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Efficiency Analysis Of Private Life Insurers In India Business

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After the gap up of the insurance industry to the private participants in 2000, there has been a haste to come in the concern which is pertinent from the figure of rivals that we presently see in the industry. This research makes an analysis of the public presentation of the private life insurance sector as a whole and the single participants in peculiar. To be specific, the paper aims at understanding the efficiency degrees by doing computations for proficient efficiency ( TE ) , pure proficient efficiency ( PTE ) and scale efficiency ( SE ) by using Data Envelopment Analysis.

The full population in the industry has been studied sing the information period from 2001-02 to 2011-12. The consequences of output-oriented DEA theoretical account on the two-input two-output instance shows that there is an huge range for betterment in the industry in order to raise the overall industry public presentation. SBI Life has been found to be the lone participant executing systematically good in all facets of efficiency.

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Keywords: India, Private Life Insurers, Efficiency Analysis, DEA

1. Introduction

There have been several reforms that have taken topographic point in the life insurance industry in the post-independence period get downing with the transition of the Life Insurance Corporation Act, 1956. The present position of the industry is a consequence of the enterprises taken by the Indian authorities after 1991 to transport out economic and fiscal reforms. With origin of the New Economic Policy in 1991 and the induction of reforms covering the banking sector and the capital market, the authorities felt the demand to convey about alterations in the insurance industry every bit good because it was understood that the function of insurance was far manner behind its possible.

The beginning of reforms procedure started in April 1993 when the Government of India appointed an Eight-member Committee on Reforms in the Insurance Sector ( popularly known as the Malhotra Committee ) headed by former Finance Secretary and RBI Governor R.N. Malhotra. It was given the duty to analyze the construction and bing ordinance of the insurance industry, to measure its strengths and failings so that it can function as an effectual instrument for the mobilisation of fiscal resources and to propose reforms in the changed economic environment.

In 1994, the Committee submitted its study which proposed several alterations, following which a radical alteration was introduced in 1999 with the transition of IRDA Act. Since so, competition has improved drastically with a significant addition in the figure of private life insurance companies from 4 in 2000-01 to 24 in 2011-12. In position of this development in the industry, it is felt necessary to find the efficiency degree of the industry and single participants because sustainability of this sector will depend upon the productiveness of the participants.

2. Literature Reappraisal

There have been several surveies made abroad to analyse the life and non-life insurance industry. In comparing to that figure, the articles to analyze this facet of public presentation in Indian insurance industry is really limited. However, there are some surveies which have focused on analysing the different public presentation facets of LIC. Some of the literatures reviewed by the research worker are given below:

Foreign Surveies:

Lee and Kim ( 2008 ) measured, analyzed and decomposed the comparative efficiency of the Korean life insurance companies. The research covered 22 registered insurance companies covering informations for 2006 merely. They used the DEA ( both BCC and CCR ) , Slack-based step and the Super-efficiency theoretical accounts to analyse the informations. Their consequences showed the mean BCC, CCR and SBM efficiency tonss to be 0.988, 0.961 and 0.892 severally. In footings of returns to scale, the figure of companies under increasing, diminishing and changeless returns to graduated table was three, seven and twelve severally. A farther in-depth analysis of the consequences showed that 12 insurance companies were both technically and scale efficient. To farther discriminate between the DMUs achieving a mark of one, they applied the super-efficiency theoretical account.

Tone and Sahoo ( 2005 ) applied DEA to analyse the cost efficiency and returns to graduated table of Life Insurance Corporation ( LIC ) utilizing clip series informations. The information set covered a period of 19 old ages from 1982-83 to 2000-2001. The consequences show that there existed heterogeneousness in cost efficiency over the period of survey. Though, it was seen that public presentation deteriorated after 1994-95 chiefly due to allocative inefficiency originating from the modernisation measures taken by LIC, there was a important addition in efficiency in 2000-01.

Qiu and Chen ( 2007 ) used the DEA attack to find the efficiency degree of Chinese life insurance companies which showed a uninterrupted diminution during the survey period 2000-03 ; the mark ranged from 0.49 to 0.64. A comparing of the consequences of Chinese and international insurance companies showed that the latter fared ill in regard of proficient efficiency tonss. The cause behind inefficiency of the Chinese insurance companies was both PTE and SE, but for the international insurance companies PTE did non play a really down drawing consequence.

