The Relationship between Life Insurance and Economic Growth: Evidence from India

Global Journal of Management and Business Studies. ISSN 2248-9878 Volume 3, Number 4 (2013), pp. 413-422 Research India Publications http://www.ripublication.com/gjmbs.htm The Relationship between Life Insurance and Economic Growth: Evidence from India Anju Verma * and Renu Bala ** *Haryana School of Business, Guru Janbheshwer University of Science and Technology, Hisar. **Haryana School of Business, Guru Janbheshwer University of Science and Technology, Hisar. Abstract This study examines the relationship between the life insurance and economic growth in India. The total life insurance premium (TLIP), and total life insurance investment (TLII), are used as proxy for life insurance and Gross Domestic Product (GDP) is used for the economic growth. The data has been compiled from the Handbook on Indian Insurance Statistics, IRDA annual reports and economic survey for the time period 1990-91 to 2010-11. The Ordinary Least Square regression model has been used for data analysis. The Breusch-Godfrey Serial Correlation LM, Heteroskedasticity: Breusch-Pagan-Godfey, Jarque- Bera, Collinearity Diagnoses tests have also applied to check robustness of the OLS regression model. The Major findings of the study are that life insurance significantly influences the economic growth in India. JEL Classification: G22, G23, E01, C01. Keywords: India, Life Insurance Premium, Investment, GDP, and OLS. 1. Introduction A life insurer at the event of un-timely policyholder s death provides the fast recovery of family members against financial crisis in return of a fee which is called premium (Spencer and Heppen, 1969; Ranade and Ahuja, 1999; Fabozzi, 2006; Pandey and

414 Anju Verma & Renu Bala Manocha, 2011). Apart from providing protection an insurer can affect the economic growth in the form of financial intermediary (Curak, et al., 2009). The rapid growth of life insurance premium not only increases the role of a life insurer as risk provider, but also increases it importance as institutional investor (Lee, et al., 2013). The function of financial intermediation of life insurance is relatively more prominent than that of nonlife insurance (Chang, et al, 2013). The life insurance sector is the largest long-term investor in Indian economy (Kallinath, 2003; Rangarajan, 2006). There is a good theoretical justification that insurance sector influences economic growth and vice versa (Haiss and Sumegi, 2008 p.425). As far as, Indian economy is concerned, the contribution of insurance in GDP has increased (Parekh & Banerjee, 2010). Thus, the present study is an effort to investigate the statistical significance of the life insurance in economic growth of India. The rest of the study is organized in following sections: section 2 describes the review of literature. Section 3 presents the need of study, and section 4 explains the objective of the study. Section 5 describes the methodology. Section 6 covers the empirical results and interpretation. Section 7 concludes the study. 2. Review of Literature Ward and Zurbruegg (2000) examined the relationship between GDP and insurance growth. With, the data set of 9-OECD countries, and these countries were Australia, Austria, Canada, France, Italy, Japan, Switzerland, UK and US. It was found that insurance premium was Granger Cause of GDP in some countries but for some countries it was not true. Muthusamy and Meera (2008) demonstrated the important role of Indian life insurance sector in economic development. Parekh and Banerjee (2010) reviewed that in India insurance sector has had significant impact on the economic development. This sector is gradually increasing and its contribution in GDP is also increasing. Han, et al. (2010) investigated the relationship between insurance development and economic growth, using the data set of 77 countries. It was found that insurance density impact plays very important role in developing countries rather than developed ones. Ching, et al. (2011) analyzed the existence of causal relationship between total assets of general insurance sector and GDP in Malaysia. It was found that the long-run relationship exists between the total assets of general insurance and GDP. And in the short-run causal relationship was absent (in both directions). Michael, Ojo (2012) examined the short and long run relationships between GDP and insurance sector growth of Nigeria. It was found that insurance sector growth positively and significantly affect the GDP. The long run relationship between the insurance growth and GDP was also confirmed. Hou, et al. (2012) investigated the impact of financial institutions and GDP in 12 Euro-countries. Two major conclusions were found: first it was from cross-country evidences that life insurance penetration and banking development do not have any significant impact on GDP. Secondly, the life insurance and banking development are significant predictors of GDP. Horng, et al. (2012) examined the relationship among the insurance demand, financial development and

