International Journal of Economics and Financial Modelling

Volume 2, Number 1 (2017) pp 25-36 doi 10.20448/811.2.1.25.36 | Research Articles

 

An Analysis of the Relationship between Insurance Risk Management and Growth of the Nigerian Economy

Nonso Fredrick Okoye 1Obiamaka Egbo 2Onuora M. Okeke 3Ebele Nwankwo 4
1 Department of Finance and Banking; University of Port Harcourt, Nigeria
2 Department of Banking and Finance, Faculty of Business Administration, University of Nigeria, Enugu Campus, Nigeria
3 Department of Political Sceince Enugu State College of Education (Technical), Nigeria
4 Department of Economics, Accounting and Finance, College of Management Sciences, Bells University of Technology, Ota, Ogun State, Nigeria

ABSTRACT

This study sought to examine the causal relationship between insurance risk management and growth of Nigerian economy within the period, 1981 to 2011. The study employed the Ordinary Least Squares technique in addition to, Johansen co-integration, Granger causality test, Error Correction Model (ECM), impulse response function and variance decomposition statistical methods of estimations. On the short run relationship, the study observed the existence of positive relationship between insurance risk management proxied by insurance various claims payment and the growth of Nigerian economy except the claim payment on marine policy which related negatively with growth of Nigerian economy within the period. Also, the study revealed the existence of equilibrium relationship between our employed variables and our ECM, denoting that 11% deviation from the equilibrium can be corrected over a year. On the direction of causal relationship, the study found no bidirectional relationship between our employed variables, however, a unidirectional relationship was observed from CPF to GDP, GDP to CPA, GDP to CPM, and GDP to CPMA. From our impulse response function, it was observed that GDP responded positively to own shock both in the long and short run, while its response to shocks from other variables was mixed. We found from our variance decomposition estimate that own shock represents the greatest source of variations in the forecast error of observed variable (GDP). Based on these findings, the study recommends among others that: Effort should limit the level protocols required by insurance sectors in the case of indemnification.

Keywords:  Insurance, Economic growth, Risk, Gross domestic product, Nigeria.

DOI: 10.20448/811.2.1.25.36

Citation | Nonso Fredrick Okoye; Obiamaka Egbo; Onuora M. Okeke; Ebele Nwankwo (2017). An Analysis of the Relationship between Insurance Risk Management and Growth of the Nigerian Economy. International Journal of Economics and Financial Modelling, 2(1): 25-36

Copyright: This work is licensed under a Creative Commons Attribution 3.0 License

Funding : This study received no specific financial support.

Competing Interests: The authors declare that they have no competing interests.

History : Received: 8 March 2017/ Revised: 2 June 2017/ Accepted: 6 June 2017/Published: 8 June 2017

Publisher: Online Science Publishing

1. INTRODUCTION

The role of insurance sector in mitigating unexpected adverse outcomes in our day to day activities cannot be over emphasised both in developed and developing countries. This has made it an attractive area of interest for scholars in recent time. As a business of assembling resources together for the sole purpose of indemnification, insurance plays a vital role in the development and growth of any economy. According to Skipper (1997) “the fundamental aspect of insurance in promoting economic growth through its structured risk management process involves; identifying the exposures to accidental loss, evaluating alternative techniques for treating each loss exposure, and choosing the best alternative.” This according to Oke (2012) permits organizations to focus their attention and capitals on their core businesses, leaving their risk to the worries of insurance firms and thereby contribute largely on the growth of the economy. Arising from the works of Haiss and Sumegi (2006) and Levine (2004) insurance contributes to the growth of an economy by means of its activities of risk transfer and indemnification which promote the financial stability of a firm. Similarly, Torbira and Ngerebo-A (2012) have argued that by reinstating the insured back to his pre-loss position, insurance sinks the aggregate risk and as such, accentuates the stock of existing capital in the economy. According to Dorfman (2005) risk management centres on rational development and execution of a plan targeted at potential unexpected adverse outcomes of business activities that will guarantee the management of individual’s and organization’s exposure to loss and to protect its investments. Nweke (2013) also holds the view that risk management role of insurance business stimulates economic growth of a nation by means of sinking investor’s panic of loss.

