DETERMINANTS OF TECHNICAL EFFICIENCY OF COOPERATIVE SUGAR MILLS OF UTTAR
PRADESH: AN APPLICATION OF DATA ENVELOPMENT ANALYSIS AND TOBIT
REGRESSION ANALYSIS
Vivek Kumar Mishra
Junior Research Fellow, Department of Economics, University of Allahabad, Allahabad (India)
ABSTRACT
The paper attempts to identify the determinants of technical efficiency of cooperative sugar mills in Uttar Pradesh. To achieve the objectives of the study and to come up with trustworthy and unbiased results, application of Tobit Regression Analysis is the key addition in this version of the paper, which is an econometric approach developed by James Tobin (1958) [2]. OTE Inefficiency, PTE Inefficiency, Scale Inefficiency, Super Efficiency (CRS), are the response variables in this analysis so, this paper uses 4 different models for each response variable. While Cane crushed, Permanent worker, Seasonal worker, Age, Sugar recovery rate (sugar produced from per quintal of cane crushed), Capacity utilization, IRS dummy (implies whether mill is operating under IRS condition or not) are the predictor variables used for every model. At 5 % level of significance, all the explanatory variables except seasonal workers have been found significant to determine the overall technical efficiency of the mills. The paper concludes with the remarks since management cannot do much in respect to number of permanent workers (due to labour laws), efforts should be concentrated on improvement in sugar recovery rate, improvement in installed capacity and capacity utilization, better maintenance of machinery, advancement in technology and efficient utilization of available manpower. Every mill has to be efficient because in this most competitive world, this is the question of survival.
Keywords: Data Envelopment Analysis, Returns to Scale, Scale Efficiency, Super Efficiency, Technical Efficiency, Tobit Regression.
I. INTRODUCTION
In India, Uttar Pradesh holds the first rank in the sugarcane production and second in sugar production after Maharashtra. With 4.5 million cane growers, contribution of around 18% to the state domestic product from agriculture sector and Rupees 400 crore to the state and national exchequer, sugar industry in Uttar Pradesh plays very crucial role in the socio-economic and political arena. With the establishment of Pratappur (Deoria) sugar mill in 1903, the evolution of modern sugar mills begun. However the pace of growth of setting up of sugar mills in India in general and in Uttar Pradesh in particular was very slow till 1932, when government of India decided to protect the domestic sugar sector. However, till 1957, the expansion of sugar mills was limited
to the private sector only. It was 1957-58 when the first cooperative sugar mill was established in bajpur (Nainital). In 1971, the private sector also came into the existence.
Around 60 years have been passed when first cooperative sugar mill was established and 50 years have been passed when the Uttar Pradesh cooperative sugar factories federation was established at Lucknow in 1963. The establishment of cooperative sugar mills was begun with the aim to boost the cooperative sugar sector as well as to improve the condition of sugarcane growers. However at present neither the condition of cooperative sugar mills nor the condition of the growers could be regarded satisfactory. The problems of under capacity utilization, low profitability, mounting cane price arrears, grower’s suicide and burning of sugarcane in the fields have become routine news. The big question; however is to identify the root causes of unsatisfactory performance of sugar industry particularly the cooperative sugar sector working in Uttar Pradesh.
II. OBJECTIVES OF THE STUDY
This paper is an extended version of a paperpresented by the author at the International Conference of recent Innovations in Science, Engineering and Management (ICRISEM 2015) at Jawahar Lal Neharu University, New Delhi, on Nov 22, 2015 and further published [1]. The present paper incorporates the suggestions and aims to explore the determinants of technical efficiency of cooperative sugar mills, which was not the part of the earlier paper. Hence this paper attempts to identify the determinants of technical efficiency of cooperative sugar mills of Uttar Pradesh.
III. METHODOLOGY OF THE RESEARCH 3.1 Data Envelopment Analysis
Data Envelopment Analysis is a non parametric optimization technique to assess the technical efficiency of any decision making unit. Being a non parametric technique it is free from the assumptions imposed with the distribution of data, making it relatively more convenient than its parametric counterpart Stochastic Frontier Analysis. The approach of Data Envelopment Analysis is based on the idea of Farrell’s (1957) [3] efficiency Frontier and further developed by Charnes, Cooper and Rhodes (1978) [4] and modified by Banker, Charnes and Cooper (1984) [5].
