Legal Stability and Determinants of Insurance Development in the Middle East and North Africa Region (MENA)

Abstract

Despite the importance of the insurance business for financial and economic development, few studies have looked at the factors that influence its growth. This research adds to the body of knowledge by empirically examining the impact of numerous factors on the development of the insurance business in 15 Middle East and North African (MENA) countries from 2000 to 2017. The study looks at macroeconomic, demographic, and institutional factors as potential drivers of the insurance industry's growth, with the insurance premium as a percentage of GDP as the dependent variable. All variables are stationary at the first difference, according to the IPS panel unit root test. The Pedroni residual cointegration test, Kao residual cointegration test, and Johansen-Fisher panel cointegration tests are then used to look for long-run associations. The cointegration tests strongly suggest that the insurance premium and the various variables have long-run correlations. Findings from the Fully-Modified OLS imply that GDP per capita, gross capital formation, and the KOF economic globalization index have a positive long-term impact on the insurance business. The insurance business is also driven by combating corruption and the rule of law. The population and regulatory quality, on the other hand, have no significant impact.

1. Introduction

The impact of financial development on economic growth has been one of the fundamental areas in economic research, with a vast body of literature supporting the connection between financial development and economic growth. (Levine & Zervos, 1998, Nguyen & Pham, 2021). Most previous studies concluded that financial services, including banking and insurance, positively affect economic growth. Such statements are expected given that well functioning financial institutions improve the efficiency of capital allocation, stimulate savings, and enhance capital formation. The importance of the insurance industry has been confirmed in several previous studies (Ward & Zurbruegg, 2000; Ul Din et al., 2017; Peleckienė et al., 2019). These studies suggest that the insurance industry positively affects economic growth through financial intermediation and risk transfer. Moreover, the insurance industry may benefit the economy as it minimizes the capital needed to cover individual losses by providing risk sharing and reducing the impact of significant losses on enterprises and households, which encourages investment and boosts economic growth.

Given the importance of financial development for economic growth, some research has focused on the factors that determine the profitability of insurance companies (Sasidharan et al., 2020; Alqirem, 2020; Ben Dhiab, 2021). Others have been looking into the factors that encourage the growth of financial services. Despite the crucial role the insurance business may play in financial and economic development, there have been few studies analyzing the factors influencing the industry’s development. In reality, most of the focus has been on the banking business, with only a smattering of attention dedicated to insurance, indicating a large vacuum in the existing literature. Among studies focusing on factors affecting the development of the insurance industry, one may cite Ward and Zurbruegg (2000) and Beck and Webb (2003). The evolution of the insurance sector is influenced by a variety of factors, including economic, legal, political, and social factors, according to these scholars. When legal and institutional rules are in place, the insurance premium is actually invested in proper instruments. In line with these considerations, there has been a growing interest in the role of legal stability in the development of the insurance business. According to Beck and Web (2003), the quality of institutions has an impact on the development of the non-life insurance business. The legal system’s relevance and the enforcement of property rights are particularly crucial for the industry’s development (Lee et al., 2018a). While some research has been done on the factors that influence the development of the insurance business, the majority of the extant studies have focused on developed countries, leaving another gap in the literature.

Between 2000 and 2017, this study adds to the current research by empirically examining the influence of numerous factors on the development of the insurance business in a sample of the Middle East and North African (MENA) nations. This work, in particular, has a double purpose. First, it examines the economic and demographic factors that influence the development of the insurance sector in the Middle East and North Africa (MENA). Second, it examines the impact of numerous institutional factors on the insurance industry’s development. As a result, this study has two major novelties. On the one hand, it takes into account several macroeconomic and demographic aspects that may have an impact on the insurance industry’s growth, such as GDP per capita, population, domestic investment, and economic openness.

On the other hand, we provide a set of institutional factors because the quality of institutions plays a significant role in promoting the insurance industry’s development. More specifically, the control of corruption, the rule of law, and the regulatory quality are introduced in the study. The empirical study is centered on identifying elements that influence the long-term development of the insurance industry. Checking the stationarity of variables, conducting a cointegration study, and lastly estimating the impact of various macroeconomic, demographic, and institutional factors on the insurance premium are all used to accomplish this.

The remainder of the paper is organized as follows. In Section 2, relevant literature is presented. Section 3 describes the data and the methodology. Section 4 analyses the empirical results, and finally, Section 5 concludes.

