MODELING SOVIET AGRICULTURE: ISOLATING THE EFFECTS OF WEATHER

• Document Type: CREST
• Collection: General CIA Records
• Document Number (FOIA) /ESDN (CREST): CIA-RDP90G01353R001800180004-4
• Release Decision: RIPPUB
• Original Classification: C
• Document Page Count: 96
• Document Creation Date: December 27, 2016
• Document Release Date: September 30, 2013
• Sequence Number: 4
• Publication Date: June 27, 1988
• Content Type: REPORT

r r:-t--77.--;,?-:, , . .-:;.
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Office of the Deputy Director for Intelligence:
Central intelligence . gency
STAT
?
2>siZ
27 June 1988
NOTE TO: Director of Central Intelligence
Deputy Director of Central Intelligence
This is a paper that has been prepared for
publication as an unclassified report. It is
one of several papers that we intend to publish
at an unclassified level that are aimed at
providing scholars and others outside the intelli-
gence area some insight into how we do our
analysis.
Attachment
Deputy Director for Intelligence
p--za ogi
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ential
Modeling Soviet Agriculture: Isolating the Effects of Weather (IF)
Scope Note
This paper is an unclassified report prepared by
Office of Soviet Analysis, CIA, to document the
development and use of a model to examine past trends in
agricultural productivity, to measure the relative
contribution of labor and capital to farm output, and to
assess prospects for meeting 12th Five?Year plan goals. The
weather modeling work builds on research reported in two
previous DI papers
Other aspects of the model
development were motivated by recent research on Soviet
agriculture by analysts in the Directorate of Intelligence.2
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STAT
STAT
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Modeling Soviet Agriculture: Isolating the Effects of Teather(U)
Directorate of Intelligence
Central Intelligence Agency
June 1988
Unclassified
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Modeling Soviet Agriculture: Isolating the Effects of Weather (U)
Information available
as of I June 1988
was used in this report
Summary
Gorbachev needs to improve food supplies dramatically to bolster popular
support for the economic restructuring program. Moscow's campaign to
'intensify' agriculture, particularly grain production, has resulted in recent
gains. But agriculture still faces serious problems, and unless strong
measures are taken to stimulate productivity on the farm, Moscow will grow
increasingly unable to meet the demand for more and better food supplies
without resorting to substantial hard currency imports.
Gorbachev has been seeking ways to overcome the gross inefficiencies of the
agro?industrial sector. Agricultural reforms since Gorbachev came to power
include the creation of the superministry Gosagroprom, endorsement of
collective contracts for farm workers, enforc;ement of stable procurement
plans, and promotion of the right of farms to directly market a portion of
planned fruit and vegetable procurement. Gorbachev's call last year for a
special Central Committee plenum to tackle comprehensive agricultural reform
suggests that more policy initiatives in agriculture are on the way. To
evaluate the effects of such initiatives, it is first necessary to isolate the
effects of weather, which often mask the influences of other variables on
agricultural performance.
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Isolating Weather Factors
A mathematical model was developed to separate the effects of weather from the
effects of other factors. In developing the model, it became clear that
weather factors alone were not sufficient to explain agriculture's dismal
showing during the 1979-82 period. When capital, labor, and productivity
changes were included in the model in addition to weather, the results tracked
closely actual fluctuations in output (see figure 1).
The rate at which weather?adjusted output is increasing has important
implications for Gorbachev's agriculture policy. Until 1979, weather?adjusted
output increased steadily, reflecting relatively stable growth of inputs,
steady but slow technological progress, and the absence of sharp swings in
government policy (see figure 2). Weather?adjusted output dropped
precipitously in 1979 and continued to decline in 1980 and 1981. During this
time, growth of deliveries to agriculture slowed as overall industrial growth
slowed, and transportation organizations were increasingly unable to keep
pace with the growing requirements. In addition, government policies specific
to agriculture were flawed as well.
;
-- Investment resources going to agriculture were wastefully allocated and
inefficiently utilized. Soviet authors have complained, moreover, about
losses of agricultural products because construction of storage
facilities and rural roads was neglected.
-- Agricultural machinery downtime increased, efficiency in the use of
inputs--especially machinery, equipment, and fertilizers--declined, and
growth in livestock herds outstripped growth in feed availability.
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Billion rubles (1982 prices)
Newaga--BENNETK
140 -
135 -
130-
125 -
120 -
115-
110 -
105
100 -
95 -
90
Figure 1
Observed Farm Output and the Model's Predictions
1964 1966 1968 1970 1972 1974 1976 1978 1980 1982 1984 1986 1988
1987 is preliminary.
Jun 10, 1988
Legend
o Actual data
? Model
E cc ti
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NEWAGB1--BENNETK
?Net of feed, seed, and waste.
5.
0
Billion rubles (1982 prices)
o CP o
1 1 1 i
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7.6
0
(78
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0
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co
0
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0
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0 ?
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In 1983, however, there was a remarkable recovery, reflecting improvements in
efficiency stemming from the Brezhnev Food Program implemented the previous
year. Since Brezhnev's death in late 1982, Gorbachev has used his influence
in the leadership to reshape the program to reflect more closely his own views
and priorities. In addition to measures targeted at increasing worker
productivity, Gorbachev has given the 'intensive technology' program a high
priority. Intensive technology, as defined by the USSR, includes many
practices routinely performed in the West--use of high?yield varieties,
planting after fallow where possible, implementing efficient field operation
schedules, and extensive use of agrochemicals. By 1984 and 1985
weather?adjusted agricultural output had nearly returned to the pre-1979
trend, and performance was clearly back on trend in 1986 and 1987.
Returns to Capital and Labor
The model results also show that the return to capital is lower in agriculture
than in any other producing sector of the economy except fuels, which
underscores the burden imposed on the rest of the economy by agriculture's
large share of investment resources. The capital elasticity was estimated to
be 0.17, indicating that a one percent increase in the capital stock results
in only 0.17 percent increase in output. The return to labor in agriculture,
on the other hand, is estimated by the model to be over four times higher than
the return to capital.
