Skip to main content
SpringerLink
Log in

Institutional drivers of life insurance consumption: a dynamic panel approach for European countries

  • Published:
The Geneva Papers on Risk and Insurance - Issues and Practice Aims and scope Submit manuscript

Abstract

The motivation behind this study resides in the heterogeneous development of life insurance across 31 European (developed and former communist) nations over the period 2002–2012. We use the dynamic panel methodology for explaining the main institutional drivers of life insurance consumption. The results show that the most significant institutional factor is governance effectiveness. Among the economic and demographic factors the interest rate and fiscal freedom exert a negative effect on life insurance consumption. Our results can be the basis for improving governance policies in former communist countries and for creating an institutional system of right incentives on the market.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2

Source: Insurance Europe (2015), European insurance for total life premiums and number of population

Similar content being viewed by others

Notes

  1. We have also considered insurance penetration as a dependent variable, but we present the results only in Appendix Tables 11, 12 and 13. The results for institutional factors do not differ significantly, but the control variables and the dummy variables become insignificant. Possible explanations of these results relate to the fact that Insurance Penetration incorporates the national GDP, while Insurance Density takes into account the population of the country. Consequently, information related to the level of economic development and to the stage of the business cycle is already present in the Insurance Penetration, generating multicollinearity issues and partially deterministic relationships between the dependent and the crisis and development dummy variables.

  2. These results are not presented in the article but are available from the authors on request.

  3. Consequently, we will not use the “ln” symbol in equations, presuming it is understood.

References

  • Alhassan, A.L., and N. Biekpe. 2016. Determinants of life insurance consumption in Africa. Research in International Business and Finance 37 (5): 17–27.

    Article  Google Scholar 

  • Anderson, T.W., and C. Hsiao. 1981. Estimation of dynamic models with error components. Journal of the American Statistical Association 76 (375): 598–606.

    Article  Google Scholar 

  • Anderson, D.R., and J. Nevin. 1975. Determinants of young marrieds’ life insurance purchasing behavior: an empirical investigation. The Journal of Risk and Insurance 42 (3): 375–387.

    Article  Google Scholar 

  • Arellano, M., and S. Bond. 1991. Some tests of specification for panel data: Monte Carlo evidence and an application to employment equations. The Review of Economic Studies 58 (2): 277–297.

    Article  Google Scholar 

  • Arellano, M., and O. Bover. 1995. Another look at the instrumental variable estimation of error-components models. Journal of Econometrics 68 (1): 29–51.

    Article  Google Scholar 

  • Asch, S.E. 1956. Studies of independence and conformity: a majority of one against a unanimous majority. Psychological Monographs 70 (9): 1–70.

    Article  Google Scholar 

  • Auerbach, A.J., and L.J. Kotlikoff. 1989. How rational is the purchase of life insurance? Working Paper w3063. National Bureau of Economic Research (NBER).

  • Beck, T., and I. Webb. 2003. Economic, demographic and institutional determinants of life insurance consumption across countries. The World Bank Economic Review 17 (1): 51–88.

    Article  Google Scholar 

  • Beenstock, M., G. Dickinson, and S. Khajuria. 1986. The determination of life premiums: an international cross-section analysis 1970–1981. Insurance: Mathematics and Economics 5 (4): 261–270.

    Google Scholar 

  • Bellemare, M., T. Masaki, and T.B. Pepinsky. 2015. Lagged explanatory variables and the estimation of causal effects. MPRA Paper 62350, University Library of Munich, Germany. Revised 23 Feb 2015.

  • Browne, M.J., and K. Kim. 1993. An international analysis of life insurance demand. The Journal of Risk and Insurance 60 (4): 616–634.

    Article  Google Scholar 

  • Chang, C.H., and C.C. Lee. 2012. Non-linearity between life insurance and economic development: a revisited approach. The Geneva Risk and Insurance Review 37 (2): 223–257.

    Article  Google Scholar 

  • Chari, M.D.R., and E.R. Banalieva. 2015. How do pro-market reforms impact firm profitability? The case of India under reform. Journal of World Business 50 (2): 357–367.

