Agriculture in the Face of Changing Markets, Institutions and Policies: Challenges and Strategies
JARMILA CURTISS, ALFONS BALMANN, KIRSTI DAUTZENBERG, KATHRIN HAPPE (eds., 2006)
Studies on the Agricultural and Food Sector in Central and Eastern Europe, Vol. 33, Halle (Saale), IAMO, pp. 337-349.
ALLOCATIVE EFFICIENCY OF CORPORATE FARMS IN THE
LENINGRAD REGION
DAVID EPSTEIN∗
ABSTRACT
The article presents an analysis of the allocative efficiency of using resources.
We compare the values of marginal products within primary types of resources
with their cost, which allows us to make conclusions concerning insufficient or
excessive use of these resources. By analyzing data from the Leningrad Region
with the Cobb-Douglas production function, the author comes to the
conclusion that there is considerable underuse and deficit both of labor and,
especially, of monetary resources. This deficit results from the low share of
profits in the revenue of agricultural enterprises (3-5 percent) and cannot be
overcome without a state policy that supports agricultural producers’ incomes,
which still remains very weak in the country.
Keywords: Allocative efficiency, corporate farms, transitional agriculture,
Russia.
1 PROBLEM DEFINITION
In an earlier paper we analyzed the differences in the financial and economic
performance and efficiency of agricultural enterprises, having divided them into
five groups using a special algorithm (EPSTEIN, 2000; EPSTEIN, 2005). The best
group in terms of financial and economic performance became Group 1, the less
successful being Group 2, while the least successful was marked as Group 5. We
saw that the differences in financial and economic performance are, by more
than 50 percent, determined by the quality of managing the enterprise. So the
question arises of how efficient is the use of different types of resources, taking
their price into account, i.e., what the allocative efficiency is. This concept is
∗
North-West Institute of the Agricultural Economics, St. Petersburg, Russia.
Email: [email protected].
338
David Epstein
related to the application of the production function and the calculation of the
marginal products of resources used. When the circumstances of competition are
close to perfect, the quantity of a resource used by the enterprise is set at a level
where this resource’s marginal product coincides with its cost. An excess in the
cost of the marginal product per unit of a resource over the cost of the resource
demonstrates an underuse of this resource, while an increase in the use of a
resource can result in an increase in the enterprise’s profit. By contrast, a
significant excess of a resource’s cost per unit over the price of the marginal
product points to the excessive use of this resource. The criterion of allocative
efficiency is thus the ratio between the marginal product of the resources and
their costs.
We would like to pay special attention to estimating the allocative efficiency of
using monetary resources (cash) by evaluating them using various indicators
(material costs1, monetary material costs2, credits and loans). Our hypothesis is
that, due to certain distinctive features of agricultural markets, without a special
state regulation system, agricultural enterprises face a significant deficit of
monetary resources. One of the indicators of this deficit is the extremely low
profitability of Russian agriculture, which amounted to an average of 4.8 percent
in 2000-2004 (ratio between profits (subsidies inclusive) and the cost of
production) (Russian Statistics Yearbook, 2004, p. 593). If there is a monetary
deficit, developing institutions as well as a general policy of financial support
for agricultural enterprises, which exists in virtually every developed country, is
needed. If there is no deficit, other institutional measures are needed, for
instance, strengthening the bankruptcy policy and reducing the state support,
which was observed in the recent years.
2 DATA AND METHODOLOGY
Classification is based on two solvency measures, defined in the caption to
Table 1. Both measures calculate a ratio of the coverage of fixed costs (in the
denominator) by value added (sales revenue less the cost of purchased and
intermediate inputs) in the nominator, but they use two different definitions of
fixed costs. K1 is calculated with the full wage cost, plus full depreciation in the
denominator. K2 is calculated with minimal wage cost3, plus depreciation of the
machinery, equipment and vehicles in the denominator. It thus provides a
measure of contribution from sales to fixed costs.
1
2
3
Purchased and intermediate inputs.
Purchased inputs.
In our analysis, we set the minimum wage at 50 % of the average wage for the Oblast.
