Using Behavioral Economics to Inform the Integration of Human Services and Health Programs under the Affordable Care Act . Notes


1 National Federation of Independent Business v. Sebelius, 567 US 1, 132 S.Ct. 2566, 183 L. Ed. 2d 450 (2012).
2 Premium subsidies vary by income, with the poorest individuals contributing 2 percent of their income towards their health insurance premium if they enroll in benchmark coverage and higher income individuals contributing a maximum of 9.5 percent. Individuals between 100 and 138 percent FPL can qualify for subsidies in states that do not expand Medicaid eligibility. In addition, consumers with incomes at or below 250 percent FPL can qualify for cost-sharing reductions that increase actuarial value.
3 “Updated Estimates of the Effects of the Insurance Coverage Provisions of the Affordable Care Act,” Congressional Budget Office [CBO], April2014.
4 Stan Dorn and Elizabeth Lower-Basch, “Moving to 21st-Century Public Benefits: Emerging Options, Great Promise, and Key Challenges” (Washington, DC: Prepared by the Urban Institute for the Coalition for Access and Opportunity, 2012),
5 Affordable Care Act, §1561 (2010).
6 “Health Insurance Exchanges Scramble to Be Ready as Opening Day Nears,” Abby Goodnough, New York Times, September 29, 2013. The hub also includes other information, such as address data from the U.S. Postal Service data base.
7 Stan Dorn, Julia Isaacs, Sarah Minton, Erika Huber, Paul Johnson, Matthew Buettgens, and Laura Wheaton, “Overlapping Eligibility and Enrollment: Human Services and Health Programs under the Affordable Care Act” (Washington, DC: Prepared by the Urban Institute for the Office of the Assistant Secretary for Planning and Evaluation [ASPE], Department of Health and Human Services [HHS], 2013).
8 Data as of September 6, 2013, see
9 Esa Eslamia, Kai Filion, and Mark Strayer, “Characteristics of Supplemental Nutrition Assistance Program Households: Fiscal Year 2010” (Princeton, NJ: Prepared by Mathematica Policy Research, Inc., for Office of Research and Analysis, Food and Nutrition Service, U.S. Department of Agriculture, 2010).
10 Dorn, Isaacs, et al., op cit.
11 Esa Eslami, Joshua Leftin, and Mark Strayer, “Supplemental Nutrition Assistance Program Participation Rates: Fiscal Year 2010: Final Report” (Princeton, NJ: Prepared by Mathematica Policy Research for the FNS Office of Research and Analysis, 2012)
12 Karen E. Cunnyngham, “State Supplemental Nutrition Assistance Program Participation Rates in 2010” (Princeton, NJ: Prepared by Mathematica Policy Research for the FNS, USDA, 2012)
13 Genevieve, M. Kenney, Nathaniel Anderson, and Victoria Lynch, “Medicaid/CHIP Participation Rates Among Children: An Update” (Washington, DC: Urban Institute, 2013)
14 Genevieve M. Kenney, Victoria Lynch, Jennifer M. Haley, and Michael Huntress, “Variation in Medicaid Eligibility and Participation Among Adults: Implications for the Affordable Care Act” Inquiry Vol. 49, no. 3 (2013): 231-253.
15 Janet Currie, “The Take-Up of Social Benefits,” in Poverty, The Distribution of Income, and Public Policy, edited by Alan Auerbach, David Card, and John Quigley (New York, NY: Russell Sage Foundation, 2006): 80-148; Dahlia K. Remler and Sherry A. Glied, “What Other Programs Can Teach Us: Increasing Participation in Health Insurance Programs,” American Journal of Public Health 93, no. 1 (2003): 67-74.
16 Robert Moffit, “An Economic Model of Welfare Stigma,” American Economic Review 73, no. 5 (1983): 1023-1035.
17 Susan Bartlett, Nancy Burstein, and William Hamilton, “Food Stamp Program Access Study, Final Report” (Cambridge, MA: Prepared by Abt Associates for Economic Research Service, USDA, 2004).
18 Beth O. Daponte, Seth Sanders, and Lowell Taylor “Why do Low-Income Households not Use Food Stamps? Evidence from an Experiment,” Journal of Nutrition Education 30, no. 1 (1999): 50-57.