The Relationship between Life Insurance and Economic Growth: Evidence 415 GDP of Taiwan. It was found that there was an equilibrium relationship between the insurance demand, financial development and GDP. The study found that in short run, GDP was Granger cause of insurance demand and financial development was Granger cause of GDP. It was finally concluded that financial development promotes GDP and GDP further promotes the insurance demand. Lee, et al. (2013) analyzed the long term and short term relationship between the GDP and real life insurance premium of 41 countries. It was found that in the long term one unit increment in the real life premium will raise the GDP by 0.06 units. The life insurance markets development determines the economic growth in the long-run and in the short term, bidirectional causalities were found between them. Chang, et al. (2013) investigated the causal relationship between the insurance activities and GDP, using a data set of 10 OECD countries. It was found that there was a significant and positive relationship between the overall insurance growth and economic growth for 5 countries out of 10 OECD countries. 3. Need of the Study The role of insurance sector in economic growth has hardly been investigated empirically in literature as compared to banking sector and stock markets (Arena, 2006; Chang, et al., 2013). The worldwide growth of insurance and its influence on the government action is a clear indication of its importance in Indian economy too (Mohananasundari and Balanagagurunathan, 2011). This fact prompts us to examine this issue empirically in India. 4. Objective of the Study To examine the significant relationship between the life insurance and GDP in India. 5. Methodology Hypothesis: There is no significant relationship between the life insurance and GDP in India. The data: This study is empirical by nature as is based on secondary data. That has been compiled from the Handbook on Indian Insurance Statistics, IRDA annual reports and economic survey. Variables Definition: the variable GDP as proxy for economic growth is taken as dependent variable. The independent variables of the study are total life insurance premium (TLIP), total life insurance investment (TLII), covering the time period from 1990-91 to 2010-11. Model: the OLS regression model has been taken as statistical tool for data analysis (Browne and Kim, 1993; Michael, 2012). In order to check the efficiency and unbiasedness of the estimated coefficients of the OLS regression model the following tests has been applied: The Breusch-Godfrey Serial Correlation LM test, Heteroskedasticity test: Breusch-Pagan-Godfey, Jarque-Bera, Collinearity Diagnoses

416 Anju Verma & Renu Bala tests. All the variables (TLIP, TLII and GDP) of the study are transformed by taking first difference for reducing the problem of serial correlation, heteroscedasticity and multicollinarity. 6. Empirical Results and Interpretation Table 1: OLS Regression Model. Dependent Variable: D(GDP) Method: Least Squares Date: 08/27/13 Sample (adjusted): 2 21 Included observations: 20 after adjustments Variable Coefficient Std. Error t-statistic Prob. C 85819.84 12634.13 6.792701 0.0000 D(TLII) 0.571622 0.204770 2.791529 0.0125 D(TLIP) 3.437962 1.046105 3.286442 0.0044 R-squared 0.861001 Mean dependent var 175106.9 Adjusted R-squared 0.844648 S.D. dependent var 103864.7 S.E. of regression 40937.95 Akaike info criterion 24.21498 Sum squared resid 2.85E+10 Schwarz criterion 24.36434 Log likelihood -239.1498 Hannan-Quinn criter. 24.24414 F-statistic 52.65154 Durbin-Watson stat 1.860152 Prob(F-statistic) 0.000000 Table 1 presents the OLS regression results and these results are substantially similar as was expected. A significant impact of life insurance on economic growth is observed in India. It is found that if the life insurance investment increases by one unit holding the other things constant then on the average GDP increases by (0.57), and it is statistically significant at 5% level. The life insurance premium increases by one unit then on the average GDP increases by (3.43) holding other things constant. It influences significantly to the GDP at 1% level.

The Relationship between Life Insurance and Economic Growth: Evidence 417 Tables 1-5 present the robustness of the OLS regression model. The R-square and adjusted R-square of the model are (0.86 and 0.85), respectively more than (0.60). The F-statistic significance is a good indicator of the overall significance of the model. The Durbin Watson is (1.86), which is near about (2), it makes clear that the residuals are not serially correlated. Again we are selecting the JB (Jarque-Bera) test, which is based on OLS residuals. To compute the Jarque-Bera test the Skewness and Kurtosis, and the OLS residuals has to compute first. It has the following statistic: JB n H : Residuals are normally distributed, which is tested with associated P-value. If the P-value is reasonably high then a researcher does not reject the normality assumption (Gujarati, 1995). The p-value is (0.95) the null hypothesis has accepted that residuals are normally distributed. 7 6 5 4 3 2 1 0-100000 -50000 0 50000 100000 Series: Residuals Sample 2 21 Observations 20 Mean -7.64e-12 Median 3374.814 Maximum 80569.49 Minimum -75300.47 Std. Dev. 38723.43 Skewness 0.010837 Kurtosis 2.644649 Jarque-Bera 0.105620 Probability 0.948560 Table 2: Breusch-Godfrey Serial Correlation LM Test. F-statistic 0.076916 Prob. F(2,15) 0.9263 Obs*R-squared 0.203026 Prob. Chi-Square(2) 0.9035 Note: H0: There is no serial correlation. Table 3: Heteroskedasticity Test: Breusch-Pagan-Godfey. F-statistic 0.262967 Prob. F(2,17) 0.7718 Obs*R-squared 0.600179 Prob. Chi-Square(2) 0.7408 Scaled explained SS 0.356584 Prob. Chi-Square(2) 0.8367 H0: The residuals are homoscedastic.