Indian Surveies:

Bawa and Ruchita ( 2011 ) examined the proficient efficiency of general insurance companies engaged in wellness insurance concern in India by using DEA. They considered informations of 10 general insurance companies for the period 2002-03 to 2009-10 including four public sector insurance companies. Some of the cardinal consequences were as follows: First, New India Assurance Company Limited and National Insurance Company Limited were the two to the full efficient insurance companies. During the ulterior one/two old ages, nevertheless, they showed an efficiency degree of less than 1 in each of those old ages. Second, they found that in about all the old ages at least one or even two public sector participants lay on the efficient frontier. However, in the ulterior old ages of the survey period, none of the public sector participants showed perfect efficiency which could be attributed to diminishing returns to scale because of entry of private participants. In the 3rd portion of the analysis, consequences showed that the average proficient efficiency of the private participants was on the rise ( from 0.062 in 2002-03 to 0.776 in 2009-10 ) in contrast to the falling tendency observed in the instance of the public sector participants ( from 0.878 in 2002-03 to 0.661 in 2009-10 ) . In the last portion of the analysis, the research worker pointed out that the overall average proficient efficiency of all insurance companies increased from 0.389 in 2002-03 to 0.730 in 2009-10.

Bedi and Singh ( 2011 ) studied the life insurance industry during the pre and post-deregulation period. They specifically studied the Life Insurance Corporation of India ( LICI ) and came to the decision that the industry every bit good as the populace sector participant were turning at a fast gait. The application of the bipartisan ANOVA technique showed the being of important difference in the public presentation of LICI and the private participants over the informations period 2001-02 to 2007-08. Furthermore, application of the ANOVA technique and t-test showed a important alteration in the investing scheme of the populace sector life insurance company.

Das ( 2012 ) looked into the position of life insurance industry in North-Eastern India. The focus country was on analysing the public presentation of LIC and to measure the market portion in that part along with understanding the selling schemes adopted by it and the challenges that it faced in the part.

Rajendran and Natarajan ( 2009 ) discussed the overall public presentation of LIC by doing a comparing between the pre and post-liberalisation period. Furthermore, they studied the current position of the participant and highlighted the challenges that the populace sector giant faced.

Sinha and Chatterjee ( 2007 ) highlighted the growing of the Indian insurance industry. In the ulterior portion of the survey, they analyzed the cost efficiencies of the life insurance companies which included LIC and the private participants. The analysis of informations for the period 2002-03 to 2006-07 suggested an incompatibility in the tendency of cost efficiency. In the initial four old ages at that place was an upward tendency after which it changed.

Tiwari and Yadav ( 2012 ) analyzed the Indian life insurance industry by utilizing 10 old ages ‘ informations from 2001 to 2010 to understand the impact of liberalisation on the sector. The variables considered for the analysis included entire premium income, entire income, market portion and figure of policies. Harmonizing to the writers, though the competitory force per unit area eroded the market portion of LIC, the trade name still dominated the head of the Indian consumers and it continued to stay the most sure trade name in the post-liberalized period.

3. Importance of the Study

From the literature that we surveyed, the research worker found no such survey either Indian or foreign that evaluated the public presentation of all Indian private participants sing a information period of around 10 old ages. There was one survey on the private participants but the sample size and the information period was found to be little. There was another paper entirely on the Life Insurance Corporation of India where the cost efficiency facet was studied by the writers. Hence, the research worker found a range of making research on this sector and analyzed the efficiency facet of the participants.

There is no other old survey which has studied the life insurance sector in the state utilizing such a comprehensive set of informations. The importance of the paper lies in the fact that it analyses the efficiency degree of the private insurance companies who are turning at a phenomenally fast gait to cognize the existent strength in the private sector as a whole.

4. Research Methodology

4.1 Aims of the Survey: The chief intent of the survey is to capture an overall thought about the efficiency degree and the operating graduated table of the overall industry and the private participants.

4.2 Sample choice: For the intent of this survey, all the private life insurance companies runing as at the terminal of March 2011-12 have been considered.

Data beginnings and informations period: The research is based on secondary informations collected from the IRDA Annual Reports.

4.4 Research tools used: In order to find comparative efficiency tonss of the private participants, a non-parametric trial like the DEA ( in this instance an output-oriented DEA theoretical account ) is used. In simple footings, this attack uses the maximization linear programming technique to make the efficient frontier. The house ( s ) which lies on the frontier are ( is ) considered to be the “ best-practice ” house ( s ) against whom the comparative efficiency degree of the other houses is determined. The unit which is found to be comparatively most efficient secures a mark of 1 ( or 100 % ) and the others get values between 0 and 1 ( or between 0 % and 100 % ) . The overall efficiency is farther divided into Pure Technical Efficiency ( PTE ) , Scale Efficiency ( SE ) and Technical Efficiency ( TE ) . For finding of the different efficiency consequences, the CCR and BCC theoretical accounts are used.

Variables used for the survey: For the intent of this survey, two variables under inputs and end products are considered. The pick of variables is based on the survey of literature. In this respect, it is, to be understood that the variable pick depends upon the information available from different relevant studies. For the intent of this research, the intermediation attack is used. The inputs used in the research are committee and operating disbursals whereas the end products include net premium and benefits paid.