Allocative efficiency of corporate farms in the Leningrad region
Table 1:
Algorithm for the solvency classification of corporate farms
Solvency groups
1 (best)
2
3
4
5 (worst)
339
K1 = (Revenue − Input costs)/(Wages + depreciation)
K2 = (Revenue – Input costs)/(Minimum wages +
farm machinery depreciation)
K1 ≥ 1
K1 < 1 and K2 ≥ 1
K2 < 1 and K2 ≥ 0
K1 < 0 and K1 ≥ −0,3
All others
If K1 is greater than 1, the farm generates some surplus after paying its workers
and covering its depreciation expenses, and can continue to grow. If K1 equals 1,
the farm can at least maintain the labor and fixed assets at a stable level, without
attrition. If, however, K1 is less than 1, the value added does not cover the fixed
costs and the farm needs to raise external capital (i.e., to borrow) in order to
grow or just stay in place. If no borrowing is possible, the farm will be forced to
reduce its labor or its asset base (or both). Yet even farms with K1 < 1 can
continue to survive if their gross earnings are sufficient to cover the minimum
(reservation) wages and the depreciation of farm machinery, equipment and
vehicles (excluding farm buildings). This less restrictive solvency measure is
captured by the ratio K2, which is calculated by the minimum wage cost plus
machinery depreciation in the denominator. If K2 is greater than or equal to 1,
the farm can manage to keep its workforce and main production assets even
without making a profit. If, however, K2 is less than one, the operating earnings
are not sufficient to cover even these minimum requirements.
The algorithm used to classify the farms into five solvency groups is shown in
Table 1. The best and the worst performers (groups 1 and 5, respectively) are
identified using only the ratio K1. Identifying the intermediate performers
(groups 2 and 3) requires the ratio K2.
To calculate the marginal products we plot a revenue production function. The
data on spending resources are partially presented in kind (labor, land) and
partially in value terms – as the costs of the used resources (material cost, fixed
assets, credits).
All agricultural enterprises (corporate farms) in the Leningrad Region that
submitted data for 2001 to the National Statistics Committee [Goskomstat] are
used as the basic body of the data. The Leningrad Region’s agricultural sector is
one of the most efficient in the country, thus, if we find here shortage of money,
this shortage could be expected for corporate farms in other regions. Besides,
this region has a considerably full database of all enterprises.
Since the allocative efficiency of various specializations (for instance, poultry
farms and, typical for the Leningrad Region, farms producing milk, potatoes and
vegetables) can differ significantly owing to different technologies used, it is
340
David Epstein
incorrect to evaluate it using one production function. Therefore, we withdrew
monospecializing farms from the sampling. The latter include poultry farms,
greenhouse farms, farms that feed pigs and cattle using purchased fodder, and
farms that produce animal fur.
The remaining sample consists of 158 farms. When plotting the regression
equations, the exact number of farms may be lower if this or that indicator is not
included in the reporting of all farms.
For our analysis we plotted the sales revenue production function in a CobbDouglas form, and, based on this, calculated the marginal products of the
following types of resources: Basic (fixed) production assets (F) in thousand
roubles; labor resources (L) in people; material costs (M) in thousand roubles;
agricultural land (S) in hectares 4.
The fixed assets’ cost estimation for a considerable number of the farms is based
on their initial cost, taking into account the rarely-done reappraisal. At the same
time, as the experience of plotting production functions shows, given a high
inflation rate, this indicator (the initial cost of the fixed assets) is often
insignificant, which is related to the drawbacks of its evaluation by the farms.
Using the depreciated cost of permanent assets is also of no statistically
important influence. However, refusal to use any indicator characterizing the
size and influence of the fixed capital would result in an overstatement of the
coefficients of other resources. In the allocative efficiency estimation, this would
mean a fortiori erroneous results. We therefore used the initial cost for the
indicator of "cost of machinery and equipment, vehicles" (we shall keep the F
symbol for this indicator). As the machinery and equipment belong to the most
frequently upgradeable part of basic capital, the inaccuracy in calculating it is
significantly lower than that when calculating the cost of fixed assets. The cost
of "machinery and equipment, vehicles" is generally a significant indicator in
the regression equations of the production function.
The Cobb-Douglas function allows us to calculate the value of the marginal
product of primary resources.
