Für neue Autoren:
kostenlos, einfach und schnell
Für bereits registrierte Autoren
Doktorarbeit / Dissertation, 2012
260 Seiten, Note: None
Letter of Declaration
List of Tables
List of Figures
List of Abbreviations
CHAPTER 1: INTRODUCTION - Main Coverage
1.2 NEED FOR THE STUDY
1.3 OBJECTIVES OF THE STUDY
1.4 HYPOTHESES OF THE STUDY
1.4.1 METHODOLOGY OF THE STUDY AND SOURCE OF DATA
1.5 CHAPTER OUTLINE
CHAPTER 2: REVIEW OF RELATED STUDIES - Main Coverage
2.2 REVIEW OF RELATED STUDIES
2.2.1 REVIEWS RELATING TO FINANCE
2.2.2 REVIEWS RELATING TO PRODUCTIVITY
CHAPTER 3: PROFILE OF THE STUDY AREA – TAMILNADU - Main Coverage
3.4 LITERACY RATE
3.7 CLIMATE AND TEMPERATURE
3.8 ADMINISTRATIVE DIVISION
3.9 THE LANGUAGES
3.10 ECONOMIC PROFILE
3.11 AGRICULTURE PROFILE
3.12 RESOURCES PROFILE
3.12.1 A WEALTH IN MINERALS
3.12.2 MARINE RESOURCES
3.13 INDUSTRIAL PROFILE
3.13.1 EXISTING INDUSTRIAL FOUNDATIONS
3.13.2 MINERAL BASED INDUSTRIES
3.13.4 TEXTILE AND READY-MADE GARMENTS
3.13.5 LEATHER-BASED INDUSTRIES
3.13.6 AGRO-BASED INDUSTRIES
3.13.7 CHEMICAL AND PETRO-CHEMICAL INDUSTRY
3.13.8 ELECTRONICS - INDUSTRY
3.14 OVERALL ECONOMIC AND INDUSTRIAL CLIMATE
3.15 POWER AVAILABILITY
CHAPTER 4: RESEARCH METHODOLOGY - Main Coverage
4.1 SUMMARY OF RESEARCH METHODOLOGY ADOPTED IN THE STUDY
4.3 CONCEPTS RELATING TO THE STUDY
4.3.1 CONCEPTS ON THE MEASUREMENT OF FINANCIAL EFFICIENCY
4.3.2 CONCEPTS ON THE PRODUCTIVITY MEASURES
4.3.3 TOOLS AND TECHNIQUES USED
4.3.4 TECHNIQUES ON MEASURING PRODUCTIVITY
4.3.5 DEA AND MALMQUIST INDEX
4.3.6 FACTOR ANALYSIS
CHAPTER 5: TRENDS IN THE FINANCIAL PERFORMANCE OF SELECTED SUGAR INDUSTRIES - Main Coverage
5.2 EFFICIENCY OF SUGAR INDUSTRIES: A RATIO ANALYSIS APPROACH
5.2.1 INVENTORY TURNOVER RATIO
5.2.2 DEBTOR TUNOVER RATIO
5.2.3 WORKING CAPITAL TURNOVER RATIO
5.2.4 FIXED ASSETS TURNOVER RATIO
5.2.5 RAW MATERIAL TO SALES RATIO
5.2.6 LABOUR CHARGES TO SALES RATIO
5.2.7 POWER/FUEL TO SALES RATIO
5.2.8 OTHER MANUFACTURING EXPENSES TO THE TOTAL SALES RATIO
5.2.9 LIQUIDITY RATIOS
5.2.10 QUICK RATIO
5.2.11 ABSOLUTE LIQUID RATIO
5.2.12 NET PROFIT TO WORKING CAPITAL
5.2.13 DEBT EQUITY RATIO
5.2.14 SOLVENCY RATIO
5.2.15 RETURN ON INVESTMENT RATIO
5.2.16 NET PROFIT TO TOTAL ASSETS RATIO
5.2.17 FIXED ASSETS TO TOTAL ASSETS RATIO
5.2.18 WORKING CAPITAL TO TOTAL ASSETS RATIO
5.2.19 NET SALES TO TOTAL ASSETS RATIO
5.2.20 DIVIDEND PAID TO TOTAL INCOME RATIO
5.2.21 NET PROFIT TO TOTAL INCOME RATIO
5.2.22 NET WORTH TO TOTAL INCOME RATIO
5.2.23 NET PROFIT TO NET WORTH
5.2.24 NET SALES TO NET WORTH RATIO
5.2.25 CURRENT ASSETS TO NET WORTH RATIO
5.2.26 INVESTMENT TO NET WORTH
5.2.27 TOTAL DEBT TO NET WORTH RATIO
5.2.28 FIXED ASSETS RATIO
5.2.29 PROFITABILITY RATIOS
5.2.30 GROSS PROFIT RATIO
5.2.31 NET PROFITS RATIO
5.2.32 NET PROFIT TO FIXED ASSETS RATIO
5.2.33 INTEREST PAID RATIO
5.3 ANALYSIS OF PRIVATE FIRMS
5.3.1 FACTOR ANALYSIS OF PRIVATE FIRMS
5.3.2 DISCRIMINENT FUNCTION ANALYSIS OF PRIVATE FIRMS
5.3.3 CONNANICAL DISRIMINENT FUNCTION FITTED
5.3.4 CLASSIFICATION OF INDIVIDUAL PRIVATE FIRMS
5.3.5 RELATIVE IMPORTANCE OF PREDICTOR VARIABLE OF PRIVATE FIRMS
5.3.6 LINEAR REGRESSION RESULTS OF PRIVATE FIRMS
5.4 ANALYSIS OF GOVERNMENT AND COOPERATIVE FIRMS
5.4.1 FACTOR ANALYSIS OF GOVERNMENT AND COOPERATIVE FIRMS
5.4.2 DISCRIMINENT FUNCTION ANALYSIS OF GOVERNMENT AND COOPERATIVE FIRMS
5.4.3 CONNANICAL DISCRIMINENT FUNCTION FITTED:.
5.4.4 CLASSIFICATION OF INDIVIDUAL GOVERNMENT AND COOPERATIVE FIRMS
5.4.5 RELATIVE IMPORTANCE OF PREDICTOR VARIABLE GOVERNMENT AND COOPERATIVE FIRMS
5.4.6 LINEAR REGRESSION RESULTS OF GOVERNMENT AND COOPERATIVE FIRMS
5.5 SUMMARY STATISTICS OF ALL SAMPLE FIRMS
5.5.1 SUMMARY STATISTICS OF NETWORTH
5.5.2 SUMMARY STATISTICS OF SALES
5.5.3 SUMMARY STATISTICS OF NET PROFIT
5.5.4 SUMMARY STATISTICS OF FIXED ASSETS
5.5.5 SUMMARY STATISTICS OF ADMINISTRATIVE EXPENSES
5.5.6 SUMMARY STATISTICS OF LABOUR COST
5.5.7 SUMMARY STATISTICS OF POWER & FUEL
5.5.8 SUMMARY STATISTICS OF RAW MATERIAL
5.5.9 SUMMARY STATISTICS OF OPERATING EXPENSES
5.5.10 SUMMARY STATISTICS OF OPERATING PROFIT
5.5.11 SUMMARY STATISTICS OF INVESTMENT
5.5.12 SUMMARY STATISTICS OF SECURED LOAN
5.5.13 SUMMARY STATISTICS OF RESERVE FUND
5.5.14 SUMMARY STATISTICS OF SHARE CAPITAL
5.5.15 ANALYSIS OF FINANCIAL PARAMETERS
5.5.16 TRENDS RESULTS OF NETWORTH
5.5.17 TREND RESULTS OF SALES
5.5.18 TREND RESULTS OF NET PROFIT
5.5.19 TRENDS RESULTS OF FIXED ASSETS
5.5.20 TREND RESULTS OF ADMINISTRATIVE EXPENSES
5.5.21 TREND RESULTS OF LABOUR COST
5.5.22 TREND RESULTS OF POWER & FUEL
5.5.23 TREND RESULTS OF RAW MATERIAL
5.5.24 TREND RESULTS OF OPERATING EXPENSES
5.5.25 TREND RESULTS OF OPERATING PROFIT
5.5.26 TREND RESULTS OF INVESTMENT
5.5.27 TREND RESULTS OF SECURED LOAN
5.5.28 TREND RESULTS OF RESERVE FUND
5.5.29 TREND RESULTS OF SHARE CAPITAL
5.5.30 REGRESSION ANALYSIS
CHAPTER 6: TRENDS IN THE PRODUCTIVITY OF SUGAR INDUSTRIES: ALL SAMPLE INDUSTRIES - Main Coverage
6.2 PARTIAL FACTOR PRODUCTIVITY
6.2.1 LABOUR PRODUCTIVITY (LP)
6.2.2 CAPITAL PRODUCTIVITY
6.2.3 CAPITAL-LABOUR RATIO (K/L)
6.2.4 ESTIMATES OF MEAN TOTAL FACTOR PRODUCTIVITY GROWTH (TFPG)
6.2.5 FIRM WISE MEAN FACTOR PRODUCTIVITY GROWTH: THE MALMQUIST INDEX
6.2.6 ESTIMATES OF ANNUAL TFPG
6.4 SWOT ANALYSIS OF THE SELECTED SUGAR MILLS
CHAPTER 7: SUMMARY, MAJOR FINDINGS, RECOMMENDATIONS AND CONCLUSION - Main Coverage
7.2 MAJOR FINDINGS OF THE STUDY
7.3 RESULTS OF HYPOTHESES TESTED
7.5 CONCLUSION OF THE STUDY
This is to certify that the thesis entitled “An A nalysis on the F inancial Performance and P roduction E fficiency of S elected S ugar M ills in the S tate of Tamil n adu, India”, submitted by me to Banasthali University, for the award of the degree of Doctor of Philosophy in Management is a bonafide record of research work carried out by me under the supervision of Dr. Harsh Purohit, Professor (Finance & Banking) WISDOM, Banasthali University, Rajasthan, India. The contents of this thesis, in full or in parts, have not been submitted to any other Institute or University for the award of any degree or diploma.
