. This increased
demand for coal for electricity is not only due to
greater energy requirements during cold weather, but
also because many thermal plants substitute coal for
natural gas during this period of tight gas supply.24
Industrial storming associated with the fourth quarter
would exert a ripple effect on coal demand both as a
fuel and for coking purposes. Also, the relatively low
level of demand in the second and third quarters,
especially as thermal plants return to gas usage, per-
mits inventory accumulation, so there would be less
need to greatly accelerate production in the fourth
quarter.25
Oil production is seasonally the least volatile energy
indicator examined. In no quarter does output diverge
from the quarterly average by more than 2 percent.
While storage theoretically could be a limiting factor,
in reality potential drilling and pumping rates are the
operative constraints to production. Thus, quarterly oil
output seems more sensitive to fluctuations in supply
than demand. Like coal, offsetting factors limit the
seasonal volatility: drilling conditions in moderate
locations are the worst in the winter, but in Central
Asia and Tyumen they are the worst in the summer.
Here again, demand exerts a limited influence on the
seasonal pattern. Petroleum products have more alter-
native uses than other forms of energy, so the heating
needs of winter do not play a dominant role in the first-
quarter demand for oil. Oil output instead peaks in the
third and fourth quarters, when the economic sectors
most dependent on oil are most active. A surge in the
demand for petroleum products in agriculture, trans-
portation, and industry plays a key role in boosting oil
demand in those particular quarters. A factor limiting
11 Ilina and Utkina. Pruzner, et al., p. 50. Also see N. S.
Neporozhnego, ed., Elektrdikatsiya SSSR (Moscow: Energiya
1970), p. 541.
Z' Ibid.
the volatility of oil demand is that mazut, like coal, is
used as a partial substitute to alleviate the natural gas
shortage during the winter.
In summary, electricity and natural gas output are the
most volatile energy series on a quarterly basis because
storage limitations make them reflect quite directly the
volatile demand for energy in the Soviet economy. Coal
and oil are more stable because production possibilities
rather than storage capacities are limiting; hence,
these fuels may be stockpiled in advance of demand.
Also, offsetting technical factors contribute to reduced
volatility in oil and coal production. Demand and con-
sequently production of energy tends to be highest in
the first quarter because of heating needs and lower
energy efficiency in industrial processes, and in the
fourth quarter because of the rapid pace of economic
activity.
Foreign trade also shows a distinct seasonal pattern
(figure 4). Weak export activity in the first quarter
probably reflects problems in the transportation
sector-inclement weather and closed ports and/or a
need to divert limited transportation resources to
higher priority activities. The rebound in the second
quarter could represent a reduction in the backlog of
undelivered goods held over from the previous quarter.
The high export activity in the final quarter is a likely
sign of storming to meet plan targets and export
commitments.
The import pattern is somewhat different from ex-
ports. First-quarter activity is about average, the sec-
ond quarter is the most active, the third quarter is
relatively inactive, and the last quarter is about aver-
age. The first two quarters may dominate imports
because of the increased reliance on imported grain.
Grain must be imported early in the year to com-
pensate for any crop shortfalls in the previous year and
to sustain livestock herds until the winter grain crop is
harvested and spring pastures are available. Likewise,
other imports may fall off toward the end of the year as
planned foreign currency allocations become tight and
some new purchases must be deferred until the follow-
ing calendar year.
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Figure 4
Seasonal Indexes for Foreign Trade, 1979
Index: 1979 average=100
I I i I
1St qtr. 2nd qtr. 3rd qtr. 4th qtr.
Price effects may also have a role here. If prices tend to
rise during the year, this would mean that for exports
the low activity of the first quarter and the high
activity of the fourth quarter would be exaggerated;
rather both would approach somewhat closer to aver-
age activity. The net effect of price changes on imports
is harder to assess because of the high share of food and
agricultural raw material imports, about one-fifth in
1978. We suspect that food prices have a high seasonal
component and that they do not rise in an uninter-
rupted manner, as most other products.
Trends in Seasonal Influences
Seasonality patterns are not necessarily stable over
time. Long-term changes in location, resource avail-
ability, labor supply, technology, industrial structure,
and even weather could affect the balance of seasonal
forces and therefore the variation in activity over the
year. Trends in seasonal patterns can be measured in
three ways: (1) changes in the role of seasonal influ-
ences in explaining quarterly variations in economic
activity, (2) changes in seasonal volatility over time,
and (3) changes in the seasonal profile itself.
