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INSURANCEINTERMEDIATIONOPERATIONSANDECONOMICGROWTH:ANALYSISOFTHENIGERIANEXPERIENCE

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INSURANCE INTERMEDIATION OPERATIONS AND ECONOMIC GROWTH: ANALYSIS OF THE NIGERIAN EXPERIENCE

Dr. Ayodele Momudu Rivers State University of Science and Technology, Port Harcourt, Nigeria

Pn11fessor Chinedu B. Ezirim Aina Sampson Olaletan

University of Port Harcourt, Port Harcourt Nigeria

ABSTRACT

/twas the central objective of this study to econometrically analyze the relationships between the different facets of the intermediation operations of insurance companies and economic growth of Nigeria. More specifically, the study sought to examine the relationships between the funds' investment, premium generation, and claims settlement operations of the insurance industry and growth or otherwise in the country's GDP over time. The results indicate that jointly or globally, the three nominated intermediation operations of the insurance companies significantly related with growth in GDP of Nigeria. Relatively, only investment operations were fouYJd to positively and significantly relate with GDP. Other variables were positive but not significant. Thus funds investment. is the strongest link the insurance industry has with the growth of the economy, which should not only be encouraged but strengthened by deliberate corporate and macroeconomic policies. Key "Vords: Insurance, Intermediation Operations, Economic Growth, Nigeria

BACKGROUND OF THE STUDY

Is there a statistical nexus between insurance intermediation operations and economic growth and development? Many studies and theorizing .have been devoted to analyzing the relationship between ecol}.omic growth and financial market deepening, with banking systems and securities markets receiving considerable attention while the insurance system received only a paltry mention (see for instance, Ezirim, 1999). It is no gainsaying that the insurance industry, the banking industry, other financial institutions and the financial markets are at least theoretically closely related in their contributions to economic growth. However, insurance performs some striking and distinctive economic functions than do other financial institutions, including banks. At least in their economic function of indemnification against risk of loss and other ancillary functions such as risk avoidance, assumption, and transfer and loss prevention, they even serve other financial service providers to make their operations more efficient. This makes it all the more imperative for particular conditions to flourish so that the industry can make a more significant contribution to the economy. ·

Even the complementarity axiom that the economy is served better when insurance contribution isjointly integrated with the contributions of other financial services providers such as banks is being verified in the light of recent documented reports. For instance, Webb, Grace, and Skipper (2002) hav~ shown that the independent -contribution of insurance is robust with the

(Ezirim, 2004; Brainard, 2008). Invariably any economy, developed or less developed should be better served in terms of boosted growth and development by the operations of insurance comparues.

The Research Problem and Objectives

It is re-iterated that the insurance companies carry out certain critical intermediation operations such as savings mobilization through premium generation, funds investment and lending, .and claims settlement. By their nature and character, these operations are expected to contribute imniensely to the growth of the economy. This is so much so since the insurance sector is the most important non-banking financial institution when consideration is given to volume of assets, investments, premium generation, and employment of human resources. Looking at these, their activities in the country ought to boost economic growth. But the economy of Nigeria has not recorded desired growth. In fact, there has been a regime of sub-optimal growth trends over the years. In view of these disturbing variances, can we say that the insurance industry has not been playing its parts well in gingering desired levels of growth over time? This is truly an empirical question. Of course, a good number of empirical works have been carried out to address this research question of whether or not insurance operations cause desired growth in many developed and less developed countries. Unfortunately, to the best of the researcher's knowledge, only a few works such as Ezirim (1999) and Okolo and Ani (2014) have uniquely analyzed the insurance-growth nexus. This creates a research gap which the present study attempts to fill.

Thus, it becomes the central objective of this study to econometrically analyze the relationships between the different facets of the intennediation operations of insurance companies and economic growth of Nigeria. More specifically the study seeks to:

i) . Determine whether or not there exist a significant relationship between the savings mobilization potential (premium generation) of the insurance industry and economic grovvth

ii) Evaluate whether or not there exist a significant relationship ·between the funds' investment operations of the insurance industry and economic growth

iii) Detennine whether or not there exist a significant relationship between the claims settlement operations of the insurance industry and economic growth

RELATED PREVIOUS EMPIRICAL STUDIES

. Agiobenebo and Ezirim (2002) examined the impact of financial intermediation on the profitability of insurance companies in Nigeria. Use was made of econometric models patterned after the partial adjustment principles. Estimation followed the least square method. The results indicate that the level of premiums relative to total assets is positively and significantly related to profitability of insurance companies. By implication, the ability of insurance companies to effectively mobilize premiums determines the extent to which they experience boost in their profitability. Contrariwise, the factors of net allocation potentials, loan levels, investments were found to be positively related, but not significant at conventional levels. It was deduced from the study that for the fact that these important aspects of financial intermediation did not exert ·significant influences on profitability would be indicative of possible improper channeling· of

inclusion of banking sector variables, and, in fact, higher levels of insurance and banking penetration jointly produce a greater effect on growth than their individual contributions combined. In other words greater synergy is achieved by the economy when the two industries complement in their growth-oriented roles. It is perhaps in realization of this fact that Brainard (7008) volunteered that very "importantly, the unique functions of insurance should be complementary to banking and financial sector deepening more broadly. For instance, insurance facilitates credit transactions such as the purchase of homes and cars and business operations, while depending in turn on well functioning payment systems and robust investment opportunities;'.