Following Zhu J (2008)[6], the basic input oriented (where inputs are minimized and outputs are kept at current level) the Charnes Cooper and Rhodes Model (CCR model) is based on the assumption of constant returns to scale and can be defined as:
* = Min S.T.
jxij ≤ xio i = 1 ………m (1)
j ykj ≥ yko k = 1………..s
j ≥ 0 j = 1………..n
* = efficiency of firm n = the number of DMUs,
ykj = amount of kth output of DMU j,
xij = amount of ith input of DMU j,
xio = ith input of DMUO
yko = kth output of DMUO
j = nonnegative scalar
The banker, Charnes and Cooper’s Model (BCC model), which assumes variable returns to scale includes an additional convexity constraint on J i.e., J = 1 to ensure that variable returns to scale assumption is satisfied.
3.2 Tobit Regression Analysis
Identification of various factors that affect mill’s efficiency is a major objective of this paper. Application of Tobit Regression Analysis to identify the determinants of technical efficiency is the key addition in this version of the paper. To achieve the objectives of the study, Tobit regression analysis is find appropriate, as technical efficiency or inefficiency score lies between the range of 0 and 1, implying it is left censored and right censored both (except super efficiency case where scores are only left censored). In such a condition censored regression analysis is necessary to obtain unbiased result, simple regression analysis like OLS cannot fulfill this requirement. The Tobit regression analysis[2] can be defined as follows:
y* = Xi + εi yi = y* if 0 ≤ yi* ≤1 yi = 0 if yi*< 0
yi = 1 if y*>1 (except super efficiency case)
Where yi = inefficiency score of ith model and yi* is a latent response variable. Xi is a vector of predictor variables and is a vector of parameters. εi ~ N(0, 2)3 Implying that εi is an independently distributed error term assumed to be normally distributed with zero mean and constant variance.
IV. EMPIRICAL RESULTS
With the help of cross sectional data (2007-08) related to the four inputs (sugarcane, permanent employee, seasonal employee and installed capacity) and one output (sugar produced) Mishra V. K.(2015)[1] applied various models of data envelopment analysis to assess the technical efficiency scores of every sugar mill. The results in the form of Overall Technical Efficiency, Pure Technical Efficiency, Scale Efficiency and Super Efficiency have been summarized in the Table1.
Table 1: Results of Data Envelopment Analysis
1 2 3 4 5 6 7 No. Mill CCR Model:
Overall Technical Efficiency (OTE)
BCC Model:
Pure Technical Efficiency (PTE)
Scale Efficiency (SE)
Super Efficiency (CRS)
Returns to Scale
1 Anpc 0.913 0.927 0.985 0.913075 IRS
2 Bagc 1 1 1 1.476678 CRS
3 Morc 0.974 0.99 0.984 0.973814 IRS
4 Ramc 1 1 1 1.167201 CRS
5 Sarc 1 1 1 1.023948 CRS
6 Belc 0.992 1 0.992 0.991998 DRS
7 Bilc 0.95 0.993 0.957 0.95041 IRS
8 Bisc 0.901 0.942 0.956 0.900754 IRS
9 Gajc 0.924 0.94 0.984 0.924459 IRS
10 Powc 0.914 1 0.914 0.914051 IRS
11 Purc 0.879 0.927 0.948 0.879239 IRS
12 Samc 0.963 0.968 0.995 0.963382 DRS
13 Satc 0.92 1 0.92 0.919729 IRS
14 Semc 0.964 0.98 0.984 0.963512 IRS
15 Snec 1 1 1 1.074092 CRS
16 Tilc 0.919 0.94 0.978 0.919142 IRS
17 Ghoc 0.875 0.938 0.933 0.875165 IRS
18 Mahc 0.89 0.92 0.967 0.890064 IRS
19 Nanc 0.895 0.913 0.98 0.895008 IRS
20 Sulc 0.93 1 0.93 0.929658 IRS
21 Kaic 0.904 1 0.904 0.903966 IRS
22 Badc 0.878 1 0.878 0.878471 IRS
23 Rasc 0.814 1 0.814 0.813642 IRS
Mean 0.930 0.973 0.957 0.963
Source: Mishra Vivek Kumar (2015)[1], International Journal of Science Technology and Management, Vol. 4, Issue 11, November 2015, p. 235.
In the table 2, column 2 contains the coded name of cooperative sugar mills working in the Uttar Pradesh, while column 3 to 7 represents the Overall Technical Efficiency, Pure Technical Efficiency, Scale Efficiency, Super Efficiency scores and Returns to scale of corresponding sugar mills respectively. The results revealed that out of 23 cooperative sugar mills there were only 4 sugar mills (Bagc, Ramc. Sarc and Snec) working efficiently under the criteria of Overall Technical Efficiency and Scale Efficiency while Rasc is least efficient mill under both criteria (0.814, 0.814). The Rasc mill however has scored Pure Technical Efficiency of 1, indicating that Rasc has been severely affected by its scale of operation or disadvantageous plat size. 11 sugar mills have been identified as Pure Technical Efficient mill having PTE score 1. Since the BCC model excludes any scale effect
from the pure technical efficiency, it is obvious to have relatively large number of PTE efficient mills than that of OTE efficient mills.