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These results demonstrate why the Soviets are concerned about productivity in
agriculture. The structure of the model implies that Moscow has three
potential policy options for increasing farm production: 1) increase the
capital stock by accelerating growth in capital investment; 2) increase the
number of workers and/or hours worked per worker, including increases in the
number of part?time workers; and 3) increase productivity. The low return to
capital relative to alternative investments in other sectors of the economy
suggests that increasing capital investment in agriculture is not in the best
interest of the overall economy. Increasing the labor input is not feasible
because the size of the labor force in agriculture is declining due to natural
demographic trends, which Moscow is powerless to change, and the leadership is
opposed to increasing part?time employment in agriculture at the expense of
production in other sectors of the economy. The only remaining policy option
is ?to increase the productivity of the labor and capital inputs.
This can be accomplished if Moscow continues to push for programs and policies
designed to increase worker efficiency. Long?standing impediments to
productivity growth must be overcome before significant progress is possible,
including:
-- Weak link between the size, quality, and costs of harvests and the
financial rewards for farm workers and managers.
-- Low quality and inappropriate assortment of farm machinery.
-- Rural living conditions that are still too stark to encourage younger,
skilled workers to stay on the farm.
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-- A rural education system that is inadequate for teaching modern
agricultural practices.
Outlook
The model was used to evaluate prospects for meeting the 1986-90 Five?Year
Plan goal for agricultural output. Farm output for 1988, 1989, and 1990 was
projected after making assumptions about capital and labor growth and
simulating alternative outcomes for weather and government policy. Model
simulations indicate that the Soviets would be able to meet their plan only if
the following three conditions prevail:
-- At least 'average' weather for 1988-90.
Continued growth of inputs from other sectors at a rate equal to that of
recent years, which was four percent in 1986, together with timely
deliveries.
-- Productivity gains equivalent at least to a one percentage point
increase in productivity growth above that required to offset employment
losses.
If any of these conditions are not met, the goal will be out of reach. Even
with good weather, substantial gains in productivity are required to meet the
five?year plan. Regardless of how successful ongoing and potential additional
agricultural policies are, however, bad weather--especially if it occurs in
both 1988 and 1989--could spawn an agricultural failure severe enough to
exacerbate current consumer dissatisfaction with food supplies and threaten
the success of Gorbachev's reform effort. While the probability that bad
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weather will occur in two consecutive years is low, the impact on Soviet
domestic policy--and foreign trade--would be high.
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Contents
Page
Summary
ii
Gorbachev Needs A Success in Agriculture
1
A Model of Soviet Agriculture
3
Factors Influencing Performance
3
Capital Stock and Investment
4
Labor
5
Material Inputs
Technology
6
Weather
7
Government Policy
8
The Model
8
Modeling Policy and Productivity Changes
10
Applications of the Model
15
Weather?Adjusted Output
15
Returns to Capital and Labor
19
Total Factor Productivity Adjusted For Weather
20
Prospects for the Future: Can the 1986-90 Plan Still Be Met? . .
23
Assumptions
23
Capital
23
Labor
24
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Page
Weather 25
Material Inputs and Technology 26
Government Policy 26
Scenarios 27
Conclusions 28
Appendixes
A. Development of the Model Al
Preliminary Models Al
Final Model A4
Testing For the Effects of the Industrial Growth Slowdown A6
Comparison of Preliminary and Final Models A7
B. Data
Farm Output
Bl
Bl
Agricultural Capital Stock Bl
Employment in Agriculture B2
Weather B3
Farm Output
Value?Added Farm Output
B5
B5
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Modeling Soviet Agriculture: IsoLating the eFfects of weather
Gorbachev Needs a Success in Agriculture
Agriculture will play an important role in determining how successful
Gorbachev will be in revitalizing the Soviet economy. The next few years will
be difficult ones for the economy as a whole as Soviet managers and workers
attempt to cope with the numerous and wide?ranging elements of the reform
program. Gorbachev has already encountered serious opposition to the pace of
reform, and additional resistance is expected as implementation spreads.
Gorbachev, who built his career in part as an agricultural expert, needs a
success in agriculture; failure to improve the food supply will not only be
damaging to Gorbachev politically, but could also undermine popular support
for the economic restructuring program.
Increasing productivity in agriculture--increasing output per unit of
inputs--is as important as increasing the supply of food because of the high
resource cost of farm production in the Soviet Union. The food production
;
sector--agro?industrial complex in Soviet parlance--in the USSR is immense,
claiming roughly one?third of total annual investment (including related
housing and services) and employing nearly 30 percent of the labor force.1
Direct farm production activity alone claims about 20 percent of annual
1. The food production sector includes not only farms but also several
branches of industry supplying farms with materials, such as tractors and
other farm machinery, repair services, and agrochemicals, and branches of
industry that process food products.
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investment and 20 percent of the labor force in comparison with less than 5
percent each in the United States. Despite the huge investment in
agriculture, however, the Soviet Union must still import large quantities of
agricultural products, particularly grain (see figure 3). Productivity
increases in agriculture would enable Gorbachev to divert resources (labor and
capital investment) from agriculture to the industrial modernization drive as
well as reduce outlays of scarce hard currency for farm products.
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NEWAG1-3ENNETK
Composition of Hard
Currency Imports
US $
Figure 3
Soviet Dependency on Other
Countries for Farm Products
Other
pr? 8.3%
1986-87
Agricultural
19.47.
Grain
Meat & Dairy
products, animal
fats, eggs
1.7%
Ruble Value of Agricultural Imports (All Countries)
The category "Other" includes vegetables, fruits, sugars,
natural fibers, animal byproducts, tobacco, spices, coffee,
tea, wine and fruit beverages.
Data for 1987 are preliminary.
12000-Thousands
10000-
8000-
6000-
4000-
2000-
0
Jun 10,1988
Legend
Meat Sc Diary
products, animal
fats, eggs
flfi Grain
MI Other
1971-75
Avg
1986-87
Avg
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A Model of Soviet Agriculture2
The impact of government policies to raise agricultural productivity is often
hard to detect because weather effects are so overwhelming that they obfuscate
the influences of policy changes and changes in quality and quantity of
inputs. To properly evaluate any new program that Gorbachev may implement, it
is first necessary to isolate the effects of each of the main factors
influencing farm production.