After the choice of the variables, their nominal values have been deflated to the basal twelvemonth 2001-02 utilizing the Consumer Price Index ( CPI ) thereby transforming them into Deflated Commission ( DEFLCOMM ) , Deflated Operating disbursals ( DEFLOPEX ) , Deflated Net premium ( DEFLNP ) and Deflated Benefits paid ( DEFLBP ) . In order to prove whether DEA can be applied on the chapfallen set, the isotonicity trial is carried out.

Table -1 Testing for Isotonicity of the Input and Output Variables

Beginning: Computed by the writer

Since, the input and end product variables are significantly positively related, it implies the fulfilment of the trial for isotonicity.

In add-on to the above, the other two pollex regulations given by Cooper et. Al. ( 2007 ) that were kept in head during variable choice include:

n a‰? P x Q, where N is the figure of DMUs, P is the figure of inputs and Q is the figure of end products, and

R = 3 ( p+q ) , where R is the entire figure of observations.

4.5 Restrictions of the survey: Data handiness was a job, since merely Annual Reports ( with informations in amalgamate signifier ) are available from the IRDA web site. A better image can be drawn if a elaborate break-up of the figures are obtained.

5. Analysis and Findingss

( I ) Efficiency analysis ( mention to Figure1 ) : The overall consequences of the private sector throw an feeling that there is huge range for betterment. The mean proficient efficiency of the sector during the last 11 old ages is found to be about 58 % merely, thereby connoting that there is a range for betterment to the extent of 42 % . It is observed from the chart that after the first seven old ages where there was a uninterrupted increasing tendency, the efficiency per centum showed a diminution during the initial old ages of the sub-prime mortgage crisis after which it revived once more.

In footings of pure proficient efficiency ensuing from managerial ability and resource allotment, it is observed that over clip at that place has non been much fluctuation with the mark during the old ages lying in the scope of 65 % – 76 % .

In comparing to these two facets, the scale efficiency consequences showed considerable betterment ; from around 65 % degrees towards the beginning of the decennary, the mark went up to about 90 % towards the terminal of the survey period.

The overall mean mark for the three classs of efficiency shows that the private sector is rather scale efficient with the mean transcending 80 % . On the other manus, proficient efficiency and pure proficient tonss averaged 58 % and 70 % severally.

From the consequences obtained, it can be observed that during the first three old ages, both managerial and scale inefficiency contributed about every bit to the overall inefficiency. However, it is noted that after that inefficient managerial public presentation led to a drastic autumn in the overall efficiency mark.

Figure-1: Average Efficiency Scores of the Private Players

( all figures are in % )

Beginning: Computed by the writer

( two ) The tabular array below ( No. 2 ) high spots the state of affairs in the private life insurance sector with respect to the figure of decision-making units ‘ ( DMUs ‘ ) on the footing of their efficiency degrees. The focal point of the tabular array is to acquire a distribution of the participants across different efficiency ranges during the period.

Table 2: Distribution of DMUs on the footing of Efficiency Scores

Efficiency

Scope

Year

2001-02

2002-03

2003-04

2004-05

2005-06

2006-07

2007-08

2008-09

2009-10

2010-

11

2011-12

Technical EFFICIENCY ( TE )

TE =1

03

01

01

03

04

03

04

03

02

02

05

0.80a‰¤TE & lt ; 1

Nothing

Nothing

01

02

01

04

03

01

01

03

02

0.50a‰¤TE & lt ; 0.80

02

03

02

02

02

03

07

10

12

10

08

TE & lt ; 0.50

06

08

08

06

07

05

03

07

07

07

08

Entire figure of private participants

11

12

12

13

14

15

17

21

22

22

23

PURE TECHNICAL EFFICIENCY ( PTE )

PTE =1

05

04

04

05

06

07

04

04

06

06

07

0.80a‰¤PTE & lt ; 1

Nothing

01

01

01

02

01

04

03

01

02

04

0.50a‰¤PTE & lt ; 0.80

03

03

04

03

02

02

07

08

10

08

06

PTE & lt ; 0.50

03

04

03

04

04

05

02

06

05

06

06

Entire figure of private participants

11

12

12

13

14

15

17

21

22

22

23

SCALE EFFICIENCY ( SE )

SE =1

03

01

01

03

04

03

04

03

02

02

05

0.80a‰¤SE & lt ; 1

02

04

03

09

08

11

10

14

16

18

16

0.50a‰¤SE & lt ; 0.80

04

03

05

Nothing

Nothing

01

03

03

03

01

Nothing

SE & lt ; 0.50

02

04

03

01

02

Nothing

Nothing

01

01

01

02

Entire figure of private participants

11

12

12

13

14

15

17

21

22

22

23

Beginning: Computed by the writer

During the last five old ages, there are around three participants on an norm who attained perfect efficiency mark. SBI Life Insurance is the lone participant that secured a comparative mark of 100 % in all old ages of the survey period. It is necessary to advert here that with respect to proficient efficiency, merely up to 2005-06, the distribution was concentrated in the “ less than 50 % ” class. After that, the maximal concentration existed in the “ 50 % to 80 % class ” .