illustration not visible in this excerpt
This is to certify that the research work titled, “An Analysis on the Financial Performance and Production Efficiency of Selected Sugar Mills in the State of Tamilnadu, India”, submitted to Banasthali University, Rajasthan, India, in partial fulfillment of the requirement for the award of the Degree of Doctor of Philosophy in Management is a record of original research work done by Girish.K.Nair under my supervision and guidance and the thesis has not formed the basis for the award of any Degree/Diploma/Associate ship/Fellowship or similar title to any candidate of any university.
illustration not visible in this excerpt
“Thanks to God” – for granting me the patience to endure the good things and challenges in life. You have made my life bountiful - May your name be exalted, honored and glorified.
I would like to take this opportunity to thank all the people who contributed to the successful completion of my thesis. It would not have been possible to write this doctoral thesis without the help and support of the kind people around me, to only some of whom it is possible to give particular mention here.
First of all, my research supervisor, Dr.Harsh Purohit, Professor, Banasthali University, who was a source of constant guidance and encouragement right from the beginning. His advice, inspirations, enthusiasm and suggestions have been invaluable in shaping this thesis and in providing me with all the support required in completing this task.
To Prof. Aditya Shastri, the Honourable Vice Chancellor, at Banasthali University – I would like express my sincere gratitude for all his support in the doctoral program. Thank you for allowing the NRI students which has made this research project possible.
Also, to Prof. Ina Shastri, The Pro-vice Chancellor, Prof. Siddharth Shastri, and the Dean of WISDOM, at Banasthali University – a sincere thank you.
Then to Mr. Hiralal Mittal, Additional Registrar at Banasthali University, thank you for supporting me through the hassle-free administrative requirements.
I would like to thank all the Managers of the sugar mills who helped me in the research and I hope the research findings will be a useful reference to them and their organizations.
The researcher is indebted to all faculty and administrative members of Stenden University, Qatar, for their constant support and encouragement throughout the research work.
I would also like to take this opportunity to acknowledge the management, faculty and administration at CMS College of Science & Commerce, Coimbatore, Tamilnadu, for contributing to my personal and professional development.
I would like to thank my wife Anitha for her patience, support and encouragement throughout my course of study and my daughter Sreenandha for her unlimited love.
And last, but definitely not least, to my dear mother, Meenakshi Kutty, and Late father Karunakaran Nair, a heart-felt thank you for your love and support; and guiding me to become an educated, responsible and respectful adult. Thanks to my parents for letting me do it ‘my way’ and for encouraging and inspiring me to reach for my dreams. To my siblings, thank you for your un-ending support and love.
Agriculture is the backbone of Indian economy. Hence, the development of the economy depends on the growth in the agriculture sector. However, with the realization on the importance of industrial development as the contributor to faster economic growth, the dual objective of the development of agriculture with a thrust being given to industrial development has taken the primary objective in the development agenda of governments. Among the various agro based industries, the sugar industry is one such industry. It is one of the traditional industries in India. It is the second largest agro-based processing industry after the cotton textiles industry in country. It has a lion's share in accelerating industrialization process and bringing socio-economic changes in under developed rural areas. Sugar industry covers around 7.5% of total rural population and provides employment to 5 lakh rural people. About 4.5 crore farmers are engaged in sugarcane cultivation in India. Sugar mills (cooperative, private, and public) have been instrumental in initiating a number of entrepreneurial activities in rural India.
However, the major raw material for the sugar industry is the sugarcane. Despite its long tradition and large area in India, in terms of productivity, sugarcane yields are unimpressive, especially where the crop is irrigated. The average productivity of sugarcane is low with certain regions reporting yields as low as 40 t/ha only. This is reflected in the production and productivity of sugar. The low sugar yield is expected to get reflected in the performance of the sugar units.
The Sugar Industry in Tamilnadu plays a vital role in the economic development of the State and particularly in rural areas. Tamilnadu is one of the leading producers of sugar in the country and its contribution is about 7% of country’s total sugar production. There are totally 38 sugar mills operating in various districts of the state of Tamilnadu. Among these mills, two are public sector mills, 16 are cooperative sugar mills and the remaining 20 are privately owned public limited companies. It is expected that due to differences in the ownership of the firms it is expected that the management of the firms and as a result the financial performance of the firms are expected to be different. Hence, for the purpose of comparison, it is decided to select firms from all the three categories.
To have fair representativeness and to understand the overall performance of the sugar industries of Tamilnadu, it was selected representative from all the three units using the random sampling technique. The sample units, for this purpose, were selected proportionately. Keeping one third of the sample selection would be a fair representative; one unit from the public sector, six from private sector and five firms from cooperative sector was selected. This gave total sample sugar firms of 12 firms.
The objectives framed for the study were: 1) to study the trends in the liquidity position of the selected sugarcane industries of Tamilnadu, 2) to examine the trends in the profitability position of the selected sugarcane industries of Tamilnadu, 3) to portray the trends in the shareholders ratio of the selected sugarcane industries of Tamilnadu, 4) to trace out the significance of the differences in the financial ratios among the selected industries of the state of Tamilnadu, 5) to estimate the productivity of the selected sugar industries of Tamilnadu and 6) to make SWOT analysis of the selected industries. The required secondary data were collected for a period of 10 years from 1999-2000 to 2008-09.
On the basis of the objectives framed, the relevant tools and techniques were identified. To analyze financial performance, ratio analysis has been used. Apart from this, the simple percentage method, simple arithmetic mean (X bar), coefficient of variation (CV), the Linear Growth Rate (LGR) and Compound Growth Rates (CGR) and ANOV were also used.
Apart from these statistical tools and techniques, to study the structure of the sugar cane industries measured in terms of output, capital and employment the rate of acceleration or deceleration has been estimated using a quadratic semi-logarithmic regression function. The partial and total factor productivity have been calculated using the sophisticated techniques like, Kendrick model, Solow model, Translog model, Data Envelope Analysis (DEA) and Malmquist Index.
The analysis of the data has provided the conclusion that in terms of financial variables considered the ratio analysis indicated that the selected sample private firms are in favourable position. However, due to poor turnover, the profitability position of the industries is far below the satisfactory level. The growth of capital is far from satisfactory level in the case of both government and private scale firms, when compared to private firms. Though both the private and the government and cooperative firms have experienced a positive growth in terms of output and capital employment, there is a significant difference between private and the government and cooperative firms in terms of growth rate as well as in terms of acceleration or deceleration in output and capital employment.