Factors Influencing Performance
Any macroeconomic model of the agricultural sector must account for six broad
categories of factors that influence production:-capital stock, labor,
material inputs (such as manufactured fertilizers), weather, technology, and
government policy. In the Soviet case, some of these factors are completely
controlled by Moscow, whereas others are only partially controlled or
completely outside government's influence. For example, Moscow controls the
flow of capital investment and material inputs into agriculture through the
planning process. - The supply of labor, on the other hand, is partly
determined by demographic trends, over which Moitow has no direct control.
Moscow can, however, influence the supply and 'quality' of the agricultural
workforce to some extent through government policies such as those directed at
relocating labor and at providing incentives to attract skilled workers to
agriculture. Weather, of course, is completely outside Moscow's control.
2. The model deals strictly with agricultural output per se, and thus does not
address other important components of the agro?industrial complex, such as the
food processing industry and the supply of industrial products to farms.
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Capital Stock and Investment. Since 1970 the. stock of machinery, equipment,
and nonresidential structures has more than tripled in Soviet agriculture.
Fixed productive capital in agriculture at the beginning of 1987 totaled 330
billion rubles, of which 61% represents nonresidential buildings and
installations, 17% represents agricultural machinery and equipment, 3.8%
represents transportation equipment, 0.5% represents draft animals, 9.5%
represents productive livestock, and 4.6% represents perennial plantings.3
But while the overall size of the capital stock has been growing, the rate at
which it is growing has been slowing since the mid-1970s (see figure 4).
Growth of the stock of tractors in agriculture, for example, has fallen from
about 3% per year in the mid-1970s to nearly zero percent in 1986.
Because technological advances in design and engineering are embodied in new
capital, capital investment is the carrier of much of the new technology
going into agriculture.4 Growth of investment in agriculture fell from a high
of 15 percent in 1971 to less than zero percent in 1984 (see figure 5). In
1986, however, investment growth rebounded to a irate approximately equal to
that of the mid-1970s (6 percent).
3. Data are from Narodnove khozvavstvo SSSR Za 70 let, p. 204. The Soviets
measure capital in 1973 'comparable' prices.
4. Capital investment in agriculture includes new machinery and equipment, new
construction and installation of new farm buildings (including new livestock
rearing facilities, irrigation and drainage systems, and agricultural research
institutions), net additions to livestock, and capital repair.
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NEWAG2--BENNETK
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14 -
12 -
(-.) - 40-
a)
a)
? 8 -
a)
0
L
(1) 6-
-$-
0
0
6_ 4
2-
1964
Figui--e- 4
Growth of Capitol Stock in Soviet Agriculture
1
1966 1968 1970
1972
1974
1976
1978
1980
1982
1984
1986
Jun 10, 1988
Legend
? Capital stock
(exclusive of
livestock)
0 Stock of tractors
used in
agriculture
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Figur'e'5
Growth of Capital Investment and Employment in Soviet Agriculture
Growth rate (percent per year)
NEWAG3--BENNETK
20 ?
1964 1966 1968 1970 1972 1974 1976 1978 1980 1982 1984 1986
Jun 10, 1988
Legend
? Capitol investment
0 Employment in hours
worked (excluding
the private sector)?
?The USSR does not report statistics on private labor, but Western estimates have remained
relatively stable during this time period.
?
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Labor. The size of the agricultural workforce in the Soviet Union is
shrinking slowly, as is its share of total employment in the economy as a
whole. Over 35 million people are presently employed in agriculture, and many
more engage in part?time farm work and gardening for personal consumption.
During the 1970s there was little change in the size of the labor supply in
terms of hours worked. Since 1984, however, agricultural employment has been
decreasing at about 1 to 2 percent per year (see figure 5). Unless the
Soviets do something to spur labor productivity, labor requirements in the
future will exceed the supply and possibly result in a serious labor shortage
in agriculture. Moscow has issued numerous decrees to improve the
productivity of the farm labor force, but the decrees have not yet had a
widespread positive effect.5
Material Inputs. Material inputs are produced by non?agricultural sectors of
the economy for use in the agricultural sector, exclusive of capital
,?
investment goods. They include chemicals, fuels, electric power, animal feed
supplements (including by?products from food processing), and machinery spare
parts.
Among the most important are manufactured fertilizers and agrochemicals.
Aided by large imports of Western equipment and technology during the 1970s,
the Soviet Union is presently the world's leading producer of manufactured
fertilizers (nitrogen, phosphate, and potassium).6 Increases in crop yields
2
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since 1960 are directly attributable to the rapid growth in fertilizer
deliveries. After 1975, however, growth of deliveries to agriculture slowed
(see figure 6) because of lags in expanding production capacities and
underutilization of existing capacities which were caused by shortages of
skilled labor, equipment failures, and transportation problems. Since 1979,
growth of fertilizer deliveries has fluctuated at about half the rate of
growth of the early 1970s.
Chemical control of insect pests, plant diseases, and weeds has also been an
important factor in increased yields, particularly for grain. Since 1984 the
Soviets have made special efforts to increase purchases of sophisticated forms
of Western herbicides, insecticides, and fungicides. In contrast to
fertilizers, more than half of the pesticides used in the USSR are imported
from the West and from Eastern Europe. Although the use of chemical
pesticides has increased in the Soviet Union, the average application rate is
still far below that of Western countries.7
Technology. Technology in agriculture encompasses both enhancements to
resources, such as new seed varieties and livestoak breeds, and innovations in
the way in which resources are used, such as crop rotation schemes and
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C3- 2
Central intelligence gency
Office of the Deputy Director for Intelligence
27 June 1988
NOTE TO: Director of Central Intelligence
Deputy Director of Central Intelligence
This is a paper that has been prepared for
publication as an unclassified report. It is
one of several papers that we intend to publish
at an unclassified level that are aimed at
providing scholars and others outside the intelli-
gence area some insight into how we do our
analysis.
