Individual health insurance reforms in the U.S.: expanding interstate markets, medicare for all, or medicaid for all? (English) Zbl 07422931

Summary: To help enhancing affordability and availability in the U.S. individual health insurance markets, we evaluate whether expanding interstate markets is associated with efficiency improvement, and the potentials of “Medicaid for All” and “Medicare for All”. This research aims to provide insights and evidence for data-driven decision making in reforming individual health insurance markets and optimizing individual health insurance operations. We employ traditional, non-oriented slack-based, order-\(\alpha\) partial frontier, bootstrapped bias-corrected, and modified context-dependent data envelopment analysis (DEA) models, as well as generalized linear, Tobit, and residual inclusion regression models. We find that higher competition or expansion is not associated with higher consumer efficiency or societal efficiency. Our results also indicate that, in minimizing premiums or expenses given enrollment and utilization of medical services, individual health plans are less efficient than Medicaid managed care plans, but more efficient than Medicare Advantage plans. Our findings imply that, for individual plans, expanding interstate markets is not accompanied with lower premiums or expenses without the sacrifice of medical services. This research suggests that it should be advisable to structure individual health insurance markets following the Medicaid managed care model but not the Medicare Advantage model. To “Medicaid-ize” individual markets, we propose to structure the individual coverage in two layers: a conditionally subsidized Medicaid managed care program with mandatory essential benefits, and an unsubsidized “Medicaid Supplement” program for optional additional coverages.


90B90 Case-oriented studies in operations research
90B50 Management decision making, including multiple objectives
62P20 Applications of statistics to economics
91G05 Actuarial mathematics