Table 1: LIST OF SUGAR INDUSTRIES BY OWNERSHIP IN THE STATE OF TAMILNADU
Table 3: INVENTORY TURNOVER RATIO OF PRIVATE FIRMS
Table 4: INVENTORY TURNOVER RATIO OF GOVERNMENT & COOPERATIVE FIRMS
Table 5: DEBTOR TURNOVER RATIO OF PRIVATE FIRMS
Table 6: DEBTOR TURNOVER RATIO OF GOVERNMENT & COOPERATIVE FIRMS
Table 7: WORKING CAPITAL TURNOVER RATIO OF PRIVATE FIRMS
Table 8: WORKING CAPITAL TURNOVER RATIO OF GOVERNMENT & COOPERATIVE FIRMS
Table 9: FIXED ASSETS TURNOVER RATIO OF PRIVATE FIRMS
Table 10: FIXED ASSETS TURNOVER RATIO OF GOVERNMENT & COOPERATIVE FIRMS
Table 11: RAW MATERIAL TO SALES RATIO OF PRIVATE FIRMS
Table 12: RAW MATERIAL TO SALES RATIO OF GOVERNMENT & COOPERATIVE FIRMS
Table 13: LABOUR CHARGES TO SALES RATIO OF PRIVATE FIRMS
Table 14: LABOUR CHARGES TO SALES RATIO OF GOVERNMENT & COOPERATIVE FIRMS
Table 15: POWER / FUEL CHARGES TO SALES RATIO OF PRIVATE FIRMS
Table 16: POWER / FUEL TO SALES RATIO OF GOVERNMENT & COOPERATIVE FIRMS
Table 17: OTHER MANUFACTURING EXPENSES TO SALES RATIO OF PRIVATE FIRMS
Table 18: OTHER MANUFACTURING EXPENSES TO SALES RATIO OF GOVERNMENT & COOPERATIVE FIRMS
Table 19: CURRENT RATIO OF PRIVATE FIRMS
Table 20: CURRENT RATIO OF GOVERNMENT & COOPERATIVE FIRMS
Table 21: QUICK RATIO OF PRIVATE FIRMS
Table 22: QUICK RATIO OF GOVERNMENT & COOPERATIVE FIRMS
Table 23: ABSOLUTE LIQUID RATIO OF GOVERNMENT & COOPERATIVE FIRMS
Table 24: NET PROFIT TO WORKING CAPITAL RATIO OF PRIVATE FIRMS
Table 25: NET PROFIT TO WORKING CAPITAL RATIO OF GOVERNMENT & COOPERATIVE FIRMS
Table 26: DEBT EQUITY RATIO OF PRIVATE FIRMS
Table 27: DEBT EQUITY RATIO OF GOVERNMENT & COOPERATIVE
Table 28: SOLVENCY RATIO OF PRIVATE FIRMS
Table 29: SOLVENCY RATIO OF GOVERNMENT & COOPERATIVE FIRMS
Table 30: RETURN ON INVESTMENT RATIO OF PRIVATE FIRMS
Table 31: RETURN ON INVESTMENT RATIO OF GOVERNMENT & COOPERATIVE FIRMS
Table 32: NET PROFIT TO TOTAL ASSETS RATIO OF PRIVATE FIRMS
Table 33: NET PROFIT TO TOTAL ASSET OF GOVERNMENT & COOPERATIVE FIRMS
Table 34: FIXED ASSETS TO TOTAL ASSETS RATIO OF PRIVATE FIRMS
Table 35: FIXED ASSETS TO TOTAL ASSETS OF GOVERNMENT & COOPERATIVE FIRMS
Table 36: WORKING CAPITAL TO TOTAL ASSETS RATIO OF PRIVATE FIRMS
Table 37: WORKING CAPITAL TO TOTAL ASSETS RATIO OF GOVERNMENT & COOPERATIVE FIRMS
Table 38: NET SALES TO TOTAL ASSETS RATIO OF PRIVATE FIRMS
Table 39: NET SALES TO TOTAL ASSETS RATIO OF GOVERNMENT & COOPERATIVE FIRMS
Table 40: DIVIDEND PAID TO TOTAL INCOME RATIO OF PRIVATE FIRMS
Table 41: DIVIDEND PAID TO TOTAL INCOME OF GOVERNMENT & COOPERATIVE FIRMS
Table 42: NET PROFIT TO TOTAL INCOME RATIO OF PRIVATE FIRMS
Table 43: NET PROFIT TO TOTAL INCOME OF GOVERNMENT & COOPERATIVE FIRMS
Table 44: NET WORTH TO TOTAL INCOME RATIO OF PRIVATE FIRMS
Table 45: NETWORTH TO TOTAL INCOME RATIO OF GOVERNMENT & COOPERATIVE FIRMS
Table 46: NET PROFIT TO NETWORTH RATIO OF PRIVATE FIRMS
Table 47: NET PROFIT TO NETWORTH RATIO OF GOVERNMENT & COOPERATIVE FIRMS
Table 48: NET SALES TO NET WORTH RATIO OF PRIVATE FIRMS
Table 49: NET SALES TO NET WORTH RATIO OF GOVERNMENT & COOPERATIVE FIRMS
Table 50: CURRENT ASSETS TO NETWORTH RATIO OF PRIVATE FIRMS
Table 51: CURRENT ASSETS TO NETWORTH RATIO OF GOVERNMENT & COOPERATIVE FIRMS
Table 52: FIXED ASSETS TO NETWORTH RATIO OF PRIVATE FIRMS
Table 53: FIXED ASSETS TO NETWORTH RATIO OF GOVERNMENT & COOPERATIVE FIRMS
Table 54: INVESTMENT TO NETWORTH RATIO OF PRIVATE FIRMS
Table 55: INVESTMENT TO NETWORTH RATIO OF GOVERNMENT & COOPERATIVE FIRMS
Table 56: TOTAL DEBT TO NETWORTH RATIO OF PRIVATE
Table 57: TOTAL DEBT TO NETWORTH RATIO OF GOVERNMENT & COOPERATIVE
Table 58: FIXED ASSETS RATIO OF PRIVATE FIRMS
Table 59: FIXED ASSETS RATIO OF GOVERNMENT & COOPERATIVE FIRMS
Table 60: OPERATING PROFIT RATIO OF PRIVATE FIRMS
Table 61: OPERATING PROFIT RATIO OF GOVERNMENT & COOPERATIVE FIRMS
Table 62: GROSS PROFIT RATIO OF PRIVATE FIRMS
Table 63: GROSS PROFIT RATIO OF GOVERNMENT & COOPERATIVE FIRMS
Table 64: NET PROFIT RATIO OF PRIVATE FIRMS
Table 65: NET PROFIT RATIO OF GOVERNMENT & COOPERATIVE FIRMS
Table 66: NET PROFIT TO FIXED ASSETS RATIO OF PRIVATE FIRMS
Table 67: NET PROFIT TO FIXED ASSET RATIO OF (GOVERNMENT & COOPERATIVE FIRMS
Table 68: INTEREST PAID TO NET PROFIT RATIO OF PRIVATE FIRMS
Table 69: INTEREST PAID TO NET PROFIT RATIO OF GOVERNMENT & COOPERATIVE FIRMS
Table 70: ROTATED FACTOR LOADINGS OF PRIVATE FIRMS
Table 71: CLUSTERING OF FINANCIAL RATIOS INTO FACTORS
Table 72: MEAN VALUES OF SELECTED FINANCIAL RATIOS OF PRIVATE FIRMS
Table 73: TESTS OF EQUALITY OF GROUP MEANS UNIVARIATE ANOVA
Table 74: DETERMINATION OF PERCENTAGE OF CORRECT CLASSIFICATION BY USING DISCRIMINANT FUNCTION
Table 75: THE RELATIVE IMPORTANCE OF RATIOS IN DISCRIMINATING BETWEEN THE GROUPS
Table 76: LINEAR REGRESSION RESULTS- DEPENENDENT VARIABLE: NPR
Table 77: ROTATED FACTOR LOADINGS OF GOVERNMENT & COOPERATIVE FIRMS
Table 78: CLUSTERING OF FINANCIAL RATIOS INTO FACTORS
Table 79: MEAN VALUES OF SELECTED FINANCIAL RATIOS OF GOVERNMENT & COOPERATIVE FIRMS
Table 80: TESTS OF EQUALITY OF GROUP MEANS OF MEDIUMD AND GOVERNMENT & COOPERATIVEFIRMS-UNIVARIATE ANOVAS
Table 81: DETERMINATION OF PERCENTAGE OF CORRECT CLASSIFICATION BY USING DISCRIMINANT FUNCTION FOR GOVERNMENT & COOPERATIVE FIRMS
Table 82: THE RELATIVE IMPORTANCE OF RATIOS IN DISCRIMINATING BETWEEN THE GROUPS
Table 83: LINEAR REGRESSION RESULTS OF GOVERNMENT & COOPERATIVE FIRM - DEPENENDENT VARIABLE: NPR
Table 84: SUMMARY STATISTICS OF NETWORTH
Table 85: SUMMARY STATISTICS OF SALES
Table 86: SUMMARY STATISTICS OF NET PROFIT
Table 87: SUMMARY STATISTICS OF FIXED ASSETS
Table 88: SUMMARY STATISTICS OF ADMINISTRATIVE EXPENSES
Table 89: SUMMARY STATISTICS OF LABOUR COST
Table 90: SUMMARY STATISTICS OF POWER & FUEL
Table 91: SUMMARY STATISTICS OF RAW MATERIAL
Table 92: SUMMARY STATISTICS OF OPERATING EXPENSES
Table 93: SUMMARY STATISTICS OF OPERATING PROFIT
Table 94: SUMMARY STATISTICS OF INVESTMENT
Table 95: SUMMARY STATISTICS OF SECURED LOAN
Table 96: SUMMARY STATISTICS OF RESERVE FUND
Table 97: SUMMARY STATISTICS OF SHARE CAPITAL
Table 98: TREND RESULTS OF NETWORTH
Table 99: TREND RESULTS OF SALES
Table 100: TREND RESULTS OF NET PROFIT
Table 101: TRENDS RESULTS OF FIXED ASSETS
Table 102: TRENDS RESULTS OF ADMINISTRATIVE EXPENSES
Table 103: TREND RESULTS OF LABOUR COST
Table 104: TREND RESULTS OF POWER & FUEL
Table 105: TREND RESULTS OF RAW MATERIAL
Table 106: TREND RESULTS OF OPERATING EXPENSES
Table 107: TREND RESULTS OF OPERATING PROFIT
Table 108: TREND RESULTS OF INVESTMENT
Table 109: TREND RESULTS OF SECURED LOAN
Table 110: TREND RESULTS OF RESERVE FUND
Table 111: TREND RESULTS OF SHARE CAPITAL
Table 112: LINEAR REGRESSION RESULTS OF PRIVATE FIRMS - DEPENDENT VARIABLE: NET PROFIT (X3)
Table 113: LINEAR REGRESSION RESULTS OF PRIVATE FIRMS - DEPENDENT VARIABLE: OPERATING PROFIT (X10)
Table 114: LINEAR REGRESSION RESULTS OF GOVERNMENT & COOPERATIVE FIRMS- DEPENDENT VARIABLE- NET PROFIT(X3)
Table 115: LINEAR REGRESSION RESULTS OF GOVERNMENT & COOPERATIVE FIRMS- DEPENDENT VARIABLE OPERATING PROFIT (X10)
Table 116: MEAN PARTIAL PRODUCTIVITY OF SAMPLE FIRMS FROM 1999-98 TO 2008-09
Table 117: TRENDS IN ANNUAL AVERAGE OF MALMQUIST INDEX OF SAMPLE FIRMS