Attachment
)4(iitilitiC>444,^S60010.564CoiRRitf-Krqq itl
Scale
adjustmenta
A,
0.507255
Capital
elasticity
2.151013
0.5837644
3.685
0.0042
Weather variables
intercepta
Al
1.643758
BOTNDRY
A,
?14.945366
4.7595395
?3.140
0.0105
1/BOTNDRY
A3
?0.043465
0.0177473
?2.449
0.0343
WINTEMP
A4
0.028784
0.0080357
3.582
0.0050
Analysis of Variancea
Degrees of Sum of
Source Freedom Squares
Mean
Square
Probability of Durbin?
F?value a greater F VE2 Watson D
Model 4
0.10621403
0.02655351
29.384 0.0001 0.8902 1.958
Error 10
0.00903678
0.00090368
Corrected
total 14
0.11525081
a The model was initially
estimated
with only six parameters, including a
parameter for the sum of A,
and Al.
The parameter for the sum of A, and Al
was determined
to be 2.15101 with a
standard error of 0.58376. A0 was
estimated to be 0.507255 (standard error=0.02076) by fitting the following
model (excluding the years 1979-82):
Log(Q/L) = A0 + BLog(K/L).
Al was then determined by solving A0+A1=2.151013 for Al.
Note: a, =
a2(W) =
A
e 0
eAl+A21101ND1Y+A3(1/HOTNDRY)+A4WINTEMP
A-4
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tests indicated that these variables did almost nothing to reduce the
remaining unexplained variation. To the extent that these variables are
positively correlated with time, the capital input is probably serving as a
proxy. In the case of hectarage sown, the year?to?year variation was perhaps
too small to measure the effects of the variable on output in the presence of
much stronger influences like weather.
Final Model
The final step in development of the model was to account for changes in
productivity that occurred during the 1979-82 period and during 1986-87. For
this purpose, the function a3(P) was created to reflect our subjective
estimate of relative changes in productivity owing to government policy
actions. The derivation of this function is explained in the main body of
this paper. By adding a3(P) to the model, it was possible to include the
years 1979-82 when estimating parameters. The results are shown in table A3.
All parameters were highly significant statistically, and the Vr2 (R2 adjusted
for degrees of freedom) was 0.970. The capital elasticity was 0.17, which is
slightly higher than the estimate made using only information on capital and
labor. Figure A2 illustrates how closely the model predictions correspond to
the historical record.
A-7
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Table A3
The Final Model
Model
Log(Q/L) = Ao + P*log(K/L) + Al + A2*H01'NDEY + A3*(1/HOTNDRY) + A4*WINTEMP
+ As*PRODCHNC
Parameter Estimates
Variable
Parameter Standard t for Ho:
Parameter estimate error Parameter=0 Probability > Iti
Scale
adjustmenta Ao 0.507255
Capital
elasticity 0 0.168433 0.0073280
Weather variables
intercepta As 1.722217
HOTNDRY Al ?15.692849 2.0572757 ?7.628
1/HOTNDRY A3 ?0.045175 0.0076127 ?5.934
WINTEMP AA 0.035548 0.0037783 9.408
Productivity
change variable As 0.048822 0.00363540 13.430
22.985 0.0001
0.0001
0.0001
0.0001
0.0001
Analysis of Variancea
Degrees of Sum of Mean
Probability of Durbin?
Source Freedom Squares Square F?value a greater F
Watson D
Model
Error 13 0.00257557 0.00019812
Corrected
total 18 0.11808509
5 0.11550951 0.02310190 116.605 0.0001 0.9698 2.123
a The parameter for the sum of Ao and As was estimated to be 2.22947 with a
standard error of 0.25126. As was estimated using the value for Ao derived in
table A2.
A?
Undlassified
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Note: al =
a2(W) =
a3(P) =
eAo
Al+A2HOTNDRY+A3(1/HOTNDRY)+A4WINTEMP
eAsPRODCHNG
A-1
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Unclassified
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0.80
0.75
0.70-
c?0.65-
0
Cr)
0
_J 0.60 -
0.55 -
0.50 -
0.45
Figure A2: Model Predictions Using Full Model
1978
1983
- 1985
1972
1975
1981
1986
(7)
-0.2
1
Feb 22, 1988
Legend
o Achiol
doh-)
0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 ? Mo,IPI
Log (K/L) pr Ddictlons
Footnote
Q is farm output in billion 1982 rubles.
L is labor in billion manhours.
K is capital in billion 1973 rubles.
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The final model is:
= al a2(W) a3(D) 0 1,1?P e,
where
Unclassified
al = e
eA,A1HOTNDRY+As(1/HOTNDRY)+A,WINTEMP
al(W) = I-
eA,PRODCIING
as(P) =
0 = capital elasticity parameter,
Q = value of agricultural output, excluding farm output used within
agriculture (such as feed for livestock and grain for seed),
billion 1982 ruble's,
K = annual capital stock in agriculture at the beginning of the year,
excluding livestock, billion 1973 rubles,
L = total work?hour employment in agriculture, billion hours,
HOTNDRY = ratio of average temperature (degrees centigrade) to cumulative
precipitation (millimeters) for April through July, weighted by
total sown area,
WINTEMP = average winter temperature (degrees centigrade) for October
through March, weighted by area sown to winter wheat,
PRODCHNG = productivity change variable,
Ao...As = statistical parameters, and
e = stochastic error term.
There are two factors that can influence agricultural output that are not
explicitly included in the model--technological progress and material inputs
(such as agrochemicals). Technological advances such as higher yielding
A? 11
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Unclassified
strains of grain or higher livestock growth rates resulting from genetic
improvements would contribute to higher growth. Similarly, increases in grain
yields can be attributed in part to increased use of fertilizers and
pesticides. Efforts to estimate the model with an additional time?trend
variable representing technological progress and variables representing
deliveries of agrochemicals to farms were unsuccessful. However, the capital
input embodies technological progress to the extent that the value of new
machinery and equipment reflects increased efficiency over the old machinery
and equipment. 'Disembodied' technological progress could also occur as a
result of more efficient management and adoption of new farming technologies.
To the extent that this disembodied technological progress is an increasing
function of time, the capital input--which is also an increasing function of
time--acts as a surrogate, or proxy, for it. For the same reason, capital
also serves as a proxy for material inputs.
