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Determinants of the market choice and the consumers behavior on the Macedonian MTPL insurance market: Empirical application of the Markov chain model

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  • Angela Blazheska
  • Igor Ivanovski

Abstract

Markov chain models are widely used for capturing the switching behavior of economic agents and its impact on the prospective market developments in various industries. The aim of this paper is to analyze the customers' altered behavior on the Motor Third Party Liability Insurance market in Republic of North Macedonia for the period of 2016–2018, in a setting where the policy tariff rates are equal for all the Insurance providers, that is, prices are excluded as a factor of transition. For that purpose, a stationary, homogeneous, first‐order Markov Chain model is deployed to detect the flows and the reasoning behind the decisions for customers' transition across companies. This approach is applied for the first time on the Macedonian Insurance Market and the findings offer valuable information to many interested parties including policymakers, insurance providers, as well as researchers in the field.

Suggested Citation

  • Angela Blazheska & Igor Ivanovski, 2021. "Determinants of the market choice and the consumers behavior on the Macedonian MTPL insurance market: Empirical application of the Markov chain model," Risk Management and Insurance Review, American Risk and Insurance Association, vol. 24(3), pages 311-331, September.
    • Handle: RePEc:bla:rmgtin:v:24:y:2021:i:3:p:311-331
    DOI: 10.1111/rmir.12192
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    References listed on IDEAS

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    1. Jacob B. Feldman & Huseyin Topaloglu, 2017. "Revenue Management Under the Markov Chain Choice Model," Operations Research, INFORMS, vol. 65(5), pages 1322-1342, October.
    2. Jeong-Gil Choi & Jin-Won Mok & Jin-Soo Han, 2011. "The Use of Markov Chains to Estimate Destination Switching and Market Share," Tourism Economics, , vol. 17(6), pages 1181-1196, December.
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    Cited by:

    1. Patricia Born & Douglas Bujakowski, 2022. "Insurance research in Central, Eastern, and Southeastern Europe: What we can learn from XPRIMM data," Risk Management and Insurance Review, American Risk and Insurance Association, vol. 25(2), pages 219-231, June.

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