Indeed, if we only have the four above-mentioned factors (L, M, F, S), then
a1
Y = CL M a2 F a3 S a4 .
(1)
The value of the marginal product is determined as the first derivative of Y of
this resource. Let us present the computations for the labor force (L):
∂Y
L
Y
= a1CLa1 −1M a 2 F a3 S a 4 ⋅ = a1 .
∂L
L
L
(2)
It is obvious that the marginal product of labor with the given number of
workers is equal to the value of the a1 coefficient to L, multiplied by the average
labor productivity calculated using the production function.
4
Using the trans-log function in this case results in significant multicollinearity.
Allocative efficiency of corporate farms in the Leningrad region
341
However, with the increase in the number of workers per person there is an
increase not only in revenue, but also in expenditures – on average, amounting to
the sum of remuneration of labor with additional social payments. This means
that if the average wage with social payments at an enterprise is lower than the
marginal product per worker, equal to a1Y/L, the additional profit per each
additional worker will increase, while profitability will increase with the growth
in the number of workers, and vice versa.
Similarly, the partial derivative of Y to M is equal to:
∂Y
Y
= a2
.
∂M
M
(3)
If the calculated value of the marginal product per rouble of material costs is
higher than one, a deficit occurs and there is an underuse of material cost5, while
if the marginal product is lower than one, there is an excessive use of material
resources, resulting in the decline of profitability.
Marginal products in other types of resources are calculated similarly.
The marginal product equations above are true if the actual production output
coincides with the amount of revenue Y (prescribed by the production
function). It also remains true if there is a permanent proportional deviation of
the actual revenue from the calculated one, which can be the case in each of the
groups.
3 RESULTS
3.1 Average allocative efficiency of the primary types of resources
Below we present the characteristics of the primary types of resources, labor,
land, capital, material resources, in general, and in the context of the five groups
of enterprises.
5
Purchased and intermediate inputs.
342
David Epstein
Farm characteristics across solvency groups
The distribution of the main financial and physical characteristics of Leningrad
Oblast farms in 2001, by solvency groups, is presented in Table 2. The number
of farms is distributed fairly uniformly, with about one-third of the farms in the
best two groups and one-third in the worst two groups, respectively.
Table 2:
1
(best)
2
3
4
5
(worst)
All
farms
Source:
Main characteristics of corporate farms in Leningrad Oblast, 2001
(per farm averages)
Value of
machines,
equipment,
vehicles
(rubles)
Annual
wages per
worker,
‘000 rubles
(including
social
deductions)
(rubles)
Depreciation
(percent of
sales
revenue)
Number
of
corporate
farms
Sales
revenue
(rubles)
Number
of
workers
Ag.
land
(ha)
Material
costs
(purchased
and intermediate
inputs)
(rubles)
11
83236
515
3397
50307
33053
44336
6.1
35
53
24
39641
18077
18157
315
186
212
2997
2578
2624
28424
16273
19105
15106
8535
8982
40181
30323
29624
6.5
9.9
9.5
34
10684
148
3158
13844
6601
22474
22.9
157
25860
234
2861
21273
11367
31700
11.9
Own calculations.
In the context of the groups, the above indicators demonstrate a natural decline
in resource security with an increase in the number of the group, with the exception
of land resources.
Below are the characteristics of the three revenue regression equations. As the
indicator of running costs, the first equation contains material costs; in the
second equation, material costs are divided, based on statistical data, into
monetary and non-monetary costs (CASH_M, NONCASH_M). In the third
equation, an additional indicator of the sum of credits and loans (CRED) was
introduced, while the material costs are, respectively, decreased by the sum of
credits and loans (MMCRED6). The equations differ in the number of farms, as
some data were missing for some farms. Credits and loans are mainly used by
relatively successful farms.
6
M minus CRED.
Allocative efficiency of corporate farms in the Leningrad region
Table 3
Regression equation characteristics of revenues from primary
factors and resources
Indicator
Dependent
variable: Sales
revenue, ‘000
roubles
Constant
Number of
workers, L,
persons.