2. Literature Review

Despite the vital role of the insurance industry in the economy, few studies have examined the factors influencing its development. Furthermore, most existing empirical research focused on the life insurance sector (Beck & Webb, 2003; Zerriaa et al., 2017). Browne et al. (2000) by focusing on a sample of 22 OECD countries between 1987 and 1993. The authors report that GDP per capita, the quality of institutions, and the market share of foreign firms influence automobile insurance.

In a seminal article, Beck and Webb (2003) examined the determinants of life insurance consumption in a sample of 68 developed and developing countries during the period 1961–2000. A wide range of economic, demographic, and institutional have been used in the empirical analysis. Results obtained from the fixed and random effects models suggest that the insurance industry is positively affected by GDP per capita and the banking sector development, while higher inflation rates exert adverse effects, while other variables, such as life expectancy, schooling, and urbanization, have no significant effects. In a similar study, Feyen et al. (2013) focussed on 90 developed and developing countries between 2000 and 2008. Moreover, the author considered life insurance and non-life insurance as proxies of the insurance industry. Findings indicated that the life sector premium is affected by GDP, the size of the public pension system, the ownership of insurance enterprises, population, and the development of the financial sector. On the other hand, the non-life sector premium reacts to GDP, the quality of institutions, and the development of the financial sector.

Between 1986 and 1996, Hwang and Gao (2003) investigated the primary factors influencing life insurance demand in China. The authors take into account a wide range of variables, including GDP per capita, educational attainment, inflation rate, and urbanization level. The empirical research demonstrates that GDP per capita, level of education, and level of urbanization have all aided the development of the insurance business in China, but the projected negative effect of inflation is not statistically significant.

Millo and Carmeci (2011) analyzed the determinants of non-life insurance in 103 Italian provinces during the period 1998–2002. The authors conclude the presence of positive effects of wealth, population density, and income and negative impact of the legal system inefficiency on the insurance demand.

Between 1999 and 2009, Zyka and Myftaraj (2014) investigated the primary factors that influence insurance demand in Albania. The study reveals that gross domestic product, population size, and the ratio of the urban population have beneficial effects on the insurance industry’s development using cointegration and causality analysis.

Brokeová and Vachálková (2016) showed how macroeconomic variables affect the development of life and non-life insurance between 1995 and 2010 using data from four Central European transition countries. GDP growth, inflation, and the unemployment rate all have negative effects on life and non-life insurance premiums, according to the authors. The only variable that has a positive impact on the development of the insurance business is the balance of payments as a percentage of GDP.

During the period 2000–2008, Gaganis et al. (2020) evaluated the influence of insurance rules on the life insurance sector in 44 developed and developing nations. The findings reveal a close link between insurance premiums on the one hand, and supervisory control over policy conditions, as well as pensions products on the other.

Lee et al. (2018a) focused on the determinants of non life insurance industry development in selected ASEAN countries (Indonesia, the Philippines, Thailand, Malaysia, and Vietnam) from 1995 to 2014. It has been shown that GDP per capita, trade openness, domestic credit provided by the financial sector, and the stock market development positively affect the insurance industry, while foreign direct investments have negative effects. Concerning institutions, the study shows that the property rights the control of corruption positively affect the development of the insurance industry. Lee et al. (2018b) examined the economic and demographic determinants of life insurance demand in 4 ASEAN countries (Malaysia, Thailand, the Philippines, and Indonesia) during the period 1990–2010. A special focus has been given to secondary and tertiary education as potential factors that may affect the demand for life insurance. According to the findings, the only economic factor affecting life insurance consumption in GDP, while the only demographic factors affecting life insurance consumption are tertiary education level and youth dependency ratio.

Other factors, such as the pace of urbanization, inflation, and life expectancy, have little effect on the demand for life insurance services. In a separate study, Lee et al. (2021) examined the factors that influenced insurance sector development in China and five ASEAN nations from 1995 to 2015. The empirical analysis used the pooled OLS, fixed effects, and random effects approach. The data imply that greater commerce has a positive impact on the non-life insurance industry’s development, whereas inflation and interest rates have negative and significant effects.

