THE INDIAN LIFE INSURANCE INDUSTRY. DEPARTMENT OF ECONOMICS.
THE M.S.UNIVERSITY OF BARODA KSHETRIMAYUM SOBITA DEVI.
A STUDY OF THE IMPACT OF LIBERALIZATION ON THE INDIAN LIFE INSURANCE INDUSTRY
DEPARTMENT OF ECONOMICS THE M.S.UNIVERSITY OF BARODA VADODARA 390002
KSHETRIMAYUM SOBITA DEVI
DEDICATED TO BABA - EMA & ALL MY TEACHERS WITH LOVE AND REVERENCE
A STUDY OF THE IMPACT OF LIBERALIZATION ON THE INDIAN LIFE INSURANCE INDUSTRY
Thesis submitted to The Maharaja Sayajirao University of Baroda, Vadodara for the award of DEGREE OF DOCOTOR OF PHILLOSOPHY IN ECONOMICS
By KSHETRIMAYUM SOBITA DEVI
Under the Guidance of DR.T. R. BISHNOI
DEPARTMENT OF ECONOMICS FACULTY OF ARTS THE MAHARAJA SAYAJIRAO UNIVERSITY OF BARODA VADODARA-390002
2011
CERTIFICATE
This is to certify that the thesis entitled ―A Study of the Impact of Liberalization on the Indian Life Insurance Industry‖ incorporates the result of an independent investigation carried out by Kshetrimayum Sobita Devi under my guidance and supervision. It has not previously formed the basis for the award of any Degree, Diploma, Fellowship or any other similar title for this or any other university.
(KSHETRIMAYUM SOBITA DEVI)
Candidate
Place: Vadodara Date:
(DR. T.R. BISHNOI)
Guiding Teacher
PREFACE
Insurance market worldwide continues to undertake procompetitive reform. As a consequent, dozens of countries have deregulated and liberalized their insurance markets. Insurance sector in India has also gone through the process of reforms following the recommendation of Malhotra Committee‘s report submitted in1996. In the progression of events towards liberalization Insurance Regulatory and Development Bill (IRDA) was passed in 1999. This along with amendments to the LIC (and also GIC) Acts paves way for the entry of private players. Since then, India‘s insurance sector is undergoing a radical change for the past decade.
This study tries to give an overview of the impacts of liberalization and deregulation processes in Indian life insurance industry. With the liberalization, there is entry of private companies in Insurance sector with attendant changes. There are changes in terms of market concentration, product innovations, marketing strategy and use of technology for processes, all attributable to liberalization and deregulation. So it is very important to evaluate and understand various impacts in terms of competition, growth, development and future prospects in the sector. The issues covered in this analysis also include the efficiency and productivity improvement in the life insurance industry due to deregulation. The main research questions explored in the study include
1. What is the present scenario of the industry? How different it is from the pre liberalization scenario? What changes may be brought about by competition in the industry?
i
2. How far the life insurance companies have improved in terms of the efficiency and productivity? 3. How did liberalization contributed in product innovation, risk management, customer service parameters in life insurance industry? 4. What are other implications of liberalization in life insurance industry?
The study covers the period from 2001-02 to 2009-10. The number of insurers taken varies from analysis to analysis. In some cases, 15 insurers are taken for the study instead of all the insurers, as the data pertaining to new entrants are not available or may deteriorate the overall result. It primarily depends on the secondary data available with Insurance Regulatory and Developmental Authority of India (IRDA), Annual reports of respective life insurance companies in India. Also text books, national as well as international articles and dailies are referred to collect relevant and required data. The thesis is divided into seven chapters in all. The first chapter gives introduction of liberalization and deregulation, purpose importance
of research. The
second
chapter
provides
a
and brief
overview of the Indian life insurance industry from the pre nationalization to nationalization and further to liberalization
breaking the
monopoly
regime of LIC of India. Chapter three confines to literature survey mostly related at
to the liberalization
home
and
abroad,
efficiency
and
productivity. Chapter
impact
of
liberalization
its
impact
and deregulation related studies on
various
four discusses
areas specially the possible
on concentration. Chapter five is devoted
to the measurement and analysis
of efficiency and productivity ii
of
Indian
life insurance industry. Chapter six is confined to innovations in
the life insurance industry. Chapter
seven
gives conclusions
of
study and policy suggestions based on the findings.
ACKNOWLEDGEMENTS
In the preparation of this study, lots of people have helped me in some way or the other and therefore acknowledgements are due to them without whose co-operation, support, encouragement and guidance this research study could not have been completed.
This study was done under the guidance of Dr. T. R. Bishnoi. I would like to acknowledge my deepest appreciation and undying gratitude for the scholarly guidance, constant encouragement and confidence he has given to me. He has been my mentor not only during the course of this work, but also in my entire graduate study. I am truly indebted to him.
I am extremely thankful to all my teachers, Department of Economics, the M. S. University of Baroda. My teachers, Prof. P. R. Joshi, Head, Department of Economics, Prof B. S. Kantawala, Prof. A. S. Rao, Prof. S.M Joshi, Prof. U. M. Naik, Prof. A.U Nanavati, Dr. A.S Mehta and Dr. V. K Padaria are the ones without whom, I may not be where I am today. I would also like to thank Hemant, Maya and the administrative staffs of the Department of Economics for their timely help and cooperation during the course of my work.
I express my gratitude to the University for providing me with an opportunity to conduct my Ph D work and also for the three years Ph D scholarship awarded to me. I also appreciate the help and co-operation iii
received from the librarian and the staffs of the Hansa Mehta Library of the University.
I am extremely grateful to Dr Dilip K. Chellani, Head, Department of Banking and Insurance, Faculty of Commerce, the M.S. University of Baroda for his suggestion and guidance. My grateful thanks are also due to Amitkumar Vaidya, the dy.Manager LIC of India, Vadodara for helping me to access the library at its office premises. My thanks are extended to Dr. Ramesh D. Smarth, Retd. General Manger, GIC and Mr. S.J Gidwani, Secretary General, Insurance Institute of India, Mumbai for contributing much to my understanding of the subject.
My fond thanks are due to my friends, Santa, Nanda, Menaka, Sofia, and Vishakha and also to my juniors Chandabadani and Seema. They gave lots of energy and freshness to life. Moments with them during the study and hostel life will be cherished throughout.
In a very special category, my love and respect are extended to my Baba and Ema. They are and will always be source of inspiration, encouragement and strength to me throughout this work and my life. I am also indebted to my sisters and brothers. Eche, Chemcha, Echao, Chenao, Bochou and Bocha‘s support and eagerness to complete my work was an inspiration to me. Guna‘s support as well pressure to complete my work was truly helpful. Lastly but not the least, I express my sincere thanks to Cha for believing in me.
iv
No one mentioned above shares any responsibility for any error, if any, or conclusion arrived at in this thesis, for which I take full responsibility.
Vadodara
(KSHETRIMAYUM SOBITA DEVI)
December, 2011
v
CONTENTS
PAGE PREFACE
i-iii
ACKNOWLEDGEMENTS
iii-v
LIST OF TABLES
viii-xii
LIST OF BOXES, CHARTS, FIGURES AND APPENDICES CHAPTER I:
INTRODUCTION
1.1
Liberalization and Deregulation Concept
1.2
Purpose and Methodology of the Study
1.3
Importance and Limitations of the Study
1.4
Chapter Outline
CHAPTER II:
1-8
AN OVERVIEW OF INDIAN LIFE INSURANCE INDUSTRY
2.1
Life Insurance: Defined
2.2
Indian Life Insurance History
2.2.1
Pre-nationalization Phase
2.2.2
Nationalization and LIC‘s Monopoly Regime
2.2.3
Post Liberalization Industry Scenario
2.3
xi-xii
9-30
Summary
CHAPTER III:
LITERATURE REVIEW
31-52
CHAPTER IV:
CONCENTRATION
53-84
4.1
Concentration-Concept
4.2
Methodology
4.3
Results Analysis
4.4
Insurance Penetration and Density
4.5
Spread of Insurance Business in Rural Areas
4.6
Summary
vi
CHAPTER V: 5.1
EFFICIENCY AND PRODUCTIVITY Efficiency
5.1.1
Concept of Efficiency
5.1.2
Estimation Technique
5.1.3
Result Analysis
5.1.4
Main Points
5.2
Productivity
5.2.1
Concepts
5.2.2
Methodology
5.2. 3
Results Analysis
5.2.4
Main Points
CHAPTER VI:
INNOVATION
6.1
Product
6.2
Quality of Customer Services
6.3
Technology
6.4
Marketing Strategies
6.5
Summary
CHAPTER VII
85-129
CONCLUSION
7.1
Summary of the Study
7.2
Directions for Future Research
130-166
167-176
APPENDICES
177-181
BIBLIOGRAPHY
182-200
vii
LIST OF TABLES TABLE
Page
2.1
Number of life insurance companies: 1929-1939
15
2.2
Growth of life business in India: 1914-1948
16
2.3
Life insurance business in force in India: 1949-1955
17
2.4
Growth statistics of LIC of India
19
2.5
Performance of LIC from 1991-92 to 1999-00: Individual Assurance
2.6
20
Performance of total life business of LIC in force from 1991-92 to 1999-00
2.7
20
Premium underwritten by life insurers and their market shares (Rs Crores)
27
2.8
New policies issued and their market share
29
4.1
Market share and harfindahl index of all life insurers (total premium)
4.2
58
Market share and harfindahl index for private life insurers (total premium)
60
4.3
Entropy for all life insurers (total premium)
61
4.4
Entropy of all private life insurance firms (total premium)
4.5
Market share and HHI of life insurance industry (equity share capital)
4.6
62
64
Market share and HHI of all private life insurance firms (equity share capital)
65
4.7
Entropy of all life insurance firms (equity share capital)
67
4.8
Entropy of all private life insurance firms
4.9
(equity share capital)
68
Market share and HHI of all life insurance firms (total assets)
70
4.10 Market share and HHI of all private life insurance firms (total assets)
71
viii
4.11 Entropy for all life insurance firms (total assets)
72
4.12 Entropy of all private life insurance firms (total assets)
73
4.13 HHI, E, CR1and CR4 compared (total premium)
76
4.14 HHI, E, CR1 and CR4 compared (equity share Capital)
76
4.15 HHI, E, CR1 and CR4 compared (total assets)
76
4.16 Insurance penetration and density in India (1999-2009)
79
4.17 Growth of LIC of India‘s rural new business (1994-95 to 2009-10)
80
4.18 Growth of postal life insurance (PLI) and rural postal life insurance (RPLI) in India (2001-02 to 2009-10)
82
5.1
Descriptive statistics of inputs (Rs in lakh)
105
5.2
Descriptive statistics of outputs (Rs in lakh)
105
5.3
Gross efficiency scores at constant return to scale (CRS) i.e. technical efficiency (TE)
5.4
107
Efficiency scores at variable return to scale (VRS) i.e. pure technical efficiency (PTE)
108
5.5
Scale efficiency scores of the companies
110
5.6
Year-wise CRS, VRS and SE of insurers
112
5.7
Number of insurers by level VRS efficiency
115
5.8
Number of insurers by level of scale efficiency
115
5.9
Number of insurers by level of CRS efficiency
116
5.10 Malmquist productivity index (MPI) of the insurers
126
5.11 MPI decomposed into technical efficiency change (TEC) and technical change (TC)
127
6.1
Number of products offered by life insurers in India
132
6.2
Number of riders attached to life insurance policies in India
133
6.3
Trends in life insurance business-unit linked insurance plans
133
6.4
Individual business within India-business in force (Number of policies in ‗000)
134
ix
6.5
Link and non linked business to total business in force (percent)
6.6
135
Total death claims (claim pending at start of year +claims initiated) in number of policies
139
6.7
Total death claims paid in number of policies
140
6.8
Percentage of death claims paid to total claims in number of policies
6.9
141
Total death claims (claim pending at start of year+claims initiated) in benefit amount for all firms
142
6.10 Total death claims paid in benefit amount by all insurance firms
143
6.11 Percentage of death claims paid to total claims in benefit amount
144
6.12 Top 5 in the highest percentage of claim paid in benefit amount
145
6.13 Death claim repudiated in number of policies
147
6.14 Percentage of claims repudiated to total claims in number of policies
148
6.15 Death claim repudiated in benefit amount by all firms
149
6.16 Percentage of claims repudiated to total claims in benefit amount by all firms
150
6.17 Top 5 in the lowest percentage of claim repudiated in benefit amount
151
6.18 Death claims pending at the end of the year in number of policies
153
6.19 Percentage of death claims pending to total claims in number of policies
154
6.20 Death claims pending in benefit amount at the end of
x
the year
155
6.21 Percentage of death claims pending to total claims in benefit amount
156
6.22 Top 5 in the lowest percentage of claim pending in benefit amount
157
6.23 Status of grievances-life insurers
159
6.24 Performance of Ombudsman –life insurance
160
6.25 Information technology and related expenses, Rs. 000‘s
162
6.26 New business underwritten (Life) through various intermediaries - in percent 7.1
164
Summary of CR4 (%) in terms of total premium, equity share capital and total assets
7.2
170
Summary of average efficiency and productivity of
7.3
Indian life insurance industry
172
Summary of distribution of insurers by level of efficiency
172
LIST OF BOXES, CHARTS, FIGURES AND APPENDICES
BOX 2.a
Milestones of insurance regulations in the 20th Century
11
2.b
Life insurance companies in India
24
5.1
Summary of studies of insurance industry‘s efficiency
88
5.2
Overview of input and output used by authors in DEA based efficiency analysis of Life Insurance firms
101
CHART 4.1
HHI and Entropy of life insurance industry (total premium)
63
4.2
HHI and Entropy of private life insurers (total premium)
63
xi
4.3
HHI and Entropy of life insurance industry (equity share capital)
69
4.4
HHI and Entropy of private life insurers (equity share capital)
69
4.5
HHI and Entropy of life insurance industry (total assets)
74
4.6
HHI and Entropy of private life insurers (total assets)
74
6.1
Chart showing performance of Ombudsman-Life insurance industry
160
FIGURE 5.1
Efficiency frontiers under CRS and VRS
5.2
Malmquist Index of TFP (Input based) and Input distance Functions
92
121
APPENDIX A.1
Total life insurance premium (Rs Crore)
177
A.2
Equity share capital if life insurers (Rs Crores)
178
A.3
Total assets of life insurers (Rs Crores)
179
A.4
Values of input variables viz. commission expense and operating expense (in lakh)
A.5
180
Values of output variables viz. premium and benefit paid (in lakh)
181
xii
CHAPTER I INTRODUCTION Liberalization of domestic financial market had been common characteristic in many countries which includes industrially developed countries as well as the developing countries. However, there is a substantial amount of debate in economic research benefits
of deregulation. The
theoretical
regarding
rationale
for
the
industry
deregulation is rooted in neoclassical economic studies beginning in the 1960s which questioned the merits of economic regulation (Oetzel J M and Sudeshna Ghosh Banerjee, 2007). Neoclassical economists tend to support deregulation and often argue that countries should rapidly deregulate
industries
1971). In theory,
and
this
professionalism in the
liberalize markets (Becker, 1983; Stigler,
will
promote
greater
efficiency,
greater
market, introduction of more products and
services and increase in the size of the market. It is argued that multinationals
can also help to create more consumer confidence
and improve market conduct practices. They can also enhance the stability and
image
of
the
industry
by
encouraging
financial
transparency and the adherence to internationally accepted disclosure requirements.
In contrast, a growing number of economists are
questioning the benefits of deregulation (Chang and Xu, 2006; Rodrik, 2001; Winters, 2004; Snyder, 2001). In the context services, deregulation
generally
leads to market
of insurance liberalization.
Countries are liberalizing their insurance market at different rates and to
different degree. Additionally, each country has its own unique
political and economic characteristics .Furthermore, the best route from monopoly to competition differs substantially in different setting. Therefore there is no single set of direction that can guide the challenging 1
journey from monopoly to competition. And so outcomes of deregulation policy differ from country and country and need to analyze each of them separately to understand the end result of a particular liberalization and deregulation policy.
In India, New Economic Policy (NEP) was introduced in 1991 with the
main idea of globalization, privatization, deregulation and
liberalization. As a consequent, insurance market liberalization process was initiated in 1993 and was finally opened in 1999. Since then there has been changes in insurance market and in the basic structure of business. The life insurance market was opened to private players because of low penetration of life insurance, non availability of customer oriented products, low level of customer satisfaction, higher premium rates and lack of professionalism on the part of the insurer and a very low spread of life insurance in the country. In addition, signing of the GATT (General Agreement on Tariff and Trade) made way for the opening of the insurance sector to global players. The opening of the Indian insurance sector was aimed at fostering competition and innovation with greater variety of products and growth of the insurance business. This thesis will highlight the post liberalization scenario in the life insurance sector in India to see whether the objective behind the opening of the life insurance have been achieved or on the right direction towards achieving the objective laid down by the policy makers.
1.1-Liberalization and Deregulation Concepts: Liberalization denotes a reduction of government or other barrier to market access, especially as relates to foreign insurers (Skipper, 1996) and deregulation relates to lessening of national regulation on 2
existing firms in the industry. Liberalization and deregulation seek to improve economic welfare by bringing a more efficient allocation of the country‘s resources in the long run. A perfectly deregulated and liberalized insurance market would therefore means that the regulations are limited to indispensable minimum and concerns the questions such as (Sterzynski Maciej, 2003) a) Who is authorized to operate an insurance business? b) How many firms will be allowed to operate? c) What products might be considered as insurance products? d) What should be the price of insurance? e) What should be prudential norms relating to capital, liquidity, quality of risk and service benchmarks? f) What are the distribution channels? Regulation means infringement of free competition on the markets. It occurs through legal limitations on the competition or by agreements among market participants. The deregulation is an occurrence opposite to regulation. It means a reduction of existing limits or controls of the state on a lower level. Liberalization results from the deregulation process. That means directions and magnitude of liberalization are determined by the actions of deregulation characters.
Liberalization
policies that remove barriers to entry and empower consumers to discipline industry suppliers typically are better methods for fostering vigorous long term industry competition (Armstrong Mark and David E. M. Sappington, 2006). There are several potential benefits of liberalized regime as given by Skipper (1997). First, liberalization facilitates better customer services 3
and value. It enhances competition in a wider geographical range and so creates stronger and more competitive local insurance industry. With increased competition there is further scope for greater motivation to generate new and innovative products, more responsible to customer needs and desires, better and broader range of quality goods and services and seek less costly means of marketing and servicing. Open markets also help firms tap into world markets, increase their sales potential, benefits from economies of scale, and spread the fixed costs of research and development over a wider customer base. Moreover liberalization allows insurers to diversify risks, in the sense that they can channelize resources to where returns are highest as well as secure access to capital at the lowest possible cost. To sum up, liberalization renders firms more competitive domestically and internationally. Secondly, liberalization helps in mobilizing domestic savings. There are various studies suggesting a positive correlation between domestic saving and economic development. (Lean Hooi Hooi and Yingzhe Song , 2009; Suemegi Kjell and Peter Haiss 2008). Other things remaining same, the greater the number and variety of quality financial intermediaries within the market, the higher should be the national saving rate. Hence, a more liberalized market regime with greater foreign insurers involved in could contribute saving and so to economic development. Thirdly,
liberalization
makes
possible
the
transfer
of
technological knowhow. Transfer of ideas, bringing of new and better skills and knowhow, training programs, technology and managerial techniques to host country are facilitated in the liberalized market.
4
Lastly, liberalization promotes additional capital inflow. A domestic country benefits from liberality in that the foreign insurers bring an additional source of financial capital. The additional financial capital can be used to finance additional projects. 1.2-Purpose and Methodology of the Study Life insurance sector is the major part of total insurance sector in India and so can be able represent majority of the reform‘s implication to the industry. The decision to introduce competition into an industry is only the beginning of a journey down a long and winding road that can represent many obstacles and detours. With the liberalization and entry of private companies in Insurance sector there are various changes brought in .There are changes in terms of efficiency of life insurance firms, market concentration level and product innovations which are generally attributable to liberalization and deregulation. So it is very important to evaluate and understand the impact in terms of growth and development and other future prospects in the sector and try to find out how the reforms have benefited the insurance sector in India. This thesis therefore, attempts to study the impacts of liberalization and deregulation processes in India, specifically in life insurance industry. The impact of liberalization can be viewed as a two tier process; namely impact on financial performance as well as on the overall functioning of the market. In the context of overall performance, the growth in terms of volume of business, penetration and density, market share etc are examined. The necessity to study the financial performance of the industry arises from the fact that the industry suddenly had a paradigm shift and insurers seem to act efficiently resulting in growth of 5
each insurer.
Innovative distribution systems are employed as
competition is fierce, which compels the insurers to price their products lower, resulting in lower profit margin. Therefore, this study takes into account the efficiency and productivity improvement in the life insurance industry in the wake of deregulation. To sum up, following research questions are examined in the thesis. 1) What is the present scenario of the industry? How different it is from the pre liberalization scenario? 2) The competition in the sector is expected to increase. So what is the present state and nature of competition? What changes have taken place in the market structure of life insurance industry? 3) Whether all firms are efficient or not? Whether or not the efficiency and productivity of the insurance market is improving after liberalization? 4) How did liberalization contributed in product innovation, risk management, customer service benchmark in life insurance industry? 5) What are the implications of liberalization on spread and coverage of social security measures? There are 23 life insurers including LIC of India as on 31st August 2010. However number of insurers taken varied in the analysis, as the insurers which entered into the industry after 2005 are not considered for short of data. The analysis depends on the secondary data available with Insurance Regulatory and Developmental Authority of India (IRDA) and the Annual Reports of respective life insurance companies in India. Also text books, national as well as international articles and dailies are referred to collect relevant and required data. The statistical tools used in 6
the research include Herfindahl Hirschman index (HHI), Entropy (E) and Data Envelopment Analysis (DEA). Other simple statistical tools were also used, as required. To examine the state and nature of competition, the Herfindahl Hirschman Index, Entropy and concentration Ratio of the industry are calculated using firm level share of total premium, equity share capital and total assets. To calculate the firm level technical efficiency, pure technical efficiency and scale efficiency, the technique of the data envelopment analysis was used. The productivity and its components technical efficiency change and technical change over the years were calculated for each firms using Malmquist Productivity Approach. Each of the statistical method used are explained in details in the respective chapter. 1.3-Importance and Limitations of the Study The importance of the research lies in the fact that the liberalization is an important phase of insurance industry. The need for private entry has been justified on various grounds such as enhancing the efficiency of operation, achieving a greater density and penetration of life insurance in the country, greater mobilization of long term saving etc. However there are emerging issues in the light of liberalization which required constant monitoring of the policy programmes. And there is little research in these aspects. Present study will strengthen the knowledge and literature in the field and also helpful to policy programs, potential insurers, researchers, academicians and general readers who are interested in Indian life insurance sector. Overall, the thesis is expected to provide a brief overview on the overall impact of insurance sector reform measures on life insurance sector in India.
7
There are many other issues or effects of liberalization and all of them cannot be examined here. The main problem faced was the dearth of data. Due to time lag in publishing official data, the data considered was from financial year 2001-02 to 2009-10. Sometimes, there was lack of consistency and uniformity in the format of the data or annual reports published. Therefore the study has limited itself to analyze and interpret the impact of insurance liberalization on the topics of concentration, efficiency, productivity and innovation. In analyzing these topics, the methodology and variables used are also restricted to the available data. The results from this study need to be interpreted with some amount of caution as ten years may not be sufficient time for a complete overhaul of the industry, and many trends may only be indicative.
1.4-Chapter Outline
This thesis consists of seven chapters. Chapter two starts with a brief overview of the Indian life insurance industry during the pre nationalization and post nationalization period which had the monopoly regime of LIC of India. It follows the discussion on whole process of liberalization and the present industry scenario. Chapter three provides review of literature mostly related to the liberalization and deregulation related studies, its impact on various areas specially efficiency and productivity and in context to India. Chapter four examines the general impact of liberalization and estimates its impact on industry concentration. Chapter five is devoted to the efficiency as well as productivity of Indian life insurance industry. Chapter six discusses and analyses the innovations in the life insurance sector under various heads. Chapter seven gives important conclusions and suggestions.
8
CHAPTER II AN OVERVIEW OF INDIAN LIFE INSURANCE INDUSTRY
The insurance sector in India is nearly 193 years old and can be termed as in the third phase of its existence. Today, the life insurance business ranked 9th among the 156 countries and the share of life insurance sector in global market was 2.45 percent in 2009.This chapter defines the concept of life insurance and gives an overall summary of the long and eventful journey of Indian life insurance industry -From the British Raj to Monopoly Raj to Swaraj.
2.1-Life Insurance: Defined
Insurance business in India is classified primarily as Life Insurance and General Insurance. Life Insurance is basically associated with risk of human lives. It provides protection to household against the premature death of its bread winner or income earning member. Individuals buy life insurance product by paying certain amount of money which is called premium to the life insurance company for contractual agreements to provide a shield in case of eventualities. Therefore, Life insurance is a contract under the provision of the respective national laws or conventions or commercial practices that agree to pay a contracted sum of money to the person whose life is insured in the event of death or on the happening of any other event agreed upon by the parties to the contract. According to the section 2(11), of the Insurance Act of 1938, life insurance business in India is defined as follows: ―Life insurance business‖ means the business of effecting contracts of insurance upon human life, including any contract whereby the payment of money is assured on death (except death by accident only) and the happening of 9
any contingency dependent on human life, and any contract which is subject to payment of premiums for terms dependent on human life and shall be deemed to include
the granting of disability and double or triple indemnity accident
benefits, if so provided in the contract of insurance;
the granting of annuities upon human life; and
the granting of superannuation allowances and annuities payable
out of any fund applicable solely to the relief of and maintenance of person engaged or who have been engaged in any particular profession, trade or employment or of the dependent of such persons.
2.2-Indian Life Insurance-History
The journey of Indian Insurance Industry has so far been very eventful in the way that it has come in full circle from privatization of insurance firms to creation of monopoly and back to privatization and liberalization. The journey can be divided into three phases viz. pre nationalization phase (Before 1956), nationalized era (1956-2000) and liberalization era (2000 onwards). The first phase was the long growth phase before the nationalization of life insurance and characterized by unfettered market access. In the second phase the entire sector become state monopoly. The third phase after 2000 was of liberalization and was characterized by several new players competing with the large public sector giant i.e. LIC of India. Insurance Regulatory and Development Authority (IRDA) has now undertaken the sole responsibility to control and regulate insurance business. The Box 2.a provides the sequence of the journey of Indian Insurance up to starts of the liberalization. Each of these phases is discussed briefly as follows.
10
Box 2.a: Milestones of insurance regulations in the 20th Century Year
Significant regulatory events
1912
The Indian Life Insurance company Act
1928
The Indian Insurance Companies act
1938
The Insurance Act: comprehensive Act to Regulate insurance business in India
1956
Nationalization of life insurance business in India with monopoly awarded to LIC of India
1972
Nationalization of general insurance business in India with formation of a holding company General Insurance Corporation
1993
Setting up of Malhotra Committee
1994
Recommendations of Malhotra Committee published
1995
Setting up of Mukherjee Committee
1996
Setting up of (interim) Insurance Regulatory Authority(IRA) Recommendations of the IRA
1997
Mukherjee Committee report submitted but not made public
1997
The Government gives greater Autonomy to LIC, GIC and its subsidiaries with regards to the restructuring of boards and flexibility in investment norms aimed at channeling funds to the Infrastructure sector.
1998
The cabinet decides to allow 40% foreign equity in private insurance companies and 14% to Non-resident Indians and foreign institutional Investors.
1999
The Standing Committee headed by Murali Deora decides that foreign equity in private insurance should be limited to 26%.IRA bill is renamed the Insurance Regulatory and Development Authority bill.
1999
Cabinet clears Insurance Regulatory and Development Authority Bill
2000
President gives Assent to the Insurance Regulatory and Development Authority Bill
*Source: Tapen Sinha, CRIS Discussion Paper Series-2002.X, the University of Nattingham, Mexico.
11
2.2.1-Pre nationalization phase:
Though the concept of insurance is largely a development of the recent past, particularly after the industrial era-past few centuries nevertheless its beginning date backs almost 6000 years. In a way there has always been some form of Insurance in informal nature. Joint family system which was the basic unit of Indian society provided the objective of life insurance such as taking care of financial needs of the family in case of the premature death of the principal wage earner, provisions for the old age. In its modern form, the first insurance company in India was the Oriental Life Insurance Company started in 1818 in Kolkatta. This company was owned by the European and it looked after the needs of Europeans only. Bombay Mutual Life Assurance Society heralded the birth of first Indian life insurance company in the year 1870, and covered Indian lives. As for the regulation, it was only in 1912 that the Life Insurance Companies Act was enacted and the comprehensive legislature for regulating and administering Indian industry started. Prior to this, India had no legislation to regulate insurance business and the Indian insurance companies were governed by the Companies Act of 1866. There were two important Insurance Acts enacted during this phase namely Insurance Act 1912 and Insurance Act 1938.
Insurance Act 1912:
It was modeled on the basis of the Insurance
Companies Act 1870 of the UK and the one enacted in 1909 replacing the Act of 1870. The Indian Life Insurance Companies Act, 1912 made it necessary that the premium rate tables and periodical valuations of companies should be certified by an actuary. But the Act discriminated between foreign and Indian companies on many accounts, putting the Indian companies at a disadvantage. Apart from the Life Insurance 12
Companies Act, 1912, another legislation called Provident Insurance Societies Act was also passed in 1912. The main features of these two acts were These were the first legislations that specifically made to regulate the life insurance business in India; These acts were only meant to control and regulate the Indian insurance companies and not for foreign companies, which were operating in India through the model used in these acts, were the same as the British Act of 1909. These acts did not include general insurance business in India.
The Indian Insurance Act 1938: This act was the first comprehensive legislation governing not only life but also non-life branches of insurance. The Act aimed to consolidate and amend the law relating to insurance business and so defined the legal framework of the insurance business in India. The insurance business in India is still governed by the provisions of this Act with several amendments made to it. The silent features of the Act were as follows; Constitution of a department of Insurance under a superintendent vested with wide
powers of supervision and control over all kinds
of insurance companies. Regulation for compulsory registration of insurance companies and for filling of returns of investment and financial condition. Provisions for deposits, to prevent insurers of inadequate financial resources or speculative concerns from commencing business. Provisions that 55% of the net life fund of an India or non-Indian insurer should be invested in India Government and approved securities with at least 25% in Indian Government Rupees 13
securities. All companies, i.e., foreign companies must invest 100% of their Indian liabilities in Indian Government and approved securities, with at least 33.3% in India Government securities. Prohibition of rebating, restriction of commission, licensing of agents etc. Maximum rates of commission were fixed at 40% of the first year‘s premium and 5% of the renewal premium in respect of the life insurance business. The agents must be licensed, to improve the status of the profession. Periodical valuation for Indian business of foreign companies and the business of the Indian companies. Provision for policyholders‘ directors, making it possible for the representatives of policyholders to be on the Board of directors. Standardization of policy conditions required all companies to file standard forma and tables of premia approved by an Actuary. Under this requirement, the initial deposit for life insurance business was raised from Rs 25,000 in Government securities to Rs 50,000 in cash or approved securities, which is subsequently to be raised by installments to Rs 2 lakh within a specified time limit.
Business during the Pre nationalization phase: During the period 1870 to 1939, number of life insurance companies were formed and exited from operation. The number of life Indian life insurance companies grew from 30 in 1912 to 116 in 1939 and remained at 154 in 1955. Growth of life business during the period 1914 to 1955 is shown in table 2.2 and 2.3. (The data were taken from different sources and are differ in the variables presented; therefore the tables are shown separately) The total business in force which was 22.44 crore in 1914 grew to 712.76 crore in 1948. The year 1943 marked the beginning 14
of a period of steady and progressive increase in the volume of new life insurance business, Rs 62.94 crore in 1943; Rs 95.20 crore in 1944; Rs 122.78 crore in 1945( Tryst with Trust (1991) LIC of India ). It was in 1945 that for the first time, the volume of Indian insurance business cross Rs 100 Crores mark. The foreign insurance companies found it difficult to withstand the competition from Indian life insurance companies and were able to get a meager amount of business share during the period. (Mitra
Debabrata and Ghosh Amlan, 2010). The total new business
written by Indian life insurance companies were Rs 255 crore and Rs 260.84 crore in 1954 and 1955 respectively.
Table 2.1: Number of life insurance companies: 1929-1939 Year
company
Promoted
Exited
Remained
1870-
Indian companies
58
28
30
1912
Foreign companies
30
21
09
Total
88
49
39
1929-
Indian companies
176
60
116
1939
Foreign companies
05
02
03
Total
181
62
119
Indian companies
--
--
154
provident --
--
91
--
--
245
1955
Foreign and societies Total
*Source: Desai G. R (1973) ―Life Insurance in India: Its History and Dimensions of Growth‖, Macmillan.
15
Table 2.2: Growth of life business in India: 1914-1948 Year
Insurers
Number Total number
Total
Total life
of
of policies in
business in
fund( Rs
insurers
force
force(Rs
crore)
crore) 1914
Indian
44
--
22.44
Non Indian
--
--
--
Indian outside
--
--
--
Total
44
--
22.44
Indian
68
513925(68.61)
84.89(32.85)
Non Indian
--
220703
69.76
Indian outside
--
14369
3.77
Total
68
748997
258.42
Indian
179
1371963(84.25) 225.51(74.17)
Non Indian
16
181247
60.12
Indian outside
--
75171
18.40
Total
195
1628381
304.03
Indian
200
2376000(87.55) 459.43(80.17)
Non Indian
15
261000
91.85
Indian outside
--
77000
21.79
Total
215
2714000
573.07
Indian
189
2791000(90.15) 566.38(79.46)
Non Indian
20
234000
101.08
Indian outside
--
202000
45.30
209
3016000
712.76
6.36
India
1930
20.53
India
1940
62.41
India
1945
107.4
India
1948
150.39
India Total
*Source: Tryst with Trust (1991) LIC of India, Bombay, India. Figures in brackets show percentage of the total. 16
Table 2.3: Life insurance business in force in India: 1949-1955
Year
New business
Total business in force
Number of
Amount(Rs in
Number of
Amount(Rs
policies (in
Crores)
policies (in
in Crores)
lakhs)
lakhs)
1949
5.44
142.20
33.03
765
1950
4.98
139.50
32.80
780
1951
4.74
147.90
34.14
873
1952
5.34
146.70
39.25
922
1953
5.58
155.20
40.79
966
1954
7.73
255.25
47.82
1177
1955
8.31
260.28
47.92
1220
*Source: Bhave S.R (1970), Saga of Security: Story of Indian Life insurance (1870-1970), LIC of India, Bombay. 2.2.2-Nationalization and LIC‘s monopoly regime (1956-2000): The announcement by C. D. Deshmukh, the then Finance minister of India on January19, 1956 that the government will take over the life insurance business of all national and foreign companies in India was the first vital move in nationalizing life insurance in India. Thereafter as a first legislative step in nationalization of life insurance business, the life insurance
(Emergency provision) Ordinance and followed by
replacement of the ordinance by the Life Insurance (Emergency Provision) Act 1956 was made. The ordinance and act provided for Government control of 245 companies comprising 154 Indian Insurers, 75 provident societies and 16 non Indian insurers. Based on the 17
ordinance, the bill for nationalizing the life insurance business in India was piloted in the Parliament on February 18, 1956 as a Finance bill. After a reference to a joint select committee of the parliament and adoption by both the houses of the parliament with the assent of the president of India, the Bill came into force on July1, 1956. And the Life Insurance Corporation of India was constituted on September 1, 1956 under the Act (LIC Act Number 31of 1956 dated June 18, 1956).
