MEASURING PERFORMANCE AND EFFICIENCY GROWTH OF THE SELECTED INDIAN LIFE INSURANCE COMPANIES: A TOTAL FACTOR PRODUCTIVITY APPROACH

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1 MEASURING PERFORMANCE AND EFFICIENCY GROWTH OF THE SELECTED INDIAN LIFE INSURANCE COMPANIES: A TOTAL FACTOR PRODUCTIVITY APPROACH ABSTRACT JOY CHAKRABORTY*; DR. PARTHA PRATIM SENGUPTA** *Assistant Professor of Finance and Accounts, Dr. B. C. Roy Engineering College, Durgapur , West Bengal, India. **Professor, Department of Humanities & Social Sciences, National Institute of Technology, Durgapur , West Bengal, India. In the pre-reform era, Life Insurance Corporation of India (L.I.C.I) dominated the Indian Life Insurance market. But the situation drastically changed since the beginning of the year With the development of the IRDA Act in 1999, private players started entering into the life insurance market. At the end of the FY , there were 22 life insurance companies (inclusive of 1 public sector player, L.I.C.I) operating in India. The 21 private life insurers enjoyed a market share of 29.90%. Pinnacle Research Journals 1 The present paper attempts to compare the performance and efficiency in terms of Total factor Productivity (TFP) growth of 13 Indian life insurance companies (12 private sector and 1 publicsector) in respect of Catch-up efficiency and Frontier-shift efficiency for the FYs ranging from to using Data Envelopment Analysis (DEA)-Solver Software version 5.0. For this purpose, Net Premium and Number of products launched during the year has been taken as the output indicators and expenses along with expenses has been taken as the inputs. KEYWORDS: Life insurance, DEA, Malmquist TFP growth, Catch-up,, Frontiershift. INTRODUCTION During the last one decade, the life insurance sector in India underwent major structural changes owing to the deregulation of entry and the introduction of a market regulator. Between and , the life insurance companies sold a total of policies. Out of these, L.I.C.I sold policies (80% of the total). The remaining 20% went to the 12 private Life Insurers. In , the life insurance companies sold a total of new policies. Of this, L.I.C.I. sold policies (market share: 94.21%) while the private sector life insurers sold new policies (market share: 5.79%). This again reflected a sharp rise in the market share of private life insurers from 5.79% in to 20% at the end of In view of

2 this, the present paper makes an attempt to compare 13 major Indian life insurance companies for the seven year period to in terms of Total Factor Productivity (TFP) growth based on the catch-up and frontier-shift efficiency effects. The paper proceeds as follows: Section-1 provides an overview of the Indian life insurance sector. Section- 2 provides a brief discussion of the methodology. Section- 3 provides a summary of the results. Finally, section-4 makes the concluding observations. SECTION 1: THE LIFE INSURANCE SECTOR IN INDIA The Insurance laws in India were developed in the footsteps of English Insurance Laws. The period between 1928 and 1956 held a significant aspect in the development of insurance laws in India. The enactment of the Insurance Act, 1938 provided stability to the growing insurance business and the earlier legislations were consolidated and amended to protect the interest of the insuring public. The next significant piece of legislation that came into effect was the introduction of the Life Insurance Act on September 1, The nationalization of the life insurance business in India saw the emergence of country s only public-sector life insurance company Life Insurance Corporation of India (L.I.C.I.), which was formed as a result of the takeover of 170 companies and 75 provident fund societies. Pinnacle Research Journals 2 The Government of India appointed a high-powered committee entitled Malhotra Committee in 1993 under the chairmanship of R.N.Malhotra, former Finance Secretary and Governor of Reserve Bank of India. The Committee submitted its report in January 1994 that emphasized upon the private-sector participation in the life insurance business thereby lifting the entry barriers of private players and allowing foreign players to enter the market with some limitations on foreign direct investment. The government accepted the recommendation of Malhotra Committee report in principle. This opened the floodgates of opportunities for the private players in India to diversify themselves into the insurance business either through joint ventures or as a standalone player. In April 2000, the commercial banks have been permitted to undertake insurance business subject to maintenance of an arm s length relationship by the banks with their insurance subsidiaries. As on 31 st August, 2010, there were 23 registered life insurance companies that were operating in India with 1 public-sector player and 22 private insurers, with more proposals being on the pipeline. SECTION 2: CATCH-UP EFFECT, FRONTIER-SHIFT EFFECT & MALMQUIST TOTAL FACTOR PRODUCTIVITY INDEX THE METHODOLOGICAL ISSUES In the present study we are concerned about the productivity change and efficiency of 13 life insurance companies during the period from to The performance of a Decision Making Unit (DMU) between two time-periods is typically described in terms of productivity change or efficiency. Productivity change of a DMU can be defined as the product of Catch-up and Frontier-shift terms. Measurement of catch-up (or recovery) term, on the other hand, relates to the degree to which a DMU improves or worsens its efficiency. Again, measurement of Frontier-shift (or innovation) term reflects the change in the efficient frontiers between the two