Guerineau and Sawadogo (2015) look at the case of 20 nations in Sub-Saharan Africa from 1996 to 2011. Institutional, demographic, and macroeconomic aspects are all taken into account in the study. The study finds that government stability and property rights protection have a positive and considerable impact on the insurance industry’s growth. The young dependency ratio and life expectancy, on the other hand, have a negative impact on the growth of life insurance.

Regarding studies on the MENA region, one may note that few studies have been conducted. Zerriaa and Noubbigh (2016) examined the factors that influenced life insurance development in 17 MENA countries from 2000 to 2012. The empirical study demonstrates that GDP per capita, financial development, educational attainment, life expectancy, inflation, and interest rates are all positively related to the insurance business, based on the pooling OLS, fixed effects, random effects, and feasible GLS. The young dependency ratio, on the other hand, has a negative impact.

Using data on life insurance from 1990 to 2014, Zerriaa et al. (2017) looked into the case of Tunisia. Income and financial development, urbanization, and life expectancy all have positive effects, according to the literature, whereas the effects are negative. Between 1996 and 2017, Benziane and Shah (2019) examined the financial and institutional factors of insurance business development in three MENA countries: Algeria, Morocco, and Tunisia. GDP per capita, private sector credit, money supply, regulatory quality, and, lastly, the rule of law are among the variables examined. The results of the pooled OLS, fixed effects, and random effects models demonstrate that GDP per capita and private sector credits have a positive and significant influence on insurance industry development, whereas the money supply ratio has a negative relationship. In terms of institutional variables, the findings reveal that regulatory quality is critical for the development of the insurance market in the MENA countries studied.

3. Data and Methodology

3.1. Data

The current study intends to investigate empirically the factors influencing the development of the insurance business in several MENA countries, including Algeria, Bahrain, Egypt, Iran, Jordan, Kuwait, Malta, Morocco, Oman, Qatar, Saudi Arabia, Syria, Tunisia, UAE, and Yemen. Between the years 2000 and 2017, the research was conducted. The availability of data limits the number of countries and time periods that can be chosen. The total of life and non-life insurance premiums as a percentage of GDP is used to quantify insurance activity. We use the gross domestic product per capita (constant 2010 US$) as a proxy for economic activity and gross capital formation as a percentage of GDP as a proxy for investment in the economy as macroeconomic variables. Furthermore, the KOF economic globalization index, which captures trade and capital flow liberalization, is used to measure economic openness. We introduce the whole population as a demographic variable. We introduce the control of corruption, the rule of law, and regulatory quality as institutional variables. The Worldwide Governance Indicators provided these factors. The rest of the data comes from the World Bank database. Except for insurance premium as a percentage of GDP, we apply the logarithmic transformation for all variables.

3.2. Empirical Specification

As explained earlier, the research focuses on the macroeconomic, demographic, and institutional determinants of insurance premium. The model to be estimated is written as follows:

PREMIUM_it = α₀ + α₁ GDDPC_it + α₂ GCF_it + α₃ POP_it + α₄ KOF_it + α₅ INSTITUTIONS_it + ε_it        (1)

where PREMIUM is the dependent variable measured by the insurance premium as a share of GDP, GDPPC is the gross domestic product per capita, GCF is the gross capital formation as a share of GDP, POP is the total population, KOF is the economic globalization index. Finally, is the variable measuring the institutional quality. i and t represent the country and the year. ε_it is the residual term. Given that we use three indicators of institutions, the following three models are estimated:

PREMIUM_it = α₀ + α₁ GDDPC_it + α₂ GCF_it + α₃ POP_it + α₄ KO F_it + α₅ CCORR_it + ε_it       (2)

PREMIUM_it = α₀ + α₁ GDDPC_it + α₂ GCF_it + α₃ POP_it + α₄ KOF_it + α₅ RLAW_it + ε_it        (3)

PREMIUM_it = α₀ + α₁ GDDPC_it + α₂ GCF_it + α₃ POP_it + α₄ KO F_it + α₅ RQUAL_it + ε_it        (4)

Where CCORR stands for corruption control, RLAW for rule of law, and RQUAL for regulatory quality. Before estimating Equations 2–4, make sure that all the variables in the analysis are stationary. The stationarity of all variables in level and first difference is checked using the panel unit root test. The presence of a unit root in the series is the null hypothesis of the test. Then we look at if there are any long-term correlations between the insurance premium and the variables in models 2–4. The residual cointegration test by Pedroni (1999, 2004) is used to confirm the presence of a long-run relationship. We also use the Kao residual cointegration test devised by Kao (1999) and the Maddala- Fisher panel cointegration test recommended by Maddala and Wu (1999) to assess the results’ robustness. The absence of cointegration between the variables is the null hypothesis in these three-panel cointegration tests. Finally, in the case of cointegration, we use the Fully Modified Least Squares estimator developed by Phillips and Hansen (1990) and Phillips (1995) to estimate the long-run determinants of insurance sector development.