LIC Act, 1956: It is an act which provides for the nationalization of life insurance business in India by transferring all such business to a corporation established for the purpose and to provide for the regulation and control of the business of the corporation and for matters connected therewith or incidental thereto. Post nationalization growth: At the time of nationalization, the LIC of India took over total life business of over Rs 1,128.06 Crores, under 47.82 lakh policies of which the share of Indian insurers was around 87.25% and that of Non –Indian insurers was 12.28( Harinarayan 2008). New business written by LIC of India at the end of December 1957 stood at Rs 281.90 crore under 794,585 policies. The business volume at the end of 1957 including bonuses stood at Rs 1474 crore under 56.86 lakh policies. (Mitra Debrabarta and Ghosh A, 2010). The decade of eighties was an eventful one. Table 2.4 shows the growth statistics of LIC of India during the eighties. Table 2.5 and 2.6 shows the performance of LIC in terms of number of policies, sum assured and annual premium received since 1991-92 to 1999-00 for individual Assurance and total life business in force. The number of policies, sum assured, and the annual premium received all shows an increasing trend over the years. 18
2.2.3-Post liberalization industry scenario: The liberalization of insurance sector was not a decision taken fortnight. There were several reasons and certain developments at the national as well as global front which convinced the Government of India to move towards opening up of the sector. Globally, there were international
compulsions
and
pressures
as
also
deregulation,
globalization and privatization are the routes that were found to have been successful in many parts of the world (Japan, China and Brazil). By signing the GATT accord, the government of India required to commit to opening up of insurance sector to private sector to both domestic and foreign operator. Table 2.4: Growth statistics of LIC of India (Policies are in lakh and amount in Crores) Year
Premium
Investment
Total
income
income
Total
in Annual
Annual
business in force
new
number
force
business
of
policies
new
policies 1984-85
1559.13
950.58
33,950.50
265.31
5,375.93
27.00
1985-86
1782.28
1126.98
40,617.10
280.47
7,088.45
32.94
1986-87
2097.21
1334.17
48,150.64
298.60
9,107.59
38.76
1987-88
2671.88
1557.21
59,067.69
324.81
12,467.58
47.64
1988-89
3432.72
1884.83
74,429.00
361.34
17,268.58
59.87
1989-90
4489.39
2278.29
94,823.00
403.98
23,319.53
74.01
Source: Tryst with Trust (1991) LIC of India, Bombay, India.
19
Table 2.5: Performance of LIC from 1991-92 to 1999-00: Individual Assurance Year
Number of policies
Sum assured
Annual premium
(Lakh)
(Rs Crores)
Received (Rs Crores)
1991-92
92.40
32064.00
1790
1992-93
100.00
325957.00
2038
1993-94
107.25
41814.00
2508
1994-95
108.74
55228.00
2534
1995-96
110.20
51816.00
2814
1996-97
122.68
56740.50
3345
1997-98
133.11
63617.69
3841
1998-99
148.44
75316.28
4863
1999-00
169.77
91214.25
6008
Source: Compiled from Annual Reports of LIC of India
Table 2.6: Performance of total life business of LIC in force from 199192 to 1999-00 Year
Number of policies
Sum assured
Annual premium
(Lakh)
(Rs Crores)
Received (Rs Crores)
1991-92
508.63
1,45,929
5946
1992-93
566.12
177268
7146
1993-94
608.00
207601
8758
1994-95
654.52
253333
10385
1995-96
708.78
294336
12094
1996-97
776.66
343018
14500
1997-98
849.15
398959
17066
1998-99
916.37
457435
20234
1999-00
1012.99
534589
24540
Source: Compiled from Annual Reports of LIC of India 20
In the national context, the insurance sector reform was part and parcel of the wave of reforms that swept across the country in the 1990s.
A
committee under the chairmanship of R N Malhotra was appointed in 1993 by Government of India to look into all the aspects of life insurance in India. The report of the committee was submitted in January 1996.
The Malhotra Committee made the following recommendations for insurance sector reform (mentioned here are related to life insurance only): The central and zonal offices of the LIC should be reconstructed and reorganized such that, while central office should concentrate on policy formulation, review ,evaluation, product development, pricing, actuarial valuation, investments, personnel policies, system development etc., the zonal offices should look after the insurance business and related matters. The government stake in the insurance companies should be reduced to 50 per cent. All insurance companies should be given greater freedom to operate. Private insurance companies with a minimum paid up capital of Rs 100 crore should be allowed to enter the insurance industry. The promoter‘s holding in a private company should not be less than 26% and more than 40% of the paid up capital. No person other than the promoter should hold more than one percent of equity. The insurance companies should not be allowed to deal in both life and general insurance through a single entity. Foreign companies may be allowed to enter industry selectively and in collaboration with the domestic companies. 21
Postal life insurance should be allowed to operate in the rural market. The insurance regulatory body be set up, and the controller of insurance should be separated from the finance ministry and made independent. Regulatory, prudential norms should be finalized to ensure a level playing field. The capital of LIC should be raised from the present Rs 5 crore to 200 crore,50 % of which should be held by the government and remainder should be held by the public at large including the LIC employees for whom a suitable proportion be reserved. Mandatory investment of LIC in government securities should be reduced from 75% to 50%. Thereafter the following major steps were taken up for liberalization: In January 1996, the Interim Insurance Regulatory Authority (IRA) was appointed. IRA bill was introduced in the parliament on December 20, 1996 with a proposed equity share of 40 percent to foreign company. However the bill was withdrawn due to disagreement from the opposition party demanding a reduced share of foreign equity. A new IRA bill was introduced on December 5, 1998 with a proposed foreign equity share of 26 percent. However, this time the bill lapsed due to the fall of the then government (NDA) in April 1999. The IRA was revised and renamed as Insurance Regulatory and Development Authority (IRDA) bill and passed in both the houses of Parliament on December 2, 1999 (Lok Sabha) and December 7, 1999 (Rajya Sabha) respectively.
22
The IRDA bill became Act 41 of 1999 and from April 19, 2000 the IRDA became actually effective vide Government of India‘s notification number277. The main features of the IRDA Act can be summed up as follows: The IRDA Act amended the LIC Act 1956 and GIC Act 1972, withdrawing the exclusive privilege of LIC of India and GIC and its subsidiaries to carry on life and general insurance business respectively. Thus the two sectors were opened up for competition from the private sectors. The Insurance Regulatory and Development Authority (IRDA) is an autonomous body constituted to regulate and develop the insurance and reinsurance business in India. Under the IRDA Act, an ‗Indian insurance company‘ will be allowed to conduct insurance business provided it satisfies the following conditions: It must be formed and registered under the Companies Act, 1956; The
aggregate
holdings
of
equity
shares
by
a foreign
company, either by itself or through its subsidiary companies or its nominees, should not exceed 26% paid up equity capital of the Indian insurance company; ( the Government has proposed an enhancement in the FDI limit from 26% to 49% but this is yet
to
be
notified
in the
Insurance
Regulatory
&
Development Act ). Its sole purpose must be to carry on the life insurance business or general insurance business or reinsurance business. Post reform developments: With the enactment of liberalization, the insurance industry has gone through a sea change. Major visible changes included the entry of 23
Box 2.b: Life insurance companies in India* Sr.
Insurers
Foreign partner
No.
Date of
Year of
registration
operation
1
HDFC Standard LI Co. Ltd
Standard life Assurance, UK
23.10.2000
2000-01
2
Max New York LI Co. Ltd
New York Life, USA
15.11.2000
2000-01
3
ICICI-Prudential LI Co.
Prudential, UK
24.11.2000
2000-01
Ltd 4
Om Kotak LI Co. Ltd
Old Mutual, SA
10.01.2001
2001-02
5
Birla Sun LI Co. Ltd
Sun Life, Canada
31.01.2001
2000-01
6
Tata-AIG LI Co. Ltd
American International Assurance Co.
12.02.2001
2000-01
USA 7
SBI LI Co. Ltd
BNP Paribas Assurance, SA, France
29.03.2001
2001-02
8
ING Vysya LI Co. Ltd
ING Insurance International B.V,
02.08.2001
2001-02
Netherlands 9
Allianz Bajaj LI Co. Ltd
Allianz, Germany
03.08.2001
2001-02
10
MetLife India insurance
MetLife International Holdings Ltd.
06.08.2001
2001-02
Co. Ltd
USA
11
Reliance LI Co. Ltd
--
03.01.2002
2001-02
12
AVIVA
Aviva International Holdings Ltd. UK
14.05.2002
2002-03
13
Sahara LI Co. Ltd
--
06.02.2004
2004-05
14
Shriram LI Co. Ltd
Sanlam, SA
17.11.2005
2005-06
15
Bharti AXA LI Co. Ltd
AXA Holdings, France
14.07.2006
2006-07
16
Future Generali India LI
SMNPL Generali, Italy
04.09.2007
2007-08
Co. 17
IDBI Fortis LI Co. Ltd
Fortis, Netherlands
19.12.2007
2007-08
18
Canara HSBC OBC LI Co.
HSBC,UK
08.05.2008
2008-09
Ltd 19
Aegon Religare LI Co. Ltd
Religare, Netherlands
27.06.2008
2008-09
20
DLF Pramerica LI Co. Ltd.
Prudential of America, USA
27.06.2008
2008-09
21
Star Union Dai-ichi LI Co.
Dai -ich Mutual life Insurance of
26-12-2008
2009-10
Ltd.
Japan
22
IndiaFirst LI Co.Ltd.
--
5-11-2009
2009-10
23
LIC of India
--
01.09.1956
1956
* As on 31 August 2010.
24
private companies, and fall in market share of LIC of India. Looking at the present scenario, a broad summary of these changes in industry scenario is put in as below. Private sector entry: The first private life insurance was registered with IRDA in October 2000 and started operation shortly thereafter. Since then the number of private companies registered has increased manifold. A summary of the private players operating in the life insurance sector is shown below in Box. 2. b. Business performance and market share:
With the entry of private players, each player was trying their best to make their presence felt in the market. In such a scenario of steep competition, the market share of LIC was only to come down. Table 2.7 shows the premium underwritten by LIC, Pvt. Insurers and their growth over the previous year as well as the market share of LIC and Pvt. Insurance Company. The market size of Indian life industry in terms of premium in 2009-10 has grown more than five times the size in 2001-02. The growth percentage varied from year to year, but in all the years after liberalization, growth percentage exceeds 10 percent. In the year 200102, premium underwritten by the industry was Rs 50094.45 Crores, of which Rs 49821.91 and Rs 272.54 Crores were written by LIC and private players respectively. The premium underwritten by the industry during 2002-03 was 55738.11 crore, of which Rs 54628.49 Crores was underwritten by LIC and Rs. 1109.62 Crores was written by private players. Overall, the industry witnessed a growth of 11.27 percent in terms of gross premium. In terms of market share, LIC held 98% of the life market, which was 99.46 in 2001-02, with the private players capturing 2 %. The renewal premium of LIC exhibited a growth of 27.87 25
% from 30233.14 Crore to 38651.73 crore in 2002-03. In case of private players, the renewal premium collected show a growth of 3701% from Rs 4.03 to Rs 153.37 crore in 2002-03. In 2003-04, the life insurance premium recorded was Rs 66653.16 crore i.e. 19.56 % growth from the previous year. The contribution of first year premium, and renewal premium was Rs 19788.33 crore and Rs 46865.43. In 2004-05, the life insurance industry underwrote Rs 26217.64 crore in the first year premium inclusive of single premium. The contribution of total premium was 82854.79 i.e. 24.31% growth from previous year. In 2005-06, life insurance industry recorded a growth of 27.78 % with premium income Rs 105875.76 crore. The contribution of first year, single premium and renewal premium to the total premium was Rs 21254.91 crore, Rs 17530.62 crore and Rs 67090.21 crore respectively. There was significant growth in the single premium. In 2006-07, Life insurance industry recorded a premium income of Rs 156075.86 crore exhibiting growth of 47.38 % from the previous year. The contribution of first year, single premium and renewal premium to the total premium was Rs 45361.17 crore, Rs 30288.04 crore and Rs 80426.64 crore
respectively. The total premium of LIC and private
insurers grew at 17.19 and 82.50 % respectively in 2007-08.The renewal premium underwritten by life insurance industry grew by33.83 % during 2007-08. The single premium income received by the life insurers recorded 31.05 % growth during 2009-10 as against the negative 3.06 % growth in 2008-09. The contribution of regular premium to the total premium stood at 22.87% during 2009-10 while it was 22.26% in 2008-09. The renewal premium had gone up by 15.69 % while it was 25.22% during 2008-09. 26
This year, private insurers reported higher growth of 35.11 % renewal premium then the 10.03 % reported by LIC. The market share of LIC was seen decreasing over the years from full monopoly of 100 percent to 99.46 percent in 2000-01and standing at 70.10 percent in 2009-10. In the first three years, the private insurers could grasp a market share at a pace of only around 2 percent per year. In the remaining three years till 2008-09, they could snatch at a rate of 4-5 percent per annum except 6 percent in 2007-08. However in 2009-10 LIC only loose only 0.82 percent against private insurers. The overall market share of LIC was seen decreasing over the years. The decline in market share was mostly seen in first year premium. While the decline in renewal premium was seen at rather very slow pace. The premium underwritten through single premium was decline but there was an upsurge in single premium received for LIC in the last three years. Table: 2.7: Premium underwritten by life insurers and their market shares (Rs Crores) Year
Premium
LIC
Growt
Pvt
h in % 2001-
Regular
02
Single
Growt
LIC and Pvt.
Growth
h in %
Total
in %
1st year
19588.77(98.65)
--
268.51(1.35)
--
19857.28(100)
--
Renewal
30233.14(99.99)
--
4.03(0.01)
--
30237.17(100)
--
Total
49821.91
--
272.55
--
50094.46(100)
--
*1st year
15976.76(94.30)
18.44
965.69(5.70)
259.65
16942.45(100)
-14.68
Renewal
38651.73
27.85
153.37
3701.2
38805.10(100)
28.34
2002-
Regular
03
Single
2 Total 2003-
Regular
04
Single 1st year*
54628.49
9.65
1119.06
310.59
55747.55(100)
11.28
17347.62(87.44)
8.58
2440.71(12.56)
152.74
19788.33(100)
16.79
27
Renewal
46185.81(98.55)
19.49
679.62(1.45)
343.12
46865.43(100)
20.77
Total
63533.43(95.29)
16.30
3120.33(4.71)
178.83
66653.76(100)
19.56
8994.82(87.02)
62.32
1341.48(12.98)
239.46
10336.30(100)
74.11
1 year
11658.24(73.41)
-1.25
4223.09(26.59)
106.46
15881.33(100)
14.65
Renewal
54474.23(96.18)
17.95
2162.93(3.82)
218.26
56637.16(100)
20.85
Total
75127.29(90.67)
18.25
7727.51(9.33)
147.65
82854.79(100)
24.31
2005-
Regular
13728.03(64.59)
17.75
7526.88(35.41)
78.23
21254.91(100)
33.84
06
Single
14787.84(84.35)
64.40
2742.78(15.65)
104.46
17530.62(100)
69.60
1st year
28515.87(73.52)
38.07
10269.66(26.48)
84.55
38785.54(100)
47.94
Renewal
62276.35(92.82)
14.32
4813.86(7.18)
122.56
67090.21(100)
18.46
Total
90792.22(85.75)
20.85
15083.53(14.25)
95.19
105875.76(100)
27.78
Regular
29886.35(65.89)
117.7
15474.83(34.11)
105.56
45361.17(100)
113.40
2004-
Regular
05
Single st
200607
0 Single
26337.22(87.04)
78.10
3950.82(12.96)
42.96
30288.04(100)
72.60
1 year
56223.56(74.35)
97.17
19425.65(25.65)
88.84
75649.21(100)
94.96
Renewal
71599.28(89.03)
14.97
8827.36(10.97)
83.33
80426.64(100)
19.87
Total
127822.84(81.92)
40.79
28253.01(18.08)
87.08
156075.86(100)
47.38
2007-
Regular
26222.00(47.77)
-12.26
28666.15(52.23)
85.24
54888.16(100)
21.00
08
Single
33774.56(86.99)
28.24
5049.80(13.01)
27.82
38824.36(100)
28.18
1 year
59996.57(64.02)
6.71
33715.959(35.98)
73.56
93712.52(100)
23.88
Renewal
89793.42(83.42)
25.41
17845.47(16.58)
102.16
107638.89(100)
33.83
Total
149789.999(74.39)
17.19
51561.42(25.61)
82.50
201351.41(100)
29.01
2008-
Regular
19140.61(38.43)
-27.01
30229.95(61.57)
5.46
49370.56(100)
-10.05
09
Single
34038.47(90.70)
0.78
3597.20(9.30)
-28.77
37635.67(100)
-3.06
1st year
53179.08(60.89)
-11.36
33827.15(39.11)
0.33
87006.23(100)
-7.16
Renewal
104108.96(77.43)
15.94
30676.07(22.57)
71.90
134786.61(100)
25.22
Total
157288.04(70.92)
5.01
64503.22(29.08)
25.10
221791.26(100)
10.15
2009-
Regular
26184.48(43.13)
36.80
34529.75(56.87)
12.61
60714.23(100)
21.91
10
Single
45337.42(92.19)
33.19
3842.37(7.81)
10.13
49179.79(100)
31.05
1st year
71521.90(65.08)
34.49
38372.12(34.92)
12.36
109894.02(100)
25.84
Renewal
114555.419(73.64)
10.03
41000.94(26.36)
35.11
155556.35(100)
15.69
Total
186077.31(70.10)
18.30
79373.06(29.90)
23.06
265450.37(100)
19.69
st
st
Source: Annual Reports of IRDA. Figures in brackets are market share in percent). *FY Premium includes single premium.
28
Table: 2.8: New policies issued and their market share
Year
New policies issued Pvt
LIC
Total
2002-03
825094(3.25)
24545580(96.75)
25370674(100)
2003-04
1658847(5.79)
26968069(94.21)
28626916(100)
2004-05
2233075(8.52)
23978123(91.48)
26211198(100)
2005-06
3871410( 10.92)
31590707(89.08)
35462117(100)
2006-07
7922274(17.17)
38229292(82.83)
46151566(100)
2007-08
13261558(26.07)
37612599(73.93)
50874157(100)
2008-09
15010710(29.48)
35912667(70.52)
50923377(100)
2009-10
14362000(26.98)
38863000(73.02)
53225000(100)
Source: Annual Reports of IRDA
Table 2.8 shows the business performance in terms of new policies issued. Over the years, the number of new policies issued by private insurers was increasing. The number of new policies issued by LIC was also increasing but it was declined in 2004-05 as well as in 2007-08 and 2009-10. The share of private life insurers in new policies issued has increased from 3% in 2002-03 to 29.5% in 2008-09 and that of Lic decreased from 97% to 70.5 respectively.
2.3-Summary:
Insurance has come a long way since the time the business was tightly regulated and concentrated in the hand of state owned insurance company. Two important Insurance Acts were enacted during the pre nationalization phase viz. Insurance Act 1912 and Insurance Act 1938. For the first time, Indian Life insurance businesses crossed Rs 100 Crores 29
mark in 1945. The total new business written by Indian life insurance companies were Rs 255 crore and Rs 260.84 crore in 1954 and 1955 respectively. The total business in force which was 22.44 crore in 1914 grew to Rs 1220 Crore in 1955. The Life Insurance Corporation of India was constituted on September 1, 1956 under the Act (LIC Act Number 31of 1956 dated June 18, 1956). The total business in force in 1957 was Rs 1474 Crore and it was increased to Rs 534589 Crore in 1999-00. During the monopoly regime of LIC of India, the number of policies, sum assured, and the annual premium received were seen increasing over the years. However, the insurance sector was liberalized in 2000 for various reason such as lack in depth, diversity and reach( geographically as well as in terms of insurable population), poor customer service and need for global dimension etc. When seen in the context of overall performance, the insurance sector was seen to have made a robust growth in the post liberalization era in terms of huge volume of business underwritten by the companies and overall growth of the market. The market share of private insurers was 29.90 in 2009-10 from 0.054 in 2001-02. The market size of the life insurance industry has grown from Rs 50094 Crore to Rs 265450 Crore in 2009-10(429.90% growth). Overall the market was widening, and insurers were competing hard to have more market share.
30
CHAPTER III LITERATURE REVIEW This chapter provides comprehensive survey of literature on the topic of insurance sector liberalization and related trends.
Insurance
markets worldwide have changed in the last two decades. Liberalization, deregulation,
globalization
of
insurance
institutions,
intensified
competition, electronic commerce, bancassurance etc. are among the challenges faced by insurance markets now. These developing trends pose both global and local challenges for insurance firms. Analysis of various key insurance markets highlights various homogeneous trends in the global insurance market. First, the process of deregulation can be seen in most part of the world. Several countries have deregulated their insurance market at the national and regional level, which include Europe, Japan and United States. The second trend is the promotion of globalization throughout the world. For instance, China committed to liberalize the insurance sector with its entry into World Trade Organization (WTO) in 2001. The third major trend is the wave of privatization. The number of government owned insurance companies is becoming smaller and some countries have almost entirely eliminated government-run insurance companies. The trend is true for developed as well as emerging economies. France, China and India are among others countries which have gradually dismantled the former government insurance monopoly. Increased sophistication in insurance technology, internationalization of insurance market, reforms in pension and health insurance, evolution of new product and distribution system are other key trends, which are seen at more or less the same level in various countries worldwide. These trends can be summed up as liberalization, deregulation, privatization and globalization of insurance sector. 31
The implications of liberalization, deregulation and globalization vary according to country. Skipper Harold D., Jr. C.V. Starr and J. Mack Robinson (2000) in their study gave an in-depth knowledge on the issues and concerns of insurance market liberalization.
Dozens of countries
have deregulated and liberalized their insurance markets with the belief that competitive markets are better at enhancing consumer choice and welfare than the rigidly regulated insurance market. Also, research on market liberalization and international expansion of service gave a notion that market liberalization will have a positive impact on firm performance (Reardon,
Erramilli
and
Dsouza, 1996;
Contractor,
Kundu
and
Hsu, 2002; Vachani, 1997). Megginson William L, Robert C. Nash, Matthias Van Randenborgh (1994) in their study compared the financial and operating performances of firms before and after the privatization. They included 61 companies from 18 countries and 32 industries that experience full or partial privatization through public share offerings during the period 1961 to 1990. They found significant increase in profitability, output per employee, capital spending,
and total
employment and concluded that the newly privatized firms benefited from improved
operating
and financial performance
while
maintaining total employment. Megginson William L and Netter (2001) provided an extensive survey of the empirical literature and concluded that privatization leads to increase in productivity in banks. Another study by Kikeri and Nellis (2004) also reached similar conclusion. Their findings thus suggested that liberalization and deregulation together promote efficiency. But, to what extent the mere change of ownership (privatization) or the strengthening of competition (liberalization) is responsible for an efficiency gain is still unclear. Moreover privatization and liberalization often take place simultaneously, and so it is hard to 32
disintegrate the effects simultaneously. The literatures detailing the impact of liberalization and deregulation on insurance industry‘s performance is still scant. The studies on the impact of deregulation and liberalization reforms will be reviewed in this chapter for different countries including India. Of the researches, insurer‘s efficiency improvement has been the main concern.
European Union:
There international
is a growing
interest and
competitiveness
and
concern efficiency
about
the
of European
financial institutions in general and insurance companies in particular. Over the past 15 years, the European Union has gradually deregulated the financial services sector through a series of banking and insurance directives with a view to creating a single European market in financial services. The implementation of European Union‘s (EU) third directives introduced in July 1994 has changed the face of European insurance market from a tightly regulated to competitive regime. The EU directives deregulated insurance market, which was earlier carried out by each insurer‘s home country. However, the impact of deregulation is likely to vary across different member countries. An overview of some of the studies done to see consequences of the deregulated and liberalized European insurance market to its member countries such as Austria, Germany, Italy, Portugal, and UK are reviewed here.
Fenn Paul, Dev Vencappa, Stephen Diacon, Paul Klumpes and Chris O’Brien (2008) estimated the cost and profit efficiency of major European countries in the wake of insurance liberalization. Their study was based on 14 major European countries from the period 199533
2001 and used stochastic frontier methodology to model the efficiency of the companies during the deregulation period. The results suggested that most of the European insurers were operating under conditions of decreasing costs (increasing return to scale) and that company size and market share were factors that significantly determine X efficiency with respect to cost as well as profits. Cost efficiency was found relatively higher for smaller companies. Profit efficiency by contrast was found increasing with size for firms in all insurance sectors. Therefore they concluded that larger firms, and those with high market shares, tend to have more cost inefficiency but less profit inefficiency.
A similar work by Sterzynski Maciej, L. L.M (2003) studied the impact of liberalization and deregulation processes in European Community which was enabled to create a Single Insurance market (SIM) under the Third Generation of Insurance Directives. For the period 1995 to 2000, they found out that there was general reduction in number of companies while a serious increase in gross premium growth was observed. During the period, 70 percent of non life insurance was concentrated only in five Member States such as: Germany France, the Netherlands, Spain and UK .Moreover, up to 67.8% of all life insurers were concentrated in UK, Germany, the Netherlands Denmark and France. Another major change observed during the period was dominance of life insurers over non life insurers.
A study by Mahlberg Bernherd and Thomas Url (2003) measured the effects of liberalization on technical efficiency and productivity development of the Austrian insurance industry. They used Data Envelopment Analysis (DEA) to seven year‘s individual firm level data (from 1992-1999). They found out signs for single market effects in 34
terms of reduction in the dispersion of DEA efficiency scores over time and the more homogeneous productivity development in the last years of their sample. There was development of efficiency in the insurance business over time. Ennsfellner Karl C, Danielle Lewis and Randy I. Anderson (2004) examined the production efficiency in the Austrian insurance industry using Bayesian stochastic frontier to obtain aggregate and firm specific estimates of production efficiency across insurer types and time. The objective of their study was to determine whether the changes in market structure and regulatory environment have had an influence on the production performance of the Austrian insurance companies. Austria became a member of European Union in 1995, so to study the effect of single market; they test the hypothesis that Austrian insurance industry has not been affected by the efficiency building program. The period of their study was from 1994 to 1999. The study provides strong evidence that the process of deregulation had positive effects on the production efficiency of the Austrian insurers.
The German insurance market which was one of the most regulated markets in the Europe was liberalized in the mid 1990‘s. Since then there have been changes in the regulation and supervision of German life insurance towards a very detailed but less intrusive regulation centering
on
the
supervision
of insurance
firms
and
preventing
insurers‘ insolvency. There are also quite a few studies on
the development and determinants of efficiency and productivity in Germany during deregulation and thereafter. Those who studied the extent to which the European directive and subsequent liberalization‘s aim has been achieved in German Insurance market include Hussels and Ward (2004), Mahlberg and Url (2007), Luhnen Michael (2008), Gamarra Lucinda Trigo (2008). Hussels and Ward (2004) analyzed the 35
German life insurance industry over the period of 1991 to 2002 and assessed the cost efficiency and TFP with the help of data envelopment analysis of balanced panel data of 31. They found an overall average growth in efficiency and productivity. Mahlberg and Url (2007) examined the development of the German insurance industry for the years 1991-2001, using DEA and Malmquist analysis. They found that the total factor productivity (TFP) increased during the observation period, although the liberalization process did not lead to converging efficiency scores. Luhnen Michael (2008) provided a comprehensive analysis of efficiency and productivity in the German
property-liability insurance
industry using data envelopment analysis (DEA) for the period 1995– 2006. Gamarra Lucinda Trigo (2008) used data from 1995-2002 and frontier efficiency methodology in the study. The study analyzed if the aims of liberalization process have been achieved in the German life insurance market. She found the evidence that the industry experienced a positive TFP growth but technical cost efficiency did not increase during the observed period and concluded that the effect of liberalization have only been partially achieved in German life insurance market.
Another study by Hussels and Ward (2006) examined the impact of deregulation in German and UK life insurance markets from the period 1991 to 2002 using 31 and 76 life insurance companies from German and UK respectively. They studied cost efficiency and further decomposed it into technical, Allocative and scale efficiency. They attempted to study the impact of insurance market deregulation by comparing German life insurance market and the U K market before and after the point of deregulation. Their result suggested that German insurance firms showed better in cost efficiency than that of UK before and after deregulation in the inter industry analysis. While in intra industry analysis, UK intra 36
industry efficiency is higher than that of Germany. However, the analysis showed lack of evidence linking deregulation to improving efficiency levels or development of total factor productivity.
The major structural change in Portugal insurance market started in 1984 with its opening to private enterprise. In 1986, Portugal became a member of European Union and therefore adjusted towards freer market condition. Barros Pedro. P. and Luis M.B. Cabral (1991) evaluated the impact of entry on market competitiveness, in particular on the level of domestic social surplus. They presented a model which was subject to entry by foreign firms and derived test for the marginal as well as the global effect of foreign entry on domestic welfare. Applying the tests, they found the negative global effect of entry in Portuguese life insurance in 1989.However the marginal effect of entry was likely to be positive implying that additional foreign entry would increase the domestic welfare.
The Italian Insurance market has undergone significant deregulation in 1990, when banks were first permitted to own controlling interests in insurers, and since 1992, when implementation of European economic unity
began. Cummins, J. D., Turchetti, G., Weiss, M. A., (1996)
studied an analysis of technical efficiency and productivity growth in the Italian insurance industry. The analysis made use of data based on a sample of 94 Italian life and non-life insurance companies over the period 1985- 1993. For the study, Input-oriented Data envelopment analysis was used to estimate production frontiers for each year of the sample period. While, Productivity growth was measured using Malmquist indices, which were also decomposed into technical efficiency change and technical change. The results indicated that technical efficiency in the 37
Italian insurance industry ranged from 70 to 78 percent during the sample period. There was almost no efficiency change over the sample period. However, productivity declined significantly over the sample period, with a cumulative decline of about 25 percent.
Cummins, Rubio-Misas, and Zi (2004)
used the DEA
method in examining the effect of organizational structure on efficiency
by analyzing stock and mutual Spanish
insurers from
1989 to 1997. A further study by Cummins David and Maria RubioMisas (2006) provided new information on the effects of deregulation and consolidation in financial services market by analyzing the insurance industry. In that they analyzed the causes and effect of consolidation using modern frontier efficiency analysis to estimate cost, technical and allocative efficiency as well as Malmquist analysis to measure the total factor productivity changes. The period of their study was from 1989 to 98. The paper aimed at analyzing scale economies and efficiency in the Spanish insurance industry to determine whether or not the deregulation has had intended effects. They measured efficiency by estimating ‗‗best practice‘ production function and cost frontiers for each year of the sample period, using data envelopment analysis (DEA), a non parametric technique. They also measured the total factor productivity growth using the Malmquist index approach, an extension of the data envelopment approach, because productivity should have improved over the sample period if deregulation has had intended effects. The sample data of their analysis included firms specializing in life insurance and non-life insurance as well as diversified firms offering both types of insurance. For output measurement a modified version of the value added approach was adopted and five outputs viz. non life insurance losses incurred, life insurance losses incurred, reinsurance reserves, reserves for primary 38
insurance contracts and invested assets were used while
four inputs
namely labor, business services, debt capital, and equity capital were taken. The result of the study showed that the deregulation has led to dramatic changes in the Spanish insurance industry such as decline in the number of firms, increase in the average size of firms and also the unit prices declined significantly in both life and non life insurance. There was significant growth in the TFP over the sample period. However the number of firms operating with increasing return to scale has reduced while the number of firms operating with decreasing return to scale has increased.
USA:
There is no significant deregulation as such taken place in US in the past 20 years. However, bank deregulation in the US in 1980s, has significantly affected insurance industry also. Bank were permitted to offer specific types of insurance including life insurance and annuities which were earlier been excluded from the insurance market. Cummins, Tennyson, examine
the
and Weiss (1999) also used the DEA method to efficiency of insurers
and its relationship with the
mergers and acquisition. They estimated Cost and Revenue efficiency over the period 1988 to 1995.They found out that acquired firms achieve greater efficiency gains than firms that have not been involved in merger and acquisition, and concluded that overall, mergers and acquisitions in the life insurance industry have had a beneficial effect on efficiency. Chidambaram N.K, Thomas A. Pugel and Anthony Saunders (1997) studied the performance of U.S property liability insurance industry for the years 1984 through 1993.The main focus of the study was on the differences in performance across lines of insurance. Intensity of 39
competition can influence the performance of an industry. So they tried to draw the impact of such an intensity of competition on insurance prices nationally across different property liability insurance lines. As such they examined the role of variables representing differences in competition intensity across lines of business. They adopted an industrial organization approach, focusing on the economic loss ratio as a measure of pricing performance. Of the various determinants of the variation in economic loss ratio across different lines, the four determinants considered are concentration ratio, direct/ agency ratio, investment ratio, standard deviation of the economic loss ratio and it was calculated for each line using data over the ten year sample period. The result of the study showed that the concentration ratio of the line and the share of direct writers in the line were found to be significant determinant of performance.
China: The monopoly of People‘s Insurance Company of China (PICC) was lifted with the establishment of three new Chinese insurance companies in 1986, 1988 and 1991 respectively. American
Insurance
Assurance won regulatory approval in 1992 to set up a branch in Shanghai, marking the first step to officially signal the opening of Chinese market to foreign insurers. The establishment of the insurance law of the People‘s Republic of china in 1995 also made provision for companies to underwrite either property insurance or life insurance, not both at the same time. In addition, the China Insurance Regulatory Commission
(CIRC) was established in 1998 and undertook the
supervisory control of all the insurance companies. With pressure from the international Insurance community, the CIRC encouraged China‘s 40
government to issue licenses to new domestic and foreign insurers. Finally, China‘s transformation continued with their membership in the WTO becoming official on December 11, 2001. Whalley John (2003) in his paper documented and assessed the policy changes in China‘s three service categories (Banking, Insurance and Telecoms) since its commitment to World Trade Organization (WTO). In case of insurance he expressed rather easy route compare to Banking to achieve the commitment to implementation of WTO. This may be because, foreign entry to the Chinese insurance market was both already possible and allowed, but foreigners seemingly did not take up new entry opportunities quickly. He expressed that whether the effects of liberalization will be beneficial or harmful for China was ambiguous. Leverty Tyler, Yijia Lin and Hao Zhou (2004) conducted an in depth analysis of the efficiency and productivity of the Chinese Insurance Industry after the state monopoly was dissolved allowing foreign owned insurers. They estimated total technical efficiency, purely technical efficiency and scale efficiency using DEA. Also they utilized the Malmquist approach to measure evolution of productivity and efficiency of Insurers over time. The dataset covered was 1995 to 2002 for property casualty insurers and 1992to 2002 for the life insurers. They observed growth in the annual average productivity over the sample period for property-casualty (PC)
insurers as well as life insurance market.
Also, they found out that
regulatory restrictions on foreign insurer,
product diversity and geographical dispersion inhibit foreign property casualty firm‘s efficiency. The gain in productivity in insurance industry is mostly accumulated by Chinese domestic insurance companies. Overall their results shows increase in social welfare following liberalization of the insurance market.
Yang Mingliang (2006) in his study of the
Chinese insurance market particularly property insurance used DEA 41
analysis to estimate the efficiency. Malmquist Index Approach was used to measure the efficiency change and technical change. He used data from the yearbook of China insurance from 2000-2005. Their finding showed that the Chinese property-liability insurance market was experiencing a decreasing efficiency during 2000 to 2004 and there was also a negative growth in total factor productivity during the period. Wei Huang (2007) evaluated the profit and cost efficiency of China‘s insurance firms for the period 1999 to 2004. He used Stochastic Frontier Approach (SFA),a parametric technique and showed that insurance
industry reported
inferior cost efficiency and profit efficiency during the period. The stateowned companies were less cost efficient than non-state-owned insurance companies though they had the advantage of profit efficiency. He also investigated the relationship between efficiency scores and specific features of China‘s insurance companies and identified the determinants of efficiency scores. For that, the efficiency value calculated was used in the regression analysis to find possible factors. Then the significant level of each coefficient and values were processed to determine the real factors and their function. He found out that the corporate governance structure, organizational forms, business mode, asset size and product diversification are among the main factors affecting efficiency. Chen Bingzhen, Powers M .R and Qui Joshep (2009) studied the structure and characteristics of the Chinese life insurance industry, with special focus on the impact of the regulatory changes and the entry of foreign life insurers. They used DEA to find efficiency and malmquist Index for productivity of insurers using data from 2001-2006. Their study concluded that domestic life insurers generally have better efficiency performance. And in case of productivity, one half of the insurers taken showed an increase in Malmquist Index across years.
42
Japan: In Japan, the ―Big Bang‖ financial reforms of the late 1990‘s aimed to make the Tokyo financial market comparable in the scale and in the variety and sophistication of financial products to markets in London, New York and continental Europe. The first drastic changes in Japan‘s insurance industry was the introduction of life insurance business lawin 1996. The law aimed at promotion of deregulation and liberalization, maintenance of sound management, and carrying out fair business operation. The new law enabled companies
to
enter
each
life and non-life insurance
other‘s sector through subsidiaries.