 

Following the argument by most scholars, we can deduce that insurance activities especially in the area of risk management and loss indemnification can be a source of confidence to investors and as such, help in stimulating the growth of an economy. However, despite the demonstrated efficacy of risk management role of insurance companies in mitigation of losses as well as the adverse consequences that random shocks may have on capital investment in the economy as revealed in the works of Haiss and Sumegi (2006) and Levine (2004) more attention has been dedicated by researchers on banks and the development of several nations economy with little emphasis on non- banks financial institutions such as insurance. Based on this, insurance activity in Nigeria is still faced with little and uneven development especially in the area of non-life policy. This has led to high level of risk in economic undertakings by individuals and firms. Against this background, a study of insurance activities and growth of Nigerian economy with greater emphasis on risk management and loss indemnification becomes relevant.This work is structured into five sections, beginning with section one which is the introduction. Section two   reviews related literature. Section three dwells on the research methodology.  Data are presented and analysed in chapter four while section five contains our concluding remarks and policy recommendations.

3. MATERIALS AND METHODS

For clarity of purpose, this section is further divided into subsections as presented below:

3.1. Research Design

The study adopted the ex post facto design.

3.2. Data and Variables Description

Data used in this study are of time series nature. They were data on insurance various claims payment comprising of Claim payment on fire policies, Claim payment on accidents policies, Claim payment on motor vehicles, Claim payment on employers liabilities, and Claim payment on marine policies and GDP over the years 1981 to 2011 as presented in table 1  below. However, the study would have expanded the years up to 2014 but the absence of data on various claims beyond 2011 proved the effort abortive.

Table-1. Variable Representation
Year GDP CPF CPA CPM CPE CPMA
1981 94.33 6.3 3.7 47.0 1.3 10.0
1982 101.01 6.8 5.5 44.7 1.5 10.4
1983 110.06 6.0 5.6 55.6 1.2 5.4
1984 116.27 5.3 6.3 53.7 1.2 8.0
1985 134.59 (0.0) 6.4 54.2 0.9 (0.0)
1986 134.6 6.9 5.9 54.2 0.8 11.4
1987 193.13 16.4 8.4 55.6 8.0 3.3
1988 263.29 16.5 11.2 67.8 0.8 30.2
1989 382.26 47.0 28.8 73.1 2.0 110.0
1990 328.61 61.5 30.8 114.5 2.3 37.3
1991 545.67 80.4 42.8 164.8 5.6 58.0
1992 875.34 114.8 66.8 267.4 8.3 81.2
1993 1,089.68 1,161.0 448.7 607.3 12.8 119.5
1994 1,399.70 267.4 193.8 605.2 22.0 132.4
1995 2,907.36 194.5 207.1 563.6 9.6 184.4
1996 4,032.30 342.7 276.9 712.3 54.5 191.8
1997 4,189.25 349.1 376.6 780.9 42.0 106.1
1998 3,989.45 388.1 396.7 832.9 39.8 129.5
1999 4,679.21 891.0 1,649.0 1,824.7 93.8 1,068.9
2000 6,713.57 1,107.7 806.3 1,804.2 112.4 440.8
2001 6,895.20 1,164.7 957.8 2,315.9 132.4 790.7
2002 7,795.76 1,857.9 109.3 2,818.7 110.8 900.9
2003 9,913.52 1,681.7 2,266.8 3,040.2 126.8 1,240.6
2004 11,411.07 2,724.4 2,852.9 3,476.2 189.5 1,361.4
2005 14,610.88 2,766.7 3,138.2 3,733.4 153.6 1,266.2
2006 18,564.59 6,663.0 15,239.8 20,735.0 912.7 10,493.4
2007 20,657.32 1,793.4 3,829.1 6,196.1 207.5 1,904.2
2008 24,296.33 6,076.6 4,467.5 9,935.5 319.2 3,185.0
2009 24,794.24 15,124.7 6,567.5 13,040.3 337.4 4,556.6
2010 33,984.75 7,794.1 6,444.5 13,219.0 281.0 2,965.2
2011 37,409.86 8,520.5 6,820.6 13,205.6 271.1 2,889.6
Source: CBN statistical bulletin (Various years)

3.3. Model Specification  

This study modelled economic growth as a positive function of insurance risk management capturing their various claims payment, the study specify in functional form thus:

GDP       =             f (CPF, CPA, CPM, CPE, CPMA,) --------------------------------- (1)

Where:
CPF = Claim payment on fire policies,
CPA = Claim payment on accidents policies,
CPM = Claim payment on motor vehicles,
CPE = Claim payment on employers liabilities, and
CPMA = Claim payment on marine policies.
Econometrically, we have;