19 Wilde, P. (2007). “Measuring the effect of food stamps on food insecurity and hunger: Research and policy considerations.” Journal of nutrition, 137, 307-10.
20 Michael Ponza, James C. Ohls, Lonezo Moreno, and Amy Zambrowski, “Customer Service in the Food Stamp Program: Final Report,” (Princeton, NJ: Prepared by Mathematica Policy Research for the FNS, USDA, 1999)
21 Center on Budget and Policy Priorities. “A Quick Guide to SNAP Eligibility and Benefits.” Revised August 30. 2013.
22 Jennifer P. Stuber, Kathleen A. Maloy, Sara Rosenbaum, and Karen C. Jones, “Beyond Stigma: What Barriers Actually Affect the Decisions of Low-Income Families to Enroll in Medicaid?” (Washington, DC: George Washington University Center for Health Policy Research, 2000).
23 Aaron S. Yelowitz, “Public Policy and Health Insurance Choices of the Elderly: Evidence from the Medicare buy-in program,” Journal of Public Economics 78 (2000): 301-24.
24 Susan L. Ettner, “Medicaid Participation Among the Eligible Elderly,” Journal of Policy Analysis and Management 16 (1997): 237-255.
25 U.S. General Accounting Office (GAO), “Health Care Reform: Potential Difficulties in Determining Eligibility for Low Income People,” (Washington, DC: Government Printing Office, 1994).
26 Cynthia Bansak and Steven Raphael, “The Effects of State Policy Design Features on Take-Up and Crowd-Out Rates for the State Children’s Health Insurance Program,” Journal of Econometrics 125 (2006): 149-75; BarbaraWolfe and Scott Scrivner, “The Devil May Be in the Details: How the characteristics of SCHIP program affect take-up,” Journal of Policy Analysis and Management 24 (2005): 499-522.
27 An early landmark paper, for example, found that people consistently make mistakes in analyzing risk, paying excessive amounts to avoid dangers that are highly unlikely. Daniel Kahneman and Amos Tversky, “Prospect Theory: An Analysis of Decision Under Risk,” Econometrica 42, no. 2 (1979): 263-292.
28 B.J. McNeil, S.G. Pauker, H.C. Sox Jr, and and A. Tversky, “On the elicitation of preferences for alternative therapies,” New England Journal of Medicine 306 (1982):1259-62; Daniel Kahneman and Amos Tversky, “Rational Choice and the Framing of Decisions”, The Journal of Business 59, no. 4, part 2 (1986): S251-S278.
29 Marianne Bertrand, Sendhil Mullainathan, and Eldar Shafir, “Behavioral Economics and Marketing in Aid of Decision Making Among the Poor,” Journal of Public Policy and Marketing 25, no. 1 (2006): 8-23.
30 David Laibson, “Golden Eggs and Hyperbolic Discounting,” The Quarterly Journal of Economics 112, no. 2 (1997): 443- 478; Richard Thaler, “Mental Accounting and Consumer Choice,” Marketing Sicence 4, no. 3 (1985): 199-214; Ted O’Donoghue and Matthew Rabin, “Procrastination in Preparing for Retirement,” in Behavioral Dimensions of Retirement Economics, edited by Henry Aaron (Washington, DC: Brookings Institution and Russell Sage, 1998): 125-56; Katherine Baicker, William J. Congdon, and Sendhil Mullainathan, “Health Insurance Coverage and Take-Up: Lessons from behavioral economics,” The Milbank Quarterly 90, no. 1 (2012): 107-134.
31 Some evidence for this factor is that the workers most likely to avoid making a decision to draw down their employers’ matching contributions in the “$100 Bills on the Sidewalk” study were those with the least level of financial sophistication and knowledge, both in self-perception and objective terms. James J. Choi, David Laibson, and Brigitte C. Madrian. $100 Bills on the Sidewalk: Suboptimal Investment in 401(K) Plans. NBER Working Paper 11554. August. 2005. See also the studies described below, in which Medicare beneficiaries with greater numeracy and cognitive challenges experienced greater difficulty processing information about health plan choices and, in some cases, were less likely to make a choice or enroll.
