Estimating consumers’ behaviour in motor insurance using discrete choice models

dc.contributor.authorDragos, Cristian Mihai
dc.contributor.authorDragos, Simona Laura
dc.contributor.otherEkonomická fakultacs
dc.date.accessioned2017-12-20
dc.date.available2017-12-20
dc.date.issued2017-12-20
dc.description.abstractInsurance is a financial service in which consumption is highly affected by the characteristics of the potential buyer and his perceptions about the offered product. Motor insurance with its two components – the Motor Third Party Liability Insurance (MTPL) and the Motor Damage insurance – constitutes the largest line of business of the non-life insurance sector in Europe. The present study models the voluntary motor damage insurance consumer behaviour using discrete choice models, hypothesizing a hierarchical and a non-hierarchical decision. The sample consists of 311 car owners from Cluj County, Romania. The econometric estimations use binary logit, multinomial logit and nested logit models. The predictive power of these models is compared by means of the Receiver Operating Characteristic curve for discrete choice models. The results reveal that the main factors affecting the purchase of a voluntary motor insurance policy are risk preference/aversion, the distance travelled by car, the driver’s education level and the ratio between the driver’s income and the car price. In contrast to previous studies who estimated the risk profile only through proxy variables without accounting for any behavioural aspects, our study has successfully integrated the risk profile of the policyholders as a self-standing explanatory variable. Since the explanatory variables are representative not only for a particular geographical area, the highlighted behaviour may be applied to all cases where motor damage insurance is voluntary.en
dc.formattext
dc.format.extent15 stran
dc.identifier.doi10.15240/tul/001/2017-4-007
dc.identifier.eissn2336-5604
dc.identifier.issn1212-3609
dc.identifier.urihttps://dspace.tul.cz/handle/15240/21381
dc.language.isoen
dc.publisherTechnical university of Liberec, Czech Republicen
dc.publisherTechnická Univerzita v Libercics
dc.publisher.abbreviationTUL
dc.relation.isbasedonAwunyo-Vitor, D. (2012). Comprehensive Motor Insurance Demand in Ghana: Evidence from Kumasi Metropolis. Management, 2(4), 80-86. doi:10.5923/j.mm.20120204.01.
dc.relation.isbasedonBarone, G., & Bella, M. (2004). Price-elasticity based customer segmentation in the Italian auto insurance market. Journal of targeting, measurement and analysis for marketing, 13(1), 21-31. doi:10.1057/palgrave.jt.5740129.
dc.relation.isbasedonBusu, M., & Gyorgy, A. (2016). Real Convergence, Steps from Adherence to Integration. Countries from Central and Eastern Europe. Amfiteatru Economic, 18(42), 303-316.
dc.relation.isbasedonCohen, A. (2005). Asymmetric information and learning: Evidence from the automobile insurance market. Review of Economics and statistics, 87(2), 197-207. doi:10.1162/0034653053970294.
dc.relation.isbasedonCurrie, J. (1995). Gender gaps in benefits coverage. In D. Lewin, D. Mitchell, & M. Zaidi (Eds.), Handbook of Human Resources. Greenwich CT: JAI Press.
dc.relation.isbasedonDewar, D. M. (1998). Do those with more formal education have better health insurance opportunities? Economics of Education Review, 17(3), 267-277. doi:10.1016/S0272-7757(97)00034-4.
dc.relation.isbasedonDragos, C. (2010). ROC curve for discrete choice models an application to the Romanian car market. Applied Economics Letters, 17(1), 75-79. doi:10.1080/13504850701719793.
dc.relation.isbasedonDragos, S. (2007). Life Insurance Pricing. Cluj-Napoca: University of Cluj Press, Romania.
dc.relation.isbasedonDwight, D., & Russell, T. (1995). The causes and consequences of rate regulation in the auto insurance industry. In D. Bradford (Ed.), The Economics of Property Casualty Insurance (pp. 81-112). University of Chicago Press.
dc.relation.isbasedonEuropean Commission. (2009). Retail Insurance Market Study. [MARKT/2008/18/H–Final Report, Europe Economics].
dc.relation.isbasedonFinkelstein, A., & Poterba, J. (2004). Adverse selection in insurance markets: Policyholder evidence from the UK annuity market. Journal of Political Economy, 112(1), 183-208. doi:10.1086/379936.
dc.relation.isbasedonGencer, Y. G., & Akkucuk, U. (2017). Measuring Quality in Automobile Aftersales: AutoSERVQUAL Scale. Amfiteatru Economic, 19(44), 110-123.
dc.relation.isbasedonHöfter, R. H. (2006). Private health insurance and utilization of health services in Chile. Applied Economics, 38(4), 423-439. doi:10.1080/00036840500392797.
dc.relation.isbasedonHsu, Y. C., Chou, P. L., Chen, Y. M. J., & Lin, J. J. (2014). Mixed Logit Model of Voluntary Selection of Automobile Insurance. Journal of Information and Optimization Sciences, 35(5-6), 503-528. doi:10.1080/02522667.2014.961823.