Table 118: FIRM WISE AVERAGE OF MALMQUIST INDEX
Table 119: TRENDS IN THE MALMQUIST INDEX BY FIRMS AND BY YEAR
Figure 1: INVENTORY TURNOVER RATIO OF PRIVATE FIRMS
Figure 2: INVENTORY TURNOVER RATIO OF GOVERNMENT & COOPERATIVE FIRMS
Figure 3: DEBTOR TURNOVER RATIO OF PRIVATE FIRMS
Figure 4: DEBTOR TURNOVER RATIO OF GOVERNMENT & COOPERATIVE FIRM
Figure 5: GRAPH SHOWING WORKING CAPITAL TURNOVER RATIO OF PRIVATE FIRMS
Figure 6: GRAPH SHOWING WORKING CAPITAL TURNOVER RATIO OF GOVERNMENT & COOPERATIVE FIRMS
Figure 7: GRAPH SHOWING FIXED ASSETS TURNOVER RATIO OF PRIVATE FIRMS
Figure 8: GRAPH SHOWING FIXED ASSETS TURNOVER RATIO OF GOVERNMENT & COOPERATIVE FIRMS
Figure 9: RAW MATERIAL TO SALES RATIO OF PRIVATE FIRMS
Figure 10: RAW MATERIAL TO SALES RATIO OF GOVERNMENT & COOPERATIVE FIRMS
Figure 11: LABOUR CHARGES TO SALES RATIO OF PRIVATE FIRMS
Figure 12: LABOUR CHARGES TO SALES RATIO OF GOVERNMENT & COOPERATIVE FIRMS
Figure 13: QUICK RATIO OF PRIVATE FIRMS
Figure 14: QUICK RATIO OF GOVERNMENT & COOPERATIVE FIRMS
Figure 15: ABSOLUTE LIQUID RATIO OF PRIVATE FIRMS
Figure 16: ABSOLUTE LIQUID RATIO OF PRIVATE FIRMS
Figure 17: ABSOLUTE LIQUID RATIO OF GOVERNMENT & COOPERATIVE FIRMS
Figure 18: DEBT EQUITY RATIO OF PRIVATE FIRMS
Figure 19: EQUITY RATIO OF GOVERNMENT & COOPERATIVE
Figure 21: RETURN ON INVESTMENT RATIO OF GOVERNMENT & COOPERATIVE FIRMS
Figure 20: RETURN ON INVESTMENT RATIO OF PRIVATE FIRMS
Figure 22: OPERATING PROFIT RATIO OF PRIVATE FIRMS
Figure 23: OPERATING PROFIT RATIO OF GOVERNMENT & COOPERATIVE FIRMS
List of Abbreviations
illustration not visible in this excerpt
India has become largest producer of sugar cane/sugar producing 280 MnT of cane and 16.5 MnT of sugar in 1995-96, making it the largest producer of sugar in the world, representing about 20 per cent of cane sugar production. India also produces another 10 Million tones of traditional sweeteners (gur 9 million tones and khandsari one million tones1). In the year 2011-12 the actual production of sugarcane is expected to be around 380 million tons, up from 346 million tons in 2010-11. As a result of higher sugarcane production it is estimated for the year to around 26 to 26.5 million tones production in 2011-12. Similarly, the annual consumption of sugar is expected to be 22 to 23 million tones. India also has a large consumer base, thus makes it quite vulnerable to international sugar market, in the event of surplus or deficit situation. At the same time it has good potential and prospects.
Sugar production commenced in 1920's but it got industry status in late 20's/early 30's when India had 29 sugar mills producing just 100,000 tons of sugar. The industry, facing competition from imported sugar, sought tariff protection. Sugar production picked up under the Sugar Industry Protection Act passed in 1932 and country became self sufficient in 1935. Also cane pricing act was enforced to provide good cane price to farmer. This was followed by land reforms putting ceiling on land holdings to protect small farmers, formation of cane grower cooperatives and setting up of sugar mills jointly with farmers called as cooperative mills on ownership and sharing basis. Today this sector produces 60 per cent of country's production.
Under the structured Industrial Development Policy, sugar industry was part of the Five-Year Plans introduced in 1951 and has been under the direct control of the Government ever since. Sugar industry is highly politicized and so closely controlled by the Government which has no parallel in the industry. Government control covers all aspects of sugar business that is, licensing/capacity/cane area, procurement/ pricing/sugar pricing/distribution and imports and exports.
Sugar scene in India has been that of protectionism. The mills, the farmers and the consumers all have been protected one way or another. Whereas the protection to farmer and consumer has been consistent, it has not been so consistent for the mill owners.
Overall government policy has given impressive results. The production has gone up to 16.5 MnT per capita consumption up from 5 to 13kg over a period of 3 decades. There is a potential - what is needed, is some changes in policy to make it world class player.
Winds of liberalization have touched sugar also. Licensing is liberalized. The imports freely allowed. Exports deregulated. Competition became intense. Customer more demanding on quality and service.
A moot question that arises in this context is that because of low productivity in agriculture and the increasing competition faced, to what extent the Indian sugar industry could be financially viable. This doubt is attempted to be clarified by examining a sample of sugar industries operating in the state of Tamilnadu.
Agriculture is the backbone of the Indian economy. Since 63 years of independence more than 45.50 per cent of the population depends on agriculture. It gives direct and indirect employment to more than 52 per cent of the population. However, the development experience of the developed countries indicates that it is only through the process of industrialization, the economy can reach a higher rate of growth. From the welfare point of view, in the Indian context it was realized that the objective of industrialization should not neglect the agricultural. Hence, the government has the dual objective of achieving faster industrialization by sustaining agriculture. It was felt that it is only the development of agro based industries which can help to achieve this dual goal. Among the various agro based industries, Sugar industry constitutes one of the most important agro-based industries in India. Although this industry has a long tradition in this country, it started growing in an organized way during the 1930s after introduction of the Sugar Industry Protection Act in 1932.
Sugarcane forms the basic raw material for the sugar industry. There are 35 million farmers growing sugarcane and another 50 million depend on employment generated by the 571 sugar factories and other related industries using sugar. In Uttar Pradesh, Maharashtra and Tamilnadu, sugarcane plays a major role in the state economy.
During the last 10 years, sugarcane production in India has been fluctuating between 233 million tones and 355 million tones. Similarly, the productivity at the farm level is as low as 40 t/ha. With such low yields and fluctuations in production, and India having the second largest area under sugarcane cultivation in the world next to Brazil, the industry is in for big trouble.
The problem is going to further deteriorate due to variability of rainfall influenced by climate change. So, unless sugarcane farmers are provided with options of high yields with much less water, India will find it difficult to meet its growing demand for sugar. Under such situation development of new technology involves less input to produce more will be the viable option.
Sugarcane in India is grown in two distinct agro-climatic regions - the Tropical (largely comprising Maharashtra, Karnataka, Gujarat and Tamilnadu) and the Sub-tropical (Uttar Pradesh, Punjab, Haryana and Bihar).
