Do Ordeals Work for Selection Markets? Evidence from Health Insurance Auto-Enrollment
Are application hassles, or “ordeals,” an effective way to limit public program enrollment? We provide new evidence by studying (removal of) an auto-enrollment policy for health insurance, adding an extra step to enroll. This minor ordeal has a major impact, reducing enrollment by 33% and differentially excluding young, healthy, and economically disadvantaged people. Using a simple model, we show that adverse selection – a classic feature of insurance markets – undermines ordeals’ standard rationale of excluding low-value individuals, since they are also low-cost and may not be inefficient. Our analysis illustrates why ordeals targeting is unlikely to work well in selection markets.
A previous version was circulated with the title, “Reducing Ordeals through Automatic Enrollment.” We thank Amina Abdu, Kendra Singh, Mike Yepes, and Olivia Zhao for excellent research assistance. We thank Jason Abaluck, Manasi Deshpande, Keith Ericson, and Ben Handel for thoughtful and constructive discussant comments. For helpful feedback and suggestions, we thank Hunt Allcott, Marcella Alsan, Chris Avery, Peter Blair, Zarek Brot-Goldberg, Sam Burn, Amitabh Chandra, Leemore Dafny, Amy Finkelstein, Peter Ganong, Josh Gottlieb, Jon Gruber, Gordon Hanson, Nathan Hendren, Alex Imas, Tim Layton, Jeff Liebman, Lee Lockwood, Amanda Kowalski, Brigitte Madrian, Sendhil Mullainathan, Matthew Notowidigdo, Carol Propper, Wesley Yin, Richard Zeckhauser, and seminar participants at the AEA meetings, ASHEcon, Boston-Area IO Conference, Covered California, Harvard Kennedy School, Harvard-MIT-BU Health Economics, Imperial College London, Massachusetts Health Connector, Queen Mary University, USC Schaeffer, and NBER Health Care, Public Economics, and Economics of Aging meetings. We thank the Massachusetts Health Connector (particularly Michael Norton and Marissa Woltmann) for assistance in providing and interpreting the data. We gratefully acknowledge data funding from Harvard’s Lab for Economic Applications and Policy and research support from Harvard Kennedy School’s Rappaport Institute for Public Policy, Harvard’s Milton Fund, and the National Institute on Aging, Grant Number T32-AG000186 (via the NBER). The research protocol was approved by the IRBs of Harvard University and the NBER. The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research.
The data cost for this projected was covered by funding from the Lab for Economic Applications and Policy (LEAP) at Harvard University. I acknowledge funding support from National Institute on Aging Grant No. T32-AG000186 (via the National Bureau of Economic Research). I have not received any financial support from an interested party in this research. I am not an officer, director, or board member of any relevant non-profit organizations or profit-making entities.