No clear relationship exists between economic develop-
ment and the relative importance of seasonal influ-
ences. Development can reduce the importance of fac-
tors like weather that are highly seasonal; it can also
increase the importance of budgetary and financial
systems that have strong seasonal rhythms. To see
whether the role of seasonal forces in Soviet economic
activity is changing, we divided the 1960-79 period into
four five-year subperiods. The activity levels of each
shorter period can then be analyzed separately to
determine the shares of the variations attributable to
irregular disturbances, the growth trend, and seasonal
influences. The analysis here is limited to activity
changes over one- and two-quarter spans since seasonal
influences generally are most important in explaining
variations over short periods.
Summary results for the six series (table 2) allow few
generalizations about the changing strength of sea-
sonal forces over time. The importance of seasonal
factors varies considerably for most of the series, but
there has been no general tendency toward a reduction
in seasonal influences. There is some suggestion of a
connection between the relative growth over the period
and the explanatory power of seasonal influence-
seasonal factors become important as the annual
growth rate falls. Oil and coal output have shown
stronger seasonality in recent periods, probably be-
cause long-term growth has been slowing in both cases.
The importance of seasonal forces in the rapidly grow-
ing but slowly decelerating natural gas sector has
decreased somewhat in recent periods. No pattern
emerges in electricity, although its seasonal component
has consistently been among the highest of the series
studied. The significance of the seasonal component in
export activity has been relatively stable but has be-
come somewhat less important in explaining change
over two quarters. There has been some shift to less
seasonal influence in quarter-to-quarter changes in
imports, but seasonal factors still dominate in these
comparisons.
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Relative Importance of Seasonal
Factors in Explaining Variation
in Activity Over Selected Periods
One-Quarter Two-Quarter
Span Span
Oil and gas condensate
1960-64
1965-69
1970-74 32.5
1975-79 59.3
Natural gas
1960-64 _51.4-
1965-69 89.5
1970-74 80.0
Coal
1960-64 75.9
1965-69 71.6
1970-74 60.6
1975-79 90.9 90.4
Electricity
1960-64 95.9 89.4
1965-69 87.3 92.9
1970-74 90.2 94.6
Exports
1960-64 96.0 82.8
1965-69 86.6 81.6
1970-74 94.8
1975-79 93.5
Imports
1960-64
1965-69 87.8 72.9
1970-74 88.5 11.3
To test for changes in seasonal volatility, we computed
the standard deviation of each year's seasonal adjust-
ment indexes. If there were no seasonal volatility in a
given year, the standard deviation would be zero. A
large standard deviation indicates that the series is
subject to extreme seasonal fluctuations, whereas vari-
ations in the standard deviation over time measure the
shift in seasonal volatility. The standard deviations are
Figure 5
Annual Standard Deviations of Indexes of
Seasonality for Energy Production
0.5 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
1960 1965 1970 1975
Natural
gas
shown in figures 5 and 6 for 1960-79, using a logarith-
mic scale so that equal vertical movements represent
equal percentage changes in volatility.
Figure 5 confirms that seasonal fluctuations are more
intense in electricity and natural gas than in coal and
oil. While the volatility of electricity has been high, it
also has shown the most stable seasonal pattern. Some
of this stability, however, may reflect the statistical
procedures used to complete the raw data series that
we have discussed above.
Natural gas production has shown a considerable de-
cline in volatility, although seasonal variations are still
large. The seasonal volatility of oil production has been
relatively stable over the two decades. But after declin-
ing for a time, volatility has risen slightly since 1975.
The growing share of Siberian oil in the national total
and the declining production in regions with a less
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Figure 6
Annual Standard Deviations of Indexes of
Seasonality for Foreign Trade
Exports
Imports
0.5 I I I I I I I I I I I I I I I I 1 1 1
1960 1965 1970 1975
severe climate may account for the turnaround. Coal
output has become more seasonal over the last 10
years. This probably reflects the expansion of strip
mining at the expense of shaft mining.
