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

  • Hanming Fang
  • Edward Kung

Previous research has shown that the reasons for lapsation have important implications regarding the effects of the emerging life settlement market on consumer welfare. We present and empirically implement a dynamic discrete choice model of life insurance decisions to assess the importance of various factors in explaining life insurance lapsations. In order to explain some key features in the data, our model incorporates serially correlated unobservable state variables which we deal with using posterior distributions of the unobservables simulated from Sequential Monte Carlo (SMC) method. We estimate the model using the life insurance holding information from the Health and Retirement Study (HRS) data. 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 smaller, 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. We also suggest the implications of these findings regarding the effects of the emerging life settlement market on consumer welfare.

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Paper provided by National Bureau of Economic Research, Inc in its series NBER Working Papers with number 17899.

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Date of creation: Mar 2012
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Handle: RePEc:nbr:nberwo:17899
Note: IO PE
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  1. 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 151-56, May.
  2. Igal Hendel & Alessandro Lizzeri, 2003. "The Role Of Commitment In Dynamic Contracts: Evidence From Life Insurance," The Quarterly Journal of Economics, MIT Press, vol. 118(1), pages 299-327, February.
  3. Yingyao Hu & Matthew Shum, 2008. "Nonparametric identification of dynamic models with unobserved state variables," CeMMAP working papers CWP13/08, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
  4. 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.
  5. He, Daifeng, 2009. "The life insurance market: Asymmetric information revisited," Journal of Public Economics, Elsevier, vol. 93(9-10), pages 1090-1097, October.
  6. Jason R. Blevins, 2011. "Sequential Monte Carlo Methods for Estimating Dynamic Microeconomic Models," Working Papers 11-01, Ohio State University, Department of Economics.
  7. John Cawley & Tomas Philipson, 1997. "An Empirical Examination of Information Barriers to Trade inInsurance," University of Chicago - George G. Stigler Center for Study of Economy and State 132, Chicago - Center for Study of Economy and State.
  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. 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.
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