418 Anju Verma & Renu Bala Table 3 exhibits that the null hypothesis of homoscedasticity has been accepted. The Breusch-Pagan-Godfrey test makes clear that the residuals of the model are free from heteroscedasticity. The coefficient estimates of the OLS regression model are efficient. Table 4: Collinearity Statistcs. Coefficientsa Model Unstandardized Coefficients Standardized Coefficients t Sig. Collinearity Statistics B Std. Error Beta Tolerance VIF 1 (Constant) 85819.820 12634.124 6.793.000 TLII.572.205.446 2.792.013.320 3.123 TLIP 3.438 1.046.525 3.286.004.320 3.123 a. Dependent Variable: GDP Here is rule of thumb if the Variance Inflation Factor of a variable exceeds 10 than that variable is said to be highly collinear (see Gujarati, 1995). Table-4 exhibits that Variance Inflation Factor of both variables as follows: (3.1 and 3.1) it means the problem of highly co linearity does not exist in among the variables. The, tolerance is (0.32 and 0.32). Table 5: Collinearity Diagnostics. Model Dimension Eigenvalue Condition Index Variance Proportions (Constant) TLII TLIP dimension0 1 dimension1 1 2.503 1.000.06.03.02 2.400 2.501.94.07.05 3.096 5.100.00.90.92 a. Dependent Variable: GDP Here is rule of thumb that if Conditional Index is between 10 and 30 there is moderate to strong multicollinearity (see Gujarati, 1995). Maximum eigenvalue CI Minimum eigenvalue The table-5 describes that Conditional Index is (1.0, 2.05 and 5.1), and it makes clear that the regression model is free from severe multicollinearity problem. In summing up it is concluded that all these above mentioned tests confirmed that the

The Relationship between Life Insurance and Economic Growth: Evidence 419 estimated coefficients of the OLS regression model are the best, linear, efficient and unbiased. Conclusions These results provide empirical evidence that life insurance has both positive as well as significant impact on the economic growth in India. The null hypothesis has been rejected. In current scenario, India has huge insurable population, is good hope for life insurance sector. Scope for further studies: Further research work need to be done in the same direction by applying different time period and different methodologies such as simultaneous equation model, Vector auto-regression, Granger causality and cointegration. The results may be more interesting than current study. References [1] Arena, M. (2006). Does Insurance Market Activity Promote Economic Growth? A Cross-Country Study for Industrialized and Developing Countries. World Bank Policy Research Working Paper, 1-21. [2] Browne, M. J., & Kim, K. (1993). An International Analysis of Life Insurance Demand. The Journal of Risk and Insurance, 60(4), 616-634. [3] Chang, T., Lee, Chien-Chiang, & Chang, Chi-Hung (2013). Does Insurance Activity Promote Economic Growth? Further Evidence Based on Bootstrap Panel Granger Causality Test. The European Journal of Finance, 1-24. [4] Ching, K. S., Kogid, M., & Mulok, D. (2011). Insurance Funds and Economic Growth in Malaysia: Future Empirical Evidence. Interdisciplinary Review of Economics and Management, 1(1), 1-9. [5] Curak, M., Loncar, S., & Poposki, K. (2009). Insurance Sector Development and Economic Growth in Transition Countries. International Research Journal of Finance and Economics, 34, 29-41. [6] Fabozzi, F. J., Modigliani, F., Jones, F. J., & Ferri, M. G. (2006). Insurance Companies. Foundations of Financial Markets and Institutes (3 rd ed. Chap.7). Pearson Education Publication. [7] Gujarati, D. N. (1995). Basic Econometrics (3 rd ed.). Tata McGraw-Hill, Publication. [8] Haiss, P., & Sumegi, K. (2008). The Relationship between Insurance and Economic Growth in Europe: A Theoretical and Empirical Analysis. Empirica, 35, 405-431. [9] Han, L., Li, D., Moshirian, F., & Tian, Y. (2010). Insurance Development and Economic Growth. The Geneva Studys on Risk and Insurance-Issue and Practice, 35, 183-199. [10] Holyoake, J., & Weipers, W. (2002). Insurance (4 th ed.). Delhi: A. I. T. B. S. Publication.