3 time-periods. In fact, both can serve as good indicators for evaluating the performance of a DMU. 2.1: CATCH-UP AND FRONTIER-SHIFT EFFECTS OF A DECISION MAKING UNIT (DMU): A PRESENTATION For a set of n DMUs {x j, y j ) {j = 1... n) each having m inputs denoted by a vector x j Є R n and q outputs denoted by a vector y j Є R q over the periods 1 and 2. We assume x j > 0 and y j > 0. The notations {x o, y o ) 1 = (x o 1, y o 1 ) and {x o, y o ) 2 = (x o 2, y o 2 ) are employed for designating DMUo (o = l,..., n ) in periods 1 and 2 respectively. The production possibility set {X, Y) t {t = 1 and 2) spanned by {x j, y j ) t (j = 1,..., n) is defined by: {X,Y} t = {(x, y)i x jג x j t, 0 y jג y j t,{ 0 ג U, גe, L Where e is the row vector with all elements equal to one, ג Є R n is the intensity vector, and L and U are the lower and upper bounds for the sum of the intensities. {L, U) = {(0, ), (1, 1), (l, ) and (0, 1)} correspond to the CCR (Constant Returns to Scale = CRS), BCC (Variable Returns to Scale = VRS), IRS (Increasing Returns to Scale) and DRS (Decreasing Returns to Scale) models. The production possibility set {X, Y) t is characterized by frontiers that are composed of (x, y) Є (X, Y) t such that it is not possible to improve any element of the input x or any element of the output y without worsening some other input or output. We call this frontier set the frontier technology at period t. In the Malmquist index analysis, the efficiencies of DMUs {x o, y o ) 1 and {x o, y o ) 2 are evaluated by the frontier technologies 1 and 2 in several ways. A. CATCH-UP EFFECT The catch-up effect from period 1 to 2 is measured by the following formulae: Pinnacle Research Journals 3 Catch-up = of (x o, y o ) 2 with respect to period 2 frontier of (x o, y o ) 1 with respect to period 1 frontier The catch-up effect is determined by the efficiencies being measured by the distances from the respective frontiers. The catch-up effect (in an input orientation) can be computed as follows: Catch-up = BD BQ AC AP (Catch-up) > 1 indicates progress in relative efficiency from period 1 to period 2, while (catchup) = 1 and (catch-up) < 1 indicates no change and regress in efficiency respectively.

