Introduction:
The owed goal of Indian Planning Process is not only the economic development but also the rural transformation. The cooperatives endowed with peoples' participation and govt. support thrive to achieve the basic principle laid down
in the plans. The cooperative agro-processing unit next to textile industry is one of the biggest sector in India. Of these units, the sugar cooperatives dominate the sugar economy for a quite long time and considered as one of the
most effective economic domains in developing the total rural economy.
The sugar industry employed about Rs. 16000 crores having a turnover of Rs. 20000 crores. The total cane price paid to the growers was around Rs. 12000
crores. The total contribution to central and state exchequer was to the tune of Rs. 1600 crores. Further, it provided direct employment to five lakh people and indirectly engaged about 45 million people in its operation which
approximately covering 7.5% of the total rural population. Cooperative sector made this gigantic growth as possible in our country. Out of total 426 factories 249 were under the fold of cooperatives, producing 57.7% of the total
sugar production. The total capacity of sugar factories was 86.17 lakh tones as against the total industry capacity of 150.87 lakh tones. Thus, to make the sector more vibrant and dynamic one has to synchronize and harmonize the
efforts of sugar cooperatives through collective action.
OBJECTIVES AND HYPOTHESIS
The broad objective of the study is to explain the variations in the performance of sugar cooperatives in terms of
procurement of cane, processing and marketing through collective action. The broad hypothesis of this study was that the collective action problem is not resolved satisfactorily in all the sugar cooperatives.
RESEARCH MODUS OPERANDI
In the study tried to explain involvement of the stakeholders in the three basic functions namely, procurement of cane, processing and marketing through collective action process. And
concentrates only on the role of members and employees in these three functions and tries to explain the variation by using regression analysis. The regression model is applied on the spirit of coase theorem. Even though coarse
theorem may not be much useful to explain the variations (Mancur Olson, 1995), in the absence of standard model we try to observe the bargaining power of the stakeholders in maximizing the wealth of the institutions and in sharing
the residual returns. A diagnostic procedure using T- Test is carried out to identify the significant variables. As such, there were 24 variables were identified. To run the regression models, both the explained (Y) and
intermediate (Z) variables are generated and their brief description is presented in the following tables.
Table No 1 Intermediate Variable (Z)
VARIABLE (Z) : FORMULA RATIONAL
PSUPPLY : 1 00-MFSU : It indicates the members' satisfaction and their continuing in supply of cane. DEPOMEMB : NRD/TOTAL MEMBERS The ability of the members to contribute towards equity out of the cane price.
DEPOCAP : NRD/TOTAL CAPACITY : It depicts the NRD contribution to per capacity. PTUIL : % OF CAPACITY UTILISATION : Higher the % of capacity utilization indicates the overall efficiency of the mills
And increase the profitability of the mills PHOUR HOURS : LOSS Lower the % of hour loss reveals the effectiveness in the operation of the mills. SALECAP : SUGAR SALES/ CAPACITY : It explains the ability of the Mills in
sale of sugar per capacity, which influence the profit of the mills. BYCAP : SALE OF BY-PRODCUT/ CAPACITY : It describes the sale of by product per capacity of the mills. SUGARP : AVERAGE SUGAR PRICE : It is an
average price realized by the mills, which comprised of both levy price & open market price. Higher the average price greater the profit. AVCOST : AVERAGE COST OF PRODUCTION : The cost of production includes the cane cost,
conversion cost and overhead costs. Higher the cost lowers the profit. RECOV NET ROCOVERY OF JUICE : It indicates the juice extracted from cane. Higher the juice recovery reveals the efficiency of the firm in terms of
procurement and processing functions.
CONSTRUCTION OF EXPLAINED VARIABLES (Y)
The Explained variables (Y) are constructed as under:
PROCAP1 (y1)= (sugar x sugar price (sugar p)-total cost of production)/Capacity
The PROCAP1 is defined as the net income from the sale of sugar for per capacity (even though in strict accounting terms it may not true).
Higher the PROCAP1 indicates that the mills able to generate more net income by either increasing its average sale price or by reducing the cost of production.
PROCAP2 (y2)= (PROCAP1+miscellaneous income)/capacity
It denotes the gross income earned by the mills after including all the incomes. Increase in the PROCAP2 explains the ability of the mills in sugar sales and byproducts and other incomes.