In recent times, however, some researchers have started documenting in appreciable number the specific contributions of insurance to the growth of the economy of various developed and developing co'untries. As in Brainard (2008),"the evidence suggests thaf insurance contributes materially to economic growth by improving the investment climate and promoting a more efficient mix of activities than would be undertaken in the absence of risk management instruments". Lending credence to this position are the works ofHak (1996), Ward & Zurbruegg (2000), Ward and Zurbruegg'(2003), Jonathan and Morduch (2004), Caessens (2005), and Moser (2007)~ Haiss & Stimegi (2008), Arena (2006) , Arena (2008), Hongbing, Meng, & Wenhua, (2013), Lee, Leen & Chiu (2013), Chang, Lee & Chang (2013), Bahrami (2014), and Okolo and Ani (2014).to mention only a few.

In low income countries that characterize the less developed economies (LDEs) of Africa and Asia, life coverage constitute a small proportion of the effective total insurance demand which would make a typical observer presume that life insurance contribution would be low. This is still an empirical issue, however, but the case should be different for high income countries (HICs) where life policies are expected to make substantial contribution to growth in view of wealth-boosting effects. If these arguments are taken, then the position of Brainard (2008) can easily be verified that "the relationship between per capita income levels and insurance penetration is also strong in the reverse direction - with rising income a strong driver of life insurance coverage". From the investment point of view, life insurance companies serve an important function as institutional investors who assist in providing capital for infrastructural

· developments as well as other long term investments. They also provide professional oversight to these investments. These life insurance providers are well suited for these when consideration is given to the long term nature of their insurance liabilities, sizeable reserves, and predictable premiun1s. It must . be subm!tted that ''these benefits are fully realized only in markets where insurance providers invest a substantial portion of their portfolios domestically" (Brainard, 2008). '

Looking at the above, the insurance mechanism can be rightly described in the words of Ezirim and Isitor (2005) as "an indispensable tool for development both at micro and macro . levels of the economy", with insurers performing the distinctive functions including production and or marketing, underwriting, rate making, claims settlement, funds investment, and reinsurance purchase. Insurance companies engage in the all-pervading function of fmancial intermediation. By this, the insurance industry mobilizes funds from the surplus economic agents (such as by premium generation, equity and debt capital) and channels it to the deficit agents in the economy (by way of investments, loans and claims payment). When these and other important functions are carried out in the most efficient manner, insurance industry becomes a well-functioning system that guarantees better pricing of risk, greater efficiency in the overall mobilization and allocation of capital and mix of economic activities, and higher productivity

funds into avenues other than the traditional profit centres that inform the intermediation activities. Further, the study revealed that profitability in previous periods, more than anything · else, significantly account for profitability in current periods. This is explained through the angle of the information content, confidence generation, and image boosting potentials that increase patronage. Finally, it was also underscored that in any one year, during the period 1970 through . 1997, the insurance industry, on the average, achieves only about 27.3% of its expected optimun1 level of profitability. By implication, it was easy to see a clear case of sub-optimality of industrial operations.

In another related study, Ezirim (2004) further analyzed the r~lationship between intermediation operations and profitability of insurance companies in a developing African economy (Nigeria). In this case the emphasis was on comparing the effects of the intermediation operations on two different indices of profitability, namely return on assets (ROA) and return on equity (ROE). The critical indicators of financial intermediation used were resource use index, liability composition, asset composition, expenses ratio, liquidity ratio, and capital adequacy. Multivariate regression . equations were specified to capture identified relationships, while estimation followed the linear and log-linear least squares. The results showed that the ROE · model behaved better thari the ROA model in capturing and expressing the relationship between profitability and intermediation indicators. The results agreed with the earlier study by Agiobenebo and Ezirim (2002) in respect of a suffocating preponderance of sub~optimality syndrome in the management and use of available funds by the institutions studied.