Fukuyama H. and William L.Weber (2001) examined the efficiency and productivity growth of non life insurance companies in Japan during the period 1983-94. They estimated output technical efficiency using three Efficiency measures namely Farrell, Russell and Zieschang measures. The three efficiency measures were used to construct the Malmquist index of productivity growth which can be decomposed into an index of efficiency change and an index of technological change. A sample of 17 Japanese non life insurance companies was used to empirically examine whether there were significant differences in measured productivity change for the three measures above. Farrel, Zieschang based measures indicated no significant increase or decrease in productivity while Russell based Malmquist index showed significant productivity growth. It was found out that Farrel, Russell and Zieschang based decomposition of Malmquist index all exhibited a significant positive correlation. Fukuyama (1997) examined the efficiency and productivity in Japanese life insurance industry using the data from 1988 to 1991.He concluded that efficiency and productivity performance differed from time to time across two ownership type viz. mutual 43
insurance companies and stock companies under different economic conditions. Therefore clear difference in efficiency and productivity between the two ownership types could not be established. Yoshihiro Asai, Yanase Noriyoshi, Tomimura Kei and Ozeki Junya( 2007) studied the efficiency and productivity of life insurance industry in Japan after mid 1990s. They employed DEA and Malmquist Index to calculate the efficiency and productivity of life insurance companies in Japan over a period of 9 years from 1996 to 2004. Their result showed no change in the efficiency of life insurance companies in Japan but productivity of insurance companies in Japan increased during the sample period. The productivity of stock companies dramatically increased while productivity of mutual companies decreased during the sample period. Souma Toshiyuki and Yoshiro Tsutsui (2005) examined the change
in
the
insurance industry
level over
of competition in
the
Japanese
life
the period of 17 years from 1986 to 2002.
Utilizing the regression equations they established that there has been a change in the degree of competition during that period. Their results suggested that competition has become stronger since
1995 but the
competition in the recent years was more lax than the pre war period and so indicated potential for more competition. Other Countries: Oetzel J.M. and S.G. Banerjee (2008) explored the relationship between market liberalization and insurance firm performance in emerging markets and developing countries to specifically determine whether or not market liberalization has a positive impact on insurance firm performance. They also studied whether there were performance differences between foreign and local insurance firms. A sample of 383 companies located in 31 EMDCs (Emerging Markets and Developing 44
Countries) was tasted using moderated time series cross section regression analysis for the time period 1998 to 2003. Their result suggested that market liberalization indeed have significant direct effect on firm profitability for all insurers operating in the host country. Local and foreign firms showed no significant difference in profitability between them.
Korea and Philippines undertook modest deregulation and liberalization efforts. Boonyasai, Grace and Skipper (2004) examined the impact of liberalization and deregulation on four life insurance markets viz. Korea Philippines, Taiwan and Thailand .The data collected were from the late 1970s or 1980s, depending on data availability for each country. For Korea, the life insurance company population varied from 6 to 33 during the study period from 1980 to 1997.
Philippine life
insurers ranged in number of firms from 24 to 33 during the study period, 1987 to 1997.The number of Taiwanese life insurance companies varied from 8 to 31 during the study period, 1983 to 1997. Finally, the number of Thai life insurers varied from 11 to 13 during the study period, 1978 to 1996.Using DEA to measure cost efficiency they found that liberalization and deregulation of Korean and Philippine life insurance industry have stimulated increase and improvement in productivity. However for Taiwanese and Thai Life insurance firms, liberalization has had little effects on increases and improvements in productivity. Their results suggest that liberalization should be closely followed by deregulation or otherwise a restrictive regulatory environment will reduce the welfare gain.
Taiwan has been opening its financial market to foreign countries since 1986. It took few changes in national regulation accompanied by 45
relaxation of market restriction. Jeng Vivian and Gene C. Lai (2008) examined the impact of deregulation and liberalization on efficiency of Taiwanese life insurance industry from 1981-2004 using DEA.
The
efficiency performances as well as changes in efficiency and productivity over time were also calculated using Malmquist index approach. Their results showed that the deregulation and liberalization did not have major adverse impact on the technical, cost and revenue efficiency performances of the existing domestic firms in the long run. Liu (1994) and Chang (1998) also studied the efficiency change in Taiwan‘s life insurance industry. In the case of Liu, the efficiency change from the year 1986 to 1993 was considered and examined the technical efficiency of life insurers using DEA. By further decomposing technical efficiency into scale efficiency and pure technical efficiency, he found that the efficiency performances of foreign insurers were usually poor in the first 2 years, but the inefficiency was mostly due to low-scale efficiency scores. After reaching constant return-to-scale in 2 years, foreign insurers tend to largely improve (1998) used
their technical
efficiency
performances. Chang
the X- efficiency analysis to examine the efficiency
change of existing domestic firms from the year 1975 to 1996. His results showed that the X-inefficiency of domestic firms on average decreased after the deregulation and liberalization. Therefore, he claims that the market competition after the deregulation and liberalization has improved the efficiency performances of existing domestic firms.
Konan Denise Eby and Keith E. Maskus(2006) analyzed the impact of services liberalization in terms of welfare, output and factor prices in Tunisia using a computable general equilibrium (CGE) model to compare it with the goods- trade liberalization. They found out that reducing services barriers generated larger welfare gain and low adjustment costs 46
compare to trade liberalization. Services liberalization increased economic activity in all sectors and raised the real returns to both capital and labor.
India:
Following deregulation of Indian insurance industry, concerns were expressed to look into the scenario of the industry as well as likely changes to be followed. Ranade Ajit and Rajeev Ahuja (1999) in their study identified the emerging strategic issues in light of liberalization and private sector entry into insurance. They justified the need for private sector entry on the basis of enhancing
the efficiency of operations,
achieving a greater density and penetration the country, and for a greater mobilization
of life- insurance in of long term savings for
long gestation infrastructure projects. They pointed out that LIC, with its 40 years of experience and wide reach, was in an advantageous position. They also pointed out the need to handle strategic
issues
carefully. Accordingly LIC should adapt to liberalized scenario such as changing
demography
for a wider variety
and demand for
of products,
pensions,
demand
and having greater freedom
in
its investments. Ranade Ajit and Rajeev Ahuja (2000) in another study looked into the regulatory issues of insurance sector in India. In the Indian insurance
market,
the regulator
entrants of a level
playing field
vis-a-vis
must
assure
hitherto
new
monopoly
incumbents. They were of the view that the regulator must focus initially on financial and
soundness
contract
and prior experience
standardization, and serving
of entrants, tariff
weaker section of the
society. Another primary objective of regulation has to be protection of customer‘s interest as in most countries with longer tradition of a 47
competitive insurance industry. Pant Niranjan (1999) addressed the need for a more cogent legislation than the Insurance Regulation Development Bill 1999. He viewed that liberalization of the insurance sector in India will see the increasing involvement of the large and powerful insurance companies of the
world in the Indian insurance
industry. It was therefore essential to have the support of a stronger regulation to turn this involvement into a positive factor for the growth of the Indian
insurance sector in particular and the Indian economy in
general. Pant Niranjan (2000) in another paper discussed the development agendas for insurance regulation in India. For him, the task of IRDA is to establish and promote fair competition in such a way that sustainable growth in the national insurance market is also achieved. Also the availability and affordability of insurance service for the weaker sections should be one of the important agenda for social development. He also mentioned that the regulator need to establish priority areas for financial management, accounting and reporting issues in insurance keeping in mind the two foremost financial issues viz. security and solvency. Rao Tripati (1999) studied the pattern and growth of life insurance business in India since its nationalization in 1956. His analysis focused specifically in the growth of new business, business in force, income and outgo (financial outflow) of life fund i.e. institutional savings and business by different zones of LIC. These indicators were compared with the related macro variables. He found out that in the decade beginning 1983-84, there was a significant growth in new business both in terms of policies and sum assured. The business in force showed an increasing trend since the early 1980‘s. The analysis of the zonal business revealed that business was greater in the more urbanized zones. The income and outgo analysis has revealed that even with lower sum assured and increased rural business, the LIC has succeeded in converting a 48
growing income into life insurance fund. In spite of all this, life business continued to be low in terms of coverage and contribution to national income and saving. He concluded that there was large potential for future development in life business in India. Rao Tripati (2000) in his paper maintained that the issue of privatization and foreign participation must be approached cautiously with a
'step-by-step approach', and should be
preceded by microeconomic institutional and legal reforms. According to him, the macroeconomic implications
of privatization
and foreign
participation in the insurance sector, especially the life insurance sector, are far-reaching. The life insurance industry was coterminous with the Life Insurance Corporation (LIC) of India and was dominant in two aspects: pooling
and redistributing risks across millions of
policyholders and performing financial
intermediation. Ahuja Rajeev
(2004) appraised the development record of Indian insurance industry in the wake of first four years of liberalization. He however pointed out four areas in which the insurance regulator needs to quickly move forward namely pensions and health insurance, phasing off tariff, strengthening of self regulation and reviewing of capital requirement. According to him the success of the competition in financial sector ultimately depends on the efficiency of regulation. So, constant reviewing and fine tuning of the rules by regulator was also suggested to keep pace with the development in the markets.
Rajendran and Natarajan (2009) found out the
remarkable improvements that the acceptance and adaptation of Liberalization Privatization and Globalization has brought about in the Indian Life Insurance Industry specifically to LIC of India. They first compared the overall performance of LIC of India between pre and post LPG era and secondly examined the current status, volume of competitions and challenges faced by LIC of India. The growth of LIC was compared in terms performance indicators such as annual business, 49
business in force, group business in force and life fund between the period 1957 and 2007.For this they have taken the secondary data from the annual reports of LIC of India. They used method of least squares for the data analysis and linear trend in future growth of insurance was predicted. Their analysis concluded that LPG was incorporating a positive influence on the performance of LIC of India showing that the business in India, business outside India as well as the total business of LIC of India was always in increasing trends. Sen Subir and S Madheswaran (2006) in their paper tried to analyze the structure of the post liberalization period of the Indian insurance industry. The econometric analysis was carried out to select the best measure of concentration from a set of eight concentration ratios of largest firms and Herfindahl index. The analysis suggested that even after five years, domination of the public entities was witnessed in both life as well as non life insurance but private sector firms were gradually coming up in terms of profits. Rastogi Shilpa and Runa Sarkar (2007) in their study
identified
the causes and the
objectives with which the sector was reformed in 2000 and concluded that the hybrid model of privatization with regulation adopted by the Government has yielded positive results and the sector has started to look up.
Sinha Ram Pratap and Biswajit Chatterjee (2009)
calculated the cost efficiency of 14 life insurance companies in India for the period 2002-03 to 2006-07.They estimated the cost efficiency using DEA method and found out an upward trend in the last three years i.e. between 2002-03 and 2004-05. The efficiency in the last two years however was in the reversed trend. Anand Mohit in his study tried to bring out the impact of Joint Venture Companies on Innovation and Growth in Indian Insurance Industry. His concepts of innovation were in terms of product and 50
process. Mitra Debabrata Ghosh Amlan (2010) studied the relationship between life insurance sector reforms in India and the development of life business in post reform period using data from 1990-91 to 2007-08. They constructed an index to measure the reforms and then used VAR-VECM model to find out the long run relationship. The Granger causality test suggested that life insurance sector reforms improved the overall development of life insurance in the recent years in India. The VEC Granger causality test showed that the relationship between the insurance sector reforms and development of life insurance sector in India was bidirectional.
Gosalia Chirag (2008) assessed the financial performance of the Indian non -life insurance industry from the year 2003 to 2007. Various financial ratios including claim ratio and combined ratio were used to analyze the financial ratio using secondary data available with IRDA‘s official gazettes and journals. He also assessed whether the existing insurers were compliance with IRDA regulations specifically the Solvency margins and Rural and social sector obligations. The study revealed that public insurers were dominating over private because of their existing base and none of the private insurers were highly profitable. However private insurers were growing aggressively posing for a strong competition with high level of penetration and profitability in the long run. Sinha Ram Pratap (2010) in his study compared the efficiency of 15 Indian life insurance companies using revenue maximizing approach. Using data from the period 2005-06 to 2008-09, he found that LIC of India was the only efficient insurer throughout the years while it was closely followed by Sahara. Shukla Sneha S. (2010) analyzed the structure of Indian life insurance industry and competition among the insurance companies. She observed that liberalization gave a positive 51
push towards growth of insurance sector as well as the economy and changed the structure of the industry. To understand the impact of the changes and analyze the state of competition, Concentration Index and Herfindahl-Hirschman Index (HHI) of concentration was used. The major findings show a concentration decline and increased competition in the life insurance industry. All these studies, mostly the relating to foreign countries examined impact of liberalizations in terms of efficiency and productivity and hence do not provide comprehensive picture of overall impact and other benefits resulting from reforms. In case of India, most of the studies available analyzed Indian industry scenario in varying aspects such as emerging strategic and regulatory issues in light of liberalization, appraisal of industry development, structure, innovation etc. However Indian insurance is in the starting point of a long journey of liberalization and therefore the result may not be sufficient time for a complete overhaul of the industry, and many trends may only be indicative. More literature in these regard is impending at this stage. This thesis attempts to contribute to field of insurance sector research by examining the changing industry scenario in terms of concentration, efficiency and productivity and other benefits of insurance reforms.
52
CHAPTER IV CONCENTRATION This chapter analyses the effect of liberalization on the market structure in terms of concentration of life insurance industry. This chapter first discusses the concept of concentration and gives measurement in various indicators namely concentration ratio, herfindahl index and entropy. The later part of the chapter examines various other effects of liberalization such as penetration and density and the spread of insurance in rural areas. 4.1 Concentration-Concept:
Market concentration is commonly used to represent the level of market competition. Concentration may have far-ranging and long-lasting implications for financial sector efficiency, stability, competitiveness. (Demirguc-Kunt Asli and Ross Levine, 2000). The appropriate policies, regulations, and institutions are essential for long-run economic growth. Some argue that concentration will intensify market power and thereby stymie competition and efficiency. While excessive competition may create
an
unstable
environment,
insufficient
competition
and
contestability in the insurance sector may breed inefficiencies. For these reasons, policymakers are concerned about concentration and adopt regulatory regime affecting the competitiveness in the industry. The presence or entry of large number of firms doesn‘t necessarily mean that the market is automatically competitive. The presence of foreign insurers also does not automatically mean that the market is more advanced in terms of product development. The question is whether it is contestable or not. Since the government has opted for liberalization from the monopoly regime of LIC to allow fresh entry, encouraging competitiveness is one 53
important objective of the liberalization of Indian insurance industry. Sen Subir and S Madheswaran (2006) and Sukla Sneha (2010) in each of their study analyzed the structure of Indian insurance industry after it went through liberalization phase. They carried out econometric analysis to select the best measure of concentration from a set of eight concentration ratios of the largest firms and herfindahl index .Their study witnessed the domination of the public entities even after five years of liberalization and also the coming up of private sector firms for profits. Their study restricted to the analysis of predicting the number of firms possibly in association or controlling the business. Shukla‘s finding has also shown that entry of large number of private players has changed the structure of the industry with a decline in concentration and increased competition in the life insurance industry.
The concept of industrial concentration has been extensively discussed and debated in the economic literature. Despite the many different approaches to its measurement, general agreement prevails about the constituting elements of concentration measures, i.e. the number of firms (fewness) and the distribution of firm sizes (inequality) in a given market (Bikker .J A. and K. Haaf, 2000). However, the classification of concentration measures in the literature is not systematic. The most frequently used measures of concentration in the empirical literature include k bank Concentration Ratio, Herfindahl-Hirschman Index , comprehensive industrial concentration index(CCI), HallTideman Index and the Rosenbluth Index, Hannah and Kay Index, U index, Hause Indices and entropy measures.
54
4.2-Methodology: Market share identifies the shares of specific firms within a market. This study measures market shares of life insurers. For this study, concentration ratios, Herfindahl Hirschman index (HHI) as well as Entropy (E) is calculated taking market shares in terms of a) total premium b) equity share capital and c) total assets respectively. The concentrations measures are calculated for each insurance company for each year from 2001-02 to 2009-10. The HHI and entropy is calculated for all the life insurance firms with and without LIC of India. Because LIC still plays dominant role in life insurance industry and so an index excluding its share may give a fair knowledge of competition among the private players after liberalization. Concentration Ratio: It is the most frequently used measures of concentration in the empirical literature. The concentration ratio
is
the
measure of the percentage market share in an industry held by the largest firms within that industry. For example, if 3 firms dominate a specific industry, holding 80% market share of the industry, the concentration ratio of the industry would thus be 80%. So the market share of the K firms in the market takes the form given below, giving equal emphasis to the k leading firms, but neglecting the many small firms in the market.
There are no rules for the determination of values of the k, so that the number of firms included in the concentration index is a rather arbitrary decision. The concentration ratio may be considered as one point on the concentration curve, and it is a one-dimensional measure ranging between 55
zero and unity. The index approaches zero for an infinite number of equally sized firms (given that the k chosen for the calculation of the concentration ratio is comparatively small as compared to the total number of firms) and it equals unity if the firms included in the calculation of the concentration ratio make up the entire industry. For this study concentration ratio are calculated taking the value of k as 1 (CR1) and 4(CR4) respectively. CR1 is calculated to determine the share of top company. The Four Firm Concentration Ratio (CR4) calculates the market share of the top four companies in the industry. This calculation determines if an industry is an oligopoly, a monopoly or neither. A Four Firm Concentration Ratio below 40 percent shows there is monopolistic competition. A Four Firm Concentration Ratio above 60 percent shows there is an oligopoly.
Definition of HHI: The Herfindahl-Hirschman index (HHI) or simply herfindahl index is regarded as the most widely treated summary measure of concentration in the theoretical literature and often serves as a benchmark for the evaluation of other concentration indices. It measures the size of the firm in relation to the industry and so indicates the amount of competition among them. The Herfindahl-Hirschman Index (HHI) of competition therefore is a measure of the competitiveness of a market overall. It is not a measure specific to any one insurer, though it is a function of each insurer‘s market share. The herfindahl index stresses the importance of larger firms by assigning them a greater weight than smaller firms, and it incorporates each firms individually, so that arbitrary cut-offs and insensitivity to the share distribution are avoided. Where Si is the market share of the firm i, 56
The HHI index ranges between 1/ n and 1, reaching its lowest value (the reciprocal of the number of firms) when all firms in a market are of equal size, and reaching unity in the case of monopoly. So a decrease in the H indicates an increase in competition. The maximum value of HHI will be 1 when there is monopoly and the minimum value of HHI is 1/n if there are n firms of equal size. Entropy: Entropy is similar to HHI except the weights assigned to smaller units in case of Entropy. It is defined as the reciprocal of
OR
The entropy index is not restricted between 0 and 1, unlike most of the other measures of concentration presented above. The value of the Entropy varies inversely to the degree of concentration. It approaches zero if the underlying market is monopoly and reaches its highest value when market shares of all firms are equal and market concentration is lowest. For a given number of firms, the index falls with an increase in inequality among those firms and the weights the index attaches to market share decreases in absolute terms as the market share of a firm becomes larger.
57
Table 4.1: Market share and herfindahl index of all life insurance firms (total premium) Insurers LIC ICICI
2001-02 2002-03 2003-04 2004-05 2005-06 2006-07 2007-08 200809 0.9946 0.9799 0.9532 0.9067 0.8575 0.8245 0.7439 0.7092 0.0023 0.0075 0.0148 0.0285 0.0402 0.0510 0.0674 0.0692
Max
0.0008
0.0017
0.0032
0.0050
0.0074
0.0097
0.0135
0.0174 0.0183
HDFC
0.0007
0.0027
0.0045
0.0083
0.0148
0.0184
0.0241
0.0251 0.0264
Birla
0.0006
0.0026
0.0081
0.0110
0.0119
0.0115
0.0163
0.0206 0.0207
Tata
0.0004
0.0015
0.0038
0.0060
0.0083
0.0088
0.0102
SBI
0.0003
0.0013
0.0034
0.0073
0.0102
0.0189
0.0279
0.0124 0.0132 0.0325 0.0381
Kotak
0.0002
0.0007
0.0023
0.0056
0.0059
0.0063
0.0084
0.0106 0.0108
Bajaj
0.0001
0.0012
0.0033
0.0121
0.0296
0.0278
0.0483
0.0479 0.0430
ING V.
0.0001
0.0004
0.0013
0.0041
0.0040
0.0046
0.0058
Met Reliance
0.0000 0.0000
0.0001 0.0001
0.0004 0.0005
0.0010 0.0013
0.0019 0.0021
0.0032 0.0065
0.0058 0.0160
0.0065 0.0062 0.0090 0.0096
Aviva
--
0.0002
0.0012
0.0031
0.0057
0.0074
0.0094
0.0090 0.0090
Sahara
--
--
--
0.0000
0.0003
0.0003
0.0007
0.0009 0.0009
Shriram
--
--
--
--
0.0001
0.0012
0.0018
Bharti Future
---
---
---
---
---
0.0001 --
0.0006 0.0000
0.0020 0.0023 0.0016 0.0025
IDBI
--
--
--
--
--
--
0.0001
0.0014 0.0022
Canara H
--
--
--
--
--
--
--
0.0013 0.0032
DLF Par
--
--
--
--
--
--
--
0.0000 0.0006
Aegon R
--
--
--
--
--
--
--
0.0001 0.0001
Star union --
--
--
--
--
--
--
0.0002 0.0020
IndiaFirst
--
--
--
--
--
--
Sum
1.0000
1.0000
1.0000
1.0000
1.0000
1.0000
-1.0000
HHI
0.9890
0.9600
0.9090
0.8230
0.7390
0.6840
0.5630
-0.0008 1.0000 1.0000 0.5130 0.5010
0.7010 0.0623
0.0222 0.0249
0.0007 0.0020
4.3-Results Analysis: In terms of total premium, year wise market share of different life insurance firms is shown in table 4.1 and 4.2 respectively with their HHI. In all the years taken, LIC dominated the industry though its share was decreasing over the years. Till 2004-05, it occupied 90% of the total premium but was reduced to 70% in 2010. All the private life insurers 58
200910
have shown increasing market share over the years. ICICI, Bajaj, SBI, and HDFC, has market share of 6 percent, 4 percent 3 percent and 2 percent in 2009-10 respectively. The herfindahl index of industry was reducing over the years with 0.989 in 2001-02 to 0.501 in 2009-10. The herfindahl index of private industry was reducing over the years with 0.2392 in 2001-02 to 0.1096 in 2009-10.For the private firms, Met and Reliance have shown an increase in market share throughout the year from 2001-02 to 2009-10. However their market shares were comparatively low standing 3 and 8 percent respectively. ICICI started with 42 percent market share but reduced in successive years to 20% in 2009-10. It however, dominated the private insurance market throughout the years. SBI has shown increasing trend of market share except in 2005-06. Its market share increased significantly with a mere 5% in 200102 to 12 % in 2009-10. Bajaj has also increased it share from 2% in 200102 to 14 % in 2009-10.
Table 4.3 and 4.4: The entropy index of life insurance industry and that of private life insurance market (excluding LIC) are shown in table 4.3 and 4.4. The entropy of life insurers in terms of total premium was decreasing over the years, from 0.96 in 2001-02 to 0.26 in 2009-10. For private life insurers also, it was decreasing except in the year 2005-06 which increased to 0.1186 from 0.1159 in 2004-05. The chart representing HHI and entropy for life insurance industry and the private life insurers in terms of total premium is shown in chart 4.1 and 4.2 respectively. For the industry as well as private insurers, the HHI was greater than that of entropy index.
59
Table 4.2: Market share and herfindahl index for all private life firms (total premium) Insurers
2001-02
2002-03
2003-04
2004-05
2005-06
2006-07
2007-08
2008-09
2009-10
ICICI
0.4270
0.3732
0.3170
0.3059
0.2825
0.2908
0.2630
0.2381
0.2083
Max
0.1429
0.0863
0.0690
0.0535
0.0523
0.0551
0.0526
0.0598
0.0612
HDFC
0.1228
0.1330
0.0954
0.0889
0.1041
0.1050
0.0942
0.0863
0.0883
Birla
0.1037
0.1286
0.1723
0.1185
0.0835
0.0653
0.0635
0.0709
0.0694
Tata
0.0776
0.0726
0.0813
0.0643
0.0584
0.0503
0.0397
0.0426
0.0440
SBI
0.0539
0.0647
0.0723
0.0778
0.0713
0.1076
0.1090
0.1118
0.1273
Kotak
0.0278
0.0360
0.0483
0.0603
0.0412
0.0357
0.0328
0.0363
0.0361
Bajaj
0.0262
0.0618
0.0708
0.1296
0.2077
0.1581
0.1886
0.1647
0.1439
ING V.
0.0154
0.0189
0.0284
0.0439
0.0282
0.0260
0.0225
0.0224
0.0207
Met
0.0018
0.0071
0.0092
0.0106
0.0137
0.0181
0.0225
0.0310
0.0320
Reliance
0.0010
0.0058
0.0100
0.0138
0.0149
0.0369
0.0626
0.0765
0.0832
Aviva
--
0.0120
0.0261
0.0328
0.0398
0.0422
0.0367
0.0309
0.0300
Sahara
--
--
--
0.0002
0.0018
0.0019
0.0028
0.0032
0.0032
Shriram
--
--
--
--
0.0007
0.0067
0.0069
0.0068
0.0077
Bharti
--
--
--
--
--
0.0003
0.0023
0.0056
0.0084
Future
--
--
--
--
--
--
0.0000
0.0024
0.0068
IDBI
--
--
--
--
--
--
0.0002
0.0049
0.0072
Canara H
--
--
--
--
--
--
--
0.0046
0.0106
DLF Par
--
--
--
--
--
--
--
0.0001
0.0021
Aegon R
--
--
--
--
--
--
--
0.0005
0.0005
Star U
--
--
--
--
--
--
--
0.0008
0.0067
India F
--
--
--
--
--
--
--
--
0.0025
Sum
1.0000
1.0000
1.0000
1.0000
1.0000
1.0000
1.0000
1.0000
1.0000
Pvt HHI
0.2392
0.1961
0.1649
0.1523
0.1565
0.1475
0.1413
60
0.1239
0.1096
Table 4.3: Entropy for all life insurance firms (total premium)
Insurers
2001-02 2002-03 2003-04 2004-05 2005-06 2006-07 2007-08 200809 LIC 0.0024 0.0086 0.0198 0.0386 0.0572 0.0691 0.0956 0.1058 ICICI 0.0061 0.0159 0.0271 0.0441 0.0562 0.0660 0.0789 0.0803 Max 0.0024 0.0048 0.0080 0.0115 0.0158 0.0195 0.0252 0.0306 HDFC 0.0021 0.0069 0.0105 0.0173 0.0271 0.0320 0.0390 0.0402 Birla 0.0018 0.0067 0.0169 0.0216 0.0229 0.0222 0.0291 0.0348 Tata 0.0014 0.0041 0.0092 0.0133 0.0173 0.0181 0.0203 0.0236 SBI 0.0010 0.0037 0.0084 0.0155 0.0202 0.0326 0.0434 0.0484 Kotak 0.0006 0.0023 0.0060 0.0127 0.0131 0.0138 0.0174 0.0209 Bajaj 0.0006 0.0036 0.0082 0.0232 0.0452 0.0432 0.0636 0.0632 ING V. 0.0003 0.0013 0.0038 0.0098 0.0096 0.0107 0.0129 0.0142 Met 0.0000 0.0005 0.0015 0.0030 0.0053 0.0079 0.0129 0.0184 Reliance 0.0000 0.0005 0.0016 0.0037 0.0057 0.0142 0.0288 0.0368 Aviva -0.0009 0.0036 0.0077 0.0127 0.0158 0.0190 0.0184 Sahara ---0.0001 0.0009 0.0012 0.0022 0.0028 Shriram ----0.0004 0.0034 0.0049 0.0053 Bharti -----0.0002 0.0019 0.0045 Future ------0.0001 0.0022 IDBI ------0.0002 0.0041 Canara H -------0.0038 DLF Par. -------0.0001 Aegon R -------0.0005 Star U -------0.0008 India F ---------Log E 0.0189 0.0598 0.1246 0.2219 0.3098 0.3698 0.4954 0.5598 NE 1.0440 1.1480 1.3320 1.6670 2.0410 2.3430 3.1290 3.6290 E=1/NE 0.9579 0.8711 0.7508 0.5999 0.4900 0.4268 0.3196 0.2756
61
200910 0.1082 0.0751 0.0318 0.0417 0.0349 0.0248 0.0540 0.0212 0.0588 0.0137 0.0193 0.0399 0.0183 0.0029 0.0061 0.0066 0.0055 0.0057 0.0079 0.0020 0.0006 0.0054 0.0024 0.5866 3.8600 0.2591
Table 4.4: Entropy of all private Life insurance firms (total premium)
Insurers
2001-02 2002-03 2003-04 2004-05 2005-06 2006-07 2007-08 200809 ICICI 0.1578 0.1598 0.1582 0.1574 0.1551 0.1560 0.1526 0.1484 Max 0.1208 0.0918 0.0801 0.0680 0.0670 0.0694 0.0673 0.0732 HDFC 0.1118 0.1165 0.0974 0.0934 0.1023 0.1028 0.0967 0.0918 Birla 0.1021 0.1146 0.1316 0.1097 0.0900 0.0774 0.0760 0.0815 Tata 0.0861 0.0827 0.0886 0.0766 0.0720 0.0653 0.0556 0.0584 SBI 0.0684 0.0769 0.0825 0.0863 0.0818 0.1042 0.1049 0.1064 Kotak 0.0433 0.0520 0.0636 0.0736 0.0571 0.0517 0.0487 0.0523 Bajaj 0.0414 0.0747 0.0814 0.1150 0.1418 0.1267 0.1366 0.1290 ING V. 0.0279 0.0326 0.0439 0.0596 0.0437 0.0412 0.0370 0.0369 Met 0.0049 0.0152 0.0187 0.0209 0.0255 0.0315 0.0371 0.0467 Reliance 0.0031 0.0129 0.0199 0.0257 0.0272 0.0529 0.0753 0.0854 Aviva -0.0231 0.0413 0.0487 0.0557 0.0580 0.0527 0.0467 Sahara ---0.0008 0.0050 0.0051 0.0071 0.0080 Shriram ----- 0.0022 0.0145 0.0150 0.0147 Bharti -----0.0010 0.0061 0.0126 Future ------0.0002 0.0062 IDBI ------0.0008 0.0114 Canara H -------0.0107 DLF Par. -------0.0002 Aegon R -------0.0016 Star U -------0.0024 India F ---------log E 0.7674 0.8528 0.9072 0.9356 0.9263 0.9576 0.9697 1.0245 NE 5.8480 7.1290 8.0724 8.6298 8.4333 9.0780 9.3240 10.5780 E=1/NE 0.1710 0.1403 0.1239 0.1159 0.1186 0.1102 0.1073 0.0945
62
200910 0.1419 0.0743 0.0930 0.0804 0.0597 0.1140 0.0521 0.1211 0.0349 0.0478 0.0899 0.0456 0.0079 0.0163 0.0175 0.0148 0.0154 0.0210 0.0056 0.0016 0.0145 0.0066 1.0758 11.9070 0.0840
chart 4.1: HHI and Entropy of life insurance industry( total premium)
hhi and entropy
1.2 1 0.8 0.6
hhi
0.4
entropy
0.2 0
Chart 4.2: HHI and Entropy of private life insurers (total premium) 0.3
hhi and entropy
0.25 0.2 0.15 hhi 0.1
entropy
0.05 0
63
Table 4.5: Market share and HHI of all life insurance firms (equity share capital) Insurers
2002
2003
2004
2005
2006
2007
2008
2009
2010
LIC
0.0030
0.0022
0.0015
0.0011
0.0008
0.0006
0.0004
0.0003
0.0002
ICICI
0.1138
0.1902
0.2081
0.2125
0.2011
0.1615
0.1139
0.0782
0.0679
Max
0.1498
0.1141
0.1067
0.1071
0.0946
0.0902
0.0840
0.0977
0.0875
HDFC
0.1007
0.0976
0.0788
0.0735
0.1052
0.0986
0.1034
0.0984
0.0936
Birla
0.0899
0.0806
0.0894
0.0804
0.0781
0.0827
0.1036
0.1030
0.0937
Tata
0.1108
0.0828
0.0712
0.0737
0.0759
0.0673
0.0708
0.0832
0.0914
SBI
0.0749
0.0560
0.0540
0.0804
0.0721
0.0615
0.0813
0.0548
0.0476
Kotak
0.0605
0.0588
0.0466
0.0486
0.0415
0.0407
0.0391
0.0280
0.0243
Bajaj
0.0899
0.0672
0.0463
0.0345
0.0255
0.0185
0.0123
0.0083
0.0072
ING V.
0.0659
0.0761
0.0755
0.0747
0.0832
0.0849
0.0642
0.0558
0.0485
Met
0.0659
0.0492
0.0493
0.0540
0.0399
0.0652
0.0619
0.0866
0.0844
Reliance
0.0749
0.0560
0.0493
0.0499
0.0562
0.0817
0.0933
0.0636
0.0554
Aviva
--
0.0693
0.0749
0.0735
0.0779
0.0933
0.0817
0.0817
0.0899
Sahara
--
--
0.0484
0.0361
0.0266
0.0193
0.0189
0.0127
0.0110
--
--
--
--
0.0212
0.0154
0.0102
0.0068
0.0059
--
--
--
--
0.0002
0.0185
0.0298
0.0366
0.0538
--
--
--
--
--
--
0.0150
0.0257
0.0334
--
--
--
--
--
--
0.0163
0.0247
0.0214
--
--
--
--
--
--
--
0.0219
0.0238
--
--
--
--
--
--
--
0.0075
0.0105
--
--
--
--
--
--
--
0.0164
0.0271
--
--
--
--
--
--
--
0.0082
0.0119
-1.0000
-1.0000
-1.0000
-1.0000
-1.0000
-1.0000
--
0.0095
total
-1.0000
1.0000
1.0000
HHI
0.0975
0.0993
0.0997
0.1016
0.0988
0.0889
0.0799
0.0717
0.0680
Shriram Bharti Future IDBI Canara H DLF Par. Aegon R Star U India F
64
Table 4.6: Market share and HHI of all private life insurance firms (equity share capital) Insurers
2002
2003
2004
2005
2006
2007
2008
2009
2010
ICICI
0.1142
0.1907
0.2084
0.2128
0.2013
0.1616
0.1140
0.0782
0.0680
Max
0.1502
0.1144
0.1069
0.1072
0.0947
0.0902
0.0840
0.0977
0.0875
HDFC
0.1010
0.0978
0.0789
0.0736
0.1053
0.0987
0.1034
0.0984
0.0936
Birla
0.0901
0.0807
0.0895
0.0805
0.0781
0.0827
0.1037
0.1030
0.0937
Tata
0.1112
0.0830
0.0713
0.0738
0.0759
0.0674
0.0708
0.0833
0.0914
SBI
0.0751
0.0561
0.0540
0.0805
0.0722
0.0616
0.0814
0.0548
0.0476
Kotak
0.0607
0.0589
0.0467
0.0487
0.0415
0.0407
0.0391
0.0280
0.0243
Bajaj
0.0901
0.0673
0.0463
0.0345
0.0255
0.0185
0.0123
0.0083
0.0072
ING V.
0.0661
0.0763
0.0756
0.0748
0.0832
0.0850
0.0643
0.0558
0.0485
Met
0.0661
0.0493
0.0494
0.0541
0.0399
0.0653
0.0619
0.0866
0.0845
Reliance
0.0751
0.0561
0.0494
0.0499
0.0562
0.0818
0.0934
0.0636
0.0554
Aviva
--
0.0694
0.0750
0.0736
0.0779
0.0934
0.0817
0.0818
0.0899
Sahara
--
--
0.0485
0.0361
0.0267
0.0193
0.0189
0.0127
0.0110
Shriram
--
--
--
--
0.0212
0.0154
0.0102
0.0069
0.0059
Bharti
--
--
--
--
0.0002
0.0185
0.0298
0.0366
0.0538
Future
--
--
--
--
--
--
0.0151
0.0257
0.0334
IDBI
--
--
--
--
--
--
0.0163
0.0247
0.0214
Canara H
--
--
--
--
--
--
--
0.0219
0.0238
DLF Par.
--
--
--
--
--
--
--
0.0075
0.0105
Aegon R
--
--
--
--
--
--
--
0.0164
0.0271
Star U
--
--
--
--
--
--
--
0.0082
0.0119
India F
--
--
--
--
--
--
--
--
0.0095
total
1.0000
1.0000
1.0000
1.0000
1.0000
1.0000
1.0000
1.0000
1.0000
Pvt.HHI
0.0981
0.0998
0.1000
0.1019
0.0990
0.0890
0.0800
0.0718
0.0680
The market share of life insurance industry in terms of equity share capital and HHI is shown in table 4.5 and that of private life insurers in terms of equity share capital and HHI is shown in table 4.6. The market share of LIC was decreasing over the years standing 0.02 % in 2010 from 0.03% in 2002. This is because LIC equity share was fixed at 5 crore only while the total equity share capital of the industry was increasing with the increase in private insurers. The HHI of the industry is at 0.09 in the first three years and increased to 0.10 in 2005. From 2006 onwards it was 65
decreasing to 0.068 in 2010. Among private insurers, the market share of Max was highest in 2002 but ICICI stood highest for 6 years from 2003 to 2008. In 2009 and 2010, Birla stood first among life insurers in market share of equity share. The HHI of the private insurers was at 0.09 in the first two years and increased to 0.10 in 2004 and 2005. From 2006 onwards it was decreasing to reach 0.068 in 2010. Entropy in terms of equity share capital for life insurance industry and that of private insurers is shown in table 4.7 and 4.8 respectively. Like HHI, the entropy of the industry is decreasing in the first three years from 0.093 to 0.086 and then increased to 0.087 in 2005. From 2006 onwards it was decreasing from 0.084 to 0.059 in 2010. The entropy of the private insurers was also decreasing in the first three years and increased at 0.087 in 2005. From 2006 onwards it was decreasing and stood at 0.059 in 2010. The combined chart of HHI and entropy for life insurance industry in terms of equity share capital and that of private insurers is shown in chart 4.3 and 4.4 respectively. For the industry as well as private insurers, the HHI was greater than that of entropy index.