4 (B) FRONTIER-SHIFT EFFECT With reference to Figure-1, we find that the point C of (x o, y o ) 1 on the frontier of period 1 moved to point E on the frontier of period 2. Thus, the frontier-shift effect at (x o, y o ) 1 is evaluated by: Ф 1 = AC/ AE This is equivalent to Ф 1 = AC AP AE AP This implies that : Ф 1 = of (x o, y o ) 1 with respect to period 1 frontier of (x o, y o ) 1 with respect to period 2 frontier Similarly, the frontier-shift effect at (x o, y o ) 2 is expressed by as follows: Ф 2 = BF BD = of (x o, y o ) 2 with respect to period 1 frontier BQ BQ of (x o, y o ) 2 with respect to period 2 frontier Using Ф 1 and Ф 2, we define Frontier-shift effect by their geometric mean, i.e. Frontier-shift = Ф = Ф 1. Ф 2 Pinnacle Research Journals 4 A Frontier-shift value of greater than 1 indicates progress in the frontier technology around DMUo from period 1 to period 2. A Frontier shift value of less than 1 and equal to 1 indicates regress in the frontier technology and no change respectively. 2.2: CONCEPT OF MALMQUIST PRODUCTIVITY INDEX The paper also tries to measure the productivity change in the observed public and private sector life insurance companies. For a detailed study on the concept of total factor productivity Malmquist approach, one may refer to Caves, Christensen and Diewert (1982), Diewert (1992), Førsund (1997), Coelli, Rao, and G. Battese (1998) and Ray(2004). In the paper, output based Malmquist Productivity Index has been used based on the measures of catch-up and Frontier-shift effects. Following W.W.Cooper, L.M.Seiford and K.Tone (2007, 2 nd edition), the Malmquist Index (MI) is defined as: MI = 1 (x o, y o ) 2 X 2 (x o, y o ) (x o, y o ) 1 2 (x o, y o ) 1

5 This implies MI as the geometric mean of the two efficiency ratios: the one being the efficiency change measured by the period 1 technology and the other the efficiency change measured by the period 2 technology. In simple words, the Malmquist Index (MI) can be expressed as the product of (Catch-up) and (Frontier-shift), i.e. MI = (Catch-up) X (Frontier-shift) Where the first term represents the relative change in performance and the second term represents the relative change in the frontier used to evaluate these performances. A MI value of greater than 1 indicates progress in the total factor productivity of the DMUo from period 1 to period 2. Whereas, MI values of less than 1 and equal to 1 indicates deterioration in total factor productivity and no productivity change respectively. 2.3: OUTPUT-INPUT RELATIONSHIP USED IN THE PRESENT STUDY In the present study, we have taken two outputs: Net Premium, Number of products launched during the year = ( + ). That is, on the input side, we have been considered the following two: and. 2.4: DATA SOURCES Pinnacle Research Journals 5 The data relating to inputs and outputs of the 13 life insurers (both public & private) have been taken from the data published by the Insurance Regulatory & Development Authority (IRDA) Annual Reports. The period of analysis is for a 7 year period from to Nominal data in respect of premium, operating expenses and commission expenses have been deflated by appropriate price deflator. SECTION 3: SUMMARY OF RESULTS The paper attempted to estimate the change in Total Factor Productivity (with decomposition) of 13 life insurance companies for a 7-year period ranging from to using the Malmquist Total Factor Productivity Index. We have selected only 13 life insurers who had their operation in all the 7-years. In order to measure Malmquist Productivity Index and the Catch-up & Frontier-shift efficiency measures, specification of outputs and inputs is essential. We have used the Data Envelopment Analysis Solver-Pro (Malmquist-Radial) Program, popularly known as DEA-Solver Software Pro-5.0 version to measure the Malmquist Productivity (TFP) growth index as well the Catch-up & Frontier-shift of the 13 life insurers. Tables 1-14 present the descriptive statistics and correlation matrices relating to the input/output variables for the observed life insurers for the period from to Tables present the company-wise Catch-up and Frontier-shift efficiency of the individual life insurers. Table 17 shows the changes in Malmquist Total Factor Productivity for the period against all 13 life insurers.