Testing For the Effects of the Industrial Growth Slowdown
The model was used to conduct a statistical test to determine if the
industrial growth slowdown during 1979-82 had a detrimental affect on
agricultural performance. The final model presented in table A3 was
re?estimated after replacing the function a3(P) by a dummy variable (DUM)
consisting of
'i's
for the years 1979-82 and 'O's for all other years.4 A
4. A dummy variable is a time?series sequence of l's and O's. Use of the
dummy variable in hypothesis testing is equivalent to performing an analysis
of variance and testing for significant group effects--where the two time
periods represent two groups--while simultaneously accounting for variation
between the two groups that is due to differences in capital and labor inputs
and weather.
Unclassified
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Unclassified
parameter value for DUN that is not significantly greater than zero would
suggest that non?weather factors other than capital and labor had little to do
with the poor agricultural performance during this time. As shown in table
A4, the parameter for DUM was highly significant statistically (that is, the
probability of a greater t?value under the null hypothesis that the
parameter's true' value is zero was less than 0.0001), indicating that
non?weather factors other than capital and labor were indeed responsible for
the associated growth slowdown in agriculture during 1979-82.
Comparison of Preliminary and Final Models
To perform simulations with the model, it is important that the parameters be
measured without significant bias. One source of bias common to econometric
models is 'multicolinearity.' Multicolinearity is a sample problem for which
the sample does not provide 'rich' enough information on the explanatory
variables (such as BOTNDRY, WINTEMP, K, and L) to prevent one variable from
inordinately influencing the parameter estimate of another variable. In other
words, multicolinearity is a problem when the explanatory variables are not
sufficiently independent to meet the requirements of the model.
In the final model presented above, there is potential for multicolinearity
between the functions al(W) and a3(P). One way to determine if
multicolinearity is a problem is to compare parameter estimates of the full
model with parameter estimates for a restricted model. Such a comparison can
be made here by contrasting the preliminary model in table A2, which excludes
423(P), with the final model in table A3, which includes a,(P). Parameter
A? (3 Unclassified
Draft
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Unclassified
Table A4
Testing For Effects of the Industrial Growth Slowdown
Model
Log(Q/L) = A0 + Oalog(K/L) + A, + li,*HOINDRY + A3*(1/HOTNDRY) + A,*WINTEMP
+ As*DUM
Parameter Estimates
Variable Parameter
Parameter
estimate
Standard
error
t for Ho:
Parameter=0
Probability > Id
Scale
adjustmenta
Capital
A,
0.507255
elasticity
0
0.168434
0.0160888
10.469
0.0001
Weather variables
intercepta
Al
1.607773
HOTNDRY
A,
?14.646667
4.41654502
?3.316
0.0056
1/HOTNDRY
A,
?0.042382
0.01635564
?2.591
0.0224
WINTEMP
A,
0.026284
0.00769052
3.418
0.0046
Dummy variable
for 1979-82
A,
?0.104958
0.01952918
?5.374
0.0001
Analysis of Variancea
Degrees of Sum of Mean Probability of Durbin?
Source Freedom Squares Square F?value a greater F Vr2 Watson D
Model 5 0.10619527 0.02123905 23.222 0.0001 0.8606 1.552
Error 13 0.01188982 0.00091460
Corrected
total 18 0.11808509
A? ;'1/ Unclassified
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a The parameter for the sum of Ao and AI was estimated to be 2.11503 with a
standard error of 0.53949. Al was estimated using the value for Ao derived in
table A2.
A?
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Draft
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Unclassified
estimates for the two models differ very little. Furthermore, there is little
difference in model predictions for years other than 1979-82 and 1986-87, as
shown in table A5. In the final model, the function a3(P) adjusts for the
additional non?weather factor influencing farm output during 1979-82 and
1986-87 and thus produces better predictions for those years. Most
importantly, trends in the weather?adjusted farm output series created using
the two models are almost identical (see table A5), even for 1979-82 and
1986-87. These results indicate strongly that if multicolinearity between
weather and the productivity change variable exists, it is not biasing
parameter estimates for the weather variables to any significant extent.
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'fable A5 Billion 1982 Rubles
Comparison of Preliminary and Final Models
Farm Output
Predicted Weather-Adjusted Farm Output
Year
Actual
Data
Final
Model
Preliminary
Model
Final
Model
Preliminary
Model
Differencea
1968
105.061
106.393
106.242
105.757
105.901
-0.144
1969
100.303
100.732
101.856
105.939
104.788
1.151
1970
112.535
112.464
111.797
108.749
109.428
-0.679
1971
111.388
110.707
110.350
109.685
110.094
-0.409
1972
104.660'
104.986
105.826
110.766
109.962
0.804
1973
121.807
119.841
118.830
115.747
116.833
-1.086
1974
119.629
121.073
120.394
115.221
115.994
-0.773
1975
109.410
109.094
108.309
118.221
119.233
-1.012
1976
118.060
114.802
115.811
122.192
121.312
0.880
1977
122.829
123.288
123.185
120.685
120.986
-0.301
1978
126.605
125.758
125.201
124.472
125.252
-0.780
1979
118.927
120.991
126.886
116.730
117.106
-0.376
1980
113.740
113.732
125.816
114.749
114.608
0.141
1981
112.500
111.332
127.930
112.040
113.135
-1.095
1982
120.788
120.174
-138.134
114.578
115.672
-1.094
1983
128.638
130.706
136.083
126.500
127.892
-1.392
1984
128.046
129.277
129.442
133.780
133.955
-0.175
1985
125.992
127.435
129.114
132:537
131.169
1.368
1986
136.287
134.448
128.699
142.117
141.792
0.325
1987
132.032
131.575
127.060
139.566
138.048
1.518
a Final model predictions minus preliminary model predictions.
A- 17 Unclassified
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Unclassified
Appendix B
Data
Parameters of the model were estimated using data from 1968 through 1986. The
period was not extended to 1987 because employment data for 1987 were not
available at the time of the study, and only preliminary data on farm output
were available.