66
Table 4.7: Entropy of all life insurance firms (equity share capital)
Insurers
2002
2003
2004
2005
2006
2007
2008
2009
2010
LIC
0.0076
0.0059
0.0043
0.0034
0.0026
0.0020
0.0014
0.0011
0.0007
ICICI
0.1074
0.1371
0.1419
0.1429
0.1401
0.1279
0.1075
0.0866
0.0793
Max
0.1235
0.1076
0.1037
0.1039
0.0969
0.0942
0.0903
0.0987
0.0926
HDFC
0.1004
0.0986
0.0869
0.0833
0.1029
0.0992
0.1019
0.0991
0.0963
Birla
0.0940
0.0881
0.0938
0.0880
0.0865
0.0895
0.1020
0.1017
0.0963
Tata
0.1059
0.0896
0.0817
0.0835
0.0850
0.0789
0.0814
0.0898
0.0950
SBI
0.0843
0.0701
0.0684
0.0880
0.0824
0.0745
0.0886
0.0691
0.0629
Kotak
0.0737
0.0723
0.0621
0.0639
0.0574
0.0566
0.0550
0.0435
0.0392
Bajaj
0.0940
0.0788
0.0618
0.0504
0.0406
0.0321
0.0234
0.0173
0.0154
ING V.
0.0778
0.0851
0.0847
0.0841
0.0898
0.0910
0.0766
0.0699
0.0637
Met
0.0778
0.0644
0.0645
0.0684
0.0558
0.0773
0.0748
0.0920
0.0906
Reliance
0.0843
0.0701
0.0645
0.0649
0.0702
0.0889
0.0961
0.0761
0.0696
Aviva
--
0.0803
0.0843
0.0833
0.0863
0.0961
0.0889
0.0889
0.0941
Sahara
--
--
0.0637
0.0520
0.0420
0.0331
0.0325
0.0241
0.0215
Shriram
--
--
--
--
0.0355
0.0279
0.0203
0.0147
0.0132
Bharti
--
--
--
--
0.0007
0.0320
0.0454
0.0526
0.0683
Future
--
--
--
--
--
--
0.0274
0.0409
0.0493
IDBI
--
--
--
--
--
--
0.0291
0.0397
0.0357
Canara H DLF Par. Aegon R Star U
--
--
--
--
--
--
--
0.0363
0.0386
--
--
--
--
--
--
--
0.0159
0.0208
--
--
--
--
--
--
--
0.0293
0.0425
--
--
--
--
--
--
--
0.0171
0.0229
India F
--
--
--
--
--
--
--
--
0.0192
-Log E
1.0308
1.0480
1.0661
1.0603
1.0746
1.1011
1.1427
1.2043
1.2279
NE
10.735
11.168
11.64
11.486
11.874
12.62
13.887
16.007
16.901
E=1/NE
0.0932
0.0895
0.0859
0.0871
0.0842
0.0792
0.0720
0.0625
0.0592
67
Table 4.8: Entropy of all private life insurance firms (equity share capital)
Insurers
2002
2003
2004
2005
2006
2007
2008
2009
2010
ICICI
0.1076
0.1372
0.1419
0.143
0.1401
0.1279
0.1075
0.0866
0.0794
Max
0.1237
0.1077
0.1038
0.104
0.0969
0.0942
0.0904
0.0987
0.0926
HDFC
0.1005
0.0987
0.087
0.0834
0.1029
0.0993
0.1019
0.0991
0.0963
Birla
0.0942
0.0882
0.0938
0.0881
0.0865
0.0895
0.1021
0.1017
0.0964
Tata
0.1061
0.0897
0.0818
0.0836
0.085
0.0789
0.0814
0.0899
0.095
SBI
0.0845
0.0702
0.0685
0.0881
0.0824
0.0745
0.0886
0.0691
0.0629
Kotak
0.0739
0.0724
0.0621
0.0639
0.0574
0.0566
0.055
0.0434
0.0392
Bajaj
0.0942
0.0789
0.0618
0.0505
0.0407
0.0321
0.0234
0.0172
0.0154
ING V.
0.078
0.0852
0.0848
0.0842
0.0899
0.091
0.0766
0.07
0.0637
Met
0.078
0.0645
0.0645
0.0685
0.0558
0.0774
0.0748
0.092
0.0907
Reliance
0.0845
0.0702
0.0645
0.065
0.0703
0.0889
0.0962
0.0761
0.0696
Aviva
--
0.0804
0.0843
0.0834
0.0864
0.0962
0.0889
0.0889
0.094
Sahara
--
--
0.0637
0.0521
0.042
0.0331
0.0325
0.0241
0.0216
Shriram
--
--
--
--
--
--
0.0203
0.0148
0.0132
Bharti
--
--
--
--
0.0007
0.0455
0.0526
0.0683
0.0274
0.0408
0.0493
0.032
Future IDBI
--
--
--
--
--
--
0.0291
0.0397
0.0357
Canara H
--
--
--
--
--
--
--
0.0364
0.0386
DLFPar.
--
--
--
--
--
--
--
0.016
0.0208
Aegon R
--
--
--
--
--
--
--
0.0293
0.0425
Star U
--
--
--
--
--
--
--
0.0171
0.0229
India F
--
--
--
--
--
--
--
-LogE
--
0.0192
1.025
1.0434
1.0628
1.0576
1.0725
1.0996
1.1416
1.2035
1.2274
NE
10.593
11.05
11.55
11.415
11.816
12.57
13.85
15.9772
16.881
E=1/NE
0.0944
0.0905
0.0866
0.0876
0.0846
0.0796
0.0722
0.0626
0.0592
68
Chart 4.3- HHI and Entropy of life insurance insurance industry (equity share capital) 0.12
hhi and entropy
0.1 0.08 0.06
hhi entropy
0.04 0.02 0 2002 2003 2004 2005 2006 2007 2008 2009 2010
Chart 4.4- HHI and Entropy of private life insurers (equity share capital) 0.12
hhi and entropy
0.1 0.08 0.06
hhi entropy
0.04 0.02 0 2002
2003
2004
2005
2006
69
2007
2008
2009
2010
Table 4.9: Market share and HHI of all life insurance firms (total assets)
Insurers
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
LIC
0.9972
0.9929
0.9905
0.9850
0.9721
0.9523
0.9258
0.8912
0.8724
0.8321
ICICI
0.0008
0.0010
0.0026
0.0049
0.0092
0.0159
0.0240
0.0336
0.0345
0.0426
Max
0.0005
0.0008
0.0007
0.0009
0.0013
0.0018
0.0030
0.0047
0.0064
0.0080
HDFC
0.0009
0.0008
0.0011
0.0015
0.0024
0.0051
0.0077
0.0108
0.0116
0.0156
Birla
0.0006
0.0006
0.0008
0.0020
0.0032
0.0047
0.0062
0.0083
0.0100
0.0124
Tata
0.0007
0.0007
0.0010
0.0016
0.0024
0.0035
0.0047
0.0056
0.0073
SBI
0.0006
0.0007
0.0012
0.0026
0.0039
0.0072
0.0117
0.0152
0.0216
Kotak
0.0006
0.0005
0.0007
0.0014
0.0021
0.0028
0.0037
0.0043
0.0051
Bajaj
0.0006
0.0007
0.0010
0.0024
0.0064
0.0105
0.0158
0.0181
0.0249
ING V.
0.0004
0.0005
0.0005
0.0015
0.0016
0.0022
0.0027
0.0031
0.0037
Met
0.0005
0.0004
0.0004
0.0005
0.0006
0.0013
0.0024
0.0033
0.0046
Reliance
0.0005
0.0004
0.0003
0.0005
0.0008
0.0021
0.0049
0.0070
0.0105
0.0005
0.0006
0.0009
0.0017
0.0027
0.0036
0.0042
0.0050
0.0004
0.0003
0.0003
0.0005
0.0005
0.0007
0.0002
0.0004
0.0007
0.0008
0.0011
0.0002
0.0004
0.0005
0.0008
Future
0.0002
0.0004
0.0005
IDBI
0.0002
0.0007
0.0010
Canara H
0.0006
0.0011
DLF Par
0.0001
0.0001
Aegon R
0.0002
0.0003
Star U
0.0002
0.0008
Aviva Sahara Shriram Bharti
India F
0.0004
Sum
1.0000
1.000
1.0000
1.0000
1.000
1.0000
1.0000
1.000
1.0000
1.0000
HHI
0.9944
0.9858
0.9810
0.9703
0.9452
0.9072
0.8580
0.7960
0.7632
0.6961
70
Table 4.10: Market share and HHI of all private life insurance firms (total assets) Insurers
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
ICICI
0.2703
0.1447
0.2759
0.3254
0.3291
0.3330
0.3235
0.3090
0.2705
0.2540
Max
0.1771
0.1187
0.0746
0.0619
0.0478
0.0385
0.0410
0.0428
0.0505
0.0474
HDFC
0.3265
0.1066
0.1130
0.0993
0.0867
0.1065
0.1040
0.0993
0.0907
0.0930
Birla
0.2261
0.0867
0.0840
0.1326
0.1148
0.0980
0.0835
0.0765
0.0786
0.0736
Tata
0.0977
0.0683
0.0653
0.0567
0.0509
0.0475
0.0432
0.0436
0.0433
SBI
0.0829
0.0766
0.0831
0.0931
0.0827
0.0964
0.1074
0.1192
0.1287
Kotak
0.0819
0.0565
0.0449
0.0518
0.0445
0.0379
0.0336
0.0335
0.0303
Bajaj
0.0876
0.0782
0.0635
0.0872
0.1347
0.1410
0.1454
0.1418
0.1486
ING V.
0.0564
0.0473
0.0360
0.0538
0.0332
0.0291
0.0248
0.0246
0.0219
Met
0.0632
0.0372
0.0259
0.0172
0.0129
0.0181
0.0218
0.0262
0.0274
Reliance
0.0735
0.0376
0.0226
0.0169
0.0176
0.0284
0.0454
0.0549
0.0624
0.0508
0.0394
0.0323
0.0352
0.0361
0.0328
0.0330
0.0297
0.0127
0.0066
0.0044
0.0045
0.0042
0.0040
Shriram
0.0052
0.0058
0.0063
0.0066
0.0066
Bharti
0.0005
0.0029
0.0033
0.0038
0.0047
0.0002
0.0017
0.0033
0.0031
0.0021
0.0058
0.0057
Canara H
0.0050
0.0064
DLF Par
0.0009
0.0006
Aegon R
0.0014
0.0016
Star U
0.0018
0.0045
Aviva Sahara
Future IDBI
India F
0.0024
sum
1.0000
1.000
1.0000
1.0000
1.000
1.0000
1.0000
1.000
1.0000
1.0000
HHI
0.2621
0.0973
0.1289
0.1584
0.1581
0.1658
0.1604
0.1530
0.1331
0.1285
71
Table 4.11: Entropy for all life insurance firms (total assets)
Insurers
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
LIC
0.0012
0.0031
0.0041
0.0065
0.0119
0.0202
0.0310
0.0446
0.0517
0.0664
ICICI
0.0024
0.0031
0.0068
0.0113
0.0187
0.0286
0.0389
0.0495
0.0505
0.0584
Max
0.0016
0.0026
0.0022
0.0028
0.0038
0.0050
0.0077
0.0109
0.0141
0.0167
HDFC
0.0028
0.0024
0.0032
0.0042
0.0063
0.0117
0.0163
0.0212
0.0224
0.0282
Birla
0.0020
0.0020
0.0025
0.0054
0.0080
0.0109
0.0137
0.0173
0.0200
0.0236
Tata
0.0022
0.0021
0.0029
0.0044
0.0064
0.0086
0.0109
0.0126
0.0155
SBI
0.0019
0.0023
0.0036
0.0067
0.0095
0.0154
0.0226
0.0277
0.0360
Kotak
0.0019
0.0018
0.0021
0.0041
0.0057
0.0072
0.0089
0.0101
0.0117
Bajaj
0.0020
0.0023
0.0029
0.0064
0.0141
0.0207
0.0285
0.0315
0.0400
ING V.
0.0014
0.0015
0.0018
0.0042
0.0044
0.0058
0.0069
0.0079
0.0090
Met
0.0015
0.0012
0.0013
0.0016
0.0020
0.0038
0.0062
0.0083
0.0107
Reliance
0.0017
0.0012
0.0012
0.0016
0.0026
0.0056
0.0114
0.0151
0.0207
0.0016
0.0019
0.0027
0.0047
0.0069
0.0087
0.0100
0.0115
0.0012
0.0011
0.0011
0.0016
0.0018
0.0021
Shriram
0.0009
0.0015
0.0022
0.0026
0.0033
Bharti
0.0001
0.0008
0.0012
0.0016
0.0025
0.0001
0.0007
0.0014
0.0017
0.0008
0.0023
0.0029
Canara H
0.0020
0.0032
DLF Par
0.0004
0.0004
Aegon R
0.0007
0.0010
Star U
0.0008
0.0024
Aviva Sahara
Future IDBI
India F
0.0014
-Log E
0.0100
0.0258
0.0328
0.0479
0.0817
0.1278
0.1851
0.2544
0.2956
0.3692
NE
1.0233
1.0617
1.0789
1.1169
1.2078
1.3428
1.5311
1.7947
1.9770
2.3388
E=1/NE
0.9772
0.9419
0.9269
0.8953
0.8280
0.7447
0.6531
0.5572
0.5058
0.4276
72
Table 4.12: Entropy of all private life insurance firms (total assets)
Insurers
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
ICICI
0.1536
0.1215
0.1543
0.1587
0.1588
0.1590
0.1586
0.1576
0.1536
0.1512
Max
0.1332
0.1099
0.0841
0.0748
0.0631
0.0544
0.0569
0.0586
0.0655
0.0628
HDFC
0.1587
0.1036
0.1070
0.0996
0.0921
0.1036
0.1023
0.0996
0.0945
0.0960
Birla
0.1460
0.0921
0.0904
0.1164
0.1079
0.0989
0.0901
0.0854
0.0868
0.0834
Tata
0.0987
0.0796
0.0774
0.0707
0.0658
0.0629
0.0590
0.0594
0.0590
SBI
0.0897
0.0855
0.0898
0.0960
0.0895
0.0980
0.1041
0.1101
0.1146
Kotak
0.0890
0.0705
0.0605
0.0666
0.0601
0.0538
0.0495
0.0495
0.0460
Bajaj
0.0927
0.0865
0.0760
0.0924
0.1173
0.1200
0.1218
0.1203
0.1230
ING V.
0.0704
0.0627
0.0520
0.0683
0.0491
0.0447
0.0398
0.0396
0.0364
Met
0.0758
0.0532
0.0411
0.0303
0.0244
0.0315
0.0363
0.0414
0.0428
Reliance
0.0833
0.0536
0.0372
0.0300
0.0309
0.0439
0.0610
0.0692
0.0752
0.0657
0.0554
0.0481
0.0512
0.0521
0.0487
0.0488
0.0453
0.0240
0.0143
0.0104
0.0105
0.0100
0.0096
Shriram
0.0119
0.0130
0.0139
0.0143
0.0144
Bharti
0.0018
0.0074
0.0082
0.0093
0.0110
0.0007
0.0047
0.0081
0.0078
0.0057
0.0129
0.0128
Canara H
0.0115
0.0140
DLF Par
0.0026
0.0020
Aegon R
0.0040
0.0045
Star U
0.0050
0.0107
Aviva Sahara
Future IDBI
India F
0.0063
-Log E
0.5914
1.0266
0.9931
0.9388
0.9483
0.9322
0.9461
0.9643
1.0165
1.0287
NE
3.9030
10.6316
9.8424
8.6856
8.8777
8.5546
8.8328
9.2109
10.3872
10.6832
E=1/NE
0.2562
0.0941
0.1016
0.1151
0.1126
0.1169
0.1132
0.1086
0.0963
0.0936
73
Chart 4.5: HHI and Entropy of life insurance industry (total assets) 1.2
hhi and entropy
1 0.8 0.6
hhi entropy
0.4 0.2 0 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010
Chart 4.6: HHI and Entropy of pvt life insurance insurers (total assets) 0.3
hhi and entropy
0.25 0.2 0.15
hhi
entropy 0.1 0.05 0 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010
74
Market share of life insurance industry in terms of total assets and HHI is shown in table 4.9 and that of private insurance industry is shown in table 4.10. The market share of LIC was decreasing over the decade from 0.99 in 2001 to 0.83 in 2010. While on an average, the private insurers were grasping market share in an upward trend throughout the year taken. It may be noted that LIC‘s market share was consistently decreasing at slow pace as LIC still enjoyed 83% of market share in terms of total assets. Next to LIC, ICICI was having a market share of 4 % which was far behind. The HHI of the industry was decreasing over the years. It was 0.696 in 2010 declining from 0.994 in 2001. In the private insurance industry, the market share (as shown in table 4.10) was highest for ICICI throughout the year except in 2001. The insurers in the second place keep fluctuating over the years. The HHI was not consistently decreasing over the years but its value remained below 0.2. The HHI of the private industry was much lower than that of the industry. Table 4.11 and 4.12 show the entropy in terms of total assets for life insurance industry and that of private insurance industry. The industry entropy was decreasing over the years and in fact reduced to more than half, from 0.977 in 2001 to 0.428 in 2010. For the private industry, the entropy was decreased over the years but its value fluctuated over the years. From 2006 onwards it was consistently decreasing from 0.1169 to 0.093 in 2010. The entropy of private industry is lower than that of the industry. Chart 4.5 and 4.6 show the HHI and entropy of life insurance industry in terms of total assets and that of private insurance industry. In both the charts, HHI was higher than that of entropy.
75
Table 4.13: HHI, E, CR1and CR4 compared (total premium) 200102 No of insurers 12 Industry HHI 0.9890 total E 0.9579 HHI 0.2392 Pvt E 0.1710 CR1 (%) 99.46 CR4 (%) 99.84 Year
200203 13 0.9600 0.8711 0.1961 0.1403 97.99 99.27
200304 13 0.9090 0.7508 0.1649 0.1239 95.32 98.06
200405 14 0.8230 0.5999 0.1523 0.1159 90.67 95.83
200506 15 0.7390 0.49 0.1565 0.1186 85.75 94.21
200607 16 0.6840 0.4268 0.1475 0.1102 82.45 92.22
200708 18 0.5630 0.3196 0.1413 0.1073 74.39 88.75
200809 22 0.5130 0.2756 0.1239 0.0945 70.92 85.88
200910 23 0.5010 0.2591 0.1096 0.084 70.10 84.44
Table-4.14: HHI, E, CR1 and CR4 compared (equity share capital) Year Number of firms Industry total HHI E Pvt HHI E CR1 (%) CR4 (%)
2002 12 0.0975 0.0932 0.0981 0.0944 14.98 47.51
2003 13 0.0993 0.0895 0.0998 0.0905 19.02 48.47
2004 14 0.0997 0.0859 0.1000 0.0866 20.81 48.30
2005 14 0.1016 0.0871 0.1019 0.0876 21.25 48.04
2006 16 0.0988 0.0842 0.0990 0.0846 20.11 48.41
2007 16 0.0889 0.0792 0.0890 0.0796 16.15 44.36
2008 18 0.0799 0.0720 0.0800 0.0722 11.39 41.42
2009 22 0.0717 0.0625 0.0718 0.0626 10.30 38.57
2010 23 0.0680 0.0592 0.0680 0.0592 09.37 36.86
Table-4.15: HHI, E, CR1 and CR4 compared (total assets) Year Number of firms Industry HHI total E Pvt HHI E CR1 (%) CR4 (%)
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
05 0.9944 0.9772 0.2621 0.2562 99.72 99.95
12 0.9858 0.9419 0.0973 0.0941 99.29 99.55
13 0.9810 0.9269 0.1289 0.1016 99.05 99.5
13 0.9703 0.8953 0.1584 0.1151 98.5 99.34
14 0.9452 0.8280 0.1581 0.1126 97.21 98.71
15 0.9072 0.7447 0.1658 0.1169 95.23 97.97
16 0.8580 0.6531 0.1604 0.1132 92.58 96.8
18 0.7960 0.5572 0.1530 0.1086 89.12 95.23
22 0.7632 0.5058 0.1331 0.0963 87.24 94.02
23 0.6961 0.4276 0.1285 0.0936 83.21 92.12
In Table 4.13, HHI, E and Concentration Ratios (CR1 and CR4) are shown in terms of total premium. The HHI as well as E showed a similar trend wherein both were decreasing over the years from 2001-02 to 200910. The HHI index was always greater than the E in all the years. For the
76
industry total, HHI decreased from 0.99 in 2001-02 to 0.50 in 2009-10 and E decreased from 0.96 to 0.26. In case of private insurers, the HHI decreased from 0.23 in 2001-02 to 0.11 in 2009-10 and E decreased from 0.17 to 0.08. In terms of total premium, the concentration was almost reduced to half since monopoly regime of LIC and the competition among private insurers was also increasing over the period. This was also evident from the CR1 which is decreasing over the years. The CR4 was also decreasing over the years. In Table 4.14, HHI, E and Concentration Ratios (CR1 and CR4) in terms of equity share capital are shown. The HHI of industry as well as private insurers showed a similar trends wherein both were decreasing since 2006. However the indices showed a slight increase till the year 2005. In case of entropy, it was decreasing over the years for industry as well as private insurers except in the year 2005. The equity share of LIC was constant at 5 Crores in all the years and so the fluctuations may be due to increase in private players. The HHI index was always greater than the E in all the years. For the industry total, HHI decreased from 0.09 in 2002 to 0.06 in 2010 and E decreases from 0.093 to 0.059. In case of private insurers, the HHI decreased from 0.098 in 2002 to 0.06 in 2010 and E decreased from 0.09 to 0.059. The CR1 was fluctuating over the years. The CR4 also changed from year to year. The average CR4 of the 09 years taken was at around 45%. In Table 4.15, HHI, E and Concentration Ratios (CR1 and CR4) for market shares in terms of total assets are given. For the industry total, both HHI and E were decreasing from 0.99 and 0.97 in 2001 to 0.70 and 0.42 in 2009-10 respectively. This shows that the competition, of course, has increased in life insurance industry after liberalization but the LIC of
77
India still dominated the market. In the case of private players, the HHI sharply decreased to 0.097 in 2002 from 0.262 in 2001 but increased thereafter for two years standing 0.158 in 2004.The similar trend was also seen in Entropy also. This may due to the fact that new entrant in the industry increased in the first two years of liberalization and the number of new entrant remained same for the two years 2003 and 2004.The slight decrease in concentration in 2005 may be due to the entry of a new player (Sahara) in 2005 with huge assets. Though there were two new entrants in 2006 also, their total assets size may not be large enough to reduce the concentration level. The HHI index was always greater than the E in all the years. In terms of total assets, the concentration ratio of LIC was reduced to 83 %. This shows that LIC still dominated the market though its share was decreasing consistently and gradually. The CR4 also was decreasing over the years. 4.4-Insurance Penetration and Density
Insurance plays a significant role in shaping the economy of a nation. The contribution of the insurance sector to growth can be gauged by the rate of penetration. The life insurance penetration and density are the standard measures of the development of life business. Penetration is defined as the ratio of premium volume to the Gross Domestic Product (GDP). The rate of penetration means the quantum of premium mobilized by the insurance sector vis-à-vis the growth of Gross Domestic Product. The insurance density may also be defined as per capita expenditure on insurance premium. It has direct correlation to per capita GDP income of the country. The insurance penetration and density are presented in table 4.16 which showed that the impact of Insurance sector reform on Indian economy. The penetration and density is also compared with that of Asia 78
and world in case of Life insurance. Insurance penetration as well as density was increasing over the years since liberalization as expected. The values of above indicators were 1.93 and 8.5 in 1999 which were grown to 5.2 and 54.3 respectively in 2009 for the total industry (life and non life together). The values of above indicators were 1.39 and 6.1 in 1999 which were grown to 4.60 and 47.7respectively for life insurance in 2009. Insurance penetration was however lower comparing to Asia and world penetration in all years except in 2007. In case of insurance density it is very much behind the Asian and World density. Table 4.16: Insurance penetration and density in India (1999-2009) Year
Insurance penetration Insurance density life non- Total Life Life Non- Total Life life life Asia World Asia World 1999 1.39 0.54 1.93 --6.1 2.4 8.5 --2000 1.77 0.55 2.32 --7.6 2.3 9.9 --2001 2.15 0.56 2.71 5.84 4.68 9.1 2.4 11.5 125.0 235.0 2002 2.59 0.67 3.26 5.81 4.76 11.7 3.0 14.7 128.1 247.3 2003 2.26 0.62 2.88 5.74 4.59 12.9 3.5 16.4 140.1 267.1 2004 2.53 0.65 3.17 5.58 4.55 15.7 4.0 19.7 147.2 291.5 2005 2.53 0.61 3.14 5.16 4.34 18.3 4.4 22.7 149.6 299.5 2006 4.10 0.60 4.70 5.00 4.50 33.2 5.2 38.4 154.6 330.6 2007 4.00 0.60 4.60 4.60 4.40 40.4 6.2 46.6 156.7 358.1 2008 4.00 0.60 4.60 -4.10 41.2 6.2 47.4 -369.7 2009 4.60 0.60 5.20 -4.00 47.7 6.7 54.3 -341.2 Source: Compiled from IRDA‘s Annual Reports (Note- Insurance Penetration: Ratio (%) of Premium to GDP, Insurance Density: Ratio of Premium to Total Population)
79
4.5-Spread of Insurance Business in Rural Areas: Apart from the overall increase in penetration of insurance in the country, there is also need for increasing the insurance penetration in the rural areas. Spread of life insurance business in rural areas was poor before liberalization. IRDA therefore made it mandatory to cover rural areas in their business [IRDA (Obligations of Insurers to Rural or Social Sectors) Regulations 2000, 2002, 2008]. Table 4.17: Growth of LIC of India‘s rural new business (1994-95 to 200910) Year
No. of Policies (In Lakh)
Sum Assured (Rs. in Crore)
% Growth Rate
Policies 1994-95 1995-96 1996-97 1997-98 1998-99 1999-00 2000-01 2001-02 2002-03 2003-04 2004-05 2005-06 2006-07 2007-08 2008-09 2009-10
49.02 52.57 60.33 68.40 81.23 97.04 109.20 37.02 45.23 62.19 55.03 74.66 88.50 90.43 87.15 102.50
21571.00 21263.59 24278.73 27550.69 32372.94 44168.19 59676.42 25461.94 23574.69 35651.99 46037.01 60971.85 68497.21 59694.44 73354.97 78895.11
-7.20 14.80 13.40 18.80 19.50 12.53 -66.1 22.18 37.50 -11.51 35.67 18.54 2.18 -3.63 17.61
Sum Assured --1.40 14.20 13.50 17.50 36.43 35.11 -57.33 -7.41 51.23 29.13 32.44 12.34 -12.85 22.88 07.55
Share of the rural new business in total new business(%) Policies Sum Assured 45.10 47.70 49.20 51.40 54.70 57.50 55.53 16.94 18.90 22.79 22.97 23.65 23.16 21.67 24.28 26.39
39.10 41.00 42.80 43.30 47.00 48.70 47.76 13.65 13.37 17.85 25.18 21.21 22.60 24.06 18.81 18.84
Source: Annual Reports of LIC of India Table 4.17 shows the growth of LIC of India‘s Rural New Business (1994-95 to 2009-10). At the time of liberalization, the rural new business of LIC in terms of policies stood at 57.50% while sum assured stood at 48.70% in 1999. The percentage of the rural new business in terms of policies and sum assured was increasing since 199495 to 1999-00 but it was declined to a low of 16.94 and 13.65 in 2001-02. 80
In 2009-10 it stood at 26.39 % and 18.84 % respectively. The growth rate of rural new policies and sum assured were seen fluctuating over the years following liberalization. The number of new policies issued in rural areas stood at 102.50 lakh only which was lower than 109.20 lakh policies issued at the beginning of the liberalization in 2000-01. However, LIC was always compliant with its obligations in the rural sector and underwrote a higher percent of policies in the rural sector, than the prescribed 25% in 2009-10. Like LIC, all the twenty two life insurers in the private sector fulfilled their rural sector obligations during 200910(IRDA‘s Annual Report 2009-10). Similarly, all life insurers except HDFC Standard fulfilled their social sector obligations during 2009-10.
Postal life insurance organization has been given responsibility to promote insurance in the rural areas. The growth of postal life insurance is given in table 4.18. The growth of postal life policies procured and sum assured was positive in all years except 2006-07.The premium income however was seen having a positive growth over the previous year in all the years after liberalization. The postal life policies procured, and sum assured has increased from 172163 and Rs 1710.02 Crores in 20001-02 to 417832 and Rs 7345.56 Crores in 2008-09. Premium income from postal life insurance has also increased from Rs 501.85 Crores in 2001-02 to Rs 2301.24 Crores in 2009-10. In case of rural postal life insurance, the premium income has increased from Rs 94.71 Crores in 2000-01 to Rs 1262.11 Crores in 2009-10. The rural postal life policies procured was declined in 2006-07 and 2007-08.
81
Table 4.18: Growth of postal life insurance (PLI) and rural postal life Insurance (RPLI) in India (2001-02 to 2009-10)
Year
2001-02 2002-03 2003-04 2004-05 2005-06 2006-07 2007-08 2008-09 2009-10
Achievement of policy, sum assured and premium income in PLI Policies % growth Sum % growth over Premium % procured over Assured Previous year Income growth during the Previous year (Rs in ( Rs in over year Crores) Crores) Previous year 172163 8.92 1710.02 20.27 501.85 13.19 212967 23.70 2110.21 23.40 590.84 17.73 276880 30.01 2846.66 34.90 698.17 18.17 344403 24.39 3830.64 34.57 904.58 29.56 364564 5.85 4533.20 19.86 1078.66 19.24 318058 -12.75 4146.67 -8.52 1211.78 12.34 355700 11.83 5020.62 21.08 1480.34 22.16 417832 17.47 7345.56 46.31 1860.52 25.68 ----2301.24 23.69 Achievement of policy, sum assured and premium income in RPLI
2001-02 2002-03 2003-04 2004-05 2005-06 2006-07 2007-08
368527 702542 1083152 1335847 1443818 1268826 1200582
127.19 90.64 54.18 23.33 8.08 -12.12 -5.38
1696.85 3347.94 5949.97 7207.96 8822.86 11116.59 9644.30
156.07 97.30 77.72 21.14 22.40 26.00 -13.24
94.71 171.36 245.32 380.87 475.09 601.02 664.70
39.75 80.93 43.16 55.25 24.74 26.51 10.60
2009-10
--
--
--
--
1262.11
43.54
Source: Directorate, postal Life Insurance, Department of post (www. mospi. govt.in) 4.6-Summary: The change in structure of life insurance with the coming of private insurers and the intensity of changes is analyzed with Concentration Ratios, Herfindahl Hirschman index (HHI) as well as Entropy (E). Both HHI and Entropy are calculated in terms of a) total premium b) equity share capital and c) total assets respectively. The concentration was declining in all the three variables taken. For the industry total, HHI decreased from 0.99 in 2001-02 to 0.50 in 2009-10 and E decreased from 0.96 to 0.26 in terms of total premium. In case of 82
equity share capital, the industry‘s HHI decreased from 0.09 in 2002 to 0.06 in 2010 and E decreased from 0.093 to 0.059. For the industry total, both HHI and E were decreasing from 0.99 and 0.97 in 2001 to 0.70 and 0.42 in 2009-10 respectively in terms of total assets. In terms of total premium CR1 has decreased from 14.98% in 2001-02 to 09.37% in 200910 while the CR4 has decreased from 47.51% to 36.86% during the period. In case of equity share capital, CR1 has decreased from 99.46% in 2001-02 to 84.44% in 2009-10 while the CR4 has decreased from 99.84% to 84.44% during the period. In terms of total assets, CR1 has decreased from 99.72 % in 2001-02 to 83.21 % in 2009-10 while the CR4 has decreased from 99.95 % to 92.12% during the period. The HHI index was always greater than the E in all the years for all the indices. LIC still dominated the market though its share was gradually decreasing consistently. For premium and total assets, the HHI and E of private insurers were always less than that of the whole industry. This shows rising competition among private players. Among the private insurers, ICICI has highest market share so far in almost all the years in terms of total premium, total assets and equity shares.
Insurance penetration as well as density has increased as expected, over the years since liberalization. The values of above indicators were 1.93 and 8.5 in 1999 which were grown to 5.2 and 54.3 respectively in 2009. The values of above indicators were 1.39 and 6.1 in 1999 which were grown to 4.60 and 47.7respectively for life insurance in 2009. Though the insurance penetration and density has increased over the years, they were lower than that of Asia and world. In terms of rural penetration, the share of rural business in total volume of insurance business was still low in India. LIC of India, which is the dominant player 83
in life insurance sector has issued only 26.39% of its new policies in rural areas and assured 18.84% in 2009-10. This was far less than the 57.50 % of new policies out of total policies issued in 1999-00. The postal life policies procured, and sum assured has increased from 172163 and Rs 1710.02 Crores in 20001-02 to 417832 and Rs 7345.56 Crores in 200809. The rural postal life insurance premium income has increased from Rs 94.71 Crores in 2000-01 to Rs 1262.11 Crores in 2009-10. The rural postal life policies procured was declined in 2006-07 and 2007-08.
84
CHAPTER-V EFFICIENCY AND PRODUCTIVITY
This chapter is concerned with the efficiency and productivity of life insurers in the wake deregulation of Indian insurance. The life insurance companies have made tremendous progress in terms of business growth following deregulation and there is little research on the efficiency analysis of life insurance companies in Indian context. Almost all the research studies attempted to discuss the impact of liberalization in terms of business performances based on premium income, policies sold etc. Efforts are also being made here to compare the relative performances of life insurers. The results will give a key to understand whether or not the aim of liberalization process has achieved in Indian life insurance market. 5.1: EFFICIENCY: 5.1.1-Concept of Efficiency: Efficiency refers to how well firms are performing relative to the existing technology in the industry. The concept of economic efficiency flows directly from the microeconomic theory of firm. In microeconomic theory of firm, production (or economic) efficiency is decomposed into technical and Allocative efficiency. A producer is said to be technically efficient if production occurs on the boundaries of producer‘s production possibilities set and technically inefficient if production occurs on the interior of the production possibilities set. That is, technical efficiency is the extent to which maximum possible output is achieved from a given combination of inputs. On the other hand, a producer is said to be
85
allocatively efficient if the production occurs in a region of production possibilities set that satisfy the producer‘s behavioral objective. 5.1.2-Estimation Technique: Firm performances can be measured using various methods; conventional financial ratios such as return on assets (ROA), return on equity (ROE), expense to premium ratios etc. However frontier methodologies have been regarded superior to the traditional methods in the economic theory. The frontier methodologies measure firm performance relative to ‗‗best practice‘‘ frontiers consisting of the other firms in the industry. Frontiers
have
been estimated to measure firm
success in employing technology (technical efficiency), attaining optimal size
(scale
maximizing
efficiency), revenues
minimizing
costs (cost
efficiency),
(revenue efficiency), and maximizing profits
(profit efficiency). Two frontier efficiency approaches has so far been used namely Parametric and non parametric approach or technique. Data envelopment analysis (DEA) and Free Disposable Hull analysis (FDH) are among the non parametric approaches used for efficiency estimation. Stochastic frontier approach (SFA), thick frontier approach (TFA), and distribution free approach (DFA) are among the parametric approach used. The economic or parametric approach requires the specification of a production, cost, revenue or profit function as well as assumption about the error terms. The mathematical programming or non parametric approach does not require specification of error terms. Both the parametric and non parametric approaches have advocates and neither has emerged as dominant till date. Box 5.1 summarizes some important techniques used so far in efficiency of insurance industry.