PROCAP3 (y3)=(PROCAP1+additional
financial benefits provided to the employees (add cap)+cane development expenditure (cdev cap)+additional cane price (smp -sap)/capacity
PROCAP3 includes net income and the additional financial benefits like, bonus,
overtime, retention allowances, leave salary and any other allowances paid to the employees. These benefits are provided to increase the productivity of the employees there by increasing the profitability of the mills. Further, the
cane development expenditure covers basically the various subsidies and incentives given to the members for the production of good quality of cane and regular supply of cane.
PROCAP4 (y4)=(PROCAP2+addcap+cdevcap+(canep - smp))/capacity
In the extended model the PROCAP4 is defined, in addition to PROCAP3, the miscellaneous income is also added.
PROCAP5 (y5)= PROCAP3+purchase tax (tax cap)
In this model, the purchase tax paid by the mills also included so as to test the collective action process in the increased value of the dependent variable.
PROCAP6 (y6)= PROCAP4+taxcap
PROCAP6 is defined
in addition to PROCAP4 and the taxes to find out the collective action process in the gross income with tax paid per capacity.
PROCAP7 (y7) = PROCAP5+depreciation per capacity (dep cap)
In this expanded model, the
depreciation per capacity is used to know the variations in the independent variables along with PROCAP5
INDEPENDENT VARIABLES (X)
Table No- 2 INDEPENDENT VARIABLES (X)
Name of The variable Rational
PUTIL : It indicates the % of capacity utilization. Increase in capacity utilization result in increase in net profit. RATIO (Non-Managerial/Managerial Employees : It explains the proportion of non-managerial
employees (both regular and seasonal) to the total managerial employees. CANEP (smp+sap+ Incentive cane price) It is the actual cane price paid by the mills to the members, which includes the SMP, SAP and the Incentive Cane
Price. SMP : It is the price fixed by the GOI and higher the SMP higher the cane supply and the profit CDEVCAP : It indicates the cane development expenditure per capacity by the mills. The mills used to provide various
subsidies for different purposes PEARLY : It denotes planting of early varieties so as to enable the mills to start the crushing early and increase the production.
PMARGIN2 : The increase in membership of both small and marginal farmers led to fluctuation in cane supply. KRABI2 : The flexibility in cropping pattern influence the cane cultivation PWELF : It includes the welfare
services provided by the mills like education, schools, hospitals, community hall and other social services to the members.
ASALARY : It is the basic salary and wages excluding the allowances which would influence the profitability ADDCAP : This variable denotes the additional financial benefits provided to the employees in the form of bonus,
overtime, retaining allowances, leave salary and other allowances. TAXCP : It indicates the purchase tax paid by the mills for per capacity on the cane purchased by them. DEPCAP : It denotes the depreciation per capacity
created by the mills, which in turn increases the cost of production and reduces the profitability of the mills. INTCAP : It explains the interest payment made by the mills to the financing agencies, which reduces the
profitability of the mills. VARCAP : It is the subsidy given to the farmers to plant early varieties and high sugar varieties. More the subsidies more the recovery and influence the profitability of the mills
AINCENT : It is the additional cane price paid to the growers to induce them to cultivate more cane
MODEL SPECIFICATION :
The model specification for the regression analysis of this study is as follows:
Model 1=y1=a+b(putil)+b(ratio)+b(smp)+b(pwelf)+b(addcap)+b(var cap)+m Where y1=procap1 Putil= % of capacity utilization Ratio= proportion of non-managerial employees to managerial employees