4. Empirical Results

4.1. Preliminary Analysis

Table 1 shows the descriptive statistics for the factors discussed. As indicated, the average insurance premium in MENA nations has been around 1.289 percent of GDP, with a maximum of 5.350 percent in Malta in 2007 and a minimum of 0.004 percent in Syria in 2017. In the same period, Qatar had the greatest GDP per capita in 2011, while Yemen had the lowest GDP per capita in 2017.

Table 1: Descriptive Statistics

The correlation matrix presented in Table 2 shows a low correlation (0.140) between the insurance premium and the GDP per capita in the selected MENA countries. Moreover, the same table shows that the KOF economic globalization index is moderately correlated with the insurance premium (0.392).

Table 2: Correlation Matrix

Furthermore, the institutional variables are highly and positively correlated with the insurance premium (0.473) for the control of corruption, 0.571 for the rule of law, and 0.441 for the regulatory quality). Finally, the institutional quality is highly correlated with GDP per capita, indicating a high positive association between the different institutional variables and the GDP per capita.

4.2. Stationarity

Checking the stationarity of variables contained in Models 2–4 is the first step in examining the primary drivers of insurance business development in MENA nations. For levels and initial difference, the IPS panel unit root test is used, which includes a constant, followed by a constant and a trend. Table 3 shows the results of the IPS unit root test. Because all p-values are higher than the 10% level, the variables in levels are not stationary in the presence of a constant. Only corruption control and regulatory quality remain stationary at 5% when a constant and a trend are included, whereas all other factors are not statistically significant.

Table 3: IPS Panel Unit Root Test Results

Notes: ***, **, and * denote the rejection of the unit root null hypothesis at the 1%, 5%, and 10% levels, respectively. Numbers in brackets are p-values.

The stationarity of variables in the first difference is then investigated. At the 1% level, the model with an intercept indicates that all variables are stationary. The population is the sole exception. When a trend is included in the model, the findings demonstrate that at the 1% level, all variables, including population, become stationary. As a result, it’s reasonable to conclude that none of the variables in Models 2–4 are stationary at levels or the first difference. We can see if there are any cointegration correlations between the insurance premium and other macroeconomic, demographic, and institutional aspects in this scenario.

4.3. Cointegration

After verifying for stationarity, we look to see if the variables have any long-term relationships. This research uses three-panel cointegration tests, as previously stated. Table 4 shows the findings of the Pedroni residual cointegration test devised by Pedroni (1999, 2004). When employing the panel PP-statistic, the panel ADF-statistic, the group PP-statistic, and the group ADF-statistic, the null hypothesis of no cointegration is rejected at the 1% level, as indicated. The null hypothesis of no cointegration, which denotes the lack of cointegration, cannot be rejected when employing the panel v-statistic, panel rho-statistic, and group rho-statistic. These findings apply to all three models (2–4). As a result, Table 4 shows that there is cointegration between the insurance premium and the various drivers.

Table 4: Pedroni Panel Cointegration Test Results

Notes: ***, **, and * denote the rejection of the null hypothesis of no cointegration at the 1%, 5%, and 10% levels, respectively.

However, to check the robustness of the results, we also use the Kao panel cointegration test and the Johansen-Fisher panel cointegration test. Results are reported in Tables 5 and 6.

Table 5: Kao Panel Cointegration Test Results

Notes: ***, **, and * denote the rejection of the null hypothesis of no cointegration at the 1%, 5%, and 10% levels, respectively.

Table 6: Johansen Panel Cointegration Test Results

Notes: ***, **, and * denote the rejection of the null hypothesis of no cointegration at the 1%, 5%, and 10% levels, respectively.

The Kao panel cointegration test, as can be seen, indicates that the variables are cointegrated at a 10% level, as the p-value of the associated ADF test is 0.055 for Model 2, 0.068 for Model 3, and 0.055 for Model 4. These findings support Table 6’s findings, implying that the insurance premium is cointegrated with its determinants in all three models studied.