47 “Facilitating Medicaid and CHIP Enrollment and Renewal in 2014,” Center for Medicare and Medicaid Services (CMS), Department of Health and Human Services (HHS), May 17, 2013.
48 Dorn, Isaacs, et al., op cit.
49 Martha Heberlein, Michael Huntress, Genevieve Kenney, Joan Alker, Victoria Lynch, and Tara Mancini. Medicaid Coverage for Parents under the Affordable Care Act. Prepared by the Georgetown University Center for Children and Families and the Urban Institute for the Atlantic Philanthropies, June 2012.
50 January Angeles, Dorothy Rosenbaum, and Shelby Gonzales. “HHS Announces Opportunity to Streamline Health Coverage for SNAP Participants: New Option for Automatic Enrollment Can Substantially Increase Health Coverage Through Medicaid While Reducing Administrative Costs.” Washington, DC: Center on Budget and Policy Priorities, June 11, 2013.
51 Since the initial implementation of these targeted enrollment strategies, the number enrolled has increased. For example, Oregon reached more than 123,000 people by February 6. “Legislature Aimed at Improving Access to Cover Oregon, IT Oversight Bills.” Cascade Business News. Feb 18, 2014. In addition, New Jersey and California have begun implementing targeted enrollment strategies.
52 For example, A Social Security Administration mailing to 16.4 million low-income Medicare beneficiaries who were identified as probably eligible for Medicare Savings Programs led just 0.5 percent to enroll. Government Accountability Office, Medicare Savings Programs: Results of Social Security Administration’s 2002 Outreach to Low-Income Beneficiaries, GAO-04-363, March 2004. Along similar lines, Mailings from Iowa and New Jersey to parents who indicated on state income tax forms that their children were uninsured caused 1 percent or less to enroll, even though, in New Jersey, parents were given a short and simple form that eliminated all income questions, and targeted media buys accompanied the mailing. B.F. Johnston, Reaching Uninsured Children: Iowa’s Income Tax Return and CHIP Project, Iowa Dpt. of Human Services, August 2010. John Guhl and Eliot Fishman, New Jersey Family Care: Express Lane Eligibility, SCI Program National Conference, July 2009. Since then, states have achieved higher response rates, but still below those recently observed with Medicaid targeted enrollment.
One difference may be that, among the eligible populations subject to previous outreach efforts, those with the greatest need for care were more likely to have already enrolled, compared to the newly eligible adult population that is a key focus of the current targeted enrollment campaigns. Dottie Rosenbaum, Center on Budget and Policy Priorities, personal communication, 2013.
53 That earlier effort achieved only a 5 percent response rate. Hoag, et al., op cit.
54 Stan Dorn, Ian Hill, and Sara Hogan, The Secrets of Massachusetts’ Success: Why 97 Percent of State Residents Have Health Coverage, (Washington, DC: Prepared by the Urban Institute for the Robert Wood Johnson Foundation and the State Health Access Reform Evaluation, 2009).
55 This approach also implements another effective Massachusetts strategy: namely, crafting state policy that incentivizes private providers to deploy their own resources in conducting outreach and enrollment. Even before that state’s 2006 reforms, the state developed a single application form for all health coverage programs, including Medicaid and uncompensated care for safety net providers. Hospitals and clinics could not receive safety net payments except for patients whose application forms were completed; state officials wanted to ensure that safety net funds were reserved for patients who were ineligible for Medicaid. As a result, public hospitals and clinics used their own money to hire large numbers of application assisters, who made a major contribution to successful enrollment into the state’s expanded coverage in 2006. Dorn, Hill, and Hogan, op cit.
56 Jennifer Edwards and Rebecca Kellenberg. “Case Study of South Carolina’s Express Lane Eligibility Processes.” CHIPRA Express Lane Eligibility Evaluation. (New York, NY: Health Management Associates, 2013), publication pending.
57 In FY 2012, the state enrolled 607,681 children. Division of Policy and Research on Medicaid and Medicare at the University of South Carolina Institute for Families in Society, SC HealthViz, a project of the SC Department of Health and Human Services,, downloaded 10/31/13.