Agricultural
land, ha
Value of
machinery and
equipment, F,
‘000 roubles
Material costs
(purchased and
intermediate
inputs), M,
‘000 roubles
Monetary
costs,
CASH_M,
‘000 roubles
Non-monetary
costs,
NONCASH_M,
‘000 roubles
Credits and
loans, CRED,
‘000 roubles
MMCRED,
‘000 roubles
N
R2
Standard error
F
Source:
Notes:
343
Equation 1
Coefficient
t - ratio
Equation 2
Coefficient
t - ratio
Equation 3
Coefficient
t - ratio
0.264
2.977
1.468
5.088
-5.152
-6.941
0.378
5.289
0.358
4.691
0.433
4.361
-0.267
-6.224
-0.202
-4.650
-0.125
-3.478
0.132
3. 870
0.116
3.359
0.135
2.749
0.796
14.268
0.423
13.174
0.360
9.874
0.166
6.322
0.552
7.694
157
0.956
0.242
832.6
126
0.966
0.216
678.2
113
0.952
0.266
420.5
Own calculations.
OLS estimation of Cobb-Douglas models in logged variables. All the coefficients
significantly different from zero (p < 0.01).
344
David Epstein
We see that the equations have high coefficient values that are of statistical
importance, and that all resources, except agricultural land, obtain positive
regression coefficients7.
The given equations allow us to calculate the marginal products of the resources
and compare them with the costs of the resources.
The evaluation of the marginal product of labor and material costs, the value of
machinery and equipment is given based on the calculated coefficients by means
of calculating logarithmic values of resources and production output (Table 4).
Table 4:
Evaluation of primary resources’ allocative efficiency
Resource
Number of
workers, persons
Material costs,
‘000 roubles
Machinery and
Equipment, ‘000
roubles
Source:
Revenue Y
Regres
ReResource
Resource
Resource
calculated
sion coeffi- source
average
excessive or
a*Y/r cost, ‘000
using
cient,
average
producinsufficient
production
roubles
value, r
tivity Y/ r
use
а
function
0.378
233.78
23,522.13
100.616
38.03
31.7
Insufficient
0.796
21273
23,522.13
1.106
0.88
1
Excessive
11367.4 23,522.13
2.069
0.27
0.1
Insufficient
0.132
Own calculations.
It is obvious that the marginal product of the number of workers is higher than
its cost, which demonstrates its underuse.
The average use of material resources is excessive, as the marginal product per
thousand roubles is only 880 roubles. However, this is the average result for all
farms, although significant differences in allocative efficiency between the
groups could be expected. Taking the hypothesis of money deficiency into
account, it is therefore appropriate to separately consider the ratio between the
marginal product and the costs of resources for the monetary and non-monetary
parts of material costs.
The marginal product of one thousand roubles in the cost of machines and
equipment amounts to 270 roubles, significantly higher than the expected
"normative cost of the resource", which is 100 roubles for machinery and
equipment, calculated based on the normative usage expectancy period of 10
years. In fact, we see that the use of machinery and equipment is highly profitable
in modern Russian conditions, while the "machinery and equipment" resource can
7
The negative sign for agricultural land has long been typical of the Cobb-Douglas
equations applied to farms in the Leningrad Region (we witnessed this effect starting with
data from 1980) and can possibly be explained by the fact that the region is dominated by
milk cattle breeding and the weaker farms located in remote parts of the region have larger
areas of agricultural land.
Allocative efficiency of corporate farms in the Leningrad region
345
be defined as highly scarce. The big gap between the value of machinery’s
marginal product and the cost of the resource demonstrates a high deficiency in
monetary resources among the corporate farms. Otherwise, the farms would
increase the amount of equipment and this gap would be significantly lower.
We will further consider allocative efficiency in the context of groups of farms,
and will then make a detailed analysis of the efficiency of using monetary
resources and credits.
3.2 Allocative efficiency of using main resources by groups of farms
Below is the ratio between the marginal product and the resource costs for each
of the five groups based on the coefficients of equation 1. They are calculated in
the same way for the whole body of farms, with the average values of resources
in each group being taken as the parameters of the groups.
