Analysis of Performance Appraisal by Collective Action in
Indian Sugar Industry


By

Dr. Pratapsinh Chauhan
Director and Professor
Smt. R. D. Gardi Institute of Business Management, Saurashtra University
Rajkot – 360 005 GJ
E-mail :
ratapsinh@lycos.com

Prof. D K Shah
Dean
Faculty of Arts, Saurashtra University
Rajkot – 360 005 GJ

Dr. Vijay Pithadia
Kidevices Chair
Department of Management Studies, Karpagam Arts & Science College
Coimbatore-641 021 TN
E-mail :
vijaypithadia@lycos.com / vijaypithadia@mailcity.com
 


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

Selected Bibliography

Acharyulu, A. V. R. (2000), "New Paradigms for Commons", paper presented in 8th Biennial Conference of IASCP, May31-June4, 2000, Indiana University, Bloomington.

Data, S.K., S. Sen and K.B. Gupta (1996)" Contract Theory and Its Application to Sugar Enterprises in India", CMA Research Study, Center for Management in Agriculture, Indian Institute of Management, Ahmedabad.

Data, S.K & K.B. Gupta (1999) "Global Competitiveness & Future of the Indian Sugar Industry," in proceedings of a seminar on Implication of WTO Agreements: threats or Challenge for Indian Agriculture and Agro-business held at Indian Institute of Management, Ahmedabad on Aug 19, 1999.

Data, Sankar (1996), "Management of Coops: A Third Sector Perspective", in Rajagopalan, (ed), "Rediscovering Co-operation, Vol-1, IRMA.

Hardin Garrett & John Baden (1977), "Managing Common", W. H. Freeman & Co., San Francisco.

Isham, Jonathan & Satu Kahkonen (1999), "Institutional Determinants of the Impact of Community Based Water Services", Working Paper No-236, Center for Institutional Reform & the Informal Sector, University of Maryland

Johnson - Jeff Deyton (2000), "Determinants of Collective Action on the Local Commons: a Model with evidence from Mexico," Journal Development Economics, Vol.62, pp.181-208.

Meinzen - Dick, Ruth et all (1999)," What Affects Organization & Collective Action for Managing Resources? Evidence From Canal Irrigation System in India", EPTD Discussion Paper No 61, International Food Policy Research Institute, Washington, D.C.

Milgrom, Paul & John Roberts (1 992), "Economic Organization & Management", Prentice Hall, Englewood Cliffs, New Jersey.

Mueller, D.C. (1989), "Public Choice-II", Cambridge University Press.

Olson, Mancur (1965) "The Logic of Collective Action: Public Goods & the Theory of Groups," Harvard University Press, Cambridge.

Ostrom, Elinor (1993),"Institutional Arrangements and the Commons Dilemma", in Rethinking Institutional Analysis & Development:-Issues, Alternatives, and Choices," International Center for Self-Governance, San Francisco

Ostrom, V. (et all, 1993)," Rethinking Institutional Analysis and Development:-Issues, Alternatives, and Choices," International Center for Self-Governance, San Francisco

Parnell, Edgar (1995), "Reinventing Co-operative: enterprise for the 21st Century", Plunkett Foundation, Oxford

Raju, K. V. (1996),"Dairy Cooperatives & Economic Rationality" in Rajagopalan (ed), "Rediscovering co-operation, Vol. -1, IRMA.

Sandler, Todd (1995),"Collective Action: Theory & Application," The University of Michigan Press

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.
 


Dr. Pratapsinh Chauhan
Director and Professor,
Smt. R. D. Gardi Institute of Business Management, Saurashtra University
Rajkot–360 005 GJ
E-mail :
ratapsinh@lycos.com

Prof. D K Shah
Dean
Faculty of Arts, Saurashtra University
Rajkot–360 005 GJ

Dr. Vijay Pithadia
Kidevices Chair
Department of Management Studies, Karpagam Arts & Science College
Coimbatore-641 021 TN
E-mail :
vijaypithadia@lycos.com / vijaypithadia@mailcity.com
 

Source : E-mail June 16, 2004

 

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