GDP       = βo + β1 CPF+ β2 CPA + β3CPM + β4CPE + β5CPMA+µi--------(2)

Where:
βo = Constant,
β1-β5 - = Estimation parameters, and
µ   = Error term.
We specify  2.in log-form as -

LGDP= βo + β1 LCPF+ β2 LCPA + β3 LCPM + β4 LCPE + β5LCPMA+µi ------ (3)

Our A-a priori expectation with respect to equation 2 are -

β1, β2, β3, β4, β5 >  0

For the purpose of detecting the presence or otherwise of unit root which is a pre-test for co-integration, we employed the Philp-Peron test statistics as -

Yt=  α + pyi-1 +  εt ……………………………. (4)

Where:
Y  = variable of choice.
α0  = intercept.
εt  =  white noise error term.

Following from this, the hypothesis to be tested becomes-:

Ho:   ẟ = 0, the time series data is non-stationary.
H1:   ẟ ≠ 0, the time series data is stationary

3.4. Error Correction Model

After establishing the existence of co-integration among our employed variables, ECM was used to ascertain the speed of adjustment and the model is presented thus:

3.5. Granger Causality

The causal relationship between GDP and our independent variables (insurance claims payment) is expressed as:

From the model, Xt is said to granger cause Yt as long as α3i is ≠ 0; similarly, in the second model, Yt is said to granger cause Xt as far as β2i is ≠ 0.  At the occurrence of the first scenario, the causation is said to be supply leading, while the second is said to be demand following. However, if both are significant, the variables are said to have a bidirectional relationship.

3.6. Diagnostic Test

Breush-Godfrey Serial correlation LM test: This was used to check the serial order correlation or autocorrelation amid the successive error terms. Breush-pagan-Godfrey Heteroskedasticity test: this shall be used for the check of heteroskedasticity of data.

4. RESULT PRESENTATION AND ANALYSIS

Results of our tests are presented in tables as shown below.

Table-2. Philip perron stationary result
Variables PP-statistics Critical value Order of integration
GDP -2.623271  (0.0106) 5% level   -1.952910 stationary at first diff I(1)
CPF -8.102115  (0.0000) 5% level   -1.952910 stationary at first diff  I(1)
CPA --1.955706 (0.0497) 5% level -1.952473 stationary at level I(0)
CPM -9.689682 (0.0000) 5% level   -1.952910 stationary at first diff I(1)
CPE -2.486525 (0.0148) 5% level  -1.952473 stationary at level I(0)
CPMA -3.129043 (0.0028) 5% level  -1.952473 stationary at level I(0)
 Source: Author’s computation

From table 2, the result of Philip perron statistics indicates that all the variables were stationary at first difference although not at the same order of integration. At 5%level for instance, only CPA, CPE, and CPMA proved to be stationary, while at the first differencing, GDP, CPF, and CPM became stationary. Therefore, having proved the stationarity of the data which stands as a pre-test for co-integration, we proceeded to other econometric analysis.

4.1. Diagnostic Tests

Table-3. Heteroskedasticity test

Heteroskedasticity Test: Breusch-Pagan-Godfrey
F-statistic 1.160867     Prob. F(5,25) 0.3557
Obs*R-squared 5.841203     Prob. Chi-Square(5) 0.3220
Scaled explained SS 5.249895     Prob. Chi-Square(5) 0.3862
Prob(F-statistic) 0.355723    
Source: Authors’ computation

Based on the result of Breush-Pagan-Godfrey test of heteroskedasticity as depicted above, the recorded F-statistic and Observed R-square were 0.486523 and 3.210264? respectively, while the reported probabilities of 0.8142 and 0.7820 which are greater than the critical probability of 5% (0.05) level of significance and as such, implies that our data are not heteroskedastic which is a desirable result. 

Table-4. Serial correlation test
Breusch-Godfrey Serial Correlation LM Test:
F-statistic 0.209379     Prob. F(2,23) 0.8126
Obs*R-squared 0.554319     Prob. Chi-Square(2) 0.7579
 Source: Authors’ computation

As shown in the table 4, the result of Breush-Godfrey serial correlation LM test with the F-statistics and Observed R-squared of 0.208541 and 0.515382 respectively, and probabilities of 0.8127 and 0.7728 which were all greater than the critical probabilities at conventional levels of significance (1%, 5%, and 10%) is an indication of an absence of serial correlation problem. Therefore, we accept the null hypothesis that the data are not serially correlated which confirm our Durbin Watson result.