Ezirim and Muoghalu (2002) undertook an econometric analysis of the net intermediation behaviour of the Nigerian financial superstructure drawing evidence from commercial banks and insurance companies. Relevant models were constructed following the multivariate regression and partial adjustment principles with both linear and log-linear specifications. The results of the relative analysis of the effects of the explanatory variables show that the Net Allocation Potential (NAP) of insurance companies is positively related to expected rate of returns, rate of economic growth, inflationary trends and the loss ratio; but negatively related to base lending rate and the savings deposit rate. Only the savings rate and the loss ratio are found to be significant at conventional levels. For commercial banks, the NAP is positively related to the minimum rediscount rate (MRR), expected rate of return on investments (EROI), rate of economic growth, inflationary trend, and financial market imperfections but negatively related to the savings rate of interest The expected return on investments, rate of economic growth and financial market imperfections are found significant; others are not. Based on these, policy options suggested range from making the level of MRR to be both market responsive and stimulating; design of policies by insurance companies with in-built incentives that are, at least, comparable to commercial banks savings deposit rate; to relaxing of the restrictive provisions of the Insurance Decree, 1997 as they affect investments. ·

As in Ezirim (2004), the insurance mechanism has been described as an indispensable tool for development both at micro and macro levels of the economy. Inherent in the functions which Insurance companies perform - production or marketing, underwriting, rate making, claims settlement, funds investment, reinsurance purchase, etc, - is the all pervading function of financial intermediation. By this, the insurance industry mobilizes funds from the surplus economic agents (such as by premium generation, equity and debt capital) and channels it to the deficit agents in the economy. In the same vein, this degree also would have implications for overall industrial/corporate performance. In developed economies, where most things tend to . work as planned, profitability of insuring financial institutions would be a positive and

significant function of their net intermediation activities. Given this assumption, models built with such indicators as return on assets (ROA) and return on equity (ROE) would behave well in the light of their relationship with the indicators of intermediation operations, and taking due cognizance of the prevailing structural efficiencies and realities. On the other hand, it is not yet well ascertained whether or not the same results are likely to be obtained using data from developing economies that are fraught with structural deficiencies and irregularities (Ezirim, 2004).

It is in the light of the above stated point that Ezirim (2004) investigated the effects of the various facets of the intermediation function on the profitability of insuring financial institutions in a developing African economy. Drawing evidence from Nigeria, the paper makes a comparative analysis of the effects of financial intermediation indicators of resource use ratio, liability composition, asset composition, expenses ratio, liquidity ratio, and capital adequacy; on the ROA and ROE indices. Multivariate regression models were constructed for the purpose and estimation done using the Least squares technique (OLS), applied to time series annual data from 1970 to 1998. The results show that the ROE model behaved better than the ROA model in expressing the relationships between profitability and the indicators of intermediation. Further, a general implication of the results of the analysis was the suffocating preponderance of suboptimality syndromes in the management and use of available funds by the institutions studied. Recommended policy directions include managerial reorientation, policy readjustments, practice of efficient portfolio construction and management, and removal of regulatory bottlenecks. This study attempts to investigate the factors that affect insurance investment behavior in Nigeria, using the Partial Adjustment Mechanism. It was discovered that insurance companies' investment behavior responded positively and significantly to the level of premiums, capital base, and profitability of operations (ROA), but inversely and significantly to the rate of growth of the economy. Investments in the previous periods though positively related, failed to be a significant vector ip. current investment decisions. It takes the Nigerian insurance industry an average of 44 months, 14 days to attain optimum level of investments, i.e. an achievement rate of 27% in a given year;-and a rate that signifies low level and/or improper allocation and utilization of investible resources. · Against these backgrounds; portfolio adjustments, management reorientation, development and training; and governmental policy review and decree amendments are suggested by the paper. ·

Ward & Zurbruegg (2000) examined the sho1t- and long-run dynamic relationships between economic growth and growth in the insurance industry for nine OECD countries using causality tests and cointegration analysis on a unique set of annual data for real GDP and total real premiums issued in each country from 1961 to 1996. Their results from the tests suggested that in some countries, the insurance industry Granger caused economic growth, and in other countries, the reverse was true. Moreover, their results indicated that these relationships are country specific and any discussion of whether the insurance industry does promote economic growth will be dependent on a number of national circumstances (Ward & Zurbruegg (2000)).

Haiss & Stimegi (2008) investigated both the impact of insurance investment and premiums on GDP growth in Europe~ applying cross-country panel data analysis from 1992 to 2005 for 29 European countries. They found a positive impact of life insurance on GDP growth in the EU-15 countries, Switzerland, Norway and Iceland. For the New EU Member States from Central and Eastern Europe, they found a larger impact for liability insurance. Furthermore their findings eIIl.phasized the impact of the real interest rate and the level of economic development

on the insurance-growth nexus. They argued that the insurance sector needs to be paid more attention in financial sector analysis and macroeconomic policy (Haiss & Silmegi (2008)).

Arena (2008) has tested whether there is a causal relationship between insurance market activity (life and nonlife insurance) Gllld economic growth. Arena (2008) has applied the generalized method of moments (GMM) for dynamic models of panel data for 55 countries between 1976 and 2004, Arena (2008) found robust evidence for this relationship. Both life and nonlife insurance have a positive and significant causal effect on economic growth. For life insurance, high-income countries drive the results, and for nonlife insurance, both high-income and developing countries drive the results (Arena (2008)).