420 Anju Verma & Renu Bala [11] Horng, M. S., Chang, Y. W., & Wu, T. Y. (2012). Does Insurance Demand or Financial Development Promote Economic Growth? Evidence from Taiwan. Applied Economics Letters, 19(2), 105-111. [12] Hou, H., Cheng, Su-Yin, & Yu, Chin-Ping (2012). Life Insurance and Euro Zone s Economic Growth. Procedia-Social and Behavioral Sciences, 57, 126-131. [13] Kallinath, S. P. (2003). Life Insurance Corporation of India, Its Products and Their Performance Evaluation: A Special Reference to Gulbarga District. Finance India, 17(3), 1037-1040. [14] Koutsoyiannis, A. (1997). Theory of Econometrics (2 nd ed.). New York: Palgrave, Publication. [15] Lee, Chien-Chiang, Lee, Chi-Chuan, & Chiu, Yi-Bin (2013). The Link between Life Insurance Activities and Economic Growth: Some New Evidences. Journal of International Money and Finance, 32, 405-427. [16] Michael Ojo, O. (2012). Insurance Sector Development and Economic Growth in Nigeria. African Journal of Business Management, 6(23), 7016-7023. [17] Mohanasundari, M., & Balangagurunathan, S. (2011). The Phase and Changes of Insurance Industry in India. European Journal of Social Sciences, 24(4), 553-564. [18] Muthusamy, A., & Meera, A. (2008). Winds of Changing Mark Life Insurance Market in India. Karaidui: Alagappa University. Retrieved from http://ffymag.com/admin/issuepdf/pvt%20life%20insurance.pdf. [19] Pandey, A., & Manocha, S. (2011). A Study of Life Insurance Products: Innovative Distribution Channels. The Journal of Indian Management and Strategy, 16(1), 47-54. [20] Parekh, A., & Banerjee, C. (2010, September). Indian Insurance Sector- Stepping into the Next Decade of Growth. Retrieved from http://mycii.in/kmresourceapplication/e000000073.2466.indian%20insuran ce%20report%20final.pdf. [21] Ranade, A., & Ahuja, R. (1999). Life Insurance in India: Emerging Issues. Money Banking and Finance, 34(¾), 203-212. [22] Rangarajan, C. (2006, July 27). The Widening Scope of Insurance. Convocation Address at the Institute of Insurance and Risk Management. [23] Spencer, R. W., & Heppen, M. J. (1969). Impact on Life Insurance Companies of Changing Economic Conditions. Financial Analysts Journal, 25(4), 73-79. Date: 08/27/13 Time: 03:52 Sample: 2 21 Included observations: 20 Autocorrelation Partial Correlation AC PAC Q-Stat Prob. *.. *. 1-0.079-0.079 0.1436 0.705.... 2-0.016-0.022 0.1495 0.928

The Relationship between Life Insurance and Economic Growth: Evidence 421.... 3 0.006 0.003 0.1506 0.985.**..**. 4-0.340-0.342 3.3242 0.505. *.. *. 5-0.076-0.148 3.4947 0.624.... 6 0.036-0.002 3.5365 0.739.... 7 0.041 0.039 3.5938 0.825... *. 8-0.033-0.167 3.6338 0.889. **.. **. 9 0.283 0.224 6.8362 0.654.... 10-0.018 0.043 6.8505 0.739... *. 11 0.037 0.097 6.9172 0.806... *. 12-0.017-0.067 6.9334 0.862 Descriptive Statistics Mean Std. Deviation N GDP 175106.9000 1.03865E5 20 TLII 70182.2600 81049.72158 20 TLIP 14301.9050 15865.11363 20 Correlations GDP TLII TLIP Pearson Correlation GDP 1.000.879.893 TLII.879 1.000.824 TLIP.893.824 1.000 Sig. (1-tailed) GDP..000.000 TLII.000..000 TLIP.000.000. N GDP 20 20 20 TLII 20 20 20 TLIP 20 20 20 Model Variables Entered Variables Removed Method dimension0 1 TLIP, TLIIa. Enter a. All requested variables entered. b. Dependent Variable: GDP Model Summary Model R R Square Adjusted R Square Std. Error of the Estimate dimension0 1.928a.861.845 40937.94626 a. Predictors: (Constant), TLIP, TLII

422 Anju Verma & Renu Bala Coefficient Correlationsa Model TLIP TLII 1 Correlations TLIP 1.000 -.824 TLII -.824 1.000 Covariances TLIP 1.094 -.177 TLII -.177.042 a. Dependent Variable: GDP ANOVAb Model Sum of Squares df Mean Square F Sig. 1 Regression 1.765E11 2 8.824E10 52.652.000a Residual 2.849E10 17 1.676E9 Total 2.050E11 19 a. Predictors: (Constant), TLIP, TLII b. Dependent Variable: GDP