SMP= statutory minimum price Pwelf= % of members benefited under various welfare schemes of the mills Add cap= additional financial benefits provided by the mills for per capacity
Var cap= varietal subsidy provided for per capacity by the mills Model 2= y2=a+b(putil)+b(ratio)+b(smp)+b(pwelf)+b(add cap)+b(var cap)+m Where y2=procap2
Model3=y3=a+b(putil)+b(ratio)+b(s mp)+b(pwelf)+b(add cap)+b(var cap)+m Where y3=procap3 Model4= y4=a+b(putil)+b(ratio)+b(smp)+b(pwelf)+b(add cap)+b(varcap)+m Where y4=procap4
Model 5= y5=a+b(putil)+b(ratio)+b(smp)+b(pwelf)+b(addcap)+b(varcap)+m Where y5=procap5 Model 6= y6=a+b(putil)+b(ratio)+b(smp)+b(pwelf)+b(addcap)+b(varcap)+m Where y6=procap6
Model 7= y7=a+b(putil)+b(ratio)+b(smp)+b(pwelf)+b(addcap)+b(varcap)+m Where y7=procap7
After identifying variables, there were 132 estimates were made to find out the fitness of the model with the use of different
independent variables. T-Test: Prior to running the regression model, a diagnostic test is conducted using the T-test and results are presented in the table below:
Table No.3 : Results of T- Test
Name of the variable Mean (N=18) Mean (N=11)
SMP : 524.60 554.98 * CONTCAP : 125.81 166.25 ** PCONT : 75.71 85.90 * INCENT : 26.71 10.89 * KCASH : 8.72 36.67 *
KRABI : 24.8 0.017** PCDMEM : 39.47 39.91 * PCDAREA : 46.67 0.017 ** MANCAP : 0.008 330545 * RATIO : 75.98 170.41 * CANE : 246055.56 28278.05 * DUR : 142.92 170.41 *
SUGAR : 20435.44 28278.05 * PUTIL : 87.48 106.55 ** SALECAP : 127716.21 153004.54 * PROCAP2 : -30271.08 27447.58 ** PROCAP3 : 7414.14 77649.64 **
PROCAP4 : 11012.20 80231.02 ** PROCAP5 : 14488.55 85499.63 ** PROCAP6 : 18086.62 88080.41 ** PROCAP7 : 19020.46 88667.23 **
Note: * Denotes the significant level at 90%
** Denotes the significant level at 99%
Prior to the estimation of the regression models T-test were performed on all variables to find out any significant difference between means. It was found in the above table that all
24 variables were significant at different levels of one tail test.
SPECIFICATION OF THE MODELS
Two basic models are developed to explain the variations in (1) net income per capacity and gross income per
capacity. These two basic models were re-estimated taking into account (1) All other financial commitment except the statutory obligation per capacity, (2) Tax per capacity (3) Depreciation per capacity and (4) Interest paid per
capacity. Alternative specifications of these features were added in stages to the dependent variables. Such alternative specifications of the model are expected to capture certain behavioral patterns of interest groups in the
sugar mills. While the left-hand side of the variable is the net worth of sugar mills to be maximized, the right hand side of the variables tries to influence their share of the net worth of the firms. Given the divergent interest
groups in the sugar mills, it is expected that their relative influence in extracting their share depend on the distribution of their weights in the factory. In the formulation of the model, we assume that interest groups are on
the left-hand side. The important interest groups are employees represented by Addcap, non-managerial labor, members represented by Pwelf and SMP, subsidy for growers, organization as whole represented by putil.
The
interest of the government has to be fulfilled in the form of tax payment. Therefore addition of tax to the net income or the gross income on the right hand side of the equation, with same independent variables on the right hand
side will bring to light the interest groups very sharply. If the coefficients are highly significant, we can conclude that government interest is important in the sugar mills, which is captured in the behavioral equation (5) and
(6). On the other hand, if the coefficients are similar even with the addition of tax payment, one could conclude that the interest of the government is minimal in the sugar mills.
