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Financial intermediation by insurance companies and capital formation: theory and empirical evidence from Nigeria.


Basically, financial intermediation refers to mechanism by which financial institutions like insurance companies provide a medium of exchange necessary for mobilization and transfer of savings from those who generate the fund (policy holders) to investments in the economic system where the funds will yield the highest return. This arrangement is expected to enhance productive activities and positively influence aggregate capital formation in the economy (Torbira, 2014). For the purpose of this study, capital formation in terms of gross fixed capital formation (GFCF) which measures the value of acquisitions of new or existing fixed assets by the business sector, government and households which are new value added in the economy that are invested to produce more goods and services rather than consumed (Ezirim, 1999).

There has been a heated debate on the nature of the relationship between financial intermediation and components of economic growth in finance literature. For instance, one group posit that financial intermediation drives economic growth and by extension capital formation. Proponents of this school of thought argued that financial intermediation facilitates the efficiency of the financial system, stimulates the restructuring and liquidation of distressed businesses as well as eliminating the inefficiency associated with the absence of fund flow. Another group argues that economic growth leads financial intermediation such that when the economy grows, it will enhance the development of the financial sector thereby boosting financial intermediation. A third group claims that a bi-directional causality exists between financial intermediation and economic growth indicators. That is financial intermediation trigger growth in the economy while, economic growth stimulates financial intermediation (Ezirim, 1999).

The economy will feel the effect of the intermediation activities of insurance companies more positively when the savings/premiums mobilized are channeled to various investments that can translate into growth in Gross fixed capital formation in the economy. This will however support the view of Agenor and Montiel (1996) who asserted that financial intermediation increase the average production of capital (thus the growth rate) in two ways: (1) by collecting, processing and evaluating the relevant information on alternative investment projects (2) by inducing entrepreneurs through the risk sharing function, to invest in riskier but more productive technologies, accepting premiums and choosing an appropriate mixture of liquid and illiquid investment. Insurance companies provide high level of risk transfer to policy holders against liquidity risk simultaneously facilitating long term investment in high return projects can boost capital formation.

Financial intermediaries such as insurance companies are credited with improving fund allocation, mobilization and allocation of savings (promoting financial intermediation), managing risk (through risk transfer and indemnification), investing and mitigating the negative financial consequences that random shocks (contingent losses) may have on investments or businesses (financial stability) in the economy (Ward & Zurbruegg, 2000; Skipper, 2001). Financial intermediation facilitates the savings and investment process through the mobilization of savings from the surplus economic unit and channeling same to investment by the deficit economic unit. In serving as an accelerator to economic growth, insurance companies seek to perform the basic function of financial intermediation.

Through institutional structures, the insurance industry attract funds from policy holders and pension scheme and transfer or allocate same to entrepreneurs, businesses, households and government for indemnity, investment and use for various projects and purposes with a view of maintaining and/or increasing the production of goods and services in the economy. This implies that insurance companies' financial intermediation ensures efficient use of funds or channeling of funds to investments that will yield the highest return (Ezirim, 1999).

The ability of the insurance industry to mobilize savings and hence contribute to investment and economic growth depends on the quality of premium mobilization (insurance intermediation ratio), and the effectiveness of insurance penetration, efficiency of claims payment and the quantum of investment derived. The prominent role of insurance which include to engage in gainful financial intermediation activities, create mechanism for risk management, creation of pool of investable fund through fund mobilization, and investment in either the money or capital market or even direct investment will stimulate the economy to achieve allocation efficiency, essentially stabilize the economy financially, create large amount of assets placed on the money and capital market and hence contribute to the growth in the output level of capital formation in the economy (Torbira, 2014).

Allocation efficiency is achieved when the insurance sector, through its intermediation role succeed in mobilizing savings and premiums, and channeling same to the investment outlet/opportunity that will guarantee the highest return on investment and pay claims on insured perils. The economy will feel the effect of the intermediation activities more positively when the insurance-intermediation-output-related index (IIORI) is on the increase or high. This index is the product of insurance intermediation and is known as the insurance-intermediation ratio (IIR). This is defined as the ratio of total insurance assets to gross domestic product (GDP). This is a modification of the financial inter-relation ratio (FIR) formulated in Ezirim (1999).

Risk management function is carried out when insurance companies help individuals, businesses and government and its agencies to manage their resources and mitigate risk efficiently through the sales of insurance policies and payment of claims to the. The availability of this insurance service is essential for the continuity of businesses and the financial stability of the economy for growth purposes. This can encourage business participants to accept aggravated risk insured (Osipitan, 2009).

Through the sales of insurance policies, insurance companies pool premiums and form reserve fund and promote financial stability through liquidity guaranteed by insurance coverage. The economy will however feel the effect of the risk management activities more positively when most businesses in the economy survive and continue to increase the output level of goods and services in the economy despite the occurrence of adverse events that lead to financial losses. This can be achieved through adequate and proper claims settlement or indemnification insured (Osipitan, 2009).

The fund mobilization activities will impact positively on the economy when insurance companies together with pension and mutual funds accumulate savings and premium to form a large pool of investable funds that will boost capital formation and stimulate economic growth. The economy will feel the effect of fund mobilization activities more positively when the insurance penetration ratio is on the increase. Insurance penetration is defined as the ratio of insurance premium to GDP (Outreville, 1990).