One shortcoming of CCR and BCC model is that it cannot distinguish between the technically mills having efficiency score 1. To overcome this shortcoming in this study, the Super Efficiency model of DEA has been applied which can distinguish between efficient mills and can identify the extreme efficient mills. The super efficiency scores have been illustrated in the column 6 of Table 1. The difference between the super-efficiency and the envelopment models is that this excludes the DMU under evaluation from the reference set in the super efficiency models. i.e., the super-efficiency DEA models are based on a reference technology constructed from all other DMUs (Zhu J 2008)[7]. One could observe from the Table 1 that Bagc could be termed as top performer amongst 23 sugar mills with the highest super efficiency score (1.476) followed by Ramc (1.167), Snec(1.074) and Sarc(1.023). While Rasc is the least efficient mill having CRS super efficiency score of about 0.813, followed by Ghoc (0.875) Badc (0.878) Purc (0.879).
Most of the mills (74 %) are facing IRS, implying that these mills are too small to reap its full potential, so these mills must increase their plant size to reach their optimum level. This result suggests that uneconomic plant size is one of the major reasons behind unsatisfactory performance of cooperative sugar mills of Uttar Pradesh.
Since 74% of the mills are operating under IRS condition, rest of the analysis is based on VRS assumption.
(Mishra V. K.)[6
The mean efficiency scores indicates that under CCR model the mean OTE score of sugar mills is 0.930, which indicates that around 7 % of inputs could be saved if utilized efficiently, without harming the production level.
The mean SE score of 0.957 shows that there are at least 5 % scale inefficiency due to disadvantageous plant size. The results reveal that in the absence of any scale inefficiency in the operation of sugar mills, there is about 3% technical inefficiency in the operations of sugar mills as indicated be mean PTE score 0.973.
The question however is how to identify key factors or determinants of the technical efficiency/inefficiency of cooperative sugar mills working in the Uttar Pradesh. In order to identify key factors that affect efficiency scores of mills, it is decided to utilize “Tobit Regression Analysis”. OTE Inefficiency, PTE Inefficiency, Scale Inefficiency, Super Efficiency (CRS), are the response variables in this analysis so, this paper uses 4 different models for each response variable. While Cane crushed, Permanent worker, Seasonal worker, Age, Sugar recovery rate (sugar produced from per quintal of cane crushed), Capacity utilization, IRS dummy (implies whether mill is operating under IRS condition or not) are the predictor variables used for every model.
Table 2 provides descriptive statistics of the variables, used in this analysis.
Table 2: Description of Variables Used in Tobit Regression
Variable Mean Std. Dev. Min Max OTE Inefficiency (=1- OTE) .0696087 .0499779 0 .186
PTE Inefficiency (=1-PTE) .0270435 .032882 0 .087
Scale Inefficiency (=1-SE) .0433478 .0468107 0 .186 Super Efficiency (CRS) .9626783 .1340804 .8136 1.4767 Cane crushed (Lakh quintals) 28.58522 13.41659 9.97 53.05
Permanent worker (No.) 218.3043 55.87521 77 291
Seasonal worker (No.) 381.0435 90.23025 197 525
Age in 2007 28.86957 7.325574 18 47
Sugar recovery rate % 8.920435 .4217223 7.78 10
Capacity utilization % 88.34783 9.073481 72 104
IRS Dummy 0 1
(Source: Author’s calculations)
The Tobit regression analysis has been conducted with the help of software STATA 10. The results of Tobit regression analysis are shown in the Table 3 below:
Table 3: Results of Tobit Regression Analysis OTE Inefficiency
(=1- OTE)
PTE Inefficiency (=1-PTE)
Scale Inefficiency (=1-SE)
Super Efficiency (CRS)#
Cane Crushed -0.0012072* 0.0046127* -0.0034666* 0.0027662 Permanent worker 0.0002864* 0.0003032 0.0002938** -0.0012791**
Seasonal worker -0.0000587 0.0000818 -0.0001754** 0.0002677
Age in 2007 0.0012162** 0.0011657 -0.0002522 0.0000139
Recovery Rate -0.076398* -0.0499175*** -0.0436653* 0.0100132 Capacity Utilization -0.0006768** -0.0032446** 0.0000275 0.0047036***
IRS Dummy -0.0012072* 0.1656612* -0.0210659 -0.0777456
Cons. .7232976 .3418298 .5494284 .6130177
Prob > Ҳ2 0.0000 0.0004 0.0000 0.0002
(Source: Author’s calculations.)