REGRESSION ANALYSIS
Model – 1 Dependant variable: Net income per capacity Model 1= y1=a+b(putil)+b(ratio)+b(smp)+b(pwelf)+b(addcap)+b(varcap)+m Where y1=procap1 Putil= % of capacity utilization
Ratio= proportion of non-managerial employees to managerial employees SMP= statutory minimum price Pwelf= % of members benefited under various welfare schemes of the mills
Addcap= additional financial benefits provided by the mills for per capacity Varcap= varietal subsidy provided for per capacity by the mills
Table No.4 : Estimate of Regression Model – 1
Variable Parameter estimate T- value
Intercept : -406927 -6.170 Putil : 874.90 2.810* Ratio : -81.72 -0.859 SMP : 653.12 4.838** Pwelf : 847.05 2. 655*
Addcap : -6.06 -2.514* Varcap : -7.49 -2.098* Adjusted R- square - 0.66. Note: ** indicates 99% of the significant level by one tail test * Denotes the 95% of the significant level by one tail test
Interpretation for Regression Model - 1
The regression estimates shown in table 3.2 show that of the six independent variables, five variables were statistically significant from zero. It is seen that an increase
in capacity utilization contributes positively to the increase in the net income of the sugar mills. The coefficient estimate shows that one- percent increase in the capacity utilization results in an increase in net income of the
sugar mill by Rs.874., which is statistically significant. We have already seen elsewhere, that average capacity utilization was around 95 percent. Any additional increase in capacity utilization will further contribute to the net
income of the firms. < BR> The second variable showed that an increase in the proportion of non-managerial employees to the managerial employees contributed to the reduction in the net income of the sugar mills. While it
is not statistically different from zero, the sign is expected one. An increase in the proportion of non-managerial employees to the managerial employees reduces the net income by Rs.81.72.
Regression Model - 2 Dependant Variables: Gross Income Per Capacity Model 2= y2=a+b(putil)+b(ratio)+b(smp)+b(pwelf)+b(addcap)+b(varcap)+m Where y2=procap2 Putil= % of capacity utilization
Ratio= proportion of non-managerial employees to managerial employees SMP= statutory minimum price Pwelf= % of members benefited under various welfare schemes of the mills
Addcap= additional financial benefits provided by the mills for per capacity Varcap= varietal subsidy provided for per capacity by the mills
Table No. 5 : Estimate of Regression Model – 2
Variable Parameter estimate T- value
Intercept : -40501 --6.141 Putil : 883.34 2.287* Ratio : -77.46 -0.815 SMP : 654.46 4.847* * Pwelf : 792.33 2.483*
Addcap : -5.88 -2.442* Varcap : -7.78 -2.178* Adjusted R- square - 0.66. Note: ** indicates 99% of the significant level by one tail test * Denotes the 95% of the significant level by one tail test
Interpretation for Regression Model – 2
In the regression model-2, the dependant variable is changed to the gross income per capacity with the same independent variables to find out the influence of those
variables gross income per capacity. The regression estimates shown in table- 2 show that of the six independent variables, five variables were statistically significant from zero. It is seen that an increase in capacity
utilization contributes positively to the increase in the net income of the sugar mills. The coefficient estimate showed that one- percent increase in the capacity utilization results in an increase in net income of the sugar mill
marginally by Rs.883.34, which is statistically significant at 95 percent level. This estimate is higher than the estimate in the previous model.
One of the significant variables in the model is the welfare benefit enjoyed
by the members of sugar mills. Though the cost of this benefit would contribute to the decline in the net income of sugar mills, the regression estimate indicated positive contribution to the gross income of the sugar mills. One-
percent increase in the proportion of people benefited contributes Rs. 792.33 increase in the gross income per capacity of the sugar mills. The value estimated for the second model is lower than the value found for model -1 at Rs.
847.05.
As expected, the additional financial benefits given to the employees reduce the gross income of the sugar mills by Rs.5.88, which is lower than previous model estimate at -7.49. It is expected that the varietal
subsidy would contribute to the increase in the gross income of the sugar mills in terms of early crushing of cane to get high juice content. However, sign is contrary to the expectation. The reason would be that higher area is
already covered under early variety and as a result late crushing takes place by the sugar mills. An increase in the area of early varietal subsidy reduces gross income of sugar mills by Rs. 7.78, which is similar to the value
found for the model -1 at Rs.7.49. The overall fit of the model is reasonable as 66 percent of variation is explained by s independent variables.
Regression Model - 3
Model 3= y3=a+b(putil)+b(ratio)+b(smp)+b(pwelf)+b(addcap)+b(varcap)+m Where y3=procap3 Putil= % of capacity utilization Ratio= proportion of non-managerial employees to managerial employees
SMP= statutory minimum price Pwelf= % of members benefited under various welfare schemes of the mills Addcap= additional financial benefits provided by the mills for per capacity
Varcap= varietal subsidy provided for per capacity by the mills
Table No 6 Estimate of Regression Model –3
Variable Parameter estimate T- value
Intercept : -432705 -6.228
Putil : 1331.67 4.061* Ratio : -162.42 -1.622 SMP : 694.70 4.885* * Pwelf : 759.83 2.261* Addcap : -4.3507 -1.714* Varcap : -6.49 -1.725* Adjusted R- square - 0.74.