The investment activities will have a positive impact on the economy when a portion of the premium income along with savings mobilized are adequately invested into various forms of insurance/pension investment as provided for in section 25(2), of the Insurance Act of Nigeria 2003, and section 73(1), of the Pension Reform Act of Nigeria, 2004. These investments can specifically and collectively translate into increase in fixed capital formation and growth in the output level of goods and services in the economy.

To grow optimally, capital formation must learn to respond appropriately to the dictates of financial intermediation activities by insurance companies. Four financial intermediation variables peculiar to insurance companies: insurance intermediation ratio (IIR), insurance penetration ratio (IPR), insurance claims payment (IICP) and total insurance investments (TII) are cardinal for the purpose of this study. The manner of the response depends on the magnitude and direction of the effect of the forces at play in the insurance industry. This manner is what this paper considers relationship between capital formation and insurance financial intermediation. Against this background, this study seeks to investigate the nature of the relationship between financial intermediation activities by insurance companies and gross fixed capital formation in the short run and long run and to examine the response of capital formation variable to shocks emanating from financial intermediation by insurance companies.

The controversy as to the nature, extent and direction of relationship between financial intermediation activities and capital formation, and the changing proxies used to capture financial intermediation activities in other financial institutions and their adequacy to insurance financial intermediation in Nigeria constitutes a research burden. However, the effectiveness and efficiency of the insurance intermediation process as well as its channels, scope and capacity varies quite considerably among economies. This is partly because of the varied levels of financial development (Levine, 1999, 2004; King & Levine, 1993). A sufficiently rigorous understanding of the channel, nature and economic implications of insurance intermediation in the capital formation process is not well documented in Nigeria. Put differently, it is still an empirical burden to determine how GFCF behave given the effect of insurance intermediation activities in Nigeria.

This study attempt to fill this gap as there is no guarantee that the results documented for both the developed and the developing economies as well as those of other forms of financial institutions would equally apply to insurance companies in Nigeria at present. This study also attempts to investigate the critical behavioral patterns of the GFCF in response to stimuli provided by the various insurance intermediation variables in Nigeria. The logical point of departure is to determine the relationship between insurance intermediation variables and the level of GFCF. Finally, the study attempts to answer the question of whether or not the Nigerian insurance intermediation behavior can be modeled aggregately to capture its influence on capital formation in Nigeria. If so, which among the variables will be significantly and positively affect the growth in the level gross fixed capital formation GFCF of the economy?


Financial Intermediation Theories

Financial Intermediation theory advocates that insurance companies should provide a mechanism for the mobilization and transfer of savings (premium) from the policyholders to investment that promises higher returns. Financial intermediation involves arrangements covering activities with respect to providing mechanism for organizing and managing the payment system, mechanism for the collection and transfer of premiums, mechanism covering the investment in financial securities, and arrangement covering financial activities complementary to insurance services (Ezirim, 1999).

Financial intermediation refer to a financial framework that provide a medium of exchange necessary for specialization, mobilization and transfer of savings from those who generate the funds to those who use the funds for investment in the economic system where the funds will yield the highest returns. This arrangement enhances productive activities and positively influences aggregate output and capital formation while helping to mitigate financial risk in the economy (Torbira, 2014; Curak, Loncar, & Poposki, 2009; Gardner & Gardner, 1998).

Reed Cotter, Gill, and Smith (1980) advanced three approaches aimed at explaining the behavior of financial institutions in respect of financial intermediation. They started with the pool of funds approach, which anchor on the premise that all funds should be pooled and allocated to various investments (Loans, Securities, Cash, etc) according to their return implication without considering the source of the funds.

The second theory called the asset allocation or conversion of fund approach distinguishes between different sources of funds and requires that the source of funds be put into consideration in subsequent allocation decision. The theory considers the source of the funds and encourages insurance companies to comply with the matching principles of insurance investment. The third approach is the linear programming theory, which requires an explicit statement of objective to be optimized and the specific constraints facing the optimizer. The first and third approaches to financial intermediation agree with the doctrines of unconstrained and constrained profit maximization. Also, the first two approaches are consistent with aggressive and conservative accommodation principles respectively. In the overall, the accumulation theory expects financial institutions' earning assets to be primarily related to and dictated by the nature of production and distribution of goods and services in the economy (Ezirim, 1999).

Some general behavioural patterns are observed from insurance companies in their performance of intermediation function. Prominent among these is the profit maximization behaviour. According to the profit maximization hypothesis, a typical insurance company, as a rational economic entity, engages in financial intermediation in order to reap all available returns from its operations. It would do this through the windows of quality service delivery (Output maximization), cost minimization and/or engaging in other profit making ventures. This would mean engaging in all lawful activities that enhance the company's assets creating ability (capital formation) as provided for in the insurance Act 2003. It could also imply being cost effective in all its operations, or doing every legal thing that adds to profit.

