AN APPRAISAL OF THE INSURANCE GROWTH OF THE ECONOMY IN NIGERIA

Abstract

This paper examines the relationship between insurance and economic growth in 20 countries for the period 2006–2015. Insurance activity is measured through three distinctive proxies such as net written premiums, penetration and density. The Hausman statistics confirmed that fixed effect model is appropriate for this data-set. This study found a positive and a significant relationship between life insurance, measured through net written premiums and density, and economic growth for developed countries while the same is true for developing countries when insurance is measured through penetration proxy. The results also reveal that non-life insurance has statistically significant, for all three proxies, relationship with economic growth for developing countries whereas, in case of developed countries, the results are only significant when insurance density is used as a proxy for insurance. Moreover, the role of non-life insurance is more significant for developing countries as compared to developed countries.

Public Interest Statement

Insurance performs similar functions as banking sector and the market stock; however, limited studies are available about the contribution of insurance from an economic perspective. Statistics reveal that the share of insurance sector is approximately 6.23% in world gross domestic product. Yet, given the huge contribution of insurance to the economy as a whole, insurance also promotes a greater sense of security, peace of mind, reduction in anxiety and fear among individuals, businesses and governments. To individuals, insurance purchase enables an individual to sustain his continuous consumption of his property in the case of theft or damaged. Insurance enables businesses to operate in a cost-effective manner by providing risk transfer mechanisms. To the Government, on the other hand, expenditure on damages caused by natural disasters such as fire, flood and other natural disasters is reduced if not eliminated due to insurance purchase.

1. Introduction

The importance of insurance, like other financial institutions such as banking and the stock market, is vital for the sustainable economic growth of any country. The risk is inherent in every human activity ranging from social life to economic activities (Din, Angappan, & Baker, 2017). The importance of insurance cannot be denied because of its economic outlook, for instance, insurance spending is 6.23% of World’s GDP (Sigma Swiss-Re, 2016). More precisely, insurance spending for developed countries is around 8–11% whereas it is 2–4% for developing countries (Din et al., 2017; Outreville, 2013). However, statistics revealed a significant reduction, from 88–67%, in the share of developed countries premium since 2005 and an upward shift in insurance premiums for emerging and developing countries (Swiss-Re, 2016).

Human behaviour, particularly risk aversion, would either lead towards avoiding these activities or excessive precaution and both of these actions would result in a social loss (Masum Billah, 2014). In absence of risk transferor entities like insurance, the stock market and the banks, the volume of such economic activities would be much lower and hence will result in an economic loss (Gollier, 1991; Ward & Zurbruegg, 2000). Insurance not only helps to smooth out the volatile economic condition (Chau, Khin, & Teng, 2013) but insurance contracts are more stable than bonds, notes, and they are an exchange of money now for money payable contingent on the occurrence of certain events (Arrow, 1921). According to the Orthodox view of insurance, it is a key instrument of risk transferring, indemnification and intermediation (Cummins & Verand, 2007; Lester, 2009; Outreville, 1990, 1994, 2013, 2015; UNCTAD, 2007).

Prudent individuals do not prefer risk; however, if unavoidable they either keep aside an accumulated surplus or maintain a sinking fund to meet the contingency. These options, accumulation or sinking fund, soak up the scarce resource, either are of no use unless contingency or insufficient to restore the position of individual. Resultantly, society will suffer from existence of risk, through exposure to the chance of reduction in general well-being due to unproductive use of resources, or fail to achieve the desired outcome. Risk transferring to the third party will reduce fear, anxiety, frustration, demoralisation or melancholy (Willett, 1901). Besides removing exaggerated fear, insurance encourages creativity, innovation, entrepreneurial activities and trade that are vital for sustainable economic growth (Cristea, Marcu, & Cârstina, 2014; Masum Billah, 2014). The underlying conception of risk sharing, in modern insurance is adopted from the practice followed by the merchants at Edward Lloyd coffeehouse of London (Liu & Lee, 2014).

Past researchers who explored the relationship between financial sector and economic growth mainly focused either on banking sector or stock market (Horng, Chang, & Wu, 2012; Levine, 1997; Merton & Bodie, 1995) while insurance remained ignored (Haiss & Sümegi, 2008; Njegomir & Stojić, 2010; Verma & Bala, 2013). Literature reported that five possible relationships could exist between insurance and economic growth negative (Zouhaier, 2014)¹, demand following (Ching, Kogid, & Furuoka, 2010), supply following² (Ward & Zurbruegg, 2000), interdependence (Ghosh, 2013) and no relationship at all (Haiss & Sümegi, 2008; Omoke, 2012). Previous studies claimed that the role of insurance in promoting economic growth is not constant rather it follows S-shape curve, for instance, insurance plays little role for developed economies (Arena, 2008; Guochen & Chiwei, 2012; Haiss & Sümegi, 2008; Han, Li, Moshirian, & Tian, 2010; Ward & Zurbruegg, 2000). In addition, studies that have been conducted to examine the relationship between insurance and economic growth utilised a single proxy such as net written premiums, penetration or density, however, the proxy choice could also affect the outcome.