Soo (1996) examined the relationship between life insurance and economic growth, theoretically and empirically; by studying the life insurance effect on three aspects of economic growth: (1) the effect of life insurance and an increase in life insurance premium tax rate on economic savings and consumption, (2) the role of life insurance in economic productivity, and (3) the causality feedback between life insurance growth and economic growth. Soo (1996) suggested that growth in the life insurance industry causes an increase in economic growth. Soo (1996) developed a dynamic optimization model to determine the effect of life insurance in individuals' process of maximizing ·their expected life-time utility. He concluded that the availability of tax-loaded life insurance affects the accumulation process of individual wealth, but not aggregate wealth. The theoretical model also suggested that a permanent increase in the annuity premium tax rate in a lending economy decreases the steady state aggregate consumption and aggregate wealth

The conditional directional feedback from life insurance growth to economic growth suggested that life insurance growth explains approximately 14% of the variance in economic growth. He found that a unit shock in life insurance growth has a positive impact on economic growth in both the short-run and the long-run. He also found that a unit shock in the insurance premium tax rate has a positive impact on economic growth but a negative impact on life insurance growth in the short-run (Soo (1996)).

Chang, Lee & Chang (2013) applied the bootstrap panel Granger causality test to test whether insurance activity promotes economic growth, using data from 10 OECD countries over the period of 1979- 2006. Their Empirical results indicated that one-way Granger causality running from all insurpnce activities to economic growth for France, Japan, Netherlands, Switzerland, and the UK, and economic growth Granger causes insurance activities in Canada (for life insurance), Italy (for total and life insurance) and the USA (for total and non-life insurance). There is a tWo-way Granger causality between life insurance activity and economic growth in the USA, while no causality between insurance activities and economic growth is found in Belgium (for al~ insurance), Canada (for total and non-life insurance), Italy (for non-life insurance) and Sweden (for life insurance). Their results also confirmed the finding of Ward and Zurbruegg (2000) showing that the insurance-growth nexus varies across countries, since their paper have previously demonstrated heterogeneity in this vein. In an analysis of a broader, though overlapping 17-country sample and taking into account banking activities, their results suggested the importance of including banking activities when investigating the insurance­growth relationship (Chang, Lee & Chang (2013)).

Lee, Leen & CQ.iu (2013) used the panel seemingly unrelated regressions augmented Dickey-Fuller (SURADF) test to re-investigate the stationarity properties of real life insurance premiums per capita and real gross domestic product (GDP) per capita for 41 countries within three levels of income c6vering 1979-2007. Their empirical results first reveal that the variables

in these countries are a mixture ofl(O) and I(l) processes, and that the traditional panel unit-root tests could lead to misleading inferences. Second, for the estimated half-lives, the degrees of mean reversion are greater in high-income countries. Third, there is concrete evidence favoring the hypothesis of a long-run equilibrium relationship between real GDP and real life insurance premiun1s after allowing for the heterogeneous country effect. The long-run estimated panel parameter results indicate that a 1 % increase in the real life premium raises real GDP by 0.06%. Finally, they determined that the development of life insurance markets and economic growth exhibit long-run and short-run bidirectional causalities. These findings offer several useful insights for policy-makers and researchers (Lee, Leen& Chiu (2013)).

Hongbing, Meng, & Wenhua, (2013) applied the bootstrap panel Grll.Ilger causality test to investigate the relationship between insurance activity and economic growth using data from 31 regions of China over the period 1997-2011. Their empirical results indicated that the direction of causality seems to be in favor of the neutrality hypothesis in 21 out of 31 regions and a one­way Granger causality running from economic growth to insurance activity in 7 regions. Regarding the direction of insurance activity to economic growth nexus, they found a one-way Granger causality from insurance activity to economic growth for Jiangsu, Zhejiang, and Shandong (Hongbing, Meng, & Wenhua, (2013)).

Outreville (2013) proposed a review of 85 empirical papers examining the relationships between insurance and economic development, that is, the insurance-growth nexus. When looking at the economic importance of the insurance sector, most papers in the past have looked at the demand side (the level of economic development is an explanatory variable among other factors that affect the demand for insurance). Because the role of the insurance sector and its contribution to development is at the agenda of international organizations and because the importance of the relationship between financial development and economic growth has been well recognized and emphasized in the field of economic development, more recent papers have examined the causality links between insurance and economic development and the role of insurance as a significant determinant in the process of economic growth (Outreville (2013).

