Three tests of information asymmetry among Medicaid adults



Information asymmetry has well known efficiency consequences for health insurance markets. Unlike private insurance, Medicaid programs are designed to attract clinically and socioeconomically vulnerable patients, and allow limited enrollee cost sharing. This suggests that the Medicaid expansion authorized by the Affordable Care Act may attract an adversely selected population that poses unknown utilization, and hence, financial risks to its federal and state financers. In this study, I test for information asymmetry by examining whether unobserved determinants of Medicaid enrollment are correlated with unobserved determinants of emergency department and primary care visits, using the Medical Expenditure Panel Survey. I use instrumental variables, correlation tests, and a simulation analysis to test for correlated unobservables. I find evidence of information asymmetry in Medicaid for emergency care use, but not primary care visits, once certain demographic and health variables are controlled. Since new Medicaid enrollees may not have “baseline” claims or diagnostic data, it may be helpful to collect information on their self-rated health status and disease history, such as obesity and heart attacks. Controlling for these variables may improve forecasts of health care use among Medicaid enrollees, and can reduce estimation bias in non-experimental studies of Medicaid.


Medicaid Moral hazard Adverse selection Econometrics 



This work was supported by the Jayne Koskinas Ted Giovanis Foundation for Health and Policy. Eric Roberts also received financial support from the Hal R. Cohen Blue Cross Blue Shield Scholarship for Health Economics. The author gratefully acknowledges comments and feedback from Martin Andersen (University of North Carolina—Greensboro); Gerard Andersen, Darrell Gaskin, and Lauren Nicholas (Johns Hopkins Bloomberg School of Public Health); and Nicholas Papageorge (Johns Hopkins University, Department of Economics). Ray Kuntz at the Agency for Healthcare Research and Quality provided invaluable assistance with the MEPS. This work does not necessarily reflect the views of the Jayne Koskinas Ted Giovanis Foundation. An earlier version of this paper was presented at the NRSA trainees conference, sponsored by the Agency for Healthcare Research and Quality, in San Diego, CA, in 2014. The author completed this research as a PhD student in the Department of Health Policy and Management at the Johns Hopkins Bloomberg School of Public Health.

Compliance with ethical standards

This study was approved by the Institutional Review Board of the Johns Hopkins University Bloomberg School of Public Health. This work was supported by the Jayne Koskinas Ted Giovanis Foundation for Health and Policy and from the Hal R. Cohen Blue Cross Blue Shield Scholarship for Health Economics.

Conflict of interest

The author has no conflicts of interest to disclose.