Note: ** indicates 99% of the significant level by one tail test * Denotes the 95% of the significant level by one tail test
In the regression model-3, in addition to net income per capacity, other additional financial
benefits given to employees, incentive price to farmers and cane development expenditure were added in order to maximize net income of sugar mills.
Interpretation for Regression Model -3
The regression
estimates shown in table- 3 show that of the six independent variables, five variables were statistically significant from zero. It is seen that an increase in capacity utilization contributes positively to the increase in the net
income of the sugar mills. The coefficient estimate showed that one- percent increase in the capacity utilization results in an increase in net income of the sugar mill by Rs. 1331.67 which greater than the value for model -2 and
model -1, which statistically significant at 99 percent level.
The second variable showed that an increase in the proportion of non-managerial employees to the managerial employees contributed to the reduction in the net
income of the sugar mills. While it is not statistically different from zero, the sign is expected one. An increase in the proportion of non-managerial employees to the managerial employees reduces the net income quite large by Rs.
162.42, which is higher than value found for the model -1 and model-2. It is expected that an increase in the price of statutory minimum price would result in the reduction in the net income of the sugar mills. However, the
estimate showed that an increase in the SMP per ton by one unit brings about an increase in the gross income per capacity by Rs. 694.70, which is much larger than value estimated for model- 1, and model -2, which is statistically
highly significant. The inference may be drawn that the SMP induces farmers to supply good quality of cane to the mills thereby enabling the mills to get high recovery from the cane supply.
Regression Model –4
Model 4= y4=a+b(putil)+b(ratio)+b(smp)+b(pwelf)+b(addcap)+b(varcap)+m Where y4=procap4 Putil= % of capacity utilization Ratio= proportion of non-managerial employees to managerial employees
SMP= statutory minimum price Pwelf= % of members benefited under various welfare schemes of the mills Addcap= additional financial benefits provided by the mills for per capacity
Varcap= varietal subsidy provided for per capacity by the mills
Table No 7 Estimate of Regression Model – 4
Variable Parameter estimate T- value
Intercept : -430828 -6.138
Putil : 1340.11 4.045 ** Ratio : -158.17 -1.563 SMP : 696.04 4.845* * Pwelf : 705.11 2.077* Addcap : -4.18 -1.629 Varcap : -6.78 -1.783 Adjusted R- square - 0.74.
Note: ** indicates 99% of the significant level by one tail test * Denotes the 95% of the significant level by one tail test
Interpretation for Regression Model – 4
The regression estimates shown in
table- 4 show that of the six independent variables, only three variables were statistically significant from zero. The variable the ratio of non-managerial employees to the total managerial employee emerges with some significance
though not statistically different from zero. The variables Addcap and Varcap were not statistically significant compared to earlier models. It is seen that an increase in capacity utilization contributes positively to the increase
in the net income of the sugar mills. The coefficient estimate showed that one- percent increase in the capacity utilization results in an increase in net income of the sugar mill by Rs. 1340.11, which greater than the value for
earlier models and it was highly statistically significant at 99 percent level.
The second variable showed that an increase in the proportion of non-managerial employees to the managerial employees contributed to the
reduction in the net income of the sugar mills. While it is not statistically different from zero, the sign is expected one. An increase in the proportion of non-managerial employees to the managerial employees reduces the net
income quite large by Rs. 158.17, which is higher lower than value found for the model 3 but greater than the value for model -1 and model-2. It is expected that an increase in the price of statutory minimum price would result in
the reduction in the net income of the sugar mills. However, the estimate showed that an increase in the SMP per ton by one unit brings about an increase in the gross income per capacity by Rs. 696.04, which is much larger than
value estimated for all earlier models, which is statistically highly significant. The inference may be drawn that the SMP induces farmers to supply good quality of cane to the mills thereby enabling the mills to get high recovery
from the cane supply.
