Abstract
We apply a utility consistent, combined multinomial choice and count data model to identify factors that affect Medicare-eligible veterans’ outpatient health care usage and provider choice. Our first stage count data regression shows that a shadow cost index for visits calculated from the second stage multinomial choice model is significant in determining the Medicare-eligible veterans’ demand of outpatient services. In the second stage, we specify a multinomial choice model to study veterans’ allocation of outpatient visits between the VA and non-VA health care facilities, and find that veterans’ actual out-of-pocket cost and the distance to the health care facility have significantly negative effects on the probability of choosing the alternative. A number of other factors including family income, means of transportation, race, employment, and disability status are also found to be important at various stages of the decision making. We find some evidence of moral hazard effect in the market for private supplemental health insurance and prescription drugs, but adverse selection does not seem to be a problem for this special population.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Ben-Akiva, M. and Lerman, S., Discrete choice analysis: Theory and application to travel demand, MIT Press, Cambridge, MA, 1985.
Burgess, J.F. Jr. and DeFiore, D.A., “The effect of distance to VA facilities on the choice and level of utilization of VA outpatient services,” Social Science Medicine 39(1), 95–104, 1994.
Burns, L.R. and Wholey, D.R., “The impact of physician characteristics in conditional choice models for hospital care,” Journal of Health Economics 11, 43–62, 1991.
Cameron, A.C. and Trivedi, P.K., “Econometric models based on count data: comparisons and applications of some estimators and tests,” Journal of Applied Econometrics 1, 29–53, 1986.
Cameron, A.C., Trivedi, P.K., Milne, F., and Piggot, J., “A microeconometric model of the demand for health care and health insurance in Australia,”Review of Economic Studies 55(4), 85–106, 1988.
Cameron, A.C. and Trivedi, P.K. Regression analysis of count data, Econometric Society Monographs No. 30, Cambridge University Press, Cambridge, 1998.
Christofides, N.L., Stengos, T., and Swidinsky, R., “On the calculation of marginal effects in the bivariate probit model,” Economics Letters 54, 203–208, 1997.
Choi, K.H. and Moon, C.G., “Generalized extreme value model and additively separable generator function,” Journal of Econometrics 76, 129–140, 1997.
Deb, P. and Trivedi, P.K., “Demand for medical care by the elderly: A finite mixture approach,” Journal of Applied Econometrics 12, 313–336, 1997.
Department of Veterans Affairs, National Survey of Veterans, 1995.
Dubin, J.A., Studies in consumer demand—econometric methods applied to market data, Kluwer, Boston, 1998.
Eichner, M.J., “The demand for medical care: What people pay does matter,” The American Economic Review 88(2), 117–121, 1998.
Ettner, S.L., “Adverse selection and the purchase of Medigap insurance by the elderly,”Journal of Health Economics 16(5), 543–562, 1997.
Greene, W., Econometric analysis, Fourth edition, Prentice Hall, 2000.
Gurmu, S., “Semi-parametric estimation of the hurdle regression models with an application to medicaid utilization,” Journal of Applied Econometrics 12, 225–242, 1997.
Hausman, J.A., Leonard, G.K., and McFadden, D., “A utility consistent, combined discrete choice and count data model assessing recreational use losses due to natural resource damage,” Journal of Public Economics 56, 1–30, 1995.
Hausman, J.A., Hall, B.H., and Griliches, Z., “Econometric models for count data with an application to the patents—R&D relationship,” Econometrica 52, 909–938, 1984.
Hausman, J.A. and Wise, D.A., “A conditional probit model for qualitative choice: Discrete decisions recognizing interdependence and heterogeneous preference,” Econometrica 46(2), 403–427, 1978.
Hoff, R.A. and Rosenheck, R.A., “Female veterans,” use of department of veterans affair’s health care services,” Medical Care 36(7), 1114–1119, 1998.
Hunt-McCool, J., Kiker, B.F., and Ng, Y.C., “Estimates of the demand for medical care under different functional forms,” Journal of Applied Econometrics 9, 201–218, 1994.