  1. AHRQ: Medical expenditure panel survey—survey background (A. f. Quality, Producer). (2009). Retrieved 4 May 2014
  2. Barnow, B.S., Cain, G.G., Goldberger, A.S.: Issues in the analysis of selectivity bias. In: Stromsdorfer, E.W., Farkas, G. (eds.) Evaluation Studies Annual Review. Sage, Beverly Hills (1980)Google Scholar
  3. Bindman, A.B., Chattopadhyay, A., Auerback, G.M.: Medicaid re-enrollment policies and children’s risk of hospitalizations for ambulatory care sensitive conditions. Med. Care 46(10), 1049–1054 (2008)CrossRefPubMedGoogle Scholar
  4. Brooks, T.: Health policy brief: Hospital presumptive eligibility. The ACA expands the policy that allows key entities to temporarily enroll people in Medicaid, creating a path to more stable coverage. Health Aff. (January 9, 2014).
  5. Buchmueller, T.C., Grumbach, K., Kronick, R., Kahn, G.G.: The effect of health insurance on medical care utilization and implications for insurance expansion: a review of the literature. Med. Care Res. Rev. 62(1), 3–30 (2005)CrossRefPubMedGoogle Scholar
  6. Cameron, A.C., Trivedi, P.K.: Microeconometrics Using Stata, Revised edition. Stata Press, Austin (2010)Google Scholar
  7. Cameron, A.C., Trivedi, P.K., Milne, F., Piggott, J.: A microeconometric model of the demand for health care and insurance in Australia. Rev. Econ. Stud. 55(1), 85–106 (1988)CrossRefGoogle Scholar
  8. Cardon, J.H., Hendel, I.: Asymmetric information in health insurance: evidence from the National Medical Expenditure Survey. RAND J. Econ. 32(3), 408–427 (2001)CrossRefPubMedGoogle Scholar
  9. Centers for Medicare and Medicaid Services: Medicaid managed care enrollment report. (2011). Retrieved 29 April 2014
  10. Chiappori, P.-A., Salanié, B.: Testing for asymmetric information in insurance markets. J. Polit. Econ. 108(1), 56–78 (2000)CrossRefGoogle Scholar
  11. Chiappori, P.-A., Durand, F., Geoffard, P.-Y.: Moral hazard and the demand for physician services: first lessons from a French natural experiment. Eur. Econ. Rev. 42, 499–511 (1998)CrossRefGoogle Scholar
  12. CMS: National summary of state medicaid managed care programs—program descriptions as of July 1, 2010. Centers for Medicare and Medicaid Services (2010)Google Scholar
  13. Cohen, A., Siegelman, P.: Testing for adverse selection in insurance markets. J. Risk Insur. 77(1), 39–84 (2010)CrossRefGoogle Scholar
  14. Courtot, B., Coughlin, T. A., & Lawton, E. A.: Medicaid and CHIP Managed Care Payment Methods and Spending in 20 States. Office of the Assistant Secretary for Planning and Evaluation, U.S. Department of Health and Human Services. Urban Institute, Washington, DC (2012)Google Scholar
  15. Currie, J., Gruber, J.: Health insurance eligibility, utilization of medical care, and child health. Q. J. Econ. 111(2), 431–466 (1996)CrossRefGoogle Scholar
  16. Currie, J., Decker, S., Lin, W.: Has public health insurance for older children reduced disparities in access to care and health outcomes? J. Health Econ. 27(6), 1567–1581 (2008)CrossRefPubMedGoogle Scholar
  17. Cutler, D.M., McLellan, M., Newhouse, J.P.: How does managed care do it? RAND J. Econ. 31(3), 526–548 (2000)CrossRefPubMedGoogle Scholar
  18. Decker, S.L., Kostova, D., Kenney, G.M., Long, S.K.: Health status, risk factors, and medical conditions among persons enrolled in Medicaid vs uninsured low-income adults potentially eligible for Medicaid under the Affordable Care Act. J. Am. Med. Assoc. 309(24), 2579–2586 (2013)CrossRefGoogle Scholar
  19. DeLeire, T., Dague, L., Leininger, L., Voskuil, K., Friedsam, D.: Wisconsin experience indicates that expanding public insurance to low-income childless adults has health care impacts. Health Aff. 32(6), 1037–1045 (2013)CrossRefGoogle Scholar