One of the significant variables in the model is the welfare benefit enjoyed by the members of sugar mills. Though the cost of this benefit would contribute to the decline in the net income of sugar
mills, the regression estimate indicated positive contribution to the gross income of the sugar mills. One- percent increase in the proportion of people benefited contributes Rs. 705.11 increase in the gross income per capacity of
the sugar mills.
Regression Model -5
Model 5= y5=a+b(putil)+b(ratio)+b(smp)+b(pwelf)+b(addcap)+b(varcap)+m Where y5=procap5 Putil= % of capacity utilization
Ratio= proportion of non-managerial employees to managerial employees SMP= statutory minimum price Pwelf= % of members benefited under various welfare schemes of the mills
Addcap= additional financial benefits provided by the mills for per capacity Varcap= varietal subsidy provided for per capacity by the mills
Table No 8 Estimate of Regression Model – 5
Variable Parameter estimate T- value
Intercept : -426599 -6.294 Putil : 1410.91 4.410 ** Ratio : -164.73 -1.686 SMP : 685.33 4.940* * Pwelf : 714.15 2.178*
Addcap : -4.30 -1.737 Varcap : -6.77 -1.846 Adjusted R- square - 0.74. Note: ** indicates 99% of the significant level by one tail test * Denotes the 95% of the significant level by one tail test
Interpretation for Regression Model – 5
The regression estimates shown in table- 5 show that of the six independent variables, only three variables were statistically significant from zero. The variable the ratio of
non-managerial employees to the total managerial employee emerges with some significance though not statistically different from zero. The variables Addcap was not statistically significant and Varcap was emerging significant not
statistically different from zero.
The second variable showed that an increase in the proportion of non-managerial employees to the managerial employees contributed to the reduction in the net income of the sugar mills.
While it is not statistically different from zero, the sign is expected one. An increase in the proportion of non-managerial employees to the managerial employees reduces the net income quite large by Rs. 164.73, which is higher
than the value found for the previous modes.
It is expected that an increase in the price of statutory minimum price would result in the reduction in the net income of the sugar mills. However, the estimate showed that an
increase in the SMP per ton by one unit brings about an increase in the gross income per capacity by Rs. 685.33, which is lower, the value estimated for all earlier models, which is statistically highly significant. The inference
may be drawn that the SMP induces farmers to supply good quality of cane to the mills thereby enabling the mills to get high recovery from the cane supply.
Regression Model - 6
Model 6= y6=a+b(putil)+b(ratio)+b(smp)+b(pwelf)+b(addcap)+b(varcap)+m Where y6=procap6 Putil= % of capacity utilization Ratio= proportion of non-managerial employees to managerial employees
SMP= statutory minimum price Pwelf= % of members benefited under various welfare schemes of the mills Addcap= additional financial benefits provided by the mills for per capacity
Varcap= varietal subsidy provided for per capacity by the mills
Table No 9 Estimate of Regression model – 6
Variable Parameter estimate T- value
Intercept : -424722 -6.163
Putil : 1419.35 4.363 ** Ratio : -160.48 -1.615 SMP : 686.68 4.867* * Pwelf : 659.43 1.978* Addcap : -4.13 -1.639 Varcap : -7.06 -1.892 Adjusted R- square - 0.74.
Note: ** indicates 99% of the significant level by one tail test * Denotes the 95% of the significant level by one tail test
Interpretation for Regression Model –6
The regression estimates shown in table-
6 show that of the six independent variables, only three variables were statistically significant from zero. The variables Addcap was not statistically significant and Varcap was emerging significant not statistically different
from zero. It is seen that an increase in capacity utilization contributes positively to the increase in the net income of the sugar mills. The coefficient estimate showed that one- percent increases in the capacity utilization
results in an increase in net income of the sugar mill by Rs. 1419.35, which greater than the value found for earlier models and it was highly statistically significant at 99 percent level.
The second variable showed that
an increase in the proportion of non-managerial employees to the managerial employees contributed to the reduction in the net income of the sugar mills. While it is not statistically different from zero, the sign is expected one.
An increase in the proportion of non-managerial employees to the managerial employees reduces the net income quite large by Rs. 160.48. It is expected that an increase in the price of statutory minimum price would result in the
reduction in the net income of the sugar mills.