4.2. OLS Result

The regression results are presented in table 5 as ahown below.

Table-5. Regression Result
Dependent Variable: GDP
Method: Least Squares
Date: 03/05/16   Time: 23:31
Sample: 1981 2011
Included observations: 31
Variable Coefficient Std. Error t-Statistic Prob.  
C 539.4301 444.9323 1.212387 0.2367
CPF 0.571796 0.260912 2.191526 0.0379
CPA 1.492611 0.750393 1.989106 0.0577
CPM 3.036988 0.431481 7.038525 0.0000
CPE 60.07751 14.03822 4.279568 0.0002
CPMA -11.89334 1.240861 -9.584752 0.0000
R-squared 0.975352 Mean dependent var 7826.232
Adjusted R-squared 0.970422 S.D. dependent var 10507.51
S.E. of regression 1807.093 Akaike info criterion 18.00881
Sum squared resid 81639632 Schwarz criterion 18.28636
Log likelihood -273.1366 Hannan-Quinn criter 18.09929
F-statistic 197.8568 Durbin-Watson stat 2.098734
Prob(F-statistic) 0.000000      

4.3. Analysis

From table 5, our R2 stood at 98% approximately indicates that over 98% variations in economic growth measured by GDP are being accounted for by our selected explanatory variables. This shows that risk management activities of insurance sector have a very high percentage influence on the growth of Nigerian economy. Interestingly, the observed Durbin Watson statistics of 2.09 is an indication that there is an absence of serial correlation and as such, the result is no spurious. Also, from the table, our observed F-statistics of 197.8568 with 0.000000 probability implies that at 5% critical level, our model demonstrated a good fit and as such, sufficiently captures insurance risk management and growth of Nigerian economy. However, on the short run-relationship between our employed variables, the OLS result as depicted above shows that all our employed variables related positively and significantly with gross domestic product over the years of our study with the exception of claim payments on marine sector which proved to be negatively and significantly related with gross domestic product. The implication of this result is that risk management activities of insurance as captured by their various claims payment has been able to mitigate the adverse effect of economic losses and thereby contributing positively and significantly with economic growth except CPMA. Table 5 is relevant in this respect.

Table-6. Johansen co-integration Result
Date: 03/05/16   Time: 23:34         
Sample (adjusted): 1983 2011         
Included observations: 29 after adjustments
Trend assumption: Linear deterministic trend
Series: GDP CPF CPA CPM CPE CPMA      
Lags interval (in first differences): 1 to 1                       
Unrestricted Cointegration Rank Test (Trace)
Hypothesized No. of CE(s) Eigenvalue Trace Statistic 0.05 Critical Value Prob.**
None * 0.988245 302.7076 95.75366 0.0000
At most 1 * 0.933884 173.8471 69.81889 0.0000
At most 2 * 0.838495 95.07299 47.85613 0.0000
At most 3 * 0.667593 42.19955 29.79707 0.0012
At most 4 0.297389 10.25905 15.49471 0.2613
At most 5 0.000808 0.023427 3.841466 0.8783
Trace test indicates 4 cointegratingeqn(s) at the 0.05 level
* denotes rejection of the hypothesis at the 0.05 level
**MacKinnon et al. (1999) p-values       

From the Johansen co-integration result obtained, our trace statistic indicates the existence of four co-integrating equations at 5% level of significance. This is evidenced on the probability values obtained which ranges from 0.0000 at none to 0.0012 at most three (3). Based on this, the study has proven the existence of long run or equilibrium relationship among our employed variables, and as such, it becomes imperative that we ascertain the speed at which any deviation at short run adjusts to the equilibrium using ECM. This is shown from our results in table 7.

Table-7. Result of Error Correction Model
Dependent Variable: D(GDP)         
Method: Least Squares     
Date: 03/07/16   Time: 08:32         
Sample (adjusted): 1982 2011         
Included observations: 30 after adjustments
Variable Coefficient Std. Error t-Statistic Prob.  
C 664.7861 219.3092 3.031274 0.0059
D(CPA) 0.718228 0.370097 1.940649 0.0647
D(CPF) -0.300823 0.187033 -1.608392 0.1214
D(CPM) 2.161534 0.343585 6.291119 0.0000
D(CPE) -11.77646 11.37998 -1.034840 0.3115
D(CPMA) -3.559975 1.350040 -2.636940 0.0147
ECM(-1) 0.106685 0.205740 0.518542 0.6090
R-squared 0.802982 Mean dependent var 1243.851
Adjusted R-squared 0.751586 S.D. dependent var 1944.357
S.E. of regression 969.0899 Akaike info criterion 16.79156
Sum squared resid 21600111 Schwarz criterion 17.11850
Log likelihood -244.8733 Hannan-Quinn criter 16.89615
F-statistic 15.62343 Durbin-Watson stat 1.214098
Prob(F-statistic) 0.000000      
Source: Authors’ computation