Agricultural Capital Stock
The Soviet definition of fixed capital includes the undepreciated value of
buildings, structures, conveying equipment, machinery and equipment (including
measurement and control instruments, laboratory equipment, and computer
hardware), vehicles, tools, and productive and draft livestock of basic herds
(but excluding young livestock, livestock allocated for fattening, and some
minor categories such as poultry, rabbits, and fur?bearing animals). Fixed
capital is broken down into productive and non?productive capital. Productive
capital is that used directly in the production process. Nonproductive
capital includes capital in the housing and municipal services sector and in
organizations and institutions of public health,-education, science, culture,
art, credit institutions, and administrative organs.
For use in fitting the model, nonproductive fixed capital was excluded, as was
productive livestock. The data used are shown in Table Bl.
B? I Unclassified
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Unclassified
Table B1
Productive Fixed Capital Stock and Capital Investment in Soviet Agriculture
Year
Beginning?of?year capital stock
(in comparable 1973 prices)
Capital investment
(in comparable 1984 prices)c
Including livestock' Excluding livestockb
1965
72
49
10.600
1966
77
54
11.308
1967
82
58
12.069
1968
87
63
13.466
1969
93
69
14.029
1970
98
74
16.000
1971
106
82
18.410
1972
116
91
20.151
1973
126
101
22.249
1974
140
113
24.179
1975
154
127
26.100
1976
167
141
27.190
1977
180
153
27.910
1978
194
167
28.895
1979
209
181
29.519
1980
223
195
29.800
1981
238
210
30.500
,1982
254
225
30.925
1983
272
242
31.978
1984
288
258
31.000
1985
303
272
31.500
1986
316
286
33.500
1987
330
300
a Narodnove khozvaystvo SSSR za 70 let., Central Statistical Administration,
Moscow, 1987, p. 100, and other years.
Based on indexes published in Narodnoye khozvavstvo SSSR za 70 let., Central
Statistical Administration, Moscow, 1987, p. 101, and other years.
c Narodnoye khozvaystvo SSSR za 70 let., Central Statistical Administration,
Moscow, 1987, p. 276, and other years.
Unclassified
ThisThis table is Unclassified. Draft
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Employment in Agriculture
Agricultural workers fall into four basic categories--workers and employees on
state farms, collective farmers, persons engaged in private farming, and
temporary workers recruited from nonfarm industries, the military, and schools
to help during peak agricultural periods, primarily the harvest season. The
Soviets report average annual employment statistics for state and collective
farms as well as the number of workers involved in temporary seasonal
activity. From this information, an estimate of total work hours in
socialized agriculture can be made (see table B2).
The Soviets do not report statistics on average annual employment in private
agriculture, but they do report data on the number of livestock on private
farms and the area allocated for private plots. Using a method developed by
the U.S. Department of Commerce, Center for International Research, an
estimate of private employment can be derived from this information (see table
B3).1 This is done using labor coefficients obtained from the Soviet
literature, as follows:
Activity Input required per unit (man?days)
Cultivation of one sown hectare
Tending one head of cattle
Tending one pig
Tending one sheep or goat
166.0
54.2
20.6
5.6
1. See Stephen Rapawy, Estimates and Projections of the Labor Force and
Civilian Employment in the USSR 1950 to 1990, Foreign Economic Report No. 10,
US Department of Commerce, Bureau of Economic Analysis, September 1976, p. 43.
B?
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'fable B2
Employment in Socialized Agriculture
Employment (million workers)
Man-days Hours worked per
per ronth year per worker Total hours 'worked (millions)
state collective state collective state collective state collective
Year farms
farms
recruits total
farms
farms
farms
farms
farms
farms
recruits total
(1)
(2)
-(3)
(4)
(5)
(6)
(7)
(8)
(9)
(10)
(11)
(12)
1968 8.548
15.782
0.5
1907
1580
16,299
24,934
790
42,023
1969 8.725
15.010
0.6
1890
1585
16,490
23,798
951
41,240
1970 8.833
14.667
0.6
24.1
22.8
19.2
1915
1613
16,917
23,655
968
41,540
1971 9.122
13.478
0.7
23.3
1924
1630
17,547
21,973
1141
40,661
1972 9.244
13.456
0.8
23.5
1924
1651
17,782
22,210
1320
41,313
1973 9.462
13.238
0.9
23.6
1932
1669
18,281
22,092
1502
41,874
1974 9.656
13.044
0.9
23.6
1932
1694
18,655
22,097
1525
42,277
1975 9.787
12.713
1.0
23.5
23.1
20.3
1940
1705
18,991
21,678
1705
42,374
1976 9.970
12.430
1.1
23.5
23.2
20.6
1949
1730
19,430
21,509
1903
42,842
1977 10.180
12.020
1.1
23.3
23.1
20.7
1940
1739
19,753
20,900
1913
42,566
1978 10.387
11.613
1.3
23.3
23.1
21.0
1940
1764
20,155
20,485
2293
42,933
1979 10.481
11.319
1.3
23.1
23.0
21.2
1932
1781
20,249
20,157
2315
42,721
1980 10.693
10.907
1.3
22.9
23.1
21.4
1940
1798
20,749
19,606
2337
42,692
1981 10.817
10.483
1.4
22.7
23.2
21.6
1949
1814
21,080
19,020
2540
42,641
1982 10.978
10.522
1.4
22.9
23.2
21.8
1949
1831
21,394
19,268
2564
43,225
1983 11.098
10.402
1.5
23.0
23.2
22.2
1949
1865
21,628
19,398
2797
43,823
1984 11.102
10.198
1.5
22.8
23.2
22.3
1949
1873
21,636
19,103
2810
43,548
1985 11.095
9.905
1.4
22.4
23.1
22.4
1940
1882
21,529
18,637
2634
42,800
1986 10.968
9.632
1.4
22.0
23.1
22.4
1940
1882
21,282
18,124
2634
42,040
Sources:
Column
Column
(2):
(1): Narodnove khozyaystvo SSSR za 70 let., Central Statistical
Administration, Moscow, 1987, P. 86, and other years.