Oluyemi (1995), Cookey (1997), and Ezirim (1999) posit that, in pursuit of profit maximization, financial institutions and indeed insurance companies usually set specific goals they desire to achieve in term of premium (income) assets/investment, or the level of relationship between them and other economic indicators. As a result of these, they adjust their output and investment towards the set or desired levels. For instance, given the present and expected levels of premium, claim settlement and investment, as well as the rate of returns on various assets, insurance companies will continue to adjust their asset portfolio in response to changes in the expected levels of these variables. This therefore suggests that the profit maximization hypothesis agrees with the partial adjustment theory (Fraster & Rose, 1973; Lambo, 1986; Nyong, 1996).

Empirical Review

Catalan, Impavido, and Musalem (2000) studied the link between insurance and economic growth by focusing on the intermediation function of the insurance sector and its transmission mechanism (contractual savings, portfolio setup and the capital market) to the growth of the economy. They examined specifically, the impact of insurance assets (contractual savings, pension fund, life and non life), market capitalization and value of stocks traded in the capital market on GDP for fourteen (14) OECD countries and five (5) developing economies over the period 1975-1997. Use was made of the ordinary least square model for Granger causality estimation in both directions.

The findings showed that contractual savings seem to correlate with market capitalization (MC) and value of stocks traded in most countries. Market capitalization correlates with pension funds while the results of the relationship between pension funds and value of stock traded is mixed. The test result shows that there was relationship between life insurance assets and market capitalization for nine OECD countries but the results for the developing economics were mixed.

The study reported a weak relationship between life insurance assets and value of stocks traded in OECD countries while the relationship in most of the developing economies is strong. It was further reported that the impact of the non-life insurance assets were almost equal to the impact of the life insurance assets on market capitalization but their impact on value of stock traded was less. There exist a relationship between contractual savings and market capitalization and between contractual savings and value of stocks traded for OECD countries, especially for economies with small and tight markets but enabling regulatory environment.

The results of the relationship between contractual savings and market capitalization as well as value of traded securities for the 5 developing economies were mixed possibly due to their different regulatory restrictions. The findings suggest that appropriate sequencing of the financial institutions development be carried out in favour of contractual savings institutions.

Park, Borde, and Choi (2002) examined the linkage between insurance penetration and gross national product (GNP) and some socio-economic factors adopted from Hofstede (1983) such as uncertainty avoidance, individualism--collectivism, power distance, masculine-feminine, SPI index, index of economic freedom. They applied the ordinary least square estimation technique on cross sectional data for thirty eight (38) countries including twelve European Union (EU) countries in 1997. On the whole, the evidence from this study suggests that there is a significant relationship between GNP, masculinity, socio-political instability, economic freedom and insurance penetration. All other factors lack importance and masculinity was dropped after checking for heteroscedasticity. Deregulation was found to be a way to facilitate growth in the insurance industry. This assertion supports the expectations of Kong and Singh (2005). Socio-political instability was found to be more a proxy for poverty than an indicator for the need to insure.

Webb, Grace, and Skipper (2002) investigated the effect of banking and insurance on the Growth of capital and output. Employing a Solow-Swan model with productivity parameters estimated across fifty five (55) countries including seventeen European Union (EU) countries for the period between1980-1996. They used the Ordinary Least Square (OLS) estimation method on a panel data and cross country data. The findings indicate that the components of banking and life insurance penetration are found to be robustly predictive of increased productivity. When split into the three areas of banking, life insurance sector and property and liability insurance sector, it is only the banking and life insurance sector that remain significantly related to GDP. Property and liability insurance penetration does not significantly correlate with GDP.

The result of the study suggests that higher levels of banking and insurance penetration jointly produce a greater effect on growth than would be indicated by the sum of their individual contributions. Their findings indicate that financial intermediation significantly correlate with GDP. This suggests that insurance intermediation is a determinant of economic growth.

Boon (2005) examined the growth supportive functions of banks, stock markets and the insurance sector in Singapore for the years 1991 to 2002. He regressed real GDP and real gross fixed capital formation against total insurance funds, stock market capitalization as percentage of nominal GDP, and loan to nominal GDP using the vector error correction model on time series data. The results showed short and long run causality running from bank loans to GDP, and a bidirectional causality between real gross fixe capital formation and bank loans. GDP growth seems to enhance stock market capitalization in the short run and the stock market capitalization significantly granger causes capital formation in the long run. Total insurance funds significantly affect GDP growth in the long run while total insurance funds influences capital formation in both the short run and long run respectively.

Kugler and Ofoghi (2005) used the components of insurance premia (disaggregated analysis) and real GDP to investigate the long run relationship between development in insurance market size and economic growth in the United Kingdom. By disaggregating total insurance premia, they attempt to solve the aggregation problem and to find whether the results of Ward's and Zurbruegg's (2000) study that reported no long run relationship will be changed. Use was made of Johansen's Trace and maximum co-integration test and Granger equations. The co-integration test result showed that in most cases, there exist a long run relationship between insurance market size and economic growth. The causality test result provided information about the possible pattern or direction of the relationship by revealing that causality runs in both directions. That is, both life, and property and liability insurance premium Granger cause growth in real GDP and vice versa.

