Efficiency Analysis Of Private Life Insurers In India Business Essay

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After the opening up of the insurance industry to the private players in 2000, there has been a rush to enter the business which is pertinent from the number of competitors that we currently see in the industry. This research makes an analysis of the performance of the private life insurance sector as a whole and the individual players in particular. To be specific, the paper aims at understanding the efficiency levels by making calculations for technical efficiency (TE), pure technical efficiency (PTE) and scale efficiency (SE) by applying Data Envelopment Analysis. The entire population in the industry has been studied considering the data period from 2001-02 to 2011-12. The results of output-oriented DEA model on the two-input two-output case shows that there is an immense scope for improvement in the industry in order to raise the overall industry performance. SBI Life has been found to be the only player performing consistently well in all aspects of efficiency.

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

1. Introduction

There have been several reforms that have taken place in the life insurance industry in the post-independence period starting with the passage of the Life Insurance Corporation Act, 1956. The present status of the industry is a result of the initiatives taken by the Indian government after 1991 to carry out economic and financial reforms. With inception of the New Economic Policy in 1991 and the initiation of reforms covering the banking sector and the capital market, the government felt the need to bring about changes in the insurance industry as well because it was understood that the role of insurance was far way behind its potential.

The commencement of reforms process 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 responsibility to examine the structure and existing regulation of the insurance industry, to assess its strengths and weaknesses so that it can serve as an effective instrument for the mobilization of financial resources and to suggest reforms in the changed economic environment.

In 1994, the Committee submitted its report which proposed several changes, following which a revolutionary change was introduced in 1999 with the passage of IRDA Act. Since then, competition has improved drastically with a substantial increase in the number of private life insurers from 4 in 2000-01 to 24 in 2011-12. In view of this development in the industry, it is felt necessary to determine the efficiency level of the industry and individual players because sustainability of this sector will depend upon the productivity of the players.

2. Literature Review

There have been several studies made abroad to analyze the life and non-life insurance industry. In comparison to that number, the articles to study this aspect of performance in Indian insurance industry is very limited. However, there are some studies which have focused on analyzing the different performance aspects of LIC. Some of the literatures reviewed by the researcher are given below:

Foreign Studies:

Lee and Kim (2008) measured, analyzed and decomposed the relative efficiency of the Korean life insurers. The research covered 22 registered insurers covering data for 2006 only. They used the DEA (both BCC and CCR), Slack-based measure and the Super-efficiency models to analyze the data. Their results showed the average BCC, CCR and SBM efficiency scores to be 0.988, 0.961 and 0.892 respectively. In terms of returns to scale, the number of companies under increasing, decreasing and constant returns to scale was three, seven and twelve respectively. A further in-depth analysis of the results showed that 12 insurers were both technically and scale efficient. To further discriminate between the DMUs attaining a score of one, they applied the super-efficiency model.

Tone and Sahoo (2005) applied DEA to analyze the cost efficiency and returns to scale of Life Insurance Corporation (LIC) using time series data. The data set covered a period of 19 years from 1982-83 to 2000-2001. The results show that there existed heterogeneity in cost efficiency over the period of study. Though, it was seen that performance deteriorated after 1994-95 mainly due to allocative inefficiency arising from the modernization measures taken by LIC, there was a significant increase in efficiency in 2000-01.

Qiu and Chen (2007) used the DEA approach to determine the efficiency level of Chinese life insurers which showed a continuous decline during the study period 2000-03; the score ranged from 0.49 to 0.64. A comparison of the results of Chinese and international insurers showed that the latter fared poorly in respect of technical efficiency scores. The cause behind inefficiency of the Chinese insurers was both PTE and SE, but for the international insurers PTE did not play a very down pulling effect.