  20. DeVoe, J.E., Baez, A., Angier, H., Krois, L., Edlund, C., Carney, P.A.: Insurance plus access does not equal health care: typology of barriers to health care access for low-income families. Ann. Fam. Med. 5(6), 511–518 (2007)CrossRefPubMedCentralPubMedGoogle Scholar
  21. Eniav, L., Finkelstein, A.: Selection in insurance markets: theory and empirics in pictures. J. Econ. Perspect. 25(1), 115–138 (2011)CrossRefGoogle Scholar
  22. Finkelstein, A., Porteba, J.: Testing for adverse selection with “unused observables”. NBER working paper No. 12112 (2006)Google Scholar
  23. Finkelstein, A., Taubman, S., Wright, B., Bernstein, M., Gruber, J., Newhouse, J.P., et al.: The Oregon Health Insurance experiment: evidence from the first year. Q. J. Econ. 127(3), 1057–1106 (2012)CrossRefPubMedCentralPubMedGoogle Scholar
  24. Haber, S.G., Khatutsky, G., Mitchell, J.B.: Covering uninsured adults through medicaid: lessons from the Oregon Health Plan. Health Care Financ. Rev. 22(2), 1–17 (2000)PubMedCentralPubMedGoogle Scholar
  25. Hausman, J.A.: Specification tests in econometrics. Econometrica 46(6), 1251–1271 (1978)CrossRefGoogle Scholar
  26. Herring, B., Adams, E.: Using HMOs to serve the medicaid population: what are the effects on utilization and does the type of HMO matter? Health Econ. 20, 446–460 (2011)CrossRefPubMedGoogle Scholar
  27. Hill, S.C., Abdus, S., Hudson, J.L., Selden, T.M.: Adults in the income range for the affordable care act’s medicaid expansion are healthier than Pre-ACA enrollees. Health Aff. 33(4), 1–9 (2014)CrossRefGoogle Scholar
  28. Holahan, J., Buettgens, M., Carroll, C., Dorn, S.: The Cost and Coverage Implications of the ACA Medicaid Expansion: National and State-by-State Analysis. Kaiser Family Foundatation, Washington (2012)Google Scholar
  29. HRSA.: Area health resource file: overview. (2013). Retrieved 10 April 2014
  30. Kaiser Commission on Medicaid and the Uninsured: Medicaid Managed Care: Key Data, Trends, and Issues. Kaiser Family Foundation, Washington (2010)Google Scholar
  31. Kaiser Family Foundation: Resuming the Path to Health Coverage for Children and Parents: A 50 State Update on Eligibility Rules, Enrollment and Renewal Procedures, and Cost-Sharing Practices in Medicaid and SCHIP in 2006. Kaiser Commission on Medicaid and the Uninsured, Washington (2007)Google Scholar
  32. Kaiser Family Foundation: Health Coverage for Children and Families in Medicaid and SCHIP: State Efforts Face New Hurdles A 50-State Update on Eligibility Rules, Enrollment and Renewal Procedures, and Cost-Sharing Practices in Medicaid and SCHIP in 2008. Kaiser Commission on Medicaid and the Uninsured, Washington (2008)Google Scholar
  33. Kaiser Family Foundation: Challenges of Providing Health Coverage for Children and Parents in a Recession: A 50 State Update on Eligibility Rules, Enrollment and Renewal Procedures, and Cost-Sharing Practices in Medicaid and SCHIP in 2009. Kaiser Commission on Medicaid and the Uninsured, Washington (2009)Google Scholar
  34. Kaiser Family Foundation: Holding Steady, Looking Ahead: Annual Findings of a 50-State Survey of Eligibility Rules, Enrollment and Renewal Procedures, and Cost Sharing Practices in Medicaid and SCHIP, 2010–2011. Kaiser Commission on Medicaid and the Uninsured, Washington (2011)Google Scholar
  35. Kaiser Family Foundation: Premiums and Cost-Sharing in Medicaid. Kaiser Commission on Medicaid and the Uninsured, Washington (2013)Google Scholar
  36. Kenney, G.M., Lynch, V., Haley, J., Huntress, M.: Variation in Medicaid eligibility and participation among adults: implications for the Affordable Care Act. Inquiry 49, 231–253 (2012)PubMedGoogle Scholar
  37. Levy, H., Meltzer, D.: What do we really know about whether health insurance affects health? (2001). Retrieved 10 May 2014
