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Why Do Life Insurance Policyholders Lapse? The Roles of Income, Health and Bequest Motive Shocks

Listed author(s):
  • Edward Kung

    (Duke University)

  • Hanming Fang

    (University of Pennsylvania)

We present an empirical dynamic discrete choice model of life insurance decisions designed to bypass data limitations where researchers only observe whether an individual has made a new life insurance decision but but do not observe the actual policy choice or the choice set from which the policy is selected. The model also incorporates serially correlated unobservable state variables, for which we provide ample evidence that they are required to explain some key features in the data. We empirically implement the model using the limited life insurance holding information from the Health and Retirement Study (HRS) data. We deal with serially correlated unobserved state variables using posterior distributions of the unobservables simulated from Sequential Monte Carlo (SMC) methods. Counterfactual simulations using the estimates of our model suggest that a large fraction of life insurance lapsations are driven by i.i.d choice specific shocks, particularly when policyholders are relatively young. But as the remaining policyholders get older, the role of such i.i.d. shocks gets less important, and more of their lapsations are driven either by income, health or bequest motive shocks. Income and health shocks are relatively more important than bequest motive shocks in explaining lapsations when policyholders are young, but as they age, the bequest motive shocks play a more important role.

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File URL: https://economicdynamics.org/meetpapers/2011/paper_188.pdf
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Paper provided by Society for Economic Dynamics in its series 2011 Meeting Papers with number 188.

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Date of creation: 2011
Handle: RePEc:red:sed011:188
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Society for Economic Dynamics Marina Azzimonti Department of Economics Stonybrook University 10 Nicolls Road Stonybrook NY 11790 USA

Web page: http://www.EconomicDynamics.org/
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  1. Jeremy T. Fox, 2007. "Semiparametric estimation of multinomial discrete-choice models using a subset of choices," RAND Journal of Economics, RAND Corporation, vol. 38(4), pages 1002-1019, December.
  2. Hu, Yingyao & Shum, Matthew, 2012. "Nonparametric identification of dynamic models with unobserved state variables," Journal of Econometrics, Elsevier, vol. 171(1), pages 32-44.
  3. Keane, Michael P & Wolpin, Kenneth I, 1994. "The Solution and Estimation of Discrete Choice Dynamic Programming Models by Simulation and Interpolation: Monte Carlo Evidence," The Review of Economics and Statistics, MIT Press, vol. 76(4), pages 648-672, November.
  4. He, Daifeng, 2009. "The life insurance market: Asymmetric information revisited," Journal of Public Economics, Elsevier, vol. 93(9-10), pages 1090-1097, October.
  5. Jason R. Blevins, 2016. "Sequential Monte Carlo Methods for Estimating Dynamic Microeconomic Models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 31(5), pages 773-804, 08.
  6. John Cawley & Tomas Philipson, 1996. "An Empirical Examination of Information Barriers to Trade in Insurance," NBER Working Papers 5669, National Bureau of Economic Research, Inc.
  7. Glenn Daily & Igal Hendel & Alessandro Lizzeri, 2008. "Does the Secondary Life Insurance Market Threaten Dynamic Insurance?," American Economic Review, American Economic Association, vol. 98(2), pages 151-156, May.
  8. Hiroyuki Kasahara & Katsumi Shimotsu, 2009. "Nonparametric Identification of Finite Mixture Models of Dynamic Discrete Choices," Econometrica, Econometric Society, vol. 77(1), pages 135-175, 01.
  9. Andriy Norets, 2009. "Inference in Dynamic Discrete Choice Models With Serially orrelated Unobserved State Variables," Econometrica, Econometric Society, vol. 77(5), pages 1665-1682, 09.
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