Among the states, Uttar Pradesh occupies half (2.25 m.ha) of the total area followed by Maharashtra (1.04 m.ha). Though Uttar Pradesh dominates in production with 134 MT followed by Maharashtra with 79 MT, in terms of productivity, Tamilnadu leads with 105 t/ha followed by Karnataka (88 t/ha) and Andhra Pradesh (82 t/ha).
Yet despite its long tradition and large area in India, in terms of productivity, sugarcane yields are unimpressive, especially where the crop is irrigated. The average productivity of sugarcane is low with certain regions reporting yields as low as 40 t/ha only. Not only is the cane yield low, the sugar yield - typically at less than 10 per cent of cane weight - is also less than satisfactory given that yields of 14 per cent of cane weight at the time of cutting (and sometimes much higher) are possible. Sugarcane cultivation and the sugar industry in India are facing serious challenges due to various internal and external factors.
The reasons for such low productivity are:
1. The improved varieties released by research organizations perform well in the initial years but lose their vigor and decline in yield in due course.
2. Water availability is unpredictable. The concern is not only the quantity of water required, but also the lack of proper water management practices. Due to this, water is either wasted or sometimes not available at the right time.
3. Unpredictable climatic aberrations, improper cultivation practices, negligence in plant protection measures, imbalanced nutrient management and other practices like mono cropping often result in low productivity, fetching low price in the market.
In addition, it is also very important to consider the enormous amount of water that goes into the sugarcane production. Approximately 25,000 kg of water is needed to produce 10 kg of sugarcane. But, the water table is depleting every year. Costs of production, moreover, are increasing not just for the small farmers but for the large industrial players as well. In future, these challenges will become even more complex with climate change inducing direct and indirect effects on crops, water, pests and diseases, and volatility in the international market.
A recent FAO report predicts sharp shortfall of sugar production in India in the year 2009. On one hand, there is the opportunity in terms of growing demand for sugar and other bi- products of sugarcane, and on the other hand, there is the decline in production and productivity due to various reasons. The rising cost of farm chemicals, along with the increasing social and environmental costs of water use by the agricultural sector and the pollution accruing to modern, input intensive production practices have begun to raise serious questions in the minds of policy makers, planners and farmers alike. Any problem affecting the sugar sector is a widespread problem, affecting a significant number of households and the economy as a whole.
As a result of wide variations in the pattern of growth between different states as well as between regions. Among the various states of the Indian union, Uttar Pradesh occupies an important place in sugar the industry. Both the terms of number of mills as well as in terms of sugar production, its share has been around 30 per cent. In spite of this high growth rate in sugar production since 1970s the industry is beset with a number of problems like shortage in sugarcane supply, obsolete technologies, low capacity utilization, poor financial performance, and discriminatory government policies. Due to these problem most of the sugar mills have been victim of high costs of production which adds to their persistent losses. Thereby the number of sick sugar mills has been rising during the last two decades.
In order to investigate the causes of sickness in sugar industry several studies have been conducted and the major findings of most of the studies attribute the causes of sickness either to raw material shortages or to defective government policies towards sugar industry or to inefficient management. A limited attempt has been made to diagnose the problem of sickness by using financial ratios which has its applications in various other industry studies. To fill such a gap this present study is aimed:
1. Identifying the financial ratios which determine the health of the sugar industry.
2. With the help of multiple regression models, to identify the significance of the financial factors that influences the profitability of the sugar industries.
3. To suggest policy measures for the industry.
More specifically, to accomplish these issues, a few Sugar industries of Tamilnadu have been selected. The reason behind selecting the state is that as discussed above, Tamilnadu ranks second in terms of output, yield, and the number of sugar industries. Moreover, the peculiarity of the state is that in the state of Tamilnadu, the operation of cane industries can be divided into the industries run by government, industries run by cooperatives and the industries run by private.
In this context, the differences in ownership are expected to have differences in their operations which ultimately are expected to get reflected in the financial health of the industry.
The financial health of any firm can be measured in terms of certain crucial financial ratios. These ratios include: the liquidity ratio, the profitability ratio, the activity ratio, the financial leverage ratio and the shareholders’ ratio.
Based on the above logic the objectives framed for the study can be given as below:
1. To study the trends in the liquidity position of the selected sugarcane industries of Tamilnadu.
2. To examine the trends in the profitability position of the selected sugarcane industries of Tamilnadu.
3. To portray the trends in the shareholders ratio of the selected sugarcane industries of Tamilnadu.
4. To trace out the significance of the differences in the financial ratios among the selected industries of the state of Tamilnadu.
5. To estimate the productivity of the selected sugar industries of Tamilnadu.
6. To make SWOT analysis of the selected industries.
Following null hypotheses have been framed:
1. There is no difference among the sample firms in terms of their liquidity (Ho1).
2. There growth in the profitability is higher in private sector firms than government and cooperative sector firms (Ho2).
3. The productivity is more in private sector firms than cooperative and government owned firms (Ho3).
The study relies exclusively on the secondary data collected from the selected sample firms.
188.8.131.52 Secondary Data
As discussed above the prime objective of the present research is to study the financial and output performance of the selected sugar industries of the state of Tamilnadu. This requires the selection of sample sugar industries.
184.108.40.206 Selection of sample units
As it is given in Table-1, there are totally 38 sugar mills operating in various districts of the state of Tamilnadu. Among these mills, two are public sector mills, 16 are cooperative sugar mills and the remaining 20 are privately owned public limited companies.
Table 1: LIST OF SUGAR INDUSTRIES BY OWNERSHIP IN THE STATE OF TAMILNADU
illustration not visible in this excerpt
Source: Department of Statistics, Abstract of Statistics, Government of –Tamilnadu, 2008-09.
As it has already been indicated due to differences in the ownership of the firms it is expected that the management of the firms and as a result the financial performance of the firms are expected to be different. Hence, for the purpose of comparison, it is decided to select firms from all the three categories.
To have fair representativeness and to understand the overall performance of the sugar industries of Tamil Nadu, it was selected representative from all the three units using the random sampling technique. The sample units, for this purpose, were selected proportionately. Keeping one third of the sample selection would be a fair representative; one unit from the Public sector, six from Private sector and five firms from Cooperative sector was selected. This gave total sample sugar firms of 12 firms.
Table 2: LIST OF SAMPLE SUGAR INDUSTRIES BY OWNERSHIP IN THE STATE OF TAMILNADU
illustration not visible in this excerpt
Source: Department of Statistics, Abstract of Statistics, Government of Tamilnadu, 20008-09.
However, as discussed above, though 12 sugar units have been selected for the study, an examination of the performance has been made on these sample sugar units only in a broader manner by bifurcating the sample units into public sector units and cooperative units as one category and the private sugar units as another category. The reason behind clubbing the public sector sugar units and the cooperative units as one is that the cooperative sugar units are quasi government units, and hence the government’s policies towards these types of units are expected to be the same.
However, wherever necessary, an individual unit’s performance has also been made. The list of sample firms selected for the analysis is given in Table-2.
220.127.116.11 Period of study
As indicated already the present piece of research relied exclusively on the secondary data collected from various sources. Required secondary data have been collected for a period of 10 years from 1999-2000 to 2008-09.
18.104.22.168 Scope of the study
With the fast industrial development, the present study is a maiden attempt as far as the study area is concerned. The present study attempts to examine the financial performance on the one hand and the productivity of the sample sugar industry of Tamilnadu on the other. Hence, through the study the scope of the study extends from the Financial Accounting to Industrial Economics.
22.214.171.124 Limitations of the study
However, the study is also hedged with some limitations:
1. This study depends on the secondary data and the non sampling errors associated with such type of data bound to influence the results of analysis.
2. The data on financial variables are only ‘window dressing’ and hence, to the extent that there is deviation from the collected data, the estimates are expected to have biasedness.
3. The suggestions for the present study are provided on the basis of the findings of the study which in turn were influenced by the policy of the government and the executives of the firm. Any policy changes brought about either by the executives of the firms or by the government are expected to performance of the firms and hence the conclusion of the study. This implies that the suggestion provided is not applicable to dissimilar firms and hence generalization cannot be made to dissimilar situations.
4. In the case of public sector sugar units and cooperative sugar units, the entire share capital have been contributed by state government hence the analysis pertaining to the dividends and the related aspects could not be made.
5. Finally the data is limited to scenario till 2008-09 only because large number of units in Tamilnadu has not divulged data for 2009-10 and beyond for one or the other reason making it possible to confine the scope of study only till 2008-09.
CHAPTER I provides a discussion on the need for the study, objectives, hypotheses, the data source, the sampling design, and period of study, scope, and limitations of the present study.
CHAPTER II provides a sketch on the available review of relate studies.
CHAPTER III outlines the profile of the study area namely the State of Tamilnadu.