Column (4) minus column (1) minus col?rmn (3). Values for 1968-69 were
taken from Stephen Rapawy, Civilian Employment in the USSR 1950 to
1983, CIR Staff Paper No. 10, US Department of
Commerce, Bureau of the
Census, August 1985, p. 31.
Columns
(3)
and (4): Narodnove khozyaystvo SSSR za 70 let.,
Central Statistical
years.
Administration, Moscow, 1987, p. 300, and other
Column
(5):
Narodnove khozyaystvo SSSR za 70 let.,
Central Statistical
years.
Administration, Moscow, 1987, P. 292, and other
Column
(6):
Narodnoye khozyaystvo SSSR za 70 let.,
Central Statistical
years.
Administration, Moscow, 1987, p. 288, and other
Column
(7):
Column (5) multiplied by 12 months per year
Values for 1968-69 and 1971-74 were derived
and 7 hours per day.
from data reported by
Stephen Rapawy, Civilian Employment in the
USSR 1950 to 1983, CIR
Staff Paper No. 10, US Department of Commerce,
August 1985, p. 29.
Bureau of the Census,
t
This table is Unclassified. Draft
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Column (8): Column (6) multiplied by 12 Months per year and 7-hours per day.
Values for 1968-69 and 1971-74 were derived from data reported by
Stephen Rapawy, Civilian Employment in the USSR 1950 to 1983, CIR
Staff Paper No. 10, US Department of Commerce, Bureau of the Census,
August 1985, p. 31.
Column (9): Column (1) multiplied by column (7).
Column (10): Column (2) multiplied by column (8).
Column (11): Column (3) multiplied by column (8).
Column (12): Column (9) plus column (10) plus column (11).
This table is Unclassified.
-
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Table B3
Employment in Private Agriculture and Total Employment
Private agriculture
Unclassified
Number of productive livestock
(end of year, millions)
year cattle swine
(1) (2)
1968
1969
1970
1971
1972
1973
1974
1975'
1976
1977
1978
1979
1980
'1981
1982
1983
1984
1985
1986
sheep and
goats
(3)
Sown area
(million
hectares)
(4)
Total
hours
worked
(millions)
(5)
Total hours
worked in
agriculture
(millions)
(6)
27.3 12.8 34.4
25.0 13.8 31.7
25.0 16.6 33.2
24.9 15.9 32.7
24.7 13.3 32.3
24.6 13.6 32.1
24.5 13.7 32.0
23.5 12.2 29.4
22.8 11.8 28.8
23.3 14.8 29.4
23.1 14.8 29.2
23.1 14.8 25.3
23.0 14.0 30.2
23.4 14.2 30.7
24.2 15.8 31.9
24.6 15.6 33.2
24.0 14.1 32.5
24.1 13.9 33.1
23.7 13.6 33.4
6.77
6.78
6.73
6.68
6.67
6.64
6.64
6.64
5.93
5.93
6.05
6.05
6.16
6.15
6.16
6.16
6.17
5.70
5.72
22,771
21,868
22,292
22,089
21,551
21,525
21,489
20,716
19,519
20,212
20,277
20,109
20,280
20,488
21,139
21,331
20,824
20,313
20,135
64,794
63,108
63,832
62,750
62,864
63,399
63,766
63,090
62,361
62,779
63,211
62,830
62,972
63,129
64,365
65,153
64,372
63,114
62,175
Sources:
Columns
Column
Column
Column
(1), (2), and (3): Narodnove khozyaystvo SSSR za 70 let., Central
Statistical Administration, Moscow, 1987, P. 253, and other years.
Narodnove khozvavstvo SSSR za 70 let., Central Statistical
Administration, Moscow, 1987, p. 225, and other years.
Derived from columns (1), (2), and (3); see text.
Column (5) plus column (12) from table B2.
(4):
(5):
(6):
'3-.'
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The total man?days for animal husbandry are increased by 10 percent to allow
for labor involved in tending poultry, horses, and rabbits, which otherwise
would not be included. Man?days are converted to total hours by multiplying
by 7 hours per day, the same daily work rate assigned to state and collective
farms.
Weather
Detailed meteorological data from the USSR are available through the World
Meteorological Organization. As a member, the USSR shares such information
with foreign countries. These data are part of a worldwide standardized
system that attempts to insure consistent measures of weather parameters from
year to year. Precipitation and temperature data are available for
approximately 1,000 stations located throughout the grain?growing portion of
the USSR.2 The US Air Force processes the data and applies corrective
measures to overcome reporting errors and omissions. Although the original
dataset extends to the mid-1940s, the 'correcteddataset begins in 1969. It
was possible to use the 'uncorrected' weather data for 1968 and thus extend
the dataset an additional year, but attempts to include years before 1968 in
the model were unsuccessful.3
2. Summaries of the data for 27 crop regions are reported in Climate Impact
Assessment, Foreign Countries, published by the National Oceanic and
Atmospheric Administration (NOAA). For the present study, eight additional
crop regions were created, predominately in Siberia and Kazakhstan.
3. The two weather datasets also had different area definitions, and so it was
necessary to link the two series. This was done for 1968 data as follows:
B-7
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These data were used to calculate monthly precipitation and average monthly
temperature for the agricultural area of the USSR. Two weighting schemes were
used to aggregate the data. Precipitation and temperature for the variable
HOTNDRY were weighted according to the area sown to all crops, whereas
temperature data for WINTEMP were weighted according to area sown to winter
wheat.4 HOTNDRY is the ratio of average temperature to cumulative
precipitation for April through July. WINTEMP is the average temperature for
October through March.