Both exports and imports were more volatile than any
of the energy series during the early 1960s. With the
growing importance of foreign trade in Soviet plans,
exports and imports have become less volatile and are
now less affected by seasonal factors than electricity.
Nonetheless, seasonal variations in trade activity are
still high. In fact, the volatility of imports has risen in
the last five years as a probable reflection of the
growing importance of grain and other food imports.
Of the two series, the seasonal volatility of imports has
changed more than export volatility.
The standard deviations of the seasonality indexes
show shifts in the degree of fluctuation over time but
do not indicate how patterns have changed within a
year. Only by examining the seasonal indexes directly
can such shifts be tracked. The quarterly seasonal
indexes for electricity output have been stable through-
out the period, although some changes in individual
seasonal indexes can be observed. Synthetic proce-
dures used to reconstruct part of the raw power series
may be a source of some of this stability. First-quarter
activity-always higher than average-has increased
in relative terms. The below-average activity of the
second and third quarters has remained unchanged,
while the fourth quarter has fallen from a pronounced
peak to a level similar to that of the first quarter. The
seasonal indexes for oil exhibit a marked stability
throughout the 1960s and 1970s. The only discernible
change is a slight weakening of the third quarter and a
concomitant strengthening of the fourth quarter.
The decreasing amplitude of the seasonality of natural
gas is reflected in the seasonal indexes, with each
quarter trending toward the average rate of activity.
The first and fourth quarters remain the peak periods
for gas production, although the peak quarterly rates
exceed the yearly average by smaller amounts now
than earlier. The coal quarterly profile has changed
slightly as seasonality has increased. The first quarter
has remained the most active period on a seasonal basis
and has accounted for an increasing share of annual
output. The fourth quarter displayed below-average
activity early in the 1960s, but now rivals the first
quarter in terms of seasonal output activity. Similar-
ities in the seasonality shifts of coal and electricity
illustrate the importance of coal to much of electricity
capacity.
The shifts in seasonal indexes of foreign trade have
been minimal for exports and most dramatic for im-
ports. With decreasing export volatility, some of the
seasonal indexes have moved closer to the quarterly
average. The fourth quarter remains the strongest
period, but less than formerly. In recent years, the
second quarter has become stronger and remains only
slightly below the annual peak. The seasonal indexes
for imports have changed radically. First-quarter
activity has gone from 20 percent below average to
about average. The second quarter has become the
most active period, while the fourth quarter-which
once dominated import activity-has fallen to slightly
below average. The weakest import period is now the
third quarter after having supplanted the first quarter
from this position.
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Figure 7
USSR: Electricity Generation
Billion kilowatt-hours
Adjusted
Unadjusted
I I I l 1 1 I . .III I I I I I I I I I I I I I I I I I I I I I I I I I I I I I 1 l I I I I I I I I I I 1 1 1 1 1 1 1 1 1 1 1 IIII I I I I 1111111
50 1960 1965 1970 1975 1980
Seasonally adjusted time series are derived by applying
indexes of seasonality to the official Soviet data. The
adjusted series, purged of seasonal influences, reflect
trend growth and random factors. The deseasonalized
and official series for energy production and foreign
trade are compared in figures 7 through 12. (The
appendix presents the adjusted values for each quarter
in 1960-80.) The deseasonalized figures provide a dif-
ferent view of Soviet economic growth. The contrast
between the unadjusted and adjusted series shows that
economic activity in these areas of the Soviet economy
are subject to distinct annual rhythms.