Note: *significant at 1%, ** significant at 5%, *** significant at 10%. #Tobit regression for super efficiency is only left censored.)
The values of Prob > Ҳ2 shown in the table imply that the predictor variables of the model together satisfactorily explain the variations in the response variable. It is evident from the table that cane crushed has significant (at 1%) negative impact on OTE inefficiency and scale inefficiency. However it has significant (at 1%) positive impact on PTE inefficiency. This result implies that although increase in cane crushed may cause PTE inefficiency, it has great ability to remove any scale inefficiency. Since relative magnitude of coefficient for scale inefficiency is grater than for PTE inefficiency, increase in cane crushed ultimately reduces OTE inefficiency.
The number of Permanent workers has significant (at 5%) positive impact on OTE inefficiency and scale
mill. This result can be supported by significant (at 5%) negative impact of number of Permanent workers on super efficiency. On the other hand coefficient of seasonal workers is significant (at 5%) for scale inefficiency only, indicating that increase in seasonal workers can improve the scale efficiency of mill. Age of mill is another factor that significantly (at 5%) leads to OTE inefficiency. In other words, age of mill seems to be contributing negatively to the overall technical efficiency. However for other efficiency scores age is not a significant factor.
Sugar recovery rate is a very important factor that has significant negative impact on OTE inefficiency, scale inefficiency (at 1%) and PTE inefficiency (at 10%). When we look at the magnitude of coefficients, we find that recovery rate is the most important factor in the OTE inefficiency and scale inefficiency model implying that the most important factor that cause OTE inefficiency and scale inefficiency is lower sugar recovery rate. Besides, recovery rate has significant (at 10 %) negative impact on PTE inefficiency also and its coefficient ranks second (after IRS) implying that it is crucial factor in respect to PTE also. Status of capacity utilization is another factor that has significant (at 5%) negative impact on OTE inefficiency and PTE inefficiency and this result is supported by positive significant (at 10 %) coefficient for super efficiency score. Although one can see from the table that IRS dummy has significant negative impact on OTE inefficiency, this score of CCR model does not consider increasing returns to scale, the Tobit result regarding IRS dummy should be used for PTE score that considers variable returns to scale. Now It is clear that IRS condition has significant (at 1%) positive impact on PTE inefficiency implying that it promotes inefficiency in the mills and it is the most important factor in the PTE model that cause PTE inefficiency, as the magnitude of its coefficient is maximum in the entire set of model. Results show that IRS condition of operation in mill leads to 0.165 unit increase in inefficiency. Besides, IRS condition is second most important factor in respect to scale inefficiency, however it is not significant.
V. CONCLUSION AND SUGGESTIONS
To achieve the objectives of the study and to come up with trustworthy and unbiased results, the research paper utilized the results received from various models of Data Envelopment Analysis and applied Tobit Regression Analysis. In Uttar Pradesh, sugar recovery rate hardly exceeds the level of 10% while in other sugar producing countries like Brazil, Australia it is higher than 12%. Although government has decided 2500 TCD as minimum economic size of sugar plants, the report of the task force on sugar industry for 10th five-year plan had noted that nearly 84% of sugar mills had a capacity of below 2500 TCD. While, Report of the working group on agricultural marketing infrastructure and policy required for internal and external trade for the XIth five year plan (planning commission), has noticed that installed capacity of sugar mills at all India level increased at compound annual growth rate of 3.3 % between 2000 and 2005, whereas the capacity utilization showed declining trend, from 112% in 2000 to 67% in 2005. In the light of macro level picture, stated above and the findings of the study, this paper suggests that since management cannot do much in respect to number of permanent workers (due to labour laws), efforts should be concentrated on improvement in sugar recovery rate, improvement in installed capacity and capacity utilization, better maintenance of machinery, advancement in technology and efficient utilization of available manpower. Every mill has to be efficient because in this most competitive world, this is the question of survival.
VI. ACKNOWLEDGEMENT
I am heartily thankful to my supervisor Prof. Girish Chandra Tripathi (Vice -Chancellor, Banaras Hindu University, Varanasi) for his kind guidance and encouragement during the development of this research paper.
REFERENCES
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