From table 7, our ECM value of 0.106685 is an indication that approximately over 11% disequilibrium in short run is being adjusted back to the equilibrium annually. Considering that negativity in this test indicates significance, the ECM is not rightly signed and does not show a reasonable dynamics of GDP to the explanatory variables.

4.4. Analysis of Direction of Causality

This was done by employing the Granger Causality test with the result shown in table 8.

Table-8. Granger Causality Result
Pairwise Granger Causality Tests
Date: 03/05/16   Time: 23:34
Sample: 1981 2011
Lags: 2
Null Hypothesis:
Obs
F-Statistic
Prob. 
CPF does not Granger Cause GDP
29
6.55962
0.0053
GDP does not Granger Cause CPF
19.5439
9.E-06
CPA does not Granger Cause GDP
29
0.11660
0.8904
GDP does not Granger Cause CPA
5.28639
0.0125
CPM does not Granger Cause GDP
29
0.00021
0.9998
GDP does not Granger Cause CPM
10.5969
0.0005
CPE does not Granger Cause GDP
29
0.13026
0.8785
GDP does not Granger Cause CPE
2.45480
0.1072
CPMA does not Granger Cause GDP
29
0.02475
0.9756
GDP does not Granger Cause CPMA
4.19339
0.0274
Source: Authors’ computation      

A cursory look at table 8 reveals that there is no bidirectional relationship between our employed variables, however, a unidirectional relationship was observed from CPF to GDP, GDP to CPA, GDP to CPM, and GDP to CPMA. Meanwhile, no directional relationship of any kind was observed between GDP and CPE.

With respect to impulse response, our result in table 9 is instructive.

Table-9. Impulse response to one S.D innovation (shocks)
Response of GDP:
 Period
GDP
CPF
CPA
CPM
CPE
CPMA
 1  352.8825  0.000000  0.000000  0.000000  0.000000  0.000000
   (46.3357)  (0.00000)  (0.00000)  (0.00000)  (0.00000)  (0.00000)
 2  712.7930  445.5381 -150.2083 -275.2191  146.9859  388.4754
   (174.317)  (126.802)  (108.970)  (98.3831)  (88.8704)  (88.2438)
 3  749.1952  162.2482 -197.7265 -262.3121  166.5136  158.5324
   (249.738)  (190.087)  (180.114)  (156.755)  (147.498)  (205.766)
 4  964.9543 -398.7275 -296.7184 -276.7468  115.6562  243.5147
   (307.365)  (206.785)  (228.357)  (205.192)  (187.574)  (223.010)
 5  806.7825  506.4886 -121.3214 -42.89496  489.2134  502.6698
   (369.075)  (317.819)  (310.123)  (276.929)  (275.721)  (281.929)
 6  1072.741  712.0115 -426.8673 -313.1428  67.60833  188.5009
   (440.251)  (348.493)  (346.743)  (275.904)  (333.051)  (359.338)
 7  1265.129 -568.3925 -802.6774 -212.7225  433.3657  410.8613
   (559.308)  (555.182)  (426.521)  (326.301)  (421.036)  (429.634)
 8  1381.683  245.3877 -140.9334 -66.33712  704.9590  182.1812
   (677.859)  (770.238)  (497.595)  (366.983)  (565.333)  (546.589)
 9  1566.384  1583.550 -521.1246 -440.6291  67.53235  600.8894
   (813.623)  (921.867)  (548.387)  (462.396)  (775.184)  (583.877)
 10  1954.987  166.3731 -1345.494 -620.1609  575.4731  737.6306
   (979.184)  (1477.26)  (723.451)  (552.365)  (926.045)  (740.024)
Source: Author’s computation

In the above table, we report the result of the impulse response estimate to one standard deviation shock in each of the variables in the VAR system for ten years period. The result shows that response of GDP to own shock at short run is positive at 72% and 13.81% at long run. However, impulse responses of GDP to shocks emanating from our dependent variables at short run are positive for CPF, CPE, and CPMA at 44.5%, 14.6%, and 38.8% respectively and negative for CPA and CPM at 15% and 27.5% respectively. Meanwhile, at long run, impulse response of GDP to shocks from CPF, CPA, CPM, CPE, and CPMA retained the same sign but varies in values with 24.5%, 14%, 66%, 70.4%, and 18.2% respectively for CPF, CPA, CPM, CPE, and CPMA (see table 9).