Regression Model - 7
Model 7= y7=a+b(putil)+b(ratio)+b(smp)+b(pwelf)+b(addcap)+b(varcap)+m Where y7=procap7 Putil= % of capacity utilization
Ratio= proportion of non-managerial employees to managerial employees SMP= statutory minimum price Pwelf= % of members benefited under various welfare schemes of the mills
Addcap= additional financial benefits provided by the mills for per capacity Varcap= varietal subsidy provided for per capacity by the mills
Table No 10 Estimate of Regression Model – 7
Variable Parameter estimate T- value
Intercept -402982 -5.821 Putil 1385.31 4.239 ** Ratio -177.80 -1.781 SMP 651.90 4.600* * Pwelf 674.33 2.014* Addcap -3.92 -1.551 Varcap -6.82 -1.821
Adjusted R- square - 0.74. Note: ** indicates 99% of the significant level by one tail test * Denotes the 95% of the significant level by one tail test
Interpretation for Regression Model – 7
The
regression estimates shown in table- 7 show that of the six independent variables, only three variables were statistically significant from zero. The variables Addcap was not statistically significant and Varcap was emerging
significant not statistically different from zero. It is seen that an increase in capacity utilization contributes positively to the increase in the net income of the sugar mills. The coefficient estimate showed that one- percent
increases in the capacity utilization results in an increase in net income of the sugar mill by Rs. 1385.31 and it was highly statistically significant at 99 percent level.
The second variable showed that an increase in the
proportion of non-managerial employees to the managerial employees contributed to the reduction in the net income of the sugar mills. While it is not statistically different from zero, the sign is expected one. An increase in the
proportion of non-managerial employees to the managerial employees reduces the net income quite large by Rs. 177.80. It is expected that an increase in the price of statutory minimum price would result in the reduction in the net
income of the sugar mills. However, the estimate showed that an increase in the SMP per ton by one unit brings about an increase in the gross income per capacity by Rs. 651. 90, which are, lower the value estimated for all earlier
models, which is statistically highly significant. In the regression model -7, the coefficient of putil is demur compared to model 6
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Acknoeledgement:
We reconcer Michael J. Rubach [University of Central Arkansas], John Williams [University of Leicester], Andrew Harver [The University of North Carolina at Charlotte], Jack Drexler [Oregon State University], Andrea Arcarola
[St. Francis College], David P. Miller [The University of Oklahoma], Ake Nordlund [Copenhagen University], Vivian Lau [The Hongkong Polytechnic University], Yvonne C. Montoya Zamora [Community Colleges of Spokane], Jodi L.
Hutchinson [Saginaw Valley State University], George C. Stonikinis Jr., [Long wood University], Terri Gutierrez [University of Northern Colorado], John M. Thornton [Washington State University–Tri-cities], Paul H. Schwinghammer
[Minnesota State University] & Robin Delong [Winona State University],B. Sudhakar [PSGR Krishanmmal College for Women], A. Kumar [Bhavnagar University], R.S. Shah [Narmada College of Management], Daxa Gohil [Saurashtra
University], M.S. Subhash [Kaushali Institute of Management Studies], S. K. Mangal [University of Rajas than], Umesh Holani [Jiwaji University], M. G. Korgaonkar [Indian Institute of Technology – Bombay], M. G. Shirahatti [Lala
Lajpatrai Institute of Management] & Asha Panch Pandey [Institute of Business Management & Research] for valuable coadjuvancy as well as MBA Students Asim Kumar, Ashish Kumar Gupta, Rajesh Shukla, Rajish Kumar, Rohit Shah,
Swet Saxena & Vivek Srivastava Aiswarya Murali, Anoop Thyagrajan, Arsha Anthony, Asna A. H., Chaitanya M.P.,Krishnaveni J., Maneesh Sasidharan, Namita Valsraj, Nithya J. Poornima S., Praveen Jose, Tripti Kishan, Viji N. &
Vinish N. Varghese Anchal Paliwal, Anurag Aggrawal, Ashish Rastogi, Ankit Sinha, Bhaskar Pant, Gurdeep Kaur, Geetima Arora, Namita Khanna, Shainda Khan & K.M. Vandana Harikesh V., Reena M.C., Sindhu Joseph, Varun T.K., Sherin
M.A., Lumina Joy & Sabira Ali. |