86
For this study, DEA is adopted for the following reasons:
(1)
Unlike the econometric approach, DEA deals with multiple outputs as well as multiple inputs, but does not require exogenous specification of the parametric form of the production function. Because, it is a nonparametric method and thereby it is not necessary to identify a functional form or
make distributional assumptions. This makes DEA
particularly useful in dealing with insurance industry which is a service industry where there is limited knowledge of underlying production technology and typically confronted with multiproduct firms (2) Indian life insurance industry is relatively small and DEA can ideally be able to handle relatively small sample sizes, convenient
decomposition of
total technical
pure technical efficiency (PTE)
(3)
It
allows
for
efficiency (TE)
into
and scale efficiency (SE); and
(4) As this approach focuses primarily on the technological aspects of production functions, it can be used to estimate productive efficiency without requiring estimates of input and output prices. Based on DEA, the
Malmquist
technique,
which
is
the
standard approach for
measuring the evolution of productivity and efficiency over time, is used in the next section of this chapter.
87
Box 5.1: Summary of studies of insurance industry‘s efficiency. Year
Author
Firm/industry
1993 1993 1993 1995
Fecher, Kessler, Perelman and Pestieau Cummins and Weiss Gardner and Grace Yuengert Cummins,Weiss and Zi
Life and Nonlife Property liability Life Life Property liability
1995
Cummins and Zi
Life
USA
Life & Non-life
Italy
DEA
Property liability Life Non life Non life LIfe Non life
US Germany, UK
DFA DEA DEA DEA DFA DEA
1993
1996 1996 1999 2000 2000 2000 2001
J D Cummins , J Turchetti and M A.Weiss Berger,Cummins and Weiss Rees et al. Jaehyum Kim Mahlberg and Url Ryan/Schellhorn H. Fukuyama and Weber
2002
Thitivadee Boonyasai, Martin F.Grace and H.D Skipper
2003
Bernhard Mahlberg, Thomas Url
2004
Ennsfellner et.al
2004 2004 2005
W.H. Greene and Dan Segal Turchetti and Daraio Tone and Sahoo
Insurance industry Life/Health, Non life Life insurance Motor Life
2006
Stephanie Hussels and DR Ward
Life Insurance
2006
Badunenko et al. J David Cumin and Maria RubioMisas
2006 2008
Yuan and Phillips
2008
Trigo Gamarra
Life Insurance
Country
Methodology
France
DEA &SFA
US US US US
SFA DFA SFA &TFA DEA SFA,DFA,DEA &FDH
Germany US Japan Korea, Philippines, Taiwan and Thailand
DEA
Austria
DEA
Austria
SFA SFA DEA DEA
Life, Non- life
US Italy India Germany &UK Ukraine
Life ,Non -life
Spain
DEA
US
SFA
Germany
SFA
Life ,Propertyliability Life
DEA,DFA DEA
(Note: DEA: Data Envelopment analysis, DFA: Distribution free approach, SFA: Stochastic frontier approach, TFA: Thick frontier approach, FDH: Free disposable hull analysis ) Estimation of efficiency using DEA: Farrell (1957) first introduced the concept of the efficiency frontier and application of DEA. It was further developed by Charnes, Cooper, and Rhodes (1978).
The DEA analysis uses a linear programming
technique to construct an envelope for the observed input output 88
combinations of all market participants under the constraint that all best practice firms support the envelope, while all inefficient firms are kept, off the frontier. The result of the DEA analysis can be used to assess the technical efficiency of individual firms with respect to the best practice or benchmark firms. It also allows decomposing the technical efficiency into pure technical and scale efficiency. Technical efficiency can be decomposed into pure technical efficiency (PTE) and scale efficiency (SE), where TE = PTE x SE, by solving additional linear programming problems. Pure technical efficiency is measured relative to a variable returns to scale (VRS) frontier, which may have segments where best practice firms operate with increasing returns to scale (IRS), constant returns to scale (CRS), and/or decreasing returns to scale (DRS).
Pure technical efficiency is the
reciprocal of the distance of firm i from the VRS frontier. Thus, the firm could achieve pure technical efficiency by moving to the VRS frontier. If the firm is operating in an IRS or DRS region of the frontier, it could further improve its efficiency by attaining CRS. Both pure technical and scale efficiency are bounded by 0 and 1. Firms with pure technical efficiency equal to 1 are operating on the VRS frontier, and a scale efficiency score equal to 1 indicates that a firm is operating with CRS. The methodology also reveals whether a non-CRS firm is operating with IRS or DRS
To estimate the technical efficiency for individual companies, Data Envelopment Analysis (DEA) developed by Charnes et al. (1978) is used in the study. Efficiency is measured here under two different assumptions, viz. 89
1) Variable Returns to Scale (VRS) model, which permits increasing and decreasing returns to scale. Here, the sum of weights of linear program is restricted to 1.This gives the measure of pure Technical Efficiency. 2) The Constant Return to Scale (CRS) model which assume a non negativity constraint instead of the VRS constraint on weights. This gives the measure of Technical efficiency. For one output and one input case, the envelope which fulfills the VRS condition
is shown in Fig.5.1 as the dashed line. The solid line in the
figure indicates the envelope of CRS. The combinations of inputs and outputs of efficient firms support the efficiency frontier whereas that of inefficient one lies to the right or below the frontiers in the Figure. Technical efficiency is defined as the ratio of the input usage of a fully efficient firm producing the same output vector to the input usage of a specified firm. The point D given is a case of inefficient firm which can either increase production using the same amount of input i.e. output maximization or decrease input holding the output constant i.e. input minimization. B indicates the point where a firm is operating optimally with available technology. At this point, the firm therefore is efficient under CRS as well as VRS. Under CRS, the ratio of distance D CRSD/0D serves the input oriented measure of technical efficiency and its value varies over the range (0, 1). The firm at D is inefficient and its ratio is smaller than 1 whereas for B, BCRS and B coincides so ratio is 1.The fraction (1- DCRSD/0D), on the other hand shows the potential input savings that a shift to technically efficient production would bring about. In case of VRS, the ratio based on VRS as reference technology provide an efficiency technology under VRS assumption. So under VRS assumption firm A, B and C are efficient.
90
The input minimization model of DEA is used which is given as Min θ0 Subject to
∑yrj λj≥ y r0,
θ0 xi0-∑x ij λ j≥ 0
θ0 free, λj≥0
∑λj=1 for VRS ∑λj≥0 for CRS Where θ0 is the efficiency score of the firm. j indicates the number of firms, j=1……….J yrj is the rth output of the j -th firm and xij is the i -th input of the j -th firm. y and x are output and input of the firms where y=1…….r and x=1,…….i. The above procedure of minimizing efficiency score of θ 0 of a single firm is repeated for each firm and thus the input oriented efficiency of each firm is obtained. Technical efficiency is decomposed into pure technical efficiency and scale efficiency. The scale efficiency (SE) which is the ratio of CRS efficiency to VRS efficiency is also calculated. OUTPUT: The lack of establishing a positive theory of the financial firm may be attributed to the incomplete application of the essential elements of the theory of the firm to financial institutions. Most of the areas ignored by writers are, appropriate classification of inputs and outputs of the financial firms by failing to consider the criteria on which financial firms make economic decision, analyzing of the technical aspects of production and cost for the financial firm. 91
Figure 5.1: Efficiency frontiers under CRS and VRS One of the major confusions in the theory of the financial firm arises over the lack of agreement concerning appropriate measures of output and inputs for the financial firm. This confusion is a direct result of the failure to carefully analyze both the technical and economic aspects of production at financial institutions. Moreover, the life insurance industry provides a good example of some of the major problems involved in measuring the production of services. In life insurance industry, defining the output is a crucial initial problem followed by the question of how to evaluate relative importance of each output so as to construct a single index of industry production. Three principal alternative methods have been used so far to measure output in financial service sector. Viz. asset 92
approach, user cost approach and value added approach (Berger and Humphery, 1992). The asset approach treats financial service firms as pure financial intermediaries i.e. borrowing funds from one set of decision makers, transforming the resulting liabilities into assets, and receiving and paying out the interest and dividend to cover the time value of funds used in this capacity. Intermediation is one of the important functions of life insurers. However intermediation alone cannot be considered as output for life insurance as it also provides many other services in addition to financial intermediation. Therefore, asset approach is not considered for measuring life insurance output. The user cost approach determines whether a financial product is an output or input on the basis of its net contribution to the revenue of financial institution. If the financial return on an asset exceeds the opportunity cost of fund or if the financial costs of a liability are less than the opportunity cost then the product is said to be a financial output. Otherwise it is classified as financial input. Theoretically, this approach is quite sound but precise data on product revenues and opportunity costs are required. Since relevant data as such are not available for Indian Life Insurance Industry, the use of user cost approach for output measurement is also ruled out for the study. The value added approach considers all assets and liability categories to have some output characteristics rather than distinguishing inputs from outputs in a mutually exclusive way. Consistent with most of the recent literatures (Cumins & Maria R, 2006; Boonyasai T& et, 2002; Yang Mingliang (2006),) on financial institution, a modified version of 93
value added approach is used for the measurement of output. The categories having significant value added, as judged using operating cost allocations are employed as important output. Others are treated as unimportant output, intermediate product or input depending on the characteristics of the specific activity under consideration. Based on the value added approach, the following discussions are made—Life insurers in general provide the following three principle services viz. risk pooling and risk bearing , real financial services relating to insured losses and intermediation. a) Risk pooling and risk-bearing: - Insurance provides a mechanism through which consumers and businesses exposed to losses can engage in risk reduction through pooling. Insurers collect premiums in advance from their customers and redistribute most of the funds to those policyholders who sustain losses. The actuarial, underwriting and related expenses incurred in risk pooling are important components of value added in the industry. The insurers also add value by holding equity capital to bear the residual risk of the pool. b) Real financial services relating to insured losses: - Life insurers provide a variety of real services for the policy holders. This includes personal financial planning, administration of group life, annuity and health insurance plans. By contracting with insurers to provide these services, policyholders can take advantage of insurer‘s specialized expertise to reduce cost associated with insurable risks. c) Intermediation: - For life insurers intermediation is the principal function, accomplished through the sale of asset accumulation products such as annuities. The insurers issue debt contracts (policies and annuities) and invest the funds until benefits are paid. In life insurance, interest credits
94
are made directly to the policyholder‘s accounts to reflect the investment income and to compensate for the opportunity costs of the funds held by the insurers. The borrowed funds are invested mainly in marketable securities such as privately placed securities and structured bonds. The net interest margin between the rate of return earned on assets and the rate credited to policyholders is the value added of the intermediation function.
Defining and measuring output in insurance industry has been a challenging task. In value added approach, usually, several types of outputs are defined, representing the single lines of business under review. Thus different output proxies are used for life and propertyliability insurers, reflecting differences in the types of insurance and data availability (Berger. A. N., Cummins, J. D., Weiss, M. A., Zi H., 2000). Premium income, weighted sum of activities, incurred benefits, additionto-reserve, present value of real losses incurred are the most commonly used output so far.
Net written premiums or net earned premiums have been used as proxies for output in various cost studies. (Fecher et.al 1993).Premiums can be viewed as including the flow of services to policyholders for a certain period .However premiums are not the quantity of output but the revenue (price times quantity). Systematic differences in price across large and small firms may leads to misleading inferences about average costs if premiums are taken as output. Doherty (1981) criticized the used of premiums as output because it results into simultaneous equation bias. However Allen (1974) and Blair et al (1975) used premium income as appropriate output considering that the product is more or less 95
homogeneous and competitive pressure compel all insurers to charge same price. In literature, there is an intense debate as to whether premiums are an appropriate proxy because they represent price times quantity of output and not output (Yuengert, 1993). In the weighted sum of activities approach, instead of unobservable implicit prices uses weighted sum of the quantity of services produced in each category. Hirshhorn and R. Geehan (1977) used life insurance industry‘s output by aggregating 29 activities of life insurance companies. Each activity was weighted by an index value and summed up for the output proxy. The activities included not only the most product line of life insurers but also different assets amounts. This approach is useful in considering the differences in aggregate output but provide little assistance in measuring the variations in activities among different firms. The method is biased for inefficient firm over efficient firm as it assumes that the value of expenses equal the value of life insurance output (i.e., ordinary life, group annuities and group life) that is, some insurers may incur more expenses than other not because they produce more output but they are less efficient. Moreover, this approach fails to recognize the risk bearing and risk pooling function of life insurers. Addition -to-reserves as a proxy of output was suggested by Yuengert (1993). These measures equal reserves set up for new business and new deposit fund and reserves set up as policies ages. The most important shortcoming of this output measurement approach as intermediation function is that it does not consider the benefits delivered to customers during the period, which is the primary service of insurers.
96
Therefore, outputs measured by addition to reserve approach may underestimate the total output of an insurance firm. Ennsfellner Karl C. and et.al (2004) used incurred benefits net of reinsurance in a given year as a proxy for risk bearing function of life and health insurance. While, total invested assets and changes in reserves net of reinsurance proxy the intermediation function for life and health insurance. Incurred losses net of reinsurance and incurred benefits net of reinsurance represent the insurer‘s payments net of reinsurance in the current year. Incurred benefits represent the risk bearing services. Several cost studies defined incurred benefit plus addition to reserve as output of life insurance industry (Cummins et al. 1996, Cummins et al. 1998) .However use of incurred benefits still have the problems although it captures the flow of services provided to customers in a certain period. Addition- to- reserves also is not immune to differences in prices, reserving methods and interest assumptions across firms. Berger, Cummins and Weiss (1997) argued that the real losses incurred are a satisfactory proxy for the amount of risk pooling and real insurance services provided. The losses incurred are defined as the losses that are expected to be paid as a result of providing insurance coverage during a particular period of time. Because the objective of risk pooling is to collect funds from the policy holder pool and redistribute them to those who incur losses, proxying output volume by the amount of losses incurred seems quite appropriate. Losses are also a good proxy for the amount of real services provided since the amount of claims settlement and risk management services are also highly correlated with loss 97
aggregates. Losses incurred are a satisfactory measure of output for coverage provided during a given year. However, insurers also perform services in connection with claims occurring in prior years or claims expected to occur in the coming years. As a proxy for these services, the real value of policy reserve is used. Boonyasai T.et al (1999) used premium income and net investment income as output. Wherein premium income represented riskbearing and risk-pooling services, and for the intermediation function of borrowing from the policyholders and investing the funds to marketable securities, net investment income is used as a proxy.
This study used both premium income and benefit paid to customers as output. Although the use of premium income as output is subjected to simultaneous bias, there are constraints imposed by data in the developing countries like India. Also there is still debate among those using the value-added approach premiums/sum added. More premiums/sum
insured studies
are use
as the
to
whether
most
claims/benefits
or
appropriate proxy for value
claims/benefits
to
proxy
output
than
insured, however, there is no recognizable trend over
time as to whether either of the two main proxies is gaining more of a following among researchers (Eling Martin und Michael Luhnen, 2009). So premium income may be use as an appropriate proxy of output for risk pooling / risk bearing function. Benefits paid are correlated with the function of real financial services of the insurer. INPUT Inputs are somewhat easier to identify and measure as compare to output in the insurance industry as units of measurement tends to be 98
tangible or at least directly observable. Insurers input can be classified into four principal groups: acquisition (marketing and distribution) input mainly agent labour, managerial and administrative input, fixed capital (office buildings and computer) and financial equity capital. Labor, fixed capital and financial equity capital are the factor of production for insurers. Equity capital is primary input into the risk pooling and risk bearing function, because the insurer must maintain the equity capital to ensure their promise to pay losses that are larger than the expected. Cost studies mainly used three inputs viz. labor, capital and materials. Labor input may consist of employees, agent and brokers. Agent and brokers are mainly responsible for marketing of products while employee‘s labor include managerial and clerical workers. The labor input volume of all the employees and agents for each company may be obtained by summing all the wages, salaries and benefits provided to employees and all the commission and benefits given to agents. There is no consensus on the measurement of capital input quantity in previous cost studies. Physical capital represents the expenditure on equipments and occupancy costs. Grace and Timme (1992), Yuengert (1993), Gardner and Grace (1993), and Kim and Grace (1995) used physical approach to measure the capital input. Wherein, the amount of physical capital used by the insurance companies in producing their outputs measured by the value of physical capital assets is used as a proxy of this input. However, Cummins and Weiss (1993), Cummins et al. (1996) and Cummins et al (1998) used the financial capital instead of physical capital to measure the capital quantity. They argue that the capital structure of insurance industries is quite different from manufacturing industries in 99
that an insurance company‘s capital consists mainly of financial capital. Financial capital is crucial input in insurance as the insurer must attain equity capital to assure policyholders that they will receive payments even if experience is below expectations. Therefore financial capital more closely represents real capital in producing output. The financial capital obtained by summing capital and surplus is used as a proxy for financial capital input. (T. Boonyasai et al). All other input associations, other than labor input, physical capital and financial capital inputs are categorized as material or business and services input. In life insurance, materials or business and services input consists of communication services, rent, equipment rentals, stationary and professional services rendered by external lawyers, physician, actuaries and accountants. Including these inputs allows the estimation to account for variation across insurers to expenditure on computers, communication services and other technology- related items. Cummins and Weiss (1998) computed the volume of business and services by dividing the expenditure on these inputs by consumer price index. Ennsfellner et al (2004) used Net operating expenses as a proxy for distribution of insurance products, the inputs of their labor force, business services and materials used in the production of insurance products. Equity capital and technical provisions proxy the inputs for the risk bearing and risk pooling function of the insurers. Following the above studies operating expenses and commission expenses are used as input proxy. The use of operating expenses and commission expense as input is justifiable because, operating costs of life insurance will take into account the labor-related expenses, capital 100
expenses, and materials consisting of all other expenses. In addition to operating expenses, commission expense is another input in line of labour input as agent and brokers are mainly responsible for marketing of products. Box 5.2: Overview of input and output used by authors in DEA based efficiency analysis of life insurance firms Author Fecher et al.(1993) Cummins et al. (1996)
Country France
Input Labour cost, Other outlays
Output Gross premiums
Italy
Fukuyama(1997)
Japan
Labor (acquisition, admin.), fixed capital expense, equity capital Labour(office, sales),capital
Sum of life insurance benefit, changes in reserve, invested assets. Insurance reserves, Loans
Donni& Fecher(1997) Cummins and Zi (1998) Cummins (1999)
15 OECD countries US
Labour
Net premiums Benefit payments ,addition to reserves Incurred benefits, addition to reserve
Cummins et al. (1999a)
U. S
Kessner and Polborn(1999) Carr et al. (1999)
Germany
Rees et al. (1999)
Germany and U.K
Labour, financial capital, materials Labor (admin., agents), business services, financial capital Home-office labor, agent labor business services (including physical capital), financial capital New business cost, administration cost Labor (admin., agents), business services, financial capital Distribution cost, administration cost
Mahlberg (1999)
Austria and Germany Germany
Administration and distribution cost(1 input) Administration and distribution cost( 1 input)
Mahlberg and Url(2000)
Germany
Administration and distribution cost( 1 input)
Mansor and Radam(2000)
Malaysia
Claims, commission, salaries,
Mahlberg (2000)
U.S
U.S
101
Incurred benefit, addition to reserves
Sum insured of new and inforce business Incurred benefit, addition to reserves Total premium income and change in total premium income (U.K.), aggregate sum insured and change in aggregate sum insured (Germany) Claims, Change in reserves, refund of premium Claims, Change in reserves, refund of premium Claims, net change in provisions, allocated investment returns, bonuses, and returned premiums New policy issued, premiums, policy in force
Kessner (2001a)
Germany and U. K
Kessner (2001b)
Germany
Boonyasai et.al.(2002)
Korea, Philippines, Taiwan, Thailand Tunisia
Chaffai and Ouertani(2002) Mahlberg and Url(2003)
Austria
Leverty et al.(2004)
China
Cummins/RubioMisas/Zi (2004) Tone &sahoo(2005)
Spain
Barros et al. (2005)
India
Portugal
Hussels and Ward(2006) Qiu and Chen (2006) Badunenko et al.(2006) Cummins and Rubio- Misas (2006) Barros and Obijiaku(2007)
Germany, U.K China
Cummins et al. (2007)
U. S
Erhemjamts and Leverty (2007)
U.S
Diboky and Ubl (2007)
Germany
Jeng et.al (2007)
U.S
Ukraine Spain
Nigeria
expenses, other cost New business cost, administration cost, cost for capital management, reinsurance contributions -Do-
Labor, Capital, Materials
Labor, physical capital, financial capital Administration and distribution cost, cost of capital investment Business expenses, financial equity capital, debt capital Labor, business services, debt capital, equity capital Labour, business services, debt capital ,equity capital Wages, capital, total investment income, premiums issued Labor, Capital Labor, equity capital, other Fixed assets, current assets, liabilities, equity Labor, business services, debt capital, equity capital Capital, operative costs, number of employees, total investments Labor (office, agent), materials and business service, financial equity capital Labor, business services, equity capital ,policyholder-supplied debt capital Labor, business services, financial debt capital, equity capital VA: Labor, business services, capital (debt +
102
Gross and net written premiums, interest on capital
Sum insured (new and existing business), net returns on capital investments Premium income, net investment income
Total premium earned Claims, net change in provisions, allocated investment returns, bonuses and returned premiums Net premiums written ,real invested assets Life insurance losses incurred Present value of real losses incurred, ratio of liquid assets to liabilities Claim Paid, profits
Net written premium. Addition to reserves Benefit payments, addition to reserve, yield on investment Premiums life losses incurred, reinsurance reserves, invested assets Profits, net premium, settled claims, outstanding claims, investment Real value of incurred benefits, addition to reserves
Incurred benefit, addition to reserves
Gross premium, net income
VA: Number of policies, total invested assets
Klumpes (2007) Yao et al. (2007)
7 Europeans countries China
Davutyan and Klumpes (2008)
7 Europeans countries
Eling and Luhnen(2008)
36 countries
Trigo Gamarra and Growitsch (2008)
Germany
equity) FI: Surplus previous year/assets change in surplus/assets, under- writing + investment expenses/assets, policyholder debt capital/assets Labor, business services, debt capital, equity capital Labor, capital, payment and benifit Labor, business services, equity capital Labor and business service, financial debt capital, equity capital Acquisition and administration expenses, equity capital
FI: Return on Assets (ROA), three principal components of financial conditions
Premiums ,investment income Premiums, investment income Present value of losses incurred, premium, invested assets Benefit +addition to reserves, Investment Incurred benefits, additions to reserves, bonuses and rebates
(Source: Eling Martin und Michael Luhnen (2009) DATA: 23 life insurance companies were registered with IRDA including LIC of India as on31st August 2010. However, only 12 to 15 insurers were taken for the study as the insurers who have entered into the industry after 2005 are not considered for the study. The number of insurance company varied year wise as their year of registration and consequent operation vary. For the year 2001-02, only 12 life insurer‘s data are available as Aviva life insurance Co. Ltd. started its operation in 2002-03 only. Likewise from 2002-03 to 2004-05, 13 life insurers are considered and 14 insurers are taken in 2004-05 for study. From 2005-06 onwards 15 companies are taken together. The data used are from the Insurance Regulatory and Development of India‘s (IRDA) Annual Report if otherwise not mentioned. For the study, two inputs viz. commission expense and operating expense while two output viz. premium and benefit paid are taken for each life insurers. 103
The values of input
variables viz. commission expense and operating expense in lakh are shown in appendix table A.4 while that of output variables namely premium and benefit paid are put in appendix table A.5. The descriptive statistics for these inputs and outputs are shown in table 5.1 and 5.2 respectively. A firm whose input or output is 0 is excluded from the calculation of descriptive statistics irrespective of the year taken. Therefore, in 2001-02, the statistics are calculated for 12 life insurers and in that for the benefit paid; only 6 insurance companies are undertaken as the values of remaining companies are 0. In 2002-03 and 2003-04, the statistics are based on all the 13 companies. In 2004-05 and 2005-06, of the respective 14 and 15 companies, the statistics of benefit paid is calculated with one company less for each year. From 2006-07 onwards the statistics are based on all the 15 companies taken. The maximum and average values of input variables were consistently increasing over the years while that of minimum values fluctuated from year to year. The SD of the input variables were also increasing over the years but decreased in 2004-05 in case of commission expense, while it was decreased in 2005-06 in case of operating expense. The maximum, average and SD of output variable, premium, has been increasing over the years .The minimum values were fluctuating but, increasing since 2005-06 for both the outputs. This may be because the latest entrant produced the minimal output. The maximum, average and SD of benefit paid were increasing over the years except in 2008-09. 5.1.3-Result Analysis: Table 5.3 shows the gross efficiency (technical efficiency) of life insurers calculated at constant return to scale. LIC of India has got gross efficiency score of 1 in CRS models in all the 9 years from 2001-02 to 2009-10 indicating efficient throughout the years. Among private 104
insurers, SBI life was the only insurer which was at par with LIC of India in all years taken. Aviva and ICICI have shown a consistent increase in efficiency scores over the years. Table 5.1: Descriptive statistics of inputs (Rs in lakh) Year
2001-02 2002-03 2003-04 2004-05 2005-06 2006-07 2007-08 2008-09 2009-10
Num ber of firms 12 13 13 14 15 15 15 15 15
Commission expenses max min Average
SD
Operating expenses max min Average
SD
451791 499861 573384 624517 709492 916907 956810 1003324 1211031
130293.1 138284.9 193971.4 165241.7 180606.6 232212.6 240483.2 250793 303717.9
426040 462109 504233 598718 604156 708584 830932 906429 1224582
121928.6 126309.1 136794.9 155795.2 150537.5 175399.3 206855.4 219297.6 297200.3
7 167 547 66 379 668 2055 2415 2368
38057.58 39636.38 47367.23 50704.43 57569.87 81787.13 97609.4 102033.9 117569.1
653 2330 4465 177 1121 1542 2373 3973 3700
38951.42 41932.08 49426.77 58682.86 64073.6 89998.47 133358.1 162631.8 178966.7
Table 5.2: Descriptive statistics of outputs (Rs in lakh)
Year
2001-02 2002-03 2003-04 2004-05 2005-06 2006-07 2007-08 2008-09 2009-10
No of fir ms 12 13 13 14 15 15 15 15 15
Premium max
min
Average
SD
Benefit Paid max min
Average
SD
4982191 5462849 6316760 7512729 9079222 12782284 14978999 15728804 18607731
28 647 2873 174 2766 5100 14349 20647 25059
417453.7 428827.31 509907.3 591820 705811.7 1040384 1341357 1469800 1745930
1437523 1512574.18 1744932 1992843 2319415 3255138 3790376 3965317 4685879
1747664 2053039 2392375 2844045 3392711 5328646 5655033 5247814 7913066
291325.3 158040.1 184565.9 221164.2 251499.1 371433.4 411235.9 388911.7 636971.3
713457.3 569376.3 663364.3 788082.7 904259 1371559 1451546 1345301 2020865
3 6 50 260 22 157 382 618 1483
(Note: Firms with its value 0 are excluded from calculation of average and others) Bajaj‘s efficiency score has shown an increasing trend till 2005-06 which has thereafter fallen for two years and then increased after 200809.Birla‘s efficiency scored decreased in the year 2008-09 only while Kotak‘ s efficiency score went down for two years from 2007-08 to 2008105
09. HDFC‘s score went down in 2004-05 and in 2007-08 to 2008-09 as well. ING Vysya has shown an increasing efficiency score throughout the years except a negligible fall in the year 2005-06 and after 2008-09. Sahara has shown a decrease in score in the year 2007-08 while the same was happened in 2008-09 in case of Shriram.
Met has shown a
decreasing trend in the years 2006-07 and 2007-08 while max‗s efficiency score was slightly down in 2007-08 only. Reliance‘s efficiency score was down for continuous three years from 2006-07 to 2008-09. Tata has shown an increasing trend throughout the year except in the year 2004-05 and 2008-09. In the year 2001-02, the number of efficient insurers stood at two being LIC and SBI. The lowest efficiency score of the year was 0.052 (Reliance). In the next three years too only SBI and LIC remained efficient. Reliance was the most inefficient insurer in 2002-03, met in 2003-04 while it was Sahara in 2004-05. Three insurers were efficient while Shriram was most inefficient with CRS score of 0.203 in 200506.From the year 2006-07 to 2008-09, Met remain most inefficient while only three insurers remain efficient. In 2009-10 too Met has highest inefficiency score of 0.428 while the number of efficient insurers increased to 4. Table 5.4 represents the efficiency scores (pure technical efficiency) of life insurers calculated at variable returns to scale. In 200102 and 2002-03, ICICI was the only insurer with efficiency score above 0.5 but less than 1. In 2003-04, the number increased to three with HDFC and Birla joining the group with efficiency score of 0.509 and 0.516. In the year 2004-05, there were four insurers with VRS efficiency score above 0.5 but less than 1.This year, ICICI joined the group of efficient insurers increasing the number of efficient insurers to three. 106
Table 5.3: Gross efficiency scores at constant return to scale (CRS) i.e. technical efficiency (TE) Insurer Lic Aviva Bajaj birla Hdfc Icici ING Kotak Met max reliance Sbi tata sahara shriram
2001- 2002- 200302 03 04 1.000 1.000 1.000 0.166 0.195 0.138 0.257 0.285 0.287 0.305 0.516 0.297 0.438 0.509 0.484 0.559 0.635 0.106 0.108 0.210 0.126 0.188 0.384 0.053 0.122 0.179 0.182 0.224 0.283 0.052 0.100 0.238 1.000 1.000 1.000 0.206 0.288 0.388
200405 1.000 0.293 0.504 0.532 0.497 0.775 0.409 0.666 0.219 0.304 0.527 1.000 0.347 0.151
200506 1.000 0.386 0.674 0.572 0.844 0.990 0.402 0.711 0.329 0.392 1.000 1.000 0.439 0.473 0.203 0.328 0.366 0.448 0.516 0.628
200607 1.000 0.428 0.487 0.588 0.908 1.000 0.501 0.821 0.313 0.439 0.677 1.000 0.478 0.509 0.417 0.638
200708 1.000 0.526 0.421 0.612 0.865 1.000 0.704 0.67 0.273 0.436 0.667 1.000 0.541 0.443 0.507 0.644
200809 1.000 0.598 0.625 0.507 0.658 1.000 0.596 0.536 0.325 0.634 0.403 1.000 0.522 0.496 0.477 0.625
200910 1.000 0.763 0.697 0.623 0.783 1.000 0.524 1.000 0.428 0.787 0.749 1.000 0.914 0.608 0.496 0.758
Average
Again in 2005-06 the number of efficient insurers doubled standing at six whiles the number of insurers with efficiency above 0.5 were at four. In the year 2006-07, the number of efficient insurers went down to four but efficiency score above 0.5 went up to seven. In the year 2007-08, there were five efficient insurers. The insurers with efficiency score above 0.5 were eight and so only two insurers have efficiency score below 0.5. Aviva‘s efficiency score was seen increasing throughout the years. There were 5 efficient insurers in 2008-09 and 2009-10 each.
107
Table 5.4: Efficiency scores at variable return to scale (VRS) i.e. pure technical efficiency (PTE)
Insurer
2001- 2002- 200302 03 04 1.000 1.000 1.000 0.166 0.195 0.138 0.257 0.285 0.323 0.305 0.516 0.297 0.449 0.509 0.651 0.887 0.825 0.106 0.108 0.210 0.126 0.188 0.384 0.053 0.122 0.179 0.202 0.224 0.283 0.052 0.100 0.238 1.000 1.000 1.000 0.206 0.288 0.388
200405 1.000 0.293 0.504 0.532 0.497 1.000 0.409 0.666 0.219 0.304 0.527 1.000 0.347 0.151
200506 Lic 1.000 Aviva 0.397 Bajaj 0.674 birla 0.58 Hdfc 0.855 Icici 1.000 ING 0.414 Kotak 0.732 Met 0.36 max 0.405 reliance 1.000 Sbi 1.000 tata 0.446 sahara 1.000 shriram 1.000 Average 0.346 0.392 0.462 0.532 0.724
200607 1.000 0.440 0.688 0.596 0.911 1.000 0.530 0.86 0.341 0.447 0.704 1.000 0.489 1.000 0.931 0.729
200708 1.000 0.585 1.000 0.633 0.877 1.000 0.791 0.730 0.314 0.461 0.698 1.000 0.583 1.000 0.762 0.762
200809 1.000 0.683 1.000 0.521 0.670 1.000 0.718 0.578 0.358 0.646 0.416 1.000 0.573 1.000 0.780 0.730
200910 1.000 0.783 0.699 0.630 0.825 1.000 0.606 1.000 0.453 0.787 0.751 1.000 0.921 1.000 0.626 0.805
LIC and SBI were efficient throughout the years taken in study with VRS efficiency score of 1. Aviva and Max have shown a consistent increase in efficiency scores over the years. Bajaj‘s efficiency score has shown an increasing trend except in 2009-10. Birla‘s efficiency score increased from 2002-03 till 2007-08 but has fallen in the year 2008-09. Kotak‘s efficiency score went down for two years from 2007-08 to 200809. HDFC‘s score went down in 2004-05 and in 2007-08to 2008-09 as well. ICICI remained efficient from 2004-05 onwards. ING Vysya has shown an increasing efficiency score throughout the years except a negligible fall after 2008-09. Met has shown a decreasing trend in the years 2006-07 and 2007-08 and its efficiency remained less than 0.5 in all years. Reliance‘s efficiency score was down for continuous three years 108
from 2006-07 to 2008-09. Tata has shown an increasing trend throughout the year except in the year 2004-05 and 2008-09. Sahara remained efficient throughout the year except in the first year i.e.2004-05. Shriram has shown a decreasing trend in score except a slight improvement in 2008-09.
Table 5.5 is the scale efficiency of life insurers which is the ratio of CRS efficiency score to VRS efficiency score. In the year 2001-02, nine insures out of twelve have scale efficiency of 1 which indicated their operation at most productive scale size. The number of insurers operating at best productive scale was 10 out of 13 in the next year. In 2003-04 and 2004-05, ICICI was the only insurer operating below the best scale of production. In 2005-06 only four insurers were operating at most productive scale while remaining eleven insurers were operating at SE below 1, showing they have scope for improvement in their scale of operation and can therefore improve their efficiency too. There were three insurers at their most productive scale of operation while remaining 12 have scope for improvement in 2006-07, 2007-08 and 2008-09. In 2009-10, 5 insurers were at their best scale of efficiency and 7 out of remaining 10 insurers have scale efficiency above 0.9 which is almost close to best scale of production.
