Does service-level spending show evidence of selection across health plan types?
AbstractThe paper examines whether patterns of service level spending in capitated managed care plans differ from those in traditional non-managed care health plans. We apply the service selection model of Ellis and McGuire (2007) to recent, highly disaggregated commercial insurance data from Medstat MarketScan. Rankings of services by selection incentives give largely the same rankings as the EM results for Medicare. We next calculate selection indices separately for four types of health plans: non-managed care comprehensive, preferred provider organization (PPO) plans, managed care point of service (POS) and health maintenance organization (HMO) plans. Our results imply high correlations and similar rankings of selection indices across plan types. We then test whether services predicted to be underprovided indeed have less than average rates of spending by managed care plans, while non-managed care plans have above average rates of spending. Stronger evidence of selection distortions among the four plan types is found when decomposing spending by type of service and provider specialty than by place of service.
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Bibliographic InfoPaper provided by Boston University - Department of Economics in its series Boston University - Department of Economics - Working Papers Series with number wp2007-43.
Date of creation: Dec 2007
Date of revision:
Health plans; Adverse selection; Managed care;
Other versions of this item:
- Randall P. Ellis & Shenyi Jiang & Tzu-Chun Kuo, 2013. "Does service-level spending show evidence of selection across health plan types?," Applied Economics, Taylor & Francis Journals, vol. 45(13), pages 1701-1712, May.
- I11 - Health, Education, and Welfare - - Health - - - Analysis of Health Care Markets
- C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
- D12 - Microeconomics - - Household Behavior - - - Consumer Economics: Empirical Analysis
This paper has been announced in the following NEP Reports:
- NEP-ALL-2009-06-10 (All new papers)
- NEP-HEA-2009-06-10 (Health Economics)
- NEP-IAS-2009-06-10 (Insurance Economics)
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.:
- Randall P. Ellis & Thomas G. McGuire, 2006.
"Predictability and Predictiveness in Health Care Spending,"
Boston University - Department of Economics - Working Papers Series
WP2006-001, Boston University - Department of Economics.
- Ellis, Randall P. & McGuire, Thomas G., 2007. "Predictability and predictiveness in health care spending," Journal of Health Economics, Elsevier, vol. 26(1), pages 25-48, January.
- Cao, Zhun & McGuire, Thomas G., 2003. "Service-level selection by HMOs in Medicare," Journal of Health Economics, Elsevier, vol. 22(6), pages 915-931, November.
- Partha Deb & Chenghui Li & Pravin K. Trivedi & David M. Zimmer, 2006. "The effect of managed care on use of health care services: results from two contemporaneous household surveys," Health Economics, John Wiley & Sons, Ltd., vol. 15(7), pages 743-760.
- Frank, Richard G. & Glazer, Jacob & McGuire, Thomas G., 2000. "Measuring adverse selection in managed health care," Journal of Health Economics, Elsevier, vol. 19(6), pages 829-854, November.
- Anupa Bir & Karen Eggleston, 2006.
"Measuring Selection Incentives in Managed Care: Evidence from the Massachusetts State Employee Insurance Program,"
Discussion Papers Series, Department of Economics, Tufts University
0605, Department of Economics, Tufts University.
- Karen Eggleston & Anupa Bir, 2009. "Measuring Selection Incentives in Managed Care: Evidence From the Massachusetts State Employee Insurance Program," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 76(1), pages 159-175.
- Arlene Ash & Randall P. Ellis & Gregory Pope & John Ayanian & David Bates & Helen Burstin & Lisa Iezzoni & Elizabeth McKay & Wei Yu, 2000. "Using Diagnoses to Describe Populations and Predict Costs," Papers 0099, Boston University - Industry Studies Programme.
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