6 3.1: DESCRIPTIVE STATISTICS OF INPUT/OUTPUT VARIABLES Tables 1-14 below present the descriptive statistics and correlation matrices relating to the input/output variables for the 13 life insurers for the 7-year period ranging from to TABLE-1 DESCRIPTIVE STATISTICS OF INPUTS & OUTPUTS ( ) (ALL FIGURES IN RS. CRORES) Net Premium (Output) (output) Maximum Value Minimum Value Average Value Standard Deviation TABLE-2 CORRELATION MATRIX OF INPUTS & OUTPUTS ( ) No. of New Net Premium Pinnacle Research Journals Net Premium

7 TABLE-3 DESCRIPTIVE STATISTICS OF INPUTS & OUTPUTS ( ) (ALL FIGURES IN RS. CRORES) Net Premium (Output) (output) Maximum Value Minimum Value Average Value Standard Deviation TABLE-4 CORRELATION MATRIX OF INPUTS & OUTPUTS ( ) Net Premium Net Premium Pinnacle Research Journals 7 TABLE-5 DESCRIPTIVE STATISTICS OF INPUTS & OUTPUTS ( ) (ALL FIGURES IN RS. CRORES) Net Premium (Output) (output) Maximum Value Minimum Value Average Value Standard Deviation

8 TABLE-6 CORRELATION MATRIX OF INPUTS & OUTPUTS ( ) Net Premium Net Premium TABLE-7 DESCRIPTIVE STATISTICS OF INPUTS & OUTPUTS ( ) (ALL FIGURES IN RS. CRORES) Net Premium (Output) (output) Pinnacle Research Journals 8 Maximum Value Minimum Value Average Value Standard Deviation TABLE-8 CORRELATION MATRIX OF INPUTS & OUTPUTS ( ) Net Premium Net Premium

9 TABLE-9 DESCRIPTIVE STATISTICS OF INPUTS & OUTPUTS ( ) (ALL FIGURES IN RS. CRORES) Net Premium (Output) (output) Maximum Value Minimum Value Average Value Standard Deviation TABLE-10 CORRELATION MATRIX OF INPUTS & OUTPUTS ( ) No. of New Net Premium Net Premium Pinnacle Research Journals 9 TABLE-11 DESCRIPTIVE STATISTICS OF INPUTS & OUTPUTS ( ) (ALL FIGURES IN RS. CRORES) Net Premium (Output) (output) Maximum Value Minimum Value Average Value Standard Deviation

10 TABLE-12 CORRELATION MATRIX OF INPUTS & OUTPUTS ( ) No. of New Net Premium Net Premium TABLE-13 DESCRIPTIVE STATISTICS OF INPUTS & OUTPUTS ( ) (ALL FIGURES IN RS. CRORES) Net Premium (Output) (output) Maximum Value Minimum Value Average Value Standard Deviation Pinnacle Research Journals 10 TABLE-14 CORRELATION MATRIX OF INPUTS & OUTPUTS ( ) Net Premium Net Premium

11 3.2: CATCH-UP & FRONTIER-SHIFT EFFICIENCY SCORES OF LIFE INSURERS Tables below present the company-wise relative Catch-up and Frontier-shift efficiency of the individual life insurers. Based on the relative catch-up efficiency over the 7- year period, we find that the lone public life insurer LICI along with the private life insurers such as ICICI Pru, Shriram Life and SBI Life demonstrated a score of 1 on a consistent basis indicating no change in relative efficiency of these life insurers across the term-period. Among the private insurers, MNYL, Om Kotak and ING Vysya with mean relative catch-up efficiency of 1.180, and respectively were found to be highly efficient over the said period. Pinnacle Research Journals 11

12 TABLE-15 INSURER WISE CATCH-UP EFFICIENCY SCORES FOR THE PERIOD FROM TO & AVERAGE CATCH-UP SCORE FOR THE AFORESAID PERIOD Pinnacle Research Journals 13 Company Name (Insurers) Catch-up Catch-up Catch-up Catch-up Catch-up Catch-up Average Catch-up LICI BSLI ICICI Pru ING Vysya HDFC Std. Life MNYL Shriram Life BAJAJ ALLIANZ SBI Life OM KOTAK TATA-AIG MET LIFE AVIVA

