Hostname: page-component-8448b6f56d-m8qmq Total loading time: 0 Render date: 2024-04-25T01:35:08.057Z Has data issue: false hasContentIssue false
  • English
  • Français

Demand Elasticity, Risk Classification and Loss Coverage: When Can Community Rating Work?

Published online by Cambridge University Press:  09 August 2013

R. Guy Thomas
R. Guy Thomas*
Affiliation:
School of Mathematics, Statistics & Actuarial Science, University of Kent, Canterbury CT2 7NF, United Kingdom, E-mail: r.g.thomas@kent.ac.uk
Get access Rights & Permissions [Opens in a new window]

Abstract

This paper investigates the effects of high or low fair-premium demand elasticity in an insurance market where risk classification is restricted. The effects are represented by the equilibrium premium, and the risk-weighted insurance demand or “loss coverage”. High fair-premium demand elasticity leads to a collapse in loss coverage, with an equilibrium premium close to the risk of the higher-risk population. Low fair-premium demand elasticity leads to an equilibrium premium close to the risk of the lower-risk population, and high loss coverage – possibly higher than under more complete risk classification. The demand elasticity parameters which are required to generate a collapse in coverage in the model in this paper appear higher than the values for demand elasticity which have been estimated in several empirical studies of various insurance markets. This offers a possible explanation of why some insurance markets appear to operate reasonably well under community rating, without the collapse in coverage which insurance folklore suggests.

Keywords

Adverse selectionloss coveragerisk classificationdemand elasticitycommunity rating
Type
Research Article
Information
ASTIN Bulletin: The Journal of the IAA , Volume 39 , Issue 2 , November 2009 , pp. 403 - 428
Copyright
Copyright © International Actuarial Association 2009

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Blumberg, L., Nichols, L. and Banthin, J. (2001) ‘Worker decisions to purchase health insurance’. International Journal of Health Care Finance and Economics, 1: 305–325.Google Scholar
Buchmueller, T. and DiNardo, J. (2002) ‘Did community rating induce an adverse selection death spiral? Evidence from New York, Pennsylvania and Connecticut’. American Economic Review, 92: 280–294.Google Scholar
Buchmueller, T.C. and Ohri, S. (2006) ‘Health insurance take-up by the near-elderly’. Health Services Research, 41: 2054–2073.CrossRefGoogle ScholarPubMed
Butler, J.R. (2002) ‘Policy change and private health insurance: Did the cheapest policy do the trick?’. Australian Health Review, 25(6): 33–41.Google Scholar
Chernew, M., Frick, K. and McLaughlin, C. (1997) ‘The demand for health insurance coverage by low-income workers: Can reduced premiums achieve full coverage?’. Health Services Research, 32: 453–470.Google ScholarPubMed
Cutler, D. and Reber, S. (1998) ‘Paying for health insurance: the trade-off between competition and adverse selection’. Quarterly Journal of Economics, 113: 433–466.Google Scholar
De Jong, P. and Ferris, S. (2006) ‘Adverse selection spirals’, ASTIN Bulletin, 36: 589–628.Google Scholar
Gale, A.P. (2007) One price fits all. Paper presented to the Institute of Actuaries Australian Biennial Convention, 2007.Google Scholar
Institute of Actuaries Australia (1994) Insurance & superannuation risk classification policy. IAA, Sydney. 16 pages.Google Scholar
Macdonald, A.S. (1997) ‘How will improved forecasts of individual lifetimes affect underwriting?’ Philosophical Transactions of the Royal Society B, 352: 1067–1075, and (with discussion) British Actuarial Journal, 3; 1009-1025 and 1044-1058.Google Scholar
Macdonald, A.S. (1999) ‘Modeling the impact of genetics on insurance’. North American Actuarial Journal, 3(1): 83–101.Google Scholar
Macdonald, A.S. (2003) ‘Moratoria on the Use of Genetic Tests and Family History for Mortgage-Related Life Insurance’. British Actuarial Journal 9: 217–237.Google Scholar
Macdonald, A.S. and Tapadar, P. (2007) ‘Multifactorial disorders and adverse selection: epidemiology meets economics’. Forthcoming in Journal of Risk and Insurance, 2010.Google Scholar
Pauly, M.V., Withers, K.H., Viswanathan, K.S., Lemaire, J., Hershey, J.C., Armstrong, K. and Asch, D.A. (2003) ‘Price elasticity of demand for term life insurance and adverse selection’, NBER Working Paper, 9925.Google Scholar
Rose, C. (1993) ‘Equilibrium and adverse selection’. RAND Journal of Economics, 24: 559–569.Google Scholar
Siegelman, P. (2004) ‘Adverse selection in insurance markets: an exaggerated threat’, Yale Law Journal 113: 1225–1271.Google Scholar
Viswanathan, K.S., Lemaire, J., Withers, K., Armstrong, K., Baumritter, A., Hershey, J., Pauly, M. and Asch, D.A. (2006) ‘Adverse selection in term life insurance purchasing due to the BRCA 1/2 Genetic Test and elastic demand’. Journal of Risk and Insurance, 74: 65–86.Google Scholar
Thomas, R.G. (2008) ‘Loss coverage as a public policy objective for risk classification schemes’. Journal of Risk & Insurance 75: 997–1018.Google Scholar
Wilson, C.A. (1979) ‘Equilibrium and adverse selection’. American Economic Review, 69: 313–317.Google Scholar
Wilson, C.A. (1980) ‘The nature of equilibrium in markets with adverse selection’. Bell Journal of Economics, 11: 108–130.CrossRefGoogle Scholar