dc.relation.isbasedonIoncică, M., Petrescu, E. C., Ioncică, D., & Constantinescu, M. (2012). The role of education on consumer behaviour on the insurance market. Procedia-Social and Behavioural Sciences, 46, 4154-4158. doi.10.1016/j.sbspro.2012.06.217.
dc.relation.isbasedonInsurance Europe. (2015). European Motor Insurance Markets. Brussels.
dc.relation.isbasedonJindrová, P., & Jakubínský, R. (2015). Significance and possibilities of major accident insurance. E&M Ekonomie a Management, 18(4), 121-131. doi:10.15240/tul/001/2015-4-009.
dc.relation.isbasedonKašćelan, V., Kašćelan, L., & Novović Burić, M. (2016). A nonparametric data mining approach for risk prediction in car insurance: a case study from the Montenegrin market. Economic Research-Ekonomska Istraživanja, 29(1), 545-558. doi:10.1080/1331677X.2016.1175729.
dc.relation.isbasedonKotler, P., & Armstrong, G. (2001). Principles of Marketing (4th ed.). NJ: Prentice Hall.
dc.relation.isbasedonLiu, H., Gao, S., & Rizzo, J. A. (2011). The expansion of public health insurance and the demand for private health insurance in rural China. China Economic Review, 22(1), 28-41. doi:10.1016/j.chieco.2010.08.006.
dc.relation.isbasedonMcFadden, D. (1973). Conditional Logit Analysis of Qualitative Choice Behaviour. In P. Zarembka (Ed.), Frontiers in Econometrics (pp.105-142). NY: Academic Press.
dc.relation.isbasedonParvatiyar, A., & Sheth, J. N. (2000). The Domain and Conceptual Foundation of Relationship Marketing. In J. N. Sheth, & A. Parvatiyar (Eds.), Handbook of Relationship Marketing (pp. 3-38). Thousand Oaks, CA: Sage.
dc.relation.isbasedonPeng, S. C., Li, C. S., & Liu, C. C. (2016). Deregulation, Pricing Strategies, and Claim Behaviour in the Taiwan Automobile Insurance Market. Emerging Markets Finance and Trade, 52(4), 869-885. doi:10.1080/1540496X.2015.1117869.
dc.relation.isbasedonQuery, J. T., Hoyt, R. E., & He, M. (2007). Service quality in private passenger automobile insurance. Journal of Insurance Issues, 30, 152-172.
dc.relation.isbasedonRundmo, T., & Moen, B. E. (2005). Demand for Risk Mitigation in Transport. In T. Rundmo, & B. E. Moen (Eds.), Risk Judgement and Safety in Transport (pp. 31-45). Trondheim: Rotunde Publ. no. 87.
dc.relation.isbasedonRundmo, T., & Nordfjærn, T. (2013). Predictors of demand for risk mitigation in transport. Transportation research part F: traffic psychology and behaviour. 20, 183-192. doi:10.1016/j.trf.2013.04.004.
dc.relation.isbasedonSaito, K. (2009). Does asymmetric information matter in the early insurance market? Evidence from the auto insurance market. Applied Economics, 41(21), 2653-2666. doi:10.1080/00036840701335546.
dc.relation.isbasedonSapelli, C., & Vial, B. (2003). Self-selection and moral hazard in Chilean health insurance. Journal of health economics, 22(3), 459-476. doi:10.1016/S0167-6296(02)00121-2.
dc.relation.isbasedonShi, P., Zhang, W., & Valdez, E. A. (2012). Testing Adverse Selection with Two‐Dimensional Information: Evidence from the Singapore Auto Insurance Market. Journal of Risk and Insurance, 79(4), 1077-1114. doi:10.1111/j.1539-6975.2012.01472.x.
dc.relation.ispartofEkonomie a Managementcs
dc.relation.ispartofEconomics and Managementen
dc.relation.isrefereedtrue
dc.rightsCC BY-NC
dc.subjectmotor insuranceen
dc.subjectrisk profileen
dc.subjectconsumer behaviouren
dc.subjectdiscrete choice modelsen
dc.subjectROC curveen
dc.subject.classificationC25
dc.subject.classificationG22
dc.subject.classificationM31
dc.titleEstimating consumers’ behaviour in motor insurance using discrete choice modelsen
dc.typeArticleen
local.accessopen
local.citation.epage102
local.citation.spage88
local.facultyFaculty of Economics
local.fulltextyes
local.relation.abbreviationE+Mcs
local.relation.abbreviationE&Men
local.relation.issue4
local.relation.volume20
Files
Original bundle
Now showing 1 - 1 of 1
Loading...
Thumbnail Image
Name:
EM_4_2017_07.pdf
Size:
1.27 MB
Format:
Adobe Portable Document Format
Description:
Článek
Collections