However, in the instance of PTE, the state of affairs is much better with many more participants achieving the mark of 100 % comparative efficiency. The figure in the “ less than 50 % ” class is less than in the instance of TE. In the instance of PTE, the per centum of participants in different classs is given below:

In the 100 % degree, it was 45.45 % in 2001-02 which decreased to 30 % in 2010-11

In the scope of 80 % to 100 % , the per centum increased from nothing in 2001-02 to 17 % in 2010-11

In the “ 50 % to less than 80 % ” class, the figure did non alter significantly and hovered around 30 % in most of the old ages.

With respect to scale efficiency, most of the participants fall in the scope of 80 % to 100 % . Looking at the tonss, we find that towards the beginning of the period, around 40 % of the DMUs fell in the mentioned zone. However, towards the terminal of the period, the per centum increased to more than 70 % .

A expression at table nos. 3 and 4 shows the distribution of the sample in footings of the graduated table of operation viz. , Increasing Tax returns, Decreasing Tax returns and the Constant Returns to Scale.

Table-3: Operating Scale in the Private Life Insurance Industry

Efficiency

Scope

Year

2001-02

2002-03

2003-04

2004-05

2005-06

2006-07

2007-08

2008-09

2009-10

2010-

11

2011-12

DRS

05

09

09

03

03

03

04

09

01

Nothing

08

IRS

03

02

02

07

07

09

09

09

19

20

10

Chromium

03

01

01

03

04

03

04

03

02

02

05

Entire

11

12

12

13

14

15

17

21

22

22

23

Beginning: Computed by the writer

DRS = Decreasing Returns to Scale, IRS = Increasing Returns to Scale, CRS = Constant Returns to Scale

It is observed from the tabular array that in about all the old ages, 10-30 % of the entire industry rivals are found to be runing at the changeless returns to scale. In other words, a important figure of insurance companies do non run at the Most Productive Scale Size ( MPSS ) .

A expression at table no. 3 shows that during the initial old ages of the private insurance concern, most of them over-utilized and operated at the supra-optimal graduated table is reflected in the DRS. However, since 2004-05, the tendency shows a alteration ; most of them are found to be runing at the sub-optimal degree ( thereby demoing IRS ) . It is, hence, clear that unless there is a important addition in the figure of insurance companies runing at the CRS, the overall industry will go on to give a blue expression. Hence, there is a demand to increase the steadfast size in order to switch to the minimal point of the long-term cost curve.

Besides given below is another tabular array ( No. 4 ) which shows the per centum distribution of the participants as per their operating returns to scale viz. CRS, IRS and DRS which is derived from the tabular array above.

Table-4: Percentage of Insurers runing at different Returns to Scale

Efficiency

Scope

Year

2001-02

2002-03

2003-04

2004-05

2005-06

2006-07

2007-08

2008-09

2009-10

2010-

11

2011-12

DRS

45.45

75.00

75.00

23.07

21.43

20.00

23.53

42.85

4.55

Nothing

34.78

IRS

27.27

16.67

16.67

53.84

50.00

60.00

52.94

42.85

86.35

91.91

43.48

Chromium

27.28

8.33

8.33

23.09

28.57

20.00

23.53

14.30

9.10

9.09

21.74

Entire

100

100

100

100

100

100

100

100

100

100

100

Beginning: Computed by the writer

DRS = Decreasing Returns to Scale, IRS = Increasing Returns to Scale, CRS = Constant Returns to Scale

The tabular array below ( No. 5 ) gives a item about the single insurance companies ‘ place during the different old ages of the survey period.