The use of deseasonalized data is especially important
in judging the intensity of random events or the timing
of turning points-for example, the possible peaking of
Soviet oil production. Oil production measured by the
quarterly unadjusted series has fallen below the level of
the previous quarter 10 times in the 80 observations in
this study. Thus, the drop in oil production between
fourth-quarter 1978 and first-quarter 1979 was not
unusual. Analysis of seasonally adjusted oil produc-
tion, however, reveals that the first-quarter 1979 drop
in deseasonalized production had happened only twice
before-in first-quarter 1969 and in second-quarter
1976. Moreover, the failure of seasonally adjusted oil
production to turn upward in second-quarter 1979 was
unprecedented in the 20 years covered by this
analysis-a development that went unnoticed in the
raw data that reported a rise in output in the second
quarter. Adjusted oil production later turned upward
again, demonstrating the severity of the winter of 1979
and its intense and unusual impact on the Soviet econo-
my. It could also suggest that oil production is nearing
its long-run peak. In reality, it is unlikely that an
economic variable will smoothly approach a peak from
which it begins a monotonic decline; rather we would
expect that as the peak is approached there will be
more frequent occurrences of short-term production
declines such as have happened since 1979. Regardless
of the cause of the 1979 winter production
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Figure 8
USSR: Natural Gas Production
Billion cubic meters
Adjusted
Unadjusted
I I I I I I II I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I IIII
0 1960 1965 1970 1975 1980
Figure 9
USSR: Oil and Gas Condensate Production
Million metric tons
1979, 1st qtr. -
Adjusted
Unadjusted
L - I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I
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Figure 10
USSR: Coal Production
Million metric tons
Adjusted
Unadjusted
I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I i I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I II
100 1960 1965 1970 1975 1980
Figure 11
USSR: Exports
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Figure 12
USSR: Imports
Million 1970 rubles
decline-weather related or nearing a long-term
peak-the seasonally adjusted data provide a better
framework for the analysis of oil production than the
unadjusted data.
Another possible value of seasonally adjusted data is to
isolate the "echo effect" from the quarterly data. This
effect may be defined as the statistical anomalies that
appear in Soviet data during a year subsequent to a
poor one. For example, in first-quarter 1980, the Soviet
press reported that industrial production had grown 5
percent over the corresponding period in the previous
year, a respectable rate of growth by anyone's stand-
ards. The real reason for the high growth rate was not
industrial expansion, however; rather the harsh winter
of 1979 brought growth to a virtual standstill with
output of many products far below the 1978 level. By
merely recouping those production losses, the echo
effect virtually guaranteed a high rate of apparent
growth for 1980 with minimal expansion of industrial
capacity.
By seasonally adjusting the series examined in this
paper and others, it becomes possible to compare dis-
parate quarters on an identical basis. For example, we
need not always compare thefirst quarter of one year
with the first quarter of another year; now we can
validly compare any quarter with any quarter in an-
other year or the same year. In this way it is possible to
more easily remove the echo effect. Since the second,
third, and fourth quarters of 1979 were only indirectly
affected by the harsh winter that year, we can compare
the first quarter of 1980 to these latter quarters to
discover the true rate of growth that is not biased
upwards by comparison with an abnormal period.
Exports are a case in point. The dislocations of the
1979 troubles caused exports in the first quarter to
drop 11 percent below 1978. If 1980 exports in the first
quarter are compared with this unusual 1979 quarter,
the impression is created that exports have risen
dramatically by 19 percent. When a comparison is
made with the seasonally adjusted third or fourth
quarters of 1979, it becomes apparent that exports in
constant prices have risen much more slowly and may
even have declined. Thus, the echo effect is neutralized
in our interpretation of economic events.
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Appendix
USSR: Quarterly Statistics
on Energy Production
and Foreign Trade
Official energy data (tables A-1 through A-4) were
compiled from various issues of Ekonomicheskaya
gazeta. Because of a change in reporting practices,
data for electricity generation through second-quarter
1971 had to be reconstructed through a combination of
partial official data and historical relationships. For-
eign trade data (tables A-5 and A-6) were obtained
from Hutchings26 and Vneshnaya torgovlya. The trade
data are reported in current rubles, but we have used
this basic data and its relationship with Soviet indexes
of trade in constant prices to derive an implicit price
deflator to convert the data to 1970 rubles. In all cases,
quarterly values were derived from published cumula-
tive totals.
The seasonally adjusted data were computed by using
the X-11 statistical routine of the Bureau of the Cen-
sus. The assumption of multiplicative seasonality was
used throughout. To limit the influence of extreme
observations, the standard default criterion of 1.5 and
2.5 standard deviations was used. This means observa-
tions more than 2.5 standard deviations beyond the
norm were discarded for purposes of deriving the in-
dexes of seasonality. Observations from 1.5 to 2.5
standard deviations received a reduced weight as they
approached 2.5 standard deviations. All other observa-
tions received a full weight.
u Hutchings, Seasonal Influences, pp. 309-310, and "Recent
Trends," pp. 247-285.