Haiss and Sumegi (2008) studied the relationship between insurance and economic growth. Identifying the channels of influence to be risk transfer, savings substitution, investment and assets, institutional, credit derivatives and contagion through the analysis of the fundamental functions of insurance and their implication for the economy, they adopted an endogenous growth model with a modified Cobb-Douglas production function. Use was made of the ordinary least square and causality technique on unbalanced cross country panel data for twenty nine (29) European countries over the 1992 to 2004 period. Real GDP was regressed against yearly Gross premium income as total sum and split into life and nonlife; other explanatory variables include real physical capital stock, human capital stock, inflation rate and interest rate.

The study reported positive and significant relationship between real GDP and physical capital. Human capital seems to be negatively related to GDP growth. Interest rate and inflation rate does not significantly correlate with real GDP. Total insurance premium income and non-life insurance consumption negatively and insignificantly affects the growth of the economy, while life insurance premium income has a positive but insignificant impact on the output level of goods and services in the economies. This suggest weak evidence for a growth supporting role of insurance.

Avran, Nguyen, and Skully (2010) provided a behavioral explanation for the impact of insurance density and insurance penetration on GDP per capital in ninety three (93) countries from 1980 to 2006. The study adopts the ordinary least square (OLS) and generalized method of moments (GMM) estimation approach in analyzing the global insurance sector. Consequently, cross country panel data for the various countries was used for the analysis. The results from the study confirm that insurance density positively and significantly causes growth in the economy while insurance penetration does not have significant effect on economic growth. The relationship was demand-leading for insurance density but not for insurance penetration.

This study concludes that insurance density in an economy helps boost risk management, intermediation and investment and implicitly enhance growth. although the results from this study are insightful, the study was based on the assumption that variations which affect insurance density will affect growth.

For instance, Majekwu, Agwuegbo, and Olowokudejo (2011) examined the impact of insurance contributions on economic growth in Nigeria over a twenty seven year period, between 1981 and 2008. The study employed the dynamic factor models on multivariate time series which analyze a functional relationship between the volume of insurance contribution and economic growth in terms of underlying but unobserved random factors. The results of the study's model analysis summarily reveal that real gross domestic product is positively correlated to insurance contributions. This implied that if insurance contribution increase, economic growth will also increase.

The finding supported that of Boon (2005) who also found in his study that total insurance funds affect both capital formation and GDP growth in the short and long run. The plausibility of the aforementioned findings may be ascribed to the fact that insurance activities rely a lot on investment. They recommended that government policy should be directed at growing the insurance industry in the country, and through such means enhance investment as well as production in order to boost the country's economic growth.


The basic questions this study seeks to answer are:

1. To what extent have changes in insurance intermediation explained changes in Gross fixed capital formation (GFCF) in Nigeria?

2. Are changes in claims payments by insurance companies important in explaining changes in Gross fixed capital formation (GFCF) in Nigeria?

3. How significantly do changes in insurance penetration explain changes in Gross fixed capital formation (GFCF) in Nigeria?

4. Is there any significant relationship between Insurance Investment and Gross fixed capital formation (GFCF) in Nigeria?

The answer to the above research questions will be answered in the section that reports the results of this study. Based on the literature review, the following hypothesis was developed and tested:

H1: There is a positive and significant relationship between Gross fixed capital formation (CFCF) and Insurance intermediation ratio (IIR), insurance claims payment (ICP), Insurance penetration ratio (IPR), and total insurance investment (TII).


Sample and Data Collections

The study sampled insurance companies whose securities are traded in the Nigerian Stock Exchange. Data for this empirical study are those on Gross Fixed Capital Formation (CFCF) as dependent variable and insurance intermediation ratio (IIR), insurance claims payment (ICP), insurance penetration ratio (IPR), and Total Insurance Investment (TII) as independent variables from 1980 through 2011.

The annual data are all converted from their absolute values to rate of change in order to capture growth and performance in both the dependent and independent variables respectively. The secondary data was sourced from the CBN statistical bulletin and annual reports. The rate of change data are expressed on yearly basis. These data are then subjected to the various estimation techniques and diagnostic test employed in this study.

Measurement of Variables

Gross Fixed capital Formation (GFCF) is measured by the value of acquisitions of new or existing fixed assets by the business sector, government and households which are new value added in the economy that are invested to produce more goods and services rather than consumed. Insurance Intermediation Ratio (IIR) is measured by the ratio of total insurance assets to nominal gross domestic product (GDP).

Insurance claims payment is measured by payments from the insurance company to the insured for covered loss in other to restore the insured to the financial position he/she was prior to the occurrence of the insured loss. Insurance penetration ratio (IPR) is measured by the ratio of insurance premium to GDP. Insurance investment is measured by the economic activities designed to increase, improve and maintain the productive quality of the existing stock of capital in an economy.

Model Specification

The authors of this study started specifying the model which is used to measure the subject of interest. After which the parameters of the specified model were obtained, check for model adequacy and examine the utility of the model for policy purpose (Gujarati, 2006).