Indian Studies:

Bawa and Ruchita (2011) examined the technical efficiency of general insurers engaged in health insurance business in India by applying DEA. They considered data of 10 general insurers for the period 2002-03 to 2009-10 including four public sector insurers. Some of the key results were as follows: Firstly, New India Assurance Company Limited and National Insurance Company Limited were the two fully efficient insurance companies. During the later one/two years, however, they showed an efficiency level of less than 1 in each of those years. Secondly, they found that in almost all the years at least one or even two public sector players lay on the efficient frontier. However, in the later years of the study period, none of the public sector players showed perfect efficiency which could be attributed to decreasing returns to scale because of entry of private players. In the third part of the analysis, results showed that the mean technical efficiency of the private players was on the rise (from 0.062 in 2002-03 to 0.776 in 2009-10) in contrast to the falling trend observed in the case of the public sector players (from 0.878 in 2002-03 to 0.661 in 2009-10). In the last part of the analysis, the researcher pointed out that the overall mean technical efficiency of all insurers 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 conclusion that the industry as well as the public sector player were growing at a fast pace. The application of the two-way ANOVA technique showed the existence of significant difference in the performance of LICI and the private players over the data period 2001-02 to 2007-08. Furthermore, application of the ANOVA technique and t-test showed a significant change in the investment strategy of the public sector life insurer.

Das (2012) looked into the status of life insurance industry in North-Eastern India. The focus area was on analyzing the performance of LIC and to assess the market share in that region along with understanding the marketing strategies adopted by it and the challenges that it faced in the region.

Rajendran and Natarajan (2009) discussed the overall performance of LIC by making a comparison between the pre and post-liberalisation period. Moreover, they studied the current status of the player and highlighted the challenges that the public sector giant faced.

Sinha and Chatterjee (2007) highlighted the growth of the Indian insurance industry. In the later part of the study, they analyzed the cost efficiencies of the life insurers which included LIC and the private players. The analysis of data for the period 2002-03 to 2006-07 suggested an inconsistency in the trend of cost efficiency. In the initial four years there was an upward trend after which it changed.

Tiwari and Yadav (2012) analyzed the Indian life insurance industry by using ten years' data from 2001 to 2010 to understand the impact of liberalization on the sector. The variables considered for the analysis included total premium income, total income, market share and number of policies. According to the authors, though the competitive pressure eroded the market share of LIC, the brand still dominated the mind of the Indian consumers and it continued to remain the most trusted brand in the post-liberalized period.

3. Importance of the Study

From the literature that we surveyed, the researcher found no such study either Indian or foreign that evaluated the performance of all Indian private players considering a data period of around ten years. There was one study on the private players but the sample size and the data period was found to be small. There was another paper exclusively on the Life Insurance Corporation of India where the cost efficiency aspect was studied by the authors. Hence, the researcher found a scope of doing research on this sector and analyzed the efficiency aspect of the players.

There is no other previous study which has studied the life insurance sector in the country using such a comprehensive set of data. The importance of the paper lies in the fact that it analyses the efficiency level of the private insurers who are growing at a phenomenally fast pace to know the real strength in the private sector as a whole.

4. Research Methodology

4.1 Objectives of the Study: The main purpose of the study is to capture an overall idea about the efficiency level and the operating scale of the overall industry and the private players.

4.2 Sample selection: For the purpose of this study, all the private life insurers operating as at the end of March 2011-12 have been considered.

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

4.4 Research tools used: In order to determine relative efficiency scores of the private players, a non-parametric test like the DEA (in this case an output-oriented DEA model) is used. In simple terms, this approach uses the maximization linear programming technique to create the efficient frontier. The firm(s) which lies on the frontier are (is) considered to be the "best-practice" firm(s) against whom the relative efficiency level of the other firms is determined. The unit which is found to be relatively most efficient secures a score of 1 (or 100%) and the others get values between 0 and 1 (or between 0 % and 100%). The overall efficiency is further divided into Pure Technical Efficiency (PTE), Scale Efficiency (SE) and Technical Efficiency (TE). For determination of the different efficiency results, the CCR and BCC models are used.

Variables used for the study: For the purpose of this study, two variables under inputs and outputs are considered. The choice of variables is based on the study of literature. In this regard, it is, to be understood that the variable choice depends upon the information available from different relevant reports. For the purpose of this research, the intermediation approach is used. The inputs used in the research are commission and operating expenses whereas the outputs include net premium and benefits paid.

After the selection of the variables, their nominal values have been deflated to the base year 2001-02 using the Consumer Price Index (CPI) thereby transforming them into Deflated Commission (DEFLCOMM), Deflated Operating expenses (DEFLOPEX), Deflated Net premium (DEFLNP) and Deflated Benefits paid (DEFLBP). In order to test whether DEA can be applied on the deflated set, the isotonicity test is carried out.