CHAPTER IV portrays the meaning, relevance of calculating the ratio analysis and productivity and the tools and techniques pertaining to the study.
CHAPTER V provides the discussion on the ratios calculated.
CHAPTER VI sketches out the production and productivity trends in the sample units.
CHAPTER VII summarizes the main findings of the study and outlines the emerging policy and research implications.
REVIEW OF RELATED STUDIES
The dual objective of the present research is to examine the financial performance of selected sugar industries in Tamilnadu and to analyze the trends in the productivity of this industry. Hence, to have methodological improvement in the project by plugging out the loop holes of the earlier studies, the studies pertaining to financial performance of industries and the studies pertaining to productivity has been reviewed in the present chapter.
In the present chapter it is attempted to provide a brief sketch on the earlier studies carried out in the area of financial performance and productivity.
The present aims at examining the performance of selected sugar industries. It was logically brought out in the earlier chapter that the financial performance of the industries has been influenced greatly by the productivity of the factor inputs more specifically, capital and labour. Hence, in the formulation of the methodology for the present chapter, an examination of the literature available in the areas of both financial performance and productivity becomes essential. Thus in the present chapter it is attempted to provide a sketch on the literature pertaining to these areas of financial performance and production and productivity of industry.
Beaver1 has taken a trend of thirty financial ratios for 79 paired failed and non-failed US firms and has found that there was a significant difference in the ratios of both category of Firms. In 1969 he has made a comparison of predictive ability of different ratios of the same paired firms, and he has identified 3 ratios namely, cash flow to total debt, net income to total assets and total debt to total assets ratio are the best indicators. He has identified that the ratios of failed firms differed significantly from those of non-failed firms and they deteriorated sharply during the 5 years prior to failure.
Altman2 took 66 firms in general and applied multiple discriminant analysis to discriminate the failed firms from the non-failed firms. On the basis of the weighted combination of 5 financial ratios, the weighted combination of working capital to total liabilities, cumulative retained earnings before interest and tax to total asset, market value of equity to book value of total debt and sales to total assets was able to predict the bankruptcy with 45% degree of accuracy. He also found that the predictive ability of the model declined very sharply when the number of years prior to the failure increased.
Smith3 in his research work has discussed the dual goals of profitability and liquidity and has suggested that achieving a trade-off between the two is the job of a financial manager. He uses rate of return as a measure of profitability and networking capital and current ratio as a measure of liquidity.
Smith4 in the second study discussed the individual and collective efforts of accounts receivable inventories and accounts payable and other accounts on probability and liquidity. The study observed that tightened inventory policy reduced unnecessary borrowing to a lower level than the faster collection of receivable or slower payments of current liabilities.
Ramamurthy5 admits profitability and solvency to be the twin objective of working capital management survival and growth of the company thus depend on its ability to meet the two sets of probability and solvency. He also viewed that if liquid asserts are adequate to pay off current liabilities, a feeling of confidence in the financial strength of the company automatically created and its reputation is sustained.
Kulshrestha6 in his study stressed that corporate liquidity is a vital factor in business. Excess liquidity, though a guarantor of solvency could reflect lower profitability, deterioration in managerial efficiency through complacency, increased speculation and unsatisfied expansion, extension of too liberal credit and dividend policies. On the other hand, poor liquidity may create frustration of business opportunities and weakening of morale. The control of liquidity requires active working capital management.
Bhabatosh Banerjee7 (1982) in his study on “corporate liquidity and profitability in India, has analyzed the trend of liquidity position of industries of medium and large public limited companies in the corporate sector of India during 1971-78 and has identified the relationship of liquidity with their profitability. His study has evidenced that in industry groups belonging to publishing and vice versa. But in other industry groups belonging tobacco, silk and rayon textiles, a rise in liquidity has been found to have a decline in profitability
Pandey and Bhat8 have analyzed the financial ratio patterns in Indian manufacturing industries, by taking 612 companies from R.B.I data tape for 1965-66 to 1984-85.They have identified 3 groups of ratios which contain the maximum amount of information about profitability and applied these ratios for the analysis of only manufacturing and processing industries. The 3 groups of financial ratios are
Vijay Kumar and Venkatachalam9 have made an empirical analysis on working capital and profitability, taking 13 firms from sugar industry, covering a period from 1982-83 to 1991-92. Correlation and regression analysis have been employed to be measure the impact of working capital ratios on profitability. Liquid ratio, receivable turnover ratio has been considered to measure their impact on profitability. The study has revealed that inventory turnover ratio and receivable turnover ratio have positively influenced the profitability and liquid ratio and cash turnover ratio have negatively influenced profitability.
George Schilling10 in his article on working capital role in maintaining corporate liquidity has explored how investment in working capital establishes the liquidity position of a company. He has identified the significance of cash conversion cycle in working capital management. He has explained how it is referred to as a company’s net liquidity float, and how it is used as a tool in arriving at optimum liquidity position. He has also illustrated how economic value added concept can be applied in working capital management. His study, thus establishes the fact that cash conversion cycle must be kept as short as possible but maintained at a length that both consistent with the current level of business activity and flexible enough to allow for achievement of overall corporate business goals as they adjust to changes in the market place.
Desai11 has made a comparative study of a few cotton mills of Ahmedabad in respect of their liquidity performance, their relationship with profitability the pattern of financing of current assets and the turnover of working capital. He has classified the selected firms into three group based on the size of the firms and it’s statistically tested to determine how for the observations of the study can be taken as a valid useful measure for future policy framework. It is observed that the liquidity and profitability of the firms are not influenced by the size.
Amit Mallika and Debasish sur12 have explored the relationship between ROI and several ratios relating to working capital management by conducting a case study on tea industry. They have used working capital management ratios and ROI for that analysis. Simple correlation, multiple correlations and multiple regression analysis have been applied to find out the relationship between ROI and each of the working capital ratio to assess the joint effect of those upon the profitability and to test the significance of cause and effect. They have also examined capital leverage of the tea industry. Their study has revealed that out of the 9 ratios selected for the study 5 ratios viz, working capital ratio, acid test ratio, current assets to sales ratio, inventory turnover ratio and debtor turnover ratio, have registered a negative correlation with ROI. The regression results have evidenced that only inventory turnover ratio has negative influence on profitability and the other 4 ratios have witnessed a positive influence on profitability. The working capital leverage of the company had recorded a fluctuating trend during the period under study and it had always been less than unity, proving that the increase in the profitability of the company was less than the proportion to the decrease in the working capital.
Desai13 has assessed the capital structure of Gujarat steel tubes ltd, for 10 years from 1980-81 to 1990-91 and found out that the real value of the equity shares has been a below their book value and also inconsistent during the entire period of study. He has found out that the company’s capital structures were imbalanced and over capitalized financial plans have continued for a long period of time, preventing the company from earning profits. In his study though he has discussed various models of prediction of sickness, he has applied Altman’s Z score model and has identified that from the year 1980-81, till the latest year 1990-91, Gujarat steel tubes ltd had been scoring less than the minimum cut off value of 2.675 as suggested by Altman. He has also applied Argent’s score system for a subjective evaluation of defects in management and accounting mistakes and symptoms. He has concluded by analyzing the reasons for the sickness of the company.
Mahammad Rafiqul lsslam14 conducted a study on “Profitability of fertilizer industry in Bangladesh” for a period from 1985-86 to 1994-95. Five out of seven fertilizer enterprises in Bangladesh under the control of Bangladesh chemical industries corporation, have been taken for the study and he has examined the earning capacity of the selected enterprises. He has also identified the responsible factors which affects the earning power of such units. Ratio analysis has been used and he has found out that none of the selected unit’s returns were consistent and all the units were played with declining profits. Higher cost of production, poor investment policy, defective capital structure, industrial unrest and frequent disruption of production process due to power cuts were found to be some of the reasons attributable for the uneven situation.
Sahu15 in his study on, analysis of corporate profitability-a multivariate approach, has made an empirical study based on the secondary data from a sample of 100 non-financial, on-government public limited companies, in eastern India for a period of ten years from 1984-85 to 1993-94. He chose profitability ratios and interest coverage ratio for the analysis. Cross sectional spearmen rank correlation of the profitability ratios for all the companies have been calculated and applied for selecting the ratio for analysis. He arrived at a single index to measure the composite profitability of a firm and ranked the companies based on the overall score.
Debasishsur16 has made a comparative analysis of liquidity management of 4 major companies in Indian power sector, covering a period from 1987-88 to 1996-97. The techniques of radio analysis, Motaal’s comprehensive rank test, and simple statistical techniques like measures of central tendency and spearman’s rank correlation analysis have been used for the analysis. The liquidity ratios like current ratio, quick ratio, current assets to total assets ratio, inventory turnover ratio and debtors turnover ratio have been used for comparison and suitable interpretations have been made Motaal’s comprehensive test is used to analysis the liquidity more precisely. To measure the closeness of association between liquidity and profitability of the companies, Spearman’s rank correlation co-efficient have been applied. The study has revealed that the inventory turnover ratio has a positive impact on profitability whereas the liquidity ratio, working capital turnover ratio and working capital to total asset have negatively influenced the profitability.