The results of the Johansen-Fisher panel cointegration test are very similar to those of the earlier panel cointegration tests. In reality, for the three models studied, the null hypothesis of no cointegration is rejected. Overall, the cointegration analysis suggests that the insurance premium and the many factors studied have long-run relationships.

4.4. Long-run Determinants of Insurance Industry Development

The long-run influence of each variable studied in Models 2–4 on the insurance premium is the final step of the empirical research performed in this work. Phillips and Hansen (1990) developed the Fully Modified Least Squares estimator, which is used to estimate long-run effects (Phillips, 1995). Results are reported in Table 7.

Table 7: Fully-Modified OLS Results

Notes: ***, ** and * indicate the statistical significance at the 1%, 5%, and 10% levels, respectively.

As can be seen, GDP per capita has had a positive impact on the growth of the insurance business in the MENA region during the last few decades. These findings hold true for all three models and are consistent with numerous prior research on the factors that influence insurance industry growth (Millo & Carmeci, 2011; Feyen et al., 2013; Brokeová & Vachálková, 2016). Gross capital formation has a positive impact on the insurance industry’s development, and this impact is determined to be highly significant across all models. For all three models, the KOF economic globalization index coefficient is positive and statistically significant. These findings suggest that as trade and capital liberalization expands, insurance premiums in MENA nations would rise. These results are in line with those of Lee et al. (2018a). More opened economies are associated with a higher demand for non-life insurance services. Table 7 also suggests that the demographic variable (population) is not statistically significant, which means that the population has not induced the development of the insurance industry during the last decades in the MENA region.

Table 7 shows some intriguing findings of the impact of institutional characteristics on insurance premiums. The results of Model 2 demonstrate that the control for corruption coefficient is positive and statistically significant at the 1% level. These findings suggest that preventing corruption and bribes is helpful to the insurance industry’s growth. In the case of Model 3, the results indicate that the rule of law coefficient is positive and statistically significant at only 10%. Even if the coefficient is positive, it is lower than those associated with corruption control. Consequently, the rule of law is important for developing the insurance industry in MENA countries but is less important than the control of corruption. The quality of contract enforcement and property rights in a country is referred to as the rule of law. The findings demonstrate the importance of these challenges in the insurance industry’s development.

Finally, Model 3’s estimation indicates that regulatory quality is insignificant to the insurance market. Despite being positive, the coefficient is not statistically significant. As a result, the insurance industry is unaffected by the government’s sound policies aimed at promoting the private sector. Overall, the analysis reveals that the primary institutional elements influencing the development of the insurance market in the MENA region are corruption control and the rule of law.

5. Conclusion

The insurance industry is extremely important to the economy. It provides for the sharing of risks, the promotion of private investment, and the acceleration of economic progress. However, there is a growing dispute about the primary variables influencing the insurance industry’s development. The goal of this research is to look at the primary determinants of insurance industry development in a sample of 15 Middle Eastern and North African countries from 2000 to 2017. Our dependent variable is the insurance premium as a percentage of GDP, with macroeconomic factors (GDP per capita, gross capital creation, and the KOF economic globalization index), demographic factors (population), and institutional factors as explanatory variables (control of corruption, the rule of law and regulatory quality). The unit root, cointegration, and long-run coefficients are all tested in the empirical study.

All variables included in the estimation are not stationary at levels and stationary at the first difference, according to the IPS panel unit root test. The Pedroni residual cointegration test, developed by Pedroni (1999, 2004), the Kao residual cointegration test, developed by Kao (1999), and the Johansen-Fisher panel cointegration test proposed by Maddala and Wu (1999) all strongly support the existence of long-run relationships between the insurance premium and the various variables studied. The findings imply that GDP per capita, gross capital creation, and KOF economic globalization have a positive long-term impact on the insurance business when using the Fully-Modified OLS estimator. Controlling corruption and upholding the rule of law are also important drivers of the insurance industry, according to the study. The population and regulatory quality, on the other hand, have no discernible impact.

*Acknowledgements:

The author gratefully acknowledges the approval and the support of this research study by the grant no. BA-2019-1-10-F-8145 from the Deanship of Scientific Research at Northern Border University, Arar, K.S.A.