    Article  Google Scholar 

  • Cummins, J.D., M. Rubio-Misas, and D. Vencappa. 2017. Competition, efficiency and soundness in European life insurance markets. Journal of Financial Stability 28: 66–78.

    Article  Google Scholar 

  • Dragos, S.L., and C.M. Dragos. 2013. The role of institutional factors over the national insurance demand: theoretical approach and econometric estimations. Transylvanian Review of Administrative Sciences 39 (9): 32–45.

    Google Scholar 

  • Duker, J.M. 1969. Expenditures for life insurance among working-wife families. The Journal of Risk and Insurance 36 (5): 525–533.

    Article  Google Scholar 

  • Eling, M., S. Pradhan, and J.T. Schmit. 2014. The determinants of microinsurance demand. The Geneva Papers on Risk and Insurance—Issues and Practice 39 (2): 224–263.

    Article  Google Scholar 

  • Engle, R.F. 1983. Wald, likelihood ratio, and lagrange multiplier tests in econometrics. In Handbook of econometrics, vol. 2, ed. Z. Griliches and M.D. Intriligator, 775–826. Amsterdam: Elsevier.

    Chapter  Google Scholar 

  • Ertl, M. 2017. Insurance convergence and post-crisis dynamics in Central and Eastern Europe. The Geneva Papers on Risk and Insurance—Issues and Practice 42 (2): 323–347.

    Article  Google Scholar 

  • Esho, N., A. Kirievsky, D. Ward, and R. Zurbruegg. 2004. Law and the determinants of property-casualty insurance. The Journal of Risk and Insurance 71 (2): 265–283.

    Article  Google Scholar 

  • European Commission. 2016. Social protection, data, expenditure on social protection. http://ec.europa.eu/eurostat/statistics-explained/index.php?title=File:Expenditure_on_social_protection,_2004-2014_(%25_of_GDP)_YB17.png.

  • Feyen, E., R. Lester, and R. Rocha. 2011. What drives the development of the insurance sector? An empirical analysis based on a panel of developed and developing countries. Policy Research Working Paper 5572, February, World Bank.

  • Guerineau, S., and R. Sawadogo. 2015. On the determinants of life insurance development in Sub-Saharan Africa: the role of the institutions quality in the effect of economic development. Etudes et Documents, n°19, CERDI.

  • Hansen, L.P. 1982. Large sample properties of generalized method of moments estimators. Econometrica 50 (4): 1029–1054.

    Article  Google Scholar 

  • Hansen, C., J. Hausman, and W. Newey. 2008. Estimation with many instrumental variables. Journal of Business and Economic Statistics 26 (4): 398–422.

    Article  Google Scholar 

  • Heritage Foundation. 2016. Index of economic freedom. Fiscal Freedom. http://www.heritage.org/index/fiscal-freedom.

  • Hwang, T., and S. Gao. 2003. The determinants of demand for life insurance in an emerging economy—the case of China. Managerial Finance 29 (5/6): 82–96.

    Article  Google Scholar 

  • Insurance Europe. 2015. Statistics No. 49 and No. 50. European insurance for total life premiums and number of population.

  • Insurance Europe. 2013. Annual report 2012–2013.

  • Kafouros, M., and M. Aliyev. 2016. Institutional development and firm profitability in transition economies. Journal of World Business 51 (3): 369–378.

    Article  Google Scholar 

  • Kaufman, D., A. Kraay, and M. Mastruzzi. 2010 The worldwide governance indicators: methodology and analytical issues. World Bank Policy Research Working Paper No. 5430. http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1682130.

  • Keefer, P., and S. Knack. 2002. Polarization, politics and property rights: links between inequality and growth. Public Choice 111 (1–2): 127–154.

    Article  Google Scholar 

  • Knack, S., and P. Keefer. 1995. Institutions and economic performance: cross-country tests using alternative institutional measures. Economics and Politics 7 (3): 207–227.