Sathish Andre varshney17 has made a case study on trade credit and company liquidity with special reference to steel authority of India and TATA iron and steel Co, to find out more liquid companies give relatively more net trade credit in these years. The period covered has been 1985-86—1996-97 and the significant variables chosen are current asset, current liabilities, liquid assets, networking capital, networking assets, cash and short term investments and cost of goods sold. Ratio analysis and multiple regression analysis have been employed. He has inferred a positive correlation of very high degree among the variables chosen. Liquidity has been taken as the dependent variable and inventory turnover and average collection period, debtor turnover and average collection period have been taken as independent. The impact of average change in net trade credit and change in stick in relation to liquidity has also been measured and a significant linear relationship is found to be existent.
Mazumder and Ghoshal18 examined the strengths and weaknesses of Indian steel industry. They prepared a SWOT analysis and identified major strengths, weaknesses, opportunities and threats in Indian steel industry. Major strengths, according to their study, included the availability of iron ore and cheaper labour. weaknesses included higher cost of capital, low labour productivity, and opportunities included wider domestic market, growth of allied sectors and major threats included substitutes and technological changes. The study concluded that if the threats and weaknesses are overcome, there will be a turnaround in the Indian steel industry.
Elbaum19 emphasized the role of government intervention and the growth of steel industry in Japan. He concluded that without industrial policy intervention, Japan might have never become a major steel producer, for it had little source of comparative advantage apart from the technical expertise and capital investments it gradually accumulated over a long extended period. At the least, without state intervention, industry development would have been substantially delayed.
Planning Commission20 necessitated the need for government intervention in the form of policy measures to make India a global producer. Some of the important areas where government support is required are – providing essential infrastructural facilities; assuring easy availability of critical inputs such as iron ore, coal, gas and power; provision of training facility for manpower development and creation of a consolidated and reliable data base for informed decision making by all stakeholders.
Mehta21 also estimated a productivity growth of 8.8 per cent in the Indian steel industry during the period 1953 to 1965. He also found the evidence of capital deepening in the production process of steel during this period.
Brahmananda22 estimated the single and total factor productivity for the sectors and sub-sectors of Indian economy during 1950-51 to 1980-81. During this period, he found that the capital productivity declined by as much as 40 per cent, but the labour productivity went up to 2 ¼ times in the registered Indian manufacturing sector. He estimated that this sector witnessed an increase in the total factor productivity at an annual rate of 0.70 per cent during 1950-51 to 1970-71 and thereafter he found it declining during 1970-71 to 1980-81.
Ahluwalia23 studied the productivity growth in Indian manufacturing during 1959-60 and 1979-80. He applied both Solow and Translog measures and found that the results of both the measures were similar. The study estimated that rubber products and miscellaneous manufacturing industries suffering a sharp decline in the total factor productivity growth whereas the foot wears and furniture industries registered a high growth rate at around 2.00 and 3.00 per cent per annum. The study estimated the declining total factor productivity growth at a rate between 0.2 and 1.3 per cent per annum during mid 60s and 70s.
Alagh24 studied the performance of Indian industrial sector at the sect-oral level by the growth rates of three measures namely index of industrial production (IIP), valued of output and net value added. He found similar pattern with varying rates of growth. In his study, he considered two time periods, 1972-73 to 1975-76 and 1976-77 to 1983-84. At the aggregate level, the industrial growth increased from 3 per cent to 4.58 per cent in IIP and 4.6 per cent to 7.6 per cent in value of production and in the case of net value added, it increased from 3.49 per cent to 5.76 per cent during the study period.
Romer25 suggested that the technological change has been an important factor to contribute output growth. Technological change arises in large part because of intentional actions taken by people who respond to market incentives and hence the technical change happens more to be endogenous rather than exogenous. In his study, he concluded that the stock of human capital (levels of education and experience) accelerated the growth but the growth did not depend on total size of labour force or the population. He found that international trade facilitates free flow of new ideas and technologies and reduces the idea-gap, which was a major source of spillovers and growth. Most of the new ideas and technologies were developed in developed countries and trade with them helped in realizing these dynamic gains to promote productivity. He further found that the use of non-rivalry nature (use of a blue print of a technology or new idea by one agent does not preclude use by other agents) of technological change was a source of increasing returns to scale and sustained long run growth.
Kumari26 estimated total and partial factor productivity and elasticity of factor substitution of public sector enterprises for 11 groups of industries in India during 1971-72 and 1987-88. In the estimation, she applied the three basic measures of productivity estimation, Kendrick, Solow and Divisia index. The study found significant variations in the growth levels of factor productivity and substitution. For chemical industries, the study estimated the annual growth of total factor productivity at 4.19 percent, 4.93 per cent and 4.80 per cent in Kendrick, Solow and Translog measures respectively. The annual growth of labour productivity at 8.39 and capital productivity at 2.82 percent was also estimated by the study. In the estimation of factor substitution, the Cobb-Douglas production function estimated constant returns to scale and the CES production function estimated unit elasticity of factor substitution for chemical industries in the Indian public sector.
Majumdar27 studied the pattern of productivity growth of Indian Industrial sector since 1950s. The study empirically proved the positive impact of liberalization measures on productivity. The reforms process was not exacerbated entry threats for the sitting incumbents in Indian industry, but the environment was equally competitive for the new entrants. Attainment of efficiency was a key survival criterion in such situations and the Indian firms had so far yielded positive efficiency out comes. The adoption of technological and organizational innovations had a very large impact on productivity at the firm level. The policy changes that took place in India in the 1990s did significantly enhance potential opportunities on one hand and increase the uncertainties and ambiguities levels on the other.
Aitken and Harrison28 found two offsetting effects of FDI. Domestically owned firms might 'benefit' from the presence of foreign firms, when the workers of foreign firms left the foreign firms, human capital might become available to domestic firms. Firm specific knowledge of foreign firms (technology) might 'spillover' to domestic firms as the domestic firms were exposed to new products, production and marketing techniques. Foreign firms might also act as a stable source of demand for inputs in an industry, which could benefit upstream domestic firms by allowing them to train and maintain relationships with experienced employees. In all these cases, foreign presence would raise the productivity of domestically owned firms. On the contrary, foreign presence could also 'reduce' productivity of domestic firms particularly in short run. A foreign firm with lower costs would have an incentive to increase production relative to its domestic competitor. In this environment, entering foreign firms producing for the local market could draw demand from domestic firms, causing them to cut production and to a fall in domestic productivity.
Desai29 studied the problems in the technological transfers in India. According to him, technological transfers did not take place properly. He highlighted the major problems in the technological transfers, the inadequacy of knowledge and skills to exploit the existing technology, lack of confidence in the successful exploitation of the projects on commercial basis and poor R&D activities. Bureaucratic inefficiency, high price of technology and import restrictions is some of the impediments to the effective technological transfers.
Mongia and Sathaye30 studied the productivity trends in selected 6 energy intensive industries including Steel industry with an elaborate survey on productivity during the period 1947-1998. They applied Kendrick, Solow and Translog index to estimate productivity growth and found the total factor productivity in steel industry was in the range of -1.6 per cent and 0.07 per cent.
Athreya and Kapur31 studied the linkage between the policy towards foreign capital and its contribution to the Indian economy. They also explained the long run conduct and performance of foreign controlled firms relative to domestic firms. In 1950s, the Indian government, in order to achieve the plan targets, allowed foreign equity participation to meet the foreign exchange needs of investment projects. In 1960s, the selectivity of government policy changed the pattern of foreign capital towards manufacturing and technology intensive industries. In 1970s, the intervention of FERA to dilute the 40 per cent of foreign equity and the exception of ‘technology intensive’ export intensive and core sector, proved more hostile to new foreign investment than the existing foreign affiliates. In 1980s, the policies of India were softened to attract foreign investment but there was only a slight increase and most part, Indian industry came to rely on foreign debt capital to meet its foreign exchange needs. The enormous increase in FDI was realized only in 1990s when India agreed to implement the reform measures in tune with IMF. The study found that the advertising intensity was greater for foreign controlled firms while expenditure on technology imports was greater for domestic firms. Export intensity was quite similar for both the firms. Technology inflows could also improve the productivity of domestic firms through spillovers as better productions and management techniques in the host country.