Table 5:
Ratio between the marginal product and the cost of resource by
groups, for labor, capital, material costs
Indicators/ Groups
1
2
3
4
5
Average
Number of farms in group, 2001
11
35
53
24
34
157
Marginal product for labor
61.04
47.50
36.68
32.41
27.25
38.03
Remuneration for one worker
with additional payments,
44.34
40.18
30.32
29.62
22.47
31.7
‘000 roubles
Ratio between marginal
product and remuneration of
1.38
1.18
1.21
1.09
1.21
1.20
labor for groups, percent
Excessive or insufficient use
Insufficient Insufficient Insufficient Insufficient Insufficient Insufficient
of labor
Marginal product per one
rouble in the cost of capital
0.33
0.35
0.28
0.27
0.21
0.27
(Machinery and equipment),
rouble/rouble
"Price" of one rouble in the
cost of machinery and
equipment calculated based on
0.1
0.1
0.1
0.1
0.1
0.1
the expected usage of 10 years,
roubles
Excessive or Insufficient use
Insufficient Insufficient Insufficient Insufficient Insufficient Insufficient
of machinery and equipment
Marginal product per one
rouble of material costs,
1.32
1.11
0.88
0.76
0.61
0.88
rouble/rouble
Excessive or insufficient use
Insufficient Insufficient Excessive Excessive Excessive Excessive
of material costs
Source:
Own calculations.
346
David Epstein
It is obvious that there is a deficiency in labor usage and that profitability grows
for all groups with an increase in the number of employees. A deficiency in the
number of qualified workers seems to impede the further increase of the total
number of employees.
A similar situation can be found using capital represented by the cost of
machinery and equipment indicator. Even in Group 5, the marginal product per
rouble invested in machinery and equipment exceeds 0.2 roubles, and is thus
twice as big as the normative return. The deficit of capital is obvious and is an
unexpected conclusion, since a number of experts think that there is abundance
in machinery, especially in weak farms. In reality, even weak farms using it
fairly effectively, though its numbers are insufficient.
The situation with material costs is radically different. Only the first two groups
use material resources profitably, though they have a deficit of these resources.
But Groups 3-5 face significant losses from each additionally-invested rouble of
material costs. The considerable decrease in material costs per production unit
are the condition for making this resource profitable.
This is a rather important conclusion: Material costs in general are effectively
used only by the first two groups. This conclusion seems unexpected at first
glance; due to the obvious deficiency of monetary resources in the enterprises
from Groups 3-5, they need to spend money efficiently. This leads us to a more
detailed analysis of the marginal product of material resources issue, taking into
account that material costs are a fairly complicated aggregation. Apart from the
monetary material costs8, they also contain self-manufactured products (seeds,
feed, dung, etc.), as well as the costs of the resources received as a result of
barter exchange. In this situation, there is an obvious overstatement of the
amount of non-monetary material costs by weak enterprises in their balance
sheets, as they are being evaluated by the self-cost, which is generally
significantly lower than the actual market price.
In the next section we will analyze the allocative efficiency of using monetary
and non-monetary material resources, as well as credits, using equations 2 and 3
as presented above (see Table 3).
3.3 Allocative efficiency of using monetary resources and credits
We included the amount of monetary resources per rouble of material costs in
Table 6. It is apparent that it is much lower than 1 for all groups of this
sampling, and is lowest for the farms in Groups 4 and 5.
8
Purchased inputs.
347
Allocative efficiency of corporate farms in the Leningrad region
Table 6:
Marginal product and efficiency of monetary and non-monetary
material costs by groups of enterprises
Indicators/ Groups
Number of farms in group
Monetary expenditure as
payment for purchased goods
and services per 1 rouble of
material costs, rouble/rouble
Marginal product of
monetary material costs,
rouble/rouble
Marginal product of nonmonetary material costs,
rouble/rouble
1
7
2
25
3
44
4
22
5
28
Average
126
0.623
0.527
0.485
0.430
0.270
0.483
1.10
1.08
0.97
0.94
1.20
1.09
1.55
1.03
0.78
0.60
0.38
0.87
The given calculations demonstrate that if monetary costs are considered as an
independent factor, the farms of all groups use monetary material costs with
a return close to the minimum necessary. The marginal product per rouble of
cash input costs in Groups 1, 2, and 5 is greater than 1 rouble, and Group 5
achieves the highest return. Farms in Group 5 thus experience the most pronounced
cash shortage, and their cash resources are accordingly used with maximum
return.