The data and summary statistics for HOTNDRY and WINTEMP are shown in Table B4
(also see figure B1). The mean and standard deviation were used to generate a
probability distribution for each variable in order to conduct the stochastic
simulation exercise. HOTNDRY and WINTEMP are positively correlated; the
Pearson correlation coefficient measured 0.595 (with a standard error of
0.139). That is, when WINTEMP is high, HOTNDRY is often--but not always--high
'corrected' value for 1969
Value for 1968 ? x 'uncorrected' value for 1968
'uncorrected' value for 1969
4. The calculation was made as follows:
35
I Share of total
L area in area i Is
i=1
Weather data
for area i
= Weighted weather data
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Table B4
Weather Data
HOTNDRY WINTEKP
Data
1968 0.065680 -0.03
1969 0.052343 -2.60
1970 0.059621 0.15
1971 0.062416 -0.10
1972 0.066987 -1.50
1973 0.059346 0.60
1974 0.052102 0.00
1975 0.085941 2.10
1976 0.058007 -1.90
1977 0.056889 -0.50
1978 0.046413 -0.10
1979 0.059482 -0.30
1980 0.049892 -1.20
1981 0.073477 1.40
1982 0.058703 0.60
1983 0.072209 1.50
1984 0.074187 0.20
1985 0.053265 -2.50
1986 0.069834 -0.60
1987 0.059304 -2.40
Percentilesa 99% 0.085942 2.1
90% 0.074187 1.5
75% 0.069834 0.6
50% (median) 0.059483 -0.1
25% 0.053266 -1.5
10% 0.049892 -2.5
1% 0.046414 -2.6
Mean
Standard deviation
0.061613 -0.376
0.0099342 1.36426
NOTE: Neither of these distributions were significantly different from the
normal distribution. Data for 1968 were excluded from calculations of summary
statistics.
a A percentile represents the probability that a value equal to or less than
the tabled value would be expected to occur, based on the 19 observations in
the original frequency distribution. For example, a value of HOTNDRY equal to
or less than 0.053266 (the value for the 25th percentile) would be expected to
occur about one-in-four years, on average.
Unclassified
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0.09-
0.08-
0.07 -
0
?
0.06-
?
0.05-
0.04
'
Degrees Centigrade
1967
3.,
Figure B1
Roo of Temperature to Preciptation,
April?July (HOTNDRY)
?
0
?
?
?
?
?
?
? ?
?
?
May 2. 1968
Averooe
?
1969 1971 1973 1975 1977. 1979 1981 1983 1985 1987
Average Winter Temperature
October?March (WINTEMP)
3
?
?
'
19 67 1969 1971 197 3 1975 1977 1979 1981 1983 1985 1987
NEWAC137-BENN:Th.
Average
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as well. Consequently, simulated values for HOTNDRY and WINTEMP were created
such that this correlation was preserved; the Pearson correlation coefficient
of simulated values was 0.585.
Farm Output
The Soviet measure of gross agricultural output is inadequate for modeling
purposes because no adjustment is made for intra?agricultural use of farm
products (such as seed and animal feed) and because Soviet gross output
statistics include a large element of waste. The measure of farm output used
in this study--net farm output--is the sum of livestock production and crop
production, less seed, feed and waste, valued in average 1982 realized prices.
Derivation of the series has previously been described in detail.5 Net farm
output is based on a sample of 28 individual crops, 10 livestock products, and
four items of livestock inventory change. These 42 products account for
.nearly 95 percent of total farm output net of intrafarm use of crops.
Value?Added Farm Output
Total factor productivity was calculated usilig value?added farm output.
Value?added farm output excludes not only production for intrafarm use, but it
also excludes the value of materials and services purchased by agriculture on
current account from nonagricultural sectors (current purchases). The time
5. See Barabara Severin and Margaret Hughes, Part III. An Index of
Agricultural Production in the USSR, in 'USSR: Measures of Economic Growth and
Development, 1950-80,' Joint Economic Committee, Congress of the United
States, December 1982, pp. 245-316.
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series for current purchases is based on 10 indexes of material inputs.6
Weather-adjusted farm output is converted to a value-added measure according
to the method presented in table BS.
6. See John Pitzer, Part I. Gross National Product of the USSR, 1950-80, in
'USSR: Measures of Economic Growth and Development, 1950-80,' Joint Economic
Committee, Congress of the United States, December 1982, pp. 88-91.
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Table B5
Derivation of Value-Added, Weather-Adjusted Farm Output
Year
Weather-adjusted
farm output
Gross
weather-adjusted
output including
current
purchases
(billion rubles)
(3)
Current
purchases
(billion rubles)
(4)
Value-added weather-
adjusted output
billion rubles index
(1) (2)
billion rubles index
(5) (6)
1968
105.757
0.923
120.361
20.1494
100.211
1.000
1969
105.939
0.924
120.568
20.9693
99.599
0.993
1970
108.749
0.949
123.766
21.6942
102.072
1.018
1971
109.685
0.957
124.831
22.7634
102.068
1.018
1972
110.766
0.966
126.062
24.2611
101.801
1.015
1973
115.747
1.010
131.730
25.9391
105.791
1.055
1974
115.221
1.005
131.132
27.5947
103.537
1.033
1975
118.221
1.031
134.546
29.2831
105.262
1.050
1976
122.192
1.066
139.066
28.3749
110.691
1.104
1977
120.685
1.053
137.350
31.1685
106.182
1.059
1978
124.472
1.086
141.661
31.6678
109.993
1.097
1979
116.730
1.018
132.850
32.1303
100.719
1.005
1980
114.749
1.001
130.595
33.1553
97.440
0.972
1981
112.040
0.977
127.511
34.0618
93.450
0.932
1.982
114.578
1.000
130.400
35.2400
95.160
0.949
4983
126.500
1.104
143.968
37.8364
106.132
1.059
1984
133.780
1.167
152.254
39.1360
113.118
1.128
1985
132.537
1.156
150.839
41.0018
109.838
1.096
1986
142.117
1.240
161.742
42.7994
118.943
1.186
1987
139.566
1.218
158.839
43.6574
115.182
1.149
Sources:
Column (1): Weather-adjusted output series from table 3.
Column (2): Column (1) divided by 114.578, the value of weather-adjusted
output for 1982.
Column (3): Column (2) multiplied by 130.4 billion rubles, which is the 1982
gross value of farm output estimated by extending the 1972
.input-output table forward to 1982. It represents complete
coverage of gross output less interfarm use, as opposed to the net
farm output measure used in this study which is based on a sample.
Column (4): Current purchases.
Column (5): Column (3) minus column (4).
Column (6): Column (5) divided by 100.211, the value of value-added
weather-adjusted output for 1968.
This table is Unclassified.
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