    Article  Google Scholar 

  • Lee, C.C., Y.B. Chiu, and C.H. Chang. 2013. Insurance demand and country risks: a non-linear panel data analysis. Journal of International Money and Finance 36: 68–85.

    Article  Google Scholar 

  • Lee, C.C., C.H. Chang, M. Arouri, and C.C. Lee. 2016. Economic growth and insurance development: the role of institutional environments. Economic Modelling 59: 361–369.

    Article  Google Scholar 

  • Lenten, L.J.A., and D.N. Rulli. 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.

    Article  Google Scholar 

  • Levine, R. 1999. Financial development and economic growth: views and agenda. World Bank Policy Research Working Paper No. 1678, November. https://ssrn.com/abstract=604955.

  • Li, D., F. Moshirian, P. Nguyen, and T. Wee. 2007. The demand for life insurance in OECD countries. The Journal of Risk and Insurance 74 (3): 637–652.

    Article  Google Scholar 

  • Liebenberg, A.P., J.M. Carson, and R.E. Dumm. 2012. A dynamic analysis of the demand for life insurance. The Journal of Risk and Insurance 79 (3): 619–644.

    Article  Google Scholar 

  • Liu, F. 2015. Herd behavior in the insurance market: a survey. International Journal of Economics and Finance 7 (11): 154–162.

    Article  Google Scholar 

  • McKinnish, T.G. 2002. Interpreting lagged effects of the independent variable: how does the local economy affect welfare caseloads? Unpublished Paper, University of Colorado. http://stripe.colorado.edu/~mckinnis/spectest081202.pdf.

  • Millo, G., and G. Carmeci. 2015. A subregional panel data analysis of life insurance consumption in Italy. The Journal of Risk and Insurance 82 (2): 317–340.

    Article  Google Scholar 

  • Outreville, J.F. 1996. Life insurance markets in developing countries. The Journal of Risk and Insurance 63 (2): 263–278.

    Article  Google Scholar 

  • Outreville, J.F. 2008. Foreign affiliates of the largest insurance groups: location-specific advantages. The Journal of Risk and Insurance 75 (2): 463–491.

    Article  Google Scholar 

  • Outreville, J.F. 2012. A survey on the relationship between relative risk aversion and the level of education, Presented at The Southern Risk and Insurance Association (SRIA) annual meeting, November 2012, Savannah, Georgia.

  • Pop-Silaghi, M., C. Jude, D. Alexa, and C. Litan. 2014. Do business and public sector research and development expenditures contribute to economic growth in Central and Eastern European Countries? A dynamic panel estimation. Economic Modelling 36 (1): 108–119.

    Article  Google Scholar 

  • Roodman, D. 2009. How to do xtabond2: an introduction to difference and system GMM in STATA. Stata Journal 9 (1): 86–136.

    Article  Google Scholar 

  • Shi, X., and Z. Yan. 2015. An international analysis of urbanizations and life insurance industry development: a new perspective. In Paper presented at the Insurance Risk and Finance Research Conference (25–26 June 2015). Nanyang Business School, Nanyang Technological University (NTU), Singapore.

  • SwissRe. 2017. Insurance: adding value to development in emerging markets, Sigma No. 4.

  • Truett, D.B., and L.J. Truett. 1990. The demand for life insurance in Mexico and the United States: a comparative study. The Journal of Risk and Insurance 57 (2): 321–328.

    Article  Google Scholar 

  • Ward, D., and R. Zurbruegg. 2002. Law, politics and life insurance consumption in Asia. The Geneva Papers on Risk and Insurance—Issues and Practice 27 (3): 395–412.

    Article  Google Scholar 

  • Ward, D., and R. Zurbruegg. 2000. Does insurance promote economic growth? Evidence from OECD countries. The Journal of Risk and Insurance 67 (4): 489–506.

    Article  Google Scholar 

  • World Bank. 2015. Indicators: Economy & Growth, Health, Urban Development, Education and Poverty. Washington, DC. http://data.worldbank.org/data-catalog/world-development-indicators.

  • Zerriaa, M., and H. Noubbigh. 2016. Determinants of life insurance demand in MENA region. The Geneva Papers on Risk and Insurance—Issues and Practice 41 (3): 491–511.