Given the theoretical underpinnings and empirical review earlier made in this study, it is hypothesized that GFCF is a positive function of insurance intermediation ratio (IIR), insurance claims payment (ICP), insurance penetration ratio (IPR) and total insurance investment (TII). Depending on the prevailing circumstances in the industry in particular and the economy at large, the relationship may be postulated to prove otherwise. Therefore, the authors construct and specify a single capital formation -insurance intermediation model patterned after multivariate regression, causality and dynamic model of linear formation as follows:

GFCF = [[mu].sub.0] + [[mu].sub.1]IIR + [[mu].sub.2]ICP + [[mu].sub.3]IPR + [[mu].sub.4]TII + [[mu].sub.t] (1)

A priori expectations are: [[mu].sub.1], [[mu].sub.2], [[mu].sub.3], [[mu].sub.4] > 0


GFCF = Gross Fixed Capital Formation

IIR = Insurance Intermediation Ratio

ICP = Claims Payment

IPR = Insurance Penetration Ratio

TII = Total Insurance Investment

[[mu].sub.t] = Error Term

Data Analysis

The analytical framework of the research consists of ten basic steps. They are: Descriptive statistical analysis, Correlation matrix, Unit root test, Diagnostic test, Ordinary least square regression method, Vector error correction mechanism (VECM), Co-integration test, Granger causality test, Impulse response analysis, Variance decomposition analysis. Generally, the study utilized some of the procedures specified in Akaike (1969), Granger (1969, 1991) Engle & Granger (1987). The E-views 7.0 software was used for data analysis.


Table 1 shows the descriptive statistics for the variables in the model. This exposes the behavior of the data on all the variables in the series. The mean of the changes in Gross Fixed Capital Formation (GFCF) is 24.2715 (median 22.2253). The mean insurance inter-relation ratio (HR) is 11.913 (median = 4.499); and the mean changes in claims payment (ICP) is 44.260 (median = 13.460). The mean insurance penetration ratio (IPR) is 4.924 (median = 4.404).

The rate of change in total insurance investment have a mean value 43.540 (median value = 18.505). The central location for each data in the series is best described by the median values in bracket. The rate of change in insurance claims payment maintained the highest mean value of 44.260 and it is followed by the rate of change in total insurance investment (43.540). The GFCF growth rate ranges from 92.26 to -31.39. The rate of change in TII and ICP maintained the first two highest standard deviation 131.67 and 112.31 in series; they are the most dynamic variables in the model. Since the mean of each of the variables in the model is greater than the median, it suggests that growth rates in all the variables are skewed to the right (Lind, March, & Wathen, 2006; Dooley, 1984).

Based on the argument of Bowman-Shelton test, the kurtosis of all other variable except that of GFCF and IIR are greater than 3. As such, the authors can posit that but for GFCF and IIR which are platytokurtic in nature ICP, IPR and TII are all leptokurtic in nature which implies that they are with higher than normal kurtosis and the weight in the tails of their probability density function is larger than normal (New bold, 1995). The result of the Jarque-Bera statistics indicates that at 5% level of alpha, the probability values (PV) of the variables in the series--GFCF, IIRICP, IPR and TII are 0.7701, 0.1478, 0, 0, 0, respectively. These results seem that the data on ICP, IPR and TII are normally distributed while those on GFCF and IIR are not normally distributed.

Table 2 shows a correlation matrix between all pairs of variables in model two (2) including Gross Fixed Capital Formation (GFCF), insurance inter-relation ratio (IIR), insurance claims payment (IICP), Insurance penetration ratio (IPR) and total insurance investment (TII).

Table 2 indicates that changes in Gross Fixed Capital Formation (GFC[]) is negatively associated with rate of change in insurance inter-relation ratio (II[]) and positively associated with rate of change in claims payment (IC[]), insurance penetration ratio (IPR) and rate of change in total insurance investment (TI[]). These results exhibit the higher rate of change in claims payment a the changes in insurance investments are also higher. An increase by one unit in insurance intermediation ratio decreases the Gross Fixed Capital formation by about 0.09%.

The more claims paid by insurance companies, the growth rate in gross fixed capital formation is increased. In addition, a rise in the insurance penetration ratio and the more insurance investment, the growth rate of gross fixed capital formation are increased. The correlation coefficients are almost all less than 0.5.

Table 3 reports the Augmented Dickey and Fuller (1981) unit root test on the series GFCF, IIR, ICP, IPR, TII at level data and at first differencing under the assumption of intercept, no trend and intercept. Comparing the ADF test statistics at the 5% critical value, the results of the unit root test reported in Table 3 indicate that all the variables are non-stationary in their ordinary levels, but they are all stationary in their first differencing level at 5% level of significance.

Detail of the result reveal that of all the variables in the model, IIR was not stationary at ordinary level but become stationary at first differencing. After stationiarizing the variables, the data can then be tested whether these variables are co-integrated or not by applying Johansen Co-integration procedure.

To further ascertain the behavior of the data on the variables in this model, four diagnostic tests were conducted as detailed in Table 4.

Normality Test

The result of Jarque-Bera (JB) normality test show that at 5% alpha value, the value of Langrage Multiplier (LM) statistics is 0.214118 with a corresponding probability value of 0.898472. This means that the residuals of the variable series specified in model 2 (i.e. GFCF, IIR, ICP, IPR and TII) are not normally distributed. The result is not in line with a priori expectation.

Serial Correlation Test

The Breusch-Godfrey first order serial correlation L.M. test result in Table 4 indicates that the probability value of the observed F-statistic of 0.349488 is larger than the critical alpha value of 5%. This suggests that the successive error terms are serially correlated and it is in agreement with the position of theory, since the langrage multiplier (LM) value and its corresponding probability are not significant at 0.05.