Hurd, M.D. and McGarry, K., “Medical insurance and the use of health care services by the elderly,” Journal of Health Economics 16(2), 129–153, 1997.
Lahiri, K. and Gao, J., “Bayesian analysis of nested logit model by Markov chain Monte Carlo,” Journal of Econometrics 111, 103–133, 2002.
Manning, G., Newhouse, J.P., Orr, L., Duan, N., Keeler, E.B., Leibowitz, A., Marquis, H.K., Marquis, M.S., and Phelps, C.E., “A Two-part model of the demand for medical care: Preliminary results from the Health Insurance Study,” in Health, economics, and economics of health (J. Van Der Gaag and M. Perlman, eds.), Noth Holland, Amsterdam, 1980.
Manning, G., Newhouse, J.P., Duan, N., Keeler, E.B., Leibowitz, A., and Marquis, M.S., “Health insurance and the demand for medical care: Evidence from a randomized experiment,” American Economic Review, 251–277, 1987.
McConnell, K. E., “Consumer surplus from discrete choice model,” Journal of Environmental Economics and Management 29, 263–270, 1995.
McFadden, D.L., “Econometric models of probabilistic choice,” in Structural analysis of discrete data with econometric applications (C. Manski and D. McFadden eds.), MIT Press, Cambridge, MA, 198–272, 1981.
McGuirk, M.A. and Porell, F.W., “Spatial patterns of hospital utilization: the impacts of distance and time,” Inquiry 21, 84–95, 1984.
Mooney, C., Zwanziger, J., Phibbs, C.S., and Schmitt, S., “Is travel distance a barrier to veterans,” use of VA hospitals for medical surgical care?” Social Science Medicine 50(12), 1743–1755, 2000.
Nemet, G.F. and Bailey, A.J., “Distance and health care utilization among the rural elderly,” Social Science Medicine 50(9), 1197–1208, 2000.
Newhouse, J.P. et al., Free for all: Lessons from the RAND health insurance experiment, Harvard University Press, Cambridge, MA, 1993.
Pohlmeier, W. and Ulrich, V., “An econometric model of the two-part decision making process in the demand for health care,” Journal of Human Resources 30(2), 339–359, 1995.
Schellhorn, M., “Health services utilization of elderly Swiss: Evidence from panel data,” Health Economics 9(6), 533–545, 2000.
Small, K.A.,Urban transportation economics, Harwood Academic Publishers, Chur, 1992.
Small, K. and Rosen, H., “Applied welfare economics with discrete choice models,” Econometrica 49, 105–130, 1981.
United State General Accounting Office, Veterans,” health care: How distance from VA facilities affects veterans,” use of VA services, (GAO/T-HEHS-96-31, Dec. 20), 1995.
United State General Accounting Office, Veterans,” health care: Use of VA services by Medicare-eligible veterans. (GAO/HEHS-95-13, Oct. 24), 1994.
Windmeijer, F.A.G. and Santos Silva, J.M.C., “Endogeneity in count data models: an application to demand for health care,” Journal of Applied Econometrics 12, 281–294, 1997.
Wolf, J.R. and Godddeeris, J.H., “Adverse selection, moral hazard, and wealth effects in the Medigap insurance market,” Journal of Health Economics 10(4), 433–459, 1991.
Yen, S.T., Tang, C., and Su, S.B., “Demand for traditional medicine in Taiwan: A mixed Gaussian-Poisson model approach,” Health Economics 10(3), 221–232, 2001.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Lahiri, K., Xing, G. Modeling Medicare-Eligible Veterans’ Demand for Outpatient Services: A Two-Stage Approach. Health Serv Outcomes Res Method 4, 221–240 (2003). https://doi.org/10.1007/s10742-005-5558-9
Received:
Revised:
Accepted:
Issue Date:
DOI: https://doi.org/10.1007/s10742-005-5558-9