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Electricity Production
1960 73.4 68.6 62.1 87.9
1961 81.6 75.4 72.4 97.6
1962 94.0 86.0 82.3 106.7
1963 107.0 97.0 92.3 115.7
1964 117.3 105.7 105.1 130.9
1965 129.7 116.3 117.0 144.0
1966 140.6 125.4 126.7 152.3
1967 154.6 137.4 136.3 160.7
1968 166.0 147.0 149.1 175.9
1969 181.2 159.8 160.4 187.6
1970 193.1 169.9 173.6 203.4
1971 210.4 184.6 187.0 218.0
1972 229.0 198.0 199.0 232.0
1973 240.0 209.0 214.0 252.0
1974 258.0 225.0 228.0 264.0
1975 272.0 241.0 241.0 284.0
1976 297.0 256.0 255.0 303.0
1977 307.0 265.0 265.0 313.0
1978 321.0 279.0 276.0 326.0
1979 332.0 289.0 283.0 335.0
1980 354.0 298.0 NA NA
First Second Third Fourth First Second Third Fourth
Quarter Quarter Quarter Quarter Quarter Quarter Quarter Quarter
69.4
72.3
70.6
79.0
77.1
79.6
82.1
87.9
88.6
91.0
92.9
96.4
100.7
103.0
103.4
105.1
110.1
112.6
116.8
119.6
121.5
124.3
128.9
132.4
131.5
134.3
138.6
140.9
144.3
147.3
148.3
149.5
154.6
157.6
161.8
164.5
168.3
171.5
173.7
176.0
179.0
182.5
187.8
191.0
194.7
198.5
202.2
204.8
211.9
213.0
215.2
218.0
221.9
224.8
231.6
236.6
238.5
242.0
247.2
247.7
251.4
259.1
261.8
266.1
274.4
275.0
277.7
283.6
283.6
284.5
289.3
292.5
296.5
299.4
301.8
304.3
306.7
310.2
309.6
312.6
326.9
319.7
NA
NA
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First
Quarter
Second
Quarter
Third
Quarter
Fourth
Quarter
First
Quarter
Second
Quarter
Third
Quarter
Fourth
Quarter
1960
129.0
128.0
130.0
126.0
127.8
129.0
129.0
127.2
1961
129.0
126.0
129.0
126.0
127.8
127.0
128.0
127.1
1962
130.0
128.0
129.0
130.0
128.7
129.1
128.1
131.0
1963
131.0
131.0
135.0
135.0
129.7
132.2
134.2
135.8
1964
138.0
135.0
142.0
139.0
136.7
136.3
141.3
139.6
1965
144.0
143.0
145.0
146.0
142.6
144.6
144.4
146.4
1966
147.0
146.0
146.0
146.0
145.5
148.0
145.4
146.1
1967
149.0
146.0
151.0
149.0
147.3
148.3
150.5
148.8
1968
151.0
145.0
149.0
149.0
149.1
147.5
148.7
148.6
1969
150.0
147.0
154.0
157.0
147.8
149.7
154.1
156.5
1970
158.0
152.0
155.0
159.0
155.3
154.8
155.6
158.5
1971
162.0
165.0
151.0
163.0
158.9
167.9
152.0
162.5
1972
166.0
161.0
163.0
165.0
162.4
163.7
164.5
164.5
1973
171.0
164.0
164.0
169.0
167.1
166.8
165.7
168.4
1974
173.0
167.0
171.0
173.0
169.0
169.9
172.9
172.2
1975
177.0
171.0
174.0
179.0
172.9
174.0
176.0
178.0
1976
181.0
175.0
176.0
180.0
176.8
178.1
178.2
178.9
1977
184.0
178.0
179.0
181.0
179.8
181.1
181.4
179.9
1978
185.0
177.0
178.0
184.0
180.7
180.0
180.5
182.8
1979
187.0
179.0
174.0
179.0
182.6
182.0
176.5
177.9
1980
186.0
176.0
NA
NA
181.6
179.0
NA
NA
Declassified and Approved For Release 2012/03/16: CIA-RDP08SO135OR000200360002-5