White-Heteroskedasticity Test

given the predetermine probability level of 0.05, the test result reveal that the L.M. value of 10.94783 which correspond with the probability value of 0.631469 as well as the probability of the F-statistics of 0.804964 suggest that the successive variance of the error terms are Heteroskedastic in nature which is not in agreement with the basic ordinary least square (OLS) assumptions. This means that the successive variance of the residuals is not equally spread.

Ramsey RESET Test

Ramsey RESET Test has been conducted to test the functional form of the model. The result of the Ramsey RESET Test in Table 4 reveals that the probability value of the L.M. CHQ statistic 0.6379 and that of the F-statistic 0.6744 are greater than the critical probability value of 0.05. As such, the authors reject the alternative hypothesis proposing that the model is not misspecified implying that the authors cannot ascertain the correct specification of this model as well as the exact relationship between the growth in Gross Fixed Capital Formation and the performance of aggregated insurance industry variables.

The OLS regression results of the relationship between GFCF and IIRICP, IPR, TII are reported in Table 5. The multiple-regression results in Table 5 exhibit that out of the four insurance industry variables, only changes in the insurance claims payment (ICP) is positively and statistically significant with the growth in Gross Fixed Capital Formation (GFCF) a 1% rise in the amount of claims paid will lead to about 0.11%. The coefficient estimate of insurance intermediation ratio is 0.143; it is insignificantly and negatively related with the changes in GFC[]. This suggests that a 1% increase in IIR may reduce GFCF by about 0.14%. IPR and TII have weak but positive relationship with GFCF. The adjusted [R.sup.2] of 0.14 indicate that about 14% of the adjustments in Gross Fixed Capital Formation could be explained by the specified independent variables in the model.

Table 6 reports the Vector Error Correction Mechanism result. The Vector Error Correction Test investigates how fast the specified variables in the model could adjust and come to a long-run equilibrium position should there be short-run equilibrium distortion.

Noting that negative sign denotes significance, the authors observed that, in case of short-run equilibrium distortion, the growth in GFCF can be adjusted back to equilibrium position at a speed of about 40% while the insurance penetration ratio can be restored back at a speed of about 94%.

To verify the existence of co-integrating relationship or whether the variables share mutual stochastic trend and are linked in a common long-run equilibrium, the Johansen cointegration procedure was utilized based on maximum eigen value and the likelihood ratio. The result is presented in Table 7.

Data analysis in Table 7 indicated that the first hypothesis of no co-integrating vector is rejected since the observed statistic is 92.57 against the critical value of 68.52 at 95% confidence level and the second null hypothesis of one co-integrating vector or less can equally be rejected against the alternative hypothesis of 2 co-integrating vectors at 5% level of significance. The observed statistics is 61370 while the critical value is 47.21. The third null hypothesis of at most 2 co-integrating vector is also rejected against the alternative hypothesis of 3 co-integrating vector at 5% level of significance since the likelihood ratio of 34.90 is greater than the critical value of 29.68.

As shown in Table 7, it is clear that the hypothesis of at most 3 co-integrating vector cannot be rejected. It can be seen that at 95% level of confidence, the observed statistic of 17.23 is greater than the critical value of 15.41, as such the hypothesis of 3 co-integrating vector cannot be rejected. As shown in Table 7, the result indicates that there are four co-integrating vectors. This implies that there is a long run equilibrium relationship between Gross Fixed Capital Formation and insurance industry intermediation activities in Nigeria.

Table 8 also shows the empirical results of the pairwise granger causality test conducted on the pairs of the variables in the model. The result of the Pairwise test conducted with a maximum lag of 6 on the first difference of the linear form of the variables show that changes in claims payment alone proceed changes in Gross Fixed Capital Formation at the aggregated level. That is, causality runs uni-directionally from total claims paid by policy holders to raise the level of GFCF in the Nigerian Economy (at 95% confidence level).

Table 9 presents the dynamic effects of insurance intermediation ratio (IIR), insurance claims payment (ICP) Insurance penetration ratio (IPR) and total insurance investment (TII) on Gross Fixed Capital Formation (GFCF) over a ten year period. The ordering of the variables is IIR -> IICP -> IPR -> TII -> GFCF. Data analysis in Table 9 reveals that the impulse response of GFCF to own innovation represent the dominant source of adjustment in the variable and it is positive 19.50% in the first year, 4.43% in the second year, falls to negative -5.56% and -2.75% in the third and fourth years respectively. Again, rise to positive 1.25% in the fifth year and fluctuate to 0.14% in the 10th year.

The impulse response of GFCF to shocks emanating from the explanatory variables (IIR, ICP, IPR and TII) in the first year is -0.20%, -1.11%, 0.65%, and -1.16%, respectively. It fluctuated in value and sign in response to all the independent variables over the ten years period, and assumed the values -0.001%, -0.008%, 0.005% and -0.008% respectively in the 10* year. This implies that GFCF response to innovations flowing from the explanatory variables is not consistent in nature and the responsiveness of GFCF is not predictable and do not follow any valid pattern.