LIC and SBI remained at best productive scale throughout the sample period. Aviva‘s SE remained above 0.85 but slightly less than 1.Bajaj was operating at best productive scale till 2005-06 but not in the remaining years. Birla and HDFC have SE less than 1 but more than 0.90 from 2005-06 to 2009-10.Their SE before 2005-06 were 1 except in 2001-02 for Birla and 2002-03 for HDFC. ICICI has SE equivalent to 1 109
from 2006-07 onwards. ING, Met and Tata could have SE of 1 till 200405 and till 2005-06 for Reliance. Kotak was at most productive scale of operation from 2001-02 till 2004-05 and in 2009-10; in between also it remained almost
near to best scale of production. Max was at best scale
of operation from 2002-03 till 2004-05 and in 2009-10.Sahara was scale efficient in 2004-05 only while Shriram was never operating at productive scale. Their SE was quite low compare to other insurers remaining in between 0.2 to 0.7. Table 5.5: Scale efficiency scores of the companies
Insurer
200102
200203
200304
200405
200506
200607
200708
200809
200910
Lic
1.000
1.000
1.000
1.000
1.000
1.000
1.000
1.000
1.000
1.000
1.000
1.000
0.972
0.973
0.899
0.876
0.974
Aviva Bajaj
1.000
1.000
1.000
1.000
1.000
0.708
0.421
0.625
0.997
birla
0.889
1.000
1.000
1.000
0.986
0.987
0.967
0.973
0.989
Hdfc
1.000
0.975
1.000
1.000
0.987
0.996
0.986
0.982
0.949
Icici
0.743
0.630
0.770
0.775
0.990
1.000
1.000
1.000
1.000
ING
1.000
1.000
1.000
1.000
0.971
0.945
0.890
0.830
0.865
Kotak
1.000
1.000
1.000
1.000
0.971
0.955
0.918
0.927
1.000
Met
1.000
1.000
1.000
1.000
0.914
0.918
0.869
0.908
0.945
max
0.901
1.000
1.000
1.000
0.968
0.982
0.946
0.981
1.000
reliance
1.000
1.000
1.000
1.000
1.000
0.962
0.956
0.969
0.997
Sbi
1.000
1.000
1.000
1.000
1.000
1.000
1.000
1.000
1.000
tata
1.000
1.000
1.000
1.000
0.984
0.978
0.928
0.911
0.992
1.000
0.473
0.509
0.443
0.496
0.608
0.203
0.448
0.665
0.612
0.792
0.895
0.891
0.859
0.873
0.941
sahara shriram Average 0.961
0.970
0.982
0.984
110
Table 5.6 shows the efficiency scores of life insurance firms viz. CRS VRS and Scale efficiency. For the year 2001-02, the efficiency scores of 12 life insurers are given in table 5.6. LIC and SBI have efficiency score of 1 for both the efficiency frontiers viz. CRS, VRS and therefore has SE of 1. This indicated that both the insurers were efficient and operating at the most productive scale size. The remaining 11 companies were inefficient as their efficiency scores are less than 1 for both CRS and VRS assumption. However except three insurers, viz. Birla, ICICI and Max, remaining six insurers have scale efficiency of 1. This indicated that these six insurers were inefficient even though operating at their most productive scale size while the remaining four has scope for efficiency improvement if operated at most productive scale size. Of the 12 insurers Reliance has lowest efficiency score of 0.52 in both CRS and VRS. For the year 2002-03 with 13 insurers taken, LIC and SBI have efficiency scores of 1. All the insurers except HDFC and ICICI have scale efficiency slightly less than 1 which indicated their operation at their best productive scale. Among the insurers ICICI was the only insurer with its efficiency score both in CRS and VRS more than 0.5 but less than 1.Reliance was lowest in efficiency score with 0.1. In the year 2003-04 given in Table 5.6, of all the 13 insurers taken together, only ICICI have shown scale efficiency less than 1. ICICI with scale efficiency of 0.770 and gross efficiency of 0.635 and 0.825 at CRS and VRS respectively showed a scope for improvement in efficiency.LIC and SBI stood most efficient this year too and next to them were Birla, HDFC and ICICI with efficiency scores above 0.50.The remaining insurers can be termed as highly inefficient as their efficiency is below 0.50 even at operating in their most productive scale with scale efficiency 111
of 1. Met scored lowest rank in efficiency with only 0.179 for both CRS and VRS. Table 5.6: Year-wise CRS, VRS and SE of Insurers Company Lic Aviva Bajaj birla Hdfc Icici ING Kotak Met max reliance Sbi tata sahara shriram
2001-02 CRS VRS 1 1
SE 1
0.138 0.287 0.297 0.484 0.106 0.126 0.053 0.182 0.052 1 0.206
1 0.889 1 0.743 1 1 1 0.901 1 1 1
0.138 0.323 0.297 0.651 0.106 0.126 0.053 0.202 0.052 1 0.206
2004-05 Company CRS VRS Lic 1 1 Aviva 0.293 0.293 Bajaj 0.504 0.504 birla 0.532 0.532 Hdfc 0.497 0.497 Icici 0.775 1 ING 0.409 0.409 Kotak 0.666 0.666 Met 0.219 0.219 max 0.304 0.304 reliance 0.527 0.527 Sbi 1 1 tata 0.347 0.347 sahara 0.151 0.151 shriram
SE 1 1 1 1 1 0.775 1 1 1 1 1 1 1 1
2002-03 CRS VRS 1 1 0.166 0.166 0.257 0.257 0.305 0.305 0.438 0.449
SE 1 1 1 1 0.975
2003-04 CRS VRS 1 1 0.195 0.195 0.285 0.285 0.516 0.516 0.509 0.509
SE 1 1 1 1 1
0.559 0.108 0.188 0.122 0.224 0.1 1 0.288
0.630 1 1 1 1 1 1 1
0.635 0.21 0.384 0.179 0.283 0.238 1 0.388
0.770 1 1 1 1 1 1 1
0.887 0.108 0.188 0.122 0.224 0.1 1 0.288
2005-06 CRS VRS 1 1 0.386 0.397 0.674 0.674 0.572 0.58 0.844 0.855 0.990 1 0.402 0.414 0.711 0.732 0.329 0.36 0.392 0.405 1 1 1 1 0.439 0.446 0.473 1 0.203 1
112
SE 1 0.972 1 0.986 0.987 0.990 0.971 0.971 0.914 0.968 1 1 0.984 0.473 0.203
0.825 0.21 0.384 0.179 0.283 0.238 1 0.388
2006-07 CRS VRS 1 1 0.428 0.44 0.487 0.688 0.588 0.596 0.908 0.9113 1 1 0.501 0.53 0.821 0.86 0.313 0.341 0.439 0.447 0.677 0.704 1 1 0.478 0.489 0.509 1 0.417 0.931
SE 1 0.973 0.708 0.987 0.996 1 0.945 0.955 0.918 0.982 0.962 1 0.978 0.509 0.448
Table 5.6 continue
Company Lic Aviva Bajaj birla Hdfc Icici ING Kotak Met max reliance Sbi tata sahara shriram
2007-08 CRS VRS 1 1 0.526 0.585 0.421 1 0.612 0.633 0.865 0.877 1 1 0.704 0.791 0.67 0.73 0.273 0.314 0.436 0.461 0.667 0.698 1 1 0.541 0.583 0.443 1 0.507 0.762
2008-09 CRS VRS 1 1 1 0.899 0.598 0.683 0.421 0.625 1 0.967 0.507 0.521 0.986 0.658 0.67 1 1 1 0.890 0.596 0.718 0.918 0.536 0.578 0.869 0.325 0.358 0.946 0.634 0.646 0.956 0.403 0.416 1 1 1 0.928 0.522 0.573 0.443 0.496 1 0.665 0.477 0.78 SE
SE 1.000 0.876 0.625 0.973 0.982 1.000 0.830 0.927 0.908 0.981 0.969 1.000 0.911 0.496 0.612
2009-10 CRS VRS 1 1 0.763 0.783 0.697 0.699 0.623 0.63 0.783 0.825 1 1 0.524 0.606 1 1 0.428 0.453 0.787 0.787 0.749 0.751 1 1 0.914 0.921 0.608 1 0.496 0.626
SE 1.000 0.974 0.997 0.989 0.949 1.000 0.865 1.000 0.945 1.000 0.997 1.000 0.992 0.608 0.792
As in table 5.6, 14 life insurers were taken in the year 2004-05.Of them, ICICI was the only insurer which was not operating at its most productive scale size with scale efficiency of 0.775.It therefore has scope for improvement in CRS efficiency score if operated at best scale of operation. This was supported by the fact that ICICI has efficiency score of 1 at variable return to scale. Bajaj, Birla, Kotak and Reliance have shown efficiency scores above 0.50 at their best productive scale of operation.LIC and SBI still stood most efficient this year too. The newly entrant Sahara was the most inefficient among the insurers taken with gross efficiency score of 0.151 for both CRS and VRS. In the year 2005-06 in addition to LIC and SBI having efficiency score of 1 each in CRS and VRS at their most productive scale, Reliance has also joined the group. Of the 15 insurers taken together, no insurers except Aviva and above three efficient insurers were operating at their best scale of production. Sahara and Shriram are operating at a very low scale efficiency which was less than 0.5.However their VRS efficiency 113
scores stood at 1 though Shriram has lowest CRS efficiency score of 0.203. The remaining insurers were operating at SE which was very near to most productive scale size with above 0.9. Bajaj, Birla, HDFC, ICICI and Kotak have CRS efficiency scores more than 0.5 and their VRS efficiency scores were also more than 0.5 with ICICI touching 1. In 2006-07, of the 15 insurers LIC, SBI and ICICI were most efficient at their best scale of operation. On an average, the insurers were operating very near to their most productive scale of operation. Aviva, Met, Tata and Max were among the inefficient insurers scoring less than 0.5 in both CRS and VRS. Shriram and Bajaj scored CRS efficiency less than 0.5 but their VRS efficiency scores stood more than 0.5. In 2007-08 too, LIC, SBI and ICICI were most efficient at their best scale of operation. Met stood lowermost in efficiency ranking for both CRS and VRS with 0.273 and 0.314respectively, even at its scale efficiency of 0.869. Bajaj and Sahara‘s scale efficiency were below 0.5 but very interestingly have VRS efficiency of 1 each. Other insurers were above 0.8 in SE which was very near to 1. On an average, insurers have efficiency scores of more than 0.5.
In 2008-09, LIC, ICICI and SBI were the best insurers with CRS VRS and SE standing each at 1. Bajaj and Sahara have VRS efficiency of 1 each. This year too, Met remained the lowermost scorer of CRS and VRS efficiency; however Sahara did have lowest SE with 0.496.
In case of 2009-10, in addition to the three efficient insurers viz. , LIC, ICICI and SBI, Kotak was seen having jumped into the league of efficient 114
insurer from all the tree dimensions. In fact this year except three insurers viz. ING, Sahara and Shriram, all other remaining insurers have SE above 0.9. Met had continued to be lowest scorer of CRS as well as VRS efficiency. Table 5.7: Number of insurers by level VRS efficiency
VRS
200102 No.of firms 12 eff> 0.90 3 eff>0.75 0 0.75>eff> 0 0.60 .60>eff>.4 0 5 .45>eff 9
200203 13 2 1 0
200304 13 2 1 0
200405 14 3 1 0
200506 15 6 1 1
200607 15 6 1 2
200708 15 5 2 4
2008 -09 15 5 1 4
2009 -10 15 6 4 4
0
2
3
1
3
3
3
1
10
8
7
6
3
1
2
0
Table 5.8: Number of insurers by level of scale efficiency SE No.of firms eff> 0.90 eff>0.75 0.75>eff> 0.60 .60>eff>.4 5 .45>eff
200102 12
2002- 200303 04 13 13
200405 14
200506 15
200607 15
2007 -08 15
2008- 2009 09 -10 15 15
9 0 1
12 0 1
12 0 1
13 0 1
13 0 0
12 0 1
9 3 1
10 2 2
12 2 1
1
0
0
0
1
1
1
0
1
0
0
0
1
1
0
0
2
(Note-CRS=Constant Return to Scale. VRS=Variable Return to Scale. SE= Scale Efficiency=CRS/VRS)
The average efficiency in case of CRS, the average was found to be increasing over the years from 0.328 in 2001-02 to 0.758 in 2009-10 but slightly decreased in 2008-09. For VRS also the efficiency was increasing 115
over the years except a slight fall in 2002-03.The average scale efficiency however keep fluctuating over the years. In fact it was lowest in 2007-08. Table 5.9: Number of insurers by level of CRS efficiency CRS
2001 -02
200203
200304
200405
200506
200607
200708
No.of firms eff> 0.90 eff>0.75 0.75>eff> 0.60 .60>eff>.45 .45>eff
12 2 0 0 1 9
13 2 0 0 1 10
13 2 0 1 2 8
14 2 1 1 4 6
15 4 1 2 2 6
15 4 1 1 5 4
15 3 1 4 3 4
200 809 15 3 0 3 7 2
200 910 15 5 3 4 2 1
Table 5.7, 5.8 and 5.9 are year wise number of insurers dividing them in 5 different groups according to their VRS, SE and CRS efficiency scores. Number of insurers with efficiency more than 0.90 was increased from 3 in 2001-02 to 6 in 2009-10 for VRS, 9 to 12 insurers in case of SE while 2 to 5 insurers in case of the CRS. Though almost all the insurers were earlier in the group of efficiency less than 0.45 categories it decreased over the years. In 2009-10 no insurer was included in this group for VRS while only 1 insurer remained in the group for CRS. For VRS, it can be seen that insurers started falling in the category of efficiency score 0.6 to 0.75 from the years 2006-07 onwards. 5.1.4-Main Points: The efficiency scores of 15 Indian life insurers have been estimated from the year 2001-02 to 2009-10. SBI and LIC were the only two insurers who remain efficient throughout the years, in terms of CRS VRS and SE.
As far as the average efficiency score of the industry was 116
concerned, VRS efficiency has doubled from 0.418 in 2001-02 to 0.805 in 2009-10. In case of CRS too; the average Efficiency scores has doubled from 0.328 in 2001-02 to 0.758 in 2009-10 and this is a very healthy sign. Two-third of insurers has got SE more than 0.90 which was almost near to 1. Thus it showed that liberalization has contributed in efficiency gains of firms over the years.
5.2: PRODUCTIVITY 5.2.1-Concepts: A
firm or
industry
is considered to be inefficient if it could
produce more output with existing inputs, i.e. the firm is not on the production possibility curve, but within it. Productivity relates the quantity of output produced to one or more
inputs
used
in
its
production, irrespective of the efficiency of their use. Productivity describes the relationship between output and the input that are required to generate that output. Productivity without efficiency is usually very expensive. Efficiency does not always lead to productivity. Productivity is equated with technological change and is measured as favorable shift in the production function. Productivity changes are often defined in terms of Total factor productivity. The TFP measures the change in outputs that are not attributable to change in input. According to Coelli et al (1998), TFP is the overall productivity measure that encompasses the productivity of all production factors or outputs. TFP gains include effects of technical change, economies of scale, capacity utilization, market inefficiency, qualitative changes in inputs and X-efficiency. These non input factors make the input factors more productive, hence enabling more production with the same quantity of inputs. There are three main 117
approaches to measure TFP viz. Growth accounting (index number) approach, econometric approach and Frontier model approach. The Index approach is based on the indices of output and inputs. Indices are made under the implicit assumption of a particular production function. In econometric approach, the production function or its dual in the form of cost or profit function is explicitly estimated. It aims at deriving the different components of productivity from the parameter estimated by fitting the function. The frontier model studies how far a decision making unit is from the efficiency frontier. Efficiency Frontier can further be studied either through econometric approach (EA) or non parametric approach (PA). EA uses parametric representation of technology along with a two part composed error term. One part of the error term represents statistical noise and is generally assumed to follow a normal distribution. The other part represents inefficiency and is assumed to follow a particular one sided distribution. The non parametric approach uses mathematical programming known as DEA. It uses linear programming method to estimate the efficiency frontier to evaluate the relative efficiency of a firm or organization. 5.2.2-Methodology: Productivity improvement is critical for life insurers facing increasing competition in the aftermath of insurance liberalization in India. Productivity growth over time, where productivity growth is defined as the change in output due to technical efficiency change and technical change over time (Grosskopf, 1993,
and
Fare,
et
al.,
1994) is measured and to measure efficiency change and technical change, the Malmquist index approach (Grifell-Tatjé and Lovell, 1993, Färe, et al., 1994), (DEA- based methodology) is adopted. The firm level 118
data of 9 years is considered from the year 2001-02 to 2009-10.The number of firms taken varied from 12 to 15. Malmquist Productivity Index (MPI): Malmquist Productivity Index (MPI) is a nonparametric model, which is derived from Data Envelopment Analysis (DEA).
It is a
bilateral index that can be used to compare the production technology of two economies. MPI makes use of distance functions to measure productivity change. Distance functions describe a multi-input, multi output production technology without the need to specify a behavioral objective (such as cost minimization or profit maximization). It can be defined using input or output orientated distance functions. This approach was first proposed by Caves, Christensen and Diewert (1982) and named it after Malmquist (1953), who proposed to construct quantity indices as ratio of distance functions for use in consumption analysis. An input distance function characterizes the production technology by looking at a maximal proportional contraction of the input vector, given an output vector. An output distance function considers a maximal proportional expansion of the output vector, given an input vector. The MPI or the total factor productivity (TFP) calculated in this study measures the change in the production frontier and how the current frontier relates to the firms‘ frontiers over time. The growth in TFP has two major components: technological change and efficiency change. Technological change is represented by a shift in the production frontier while efficiency change is based upon an index of a firm‘s efficiency relative to past and future frontiers. Distance functions can be used to compare the firm‘s efficiencies in periods t and t+1. The input distance function is the same as the 119
reciprocal of the minimum equi-proportional contraction of the input vector x, given outputs y, i.e. Farrell's (1957) measure of input technical efficiency. Input technical efficiency TE(x,y) is therefore defined as
TE(x,y) for each decision making unit can be obtained by linear programming (Charnes, et al., 1994). To provide some intuition into the interpretation of the input distance function, consider the single output, single input frontier portrayed in Figure 1in the appendix. The lines V and V
t+1
t
represent the production frontiers in periods t and t+1,
respectively. The boundary V t represents the minimum inputs needed to produce any given level of output in period t. Thus, input-output combinations observed among firms in period t lie on or to the right of V t. Firms on the frontier are considered fully efficient, while those to the right of V
t
are inefficient. The type of efficiency considered here is
technical efficiency, i.e., firms on the frontier are using the most efficient available technology, while those to the right of the frontier are not using this technology. To illustrate the distance function, consider a firm operating at point (x t, y t), where x t and y t represent, respectively, the firm's input and output in period t. The firm's input quantity x
t
is
represented by the distance 0e along the horizontal axis. By adopting the most efficient technology, this firm could operate on the frontier, using input quantity 0b. The value of the input distance function for this firm is equal to 0e/0b, and its Farrell technical efficiency ratio is the reciprocal 0b/0e. The input distance function value, D (x t, y t) = 0e/0b ≥ 1, while its Farrell technical efficiency, TE = 0b/0e≤1.
120
Fig-5.2-Malmquist Index of TFP (Input based) and Input distance Functions. If technology is improving over time, we will observe shifts in the frontier. For example, in Figure 5.2, the frontier labeled V
t+1
represents
the frontier in period t+1. The improved technology represented by V
t+1
enables efficient firms to produce all levels of output using less of the input than was required by technology V t. For example, suppose that our hypothetical firm has input-output combination (xt+1, y
t+1)
in period t+1.
Because of technical progress, this firm operates to the left of V t, i.e., its input-output combination would have been infeasible using period t technology, but is feasible using period t+1 technology. This firm is also more efficient relative to the period t+1 frontier than it was relative to the period t frontier, because its operating point is closer to the frontier in t+1. In distance function terms, Dt+1(xt+1,yt+1) = 0d/oc < Dt(xt,yt) = 0e/0b, where superscripts on D indicate the time period of the frontier from 121
which the distance is computed. Distance functions are estimated by solving linear programming problems. For example, the distance function is obtained by solving the following linear programming model, for each firm, i = 1, 2... I, for each year of the sample period (time superscripts are suppressed):
where X is an K x I input matrix and Y an N x I output matrix for all sample firms, Xi is a K x 1 input vector and Yi an N x 1 output vector of firm i, and λi is an I x 1 intensity vector (the inequalities are interpreted as applying to each row of the relevant matrix). The distance function representation is used to define the Malmquist index of total factor productivity. To determine whether productivity change has occurred between periods t and t+1, either the period t frontier or the period t+1 frontier can be used as point of reference. With respect to the period t frontier, an input-oriented Malmquist productivity index can be defined as:
122
The input oriented Malmquist productivity index for the period t+1 frontier is defined by
Where, Mt measures productivity growth between periods t and t+1 using the technology in period t as the reference technology while, M
t+1
measures the productivity growth with respect to the technology in period t+1. To avoid an arbitrary choice of reference technology, the inputoriented Malmquist productivity index is defined as the geometric mean of Mt and M t+1 ( Färe, et al., 1994)
This productivity index can be decomposed into measures of technical efficiency change and technical change, by factoring as follows:
123
(Note-This Malmquist productivity index is input-oriented so, the numerator and denominator are reversed compared with those in Fare et al. (1994), in which they use output-oriented Malmquist index. ) The first ratio in equation above, in parentheses, represents technical efficiency change, i.e., the relative distance of the input-output bundle from the frontier in period t and t+1. It can be noted here that both the numerator and denominator of the ratio must be greater than or equal to 1 and that values closer to 1 represent higher efficiency. Thus, if technical efficiency is higher in period t+1 than in period t, the value of this ratio will be > 1; while if efficiency declines between the two periods, the value of the ratio will be < 1 The second factor in equation above is a geometric mean, representing technical change (shifts in the frontier) between period t and t+1. If technical improvement occurs, both ratios comprising the geometric mean will exceed 1. Thus, values of the second factor > 1 imply technical progress and values < 1 imply technical regress.
The distance functions D t(x t, y t), D t (xt+1, y t+1 ), D t+1(x t, y t) and D
t+1
(xt+1, y
t+1)
) are measured by solving mathematical programming
problems. So In order to measure the Malmquist TFP change for the i th firm, between two adjacent periods four distance functions are calculated. This requires the solving of four linear programming (LP) problems. The required LP problems under the assumption of a CRS technology are:
124
(1)
Where , D t(x i t, y i t) is the distance of the time t input-output bundle from the time t frontier for the firm I i.e it is distance function measuring the efficiency of conversion of input xt to output yt at the period t (2)
Where , D
t+1
(x i t, y i t) is the distance of the time t input-output
bundle from the time t+1 frontier for the firm i. (3) (D t+1(xit+1 y it+1))-1= min θi subject to Y t+1 λi ≥ yi t+1, Xt+1 λ i≤θi xi t+1 λi ≥ 0, θ0 free, 125
(4) (D t(xit+1 yit+1))-1= min θi subject to Y t λi ≥ yi t+1, Xt λ i≤θi xi t+1 λi ≥ 0, θ0 free,
Table 5.10: Malmquist productivity index (MPI) of the insurers MPI Company 2002- 2003- 2004- 2005- 200603 04 05 06 07 Lic 1.050 0.961 0.953 0.883 0.834 Aviva 1.257 0.870 1.165 0.982 Bajaj 0.688 0.978 0.953 0.872 0.797 birla 0.731 0.980 0.912 0.994 0.867 Hdfc 0.763 0.979 2.062 1.239 1.020 Icici 0.747 0.978 0.887 0.990 1.014 ING 0.978 1.115 0.869 1.258 1.017 Kotak 0.708 1.074 0.874 1.144 0.987 Met 28.263 30.352 26.360 38.921 1.004 max 0.708 0.977 0.872 1.091 0.943 reliance 1.302 1.468 0.929 1.640 1.042 Sbi 1.115 1.258 0.922 1.372 0.977 tata 0.737 0.978 0.893 1.079 0.853 sahara 1.160 1.007 shriram 0.890 Total Average 0.87 1.08 1.00 1.15 0.95
200708 0.996 0.917 0.819 0.931 0.919 0.970 0.934 0.904 0.930 0.925 0.865 0.859 0.922 0.919 0.955
200809 1.014 0.771 0.961 0.896 0.856 0.652 0.720 0.845 0.921 0.970 0.800 1.011 0.790 0.949 0.971
0.92
0.88
Company 2009- average 10 0.955 0.96 0.842 0.97 1.204 0.91 1.144 0.93 1.253 1.14 0.784 0.88 0.820 0.96 1.042 0.95 0.940 0.95 0.867 0.92 1.070 1.14 0.857 1.05 1.122 0.92 0.976 1.00 0.962 0.94 0.99
0.98
(Note: The MPI of Met is not considered for the years from 2001-02 to 2005-06 in average calculation as its MPI is abnormally high and so may affect the overall average. The same is considered for TEC and TC also.) Table 5.10 shows the MPI index of 15 life insurers from the year 2002-03 to 2009-10. The Malmquist results for 2002-03 would mean the change in 126
productivity from 2001-02 to 2002-03 . The 2006-07‘s Malmquist index (0.834) showed a decline in productivity of about 16.6 percent ((1 minus 0.834) times 100) for LIC which was the highest decline of the company in 8 years taken. The company on an average have 4% decline in productivity in 8 years. Aviva‘s and Max‘s MPI improved only in 200506 while Bajaj and Birla in 2009-10 only. HDFC, SBI, Reliance and Sahara are the four insurers whose average productivity has improved over the years. ICICI‘s MPI improve only in 2006-07 while Shriram‘s MPI never improved. ICICI showed highest average productivity decline of 12 percent while HDFC and Reliance showed highest average improvement of productivity with 14 percent. The Malmquist indices of the industry total showed improvement in productivity in only three of the eight two-year comparisons and productivity regress in five of the eight comparisons. Table 5.11: MPI decomposed into technical efficiency change (TEC) and technical change (TC) Company Lic Aviva Bajaj birla Hdfc Icici ING Kotak Met max reliance Sbi tata sahara Total Average
2002-03 TEC 1.000 -1.862 1.063 1.475 1.155 1.019 1.492 2.302 1.231 1.923 1.000 1.390 1.328
TC 1.05 -0.370 0.688 0.518 0.647 0.695 0.511 12.278 0.576 0.677 1.115 0.527 0.67
2003-04 TEC 1.000 1.175 1.109 1.692 1.162 1.136 1.944 2.043 1.467 1.263 2.380 1.000 1.347
TC 0.961 1.070 0.882 0.579 0.842 0.861 0.573 0.526 20.687 0.773 0.617 1.258 0.726
1.438
127
0.806
2004-05 TEC 1.000 1.503 1.768 1.031 0.976 1.220 1.948 1.734 1.223 1.074 2.214 1.000 0.894 1.364
TC 0.953 0.579 0.539 0.885 2.112 0.727 0.446 0.504 21.546 0.811 0.420 0.922 0.998 0.825
2005-06 TEC 1.000 1.317 1.337 1.075 1.698 1.277 0.983 1.099 1.502 1.289 1.865 1.000 1.265 3.132 1.411
TC 0.883 0.884 0.652 0.925 0.730 0.775 1.280 1.041 25.908 0.846 0.865 1.372 0.853 0.370 0.883
Table 5.11 continues. MPI decomposed Company average Company 2006-07 2007-08 2008-09 2009-10 TEC TC TEC TC TEC TC TEC TC TEC TC 1.000 0.835 1.000 0.996 1.000 1.014 1.000 0.955 1.00 0.96 Lic 1.109 0.885 1.229 0.746 1.137 0.678 0.627 0.660 1.25 0.79 Aviva 0.722 1.103 0.864 0.947 1.485 0.648 1.115 1.080 1.31 0.78 Bajaj 1.028 0.844 1.041 0.895 0.828 1.082 1.229 0.931 1.12 0.85 birla 1.076 0.948 0.953 0.965 0.761 1.125 1.190 1.105 1.16 1.04 Hdfc 1.010 1.004 1.000 0.970 1.000 0.652 1.000 0.784 1.10 0.80 Icici 1.246 0.816 1.405 0.665 0.847 0.850 0.879 0.933 1.28 0.78 ING 1.154 0.855 0.816 1.108 0.800 1.057 1.866 0.558 1.38 0.77 Kotak 0.951 1.055 0.872 1.066 1.190 0.774 1.317 0.713 1.08 0.90 Met 1.120 0.842 0.993 0.931 1.454 0.666 1.241 0.698 1.21 0.77 max 0.677 1.540 0.985 0.878 0.604 1.324 1.859 0.575 1.56 0.86 reliance 1.000 0.977 1.000 0.859 1.000 1.011 1.000 0.857 1.00 1.05 Sbi 1.089 0.784 1.132 0.814 0.965 0.818 1.751 0.641 1.23 0.77 tata 1.076 0.936 0.870 1.055 1.120 0.848 1.226 0.796 1.48 0.80 sahara 2.054 0.433 1.216 0.785 0.941 1.032 1.040 0.925 1.31 0.79 shriram Total 1.087 0.924 1.025 0.912 1.009 0.905 1.223 0.814 1.24 0.85 Average
Table 5.11: The technical efficiency change and technical change results for each year from 2002-03 to 2009-10 are given in table 5.2.2. For LIC, the decline in productivity was mainly due to technical regress as its efficiency remained 1 in all the years. The productivity of the year 200203 and 2008-09 were improved because, its technology progressed i.e. more than 1 in those two years. Aviva‘s efficiency was improving all the 128
years except in 2009-10(0.6271‹1) but technically regressing throughout. ICICI‘s efficiency improved all the years but experienced no technical progress in all the years. Kotak showed technical progress in 2007-08 and 2008-09 while ING in 2005-06 though their TECs were declined. SBI showed technical progress in four out of eight years and also maintained efficiency of 1 in all the years. Like SBI, LIC could maintain efficiency of 1 in all years including those two years of technical progress. However no other insurers could simultaneously experienced efficiency as well as technical progress at a time. 5.2.4-Main Points: This chapter estimated the MPI and it two components, technical efficiency change (TEC) as well as technical change (TC). Out of the 15 insurers, only 4 insurers could have improved average productivity. In 2007-08 no insurers could make productivity improvement. At start, i.e. in 2002-03 and 2003-04, all the insurers showed efficiency improvement which was slightly declined in the next two years and it grew in 2007-08 and 2008-09. However in 2009-10, 14 out of 15 insurers showed efficiency improvement. In a given year, insurers either improve efficiency and regressed technology or decline efficiency and progress technology except SBI and LIC who maintain efficiency with technical progress.
129
CHAPTER VI INNOVATIONS
Innovations are the introduction or adoption of new ideas, process, product or services, developed internally or acquired from external environment. The adoption of innovation flows from and is contingent upon an organization‘s repertoire of technical, strategic and administrative skills. Liberalization has given way for establishment of new companies in Insurance sector and consequently competition is leading to innovation. Therefore this chapter of the thesis examines new innovations visible in the wake of insurance sector‘s liberalization. This chapter of innovation is discussed under the following headings 6.1) Product 6.2) Quality of customer services 6.3) Technology 6.4) Marketing Strategies 6.5) Conclusion
6.1) Product: Products offered before liberalization and after liberalization are given in this section. Before liberalization, the range of product available was very limited. India has an enormous middle class that can afford to buy life insurance product as per their need. However after the liberalization, there was a major change in the insurance product offered 130
by the insurance companies and insurance services covers opted for by the customers.
It is difficult to specify exact number of policies or
product that LIC or others have, since time to time; some products were introduced or withdrawn with small variation of time and may be counted as new product. Table 6.1 shown is the number of products offered by life insurance companies per year. Of the 18 life insurance companies taken together, 4 companies were offering more than 10 products per year on an average, and only two companies have offered less than 5 products per year. The remaining 12 companies have on average offered more than 5 but less than 10 products per year. The number of product offered was varied irrespective of the year and there was no trend of either increasing or decreasing for all the companies. As of now, the relationship between the number of product offered and the growth of the company is not looking into but it is important to mention here that the number of product available in the market has increased tremendously in the wake of liberalization.
In table 6.2 the number of riders attached with
insurance policies is shown. 4 insurance companies have more than 20 riders attached with, in total and 8 companies have more than 10 riders taken altogether. Remaining 6 insurers out of 18 insurers taken together have less than 10 riders in total.
A thorough analysis showed that there was a shift from the traditional life insurance product to unit linked insurance policies as shown in Table 6.3. The unit link insurance plans constituted more than 70% of the total industry‘s plans in 2007-08. The private insurers offered more than 90% of their plans in unit link plans in 2007-08.
131
Table-6.1: Number of products offered by life insurers in India
Company
Number of Products offered during the financial year 200 200 200 200 200 200 200 200 1-02 2-03 3-04 4-05 5-06 6-07 7-08 8-09
LIC Aviva
07
13 18
05 10
06 04
06 02
08 24
06 18
09 12
08 16
Com pany wise total 68 104
Max NY HDFC ICICI
03 06 06
03 02 14
05 07 10
07 04 16
06 05 08
06 11 27
14 04 10
03 13 13
10 19 17
57 71 121
9 9 9
Met SBI lIfe Tata AIG Birla Sun Bajaj Al
03 06 21 07 08
12 05 07 05 05
07 09 11 01 13
06 02 02 02 10
02 04 01 06 06
08 09 08 18 15
07 13 11 09 14
05 05 09 06 16
16 14 19 14 22
66 67 89 68 109
9 9 9 9 9
ING Vys Reliance*
06 05
05 00
04 11
04 03
05 00
09 09
07 05
03 15
10 31
53 79
9 7
Kotak Sahara Shriram Bharti Ax Future G IDBI life Yearwise total 2 Industry Average
07
09
05
10 06
04 03 03
17 06 08 08
85
98
98
82
61
191
07 02 06 05 04 02 144
09 03 02 05 13 06 147
16 05 13 17 11 06 264
9 6 5 4 3 3 --
12 7.08
12 8.16
13 7.54
14 5.86
14 4.36
16 11.9 4
18 8.00
18 8.17
18 14.6 7
84 25 32 35 28 14 117 0 18 65
7.56 13.0 0 6.33 7.89 13.4 4 7.33 7.44 9.89 7.56 12.1 1 5.89 11.2 9 9.33 4.17 6.40 8.75 9.33 4.67 --
---
---
2009 -10
1
Firm ‘s aver age
Ra nk
9 8
9 2
---
*Formerly called AMP Sanmar (1 is the number of years taken for average calculation.2 is the number of companies taken for average calculation. Data are from IRDA‘s annual report .The number of new product launched in each year is mentioned here irrespective of whether withdrawn or not. The company wise average is calculated for the year product was launched. In case of the year where no product or riders were launched then that year is excluded from the average calculation.
132
13 8 1 11 10 5 9 3 14 4 6 16 12 7 6 15 --
Table 6.2: No of riders attached to life insurance policies in India
Company
LIC Aviva Max NY HDFC ICICI Met SBI lIfe Tata AIG Birla Sun Bajaj Al ING Reliance* Kotak Sahara Shriram Bharti Ax Future G IDBI Life Total 2 Average
2001 -02 01 02 09 00 03 02 08 07 05 04 02 10
2002 -03 00 03 04 00 07 03 02 03 01 04 00 06 05
2003 -04 02 00 02 01 04 07 02 01 03 02 01 00 00
53 11 4.8
38 10 3.8
25 10 2.5
NUMBER of RIDERS 2004 2005 2006 -05 -06 -07 00 01 02 01 02 00 03 00 00 00 00 04 00 00 05 00 00 02 00 00 00 01 00 00 01 00 01 00 02 05 01 00 00 01 00 00 00 00 00 01 02 01 02 04 02
09 7 1.3
09 5 1.8
26 9 2.9
2007 -08 02 02 00 00 02 00 00 01 06 02 00 03 00 00 01 00 15 04 38 10 3.8
2008 -09 01 06 07 00 00 02 00 03 00 01 00 00 01 00 00 00 01 03 25 9 2.8
2009 -10 00 00 00 00 00 00 15 00 00 04 00 03 09 00 01 02 05 00 39 7 5.6
Comp -any total 09 14 18 14 18 17 21 17 19 25 06 15 25 04 08 04 21 07 262 18 14.6
1 Ave rage 6 5 5 3 4 5 4 6 6 8 3 4 3 5 2 3 2 -
Table 6.3: Trends in life insurance business-unit linked insurance plans Year 2005-06 2006-07 2007-08 2005-06 2006-07 2007-08
lic Pvt Unit linked business (%) 29.76 82.30 46.31 88.75 62.31 90.33 Non- linked business (%) 70.24 17.70 53.69 11.25 37.69 9.67
industry 41.77 56.91 70.30 58.23 43.09 29.70
Source: IRDA‘s annual report-2007-08
133
1.5 2.8 3.6 4.7 4.5 3.4 5.3 2.8 3.2 3.1 2 3 6.3 1.3 2 2 7 3.5 3.4 ---
Table 6.4 and 6.5 represent the link and non linked life insurance business in force within India in terms of number of policies and their percentage to total business in force for the year 2005-06 to 2009-10. Link businesses are the insurance policies that combine risk coverage with investing in stock/ debt markets. The non linked business taken includes life business and General annuity and pension business. Health business in force is not considered in the linked as well as non- linked business. In case of private insurers, the percentage of linked business increased from 46.82 percent in 2005-06 to 76.38 in 2009-10 and that in case of LIC was 2.10 to 17.76. Both private and public insurers have shown an increasing trend throughout the years taken. For the life insurance industry, the percentage of linked business to total business stood at 25.11 in 2009-10. So for obvious reason, the percentage of non linked business to total business showed decreasing over the years for both private and LIC of India. Only 23.62 percent constituted the non linked business of private insurers and 82.24 % for LIC in 2009-10. The percentage of non- linked business to total business was decreasing over the years. Overall it can be concluded that the dominance of linked business to non linked was increasing in case of private insurers, however in case of LIC, traditional policies still dominated the linked business but its percentage was decreasing.
134
Table 6.4: Individual business within India-business in force (Number of policies in 1000) Year
Insurer
Life business(LB)
Gen annuity ( GA)
200506
Pvt. LIC total Pvt. LIC total Pvt. LIC total Pvt. LIC total Pvt. LIC total
3545 179564 183109 4745 189419 194164 5741 192428 198169 7530.66 210154.04 217684.70 9006.99 226057.89 235064.88
263 2923 3186 284 2909 3193 306 2829 3135 371.83 2788.78 3160.61 403.82 2779.84 3183.66
200607 200708 200809 200910
Non Linked Business, NLB=LB +GA 3808 182487 186295 5029 192328 197357 6047 195257 201304 7902.49 212942.8 220845.3 9410.81 228837.7 238248.5
Linked Grand business(L) Total=L+NLB
3352 3914 7266 8438 20240 28678 17532 38582 56114 25264 44682 69946 30427.87 49434.28 79862.15
7160 186401 193561 13467 212568 226035 23579 233839 257418 33166.49 257624.8 290791.3 39838.68 278272 318110.7
Table 6.5: Link and non linked business to total business in force (percent) Linked Business
2005-06
2006-07
2007-08
2008-09
2009-10
Pvt.