Still on the output - insurance performance nexus, Okolo and Ani (2014) used the two­stage least square regression and vector auto-regression techniques to examine the influence of insurance on foreign direct investment and its transmitted effect on economic growth in Nigeria. The regression results show that the coefficient for insurance premium has a positive statistically significant relationship with foreign direct investment and a positive but insignificant effect on gross domestic product. The VAR result indicated that insurance premium (IP) impacted on foreign direct investment in the first year and on gross domestic product in the third year. These led them to conclude that insurance has significant effect on foreign direct investment and economic growth in Ni~eria. The model specifying GDP as a function of insurance premium, trade openness, exchange rate, and foreign direct investments in a study aimed at investigating the effects of insurance on GDP appears to be a cluster of not-too-relevant and not-too-related explanatory variable. This would suggest a re-specification of the model. A VAR analysis using GDP and Insurance premium may prove more plausible. More so, considering that much of the contribution from the insurance sector would come from their investments in the domestic economy than just premium generation, a re-specification using insurance investments as the primary argument would be indicated in this circumstance. Premium as we know boosts aggregate national savings but not all premiums are invested, so the proportion that is invested is considered more plausible an argument explaining output.

From the reviewed studies, it is obvious that there are more of works that draw evidence from countries outside of Africa than those from within. Only a few utilized the Nigerian data. None of the studies reviewed investigated the relationships between such variables the ability to generate investible funds through premiums defined as the insurance penetration ratio, insurance investments and claims settlement potentials of the insurance companies. More so most of the works covered the periods prior to 1998. A more recent data would expectedly provide fresh insights aboutthe chosen area of study.

Research Methodology

Research Design, Data Collection, and Model Specification

The ex-poste investigative type of research design was adopted in this study. The data required are obtained from the statistical Bulletin, Annual Reports and Statement of Accounts, Economic and Financial Review, CBN Briefs and Bullion for various years, an~ the Nigerian Insurance Year Book for various years. In carrying out the study, we adapted and applied the models developed in (Ezirim, 1999). Accordingly, linear regression equation is derived to express the resultant functional model. Accordingly, it is hypothesized that the growth in the GDP (GDPR) of the country is a function of the overall intermediation operations of the insurance industry which manifests in their level of funds mobilization potentials or the insurance penetration ratio (IPR) represented by total premiums (TP) to GDP ratio, level of total insurance investments in the given period (TII), the claims payment to policyholders that suffer loss (CP), and the insurance intermediation ratio (IIR). We can write the relationship between these variables .functionally as

GDPR = 1NV1 (IPRt. CPt. INV, IIR1); fl> O; f2 > O; f3 > O; f4 > 0 . ...... (1)

The direction or nature of relationships existing between the explanatory variables and the explained variable has been hypothesized following a priori reasoning in Ezirim (1999). This means that all the variables are expected to have a positive relationship with economic growth defined as growth in GDP. Accordingly, the data for the study relate to rates of change in the annual data of Nigeria GDP (GDPR), Insurance premium ratio (IPR), Total insurance investments (TII), claim~ payments (CP), and the insurance intermediation ratio (IIR). Estimation was done using the Least Squares technique and augmented Dickey-Fuller tests. E­Views 7.0 software was used.

Descriptive Analysis of Data

It can be seen that the rate& of change in GDP of the Nigerian economy fluctuated very violently as shown in Figure 1. In Figure 2, we see the distribution of the insurance intermediation or the insurance inter-relation r~tio. The fluctuation was not as violent as in the case of GDP growth. From 1980 through 1995, the ratio stood above 8% but from 1996 it flattens at slightly above the zero level. In the case of ipsurance claims payment, Figure 3 reveal three most important peaks in 1993, 1999, and 2008. These were the years of high payment ratios in the Nigerian claims settlement experience. From Figure 4, the insurance penetration ratio that depicts performance in premium generation the rates appeared flat and evenly distributed both before and after l 994,

I

during which period it had its peak. The penetration seems to follow normal distribution. The last variable in our presentation is the rates of change of total insurance investment over time. From Figure 5, the rates of change fluctuated seemly evenly except in 1995, and 2000, when it

. generated very high peaks demonstrating periods of high investments by the companies.

1 ----- - ·- - -·- -·-- ---- - - - - - - -·- ---- --------------- ---- -- ----·-- - - ------------·--- - ---- - - --- ----1

I GDP I 150

100

so -GDP

0 t-. -_50 1~ __ 2._~- ~--~~ -~-~-~-~~ 1112~~~:~~~~~~~~~-2.-~~~-~2.-23 2~ 25 ~-~~~-28 29 ~o 3 ~~~ I

! ___________ . _____ ___ __ , ______ __ , _ __ _________ , _________________ ,___________ __ _ ------ ··-----·---J

Figure 1: Rates of Change in Nigeria's GDP

r· .. -- ---- ---------- ---- --- ---- --------- -------- ----.. - -------·----·---------- -------- --------·-----!