Table-5: Position of Individual Insurers in regard of Returns to Scale

Life Insurer

2001-02

2002-03

2003-04

2004-05

2005-06

2006-07

2007-08

2008-09

2009-10

2010-11

2011-12

Aegon Religare

NCB

NCB

NCB

NCB

NCB

NCB

NCB

Calciferol

I

I

I

Aviva

NCB

Calciferol

Calciferol

I

I

I

I

Calciferol

I

I

C

Bajaj Allianz

Calciferol

Calciferol

Calciferol

Calciferol

C

Calciferol

Calciferol

Calciferol

Calciferol

I

C

Bharti AXA

NCB

NCB

NCB

NCB

NCB

I

Calciferol

Calciferol

I

I

I

Birla Sun Life

Calciferol

Calciferol

Calciferol

C

I

Calciferol

I

I

I

I

Calciferol

Canara HSBC OBC

NCB

NCB

NCB

NCB

NCB

NCB

NCB

I

I

I

I

DLF Pramerica Life

NCB

NCB

NCB

NCB

NCB

NCB

NCB

C

I

I

I

Edelweiss Tokyo

NCB

NCB

NCB

NCB

NCB

NCB

NCB

NCB

NCB

NCB

I

Future Generali India Life

NCB

NCB

NCB

NCB

NCB

NCB

C

Calciferol

I

I

Calciferol

HDFC Standard Life

I

Calciferol

Calciferol

I

Calciferol

I

I

Calciferol

I

I

Calciferol

ICICI Prudential Life

C

Calciferol

Calciferol

C

C

C

C

C

C

C

C

IDBI Federal Life

NCB

NCB

NCB

NCB

NCB

NCB

C

Calciferol

I

I

I

IndiaFirst Life

NCB

NCB

NCB

NCB

NCB

NCB

NCB

NCB

I

I

C

ING Vysya Life

Calciferol

Calciferol

Calciferol

I

Calciferol

I

Calciferol

I

I

I

I

Metlife

I

I

I

I

Calciferol

I

I

I

I

I

Calciferol

Max New York Life

Calciferol

Calciferol

Calciferol

Calciferol

I

I

I

Calciferol

I

I

Calciferol

Kotak Life

Calciferol

Calciferol

Calciferol

I

I

C

I

I

I

I

Calciferol

Reliance Life

I

I

I

I

C

I

Calciferol

I

I

I

Calciferol

Sahara India Life

NCB

NCB

NCB

I

I

I

I

I

I

I

I

SBI Life

C

C

C

C

C

C

C

C

C

C

C

Shriram Life

NCB

NCB

NCB

NCB

I

I

I

I

I

I

I

Star Union-Daichi Life

NCB

NCB

NCB

NCB

NCB

NCB

NCB

I

I

I

I

Tata AIG

C

Calciferol

Calciferol

Calciferol

I

Calciferol

I

Calciferol

I

I

Calciferol

Beginning: Computed by the writer

Note: I = Increasing, D = Decreasing, C = Constant, NCB: Not Commenced Business

( three ) A expression at tabular arraies 6, 7 and 8 reveal the followers:

( a ) In regard of Technical Efficiency consequences ( see table 6 ) , the best consequences have been seen in the instance of SBI Life ( mark of 100 % ) and ICICI Prudential Life ( norm of 87.73 % ) . In fact, SBI Life is the lone insurance company that has been systematically executing good and it attained 100 % comparative efficiency mark throughout the period.

Table-6: Technical Efficiency Scores of the Private Life Insurers

( all figures are in % )