 Declassified and Approved For Release 2012/03/16: CIA-RDP08SO135OR000200360002-5
1960
1961
1962
1963
1964
1965
1966
1967
1968
1969
1970
1971
1972
1973
1974
1975
1976
1977
1978
1979
1980
First
Quarter
Second
Quarter
Third
Quarter
Fourth
Quarter
11.9
10.6
11.0
13.5
15.2
13.4
14.2
18.1
19.9
16.6
23.0
15.7
23.7
20.8
21.0
26.0
27.9
25.7
26.0
30.4
32.3
30.3
31.0
35.4
37.0
33.8
34.2
40.0
41.0
37.6
37.4
43.0
44.6
40.5
39.9
46.0
46.6
43.5
43.9
49.0
50.5
47.6
47.9
54.0
53.8
51.2
52.0
55.0
57.3
53.7
53.0
57.0
59.9
57.1
57.0
62.0
64.1
62.9
64.0
70.0
71.3
69.7
70.0
78.0
80.3
77.7
78.0
85.0
86.6
83.4
84.0
92.0
93.0
90.0
90.0
99.0
102.0
99.0
98.0
108.0
109.0
105.0
NA
NA
First Second Third Fourth
Quarter Quarter Quarter Quarter
10.9
11.2
12.0
12.9
14.0
14.1
15.4
17.3
18.4
17.4
25.0
15.0
22.0
21.8
22.7
24.8
26.0
26.9
28.0
29.0
30.3
31.7
33.3
33.8
34.8
35.4
36.6
38.1
38.6
39.4
39.9
41.0
42.2
42.3
42.4
44.0
44.3
45.3
46.5
47.0
48.2
49.4
50.4
52.0
51.5
52.9
54.5
53.2
55.0
55.3
55.4
55.3
57.7
58.6
59.5
60.3
61.8
64.5
66.7
68.1
68.9
71.4
72.9
75.8
77.7
79.5
81.3
82.5
83.9
85.3
87.6
89.2
90.2
92.0
93.8
96.0
98.9
101.2
102.2
104.7
105.7
107.4
NA
NA
Declassified and Approved For Release 2012/03/16: CIA-RDP08SO135OR000200360002-5
 Declassified and Approved For Release 2012/03/16: CIA-RDP08SO135OR000200360002-5
Table A-4
Oil and Gas Condensate Production
First Second Third Fourth First Second Third Fourth
Quarter Quarter Quarter Quarter Quarter Quarter Quarter Quarter
1961 39.1 40.6 42.3 44.0 39.8 40.8 41.7 43.6
1962 43.7 45.6 47.7 49.0 44.5 45.8 47.1 48.5
1963 48.6 50.4 53.0 54.0 49.5 50.6 52.3 53.5
1964 53.8 55.2 57.0 58.0 54.8 55.4 56.2 57.6
1965 57.7 60.3 62.0 63.0 58.7 60.6 61.0 62.7
1966 63.2 65.3 68.5 68.0 64.4 65.6 67.4 67.7
1967 68.0 71.0 75.0 74.0 69.2 71.3 73.7 73.6
1968 74.7 76.3 79.0 79.0 76.1 76.6 77.8 78.5
1969 76.7 81.3 84.0 86.0 78.0 81.6 82.9 85.4
1970 84.8 87.2 90.0 91.0 86.2 87.6 89.0 90.2
1971 90.1 91.9 94.0 96.0 91.5 92.3 93.1 95.1
1972 95.6 97.4 100.0 101.0 97.0 97.7 99.2 100.0
1973 100.0 103.0 108.0 110.0 101.4 103.3 107.3 108.9
1974 110.0 114.0 116.0 119.0 111.6 114.2 115.4 117.7
1975 118.0 122.0 124.0 127.0 119.7 122.2 123.5 125.5
1976 127.0 128.0 131.0 134.0 128.9 128.2 130.5 132.3
1977 132.0 136.0 138.0 140.0 134.0 136.4 137.4 138.0
1978 138.0 141.0 145.0 148.0 140.1 141.6 144.3 145.8
1979 143.0 144.0 148.0 151.0 145.2 144.7 147.2 148.7
1980 148.0 149.0 NA NA 150.3 149.9 NA NA
Declassified and Approved For Release 2012/03/16: CIA-RDP08SO135OR000200360002-5
 Declassified and Approved For Release 2012/03/16: CIA-RDP08SO135OR000200360002-5
First Second Third Fourth
Quarter Quarter Quarter Quarter