Table 10 reports that GFCF explains about 98.4% variation in its own shock in the second year while IIRICP, IPR and TII explains about 0.22%, 0.003%, 1.19% and 0.16% respectively. The variation explained by innovations emanating from GFCF gradually diminished from 97.345 in the 4th year to 97.099 in the 10th year while the variation explained by shocks emanating from IIRICP, IPR and TII increases from 0.56%, 0.14%, 1.42% and 0.70% in the 5th year to 0.59, 0.143%, 1.432% and 0.73% respectively in the 10th year. This shows a consistent expansion.


The study recommends the implementation of all compulsory insurances and domestic insurance of all risk in the oil and gas as well as aviation sector in Nigeria in order to boost insurance penetration and intermediation in Nigeria.


The study sought to determine the effects of insurance intermediation indices on the growth of GFCF. In the light of these effects, the study also sought to identify the GFCF behavioral pattern in response to insurance financial intermediation stimuli. The results of the short run analysis indicate that positive and significant relationship exists between total claims paid on all forms of non-life insurance policies and GFCF. By implication, the aggregated claims paid by insurance companies strongly mitigate the adverse effect of insured losses on businesses, households and corporate bodies in the economy. Insurance penetration, and total insurance investment positively, but not significantly, affects growth in capital formation while insurance intermediation ratio negatively and insignificantly affects capital formation.

In the long run, the effects of the insurance intermediation factors, on the growth of Gross Fixed Capital formation were positive and significant, showing that there is a long run equilibrium relationship between GFCF and financial intermediation activities by insurance companies in Nigeria at 5% level of significance. This is in time with the findings of Boon (2005) where total insurance funds influence capital formation in both the short run and the long run respectively. The pairwise granger causality test results reveal that causality trickle from changes in total claims paid to growth in GFCF.

This implies that indemnification of the holders of property and liability insurance policies by the insurance companies could help to maintain and improve the productive quality of the existing stocks of capital in the economy. The result confirms our priori expectations and supported the short run result. GFCF response to shocks emanating from aggregated insurance industry financial intermediation variables appear to follow a random walk pattern throughout the forecast period. Though there seems to be a significant dynamic relationship between yearly GFCF and financial intermediation by insurance companies, the relationship does not follow a define pattern.

The variance decomposition analysis shows that an increasingly large proportion of the forecast error of GFCF could be attributed to innovations in the independent variables. This implies that government policies focused on improving insurance intermediation, claims payment, insurance penetration and insurance investment activities will lead to growth in the level of capital formation in the future. The identified behavior of GFCF in response to the stimuli provided by the financial intermediation by insurance companies is largely positive and expansionary motivating growth in capital formation.

From the analysis, we can identify some consistencies in the behavior of GFCF in response to the stimuli provided by insurance financial intermediation. We can conclude that financial intermediation activities by insurance companies impacts significantly on growth in capital formation especially in the long run into the future.


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Chinedu B. Ezirim

Lezaasi Lenee Torbira

University of Port-Harcourt, Nigeria

Azuka Edith Amuzie

Office of Accountant General Imo State, Nigeria

Chinedu B. Ezirim is a Professor of Finance, Banking and Finametrics, and Dean of Graduate School of Management, Business and Trade, University of Port Harcourt, Nigeria. He is a Fellow of many international and national professional associations. He has published over 100 papers in accredited journals internationally and locally.

Lezaasi L. Torbira is a senior Lecturer in the Department of Finance and Banking, Faculty of Management Sciences and also of the Graduate School of Management, Business and Trade, University of Port Harcourt, Nigeria. He has publishes several papers in accredited international journals.

Azuka E. Amuzie is a Director of Finance in the Office of the Accountant-General, Ministry of Finance, Imo State Nigeria. She has many papers published in international journals to her credit.
Table 1

Descriptive Statistics for The Variables GFCF, IIRICP, IPR,
TII in The Model

Variable   Mean      Medium    Maximum   Minimum   Std.Dev  Skewness

GDP        24.27154  22.22529  92.25809  -31.3871  28.1742  0.287386
IIR        11.913    4.499     35.981    0.056     12.735   0.292
ICP        44.260    13.460    515.011   -79.228   112.313  2.990
IPR        4.924     4.404     16.135    2.876     2.2859   3.845
TII        43.540    18.505    595.062   -100      131.675  3.033

Variable   Kurtosis  J.B       PV

GDP        2.751814  0.522613  0.7701
IIR        1.411     3.824     0.1478
ICP        12.034    156.507   0
IPR        19.391    437.054   0
TII        12.506    169.542   0

Table 2

Correlation Matrix Result

Variables     GFCF       IIR       IICP      IPR     TII

GFCF               1
IIR         -0.089295        1
ICP         0.414201   -0.01676        1
IPR         0.017091   0.028792  -0.04946        1
TII         0.245129   -0.07154  -0.05346  -0.05906   1

Table 3

Augmented Dickey Fuller (ADF) Unit Root Test Results

Variable   ADF stat@   ADF stat@    Order of
             levels     1st Diff   integration

GFCF         -4.748      -8.932       1(1)
IIR          -1.077      -4.251       1(1)
ICP          -4.370      -6.594       1(1)
IPR          -3.414      -5.183       1(1)
TII          -5.999      -7.072       1(1)

Critical value: 1%= -3.675; 5%= -2.966; 10%= -2.622

Table 4

Diagnostic Test Results

Test Statistics      L.M. Version  Prob. Value  F-Version  Prob. Value

Normality (J.B)      0.214118      0.898472     --         --

1st order serial     1.079532      0.298802     0.907743   0.349488

White                10.94783      0.690129     0.631469   0.804964

Ramsey RESET         0.221437      0.637947     0.180542   0.674404

Table 5

Ordinary Least Square Results

                  Dependent Variable: GFCF

Variable Variables   Coefficient  Std. Error  t-Statistic  Prob.