Full Text: DOI


[1] Alam, Z.; Chen, M.; Ciccotello, C.; Ryan, H., Board structure mandates: Consequences for director location and financial reporting, Management Science, 64, 4735-4754 (2018)
[2] Aragon, Y.; Daouia, A.; Thomas-Agnan, C., Nonparametric frontier estimation: A conditional quantile-based approach, Econometric Theory, 21, 358-389 (2005) · Zbl 1062.62252
[3] Banker, R.; Charnes, A.; Cooper, W., Some models for estimating technical and scale inefficiencies in DEA, Management Science, 30, 1078-1092 (1984) · Zbl 0552.90055
[4] Banker, R.; Natarajan, R.; Zhang, D., Two-stage estimation of the impact of contextual variables in stochastic frontier production function models using data envelopment analysis: Second stage OLS versus bootstrap approaches, European Journal of Operational Research, 278, 368-384 (2019) · Zbl 1430.90401
[5] Brockett, P.; Chang, R.; Rousseau, J.; Semple, J.; Yang, C., A Comparison of HMO efficiencies as a function of provider autonomy, Journal of Risk and Insurance, 71, 1-19 (2004)
[6] Brockett, P.; Golden, L.; Yang, C., Potential “savings” of Medicare: The analysis of Medicare advantage and accountable care organizations (ACOs), North American Actuarial Journal, 22, 458-472 (2018)
[7] Brockett, P.; Golden, L.; Yang, C.; Young, D., Medicaid managed care: Efficiency, medical loss ratio, and quality of care, North American Actuarial Journal (2019), published online early view at
[8] Increasing consumer choice through the sale of individual health insurance coverage across state lines through health care choice compacts (2019), Centers for Medicare and Medicaid Services (CMS), Accessed August 16 2020
[9] Cooper, W.; Seiford, L.; Tone, K., Data envelopment analysis: a comprehensive text with models, applications, and references and dea solver software (2007), Kluwer Academic Publishers: Kluwer Academic Publishers Norwell · Zbl 1111.90001
[10] Corlette, S., & Lucia, K. (2017). Selling health insurance across state lines is unlikely to lower costs or improve choice. https://www.commonwealthfund.org/blog/2017/selling-health-insurance-across-state-lines-unlikely-lower-costs-or-improve-choice Accessed August 16 2020,.
[11] Dafny, L.; Duggan, M.; Ramanarayanan, S., Paying a premium on your premium? Consolidation in the US health insurance industry, American Economic Review, 102, 1161-1185 (2012)
[12] Dafny, L.; Gruber, J.; Ody, C., More insurers lower premiums: Evidence from initial pricing in the health insurance marketplaces, American Journal of Health Economics, 1, 53-81 (2015)
[13] Deb, P., Manning, W., & Norton, E. (2014). Modeling health care costs and counts. http://econ.hunter.cuny.edu/parthadeb/wp-content/uploads/sites/4/2014/05/ASHEcon_LosAngeles_minicourse.pdf Accessed August 16 2020,.
[14] Ding, X.; Peng, X.; Heim, G.; Jordan, V., Service mix, market competition, and cost efficiency: A longitudinal study of U.S. hospitals, Journal of Operations Management, 66, 176-198 (2020)
[15] Goetz, M.; Laeven, L.; Levine, R., Identifying the value effects and agency costs of corporate diversification: Evidence from the geographic diversification of U.S. banks, Review of Financial Studies, 26, 1787-1823 (2013)
[16] Golden, L.; Yang, C., Efficiency analysis of health insurers’ scale of operations and group affiliation with a perspective toward health insurers’ mergers and acquisitions effects, North American Actuarial Journal, 23, 4, 626-645 (2019) · Zbl 1429.91279
[17] Gormley, T.; Matsa, D., Common errors: How to (and not to) control for unobserved heterogeneity, Review of Financial Studies, 27, 617-661 (2014)
[18] Gruber, J.; Sommers, B., The Affordable care act’s effects on patients, providers, and the economy: What we’ve learned so far, Journal of Policy Analysis and Management, 38, 4, 1028-1052 (2019)
[19] Guardado, J.; Emmons, D.; Kane, C., The Price effects of a large merger of health insurers: A case study of UnitedHealth-Sierra, Health Management, Policy and Innovation, 1, 16-35 (2013)
[20] Hussels, S.; Ward, D., The impact of deregulation on the German and UK life insurance markets: an analysis of efficiency and productivity between 1991 and 2002 (2007), Cranfield University, Working Paper, SOM Research Paper Series 4/07
[21] Kaffash, S.; Azizi, R.; Huang, Y.; Zhu, J., A survey of data envelopment analysis applications in the insurance industry 1993-2018, European Journal of Operational Research, 284, 801-813 (2020) · Zbl 1441.91064
[22] Kaiser Family Foundation. Insurance market competitiveness. (2014-2017). https://www.kff.org/state-category/health-insurance-managed-care/insurance-market-competitiveness/ Accessed August 16, 2020.
[23] Karaca-Mandic, P.; Abraham, J.; Simon, K., Is the medical loss ratio a good target measure for regulation in the individual market for health insurance?, Health Economics, 24, 55-74 (2015)
[24] Mahlberg, B.; Url, T., Single market effects on productivity in the German insurance industry, Journal of Banking & Finance, 34, 7, 1540-1548 (2010)
[25] Minarik, J., We need better than Medicare for All, Journal of Policy Analysis and Management, 39, 1254-1261 (2020)
[26] Morita, H.; Hirokawa, K.; Zhu, J., A slack-based measure of efficiency in context-dependent data envelopment analysis, Omega, 33, 357-362 (2003)
[27] Oberlander, J., Navigating the shifting terrain of US health care reform—Medicare for all, single payer, and the public option, Milbank Quarterly, 97, 4, 939-953 (2019)
[28] Paradise, J. & Garfield, R. (2013). What is Medicaid’s impact on access to care, health outcomes, and quality of care? Setting the record straight on the evidence. https://kaiserfamilyfoundation.files.wordpress.com/2013/08/8467-what-is-medicaids-impact-on-access-to-care1.pdf Accessed August 16 2020,.
[29] Rudowitz, R., Garfield, R., & Hinton, E. (2019). 10 things to know about Medicaid: Setting the facts straight. http://files.kff.org/attachment/Issue-Brief-10-Things-to-Know-about-Medicaid-Setting-the-Facts-Straight Accessed August 16 2020,.
[30] Schmid, M.; Walter, I., Geographic diversification and firm value in the financial services industry, Journal of Empirical Finance, 19, 109-122 (2012)
[31] Seiford, L.; Zhu, J., Context-dependent data envelopment analysis—Measuring attractiveness and progress, Omega, 31, 397-408 (2003)
[32] Shi, B.; Baranoff, E.; Sager, T., Product diversification in health insurance with comprehensive coverage benefits U.S. health insurers, Journal of International & Interdisciplinary Business Research, 3, 14-28 (2016)
[33] Simar, L.; Wilson, P., Sensitivity analysis of efficiency scores: How to bootstrap in nonparametric frontier models, Management Science, 44, 1, 49-61 (1998) · Zbl 1012.62501
[34] Simar, L.; Wilson, P., Estimation and inference in two-stage, semi-parametric models of production processes, Journal of Econometrics, 136, 31-64 (2007) · Zbl 1418.62535
[35] Sparer, M., Redefining the “public option”: Lessons from Washington State and New Mexico, Milbank Quarterly, 98, 2, 260-278 (2020)
[36] Terza, J. (2016). Two-stage residual inclusion estimation: A practitioner’s guide to Stata implementation. https://www.stata.com/meeting/chicago16/slides/chicago16_terza.pdf Accessed August 16 2020,.
[37] Terza, J.; Basu, A.; Rathouz, P., Two-stage residual inclusion estimation: Addressing endogeneity in health econometric modeling, Journal of Health Economics, 27, 531-543 (2008)
[38] Tone, K., A slacks-based measure of efficiency in data envelopment analysis, European Journal of Operational Research, 130, 498-509 (2001) · Zbl 0990.90523
[39] Wicks-Lim, J., Medicare for All provides more for less, Journal of Policy Analysis and Management, 39, 1247-1254 (2020)
[40] Yang, C., Health care reform, efficiency of health insurers, and optimal health insurance markets, North American Actuarial Journal, 18, 478-500 (2014) · Zbl 1414.91244
[41] Yang, C., The impact of Medicaid expansion, diversity and the ACA primary care fee bump on the performance of Medicaid managed care, Journal of Insurance Regulation, 37, 1-34 (2018)
[42] Yang, C., The affordability of the individual markets in the affordable care act: Analyses of premium increases and cost reductions from an expanded cross-subsidization perspective, North American Actuarial Journal, 24, 446-462 (2020) · Zbl 1454.91210
[43] Yang, C.; Lin, H., The (mis)alignment of health insurers’ efficiency measures from different perspectives and their (un)linkage with financial ratios and asset allocation, Journal of Insurance Regulation, 36, 175-195 (2017)
[44] Yang, C.; Wen, M., An efficiency-based approach to determining potential cost savings and profit targets for health insurers: The case of Obamacare health insurance CO-OPs, North American Actuarial Journal, 21, 2, 305-321 (2017) · Zbl 1414.91245
[45] Yang, C., Health expenditures and quality health services: The efficiency analysis of differential risk structures of Medicare accountable care organizations (ACOs), North American Actuarial Journal (2020), published online early view at
[46] Zhu, J., Quantitative models for performance evaluation and benchmarking: Data envelopment analysis with spreadsheets (2009), Springer: Springer New York · Zbl 1180.90002
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