1960 986 1,212 1,161 1,361
1961 1,071 1,243 1,296 1,564
1962 1,243 1,504 1,431 1,827
1963 1,280 1,646 1,545 1,782
1964 1,366 1,620 1,562 1,907
1965 1,527 1,765 1,701 2,095
1966 1,682 1,943 2,099 2,397
1967 1,872 2,215 2,255 2,499
1968 1,873 2,217 2,255 3,400
1969 2,249 2,733 2,696 3,121
1970 2,578 2,827 2,924 3,191
1971 2,646 3,086 2,840 3,298
1972 2,767 3,084 2,918 3,454
1973 2,802 3,334 3,439 4,341
1974 3,478 4,138 3,866 4,226
1975 3,591 4,295 3,982 4,370
1976 3,759 4,517 4,323 4,896
1977 4,220 5,029 4,706 5,167
1978 4,425 5,017 5,180 5,355
1979 3,952 5,070 5,292 5,824
1980a 4,707 5,333 NA NA
First
Quarter
Second
Quarter
Third
Quarter
Fourth
Quarter
1,134
1,184
1,195
1,199
1,231
1,214
1,335
1,378
1,426
1,472
1,477
1,608
1,464
1,616
1,592
1,567
1,560
1,600
1,602
1,674
1,743
1,756
1,734
1,840
1,919
1,945
2,126
2,108
2,130
2,232
2,270
2,207
2,122
2,233
2,268
3,021
2,533
2,742
2,721
2,790
2,886
2,815
2,974
2,865
2,947
3,053
2,902
2,975
3,079
3,021
2,994
3,134
3,121
3,238
3,531
3,962
3,882
3,986
3,965
3,883
4,013
4,119
4,064
4,043
4,210
4,319
4,387
4,551
4,734
4,810
4,753
4,810
4,975
4,807
5,211
4,985
4,450
4,864
5,314
5,421
5,307
5,118
NA
NA
Declassified and Approved For Release 2012/03/16: CIA-RDP08SO135OR000200360002-5
 Declassified and Approved For Release 2012/03/16: CIA-RDP08SO135OR000200360002-5
Imports
First Second Third Fourth
Quarter Quarter Quarter Quarter
1960 1,238 1,407 1,365 1,782
1961 1,128 1,475 1,607 1,778
1962 1,376 1,672 1,631 1,904
1963 1,303 1,818 1,713 2,215
1964 1,416 1,972 1,811 2,076
1965 1,521 1,960 1,927 2,387
1966 1,626 2,065 1,824 2,140
1967 1,760 2,112 2,095 2,282
1968 1,800 2,160 2,142 3,196
1969 2,165 2,663 2,373 2,671
1970 2,491 2,826 2,475 2,767
1971 2,610 2,884 2,648 3,125
1972 2,990 3,309 3,100 3,754
1973 3,652 3,951 3,669 3,823
1974 3,393 3,927 3,811 4,475
1975 4,490 4,853 4,218 4,863
1976 4,838 5,462 4,491 4,793
1977 5,064 5,563 4,632 4,664
1978 5,584 6,119 5,129 6,099
1979 5,250 6,194 5,464 6,190
1980 a 6,447 7,056 NA NA
First
Quarter
Second
Quarter
Third
Quarter
Fourth
Quarter
1,526
1,395
1,379
1,496
1,395
1,455
1,626
1,496
1,706
1,635
1,655
1,612
1,613
1,761
1,744
1,892
1,741
1,895
1,851
1,792
1,843
1,877
1,978
2,086
1,929
1,981
1,877
1,897
2,035
2,027
2,165
2,060
2,020
2,073
2,229
2,934
2,366
2,551
2,491
2,488
2,661
2,702
2,618
2,607
2,748
2,747
2,818
2,972
3,112
3,144
3,308
3,601
3,775
3,735
3,927
3,700
3,484
3,686
4,096
4,381
4,575
4,5'6
4,561
4,815
4,888
5,044
4,891
4,784
5,084
5,104
5,077
4,677
5,583
5,596
5,642
6,129
5,238
5,658
6,018
6,226
6,428
6,443
NA
NA
Declassified and Approved For Release 2012/03/16: CIA-RDP08SO135OR000200360002-5