    Article  Google Scholar 

  • Zingales, L. 2003. The weak links. Federal Reserve Bank of St. Louis Review 7(July–Aug): 47–52.

  • Zietz, E.N. 2003. An examination of the demand for life insurance. Risk Management and Insurance Review 6 (2): 159–191.

    Article  Google Scholar 

Download references

Acknowledgements

The authors wish to thank Assoc. Prof. Litan Cristian from Babeş-Bolyai University of Cluj-Napoca, Romania, for his useful suggestions and comments concerning methodology. We thank Prof. Bernard Casey from the London School of Economics and University of Warwick and Assoc. Prof. Najat El Mekkaoui de Freitas from University Paris Dauphine for their valuable comments and suggestions on earlier versions during the 14th International Conference on Pensions, Insurance and Savings (9–10 May 2016, Paris). Recent versions of this article have also been presented at the 15th Annual European Economics and Finance Society Conference (16–19 June 2016, Amsterdam) and at the 3rd Annual Conference of Romanian Foreign Academics ERMAS (1–3 August 2016, Timişoara, Romania). Feedback obtained at these two conferences is also incorporated in this final version of the article, and we thank all those who have helped us with their suggestions.

Funding

This work was supported by a grant of the Romanian National Authority for Scientific Research and Innovation, CNCS—UEFISCDI, Project Number PN-II-RU-TE-2014-4-0745: Study of Romanian Life Insurances in International Context: Innovation, Spatial and Behavioral Modelling; Impact of Institutional Factors.

Author information

Authors and Affiliations

  1. Department of Finance, Faculty of Economics and Business, Babeş-Bolyai University of Cluj Napoca, 58-60 Teodor Mihali, Room 239, 400591, Cluj-Napoca, Romania

    Simona Laura Dragoş

  2. Faculty of Economics and Law, University of Orleans, LEO, Rue de Blois, BP 26739, 45067, Orleans Cedex 2, France

    Simona Laura Dragoş & Cristian Mihai Dragoş

  3. Department of Statistics-Forecasts-Mathematics, Faculty of Economics and Business, Babeş-Bolyai University of Cluj Napoca, 58-60 Teodor Mihali, Room 231, 400591, Cluj-Napoca, Romania

    Codruţa Mare & Cristian Mihai Dragoş

Authors
  1. Simona Laura Dragoş
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Codruţa Mare
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Cristian Mihai Dragoş
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Simona Laura Dragoş.

Appendix

Appendix

Table 9 Correlation matrix for the six institutional indicators
Full size table
Table 10 Correlation matrix between Government Effectiveness and the control factors
Full size table
Table 11 Influence of the Worldwide Governance Indicators upon the life insurance market (proxy Insurance Penetration)—regression results [short− run coefficients based on Eq. (1)]
Full size table

As shown in Table 12, none of the control variables considered significantly influence the insurance penetration. The short-run coefficients of both the level and the lag of government effectiveness remain significant, along with the lag of the dependent variable (insurance penetration). Further, we were also interested in evaluating the influence of the two dummy variables on the life insurance market, considering the effect of government effectiveness. Additionally, in this type of regression we also included the rule of law as an institutional factor as it came out to be significant at the 10% critical level in the original regression. But results in Table 13 show no significance of the crisis and of the level of development on the life insurance market when the insurance penetration is taken as a proxy.

Table 12 Regression analysis with control variables taken individually—proxy Insurance Penetration, institutional factor Government Effectiveness
Full size table
Table 13 Regression analysis with dummy variables—proxy Insurance Penetration, institutional factor Government Effectiveness and Rule of Law
Full size table

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Dragoş, S.L., Mare, C. & Dragoş, C.M. Institutional drivers of life insurance consumption: a dynamic panel approach for European countries. Geneva Pap Risk Insur Issues Pract 44, 36–66 (2019). https://doi.org/10.1057/s41288-018-0106-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1057/s41288-018-0106-3

Keywords

Search

Navigation