Information and the Demand for Supplemental Medicare Insurance
While the critical role of imperfect information has become axiomatic in explaining health care market failure, the theory is backed by little empirical evidence. In this paper we use a unique panel data set with explicit measures of information and an educational intervention to investigate the role of imperfect information about health insurance benefits on the demand for supplemental Medicare insurance. We estimate a structural discrete choice model of the demand for supplemental Medicare insurance that allows imperfect information to affect both the mean and the variance of the expected benefits distribution. The empirical specification is a structural panel multinomial probit with an unrestricted variance- covariance, including heteroskedasticity and random effects to control for unobserved heterogeneity. The model is computationally complex and is estimated by simulated maximum likelihood. The empirical results indicate that imperfect information affects the demand for supplemental Medicare insurance by increasing the variance of the expected benefits distribution rather than by systematically shifting the mean of the distribution. We find that the increase in variance due to imperfect information increases the probability of choosing not to purchase supplemental insurance by about 23%. We also found that controlling for unobserved heterogeneity is important. The goodness of fit increased by about 25% and the precision of the estimated effect of information on the variance of the expected benefits distribution improved dramatically.
|Date of creation:||Apr 1994|
|Date of revision:|
|Contact details of provider:|| Postal: National Bureau of Economic Research, 1050 Massachusetts Avenue Cambridge, MA 02138, U.S.A.|
Web page: http://www.nber.org
More information through EDIRC
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Berkovec, James & Stern, Steven, 1991. "Job Exit Behavior of Older Men," Econometrica, Econometric Society, vol. 59(1), pages 189-210, January.
- Kenkel, D.S., 1988.
"Health Behavior, Health Knowledge, And Schooling,"
10-88-3, Pennsylvania State - Department of Economics.
- Bunch, David S., 1991. "Estimability in the multinomial probit model," Transportation Research Part B: Methodological, Elsevier, vol. 25(1), pages 1-12, February.
- Vassilis A. Hajivassiliou, 1991. "Simulation Estimation Methods for Limited Dependent Variable Models," Cowles Foundation Discussion Papers 1007, Cowles Foundation for Research in Economics, Yale University.
- Small, Kenneth A & Rosen, Harvey S, 1981.
"Applied Welfare Economics with Discrete Choice Models,"
Econometric Society, vol. 49(1), pages 105-30, January.
- Harvey S. Rosen & Kenneth A. Small, 1979. "Applied Welfare Economics with Discrete Choice Models," NBER Working Papers 0319, National Bureau of Economic Research, Inc.
- repec:cup:etheor:v:8:y:1992:i:4:p:518-52 is not listed on IDEAS
- Lee, L-F., 1990.
"On Efficiency of Methods of Simulated Moments and Maximum Simulated Likelihood Estimation of Discrete Response Models,"
260, Minnesota - Center for Economic Research.
- Lee, Lung-Fei, 1992. "On Efficiency of Methods of Simulated Moments and Maximum Simulated Likelihood Estimation of Discrete Response Models," Econometric Theory, Cambridge University Press, vol. 8(04), pages 518-552, December.
- Cady, John F, 1976. "An Estimate of the Price Effects of Restrictions on Drug Price Advertising," Economic Inquiry, Western Economic Association International, vol. 14(4), pages 493-510, December.
- McFadden, Daniel, 1989.
"A Method of Simulated Moments for Estimation of Discrete Response Models without Numerical Integration,"
Econometric Society, vol. 57(5), pages 995-1026, September.
- Daniel McFadden, 1987. "A Method of Simulated Moments for Estimation of Discrete Response Models Without Numerical Integration," Working papers 464, Massachusetts Institute of Technology (MIT), Department of Economics.
- Pakes, Ariel & Pollard, David, 1989. "Simulation and the Asymptotics of Optimization Estimators," Econometrica, Econometric Society, vol. 57(5), pages 1027-57, September.
- Hausman, Jerry A & Wise, David A, 1978.
"A Conditional Probit Model for Qualitative Choice: Discrete Decisions Recognizing Interdependence and Heterogeneous Preferences,"
Econometric Society, vol. 46(2), pages 403-26, March.
- J. A. Hausman & D. A. Wise, 1976. "A Conditional Profit Model for Qualitative Choice: Discrete Decisions Recognizing Interdependence and Heterogeneous Preferences," Working papers 173, Massachusetts Institute of Technology (MIT), Department of Economics.
- Keane, Michael P, 1992. "A Note on Identification in the Multinomial Probit Model," Journal of Business & Economic Statistics, American Statistical Association, vol. 10(2), pages 193-200, April.
- Davidson, Bruce N. & Sofaer, Shoshanna & Gertler, Paul, 1992. "Consumer information and biased selection in the demand for coverage supplementing medicare," Social Science & Medicine, Elsevier, vol. 34(9), pages 1023-1034, May.
- Kenkel, Don, 1990. "Consumer Health Information and the Demand for Medical Care," The Review of Economics and Statistics, MIT Press, vol. 72(4), pages 587-95, November.
- Bunch, David S., 1991. "Estimability in the Multinomial Probit Model," University of California Transportation Center, Working Papers qt1gf1t128, University of California Transportation Center.
- Martin Gaynor & Solomon Polachek, 1990. "Measuring Ignorance in the Market: A New Method with an Application to Physician Services," NBER Working Papers 3430, National Bureau of Economic Research, Inc.
When requesting a correction, please mention this item's handle: RePEc:nbr:nberwo:4700. See general information about how to correct material in RePEc.
If references are entirely missing, you can add them using this form.