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The Relationship between Life Insurance Market Development and Economic Growth - The Analysis of Dynamic Panel Data Models
|關鍵字:||無;life insurance market development;economic growth;two-step system GMM;dynamic panel model;marginal effect;non-linear model||出版社:||應用經濟學系所||引用:||References Adams, M., J. Andersson, L.-F. Andersson, and M. Lindmark (2005), “The Historical Relation between Banking, Insurance and Economic Growth in Sweden: 1830 to 1998,” University of Wales Swansea. Alfaro, L., A. Chanda, S. Kalemli-Ozcan, and S. Sayek (2004), “FDI and Economic Growth: The Role of Local Financial Markets,” Journal of International Economics, 64, 89-112. Anoruo, E. and Y. Ahmad (2001), “Causal Relationship between Domestic Savings and Economic Growth: Evidence from Seven Africa Countries,” Africa Development Bank. Arellano, M. and S. Bond (1991), “Some Tests of Specification for Panel Data: Monte Carlo Evidence and an Application to Employment Equations,” Review of Economic Studies, 58, 277-297. Arellano, M. and O. 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Generally, financial sector contains bank, stock and insurance markets. Although there is a plenty of research study on financial sector development, less literature focuses on insurance industry. However, life insurance market has become an increasingly important part of financial sector over the last two decades. In other words, the development of insurance markets has a great contribution to economic growth. Most importantly, life insurance market development plays an increasingly important role within the insurance industry. While there is a sea of research on the demand for life insurance or the relationship between life insurance market and economic growth, the impact on economic growth considering conditional variables has not received enough attention in this respect.
This article examines empirically the relationship between life insurance market development and economic growth by using the two-step system Generalized Method of Moments (GMM) for dynamic models of panel data for 60 countries and for 1976-2005. In this paper, we use three different proxies of life insurance market development: (a) Life Insurance Penetration (LIP) is defined as the ratio of premium volume to GDP, (b) Life Insurance Density (LID) is defined as premiums per capita, and (c) Life Insurance in Savings (LIS) is defined as the ratio of premium volume to gross savings. Specially, we further take different conditional variables into consideration to assess whether and how this relationship affected by conditional variables. Moreover, our conditional variables are divided into five sets as follows: (a) economic conditions, containing savings (SAVINGS), real interest rate (REALINT), and social security (SECURITY); (b) financial conditions, including private credit by deposit money banks to GDP (CREDIT), stock market total value traded to GDP (STOCKTRA), and stock market turnover ratio (TURNOVER); (c) demographic conditions consist of young dependency ratio (YDEP), life expectancy (LIFEXP), and urbanization (URBAN); (d) income level conditions, comprising middle-income (MIC) and low-income (LIC) level dummies; and (e) regional conditions, involving Europe (EUROPE), Latin America (LATIN) and Sub-Saharan AFRICA (S-S AFRICA) dummies.
What we find is an interesting evidence that life insurance market development robustly has a positive effect on economic growth in the basic model. Moreover, in the extended model, our results clearly show that the conditional variables of SAVINGS, SECURITY, STOCKTRA, TURNOVER, YDEP, MIC, and S-S AFRICA mitigate the positive impacts of LID on economic growth, while the conditional variable of LATIN enhances the positive impact of LID on economic growth. Next, the conditional variables of REALINT and SECURITY mitigate the positive impacts of LIP on economic growth, whereas the conditional variable of LIC enhances the positive impact of LIP on economic growth. Interestingly, the conditional variable of EUROPE alleviates the negative impact of LIP on economic growth. Last, the conditional variables of TURNOVER mitigate the positive impacts of LIS on economic growth. In conclusion, in the extended model, our results roughly show that the conditional variables of MIC, S-S AFRICA, SAVINGS, REALINT, and SECURITY, STOCKTRA, TURNOVER, and YDEP alleviate the positive impacts of life insurance market development on economic growth. Oppositely, the conditional variables of LIC and LATIN strengthen the positive impacts of life insurance market development on economic growth. As we consider the marginal effect by adding conditional variables, the relationship between life insurance market development and economic growth may become ambiguous. Therefore, we can validly demonstrate that why same life insurance market development has different economic growth. In conclusion, the conditional variables may affect the relationship between life insurance market development and economic growth. This paper offers several useful insights for policy-makers and researchers. Furthermore, we find that the relationship between life insurance market development and economic growth is better described as a weak inverse U-shape. Thus, life insurance market development and economic growth, in fact, be in a non-linear form.
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