IIR                   -0.142946    0.369793     -0.38656   0.7021
ICP                    0.107905    0.041927     2.573651   0.0159
IPR                    0.690023    2.060872     0.334821   0.7403
TII                    0.057089    0.035864     1.591801   0.1231
C                      15.31544    12.36927     1.238184   0.2263

R-squared            0.250298
Adjusted R-squared   0.139231
F-statistic          2.25358
Durbin-Watson stat   1.655444

Table 6

Vector Error Correction Test Result

 Variable    Adjustment Parameter

   GFCF             -0.40
   IIR               0.24
   ICP               0.08
   IPR              -0.94
   TII               0.07

[R.sup.2] = 0.582, C coeff = - 4.80, C stat = -0.37

Table 7

Johansen Co-integration Test Results

                          5 Percent  1 Percent   Hypothesized
             Likelihood   Critical   Critical    No. of
Eigenvalue   Ratio        Value      Value       CE(s)

0.642526     92.57075     68.52      76.07         None **
0.59081      61.70999     47.21      54.46       At most 1 **
0.445263     34.9027      29.68      35.65       At most 2 *
0.421664     17.22489     15.41      20.04       At most 3 *
0.026214     0.796901     3.76       6.65         At most 4

*(**) denotes rejection of the hypothesis at 5%(1%) significance level

L.R. test indicates 4 cointegrating equation(s) at 5% significance

Table 8

Pairwise Granger Causality Test Result

Null Hypothesis:                  Observations  F-Statistic

IIR does not Granger Cause GFCF        26       1.20185
GFCF does not Granger Cause IIR                 0.69175

IICP does not Granger Cause GFCF       26       3.94126
GFCF does not Granger Cause IICP                2.62077

IPR does not Granger Cause GFCF        26       1.55121
GFCF does not Granger Cause IPR                 0.65625

TII does not Granger Cause GFCF        26       0.53905
GFCF does not Granger Cause TII                 0.81627

Null Hypothesis:                  Probability

IIR does not Granger Cause GFCF   0.36473
GFCF does not Granger Cause IIR   0.66063

IICP does not Granger Cause GFCF  0.01821
GFCF does not Granger Cause IICP  0.06858

IPR does not Granger Cause GFCF   0.2379
GFCF does not Granger Cause IPR   0.68569

TII does not Granger Cause GFCF   0.76976
GFCF does not Granger Cause TII   0.57623

Table 9

Impulse Response to one Standard Deviation Innovations

Response of GFCF:

Period    IIR         IICP       IPR        TII        GFCF

1         -0.199865   -1.11241    0.646023   -1.15978    19.49981
2         -0.045393   -0.25265    0.146725   -0.26341    4.428789
3          0.05697     0.317082  -0.18414     0.330586  -5.55827
4          0.02822     0.157064  -0.09121     0.163753  -2.75324
5         -0.012768   -0.07107    0.041271   -0.07409    1.245742
6         -0.012399   -0.06901    0.040078   -0.07195    1.209742
7          0.001482    0.008249  -0.00479     0.0086    -0.14459
8          0.004511    0.025105  -0.01458     0.026174  -0.44007
9          0.000526    0.002925  -0.0017      0.00305   -0.05128
10        -0.001399   -0.00779    0.004522   -0.00812    0.136493


Table 10

Variance Decomposition Estimation Result

                  Variance Decomposition of GFCF:

Period     S.E.      GFCF      IIR       IICP      IPR       TII

1        32.52367  100.0000  0.000000  0.000000  0.000000  0.000000
2        32.92687  98.43246  0.216158  0.002813  1.190593  0.157975
3        34.10054  97.65935  0.513530  0.007416  1.411999  0.407701
4        34.19053  97.34531  0.549323  0.104127  1.429345  0.571900
5        34.35611  97.18401  0.563002  0.141291  1.415613  0.696083
6        34.36327  97.14478  0.596148  0.141511  1.415871  0.701688
7        34.37025  97.12749  0.596392  0.141647  1.415695  0.718779
8        34.37481  97.12044  0.596313  0.141812  1.417951  0.723486
9        34.37839  97.11244  0.596326  0.141888  1.424559  0.724783
10       34.38090  97.09882  0.596254  0.143124  1.432849  0.728952
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Author:Ezirim, Chinedu B.; Torbira, Lezaasi Lenee; Amuzie, Azuka Edith
Publication:International Journal of Business, Accounting and Finance (IJBAF)
Article Type:Report
Geographic Code:6NIGR
Date:Dec 22, 2016
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