Institutional drivers of life insurance consumption: a dynamic panel approach for European countries
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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.
KeywordsInstitutional factors European countries Life insurance consumption Dynamic panel
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.
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.
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