46.82
62.66
74.35
76.17
76.38
LIC
2.10
9.52
16.50
17.34
17.76
total
3.75
12.69
21.80
24.05
25.11
Non Linked Business Pvt
53.18
37.34
25.65
23.83
23.62
LIC
97.90
90.48
83.50
82.66
82.24
total
96.25
87.31
78.20
75.95
74.89
135
6.2) Quality of Customer Services:
With liberalization of insurance sector, it has become very important for insurers to improve customer satisfaction and loyalty. In fact, service quality is an important means of differentiation and path to achieve business success in the competitive environment. Customer service is a derivative of a mix of human reactions influenced by a host stimuli emanating from within and outside the organization. Any improvement in customer services cannot be attained in isolation, unless the entire gamut of factors affecting it is taken into account and managed properly. Customers and employees need active participation in the process of developing a healthy relationship which involves a thorough overhaul of the approach of insurance company towards customer relations management. Service quality is an abstract concept, difficult to define and measure. The quality of customer services can be viewed from various sources. Most widely used service quality measurement tools include SERVQUAL and SERVPERF.SERVQUAL scale measures service quality, based on difference between expectation and performance perception of customers using 22 items and five -dimensional structure. In the SERVPERF scale, service quality is operationalised through performance only based on the same 22 items and five dimensional structure of SERVQUAL. The customer‘s perception of service quality varies. Often there is a disconnection between what customers want and what service providers offer. This is particularly true in case of services like life insurance because of the intangibility element associated with it. However there are 136
some important dimensions of overall expectation of service quality. To name some of these dimension in particular, the life insurance industry are assurance, competence, and corporate image, tangibles
and
technology etc. Assurance implies that the agent will be prepared to deliver in the terms of the life insurance policy when it is redeemed. The life insurance policyholders have primarily defined assurance in terms of well trained and informed agents, who understand intimately specific needs, approach from customer‘s point of view show clarity in explaining policy‘s terms and conditions and thereby inspire trust and confidence. Therefore, it is imperative for the service providers to provide adequate training to their agents to improve their customer interaction skills and knowledge. Competence implies that the agent will be prepared to deliver on the terms of the life insurance policy when it is redeemed. Competence also means that the customer can count on the agent to resolve any
problems should they
arise,
and that
too
promptly.
Within the purview of this attribute the policyholders accord the highest priority to ‗efficient claims settlement‘. Beyond this, the service providers need to focus on promptness in ‗grievance handling‘, that too by efficient and dependable staff. Keeping in view the two important dimensions of customer‘s perceived quality service viz. assurance and compliance, the quality of customer service is examined on the point of a) the claim settlement of the life insurance firms b) status of grievances and c) the performance of Ombudsman i.e .in terms of complaints received and dissolved. a) Status of claim: Death Claim records of the insurance company give a fair idea of their payment history. Claim settlement ratio, claim repudiation ratio and claim
137
pending ratio are three important parameters which would make us understand the quality of company‘s trustworthiness. Claim settlement ratio is the number of claims settled for every 100 claims received by the life insurance company. For example if an insurance company has received 100 claims in a year and it has settled 98 claims out of it, then the claim settlement ratio will be 98%. Higher is the claim settlement ratio for the company, the better. Likewise claim repudiation ratio or claim pending ratio are the number of claims repudiated or pending for every 100 claims received by the life insurance company. Companies with lower claim repudiation or claim pending ratio are considered good. The payment history of private as well public insurer in case of death claim-both in policy number as well as benefit amount are put here for three years from 2007-08 to 2009-10. The claim record of 15 life insurers in four parameters viz. claim settled, claim repudiated, claim written back and claim pending are examined here.
Table 6.6 shown is the total death claims (claim pending at start of year +claims initiated) in number of policies from the year 2007-08 to 200910. In individual category for private sector, the total death claim was highest for Bajaj in terms of policies in all the years. In group, SBI has got the highest claims in 2007-08 and 2009-10 and was replaced by Bajaj in 2009-10. In total, SBI has highest death claim in2007-08 and in the remaining two years Bajaj got the highest claims in terms of number of policies. For overall life insurance sector, LIC obviously stood top in all category and all the years as highest claim receiver. For the industry, the number of death claim was increasing over the years.
138
Table 6.6: Total death claims (claim pending at start of year +claims initiated) in number of policies Company Aviva Bajaj Birla HDFC ICICI ING Kotak Max Met Reliance Sahara SBI Shriram Tata LIC Pvt Total TOTAL
2007-08 individual 947 6529 1460 2062 6632 942 703 2347 341 1015 186 2311 396 1664 577322 27535 604857
Group 1841 4908 278 182 290 253 875 567 693 900 17 9546 00 1043 141428 21393 162821
Total 2788 11437 1738 2244 6922 1195 1578 2914 1034 1915 203 11857 396 2707 718750 48928 767678
2008-09 individual 1438 12011 2757 2898 10743 1523 1300 3938 729 3701 439 4260 791 2700 591097 49228 640325
Group 2819 14368 542 215 1207 153 894 475 735 1049 5 17541 2 1111 222845 41116 263961
Total 4257 26379 3299 3113 11950 1676 2194 4413 1464 4750 444 21801 793 3811 813942 90344 904286
2009-10 individual 50 23040 5921 3837 16057 1926 2280 6019 1346 8754 731 7232 1166 3495 677374 81854 759228
Group 4579 39387 911 288 2007 246 1265 11488 1039 1911 7 26630 94 985 215909 90837 306746
Total 4629 62427 6832 4125 18064 2172 3545 17507 2385 10665 738 33862 1260 4480 893283 172691 1065974
Table 6.7 and 6.8 shows the company wise total death claim paid in number of policies and their percentage to total death claim in policy number. The number of death claim paid was highest for LIC in all years in all the three categories. Among private insurers, the number of death claim paid was highest for ICICI in 2007-08 and then Bajaj in remaining two years in individual claim. SBI stood first in number of group claim paid for two years which was being replaced by Bajaj in 2009-10. In total, SBI stand as insurer with highest claim paid in policy number in 2007-08 and replaced by Bajaj in the remaining two years. However in terms of Percentage of death claims paid to total claims in number of policies, LIC stood first among the insurers in all three years in individual, followed by Birla in 2007-08, Max in 2008-09 and HDFC in 2009-10. In Group, Sahara has go 100 percent claim paid in all the three years followed by LIC and then max in 2007-08, Shriram and then LIC in 2008-09 and finally LIC then Aviva in 2009-10. In total, LIC then 139
Reliance in 2007-08, LIC then Birla in 2008-09 and in 2009-10, Aviva then LIC were the insurers with highest percentage of claim paid in number of policies. Table 6.7: Total death claims paid in number of policies Compan y
2007-08 individual
Group
Total
Aviva Bajaj Birla HDFC ICICI ING Kotak Max Met Reliance Sahara SBI Shriram Tata LIC Pvt Total TOTAL
654 5065 1334 1665 5831 526 414 2121 162 925 82 1883 155 929 553408
1769 4339 270 174 276 217 619 557 466 867 17 7949 00 553 141258
2423 9404 1604 1839 6107 743 1033 2678 628 1792 99 9832 155 1482 694666 39819
21746 575154
18073 159331
734485
2008-09 individual
Group
Total
1032 10484 2457 2549 9298 1180 1002 3545 422 3204 198 3262 312 1652 564389
2759 13300 540 212 1104 97 729 434 617 995 5 16387 2 912 222307
3791 23784 2997 2761 10402 1277 1731 3979 1039 4199 203 19649 314 2564 786696
2009-10 individu al 24 20316 5275 3497 14479 1720 1983 3943 1111 7797 461 6022 461 2732 653909
40597 604986
38093 260400
78690 865386
69821 723730
140
Group
Total
4550 38988 900 286 1863 230 1157 10421 954 1869 7 25817 82 809 215485
4574 59304 6175 3783 16342 1950 3140 14364 2065 9666 468 31839 543 3541 869394
87933 303418
157754 1027148
Table 6.8: Percentage of death claims paid to total claims in number of policies Company 2007-08
2008-09
2009-10
individual Group
Total
individual Group
Total
individual Group
Total
Aviva
69.06
96.09
86.91 71.77
97.87
89.05 48.00
99.37
98.81
Bajaj
77.58
88.41
82.22 87.29
92.57
90.16 88.18
98.99
95.00
Birla
91.37
97.12
92.29 89.12
99.63
90.85 89.09
98.79
90.38
HDFC
80.75
95.60
81.95 87.96
98.60
88.69 91.14
99.31
91.71
ICICI
87.92
95.17
88.23 86.55
91.47
87.05 90.17
92.83
90.47
ING
55.84
85.77
62.18 77.48
63.40
76.19 89.30
93.50
89.78
Kotak
58.89
70.74
65.46 77.08
81.54
78.90 86.97
91.46
88.58
Max
90.37
98.24
91.90 90.02
91.37
90.17 65.51
90.71
82.05
Met
47.51
67.24
60.74 57.89
83.95
70.97 82.54
91.82
86.58
Reliance
91.13
96.33
93.58 86.57
94.85
88.40 89.07
97.80
90.63
Sahara
44.09
100.00 48.77 45.10
100.00 45.72 63.06
100.00 63.41
SBI
81.48
83.27
82.92 76.57
93.42
90.13 83.27
96.95
94.03
Shriram
39.14
0.00
39.14 39.44
100.00 39.60 39.54
87.23
43.10
Tata
55.83
53.02
54.75 61.19
82.09
67.28 78.17
82.13
79.04
LIC
95.86
99.88
96.65 95.48
99.76
96.65 96.54
99.80
97.33
Pvt Total
78.98
84.48
81.38 82.47
92.65
87.10 85.30
96.80
91.35
TOTAL
95.09
97.86
95.68 94.48
98.65
95.70 95.32
98.92
96.36
Table 6.9 show the company wise total death claim in benefit amount for three years from 2007-08 to 2009-10. The total claim is the sum of claim pending at the start of the year and claim initiated in the year. The death claim is shown in individual and group categories and also their total for each year and each insurance company. The total individual claim exceeded the total group claim for private as well public insurer in all the three years. Company wise too, the total individual claim in benefit amount was greater than group claim except for SBI, Met and Reliance in 2007-08. In case of SBI, the group claims exceeded individual claim in
141
all the three years. The company wise total claim for each category was seen increasing over the year. Table 6.9: Total death claims (claim pending at start of year +claims initiated) in benefit amount for all firms (Rs Crores) Company Individual
Group
Total
2007-
2008-
2009-
2007-
2008-
2009-
2007-
2008-
2009-
08
09
10
08
09
10
08
09
10
Aviva
15.86
30.93
31.71
5.06
6.63
11.58
20.92
37.56
43.29
Bajaj
108.92
212.50
353.11
41.95
48.07
68.31
150.87
260.57
421.42
Birla
38.13
74.50
130.21
18
7.87
11.31
56.13
82.37
141.52
HDFC
33.86
49.24
79.75
2.15
2.84
2.48
36.01
52.08
82.23
ICICI
60.39
127.98
248.52
7.32
26.36
38.35
67.71
154.34
286.87
ING
13.62
27.77
29.68
2.51
2.17
3.89
16.13
29.94
33.57
Kotak
25.03
25.66
67.97
18.53
23.46
39.74
43.56
49.12
107.71
Max
47.69
78.11
126.55
4.55
6.92
22.90
52.24
85.03
149.45
Met
14.45
32.42
76.62
18.61
21.30
27.66
33.06
53.72
104.28
Reliance
15.13
46.29
103.47
53.51
22.08
26.42
68.64
68.37
129.89
Sahara
1.84
3.61
7.10
0.07
0.03
0.03
1.91
3.64
7.13
SBI
33.75
61.57
103.35
161.12
175.73
183.65
194.87
237.3
287
Shriram
6.04
11.12
17.42
0.08
2.11
6.04
11.2
19.53
Tata
28.28
44.15
75.56
26.04
17.86
47.58
70.19
93.42
LIC
4182.23 4444.17 5049.43 761.10
1008.62 1202.09 4943.33 5452.79 6251.52
Pvt Total
442.99
369.58
TOTAL
4625.22 5270.02 6500.45 1113.78 1378.2
825.85
19.30
1451.02 352.68
142
456.29
795.67
1658.38 5739
1195.43 1907.31 6648.22 8158.83
Table 6.10: Total death claims paid in benefit amount by all insurance firms (Rs Crores) Compnay
Individual
Group
Total
2007-
2008-
2009-
2007-
2008-
2009-
2007-
2008-
2009-
08
09
10
08
09
10
08
09
10
Aviva
6.99
22.38
25.37
4.81
6.49
11.53
11.8
28.87
36.9
Bajaj
79.46
174.42
294.92
39.38
43.64
66.05
118.84
218.06
360.97
Birla
31.06
59.11
102.76
17.63
7.72
11.06
48.69
66.83
113.82
HDFC
23.27
37.83
66.58
1.84
2.71
2.36
25.11
40.54
68.94
ICICI
44.37
101.69
218.04
6.89
22.34
33.34
51.26
124.03
251.38
ING
7.42
17.18
24.91
2.36
1.65
3.46
9.78
18.83
28.37
Kotak
19.76
16.21
55.91
13.35
18.68
35.62
33.11
34.89
91.53
Max
41.51
66.74
78.07
4.32
6.16
19.87
45.83
72.9
97.94
Met
5.44
17.13
40.50
12.94
17.83
25.82
18.38
34.96
66.32
Reliance
13.00
35.06
79.61
52.67
20.61
25.59
65.67
55.67
105.2
Sahara
0.66
1.78
4.73
0.07
0.03
0.03
0.73
1.81
4.76
SBI
25.96
46.99
92.44
129.05
151.63
170.32
155.01
198.62
262.76
Shriram
1.95
4.12
5.47
0.08
1.89
1.95
4.2
7.36
Tata
15.27
28.36
52.00
20.76
13.96
23.87
49.12
65.96
LIC
8.60
3918.72 4165.10 4799.55
759.82
1005.15 1195.28 4678.54 5170.25 5994.83
Pvt Total
316.12
629
293.91
320.33
TOTAL
4234.84
4794.1
1141.31
420.9
610.03
949.33
1562.21
5940.86 1053.73 1325.48 1616.18 5288.57 6119.58 7557.04
143
Table 6.11: Percentage of death claims paid to total claims in benefit amount (Rs Crores) Company 2007-08
2008-09
2009-10
individual Group
Total
individual Group
Total
individual Group
Total
Aviva
44.07
95.06
56.41 72.36
97.89
76.86 80.01
99.57
85.24
Bajaj
72.95
93.87
78.77 82.08
90.78
83.69 83.52
96.69
85.66
Birla
81.46
97.94
86.75 79.34
98.09
81.13 78.92
97.79
80.43
HDFC
68.72
85.58
69.73 76.83
95.42
77.84 83.49
95.16
83.84
ICICI
73.47
94.13
75.71 79.46
84.75
80.36 87.74
86.94
87.63
ING
54.48
94.02
60.63 61.87
76.04
62.89 83.93
88.95
84.51
Kotak
78.95
72.05
76.01 63.17
79.62
71.03 82.26
89.63
84.98
Max
87.04
94.95
87.73 85.44
89.02
85.73 61.69
86.77
65.53
Met
37.65
69.53
55.60 52.84
83.71
65.08 52.86
93.35
63.60
Reliance
85.92
98.43
95.67 75.74
93.34
81.42 76.94
96.86
80.99
Sahara
35.87
100.00 38.22 49.31
100.00 49.73 66.62
100.00 66.76
SBI
76.92
80.10
79.55 76.32
86.29
83.70 89.44
92.74
91.55
Shriram
32.28
0.00
32.28 37.05
100.00 37.50 31.40
89.57
37.69
Tata
54.00
44.56
50.17 64.24
79.72
69.98 68.82
78.16
70.61
LIC
93.70
99.83
94.64 93.72
99.66
94.82 95.05
99.43
95.89
Pvt Total
71.36
83.34
76.67 76.16
86.67
79.41 78.66
92.24
81.91
TOTAL
91.56
94.61
92.15 90.97
96.17
92.05 91.39
97.46
92.62
Table 6.10 to table 6.12 shows the total claim paid in benefit amount, its percentage to total claims and the top 5 insurers with the highest percentage of claim paid.LIC remained top spot in the claim paid in all the years. Among private insurers Bajaj remained as top insurer in claim paid in individual category while SBI stood first in Group category. Mention may be made here that this was because the total claim of SBI was higher in group category than the individual category. While amount 144
of claim paid were dependent on the amount of total claim asked for, the percentage of claim paid to total claim was discussed in table 6.11 and further ranked insurers according to their respective percentages in the table 6.12. Overall, LIC remains top insurer in terms of percentage of individual claim paid in all three years taken. Table 6.12: Top 5 in the highest percentage of claim paid in benefit amount Individual
2007-08
2008-09
2009-10
insurer
percentage
insurer
percentage
insurer
percentage
1
LIC
93.70
LIC
93.72
LIC
95.05
2
Max
87.04
Max
85.44
SBI
89.44
3
Reliance
85.92
Bajaj
82.08
ICICI
87.74
4
Birla
81.46
ICICI
79.46
ING
83.93
5
Kotak
78.95
Birla
79.34
Bajaj
83.52
1
Sahara
100
Sahara
100
Sahara
100
2
LIC
99.83
Shriram
100
Aviva
99.57
3
Reliance
98.43
LIC
99.66
LIC
99.43
4
Birla
97.94
Birla
98.09
Birla
97.79
5
Aviva
95.06
Aviva
97.89
Reliance
96.86
1
Reliance
95.67
LIC
94.82
LIC
95.89
2
LIC
94.64
Max
85.73
SBI
91.55
3
Max
87.73
SBI
83.7
ICICI
87.63
4
Birla
86.75
Bajaj
83.69
Bajaj
85.66
5
SBI
79.55
Reliance
81.42
Aviva
85.24
Group
total
It also remained among the top five players in group claims as well as total claims in all the three years taken. After LIC, only SBI consistently remained among the top 5 insurers in overall claim paid percentage. 145
However it was not among the top 5 insurer in group category in all the three years despite being the highest claim paid insurer amount wise in group category. Sahara stood at top for all the three years in group claim paid.
Table 6.13 and 6.14 shows the company wise total death claim repudiated in number of policies and their percentage to total death claim in policy number respectively. The number of death claim repudiated was highest for LIC in individual and in total. Among private insurers, the number of death claim repudiated was highest for Bajaj in all the three years and lowest for Sahara in 2007-08 and 2008-09 and Aviva in 2009-10 for individual claim repudiated. SBI top in number of group claim repudiated and was lowest for Sahara in all the years. In total, SBI stood as insurer with highest claim repudiated in policy number in all years and Sahara stood as lowest claim repudiated insurer in the first two years then replaced by Aviva in 2009-10. However in terms of Percentage of death claims repudiated to total claims in number of policies, LIC remained lowest claim repudiated insurers in all three years in individual as well as in total. In Group, Sahara and Shriram has got 0.00 percent claim repudiation in all the three years. In total, Kotak in 2007-08, Sahara in 2008-09 and 2009-10 has got highest percentage of claim repudiation while HDFC, Bajaj and Aviva has got lowest percentage of claim repudiation in 2007-08, 2008-09 and 2009-10 respectively.
146
Table 6.13: Death claim repudiated in number of policies
Company 2007-08
2008-09
2009-10
Individual
Group Total
Individual Group
Total
Individual
Group Total
Aviva
121
23
144
278
53
331
22
25
47
Bajaj
689
23
712
1003
113
1116
1197
26
1223
Birla
109
8
117
286
2
288
629
11
640
HDFC
87
87
139
1
140
179
1
180
ICICI
436
2
438
559
48
607
525
92
617
ING
132
24
156
119
23
142
82
7
89
Kotak
178
148
326
120
93
213
100
10
110
Max
221
10
231
306
31
337
741
420
1161
Met
73
66
139
166
21
187
80
25
105
Reliance
86
26
112
210
10
220
617
6
623
Sahara
9
00
9
54
00
54
50
00
50
SBI
164
835
999
643
887
1530
1067
779
1846
Shriram
76
76
192
192
230
00
230
Tata
413
66
479
750
101
851
452
80
532
LIC
9027
9
9036
7846
13
7859
8227
21
8248
Pvt Total
2794
1231
4025
4825
1383
6208
5971
1482
7453
TOTAL
11821
1240
13061 12671
1396
14067 14198
1503
15701
Table 6.15 to 6.17 shows the total claim repudiated in benefit amount, its percentage to total claims and the top 5 insurers with lowest percentage of claim repudiated. In individual category, Bajaj remained top private insurer with highest claim repudiated amount for the first two years which was replaced by Birla in 2009-10. Sahara remained with lowest claim repudiated amount in all three years. In group category, claim repudiated amount was highest for SBI in all three years while it was lowest by Sahara and Shriram in all three years with claim repudiated 147
amount of Rs 0.00 Crores. Overall, industry total of claim repudiated amount was highest for LIC and for private insurers; the trend was same as the case of individual category. The amount of claim repudiated as percentage of total claims and company‘s ranking accordingly is given in table 6.16 and 6.17. No company is at par with LIC except Sahara as they were the only two insurers which remained among the top 5 insurers with the lowest percentage of claim repudiated in all the years and in the three entire categories. Table 6.14: Percentage of claims repudiated to total claims in number of policies. Compan
2007-08
2008-09
y
Individu
Grou
al
p
Aviva
12.78
1.25
5.16
19.33
1.88
7.78
Bajaj
10.55
0.47
6.23
8.35
0.79
Birla
7.47
2.88
6.73
10.37
HDFC
4.22
0.00
3.88
ICICI
6.57
0.69
ING
14.01
Kotak
Group
Total
44.00
0.55
1.02
4.23
5.20
0.07
1.96
0.37
8.73
10.62
1.21
9.37
4.80
0.47
4.50
4.67
0.35
4.36
6.33
5.20
3.98
5.08
3.27
4.58
3.42
9.49
13.05
7.81
15.03
8.47
4.26
2.85
4.10
25.32
16.91
20.66
9.23
10.40
9.71
4.39
0.79
3.10
Max
9.42
1.76
7.93
7.77
6.53
7.64
12.31
3.66
6.63
Met
21.41
9.52
13.44
22.77
2.86
12.77
5.94
2.41
4.40
Reliance
8.47
2.89
5.85
5.67
0.95
4.63
7.05
0.31
5.84
Sahara
4.84
0.00
4.43
12.30
0.00
12.16
6.84
0.00
6.78
SBI
7.10
8.75
8.43
15.09
5.06
7.02
14.75
2.93
5.45
Shriram
19.19
0.00
19.19
24.27
0.00
24.21
19.73
0.00
18.25
Tata
24.82
6.33
17.69
27.78
9.09
22.33
12.93
8.12
11.88
LIC
1.56
0.01
1.26
1.33
0.01
0.97
1.21
0.01
0.92
Pvt Total 10.15
5.75
8.23
9.80
3.36
6.87
7.29
1.63
4.32
TOTAL
0.76
1.70
1.98
0.53
1.56
1.87
0.49
1.47
1.95
Total
Indivi
2009-10 Group
Total
dual
Indivi dual
148
Table 6.15: Death claim repudiated in benefit amount by all firms (Rs Crores) Individual 2007- 200808 09 Aviva 3.80 6.19 Bajaj 14.18 23.87 Birla 4.28 14.62 HDFC 2.76 3.24 ICICI 9.11 9.39 ING 2.26 5.59 Kotak 3.65 4.20 Max 5.75 9.55 Met 3.50 7.88 Reliance 1.99 4.09 Sahara 0.12 0.01 SBI 2.42 6.50 Shriram 1.43 3.39 Tata 6.66 5.08 LIC 90.54 72.45 Pvt 61.91 103.6 Total TOTAL 152.45 176.05
200910 4.57 25.79 26.48 5.89 10.98 1.58 3.61 18.16 18.48 15.80 0.25 5.93 4.10 12.03 80.36
Group 200708 0.10 0.48 0.37 0.00 0.02 0.02 2.57 0.23 0.60 0.63 0.00 11.70 0.00 1.40 0.15
200809 0.13 0.46 0.16 0.01 1.26 0.17 2.59 0.68 0.36 0.56 0.00 14.49 0.00 2.87 0.13
200910 0.03 0.20 0.25 0.02 1.99 0.17 1.23 1.37 0.91 0.16 0.00 12.17 0.00 1.68 0.28
Total 200708 3.9 14.66 4.65 2.76 9.13 2.28 6.22 5.98 4.1 2.62 0.12 14.12 1.43 8.06 90.69
200809 6.32 24.33 14.78 3.25 10.65 5.76 6.79 10.23 8.24 4.65 0.01 20.99 3.39 7.95 72.58
200910 4.6 25.99 26.73 5.91 12.97 1.75 4.84 19.53 19.39 15.96 0.25 18.1 4.1 13.71 80.64
153.65
18.12
23.74
20.18
80.03
127.34
173.83
234.01
18.72
23.87
20.46
170.72
199.92
254.47
.
149
Table 6.16: Percentage of claims repudiated to total claims in benefit amount by all firms
(Rs Crores)
2007-08
2008-09
2009-10
Individual
Group
Total
Individual
Group
Total
Individual
Group
Total
Aviva
23.96
1.98
18.64
20.01
1.96
16.83
14.41
0.26
10.63
Bajaj
13.02
1.14
9.72
11.23
0.96
9.34
7.30
0.29
6.17
Birla
11.22
2.06
8.28
19.62
2.03
17.94
20.34
2.21
18.89
HDFC
8.15
0.00
7.66
6.58
0.35
6.24
7.39
0.81
7.19
ICICI
15.09
0.27
13.48
7.34
4.78
6.90
4.42
5.19
4.52
ING
16.59
0.80
14.14
20.13
7.83
19.24
5.32
4.37
5.21
Kotak
14.58
13.87
14.28
16.37
11.04
13.82
5.31
3.10
4.49
Max
12.06
5.05
11.45
12.23
9.83
12.03
14.35
5.98
13.07
Met
24.22
3.22
12.40
24.31
1.69
15.34
24.12
3.29
18.59
Reliance
13.15
1.18
3.82
8.84
2.54
6.80
15.27
0.61
12.29
Sahara
6.52
0.00
6.28
0.28
0.00
0.27
3.52
0.00
3.51
SBI
7.17
7.26
7.25
10.56
8.25
8.85
5.74
6.63
6.31
Shriram
23.68
0.00
23.68
30.49
0.00
30.27
23.54
0.00
20.99
Tata
23.55
7.25
16.94
11.51
11.02
11.33
15.92
9.41
14.68
LIC
2.16
0.02
1.83
1.63
0.01
1.33
1.59
0.02
1.29
Pvt Total
13.98
5.14
10.06
12.54
6.42
10.65
10.59
4.42
9.11
TOTAL
3.30
1.64
2.97
3.34
1.73
3.01
3.60
1.23
3.12
150
Table 6.17: Top 5 in the lowest percentage of claim repudiated in benefit amount
Individual
2007-08
2008-09
2009-10
Insurer
Percentage
Insurer
Percentage
Insurer
Percentage
1
LIC
2.16
Sahara
0.28
LIC
1.59
2
Sahara
6.52
LIC
1.63
Sahara
3.52
3
SBI
7.17
HDFC
6.58
ICICI
4.42
4
HDFC
8.15
ICICI
7.34
Kotak
5.31
5
Birla
11.52
Reliance
8.84
ING
5.32
1
Sahara
0
Sahara
0
Sahara
0
2
HDFC
0
Shriram
0
Shriram
0
3
Shriram
0
LIC
0.01
LIC
0.02
4
LIC
0.02
HDFC
0.35
Aviva
0.26
5
ICICI
0.27
Bajaj
0.96
Bajaj
0.29
1
LIC
1.83
Sahara
0.27
LIC
1.29
2
Reliance
3.82
LIC
1.33
Sahara
3.51
3
Sahara
6.28
HDFC
6.24
Kotak
4.49
4
SBI
7.25
Reliance
6.8
ICICI
4.52
5
HDFC
7.66
ICICI
6.90
ING
5.21
Group
Total
Table 6.18 and 6.19 shown are the company wise total death claim pending in number of policies and their percentage to total death claim in policy number. The number of death claim pending was highest for LIC in individual and in total for all the years. Among private insurers, the number of death claim pending was highest for Bajaj, ICICI and again Bajaj in 2007-08 2008-09 2009-10 respectively for individual claim. SBI, 151
Bajaj and Max top in number of group claim pending for 2007-08, 200809 2009-10 respectively and was lowest for Sahara and Birla with 0.00 repudiation in all the years. In total, Bajaj stood as insurer with highest claim pending in policy number in the first two years then replaced by Max in 2009-10. However in terms of Percentage of death claims pending to total claims in number of policies, Sahara and Birla remained lowest claim pending insurers in all three years in group with 0 .00 percent claims pending. However interestingly, Sahara has got highest percentage of claim pending in 2007-08 and 2008-09 in individual as well in total category which was replaced by Sahara in 2009-10. The percentage of claim pending was shown decreasing over the years for private as well as the whole industry. All the insurers on an average have decreasing trend of percentage claim pending. Max was the only insurer whose percentage of claim pending was seen increasing over the years . Table 6.20 to 6.22 shows the total claim pending in benefit amount, its percentage to total claims and the top 5 insurers with lowest percentage of claim pending. LIC in public while Bajaj in private sector has got highest claim pending in individual category in all three years. Group –wise, Birla and Sahara were the two insurers with 0.00 amount of claim repudiated in all the three years. SBI remained on the top for claim pending in group category for the first two years which was substituted by LIC in 2009-10. In total, LIC was on top of the list of claim pending in whole industry while in private sector, SBI in 2007-08, ICICI in 2008-09 and Bajaj in 2009-10. In the life insurance industry, Sahara has highest percentage of claim pending in individual category in the first two years which was replaced by Shriram in 2009-10 as shown in table-6.21. In group category, Tata stood as highest death claim pending insurer in 2007-08 and 2009-10 while ING stood highest in 2008-09. 152
Table 6.18: Death claims pending at the end of the year in number of policies Company
2007-08 Individual Group
2008-09 Total
Individual Group
2009-10 Total
Individual Group
Total
Aviva
172
49
221.00
128
7
135
4
4
8
Bajaj
775
546
1321.00
524
955
1479
1527
373
1900
Birla
17
00
17.00
14
0
14
14
0
14
HDFC
310
8
318.00
210
2
212
161
1
162
ICICI
365
12
377.00
886
48
934
1053
41
1094
ING
281
12
293.00
216
33
249
112
8
120
Kotak
111
108
219.00
178
72
250
197
98
295
Max
5
00
5.00
87
10
97
1335
647
1982
Met
106
161
267.00
139
39
178
152
54
206
Reliance
4
7
11.00
287
44
331
334
34
368
Sahara
95
00
95.00
187
0
187
220
0
220
SBI
263
728
991.00
355
261
616
142
34
176
Shriram
165
0
165.00
287
0
287
475
12
487
Tata
322
424
746.00
298
98
396
306
96
402
LIC
14548
161
14709.00
13076
525
13601
9527
403
9930
Pvt Total
2991
2055
5046.00
3796
1569
5365
6032
1402
7434
TOTAL
17539
2216
20978.36
16872
2094
18966
15559
1805
17364
153
Table 6.19: Percentage of death claims pending to total claims in number of policies
Company 2007-08
2008-09
2009-10
Individual Group Total
Individual Group Total
Individual Group Total
Aviva
18.16
2.66
7.93
8.90
0.25
3.17
8.00
0.09
0.17
Bajaj
11.87
11.12
11.55 4.36
6.65
5.61
6.63
0.95
3.04
Birla
1.16
0.00
0.98
0.51
0.00
0.42
0.24
0.00
0.20
HDFC
15.03
4.40
14.17 7.25
0.93
6.81
4.20
0.35
3.93
ICICI
5.50
4.14
5.45
3.98
7.82
6.56
2.04
6.06
ING
29.83
4.74
24.52 14.18
21.57
14.86 5.82
3.25
5.52
Kotak
15.79
12.34
13.88 13.69
8.05
11.39 8.64
7.75
8.32
Max
0.21
0.00
0.17
2.11
2.20
22.18
5.63
11.32
Met
31.09
23.23
25.82 19.07
5.31
12.16 11.29
5.20
8.64
Reliance
0.39
0.78
0.57
4.19
6.97
1.78
3.45
Sahara
51.08
0.00
46.80 42.60
0.00
42.12 30.10
0.00
29.81
SBI
11.38
7.63
8.36
1.49
2.83
0.13
0.52
Shriram
41.67
0.00
41.67 36.28
0.00
36.19 40.74
12.77
38.65
Tata
19.35
40.65
27.56 11.04
8.82
10.39 8.76
9.75
8.97
LIC
2.52
0.11
2.05
2.21
0.24
1.67
1.41
0.19
1.11
Pvt Total
10.86
9.61
10.31 7.71
3.82
5.94
7.37
1.54
4.30
TOTAL
2.90
1.36
2.73
0.79
2.10
2.05
0.59
1.63
8.25
2.21
7.75
8.33
2.63
154
3.82
1.96
Table 6.20: Death claims pending in benefit amount at the end of the year (Rs Crores)
Company Individual
Group
Total
2007-
2008-
2009-
2007- 2008- 2009- 2007-
2008-
2009-
08
09
10
08
09
10
08
09
10
Aviva
5.07
2.36
1.77
0.15
0.01
0.02
5.22
2.37
1.79
Bajaj
15.27
14.21
32.39
2.10
3.97
2.06
17.37
18.18
34.45
Birla
2.79
0.77
0.67
0.00
0.00
0.00
2.79
0.77
0.67
HDFC
7.83
8.16
7.28
0.31
0.12
0.10
8.14
8.28
7.38
ICICI
6.91
16.90
19.50
0.41
2.74
2.27
7.32
19.64
21.77
ING
3.92
4.75
2.88
0.12
0.34
0.26
4.04
5.09
3.14
Kotak
1.62
5.25
8.45
2.61
2.19
2.88
4.23
7.44
11.33
Max
0.43
1.82
30.32
0.00
0.08
1.66
0.43
1.9
31.98
Met
5.50
7.40
17.54
5.07
1.14
0.78
10.57
8.54
18.32
Reliance
0.14
7.14
8.02
0.21
0.91
0.67
0.35
8.05
8.69
Sahara
1.06
1.82
2.13
0.00
0.00
0.00
1.06
1.82
2.13
SBI
5.36
8.08
4.98
19.79 9.61
1.16
25.15
17.69
6.14
Shriram
2.67
3.61
7.85
0.00
0.00
0.22
2.67
3.61
8.07
Tata
6.35
10.71
11.53
9.30
2.41
2.21
15.65
13.12
13.74
LIC
141.37 148.53 118.45 1.13
3.34
6.53
142.5
151.87 124.98
Pvt Total
64.92
TOTAL
206.29 241.51 273.76 41.2
92.98
155.31 40.07 23.52 14.29 104.99 116.5
155
169.6
26.86 20.82 247.49 268.37 294.58
Table 6.21: Percentage of death claims pending to total claims in benefit amount Company 2007-08
2008-09
Individual
Group Total
Aviva
31.97
2.96
Bajaj
14.02
Birla
Individual
2009-10 Group Total
Individual
Group Total
24.95 7.63
0.15
6.31
5.58
0.17
4.13
5.01
11.51 6.69
8.26
6.98
9.17
3.02
8.17
7.32
0.00
4.97
0.00
0.93
0.51
0.00
0.47
HDFC
23.12
14.42
22.60 16.57
4.23
15.90 9.13
4.03
8.97
ICICI
11.44
5.60
10.81 13.21
10.39
12.73 7.85
5.92
7.59
ING
28.78
4.78
25.05 17.10
15.67
17.00 9.70
6.68
9.35
Kotak
6.47
14.09
9.71
20.46
9.34
15.15 12.43
7.25
10.52
Max
0.90
0.00
0.82
2.33
1.16
2.23
23.96
7.25
21.40
Met
38.06
27.24
31.97 22.83
5.35
15.90 22.89
2.82
17.57
Reliance
0.93
0.39
0.51
15.42
4.12
11.77 7.75
2.54
6.69
Sahara
57.61
0.00
55.50 50.42
0.00
50.00 30.00
0.00
29.87
SBI
15.88
12.28
12.91 13.12
5.47
7.45
0.63
2.14
Shriram
44.21
0.00
44.21 32.46
0.00
32.23 45.06
10.43
41.32
Tata
22.45
48.19
32.89 24.26
9.25
18.69 15.26
12.37
14.71
LIC
3.38
0.15
2.88
0.33
2.79
2.35
0.54
2.00
Pvt Total
14.65
11.36
13.20 11.26
6.36
9.75
10.70
3.13
8.89
TOTAL
4.46
3.70
4.31
1.95
4.04
4.21
1.26
3.61
1.03
3.34
4.58
4.82
Overall, Sahara remained top for the first two years and then by Shriram in 2009-10 as insurer with highest percentage claim pending. As seen in table-6.22, LIC and Birla were among the top 5 insurers with lowest percentage of claim repudiated in individual category. In group, LIC, Sahara and Birla were among the top 5 insurers with lowest percentage of claim repudiated. Birla and LIC remained in top 5 lowest percentage of claim repudiated in total.