58 However, these application modes create the potential for self-selection. It is possible that many internet applicants could have greater cognitive reserves than some who apply through other venues. On the other hand, many consumers who qualify for subsidies in the Marketplace may ultimately have little choice but to go on line to complete the enrollment process. Most such consumers will be confronted with dozens of plan options, each with its own premiums, covered benefits, out-of-pocket cost-sharing rules, and provider networks. In many cases, consumers may need to peruse web displays of this information to choose a plan.

59 The system faced limits on transferring applicants’ full files to the proper state for processing, but expanded Medicaid coverage still began in January 2014 (from “Medicaid Applications Face Delay in Health Exchanges,” Louise Radnofsky, Wall Street Journal, September 24, 2013,
60 All plans in the exchange will provide the same essential health benefits, but plans can offer additional benefits or cover additional services within mandatory benefit categories.
61 Amy Burke, Arpit Misra, and Steven Sheingold. “Premium Affordability, Competition, and Choice in the Health Insurance Marketplace, 2014.” ASPE Research Brief, June 18, 2014, Washington, DC: Assistant Secretary for Planning and Evaluation, U.S. Department of Health and Human Services (ASPE/HHS).
62 Jeffrey Liebman and Richard Zeckhauser, “Simply Humans, Complex insurance, Subtle Subsidies,” National Bureau of Economic Research Working Paper Series (Cambridge, MA: NBER, 2008).
63 Christopher Millett, Arpita Chattopadhyay, and Andrew Bindman. “Unhealthy Competition: Consequences of Health Plan Choice in California Medicaid.” American Journal of Public Health. 2010 Nov;100(11):2235-40.
64 Eric Johnson, Ran Hassin, Tom Baker, Allison Bajger, and Galen Treuer, "Can Consumers Make Affordable Care Affordable? The Value of Choice Architecture," (New York, NY: Columbia Business School, 2013).
65 Keith M. Ericson, and Amanda Starc, "Heuristics and Heterogeneity in Health Insurance Exchanges: Evidence from the Massachusetts Connector," American Economic Review 102, no. 3 (2012): 493-97.
66 Those in the bottom quartile of cognition— based on a validated instrument that assesses orientation, attention, memory, word recognition and comprehension, and ability to count and perform simple arithmetic—were less likely to report enrollment (63.5% vs. 52.0%), subsidy awareness (58.3% vs. 33.3%), and subsidy application (25.5% vs. 12.7%) relative to those in the top quartile. Ifedayo O. Kuye, Richard G. Frank, and J. Michael McWilliams, “Cognition and Take-Up of Subsidized Drug Benefits by Medicare Beneficiaries,” JAMA Internal Medicine 173, no. 12 (2013): 1100-1107.
67 After adjusting for decision-making approach, numeracy, and level of medical training, subjects were 10.75 times as likely to pick the right plan when presented with 3 rather than 9 options. Andrew J. Barnes, Yaniv Hanoch, Melissa Martynenko, Stacey Wood, Thomas Rice, and Alex D. Federman. “Physician Trainees’ Decision Making and Information Processing: Choice Size and Medicare Part D.” PLoS ONE 8(10): e77096.
68 Sewin Chan and Brian Elbel, “Low Cognitive Ability and Poor Skill with Numbers May Prevent Many from Enrolling in Medicare Supplemental Coverage,” Health Affairs 31, no. 8 (2012): 1847-1854.
69 See Baicker et al., “Health Insurance Coverage and Take-Up,” for a recent review.
70 Sheena S. Iyengar, Gur Huberman, and Wei Jiang, “How Much Choice is Too Much? Contributions to 401(k) retirement plans,” Pension Research Council Working Paper (Philadelphia, PA: Pension Research Council, The Wharton School, University of Pennsylvania, 2004).
71 Sheena S. Iyengar and Mark R. Lepper, “When Choice is Demotivating: Can one desire too much of a good thing?” Journal of Personality and Social Psychology 79, no. 6 (2000): 995-1006 and Sheena S. Iyengar and Wei Jiang, “The Psychological Costs of Ever Increasing Choice: A fallback to the sure bet,” Working Paper (New York, NY: Columbia University, 2005) also explore the choice overload effect of extra choices on consumer decisions about retirement savings.