I i

600 r---.--________ CP ________ -=_-.. _ ·----- -------400

200 +- ---·-·------------·--·-----· . -- ---- ---

1 -~ I 0 f ·--; 3 4 . 5 -~ ·;~10·11-,1-2·~~1516

1

171~'i92021,22'2~;;26,27T2 2;0,31.321

L .. _____ ... _____ .... __ ____ -20_0 - - ----·---- ____________________ _ __ j

Figure 3: Rate of Change in ~laims Payment

r- --1 IPR

20 T------- -------------------------·-- · .. ·--·- --

15 ··1 ·- -- - --·- ------- - - - ----------- --- -------- -----

10 -i----- ----- ------------ - ---1 5 i • r~~-·--~-.-----,;;;;---- --

a ~---··1-~-r---r·---i-·r··-1-·--:--·1--r--i- I ! l

I __ 1 2 3 4 5 6 7 8 9 1011121314151617181920212223242526272829303132

Figure 4: The Insurance Pen~tration Ratio

- , ---------------- ------------------ --------------- !

Tll I

800 -i---- --------------------

600 +------------- -----!

400 r-- . 200 L----~-------------

. I

I :,~ n1 ,""" ____ ~_~""":_-~_.,,,.!!':.,~~~-6._..,_7_-__ -8_>?'1'!_~~--1 ... _ ~"l"-~.e;~-~~-~~l-4-15-~~-~~~~-------------------------- ________ ________ ____ _

Figure 5: Rates of Change in Total Insurance Investments

ESTIMATION RESULTS AND ANALYSIS

Stationarity Analysis and Long-run Implications

The study started its analysis with the unit root test to determine the stationaity imperatives of the variables, noting the curious problems associated with time series data. If the data are not stationary, the results of typical least squares manipulations would be suspect. Furthermore, conducting stationarity test would help to determine if further analysis such as cointegration or vector autoregressions would be needed to determine whether or not long-run equilibrium relationship exists. If, for instance, the variables are stationary, then there may be no urgent need to pursue cointegration test, since observed short-run results can be deemed to be sustainable in the long-run. Form the results of the unit root tests as in Table 1, it is easy to see that all the four variables, namely, GDP, CP, IPR, and TII are all stationary at level data, with the observed t­st11tistics of -4.261274, -6.944061, -3.567708, -5.998971, respectively, greater than the critical t­value of -2.960411 at 5% level of significance .. This implies they are all integrated at order, zero, i.e., I(O) series. These results are not surprising, since the data were converted to rates of change before estimation. By implication, the short-run OLS results are sustainable, in their effect, even in the long-run and the stqdy is over 95% confident to infer this. With level of confidence, the study proceeds to analyze the least square results at two levels. The first level is to determine the global utility of the model-using global statistic results such as the adjusted R2 and the F-ratio. The second level is to analyze the relative statistics and test relevant hypothesis.

Table 1: Augmented Dickey-Fuller Unit Root Test Results

Null Hypothesis: GDP has a unit root Exogenous: Constant Lag Length: 0 (Automatic - based on SIC, maxlag=7)

t-Statistic -

Augmented Dickey-Fuller test statistic -4.261274

Prob.*

0.0022

~

Test critical values: 1% level -3.661661 5% level -2.960411 10% level -2.619160

Null Hypothesis: CP has a unit root

t-Statistic Prob.*

Augmented Dickey-Fuller test statistic -6.944061 0.0000 Test critical values: 1% level -3.661661

5% level -2.960411 10% level -2.619160

Null Hypothesis: IPR has a unit root

t-Statistic Prob.*

Augmented Dickey-Fuller test statistic -3.567708 0.0126 Test critical values: 1% level -3.661661

5% level -2.960411 10% level -2.619160

Null Hypothesis: TH has a unit root

t-Statistic Prob.*

Augmented Dickey-Fuller test statistic -5.998971 0.0000 Test critical values: 1% level -3.661661

5% level -2.960411 10% level -2.619160

*MacKinnon (1996) one-sided p-values.

Global Statistical Analysis of Estimated Short-run Results

In this sub-section, the results of the short-run statistical estimation are analyzed on a global or joint basis and subsequently the hypotheses are tested. The results of estimation of the model are summarized on Table 2 that shows the relationship between the rates of change in GDP and the Insurance intermediation operations. As can be seen in the results, the intermediation operations of insurance companies jointly accounted for about21 % of the changes in or growth in GDP of Nigeria as shown by the ~adjusted R-square of 0.2145. Strangely this level of performance (21 %) is seen to be statistically significant at 5% level with F-statistic of 3.82 and probability of 0.02. On a joint or global basis, it can be inferred that the three nominated intermediation operations of the insurfil}ce companies significantly related with growth in GDP of Nigeria.

Table 2: Estimation Results of the Short-run Relationship between GDP and Operations of Insurance Companies

Variable Coefficient Std. Error t-Statistic Prob.

c 9.151814 9.879793 0.926316 0.3622 IPR 1.927738 1.759037 1.095905 0.2825 TII 0.098223· 0.030676 3.201986 0.0034 CP 0.030593 0.035944 0.851147 0.4019

R-squared 0.290565; F-statistic 3.822681 Prob(F-stat) 0.020567

Three hypotheses naturally spring up from the specific objectives formulated earlier in this study and are tested in this section.