Table-10. Variance Decomposition Estimate
Response of GDP:
 Period GDP CPF CPA CPM CPE CPMA
 1  352.8825  100.0000  0.000000  0.000000  0.000000  0.000000
 2  1049.729  57.40834  18.01423  2.047545  6.873898  1.960637
 3  1360.261  64.52393  12.15086  3.332321  7.812373  2.666127
 4  1782.621  66.87242  12.07816  4.710901  6.959102  1.973353
 5  2143.299  60.42861  13.93950  3.579203  4.854049  6.574997
 6  2563.561  59.75038  17.45786  5.274542  4.885090  4.665489
 7  3088.951  57.92783  15.41010  10.38530  3.838879  5.181661
 8  3473.515  61.63377  12.68587  8.377642  3.072379  8.216798
 9  4225.855  55.38104  22.61313  7.180926  3.163012  5.577056
 10  4977.723  55.33937  16.40949  12.48183  3.831851  5.356067
 1  352.8825  100.0000  0.000000  0.000000  0.000000  0.000000
 2  1049.729  57.40834  18.01423  2.047545  6.873898  1.960637
 3  1360.261  64.52393  12.15086  3.332321  7.812373  2.666127
 4  1782.621  66.87242  12.07816  4.710901  6.959102  1.973353
 5  2143.299  60.42861  13.93950  3.579203  4.854049  6.574997
 6  2563.561  59.75038  17.45786  5.274542  4.885090  4.665489
 7  3088.951  57.92783  15.41010  10.38530  3.838879  5.181661
 8  3473.515  61.63377  12.68587  8.377642  3.072379  8.216798
 9  4225.855  55.38104  22.61313  7.180926  3.163012  5.577056
 10  4977.723  55.33937  16.40949  12.48183  3.831851  5.356067
Source: Authors’ computation

According to Iyeli (2010) variance decomposition helps to determine the total proportion of forecast error to own innovation and to innovation in the other variables. Looking at the variance decomposition estimate above, it shows that own shock represents that greatest source of variations in the forecast error of our observed variable (GDP). For instance, own shock explains about 57.40% variations at short run and 61.63%  in the long run, while our explanatory variables (CPF, CPA, CPM, CPE, and CPMA), explains about 18.01%, 2.04%, 6.87%, 1.96, and 13.70% variations in the short run and 12.68%, 8.38%, 3.07, 8.23, and 6.01% variations in the  long run respectively. However, within the ten years, own shock and shock from other variables show a mixture of contribution to the variations in the forecast error of the explained variable (GDP).

4.5. Concluding Remarks

The study tried to ascertain the relationship between insurance risk management and growth of Nigerian economy over the years 1981 to 2011. Based on the above estimates and analysis, the study revealed that the risk management activities of insurance sector in Nigeria relates positively with the growth of the economy except in the area of marine insurance. However, using the Johansen co-integration test, the study observed the existence of equilibrium relationship among our employed variables and over 10% of any disequilibrium in the short run was found to adjust back over a year. However, a unidirectional relationship was observed from CPF to GDP, GDP to CPA, GDP to CPM, and GDP to CPMA. Following our findings and remarks, the study thereby recommends that1) The level of protocol required by insurance sectors in the case of indemnification should be greatly reduced through deliberate policy and its implementation;

  1. Effective policy should be made to strengthen the activities of insurance industry in Nigeria.

REFERENCES

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About the Authors

Nonso Fredrick Okoye
Department of Finance and Banking; University of Port Harcourt, Nigeria
Obiamaka Egbo
Department of Banking and Finance, Faculty of Business Administration, University of Nigeria, Enugu Campus, Nigeria
Onuora M. Okeke
Department of Political Sceince Enugu State College of Education (Technical), Nigeria
Ebele Nwankwo
Department of Economics, Accounting and Finance, College of Management Sciences, Bells University of Technology, Ota, Ogun State, Nigeria

Corresponding Authors

Obiamaka Egbo

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