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

Source: Computed by the author

Since, the input and output variables are significantly positively related, it implies the fulfillment of the test for isotonicity.

In addition to the above, the other two thumb rules given by Cooper et. al. (2007) that were kept in mind during variable selection include:

n ≥ p x q, where n is the number of DMUs, p is the number of inputs and q is the number of outputs, and

r = 3 (p+q), where r is the total number of observations.

4.5 Limitations of the study: Data availability was a problem, since only Annual Reports (with data in consolidated form) are available from the IRDA website. A better picture can be drawn if a detailed break-up of the figures are obtained.

5. Analysis and Findings

(i) Efficiency analysis (refer to Figure1): The overall results of the private sector throw an impression that there is immense scope for improvement. The average technical efficiency of the sector during the last eleven years is found to be around 58% only, thereby implying that there is a scope for improvement to the extent of 42%. It is observed from the chart that after the first seven years where there was a continuous increasing trend, the efficiency percentage showed a decline during the initial years of the sub-prime mortgage crisis after which it revived again.

In terms of pure technical efficiency resulting from managerial ability and resource allocation, it is observed that over time there has not been much fluctuation with the score during the years lying in the range of 65% - 76%.

In comparison to these two aspects, the scale efficiency results showed considerable improvement; from around 65% levels towards the beginning of the decade, the score went up to almost 90% towards the end of the study period.

The overall average score for the three categories of efficiency shows that the private sector is quite scale efficient with the average exceeding 80%. On the other hand, technical efficiency and pure technical scores averaged 58% and 70% respectively.

From the results obtained, it can be observed that during the first three years, both managerial and scale inefficiency contributed almost equally to the overall inefficiency. However, it is noted that after that inefficient managerial performance led to a drastic fall in the overall efficiency score.

Figure-1: Average Efficiency Scores of the Private Players

(all figures are in %)

Source: Computed by the author

(ii) The table below (No. 2) highlights the situation in the private life insurance sector with regard to the number of decision-making units' (DMUs') on the basis of their efficiency levels. The focus of the table is to get a distribution of the players across different efficiency ranges during the period.

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

Efficiency

Range

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

0.80≤TE<1

NIL

NIL

01

02

01

04

03

01

01

03

02

0.50≤TE<0.80

02

03

02

02

02

03

07

10

12

10

08

TE<0.50

06

08

08

06

07

05

03

07

07

07

08

Total number of private players

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

0.80≤PTE<1

NIL

01

01

01

02

01

04

03

01

02

04

0.50≤PTE<0.80

03

03

04

03

02

02

07

08

10

08

06

PTE<0.50

03

04

03

04

04

05

02

06

05

06

06

Total number of private players

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

0.80≤SE<1

02

04

03

09

08

11

10

14

16

18

16

0.50≤SE<0.80

04

03

05

NIL

NIL

01

03

03

03

01

NIL

SE<0.50

02

04

03

01

02

NIL

NIL

01

01

01

02

Total number of private players

11

12

12

13

14

15

17

21

22

22

23

Source: Computed by the author

During the last five years, there are around three players on an average who attained perfect efficiency score. SBI Life Insurance is the only player that secured a relative score of 100% in all years of the study period. It is necessary to mention here that with regard to technical efficiency, only up to 2005-06, the distribution was concentrated in the "less than 50%" category. After that, the maximum concentration existed in the "50% to 80% category".

However, in the case of PTE, the situation is much better with many more players attaining the score of 100% relative efficiency. The number in the "less than 50%" category is less than in the case of TE. In the case of PTE, the percentage of players in different categories is given below:

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

In the range of 80% to 100%, the percentage increased from nil in 2001-02 to 17% in 2010-11

In the "50% to less than 80%"category, the number did not change significantly and hovered around 30% in most of the years.

With regard to scale efficiency, most of the players fall in the range of 80% to 100%. Looking at the scores, we find that towards the beginning of the period, around 40% of the DMUs fell in the mentioned zone. However, towards the end of the period, the percentage increased to more than 70%.

A look at table nos. 3 and 4 shows the distribution of the sample in terms of the scale of operation viz., Increasing Returns, Decreasing Returns and the Constant Returns to Scale.