Hypothesis I: There is no significant relationship between the savings mobilization (premium generation) potential of the insurance industry and economic growth. Test Result: From Table 2, the relevant insurance intermediation operation variable for this hypothesis is insurance penetration ratio (IPR), which is the ratio of premium to GDP or total assets). The IPR variable recorded a coefficient of 1.927738 and a standard error of 1.759037 and thus at-statistic of 1.095905 (0.28). This is not significant at 5% level of significance. Thus, the IPR variable was positively and but not significantly related to GDP growth. The hypothesis of no significant short-run relationship cannot be rejected in the place of alternative hypothesis. The inference is that there is a positive but not significant relationship between the premium generation potential of insurance companies in Nigeria and the growth in the GDP of the country. That the relationship is positive however satisfies the expected sign of the model.

Hypothesis 2: There is no significant relationship between investment operations of the insurance industry and economic growth. Test Result: From Table 2;· the relevant insurance intermediation operation variable for this hypothesis is rates of change in total insurance investments (TII). The TII variable recorded a coefficient of 0.098223 anq a standard error of 0.030676 and thus a t-statistic of 3.201986 (0.003). This is significant at 5% level and even at 1 % level of significance. Thus, the TII variable was positively and significantly related to GDP growth. The hypothesis of no significant short-run relationship cannot be accepted in the place of alternative hypothesis. The inference is that there is a significant positive relationship between investment operations of insurance companies and the growth in GDP of the country. As expected, the relationship is positive and thus satisfies the expect~d sign of the model.

Hypothesis 3: There is no significant relationship between the claims settlement operations of the insurance industry and economic growth. Test Result: From Table 2, the relevant insurance intermediation operation variable for this hypothesis is claims settlement operations of insurance companies, which is defined here as the rates of change in claims payments (CP). The CP variable recorded a coefficient of 0.030593 and a standard error of 0.0'.3':5944 and thus at-statistic of 0.851147 (0.40). This is not significant at ' . 5% level of significance. Thus, the CP variable was positively and but not significantly related to

-. . GDP growth. The hypothesis of no significant short-run relationship cannot be rejected in the place of alternative hypothesis. The inference is that there is no significant relationship between the claims payments of insurance companies in Nigeria and the growth in the GDP ·of the country. The relationship is positive however satisfying the expected sign of the model.

Summary of Work and Discussion of Findings

It was the central objective of this study to econometrically analyze the relationships between the different facets of the intermediation operations of insurance companies and economic growth of Nigeria. More specifically, the study sought to examine the relationships between the funds' investment, premium generation, and claims settlement operations of the insurance industry and growth or otherwise in the country's GDP over time. This research utilizes objective measurements ·and analysis that is characteristic of the ex-poste investigative survey research design that attempts to · establish the effect of the independent variables (ability to generate investible funds through premiums, claims payment, insurance investments) on the dependent variable (economic growth or growth in GDP). These variables are observed for the period covering 1980 to 2012, implying about 32 observations. Ratios and rates of change were used in the computations. The regression analysis technique was employed to test the hypotheses.

The results indicate that jointly or globally, the three nominated intermediation operations ·of the insurance companies significantly related with growth in GDP of Nigeria. It was also found that that there is a positive but not significant relationship between the premium generation potential of insurance companies in Nigeria and the growth in the GDP of the country. It was discovered that there is a positive and significant relationship between investment operations of insurance companies and the growth in GDP of the country. Finally, it was found that there is a positive but no significant relationship between the claims payments of insurance companies in Nigeria and the growth in the GDP of the country. Evidently, among the three variables of intermediation operations of insurance companies, only the investment operations associate with growth in GDP very significantly. Others are not as significant.

From the above results, it is easy to see that all the variables put together exerted a significant influence on the economy by way of affecting the GDP positively and considerable. However, it is only the investment activities that impacted the GDP more remarkably. The reason is not far-fetcheq, Investments are direct productive activities which generate output and GDP is the aggregate 01itput. It is only reasonable that investment would contribute to boosting output. In another development, premiums generation is not found to be significant and is somewhat understandabie. For one thing, premium is a savings vehicle and unless channeled to investment cannot have a direct contribution or impact on output. Its relationship is at best ancillary · or indirect through the vehicle of investment. It is only when premiums are invested that they would affect output. That is why the relationship is positive but not significant since only the proportion that eventually gets invested that would contribute to output. On the other claims settlement or payµients to policyholders is also not a direct injection to production. When the claims are paid, the recipients decide what to do with their money. lt is doubtful if all of the money received is channeled to productive activities in the economy. For one thing not all the claimants are corporate bodies which one may say that they are producers and would thus use their monies productively to boost output. The households aniong them are bound to use theirs for consumption purposes, more or less. On .the aggregate the contribution to · GDP, though positive, would not be significant as discovered in the study . .