The allocative efficiency of using monetary resources in Groups 3 and 4 is close
to the optimum. The deficit of monetary resources in these groups is concealed
by their inadequately efficient use. For three groups out of five, the significant
deficit of monetary resources is obvious.
Non-monetary material resources are efficiently used only by Groups 1 and 2,
while in Groups 3 and 5, their marginal product is significantly lower and
declines with the number of the group. As we have already stated, this can be
explained by an overstatement of the amount of non-monetary material costs by
weak enterprises, since they evaluate their own production at its self-cost. For
weak enterprises, the latter is generally much lower than the market price. To be
able to use the non-monetary inputs efficiently, they should significantly
decrease their expenditures per rouble of revenue. By contrast, the enterprises of
Groups 1 and 2 could receive additional profit from increasing the use of nonmonetary inputs. It is important here that the deficit of monetary resources can
be found in farms of all groups.
Analysis of the allocative efficiency of monetary material resources thus clearly
points to the deficit of monetary resources. The reason of this deficit is wellknown – it lies in the disparity of prices, resulting in unfairly low profitability of
agricultural enterprises.
Our conclusion on the deficit of monetary resources is supported by the
allocative efficiency analysis of using short-term credits and loans.
348
David Epstein
In 2001, credits and loans in the Leningrad Region were given to slightly over 130
enterprises, i.e., to approximately two-thirds of the farms. Since highly-specialized
industrial enterprises were excluded from the analyzed body, 113 enterprises
remained.
Equation 3 (Table 3) demonstrates that an increase in credit results in a
statistically important positive influence on the output of farms, and the marginal
product per rouble of credit is higher than one rouble, i.e., there is a certain deficit
of credits. In Table 7, the values of credit’s marginal product for the five groups
of enterprises can be found.
Table 7:
Marginal product per rouble of credits and loans for five groups
of enterprises
Indicators/Groups
Number of farms in group
Marginal product of
credits and loans for
enterprises, rouble/rouble
Source:
1
13
2
28
3
31
4
20
5
21
Average
113
1.51
1.12
1.07
0.93
1.07
1.06
Own calculations.
The average marginal product of credits is somewhat lower than one-third of the
Central Bank of Russia’s rate, which was 24 percent in 2001. The state
reimburses no more than two-thirds of the Central Bank’s interest rate to the
farms. However, commercial credits are provided at higher rates than the Central
Bank’s rate. Thus, with the Central Bank’s rate of 24 percent, the state reimburses
no more than 16 percent, but with the commercial credit rate of, for instance,
26 percent, the farms have to pay 10 percent (the difference between 26 and 16)
themselves. For Groups 3-5 the marginal product is significantly lower than the
required 10 percent. That means that even the subsidized credit rate is too high
for the weaker groups’ farms due to low production profitability.
4 CONCLUSIONS
We can conclude that 1) credit is a deficit resource for farms in the Leningrad
Region, and 2) relatively low profitability for enterprises in Groups 3-5 is an
obstacle to its expansion.
Thus, the results demonstrate a deficit of both labor and monetary resources in
the farms, and a deficit of capital. This deficit cannot be overcome without a
state policy that supports the incomes of agricultural producers, which is now
very weak.
Allocative efficiency of corporate farms in the Leningrad region
349
ACKNOWLEDGEMENTS
The author is sincerely grateful to Prof. Bruce Gardner (Maryland University,
USA) and Prof. Zvi Lerman (Hebrew University, Israel) for their methodical help.
REFERENCES
EPSTEIN, D. (2000): Evaluating the competitiveness of agricultural enterprises. Competitiveness
of agricultural enterprises and farm activities in transition countries, Studies on the
Agricultural and Food Sector in CEE, Vol. 6,Kiel, pp. 10-21.
EPSTEIN, D. (2005): Financial performance and efficiency of corporate farms in North-west
Russia, Comparative Economic Studies, Vol. 47, No. 1, pp. 188-199.