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

Efficiency

Range

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

NIL

08

IRS

03

02

02

07

07

09

09

09

19

20

10

CRS

03

01

01

03

04

03

04

03

02

02

05

Total

11

12

12

13

14

15

17

21

22

22

23

Source: Computed by the author

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

It is observed from the table that in almost all the years, 10-30% of the total industry competitors are found to be operating at the constant returns to scale. In other words, a significant number of insurers do not operate at the Most Productive Scale Size (MPSS).

A look at table no. 3 shows that during the initial years of the private insurance business, most of them over-utilized and operated at the supra-optimal scale is reflected in the DRS. However, since 2004-05, the trend shows a change; most of them are found to be operating at the sub-optimal level (thereby showing IRS). It is, therefore, clear that unless there is a significant increase in the number of insurers operating at the CRS, the overall industry will continue to give a dismal look. Hence, there is a need to increase the firm size in order to shift to the minimum point of the long-run cost curve.

Also given below is another table (No. 4) which shows the percentage distribution of the players as per their operating returns to scale viz. CRS, IRS and DRS which is derived from the table above.

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

Efficiency

Range

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

NIL

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

CRS

27.28

8.33

8.33

23.09

28.57

20.00

23.53

14.30

9.10

9.09

21.74

Total

100

100

100

100

100

100

100

100

100

100

100

Source: Computed by the author

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

The table below (No. 5) gives a detail about the individual insurers' position during the different years of the study period.

Table-5: Position of Individual Insurers in respect 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

D

I

I

I

Aviva

NCB

D

D

I

I

I

I

D

I

I

C

Bajaj Allianz

D

D

D

D

C

D

D

D

D

I

C

Bharti AXA

NCB

NCB

NCB

NCB

NCB

I

D

D

I

I

I

Birla Sun Life

D

D

D

C

I

D

I

I

I

I

D

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

D

I

I

D

HDFC Standard Life

I

D

D

I

D

I

I

D

I

I

D

ICICI Prudential Life

C

D

D

C

C

C

C

C

C

C

C

IDBI Federal Life

NCB

NCB

NCB

NCB

NCB

NCB

C

D

I

I

I

IndiaFirst Life

NCB

NCB

NCB

NCB

NCB

NCB

NCB

NCB

I

I

C

ING Vysya Life

D

D

D

I

D

I

D

I

I

I

I

Metlife

I

I

I

I

D

I

I

I

I

I

D

Max New York Life

D

D

D

D

I

I

I

D

I

I

D

Kotak Life

D

D

D

I

I

C

I

I

I

I

D

Reliance Life

I

I

I

I

C

I

D

I

I

I

D

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

D

D

D

I

D

I

D

I

I

D

Source: Computed by the author

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

(iii) A look at tables 6, 7 and 8 reveal the following:

(a) In respect of Technical Efficiency results (see table 6), the best results have been seen in the case of SBI Life (score of 100%) and ICICI Prudential Life (average of 87.73%). In fact, SBI Life is the only insurer that has been consistently performing well and it attained 100% relative efficiency score 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

Source: Computed by the author

NCB: Not commenced business

(b) With regard to Pure Technical Efficiency results (see table 7), the best scores are obtained by the following decision-making units (DMUs): SBI Life is again in the number one position together with, IndiaFirst Life and Star Union-Daichi Insurance all of whom attained a score of 100% (or close to 100%). However, since the latter two players are very new ones, the researcher does not give much significance to the results. The two players who secured close second position are: ICICI Prudential Life and Sahara Life with a score of 95.47% and 95% respectively.

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

Source: Computed by the author

NCB: Not commenced business

(c) Scale Efficiency Results: With regard to this aspect, 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

Source: Computed by the author

NCB: Not commenced business

The insurers 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%.

(d) Amongst the private players, the players 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 players who have been operating since sectoral deregulation, Bajaj Allianz Life attains a score of 64% and Kotak Life a score of 61%.

Another observation is that the PTE result for each year exceeds the TE score. The pure technical efficiency score shows a wide range with a maximum of 100% and a low of 25%. In terms of results, of the players who are operating for at least seven years, the best score is attained by SBI Life Insurance with a result of 100% throughout the period. The other insurers who are among the best performers in this aspect are:

ICICI Prudential Life, BSLI, Bajaj Allianz, Reliance Life and HDFC Standard Life. The scores of the relatively newer players are much better in this regard.