72 Thomas Rice, Yaniv Hanoch, and Janet R. Cummings, “What Factors Influence Seniors’ Desire for Choice Among Health Insurance Options? Survey Results on the Medicare Prescription Drug Benefit,” Health Economics, Policy, and Law 5, no. 4 (2010): 437-57.
73 M. Kate Bundorf and Helena Szrek, “Choice Set Size and Decision Making: The case of Medicare Part D Prescription Drug Plans,” Medicare Decision Making 30, no. 5 (2010): 582-93.
74 CMS, “Guidance on State Alternative Applications for Health Coverage”, June 2013,
75 For example, most behavioral economics studies that employ randomized controlled experiments obtain informed consent from participants or pair informed consent with a financial incentive to participate (Johnson, Hassin, et al., op cit.; Flores et al., op cit.; Barnes et al., op cit). To ensure participant privacy and security, researchers should strip data of individually identifying characteristics, such as name, address, and social security number (see Schanzenbach et al., op cit.).
76 Certain aspects of a health coverage application, such as verification provided by the federal data hub, cannot currently be shared with human services programs. May 16, 2013,
77 This same question does not arise in the first context. SNAP agencies have the legal authority to share eligibility information with Medicaid agencies. 7 CFR § 272.1(c)(1)(i).
78 An approach not discussed in the text is the following. Once a consumer finishes the application for insurance affordability programs but before the consumer selects a health plan, the consumer could be given a chance to have his or contact information shared with SNAP for later follow-up, as described above. This would allow the consumer to consider SNAP before exhaustion results from plan choice. On the other hand, interposing SNAP in the midst of the health application process could be experienced by the consumer as a confusing and distracting interruption.
79 The authors are unaware of research that specifically examines the impact of multi-program applications, compared to health-only application forms. However, a number of studies described above find that simplified applications or combined Medicaid-CHIP application forms increase participation levels. See, e.g., Bansak and Raphael; Wolfe and Scrivner.
80 In the context of children’s coverage through Medicaid and the Children’s Health Insurance Program, participation levels are higher in states without asset tests than in states that use such tests. Kronebusch K, Elbel E. “Simplifying children’s Medicaid and SCHIP: What helps? What hurts? What’s next for the states?” Health Affairs. 2004; 23(3):233–46. Beneficiary focus groups involving seniors show that the burdensome nature of the asset test is a key barrier to participating in available Medicaid supplementation of Medicare for low-income beneficiaries. Perry MJ, Kannel S, Dulio A (Lake Snell Perry & Associates/SPRI, Washington, DC). Barriers to Medicaid enrollment for low-income seniors: focus group findings [Internet]. Washington (DC): Kaiser Family Foundation; 2002.
81 Much behavioral economics research shows that people often overestimate the ease of project completion. Business and academic organizations, for example, frequently underestimate the time and cost required by proposed projects, often leading to a failure of project completion. See, e.g., Kahneman, D. & Lovallo, D., 1993, “Timid choices and bold forecasts: A cognitive perspective on risk-taking.” Management Science, 39, 17-31. Roger Buehler, Dale Griffin, and Johanna Peetz, 2010, “The Planning Fallacy: Cognitive, Motivational, and Social Origins.” Advances in Experimental Social Psychology, 43, 1-62. One classic example involves rebates, where consumers make purchases intending to mail in rebates and obtain a reduced price, but sellers know that very few consumers do so. One study found a rebate return rate between 10 and 40 percent. Tim Silk and Chris Janiszewski , “Managing Mail-in Rebate Promotions,” Moore School of Business, University of South Carolina and Warrington College of Business, University of Florida, working paper, 2009, Another study found that the experimental subjects who were most optimistic about their likelihood of mailing in rebates were, in fact, least likely to follow through. Letzler, Robert and Joshua Taso, “Everyone Believes in Redemption: Nudges and Overoptimism in Costly Task Completion," Federal Trade Commission and Claremont Graduate University Department of Economics, working paper, 2013, In another example, 90 percent of mostly unbanked low-income consumers who attended financial education workshops in Chicago reported, after the workshops, that they expected to open specially arranged low-fee accounts. However, only approximately 50 percent actually did so. Bertrand et al., op cit.