13 Based on relative Frontier-shift efficiency, we find that only the public life insurer LICI represented a consistent progress in frontier-technology with a score above 1 throughout the 7-year period ranging from to No private life insurers could represent the same across the 7-year time-period. The private life insurers attained frontier-efficiency above 1 only in patches but not on a consistent basis over the years. Among the private life insurers, Shriram Life enjoyed the highest mean relative frontierefficiency score of 1.27 followed by BSLI with a score of 1.22 across the stated period. Nevertheless, all the life insurers enjoyed mean frontier-efficiency above 1 over the said period thereby indicating a significant progress in frontier technology. Pinnacle Research Journals 14 TABLE-16 INSURER WISE FRONTIER-SHIFT EFFICIENCY SCORES FOR THE PERIOD FROM TO & AVERAGE FRONTIER-SHIFT EFFICIENCY SCORE FOR THE AFORESAID PERIOD Company Name (Insurers) Frontiershift Frontier-shift Frontier-shift Frontiershift Frontiershift Frontiershift Average Frontiershift LICI BSLI ICICI Pru ING Vysya HDFC Std. Life MNYL Shriram Life BAJAJ ALLIANZ SBI Life OM KOTAK TATA-AIG MET LIFE AVIVA

14 3.3: MALMQUIST INDEX SUMMARY Tables-17 below shows the year-wise and insurer wise relative Malmquist total factor productivity index for the observed years from to The change in Malmquist total factor productivity Index can be decomposed into changes in Catch-up efficiency and Frontier-shift efficiency. In other words, Malmquist total factor productivity index can be represented as a product of catch-up and frontier-shift effects, which is expressed as follows:- MI = (Catch-up) X (Frontier-shift) The Malmquist Total Factor Productivity (TFP) represents the productivity of the production point (X t+1, Y t+1 ) relative to the production point (X t, Y t ). Pinnacle Research Journals 15

15 TABLE-17 MALMQUIST INDEX (MI) SCORES OF INDIVIDUAL FIRMS OVER THE YEARS ( TO ) Company Name (Insurers) MI MI MI MI MI MI Average MI Ranking (Based on TFP Change) Pinnacle Research Journals 16 LICI BSLI ICICI Pru ING Vysya HDFC St. Life MNYL Shriram Life BAJAJ ALNZ SBI Life OM KOTAK TATA-AIG MET LIFE AVIVA Source : Calculated LICI exhibited a consistent relative total factor productivity change index score of more than 1 over the 7-year time-period thereby indicating a progress in total factor productivity growth from period t to period t+1. An index value greater than 1 indicates positive TFP growth from period t to period t+1. Excepting AVIVA Life Insurance Company along with Shriram Life and Om Kotak Life Insurance Company, all the other private life insurers also showed a mean total factor productivity score of more than 1 thereby indicating a positive growth. Moreover, decomposition of the change in total factor productivity showed that the observed life insurers exhibited a more or less similar change in catch-up and frontier-shift efficiency

16 SECTION 4: CONCLUDING OBSERVATIONS In the paper, 13 life insurance companies have been compared in respect of catch-up efficiency, frontier-shift efficiency and Malmquist Total Factor Productivity growth index for a 7-year period ranging from to LICI exhibited a relative catch-up efficiency of 1 indicating no significant changes in performance over the years. In comparison, the private life insurers such as MNYL, Om Kotak and ING Vysya exhibited a mean relative catch-up efficiency score of more than 1 over the said period. Further, a comparison of the relative frontier-shift efficiency of the life insurers for the 7-year period from to reflected that the public-sector life insurer is marginally ahead of its private sector counterparts with respect to efficiency in frontier technology over the years. Moreover, LICI exhibited a consistent relative Total Factor Productivity change index score of more than 1 over the 7- year time-period thereby indicating a relative progress in total factor productivity growth from period t to period t+1. Among the private life insurers, AVIVA Life Insurance Company along with Shriram Life and Om Kotak Life Insurance Company limited recorded the highest Total Factor Productivity growth respectively. However, all the private life insurers exhibited a mean positive total factor productivity growth during the said period. Pinnacle Research Journals 17

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