The remainder of this article is organised as follows. The next section presents the institutional setting in the sample countries and reviews the literature in the field. Then the methods employed in this study are discussed followed by the research findings. The final section concludes.

2. Literature review

Studies that examined the relationship between insurance and economic growth can be counted on fingers. The study of Ward and Zurbruegg (2000) is considered to be the first that explored the relationship between insurance and economic growth for OECD countries. They measured insurance through total insurance premium proxy and apply Granger Causality to study demand or supply following relationship between insurance and economic growth. The results revealed that in some OECD countries economic growth Granger Cause insurance demand and the reverse is true for others. It is important to mention here that authors found an insignificant relationship for two OECD countries namely UK and USA. Kugler and Ofoghi (2005) also investigated the relationship between insurance and economic growth using disaggregated data. They found a significant and a positive relationship between insurance and economic growth for the UK. They argued that an insignificant result of Ward and Zurbruegg (2000) was due to use of aggregate data (life plus non-life insurance premiums).

Haiss and Sümegi (2008) apply panel data analysis over the period of 1992–2004 for 29 OECD countries to explore the relationship between insurance and economic growth. They found that insurance differently affects economic growth of countries, for example, life insurance has become more significant for 15 OECD countries while non-life insurance has the same for rest of the 14 countries. Likewise, Ege and Bahadır (2011) also explore the relationship between insurance and economic growth on the panel data of 29 OECD over the period of 1999–2008, utilising the generalised method of moments (GMM). Results indicate that there is a positive and significant relationship between insurance and economic growth.

In addition, Chang, Lee, and Chang (2014) again examined the relationship between insurance and economic growth for 10 OECD. They apply bootstrapping Granger causality model over a period of 1979–2006. They revealed that one-way Granger causality running from all insurance activities to economic growth for France, Japan, Netherlands, Switzerland and the UK. Furthermore, economic growth Granger causes insurance activities in Canada (for life insurance), Italy (for total and life insurance) and the US (for total and non-life insurance). There is a two-way Granger causality between life insurance activity and economic growth in the US, while no causality between insurance activities and economic growth is found in Belgium (for all insurance), Canada (for total and non-life insurance), Italy (for non-life insurance) and Sweden (for life insurance). They justify their results as opposed to Ward and Zurbruegg’s (2000) findings, (1) we utilise the most recent data for analysis and (2) they perform their analysis on country-to-country basis while we did it on panel framework.

A study conducted by Arena (2008) apply the generalised method of moments (GMM) on the panel data of 55 developed and developing countries for a period of 1974–2004 to investigate the relationship between insurance and economic growth taking insurance density as a proxy. They revealed that insurance at the aggregate level is significantly affecting the economic growth. Furthermore, they highlighted the, at the disaggregate level; effect of life insurance is significant for low-income countries whereas non-life is significant for developing and developed countries. Tong (2008) conducted a study to explore the relationship between insurance and economic growth for US, Germany, Sweden and South Korea. He utilised OLS, Fixed Effect and simultaneous equation modelling to investigate this relationship. He found life insurance has a significant and positive effect on economic growth for US, South Korea. However, the said relationship is negative in case of Sweden and Germany. The author claimed that as the government provides social benefits similar to life insurance, therefore, life insurance industry in European countries is not significantly contributing to the economy. On the other hand, non-life insurance has a significant and positive effect on economic growth for US, Germany, Sweden and South Korea.

Similarly, a study carried out by Kjosevski (2011) also investigated the relationship of insurance and economic growth for the Macedonia using the multiple regression models. Results highlighted that aggregate insurance industry and non-life insurance has a positive and significant effect on economic growth of Macedonia for the period 1995–2010. On the other hand, life insurance has significant but negatively affecting the economic growth of Macedonia. The author claimed that a strong banking sector (saving substitute and investment channel) could be the possible reason for the negative relationship between life insurance and economic growth for Macedonia. It is important to mention here that Tong (2008) measured insurance activity using total insurance premiums as a proxy for Europe, whereas, Kjosevski (2011) utilise insurance penetration to measure insurance activity for Macedonia.

Later on, Ćurak, Lončar, and Poposki (2009) apply fixed effect panel data test to again investigate the above-mentioned relationship for the 10 transitional countries over the period of 1992–2007. Authors established an argument, unlike the previous study where only life insurance promoting the economic growth, that insurance industry as a whole, life and non-life insurance all promotes economic growth for transitional countries. Similarly, Han et al. (2010) also explore the relationship between insurance and economic growth for 77 countries over the period of 1995–2004 using generalised methods of moments (GMM). The result supported the findings of Ćurak et al. (2009) that aggregate, non-life and life insurance have a much significant effect on economic growth for developing countries as compared to developed.

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