  38. Maddala, G.S.: Limited-Dependent and Qualitative Variables in Econometrics. Cambridge University Press, Cambridge (1983)CrossRefGoogle Scholar
  39. Manning, W.G., Newhouse, J.P., Duan, N., Keeler, E., Leibowitz, A., Marquis, M.S.: Health insurance and the demand for medical care: evidence from a randomized experiment. Am. Econ. Rev. 77(3), 251–277 (1987)PubMedGoogle Scholar
  40. Moffitt, R.: An economic model of welfare stigma. Am. Econ. Rev. 73(5), 1023–1035 (1983)Google Scholar
  41. Mortensen, K.: Copayments did not reduce medicaid enrollees’ nonemergency use of emergency departments. Health Aff. 29(9), 1643–1650 (2010)CrossRefGoogle Scholar
  42. Natoli, C., Cheh, V., Verghese, S.: Who will enroll in Medicaid in 2014? Lessons from Section 1115 Medicaid waivers. Mathematica Policy Brief 1 (2011).
  43. Pauly, M.V.: The economics of moral hazard: comment. Am. Econ. Rev. 58(3), 531–537 (1968)Google Scholar
  44. Remler, D.K., Rachlin, J.E., Glied, S.A.: What can the take-up of other programs teach us about how to improve take-up of health insurance programs? NBER Working Paper No. 8185 (2001)Google Scholar
  45. Rothschild, M., Stiglitz, J.E.: Equilibrium in competitive insurance markets: an essay on the economics of imperfect information. Q. J. Econ. 90, 629–649 (1976)CrossRefGoogle Scholar
  46. Sapelli, C., Vial, B.: Self-selection and moral hazard in Chilean health insurance. J. Health Econ. 22, 459–476 (2003)CrossRefPubMedGoogle Scholar
  47. Schnittker, J., Bacak, V.: The increasing predictive validity of self-rated health. PLoS ONE 9(1), e84933 (2014)CrossRefPubMedCentralPubMedGoogle Scholar
  48. Siddiqui, M., Roberts, E.T., Pollack, C.E.: The effect of emergency department copayments for Medicaid beneficiaries following the Deficit Reduction Act of 2005. JAMA Intern. Med. 175(3), 393–398 (2015)CrossRefPubMedCentralPubMedGoogle Scholar
  49. Sommers, B.: Insuring children of insuring families: do parental and sibling coverage lead to improved retention of children in Medicaid and CHIP. J. Health Econ. 25(6), 1154–1169 (2006)CrossRefPubMedGoogle Scholar
  50. Sommers, B.D., Kenney, G.M., Epstein, A.M.: New evidence on the Affordable Care Act: coverage impacts of early medicaid expansions. Health Affairs 33(1), 78–87 (2014)CrossRefPubMedGoogle Scholar
  51. Staiger, D., Stock, J.H.: Instrumental variables regression with weak instruments. Econometrica 65(3), 557–586 (1997)CrossRefGoogle Scholar
  52. Taubman, S.L., Allen, H.L., Wright, B.J., Baicker, K., Finkelstein, A.N.: Medicaid increases emergency-department use: evidence from Oregon’s Health Insurance experiment. Science 343(6168), 263–268 (2014)CrossRefPubMedCentralPubMedGoogle Scholar
  53. Terza, J.V.: Estimating count data models with endogenous switching: sample selection and endogenous treatment effects. J. Econom. 84(1), 129–154 (1998)CrossRefGoogle Scholar
  54. Terza, J.V., Basu, A., Rathouz, P.J.: Two-stage residual inclusion estimation: addressing endogeneity in health econometric modeling. J. Health Econ. 27(3), 531–543 (2008)CrossRefPubMedCentralPubMedGoogle Scholar
  55. Waidmann, T.A., Ormond, B.A., Boybjerg, R.R.: The role of prevention in bending the cost curve. (2011). Retrieved 10 April 2014
  56. Wherry, L.R., Burns, M.E., Leininger, L.J.: Using self-reported health measures to predict high-need cases among Medicaid-eligible adults. Health Serv. Res. 49(S2), 2147–2172 (2014)CrossRefPubMedGoogle Scholar
  57. Winkelman, R., Damler, R.: Risk adjustment in state Medicaid programs. Health Watch 57, 14–17, 32–33 (2008)Google Scholar

Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  1. 1.Department of Health Care PolicyHarvard Medical SchoolBostonUSA

Personalised recommendations