CONCLUDING REMARKS

It is the main conclusion of this work that the intermediation operations of insurance companies jointly exert considerable influence on the GDP of Nigeria. Put in other words, the intermediation operations of insurance jointly boost the GDP of Nigeria. However, only the investment operations produce an individual significant effect on GDP. The contributions of the premium generation and claims settlement operations were not significant in their effects on aggregate output.

Since the investment activities are found to significantly associate With output, then every effort should be made to encourage the insurance companies to increase their investments in order to boost economic growth. This can be by way of deliberate policy by the government through NAICOM interventions. It can also be by way of corporate action on the part of the insurance to boost their profitability. Furthermore, since all the operations jointly associates with

· growth of the economy, more or less, it is suggested that efforts should be made to improve all the intermediation activities so as to reap the benefits of scale of operation. The little contributions premiums and claims make will surely improve and augment that of the investment activity to further boost output. This is so recommended because the relationships are found to be positive. Thus it is good economic growth strategy to encourage the insurance companies to perform their intermediation operations creditably in order to boost output.

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APPENDIX 1: Rates of Change in Economy and Insurance Data YEAR GDP llR CP IPR Tll 1980 18.24342 19.11839 0.375374 3.167985 19.29613

1981 -4.05514 21.29893 25.00716 4.914987 30.89383

1982 3.044163 23.2003 6.690653 5.069669 -10.9738

1983 8.229385 20.82828 -0.74899 3.61157 17.7131

1984 12.26788 22.37215 -1.11479 3.449937 39.17522 1985 13.89746 :28.48653 -17.6683 2.875779 4.662905 1986 1.823688 35.98116 35.03712 3.675619 11.55975 1987 52.1727 25.19887 26.66975 3.86323 0.562572 1988 32.18166 30.59918 38.11843 3.498918 27.17506 1989 55.8738 2'4.40449 84.54576 3.104689 20.13901 1990 23.41007 23.67312 9.88929 3.788727 14.20086

.. 1991 16.66595 2~.23528 26.21757 4.152765 73.5686

1992 70.63314 1~.08837 58.67961 4.591865 415.8833

1993 28.3988 29.72782 337.2311 7.211779 -59.1984

1994 31.584 25.80487 -50.9969 16.13484 -12.5461

1995 114.8339 8.873937 14.71823 6.996195 132.8311

1996 39.80462 0.075937 9.622157 4.103768 22.66497 1997 3.672355 0.087031 l.403388 3.904956 3.05852 1998 -3.33842 0.089849 16.63 4.315507 -99.8331 -· 1999 17.92861 0.072908 202.7879 4.570198 25.75493 2000 43.45979 0.072548 -4.95781 4.917249 595.0622 2001 3.11992 0.079115 8.544245 6.133495 -52.6766 2002 46.29112 0.072645 12.20232 5.463514 47.91974 2003 22.78014 0.102288 37.32498 5.118611 36.50559

2004 34.45298 0.075905 28.34608 4.390547 63.35028

2005 27.70269 0.055491 2.634549 4.629732 77.57095 2006 27.39699 0.083753 515.0109 4.394588 32.76143 2007 11.27266 0.094254 -79.2281 4.313478 12.14054 2008 17.61609 0.084955 61.76413 4.413007 17.45623 2009 2.049319 0.012357 2.5 6.190026 -20.4388 2010 17.79262 0.073871 37.05481 5.103175 7.030837 2011 -15.2962 0.0933 -27.962 5.041202 -100

Source: CBN Statistical Bulletin APPENDIX 2: Descriptive Statistics of Economy and Insurance Variables

· GDP GFCF llR Tll IPR CP

Mean 24.24719 . 1276491. 11.90993 43.53971 4.909738 44.26027

Median 18.08602 27.12364 4.488113 18.50462 4.403797 13.46028

Maximum 114.8339 40846946 35.98116 595.0622 16.13484 515.0109

Minimum -15.29620 -31.38710 0.012357 -100.0000 2.875779 -79.22810

Std. Dev. 25.29360 7220784. 12.73787 131.6754 2.295512 112.3131

Skewness 1.527657 5.388159 0.292287 3.032766 3.806331 2.990392

Kurtosis 6.419161 30.03226 1.410395 12.50609 19.16925 12.03385

Jarque-Bera 28.03415 1129.163 3.824759 169.5420 425.8631 156.5069

Probability 0.000001 0.000000 0.147728 0.000000 0.000000 0.000000

Sum 775.9102 40847718 381.1179 1393.271 157.1116 1416.329

Sum Sq. Dev. 19832.76 1.62E+15 5029.852 537490.6 163.3507 391041.0

Observations 32 32 32 32 32 32