Life Insurer

2001-02

2002-03

2003-04

2004-05

2005-06

2006-07

2007-08

2008-09

2009-10

2010-11

2011-12

Aegon Religare

NCB

NCB

NCB

NCB

NCB

NCB

NCB

57.33

48.78

55.07

39.02

Aviva

0.00

16.47

20.73

34.78

39.71

42.54

53.66

59.17

54.59

72.47

100

Bajaj Allianz

20.94

33.10

42.12

94.14

100

98.44

55.83

60.93

68.38

66.97

100

Bharti AXA

NCB

NCB

NCB

NCB

NCB

92.55

31.51

41.49

38.48

62.84

69.18

Birla Sun Life

52.72

50.55

93.27

100

79.77

62.68

88.53

64.30

51.70

52.28

67.91

Canara HSBC OBC

NCB

NCB

NCB

NCB

NCB

NCB

NCB

17.94

30.36

47.93

76.10

DLF Pramerica Life

NCB

NCB

NCB

NCB

NCB

NCB

NCB

100

32.75

30.67

19.52

Edelweiss Tokyo

NCB

NCB

NCB

NCB

NCB

NCB

NCB

NCB

NCB

NCB

14.26

Future Generali India Life

0.00

0.00

0.00

0.00

0.00

0.00

100

30.11

17.35

23.52

25.33

HDFC Standard Life

56.89

66.49

75.08

58.22

83.50

89.55

86.40

59.17

61.34

67.10

67

ICICI Prudential Life

100

77.28

12.38

100

100

100

100

100

100

100

100

IDBI Federal Life

NCB

NCB

NCB

NCB

NCB

NCB

100

93.32

54.56

43.13

38.68

IndiaFirst Life

NCB

NCB

NCB

NCB

NCB

NCB

NCB

NCB

50.66

93.26

100

ING Vysya Life

13.65

11.77

22.70

46.53

39.75

49.75

64.88

59.21

52.78

41.85

48.23

Metlife

5.51

12.07

17.61

21.29

32.75

31.06

29.93

34.06

44.30

88.95

70.85

Max New York Life

46.38

27.33

33.14

33.65

39.11

43.42

46.22

44.43

46.57

41.38

41.81

Kotak Life

15.13

20.83

41.54

81.57

76.42

100

87.88

54.33

71.20

71.10

96.86

Reliance Life

5.18

9.30

23.01

51.87

100

67.41

69.43

40.83

51.24

46.26

55.94

Sahara India Life

0.00

0.00

0.00

19.31

47.42

50.95

50.21

52.31

65.48

55.46

49.49

SBI Life

100

100

100

100

100

100

100

100

100

100

100

Shriram Life

NCB

NCB

NCB

NCB

25.50

82.91

57.58

55.34

52.76

70.40

74.42

Star Union-Daichi Life

NCB

NCB

NCB

NCB

NCB

NCB

NCB

35.14

80.91

91.16

83.00

Tata AIG

100

42.30

55.13

49.53

49.57

48.26

55.21

51.92

49.95

51.48

77.14

Beginning: Computed by the writer

NCB: Not commenced concern

( B ) With respect to Pure Technical Efficiency consequences ( see table 7 ) , the best tonss are obtained by the undermentioned decision-making units ( DMUs ) : SBI Life is once more in the figure one place together with, IndiaFirst Life and Star Union-Daichi Insurance all of whom attained a mark of 100 % ( or near to 100 % ) . However, since the latter two participants are really new 1s, the research worker does non give much significance to the consequences. The two participants who secured close 2nd place are: ICICI Prudential Life and Sahara Life with a mark of 95.47 % and 95 % severally.

Table-7: Pure Technical Efficiency Scores of the Private Life Insurers

( all figures are in % )

Life Insurer

2001-02

2002-03

2003-04

2004-05

2005-06

2006-07

2007-08

2008-09

2009-10

2010-11

2011-12

Aegon Religare

NCB

NCB

NCB

NCB

NCB

NCB

NCB

62.58

63.38

64.52

39.31

Aviva

NCB

17.95

30.07

34.93

40.23

42.59

53.70

59.27

55.91

74.85

100

Bajaj Allianz

23.39

39.92

53.73

100

100

100

91.60

88.29

82.52

67.71

100

Bharti AXA

NCB

NCB

NCB

NCB

NCB

100

54.54

42.23

40.13

65.80

70

Birla Sun Life

72.39

62.70

100

100

80.60

63.64

88.91

64.99

51.85

52.44

71.40

Canara HSBC OBC

NCB

NCB

NCB

NCB

NCB

NCB

NCB

19.05

31.49

49.10

79.32

DLF Pramerica Life

NCB

NCB

NCB

NCB

NCB

NCB

NCB

100

100

100

20.10

Future Generali India Life

NCB

NCB

NCB

NCB

NCB

NCB

NCB

30.47

17.97

23.92

27.96

HDFC Standard Life

57.17

80.57

81.44

58.24

84.52

89.65

86.43

59.19

61.49

67.19

76.36

ICICI Prudential Life

100

100

45.76

100

100

100

100

100

100

100

100

IDBI Federal Life

NCB

NCB

NCB

NCB

NCB

NCB

100

93.53

64.04

45.20

41.76

IndiaFirst Life

NCB

NCB

NCB

NCB

NCB

NCB

NCB

NCB

100

100

100

ING Vysya Life

18.18

18.14

32.43

46.64

39.76

49.87

65.27

59.39

54.85

43

48.68

Metlife

100

100

100

21.63

32.76

31.14

29.94

34.23

44.90

90.62

76.88

Max New York Life

66.63

78.80

58.45

38.03

39.42

43.44

46.23

44.45

46.77

41.41

47.60

Kotak Life

28.12

31.14

54.55

82.26

78.83

100

89.33

55.06

72.97

71.91

97.10

Reliance Life

100

77.05

100

54.68

100

67.44

69.75

40.84

51.27

46.30

56.80

Sahara India Life

NCB

NCB

NCB

100

100

100

76.59

87.22

100

100

100

SBI Life

100

100

100

100

100

100

100

100

100

100

100

Shriram Life

NCB

NCB

NCB

NCB

100

100

66.82

76.22

58.85

81.65

88.82

Star Union-Daichi Life

NCB

NCB

NCB

NCB

NCB

NCB

NCB

100

100

100

93.28

Tata AIG

100

100

67.65

50.69

50.05

48.46

55.25

51.97

50.48

51.91

89.44

Beginning: Computed by the writer

NCB: Not commenced concern

( degree Celsius ) Scale Efficiency Results: With respect to this facet, see table no. 8 below.