Mahadevan and Kalirajan32 examined the criticism leveled against Singapore for experiencing insigniﬁcant total factor productivity (TFP) growth. This paper examines whether this criticism is valid in the context of the manufacturing sector of Singapore. Using new data and the stochastic production frontier approach, TFP growth is composed into technological progress and changes in technical efficiency. While the results could not reject the hypothesis that Singapore’s output growth is mostly input-driven, they show that, despite technological progress, technical inefficiency is the cause for the low and declining TFP growth in the manufacturing sector.
Singh33 estimated the total factor productivity (TFP) in Indian manufacturing sector during 1973-74 to 1993-94 for the ten industries, which constituted about 70 per cent of GDP in India. He found that food products industry showed improvement in TFP during the period and recorded a trend growth rate of 2.68 per cent followed by transport equipments industry at 2.19 per cent. The chemical industries having the highest weight of 11.4 per cent in GDP showed moderately rising trend in TFP and worked out to be 0.28 per cent per annum during the period. The study listed out the factors responsible for the growth in productivity and output namely:
i. increase in capacity utilization;
ii. efficient allocation of resources;
iii. generation of economies of scale;
iv. spillovers of external economies among industries;
v. increase in specialization and technological improvements in response to greater competition abroad;
vi. increase in R&D expenditure and
vii. increase in export and trade orientation.
In addition to these factors, the policy initiatives of economic reforms namely
a. removal of economic controls;
b. entry of MNCs;
d. financial sector reforms and
e. liberalization of trade etc., would also enhance the TFP in the country.
The study concluded that the recent policy initiatives aimed at the removal of controls and creation of competition in the industrial sector had important implications for the TFP and the process of economic growth. These changes have created a more conducive and competitive environment in the economy and this would have favorable effects on the total factor productivity.
Sharma and Upadhyay34 studied the components of total factor productivity in the Indian fertilizer industry. They used the cost function to estimate the scale of economies, technical progress, and elasticity of substitution, scale bias and technical bias. The study estimated decreasing returns to scale and increasing technical progress in the fertilizer industry. The study further found that the technical bias and scale bias were in favor of material input. With regard to factor substitution, they found that the substitution between capital and energy and capital and material led to an important implication that output could be increased by using more material and even without increasing the capacity.
Goldar and others35 in their paper studied the effect of ownership on efficiency of engineering firms in India with a comparison of technical efficiency among three groups of firms viz., firms with foreign ownership, domestically owned private sector firms and public sector firms. The study explained that the foreign ownership firms had greater efficiency than the domestic firms. It was so because, in a developing country, the foreign firms had relatively better access to advanced technology. The study concluded that the foreign firms in Indian engineering sector had greater technical efficiency than that of domestic firms and there was no significant variation in technical efficiency between private and public sector firms. The study pointed out a fact that there were indications of a process of efficiency convergence, that is, the domestic firms tended to 'catch-up' with foreign firms in terms of technical efficiency. Among the various factors responsible for inter firm variation in technical efficiency, the import intensity played a significant role. The liberalization of imports increased the access of firm to imported inputs and capital goods and thus contributed considerably to increase the efficiency of engineering firms.
Sampathkumar36 examined the assumption of homogeneity in the estimation of total factor productivity at the aggregate level in the Indian chemical sector. He classified the entire sector into five major sub-sectors and each sub-sector has further been divided into small and large firms. The study further found that there are productivity variations as the size of the firms differs. It was found in his study that large firms tend to have higher level of TFPG than the small firms.
Kim37 decomposed total factor productivity (TFP) growth into technical progress (TP), technical efficiency change (TEC), allocate efficiency change (AEC) and scale efficiency change (SEC) to Malaysian manufacturing data from 2000 to 2004. The paper also identified the factors that determine each TFP component. Empirical results show that TFP was driven mainly by TP, but plagued by deteriorating TEC. The skill and quality of workers represent the most important determinants of TE, whereas foreign ownership, imports and employee quality represent those of TP. The impact of firm size on SEC differed across industries, and AEC determinants were identified.
Nwaokoro38 examined the impact of the trade restrictions on steel imports in order to protect the US steel industry. During the period of 1963 to 1988, the industry experienced a tremendous decline in its output. Trade restrictions are implemented to limit steel imports. The overall goal of this study is to estimate the impact of the steel trade restriction regimes on the output of the industry. Beside foreign competition, the study addresses the impact of other factors - other shipments (non-steel shipments) and the prices of steel substitutes - aluminum, and plastic and rubber that may have also caused variation in steel production. The study estimated insignificant regression results which implied that the protection regimes were not statistically significant to enhance output expansion.
Their study observed a declining trend in profitability in relation to sales shareholders equity and total investment the impact of which increased with the increasing interest burden. It was also found that these 3 groups of ratios of profitability showed a consistent declining trend across most of the firms.
Hideaki et.al.39 studied the productivity dispersions across workers, firms and industrial sectors. Empirical study of data on Japanese firm’s shows that they all obey the Pareto law, and also that the Pareto index decreases with the level of aggregation. In order to explain these two stylized facts, they proposed a theoretical framework built upon the basic principle of statistical physics. In this framework, they employed the concept of super statistics, which accommodates fluctuations of aggregate demand. Their analysis demonstrates that the allocation of production factors depends crucially on the level of aggregate demand. The frontier of the production possibility set is a never-never land. The higher the level of aggregate demand is, the closer the economy is to the frontier of production possibility set.
Leo Sveikauskas40 in his paper reviewed the literature on R&D to provide guidelines for recent efforts to include R&D in the national income accounts. The main conclusions are: 1. Measures of R&D as an asset held by a particular owner must be complemented by estimates of the spillover effect of R&D in order to obtain a reliable measure of the overall effect of R&D on productivity growth. 2. If research financed by the government and research financed by business are both counted as investment, some double counting occurs and growth accounting analysis overstates the role of research relative to other factors. 3. The overall rate of return to R&D is very large, perhaps 25 percent as a private return and a total of 65 percent for social returns. However, these returns apply only to privately financed R&D in industry. Returns to many forms of publicly financed R&D are near zero. 4. Firm R&D should be allocated to the different industries in which a firm produces, rather than all credited to the firm’s main industry. An allocation procedure is proposed. 5. Much further work needs to be carried out to understand how R&D conducted in the richest countries is transmitted to developing countries. Detailed microeconomic data on firms or establishments in developing nations will be necessary to understand the channels of technology transfer more fully.
According to Vanish Kathuria et.al.41 empirical studies on total factor productivity growth (TFPG) in developing countries highlight trade openness, research and development and market structure as being the most important determinants of TFPG. The role of institutions remains overlooked in the literature on the determinants of TFPG. In their paper, they looked into the role of institutional quality as captured by effective state-business relationships (SBRs) in influencing TFPG, using Indian manufacturing as a case-study. To compute TFPG, they used firm level data for both the formal and informal manufacturing sector. They corrected for the simultaneity bias associated with the production function approach for TFPG estimation by employing a method developed by Levinsohn and Petrin. They proposed measures of effective SBRs for 15 Indian States over the period 1994-2005, and then use them in TFP growth equations to estimate the effect of SBR on TFPG. The results indicated that SBR has positively affected the TFP growth of Indian industry. The effect however is primarily for the formal sector. Their paper has two important methodological strengths. Firstly, we are able to test for the effects of effective SBR on TFPG for the combined manufacturing sector, which includes both the formal and informal segments of the manufacturing sector. Previous studies on TFPG in Indian manufacturing have estimated TFPG only for the formal manufacturing sector. This is a serious omission as nearly 35 per cent of output and 85 per cent of employment in Indian manufacturing are in the informal sector. A second strength of the empirical analysis is that they used the Levinsohn-Petrin method of calculating total factor productivity growth, which addresses the simultaneity bias in standard productivity estimates.
 Government of India, Annual Survey of Industries, 2008-09.
 Government of India, Economic Survey, 2009-10.
 Government of India, Economic/Survey, 2009-10.
 C.B. Memoria, Agricultural Problems in India, Kitab Mahal, 2007.
 S.Sankaran, Agricultural Economy in India, Margham Publications, 2010.
 United Nations, Food and Agricultural Organisation.2010.
 Government of India, Economic Survey, 2009-10.
Der GRIN Verlag hat sich seit 1998 auf die Veröffentlichung akademischer eBooks und Bücher spezialisiert. Der GRIN Verlag steht damit als erstes Unternehmen für User Generated Quality Content. Die Verlagsseiten GRIN.com, Hausarbeiten.de und Diplomarbeiten24 bieten für Hochschullehrer, Absolventen und Studenten die ideale Plattform, wissenschaftliche Texte wie Hausarbeiten, Referate, Bachelorarbeiten, Masterarbeiten, Diplomarbeiten, Dissertationen und wissenschaftliche Aufsätze einem breiten Publikum zu präsentieren.
Kostenfreie Veröffentlichung: Hausarbeit, Bachelorarbeit, Diplomarbeit, Dissertation, Masterarbeit, Interpretation oder Referat jetzt veröffentlichen!