In contrast to the scores of TE and PTE, the overall sectoral result with regard to SE shows that it stands in a good position with an average of 84%. A further investigation into the results shows that the main contributor to the low technical efficiency score is mainly PTE and not SE. The average of PTE in 2001-02 was 69.62% which came down to around 65% by the end of the study period. On the contrary, there is a jump in the scale efficiency score from an average of 70% in 2001-02 to around 90% at the end of 2011-12 showing considerable improvement during the period. Of the relatively older players, those who scored well in terms 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 relatively new ones also showed commendable results in terms of SE with values lying in the range from 50% to 95%. The other few players which achieved a relative score of more than 80% scale efficiency include: Aegon Religare, Bharti AXA Life, Canara HSBC OBC, future and IDBI Fortis.

With regard to operating scale, the researcher identified the number of players who are operating at the Most Productive Scale Size (MPSS). Overall results show that there is a fluctuating trend, thereby, depicting the inconsistency in terms of maintaining the optimum operational scale. In some years, it is a case of increasing returns and in others decreasing returns.

6. Conclusions

It is, therefore, clear from the above discussion that there is immense scope for improvement in the private life insurance industry. The efficiency levels are showing inconsistencies with respect to trends. SBI Life Insurance is the only private player which operated at the most productive scale size with constant returns during all years of the study. The finding is very close to the conclusions drawn by Sinha and Chatterjee (2009) in which they found SBI Life to be the first rank holder in terms of cost efficiency in three of the five years and second and third years in the other two years.

The management of companies has to definitely take a relook at the products, pricing and the operational strategies and figure out the areas where better and efficient usage can be done and / or more outputs can be produced. The results clearly depict that the output in terms of net premium and the benefits amount paid is much less than what could have been. The average TE score of 55% shows that the overall industry produces only 45% of their best possible output. Hence, there is also a need to reduce the expenses side in terms of management expenses and commission. A further analysis shows that the contribution towards inefficiency of the players is mainly due to PTE rather than SE. A look at the performance of the relatively new players (operating for three years or less) shows that their overall technical efficiency level is also below 60%. Moreover, during the period of study, it is seen that there is no significant improvement in the overall technical efficiency. Thus, IRDA has been taking the right measures in terms of capping commission for different kind of policies and management expenses.

Results point out that due to an under-optimal scale size for majority of the players, scale inefficiencies result. Thus, size expansion is required to reach the minimum point of the long-run average cost curve. Hence, looking into the competitive wave in the industry, for sustainability of operations all aspects of efficiency (mainly PTE) will have to be worked upon This would not only improve the performance of the individual players but also result in a better performing industry as a whole.

Furthermore, the researcher finds that there is a substantial fall in the efficiency score of all the private players during the global slowdown period after 2007-08. This is mainly due to low growth of net premium but high rate of expenses growth. Thus, it is wise on the part of the strategy thinkers to adopt right changes in order to make a cushion during such global events so that their business in least hurt. Moreover, avoidable expenses have to be cut down resulting in increased efficiency.

However, it is to be remembered that since the private players have been allowed to enter only about a year ago and the IRDA is working on how to disseminate maximum possible information in the reports, the researcher has not been able to collect all information that could have improved the study. Hence, a better picture can be obtained if the research is carried out after another decade.

7. Recommendations and Suggestions

From the above discussion, we find out that the overall performance of the private sector is very dismal with enough scope for improvement in all forms of efficiency. The low pure technical efficiency score indirectly talks about the poor managerial competence and improper allocation of resources. Moreover, since in most of the cases, the insurer is operating at the sub-optimal or the supra-optimal scale, the most efficiency level is not obtained. In other words, the insurance companies have to make necessary adjustment in their scale size so that they operate at the constant returns to scale.

8. Scope for further Research

This is one of the unique articles written on the life insurance industry in India which has covered all the players and used the maximum possible data for the purpose of study. However, many other areas that can be studied include cost-efficiency analysis, application of stochastic frontier models for analysis purpose and relating the efficiency scores with some other variables among many others.

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