Table 8: Scale Efficiency Scores of the Private Life Insurers

( all figures are in % )

Life Insurer

2001-02

2002-03

2003-04

2004-05

2005-06

2006-07

2007-08

2008-09

2009-10

2010-11

2011-12

Aegon Religare

NCB

NCB

NCB

NCB

NCB

NCB

NCB

91.60

76.97

85.35

99.26

Aviva

NCB

91.77

68.94

99.58

98.70

99.89

99.92

99.82

97.64

96.8

100

Bajaj Allianz

89.54

82.92

78.40

94.14

100

98.44

60.95

69.01

82.87

99.86

100

Bharti AXA

NCB

NCB

NCB

NCB

NCB

92.55

57.77

98.26

95.90

95.51

98.81

Birla Sun Life

72.83

80.62

93.27

100

98.97

98.50

99.57

98.93

99.71

99.68

95.12

Canara HSBC OBC

NCB

NCB

NCB

NCB

NCB

NCB

NCB

94.20

96.42

97.66

95.94

DLF Pramerica Life

NCB

NCB

NCB

NCB

NCB

NCB

NCB

100

32.75

30.67

97.24

Edelweiss Tokyo

NCB

NCB

NCB

NCB

NCB

NCB

NCB

NCB

NCB

NCB

14.25

Future Generali India Life

NCB

NCB

NCB

NCB

NCB

NCB

100

98.81

96.58

98.35

90.59

HDFC Standard Life

99.52

82.53

92.19

99.98

98.79

99.89

99.97

99.97

99.76

99.86

87.74

ICICI Prudential Life

100

77.28

27.06

100

100

100

100

100

100

100

100

IDBI Federal Life

NCB

NCB

NCB

NCB

NCB

NCB

100

99.77

85.20

95.41

92.61

IndiaFirst Life

NCB

NCB

NCB

NCB

NCB

NCB

NCB

NCB

50.66

93.26

100

ING Vysya Life

75.10

64.88

70.00

99.77

99.95

99.77

99.41

99.69

96.22

97.37

99.08

Metlife

5.51

12.07

17.61

98.41

99.96

99.74

99.96

99.52

98.66

98.16

92.16

Max New York Life

69.60

34.68

56.71

88.50

99.23

99.96

99.98

99.97

99.58

99.92

87.83

Kotak Mahindra OM Life

53.83

66.89

76.15

99.17

96.95

100

98.37

98.67

97.57

98.87

99.77

Reliance Life

5.18

12.07

23.01

94.86

100

99.97

99.54

99.98

99.94

99.91

98.48

Sahara India Life

NCB

NCB

NCB

19.31

47.42

50.95

65.56

59.97

65.48

55.46

49.49

SBI Life

100

100

100

100

100

100

100

100

100

100

100

Shriram Life

NCB

NCB

NCB

NCB

25.50

82.91

86.17

72.61

89.64

86.21

83.78

Star Union-Daichi Life

NCB

NCB

NCB

NCB

NCB

NCB

NCB

35.14

80.91

91.16

89

Tata AIG

100

42.30

81.49

97.72

99.05

99.59

99.93

99.92

98.95

99.15

86.25

Beginning: Computed by the writer

NCB: Not commenced concern

The insurance companies who are most scale efficient include: SBI Life – 100 % , Future Generali India Life – 96.87 % , HDFC Standard Life – 96.38 % , Canara HSBC OBC Life Insurance – 96.10 % , IDBI Federal Life – 94.60 % , Aviva Life – 95.21 % .

( vitamin D ) Amongst the private participants, the participants who attained more than 70 % efficiency include SBI Life, ICICI Prudential Life, IDBI Fortis, BSLI and HDFC Standard life, IndiaFirst Life and SUDI. Among the other private participants who have been runing since sectoral deregulating, Bajaj Allianz Life attains a mark of 64 % and Kotak Life a mark of 61 % .

Another observation is that the PTE consequence for each twelvemonth exceeds the TE mark. The pure proficient efficiency mark shows a broad scope with a upper limit of 100 % and a depression of 25 % . In footings of consequences, of the participants who are runing for at least seven old ages, the best mark is attained by SBI Life Insurance with a consequence of 100 % throughout the period. The other insurance companies who are among the best performing artists in this facet are:

ICICI Prudential Life, BSLI, Bajaj Allianz, Reliance Life and HDFC Standard Life. The tonss of the comparatively newer participants are much better in this respect.