156
Table 6.22: Top 5 in the lowest percentage of claim pending in benefit amount
Individual
2007-08
2008-09
2009-10
Insurer
Percentage
Insurer
Percentage
Insurer
Percentage
1
Max
0.90
Birla
1.03
Birla
0.51
2
Reliance
0.93
Kotak
2.33
LIC
2.35
3
LIC
3.38
LIC
3.34
SBI
4.82
4
Kotak
6.47
Bajaj
6.69
Aviva
5.58
5
Birla
7.32
Birla
7.63
Reliance
7.75
1
Sahara
0.00
Birla
0.00
Birla
0.00
2
Shriram
0.00
Sahara
0.00
Sahara
0.00
3
Max
0.00
Shriram
0.00
Aviva
0.17
4
Birla
0.00
Aviva
0.15
LIC
0.54
5
LIC
0.15
LIC
0.33
SBI
0.63
1
Reliance
0.51
Birla
0.93
Birla
0.47
2
Max
0.82
Max
2.23
LIC
2.00
3
LIC
2.88
LIC
2.79
SBI
2.14
4
Birla
4.97
Aviva
6.31
Aviva
4.13
5
Kotak
9.71
Bajaj
6.98
Reliance
6.69
Group
Total
b) Status of grievances-life insurers The IRDA has established the in house Consumer Grievance Cell (CGC) since 2002 to protect the interest of policyholders‘ in every possible way. The CGC has been constituted with the sole responsibility of taking up the complaints of the insurance customers- irrespective of
157
the nature of the complaints-with the insurance companies and to ensure early action on the part of the insurers for their early resolution.
Table 6.23 shows the status of complaints or grievances for the life insurers. Over the years the percentage of complaints resolved was seen increasing on an average for the industry total. The percentage of complaint resolved by private total was always higher than public insurer except in 2008-09. This may be because private insurers were relatively new and do not have many complaints as the number of policies issued by them were also comparatively low. The number of complaint received by public insurer was greater than private total till 2006-07 but was reversed since 2007-08 onwards. The year to year percentage change in number of complaint was shown decreasing or negative over the years for LIC of India. The private insurers have shown a greater increase in the number of complaints over public insurers in all years except in 2008-09.
c) The performance of Ombudsman: For providing protection to Indian consumers against malpractices and gullible brokers who are out to fleece the customers by raking in quick profits, IRDA has appointed Ombudsman in 12 cities across India to specifically deal with Insurance Grievances and speedy disposal of such cases. Complaints received from policy holders, insurance intermediaries and various other sources against different life insurance companies are being registered and tracked by the IRDA as they provide significant inputs to the marketing practices and settlement procedures adopted by insurers. These complaints primarily pertain to issues relating adjustment of premium, policy servicing, payments of claims, mis-selling 158
and complaints received from the agents. If a policyholder is dissatisfied by the outcome or the decision taken by the Insurance Company, such an individual has the liberty to approach the Insurance Ombudsman as a last resort after exhausting various available options. Each Ombudsman has been empowered to redress customer grievances in respect of insurance contracts on personal lines where the insured amount is less than Rs. 20 lakh, in accordance with the Ombudsman Scheme. Table 6.23: Status of grievances-life insurers
Year
Insurer s
Outstandi ng at the beginning of the year
Grievan ces received during the current year
Total
Resol ved during the year
Outsta nding at the end of year
200405
Public Pvt Total Public Pvt Total Public Pvt Total Public Pvt Total Public Pvt Total Public Pvt Total
498 36 534 992 133 1125 1376 403 1779 197 102 299 685 332 1017 186 272 458
704 195 899 851 540 1391 354 507 861 651 1406 2057 481 1313 1794 606 1843 2449
1202 231 1433 1843 673 2516 1730 910 2640 848 1508 2356 1166 1645 2811 792 2115 2907
210 98 308 467 270 737 1533 808 2341 163 1176 1339 980 1373 2353 642 1870 2512
992 133 1125 1376 403 1779 197 102 299 685 332 1017 186 272 458 150 245 395
200506 200607 200708 200809 200910
159
% of complai nt resolve d to total complai nt 17.47 42.42 21.49 25.34 40.12 29.29 88.61 88.79 88.67 19.22 77.98 56.83 84.05 83.47 83.71 81.06 88.42 86.41
% change in number of complai nt ---53.33 191.34 75.58 -6.13 35.22 4.93 -50.98 65.71 -10.76 37.50 9.08 19.31 -32.08 28.57 3.42
Table 6.24: Performance of Ombudsman –life insurance Particular
Total complaints Complaints disposed % of complaint dissolved
2001-
2002-
2003-
2004-
2005-
2006
2007-
2008-
2009
02
03
04
05
06
-07
8
09
-10
1967
2481
3968
5567
5526
6021
6168
6143
9524
1506
1917
3289
5020
4925
5418
5778
5586
8636
76.56
77.27
82.89
90.17
89.12
89.99
93.68
90.93
90.68
(Total Complaint is the complaint outstanding at the end of the year plus complaints receive during the year.
Chart 6.1: Chart showing performance of Ombudsman-Life insurance industry
10000
Performance of ombudsment-Life
9000 8000 c o m p l a i n t s
7000 6000 5000 4000
Total no. of complaints
3000
No of complaints disposed
2000
1000 0
years
160
6. 3) Technology: Technology is one of the major domains of innovation. Where there is a lot of scope for innovation, it can clearly highlight the difference between leaders and follower. Historically the investment in technology by the insurance sector has been low. However in the last three years, this has changed with public and private players making investments in automation, connectivity, and business intelligence solutions. Indian insurance companies are now using information technology (IT) in one form or the other at various levels. Innovative development of centralized distributed solutions, e-channels and mobile computing technologies, to provide IT based operational services to customers; employees, agents as well as development officers, are some of the explored areas in the field of technology. Internets is becoming an important medium for marketing as well as for claims management and insurance companies are exploring similar innovative ways to ramp up customer services. IT adoption is taken up by the sales and distribution teams, customer service, marketing. Now the adoption of business intelligence, customer analytics, and other advanced tools are on the horizon. Imaging and workflow automation are also being widely adopted by many insurance companies to ensure better customer service, and facilitate better policy and claims processing. Mobile computing with the help of hand-held devices is another initiative that the insurance industry is fast adopting to improve their customer services, and to improve connectivity of the sales force even in the remote locations. While LIC has been in a completely different league when it comes to IT adoption; there are many other prominent insurance players who have had some very well IT success stories. Table 6.25 gives an overview of the insurance companies‘ operating expenses on information 161
technology. The expense of four company‘s viz. Shriram, HDFC, ING and LIC were not available so not included here. The average expense of 6 years (5 in case of not available) is calculated for each company. Bajaj followed by Reliance and then ICICI has highest spending in IT on an average.
Table 6.25: Information technology and related expenses, Rs. 000‘s 2004-05
2005-06
2006-07
2007-08
2008-09
2009-10
average
Aviva
NA
61970
95074
157593
151491
152017
123629
Bajaj
NA
110982
227693
532772
557559
542950
394391.2
Birla
22569
20367
32076
122717
168501
252917
103191.2
ICICI
NA
129232
232175
389950
264476
273010
257768.6
Kotak
41310
33254
51526
74144
107519
90890
66440.5
Max
NA
99322
150891
198676
252439
493792
239024
Met
17991
24615
87231
150291
108334
13783
67040.83
Reliance
71637
72757
160370
351871
584343
569117
301682.5
Sahara
xx
28328
28723
40781
49032
46175
38607.8
SBI
35025
89287
101727
188652
203303
212960
138492.3
Tata
63300
107935
109028
199745
446580
461793
231396.8
Source: operating expense schedule, L-6. Public disclosure of insurance companies (Operating expenses are for the year ended 31 March)
On the part of IRDA, IT infrastructure
facilities
have
been
significantly enhanced and exploring the possibility of use of IT in key areas of functioning in order to cater to the growing needs of the industry. In order to improve the current grievance redressal procedure in the insurance sector, IRDA has initiated
the process of development of
Integrated Grievance Management System (IGMS).This will ensure speedy settlement of genuine claims. IRDA has also set up Insurance Grievances Call Center (IGCC). The Call Center has been made operation from July 2010.This will enable nationwide consumer service 162
help line supported by back office activity and will also provide single window system to policyholders to lodge their complaints with their respective insurers.
6.4 -Marketing Strategies: The Rising Importance of Alternative Distribution Channels Marketing includes all those activities carried on to transfer goods and services from manufacturer to consumer. Marketing channels are sets of interdependent organizations involved in the process of making a product or services available to customers. Marketing mix is a unique combination of basic ingredients of marketing viz. product, price, and place i.e. channels of distribution and promotion. Insurance being a service business requires marketing department to play a key role in delivery of services. Private sector entry has brought with it a transformation not only in product choice but also in the way life insurance is distributed. Previously, agents provided the primary method of acquiring new policies in India as they originated almost all of the LIC's business. This trend is an Asia-wide phenomenon as most of the business of life insurers in Asia is originated by agents.
Distribution has a key role to play in enlarging a domain like insurance. Although tied agency style of distribution has been largely successful in the monopolistic regime, the introduction of brokers, corporate agents and other alternate channel distribution have also contributed to the growth achieved in the competitive era .Table 6.26 shows the new business acquisition through different channels of distribution. Individual agents still dominated to the contribution of new 163
business underwritten, but its share has decreased tremendously from 95 % in 2003-04 to 60 % in 2009-10. Among others, corporate agents including bank also give a large junk of business especially for private insurers.
Table 6.26- New Business underwritten (Life) through various intermediaries - in percent
Yea r
Life Insurers
Individua l agent
Corporate agent Bank Other s s
Broker s
Direct sellin g
Referral s
Total New busines s
200 304
Pvt T LIC Industry Total Pvt T LIC Industry Total Pvt total LIC Industry Total --
60.39 99.78 95.32
10.57 0.11 1.30
6.86 0.09 0.86
0.31 0.02 0.05
14.37 0.00 1.63
7.50 0.00 0.85
100 100 100
59.30 98.79 88.65
15.42 0.87 4.61
7.75 0.3 2.21
1.23 0.04 0.35
10.05 0.00 2.58
6.25 0.00 1.60
100 100 100
59.71 98.37 85.67
16.87 1.25 6.38
8.92 0.32 3.15
0.83 0.06 0.31
6.61 0.00 2.17
7.06 0.00 2.32
100 100 100
--
--
--
--
--
--
--
Pvt total LIC Industry Total Pvt total LIC Industry Total Pvt total LIC Industry Total
53.46 82.68 72.17
18.20 1.15 7.28
9.92 0.27 3.74
1.61 0.05 0.61
16.81 15.85 16.20
100 100 100
6.96 -2.51
47.44 76.90 65.45
19.19 1.56 8.41
9.54 0.55 4.04
1.96 0.37 0.99
21.87 20.63 21.11
100 100 100
8.05 0.03 3.14
41.85 71.14 60.91
22.02 1.17 8.46
9.34 0.46 3.56
3.16 0.37 1.34
23.63 26.86 25.73
100 100 100
6.57 0.12 2.37
200 405 200 506 200 607 200 708 200 809 200 910
164
6.5-Summary: As far as innovation in product is concerned, there is a trend of unit linked insurance product blooming in the wake of privatization. Both private and public insurers have shown an increasing trend of linked business throughout the years taken. The dominance of linked business over tradition product was more in private insurance compare to LIC. Concerning customer services, on average more than 80% of the complaints were dissolved by Ombudsman cell over the years taken. The percentage of complaint dissolved to total complaint in grievances cell was increasing over the years except in 2007-08. The percentage of complaint dissolved was more for private insurance companies compare to LIC. However the raise in the number of complaint was more in case of private insurers. Following claim data, the industry witnessed a claim pending to total claim in benefit amount at a percentage in the range of around 4% while claim repudiation ratio was around 3 % and the claim paid was at around 92 %. Percentage of death claims paid to total claims in number of policies for the industry stands around 95% while claim repudiation and claim pending was at approximately 2 % each. Indian insurance companies were using information technology in one form or the other at various levels. While LIC has been in a completely different league when it comes to IT adoption; there were many other prominent insurance players such as ICICI who have had some very well IT success stories . Looking at the expense of insurers in information technology, the average expense of Bajaj followed by Reliance and then ICICI was highest. On the part of IRDA, IT 165
infrastructure
facilities
have
been
significantly enhanced and
exploring the possibility of use of IT in key areas of functioning in order to cater to the growing needs of the industry. As far marketing is concerned, individual agents still dominated to the contribution of new business underwritten, but its share has decreased tremendously from 95 % in 2003-04 to 60 % in 2009-10. Among others, corporate agents including bank also give a large junk of business especially for private insurers.
166
CHAPTER VII CONCLUSIONS
The current reforms in Indian insurance sector have facilitated many functional changes over the past decade.
This study aims to
provide an assessment of deregulation with respect to industry scenario, concentration, efficiency, productivity and innovation in the Indian life insurance industry. A summary of the main points of the analysis is presented here along with suggestions for future research.
7.1 Summary of the Study The first chapter of introduction gives a general background for the study. It discusses theories, factors and circumstances that caused liberalization of the insurance industry. The concepts of liberalization and deregulation, purpose, methodology importance, limitations etc of the thesis are discussed here. The entry of private players in insurance was needful and justifiable in order to enhance the efficiency, density and penetration of insurance in the country and also for a greater mobilization of savings needed for long gestation infrastructure projects. So this thesis purposes to evaluate the impact of reforms in terms of growth and development and other functional changes in the sector. It also tries to find out how the reforms have benefited Indian life insurance industry. An overview of present market scenario is compared to that of pre liberalization market. The changes in market structure are examined in terms of market concentration. The efficiency as well as productivity of each of the insurers is calculated and analyzed improvement therein over the years. Other changes in terms of product range, service benchmark, technology innovations, and marketing strategies are also examined. The 167
different approaches of measurement are applied depending on the available data. The second chapter gives an overview of Indian life insurance Industry. Insurance sector of India is almost 193 years old since its first insurance company, the Oriental Life Insurance Company was started in 1818. It is now in the third phase of its existence. The first phase was before the nationalization of life insurance in 1956. At that point of time, there were more than 200 private life insurers including Indian Insurers, provident societies and non Indian insurers. In the second phase, the whole life insurance industry came under state monopoly as Life Insurance Corporation of India. During the monopoly regime of LIC of India, the number of policies, sum assured, and the annual premium received were seen increasing over the years. However, the insurance sector was liberalized in 2000 for various reason such as lack in depth, diversity and reach( geographically as well as in terms of insurable population), poor customer service and need for global dimension etc. In the third phase of liberalized regime, the insurance sector was seen to have many new entrants and made a robust growth in terms of huge volume of business underwritten by the companies and overall growth of the market. In addition, India is poised to experience major changes in its insurance markets as insurers operate in an increasingly and
deregulated
liberalized environment.
The third chapter of the thesis is fully devoted to the review of literature, covering some of the important studies on the topic of the research. Liberalization, deregulation, privatization and globalization of insurance sector have been the major trend worldwide in the last two decades. Outcomes of the study on deregulation and liberalization differ 168
from analysis to analysis. On an average, the studies reviewed shows that liberalization has positive impact on efficiency and productivity growth varying from country to country and the degree of liberalization. In the Indian context, a few studies have been made on the Indian insurance sector in the wake of liberalization. All these studies relating to Indian industry covered varying aspects such as emerging strategic and regulatory issues in light of liberalization, appraisal of industry development, structure, innovation etc. However Indian insurance is at the starting point of a long journey of liberalization and so more literature on this aspect is desirable and needed. Therefore, this study will strengthen the existing literature and help to understand the emerging market dynamics. The fourth chapter entitled ―Concentration‖ deals with the post liberalized market structure of Indian life insurance industry in terms of Concentration, penetration and density and the spread of insurance in rural areas. The change in structure of life insurance with the coming of private insurers and the intensity of changes is analyzed with CR1 (market share of largest insurer), CR4 (market share of 4 largest insurer), Herfindahl Hirschman index (HHI) as well as Entropy (E). Using 9 years data from 2001-02 to 2009-10, these indices are calculated taking market shares in terms of a) total premium b) equity share capital and c) total assets respectively for each year. In terms of total premium, with an HHIindex of 0.50 and entropy of 0.25 in 2009-10 and a CR4 of 84%, the concentration of the life insurance market can be qualified as very high. However, the degree of concentration was decreased substantially from HHI of 0.99 and entropy of 0.95 and CR4 of 99% in 2001-02. For the private insurers, the HHI and entropy are 0.11 and 0.08 in 2009-10.
169
In terms of equity share capital, with an HHI-index of 0.06 and entropy of 0.05 in 2009-10 and a CR4 of 36%, the concentration of the life insurance market can be considered as very low. Also, the degree of concentration has decreased slightly from HHI of 0.09 and entropy of 0.09 and CR4 of 47% in 2001-02. For the private insurers, the HHI and entropy were 0.6 and 0.05 in 2009-10 which means the degree of concentration is very low. In terms of total assets, with an HHI-index of 0.69 and entropy of 0.43 in 2009-10 and a CR4 of 92%, the concentration of the life insurance market can be termed as very high. However, the degree of concentration was decreased substantially from HHI of 0.99 and entropy of 0.97 and CR4 of 99.9% in 2001-02. For the private insurers, the HHI and entropy were 0.12 and 0.09 in 2009-10. To sum up, the concentration was declining in all the three variables viz. premium, equity share capital and total assets. But the market shows high concentration even now on indicators of total premium and total assets. The detail is provided in table 7.1. Table 7.1: Summary of CR4 (%) in terms of total premium, equity share capital and total assets Years Number of firms Premium Equity share capital Total assets
2002
2003
2004
2005
2006
2007
2008
2009
2010
12
13
13
14
15
16
18
22
23
99.84 99.27 98.06 95.83 94.21 92.22 88.75 85.88 84.44 47.51 48.47 48.30 48.04 48.41 44.36 41.42 38.57 36.86 99.55 99.5
99.34 98.71 97.97 96.8
95.23 94.02 92.12
Though the life insurance penetration and density has increased over the years from 1.39 and 6.1 in 1999 to 4.60 and 47.7 in 2009, these were 170
lower than that of Asia and world. In terms of rural penetration, the share of rural business in total volume of insurance business was still low in India. LIC of India, which is the dominant player in life insurance sector, has issued only 26.39% of its new policies in rural areas and the sum assured was 18.84% in 2009-10. This was far less than the 57.50 % of new policies out of total policies issued in 1999-00.
The fifth chapter relates to efficiency and productivity of life insurance industry. DEA analysis provides evidence of improvement in firm level as well as industry efficiency over the years taken. The efficiency scores of 12 to15 Indian life insurers have been estimated from the year 2001-02 to 2009-10. SBI and LIC were the only two insurers which remained efficient throughout the years, in terms of CRS VRS and SE. CRS as well as VRS efficiency has almost doubled in the time period taken i.e. from 2001-02 to 2009-10. Thus it suggests that liberalization has contributed to efficiency gains of firms over the years. The MPI and it two components, technical efficiency change (TEC) as well as technical change (TC) were estimated for the insurers. Only 4 insurers could have shown improvement in average productivity while 2007-08 no insurers could make productivity improvement. However as a healthy sign, 14 out of 15 insurers showed technical efficiency improvement in 2009-10. In a given year, insurers have either improved efficiency or deteriorated efficiency. The exception was SBI and LIC which maintained efficiency with technical progress. There was technical efficiency improvement over the sample period. The mean technical efficiency ranged between 0.33 in 2001-02 and 0.76 in 2009-10. The average pure technical efficiency during the years improved from 0.35 in 2001-02 to 0.81 in 2009-10 while the mean 171
scale efficiency ranged between 0.96 in 2001-02 and 0.94 in 2009-10. Pure technical efficiency reflects the efficiency of resource allocation and internal management, whereas scale efficiency reflects whether or not the firm is operating at the optimal scale . The industry has scope for improvement in both pure technical and scale efficiency.
Table 7.2- Summary of average efficiency and productivity of Indian life insurance industry. Year
2001-
2002-
2003-
2004-
2005-
2006-
2007-
2008-
2009-
02
03
04
05
06
07
08
09
10
No. of firms
12
13
13
14
15
15
15
15
15
TE(CRS)
0.33
0.37
0.45
0.52
0.63
0.64
0.64
0.63
0.76
PTE(VRS)
0.35
0.39
0.46
0.53
0.72
0.73
0.76
0.73
0.81
SE
0.96
0.97
0.98
0.98
0.90
0.89
0.86
0.87
0.94
MPI
--
0.87
1.08
1.00
1.15
0.95
0.92
0.88
0.99
TEC
--
1.33
1.44
1.36
1.41
1.09
1.03
1.01
1.22
TC
--
0.67
0.81
0.83
0.88
0.92
0.91
0.91
0.81
(Note-MPI and its components compare changes across two-year periods ending in the indicated year.)
Table 7.3- Summary of distribution of insurers by level of efficiency. Year Number of firms TE 1> 0.90 0.90>0.75 PTE 1> 0.90 0.90>0.75 SE 1> 0.90 0.90>0.75
200102 12 2 0 3 0 9 0
200203 13 2 0 2 1 12 0
200304 13 2 0 2 1 12 0
200405 14 2 1 3 1 13 0
200506 15 4 1 6 1 13 0
200607 15 4 1 6 1 12 0
200708 15 3 1 5 2 9 3
200809 15 3 0 5 1 10 2
In terms of TE, out of 12 insurers only two insurers were having efficiency more than 0.90 in 2001-02 but this was increased to 5 firms in 172
200910 15 5 3 6 4 12 2
2009-10. In case of PTE, the number of efficient insurers was 6 in 200910. The number of insurers having the scale efficiency more than 0.90 was 12 in 2009-10. If the Insurers with efficiency more than 0.90 can be considered as efficient, the number of efficient insurers has increased significantly over the period. The sixth chapter relates to innovation in the industry. The liberalization of insurance has augured well for the sector witnessing introduction of new products in recent years. Almost 50 % of the companies were offering around 10 products per year on an average. The number of product available in the market has increased tremendously in the wake of liberalization. The sale of traditional life insurance products such as individual, whole life and term insurance remained popular but sale of new products such as single premium, unit linked, retirement products and annuity products are on the rise. Both private and public insurers have shown an increasing trend of linked business throughout the years taken. However, the dominance of linked business over traditional product was more in private insurance compare to LIC. In case of private insurers, the percentage of linked business increased from 46.82 percent in 2005-06 to 76.38 in 2009-10 and that in case of LIC is 2.10 to 17.76 The percentage of non- linked business to total business was decreasing over the years. Regarding customer services, on an average more than 80% of the complaints were dissolved by Ombudsmen cell for these years. The percentage of complaints dissolved to total complaints in grievances cell was increasing over the years. The percentage of complaint dissolved was more for private insurance companies compare to that of LIC. However the rise in number of complaints is more in case of private insurers.
173
Indian insurance companies are using information technology in one form or the other at various levels. The average expense of Bajaj on information technology was highest followed by Reliance and then ICICI. The use of the Internet to distribute life insurance products has only emerged recently and has not made a significant impact so far, partly because of the substantial advisory component of most life insurance products. Private sector entry has brought with it a transformation in the way life insurance is distributed. Brokers, corporate agents and other alternate channel distribution have come up and made their share of contribution to the growth. Individual agents still dominated to the contribution of new business underwritten, but its share has decreased tremendously. For the private sectors a large junk of business was written through corporate agents including bank. It is difficult at this stage to analyze the determinant variables for performance of life insurers. However the performance in terms of Technical efficiency can be attributed to market share in terms of premium as well as total assets and number of product launched. LIC remained at top spot in efficiency and market share of both premium and total assets. SBI which was efficient throughout the years, also have good share of premium and total assets share. ICICI was also more efficient relative to other private insurers in all the years and its market share and number of new products offered was also relatively higher than other private insurers. Birla, ICICI, LIC, HDFC and SBI were the insurers which have relatively better performance than others in all three variables examined.
174
In terms of their claim history also, Birla, ICICI, LIC, HDFC and SBI were the insurers which remained among the top 5 in of the highest percentage of claim paid in benefit amount, lowest percentage of claim repudiated in benefit amount and the lowest percentage of claim pending in benefit amount over the three. They were never at the bottom of these indicators in the three years ranking (2007-08 to 2009-10) except HDFC at the 5th bottom in 2008-09 for the lowest percentage of claim repudiated in benefit amount. In sum, the results of this study support the preposition that the reforms in the insurance sector contributed to the overall development of this sector. It has enhanced competition as indicated by the fall in concentration ratios and increased insurance density and penetration. It has provided an option to the customers to choose firms, products and riders. The firm level as well as industry level efficiency and productivity have improved. However, the Indian life insurance sector needs to improve further. The impact of increased competition is yet to be fully realized on insurance penetration in rural areas. The huge potential of the life insurance market development in rural areas is still under developed. The efficiency and productivity of private life insurers are still lagging behind that of LIC. It is likely that new entrant tend to show lower efficiency because of their higher initial cost. Though the ULIPs and single policies have contributed to high growth of life insurance over the years after liberalization, they are short term products, have low margin and have a very low proportion of risk coverage. More innovation in product market with transparency in dealing and specially claim settlement, building trust are major challenges for life insurers. Moreover, alternate distribution channels like bancassurance are under experiment and are still 175
unexplored in sale of products. Though the popularity of insurance has increased manifold, lack of customer education is also one of the major obstacles in development of insurance market. There is strong need to educate rural and semi urban masses on the need for security that protects their livelihood. The IT used in insurance industry is mostly related to Knowledge Management, Document Management System and Workflow Automation. Enhancing consumer awareness will help the life insurers to initiate, expand and sustain the business diversification by rural –urban segment, type of policies and supply chain management. 7.2 Directions for Future Research For future research, a detailed analysis of the relationship between efficiency, and the structure and performance of the life insurance should be tested. More research needs to be done to examine the channels through which concentration and the competitiveness of the financial system impact stability. Further studies on efficiency should also be conducted employing different output definition, data from different sources and different measures of efficiency such as allocative, cost, revenue and profit efficiency. Impact of liberalization and deregulation on life insurance can also be compared with that in general insurance.
Actuarial, IT, reinsurance, healthcare and pharmaceutical
sector are the other areas of insurance which need more research for their development and policy implication.
APPENDICES
176
Table A.1- Total life insurance premium (Rs. Crore) Insurers LIC ICICI Max HDFC Birla Tata SBI Kotak Bajaj ING V Met Reliance Aviva Sahara Shriram Bharti Future IDBI Canara H DLF Pra. Aegon R Star U India first Pvt total Total
2001-02 49821.91 116.38 38.95 33.46 28.26 21.14 14.69 7.58 7.14 4.19 0.48 0.28 -----------272.55 50094.46
2002-03 54628.49 417.62 96.59 148.83 143.92 81.21 72.39 40.32 69.17 21.16 7.91 6.47 13.47 ----------1119.06 55747.55
2003-04 63533.43 989.28 215.25 297.76 537.54 253.53 225.67 150.72 220.8 88.51 28.73 31.06 81.5 ----------3120.35 66653.78
2004-05 75127.29 2363.82 413.43 686.63 915.47 497.04 601.18 466.16 1001.68 338.86 81.53 106.55 253.42 1.74 ---------7727.51 82854.8
2005-06 90792.22 4261.05 788.13 1569.91 1259.68 880.19 1075.32 621.85 3133.58 425.38 205.99 224.21 600.27 27.66 10.33 --------15083.55 105875.8
177
2006-07 127822.8 7912.99 1500.28 2855.87 1776.71 1367.18 2928.49 971.51 4302.74 707.2 492.71 1004.66 1147.23 51 181.17 7.78 -------27207.52 155030.4
2007-08 149790 13561.06 2714.6 4858.56 3272.19 2046.35 5622.14 1691.14 9725.31 1158.87 1159.54 3225.44 1891.88 143.49 358.05 118.41 2.49 11.9049 -----51561.42 201351.4
2008-09
2009-10
157288.04 15356.22 3857.26 5564.69 4571.80 2747.50 7212.10 2343.19 10624.52 1442.28 1996.64 4932.54 1992.87 206.47 436.17 360.41 152.60 318.97 296.41 3.37 31.21 50.19 -64497.41 221785.45
186077.31 16531.88 4860.54 7005.10 5505.66 3493.78 10104.03 2868.05 11419.71 1642.65 2536.01 6604.90 2378.01 250.59 611.27 669.73 541.51 571.12 842.45 165.65 38.44 530.37 201.60 79373.05 265450.36
Table A.2- Equity share capital of life insurers (Rs Crores) Insurers LIC ICICI Max HDFC Birla Tata SBI Kotak Bajaj ING V. Met Reliance Aviva Sahara Shriram Bharti Future IDBI Canara H DLF Pra. Aegon R Star U India First Pvt total Total
2002 5 190 250 168 150 185 125 101 150 110 110 125 ------------1664 1669
2003 5 425 255 218 180 185 125 131.3 150.03 170 110 125 154.8 ----------2229.13 2234.13
2004 5 675 346.08 255.5 290 231 175 151.26 150.07 245 160 160 242.8 157 ---------3238.71 3243.71
2005 5 925 466.08 320 350 321 350 211.76 150.07 325 235 217.1 319.8 157 ---------4347.81 4352.81
2006 5 1185 557.43 620 460 447 425 244.58 150.23 490 235 331 458.7 157 125 1.10 -------5887.04 5892.04
178
2007 5 1312.3 732.43 801.26 671.5 547 500 330.35 150.37 690 530 664 758.2 157 125 150 -------8119.41 8124.41
2008 5 1401.11 1032.43 1271 1274.5 870 1000 480.27 150.71 790 761.08 1147.7 1004.5 232 125 366.11 185 200 ----12291.41 12296.41
2009 5 1427.26 1782.43 1796 1879.5 1519.5 1000 510.29 150.7 1019.15 1580 1160.43 1491.8 232 125 668.43 468.5 450 400 137.05 300 150 -18248.04 18253.04
2010 5 1428.14 1838.82 1968 1969.5 1920.5 1000 510.29 150.71 1019.15 1774.79 1164.65 1888.8 232 125 1131.35 702 450 500 221.3 570 250 200 21015 21020
Table A.3-Total assets of life insurers (Rs Crore) Insurers
2001
2002
2003
2004
2005
LIC
19251464
24265960
29053996
36571500
43744027
ICICI
14551
25281
77096
181195
Max
9534
20737
20859
HDFC
17571
18627
Birla
12167
2007
2008
2009
2010
55269791
64622370
79899279
86086308
113931839
412690
922549
1676194
3014625
3407870
5838374
34493
59911
106555
212636
417509
636080
1090128
31568
55301
108773
295124
539036
968898
1142462
2138664
15156
23488
73864
143950
271636
432692
746151
989855
1691763
Tata
17068
19086
36337
71124
141089
246165
421745
549643
995052
SBI
14490
21404
46264
116738
229209
499579
1047788
1502000
2957970
Kotak
14311
15791
24988
64942
123171
196142
327828
422580
696421
Bajaj
15313
21853
35369
109300
373296
730625
1419079
1786681
3414451
9849
13225
20062
67450
91988
150729
242135
309884
503896
Met
11046
10396
14449
21560
35736
93515
213173
329898
629042
Reliance
12836
10521
12600
21220
48723
147123
442999
691698
1433827
14188
21949
40467
97523
187236
320513
415076
681500
15868
18178
22806
43571
53156
92016
14411
30259
61601
82800
151404
1517.58
15166
32311
48448
108825
949.16
16617
41055
71693
20759
72790
131300
Canara H
62762
146355
DLF Par
10755
14688
Aegon R
17551
36841
Star union
23093
104577
ING V.
Aviva Sahara Shriram Bharti
2006
Future IDBI
IndiaFirst Sum
55447 19305287
24440674
29333471
37128371
44998020
179
58040496.6
69803222
89656581
98682445
136916073
Table A.4: Values of input variables viz. commission expense and operating expense (in lakh)
Company 2001-02 CE* Lic 451791 Aviva 0 Bajaj 235 birla 440 Hdfc 662 Icici 1447 ING 135 Kotak 181 Met 16 max 1186 reliance 7 Sbi 19 tata 572 Sahara sriram
OE 426040 0 2511 4816 4126 8485 2312 3698 653 8488 1123 1127 4038
2002-03 CE 499861 210 1242 2951 1977 3776 645 761 167 1849 167 187 1480
2005-06 CE 709492 10317 34187 15964 12033 28339 6913 5912 4050 13447 1433 6969 13755 379 358 *CA: Commission Expense and OE: Operating Expense OE 462109 4841 6672 8907 6973 17383 5775 6138 3044 11194 3398 2330 6353
2003-04 CE 573384 1936 5044 7713 3871 9562 1993 1920 673 4028 547 945 4158
OE 504233 9950 13237 14512 9817 28728 9891 8984 4465 16273 5219 5735 11504
2004-05 CE 624517 4593 14584 12922 7309 17796 4107 3890 1449 6509 787 2339 8994 66
OE 598718 14357 21439 17744 23075 46151 14649 11133 9538 24641 7680 12456 19802 177
Table- A.4 continue
company 2006-07 CE Lic 916907 Aviva 17886 Bajaj 94668 birla 20138 Hdfc 20993 Icici 52551 ING 9417 Kotak 8020 Met 10505 max 22852 reliance 9877 Sbi 19597 tata 19124 Sahara 668 sriram 3604
OE 708584 42749 107302 37587 57674 152296 30353 24031 23197 51370 42904 32238 35702 1542 2448
2007-08 CE 956810 21797 149686 33555 35126 81097 10555 15511 26629 38446 28969 36535 22892 2055 4478
OE 830932 66973 200434 67073 101298 291994 40370 42487 42661 88054 101685 48696 70252 2373 5090
180
2008-09 CE 1003324 15196 105155 48179 42489 69999 11038 22543 34956 39158 59691 46788 23978 2415 5599
OE 906429 77390 187579 114633 176007 273873 46392 60767 63290 160896 192297 62050 107119 3973 6782
2009-10 CE 1211031 15829 96257 51620 52550 60297 12076 16792 29251 42121 62785 75825 28085 2368 6649
OE 1224582 71019 177163 132675 150904 256915 46727 57384 68199 150439 163673 66090 102631 3700 12399
OE 604156 25498 48681 24393 39849 72500 21083 13408 16157 33932 11593 18996 29078 1121 659
Table A. 5: Values of output variables viz. premium and benefit paid (in lakh)
Company Lic Aviva Bajaj birla Hdfc Icici ING Kotak Met max reliance Sbi tata Sahara Sriram
2001-02 P* 4982191 0 714 2826 3346 11637 419 758 48 3895 28 1468 2114
BP 1747664 0 0 30 3 65 0 0 0 67 0 0 123
2002-03 P 5462849 1347 6917 14392 14883 41762 2116 4032 791 9659 647 7239 8121
BP 2053039 6 36 102 55 316 29 21 18 249 9 274 367
2003-04 P 6316760 8150 22080 53754 29776 98928 8851 15072 2873 21525 3106 22567 25353
BP 2392375 77 278 772 270 816 96 408 54 1164 50 2145 852
2004-05 P 7512729 25342 100168 91547 68663 236382 33886 46616 8153 41343 10655 60118 49704 174
BP 2844045 522 5651 3303 1572 10120 260 456 350 1242 695 4636 2282 0
2005-06 P 9079222 60027 313358 125566 156991 426105 42538 62185 20599 78813 22421 107532 88019 2766 1033
Table A.5 continue
compan y Lic Aviva Bajaj birla Hdfc Icici ING Kotak Met max reliance Sbi tata Sahara sriram
2006-07 P 1278228 4 114723 534524 176617 285587 791299 70720 97151 49271 150028 100466 292849 136718 5100 18417
BP 532864 6 7092 69854 12484 17454 72750 5051 17317 2077 8337 7891 14006 8209 157 176
2007-08 P 1497899 9 189188 972531 325713 485856 1356106 115887 169114 115954 271460 322544 562214 204635 14349 35805
BP 565503 3 18031 85140 42968 50146 201487 8958 26255 3465 13601 16242 35085 11218 527 382
2008-09 P 1572880 4 199287 1062452 446944 556469 1535622 144228 234319 199664 385726 493254 721210 274750 20647 43617
BP 524781 4 20116 75651 64644 68127 220656 13371 24304 7669 22082 15553 39675 12120 618 1276
2009-10 P 1860773 1 237801 1141971 550566 700510 1653188 164265 286805 253601 486054 660490 1010403 349378 25059 61127
* P: Premium and BP: Benefit Paid
181
BP 791306 6 63094 263020 113878 133789 720999 24544 49668 18573 58917 69342 85144 32458 1483 6595
BP 3392711 1755 65348 7379 4483 20947 3034 4197 597 4254 3279 8243 4738 22 0
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