82 If a QHP enrollee does not take action during renewal, the enrollee stays in the same health plan. 45 CFR §155.335(j). In theory, consumers should be reexamining their initial decisions in light of market changes. In practice, however, behavioral economics research raises questions about how often that will occur. With Medicare Part D coverage, for example, only 6 percent of enrollees change plans annually, despite changing premiums. Neuman, Patricia and Juliette Cubanski, ‘Medicare Part D Update — Lessons Learned and Unfinished Business,” New England Journal of Medicine, Vol. 361, No. 4, pp. 406-404, 2009. One study found that most enrollees did not understand key features of insurance coverage; more than 70 percent underestimated the savings potentially gained from changing plans; even among beneficiaries who could save $400 or more by changing plans, fewer than 20 percent did so; and that people who received a distillation of available information into simple comparisons were 65 percent more likely to change plans (28 vs. 17 percent), compared to a control group that did not receive that distilled information. Kling, Jeffrey R. Sendhil Mullainathan, Eldar Shafir, Lee Vermeulen, and Marian V. Wrobel, “Comparison Friction: Experimental Evidence from Medicare Drug Plans,” Quarterly Journal of Economics, Vol. 127 Issue 1, p199, 2012.
83 In theory, factors other than income and family size can change. In practice, however, such changes are sufficiently rare that redetermination of eligibility generally requires information only about income and family size. See, e.g., 45 CFR §155.335(b); CMS, Medicaid/CHIP Eligibility & Enrollment Webinars, “Application, Verification & Renewals,” April 19, 2012, Transcript (“they can't require the individual to re-verify their residency”) presentation slides (“At annual renewal, the State checks the quarterly wage database…. State does not re-check citizenship because it is not subject to change”)
84 See generally Wendy R. Williams. “Struggling with Poverty: Implications for Theory and Policy of Increasing Research on Social Class-Based Stigma.” Analyses of Social Issues and Public Policy, Vol. 9, No. 1 (2009): 37-56.
85 Naomi Mandel and Eric J. Johnson. “When Web Pages Influence Choice: Effects of Visual Primes on Experts and Novices.” Journal of Consumer Research, Vol. 29, No. 2 (September 2002), pp. 235-245.
86 Sabrina M. Tom, Craig R. Fox, Christopher Trepel, and Russell A. Poldrack. “The Neural Basis of Loss Aversion in Decision-Making Under Risk.” Science Vol. 315, 515 (2007);
87 Banks, Sara M., Peter Salovey, Susan Greener, Alexander Rothman, Anne Moyer, John Beauvais, and Elissa Epel (1995), “The Effects of Message Framing on Mammography Utilization,” Health Psychology, 14 (2), 178–84.
88 Another illustrative study presented experimental subjects with a choice between two hypothetical jobs. Job A had a medium-sized commuting distance and Job B had a moderately friendly work environment. One group of subjects was told that their hypothetical current, temporary job, which would soon end, involved almost no commuting distance and a highly isolated working environment. For this group, Job A, which would have increased their commute, created a loss, and 70 percent said they preferred Job B. The other group of experimental subjects was told that their current, temporary job involved an extremely long commute and a very warm and friendly office environment. For this group, Job B, which offered only a moderately friendly work environment, represented a loss, and 66 percent found Job A preferable. Even though the two available options, Job A and B, were identical to both groups, most subjects made diametrically opposed choices, depending on whether those options were seen as involving a loss or gain, compared to their current, temporary situation. For this example of loss aversion and many others, see Amos Tversky and Daniel Kahneman. “Loss Aversion in Riskless Choice: A Reference-Dependent Model.” The Quarterly Journal of Economics, Vol. 106, No. 4 (Nov., 1991), pp. 1039-1061.
89 Brian J. Zikmund-Fisher, Paul D. Windschitl, Nicole Exe, and Peter A. Ubel. ““I’ll Do What They Did”: Social Norm Information and Cancer Treatment Decisions.” Patient Education and Counseling. Vol. 85, No. 2 (Nov. 2011): 225–229. For an example involving energy conservation, see P. Wesley Schultz, Jessica M. Nolan, Robert B. Cialdini, Noah J. Goldstein, and Vladas Griskevicius. “The Constructive, Destructive, and Reconstructive Power of Social Norms.” Psychological Science. Vol. 18, No. 5 (May 2007): 429-434.

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