Dependence relationship between insurance demand and some economic, financial, and socio-demographic factors: Evidence from different groups of European countries
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DOIhttp://dx.doi.org/10.21511/ins.14(1).2023.10
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Article InfoVolume 14 2023, Issue #1, pp. 110-120
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The insurance sector is a significant component of the economy and its financial system. Therefore, sound growth and protection of the insurance industry against systemic risks are critical requirements for any country’s social and economic development. The paper analyzes the dependence between insurance demand represented by insurance penetration and various factors from economics, finance, socio-demographics, and institutions. The analysis is conducted within certain clusters of European countries, which are determined by functional clustering analysis concerning the magnitude and shape of the insurance penetration curves. The dependence is analyzed via linear mixed-effect models. The analysis shows significantly different dependencies between the clusters, proving the existence of different conditions for different European insurance markets, especially concerning economic growth, income, financial development, and unemployment. In contrast, interest rates, inflation, urbanization, and education do not play a significant role in these insurance markets. The institutional development seems insignificant for all clusters except for certain economies in transition. The findings imply that there is a need for countries across Europe to identify country-specific determinants of insurance. In that respect, European policymakers and managers can direct specific policies based on the identified determinants’ relationship with insurance, especially in developing countries.
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JEL Classification (Paper profile tab)G22, G28
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References41
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Tables2
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Figures0
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- Table 1. Coefficients and p-values of factor significance tests for all formed clusters obtained before stepwise-backward regression analysis
- Table 2. Coefficients and p-values of factor significance tests for all formed clusters obtained after stepwise-backward regression analysis
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- Aussilloux, V., Bénassy-Quéré, A., Fuest, C., & Wolff, G. (2017). Making the best of the European Single Market. Bruegel.
- Balcilar, M., Gupta, R., Lee, C., & Olasehinde-Williams, G. (2020). Insurance and economic policy uncertainty. Research in International Business and Finance, 54, 101253.
- Basse, T. (2019). The impact of the financial crisis on the dividend policy of the European insurance industry: Additional empirical evidence. Zeitschrift Für Die Gesamte Versicherungswissenschaft, 108(1), 3-17.
- Beck, T., & Webb, I. (2002). Determinants of life insurance consumption across countries (Policy Research Working Papers).
- Berends, K., McMenamin, R., Plestis, T., & Rosen, R. (2013). The sensitivity of life insurance firms to interest rate changes. Economic Perspectives, 37(2), 47-78.
- Brown, J. R., Goda, G. S., & McGarry, K. (2012). Long-term care insurance demand limited by beliefs about needs, concerns about insurers, and care available from family. Health Affairs, 31(6), 1294-1302.
- Browne, M. J., & Kim, K. (1993). An international analysis of Life Insurance Demand. The Journal of Risk and Insurance, 60(4), 616.
- Busemeyer, M. R., & Iversen, T. (2020). The Welfare State with private alternatives: The transformation of popular support for Social Insurance. The Journal of Politics, 82(2), 671-686.
- Christophersen, C., & Jakubik, P. (2014). Insurance and the macroeconomic environment (Financial Stability Report).
- Dai, W., Athanasiadis, S., & Mrkvička, T. (2021). A new functional clustering method with combined dissimilarity sources and graphical interpretation. Computational Statistics and Applications.
- Dai, W., Mrkvička, T., Sun, Y., & Genton, M. G. (2020). Functional outlier detection and taxonomy by Sequential Transformations. Computational Statistics & Data Analysis, 149, 106960.
- Dorofti, C., & Jakubik, P. (2015). Insurance sector profitability and the Macroeconomic Environment.
- Dragos, S. L. (2014). Life and non-life insurance demand: The different effects of influence factors in emerging countries from Europe and Asia. Economic Research-Ekonomska Istraživanja, 27(1), 169-180.
- ECB. (2023). Long-term interest rate statistics for EU Member States.
- Esho, N., Kirievsky, A., Ward, D., & Zurbruegg, R. (2004). Law and the determinants of property-Casualty Insurance. Journal of Risk and Insurance, 71(2), 265-283.
- Feyen, E., Lester, R., & Rocha, R. (2011). What drives the development of the insurance sector? an empirical analysis based on a panel of developed and developing countries (Policy Research Working Papers).
- Franzetti, C. (2021). Pricing export credit. Springer.
- Hwang, T., & Greenford, B. (2005). A cross-section analysis of the determinants of life insurance consumption in mainland China, Hong Kong, and Taiwan. Risk Management and Insurance Review, 8(1), 103-125.
- IMF. (2023). Financial development index database.
- In’t Veld, J. (2019). The economic benefits of the EU Single Market in goods and services. Journal of Policy Modeling, 41(5), 803-818.
- Jagric, T., Bojnec, S., & Jagric, V. (2018). A map of the European Insurance Sector – are there any borders. Economic Computation and Economic Cybernetics Studies and Research, 52(2), 283-298.
- Jarrow, R. A. (2021). The economics of insurance: A derivatives-based approach. Annual Review of Financial Economics, 13(1), 79-110.
- Lenten, L. J., & Rulli, D. N. (2006). A Time-series analysis of the demand for life insurance companies in Australia: An unobserved components approach. Australian Journal of Management, 31(1), 41-66.
- Ma, Y., & Pope, N. (2003). Determinants of international insurers’ participation in foreign Non-Life Markets. The Journal of Risk and Insurance, 70(2), 235-248.
- McGee, A. (2020). Single market in insurance: Breaking down the barriers. Routledge.
- Millo, G., & Carmeci, G. (2010). Non-life insurance consumption in Italy: A sub-regional panel data analysis. Journal of Geographical Systems, 13(3), 273-298.
- Myllymäki, M., & Mrkvička, T. (2020). GET: Global envelopes in R.
- OECD. (2023). Long-term interest rates.
- Outreville, J. F. (2013). Risk aversion, risk behavior and demand for insurance: A survey. (ICER Working Paper Series No. 11).
- Paul, S. (2011). On the measurement of unemployment in the developing and developed countries. International Journal of Development and Conflict, 1(3), 365-377.
- Pham, B. T., & Sala, H. (2021). Cross-country connectedness in inflation and unemployment: Measurement and macroeconomic consequences. Empirical Economics, 62(3), 1123-1146.
- Pinheiro, J., Bates, D., DebRoy, S., Sarkar, D., & R Core Team (2021). nlme: Linear and Nonlinear Mixed Effects Models. R package version 3.1-152.
- Schanz, K., & Treccani, P. (2023). The return of inflation: What it means for insurance.
- Simionescu, M., & Ulbinaitė, A. (2021). The relationship between insurance market and macroeconomic indicators in the Baltic States. Journal of Baltic Studies, 52(3), 373-396.
- Svirydzenka, K. (2016). Introducing a new broad-based index of Financial Development (IMF Working Papers No. 16/5).
- Swiss Re. (2022). Swiss Re sigma database. Zurich: Swiss Re Institute, Swiss Re Management Ltd.
- Treerattanapun, A. (2011). The impact of culture on non-life insurance consumption.
- Twinoburyo, E. N., & Odhiambo, N. M. (2018). Monetary policy and economic growth: A Review of International Literature. Journal of Central Banking Theory and Practice, 7(2), 123-137.
- Vimala, B., & Ramanathan, K. A. (2018). Insurance Penetration and Insurance Density in India - An Analysis. IJRAR- International Journal of Research and Analytical Reviews, 5(4), 229-232.
- World Bank. (2023a). World development indicators.
- World Bank. (2023b). Worldwide governance indicators.