In contrast to the tonss of TE and PTE, the overall sectoral consequence with respect to SE shows that it stands in a good place with an norm of 84 % . A farther probe into the consequences shows that the chief subscriber to the low proficient efficiency mark is chiefly PTE and non SE. The norm of PTE in 2001-02 was 69.62 % which came down to around 65 % by the terminal of the survey period. On the contrary, there is a leap in the scale efficiency mark from an norm of 70 % in 2001-02 to around 90 % at the terminal of 2011-12 demoing considerable betterment during the period. Of the comparatively older participants, those who scored good in footings of SE are: SBI Life – 100 % , HDFC Standard Life Insurance – 96.95 % , Aviva Life – 94 % , Birla Sun Life Insurance – 93.6 % and Tata AIG – 91 % . The comparatively new 1s besides showed applaudable consequences in footings of SE with values lying in the scope from 50 % to 95 % . The other few participants which achieved a comparative mark of more than 80 % scale efficiency include: Aegon Religare, Bharti AXA Life, Canara HSBC OBC, hereafter and IDBI Fortis.

With respect to runing graduated table, the research worker identified the figure of participants who are runing at the Most Productive Scale Size ( MPSS ) . Overall consequences show that there is a fluctuating tendency, thereby, picturing the incompatibility in footings of keeping the optimal operational graduated table. In some old ages, it is a instance of increasing returns and in others diminishing returns.

6. Decisions

It is, hence, clear from the above treatment that there is huge range for betterment in the private life insurance industry. The efficiency degrees are demoing incompatibilities with regard to tendencies. SBI Life Insurance is the lone private participant which operated at the most productive graduated table size with changeless returns during all old ages of the survey. The determination is really near to the decisions drawn by Sinha and Chatterjee ( 2009 ) in which they found SBI Life to be the first rank holder in footings of cost efficiency in three of the five old ages and 2nd and 3rd old ages in the other two old ages.

The direction of companies has to decidedly take a relook at the merchandises, pricing and the operational schemes and figure out the countries where better and efficient use can be done and / or more end products can be produced. The consequences clearly depict that the end product in footings of net premium and the benefits sum paid is much less than what could hold been. The mean TE mark of 55 % shows that the overall industry produces merely 45 % of their best possible end product. Hence, there is besides a demand to cut down the disbursals side in footings of direction disbursals and committee. A farther analysis shows that the part towards inefficiency of the participants is chiefly due to PTE instead than SE. A expression at the public presentation of the comparatively new participants ( runing for three old ages or less ) shows that their overall proficient efficiency degree is besides below 60 % . Furthermore, during the period of survey, it is seen that there is no important betterment in the overall proficient efficiency. Therefore, IRDA has been taking the right steps in footings of cresting committee for different sort of policies and direction disbursals.

Consequences point out that due to an under-optimal graduated table size for bulk of the participants, graduated table inefficiencies result. Thus, size enlargement is required to make the minimal point of the long-term norm cost curve. Hence, looking into the competitory moving ridge in the industry, for sustainability of operations all facets of efficiency ( chiefly PTE ) will hold to be worked upon This would non merely better the public presentation of the single participants but besides result in a better acting industry as a whole.

Furthermore, the research worker finds that there is a significant autumn in the efficiency mark of all the private participants during the planetary lag period after 2007-08. This is chiefly due to low growing of net premium but high rate of disbursals growing. Therefore, it is wise on the portion of the scheme minds to follow right alterations in order to do a shock absorber during such planetary events so that their concern in least injury. Furthermore, evitable disbursals have to be cut down ensuing in increased efficiency.

However, it is to be remembered that since the private participants have been allowed to come in merely about a twelvemonth ago and the IRDA is working on how to circulate maximal possible information in the studies, the research worker has non been able to roll up all information that could hold improved the survey. Hence, a better image can be obtained if the research is carried out after another decennary.

7. Recommendations and Suggestions

From the above treatment, we find out that the overall public presentation of the private sector is really blue with adequate range for betterment in all signifiers of efficiency. The low pure proficient efficiency score indirectly talks about the hapless managerial competency and improper allotment of resources. Furthermore, since in most of the instances, the insurance company is runing at the sub-optimal or the supra-optimal graduated table, the most efficiency degree is non obtained. In other words, the insurance companies have to do necessary accommodation in their graduated table size so that they operate at the changeless returns to scale.

8. Scope for farther Research

This is one of the alone articles written on the life insurance industry in India which has covered all the participants and used the maximal possible information for the intent of survey. However, many other countries that can be studied include cost-efficiency analysis, application of stochastic frontier theoretical accounts for analysis intent and associating the efficiency scores with some other variables among many others.

Cite this Efficiency Analysis Of Private Life Insurers In India Business

Efficiency Analysis Of Private Life Insurers In India Business. (2017, Jul 28). Retrieved from https://graduateway.com/efficiency-analysis-of-private-life-insurers-in-india-business-essay/

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