Comment on Automatic Reenrollment Process for Individuals with APTC

See PDF: 2020-Comment on Exchange Automatic Reenrollment

Centers for Medicare & Medicaid Services
Department of Health and Human Services
Attention: CMS-9916-P
March 2, 2020

Comment on Automatic Reenrollment Process for Individuals with APTC

Dear CMS:

I write to oppose the changes in the automatic reenrollment process in the proposed rule, “Benefit and Payment Parameters; Notice Requirement for Non-Federal Governmental Plans.”

The proposed rule solicited comment on “modifying the automatic re-enrollment process such that any enrollee who would be automatically re-enrolled with APTC that would cover the enrollee’s entire premium would instead be automatically re-enrolled without APTC.” I oppose this change because it is likely to reduce enrollment of individuals eligible for subsidies, increase the uninsured, and increase adverse selection in the Exchange. This portion of the proposed rule also seems unfair to enrollees and may diminish trust the Exchange. As a result of all these considerations, the proposed rule is unlikely to pass a cost-benefit assessment.

I am an academic researcher who has published on the design of health insurance exchanges. In particular, I have studied individual inertia and the role of automatic reenrollment defaults in Medicare Part D, the setting of optimal defaults, and interventions in Exchanges to reduce default taking and increase active decisions.

Under the proposed rule, consumers who were eligible for a zero-premium plan would be automatically reenrolled into a plan with zero APTC (that is, they would receive a bill for the full price) unless the consumer returns to update their application and receive a new determination of their eligibility for subsidies (aka advanced premium tax credits, APTC). This is a type of “default,” in that the automatic reenrollment process will determine what happens to an individual who does not make an active decision. I refer to this proposal as the “proposed default into zero-APTC plans.”

  1. The proposed rule’s default will reduce enrollment in the exchanges and increase the number of uninsured households

Most of these enrollees subject to the proposed default into zero-APTC plans would likely still qualify for substantial amount of APTC if they completed the eligibility determination process. However, it is likely that many consumers will nonetheless take the automatic reenrollment default. Taking the default often does not reflect consumers’ well thought out preferences, but instead results from behavioral frictions, such as inattention, forgetting, or procrastination [1-4]. Evidence indicates that defaults have powerful effects in many contexts [5-6]. Reenrollment defaults matter for health insurance as shown by evidence from employer-sponsored insurance [7], Medicare Part D [8] and state-based Exchanges [9]. Small barriers or nudges can increase or reduce enrollment [10,11]. Low-income individuals also take the default because it is difficult for them to keep up with the extensive administrative burden of the social safety net.

Enrollees who receive a bill for a zero-subsidy, full price health insurance plans are likely to drop coverage. Even if enrollees would eventually be eligible for subsidies when filing taxes, enrollees are unlikely to have the financial resources to pay for premiums upfront because of liquidity constraints [12]. They may also be confused and averse to the risk that they might not receive subsidies when filing taxes. As a result, enrollees who receive this default are likely to drop coverage.

  1. The proposed rule’s default will worsen adverse selection into the exchanges

Sick individuals will be less likely than healthy individuals to drop coverage as a result of receiving the proposed default into zero-APTC plans. This will lead to adverse selection. Adverse selection leads to inefficiency in health insurance markets generally. The risk adjustment system helps address adverse selection across plans in the Exchange, but the risk adjustment system does not address adverse selection into the Exchange versus out of the Exchange (e.g. selection between Exchange coverage and no coverage, or some other source of coverage). As a result, it is important to consider the effects on selection into the exchange. Worsening selection into the Exchange can raise premiums and even lead to a “death-spiral.”

  1. The proposed rule’s default is unfair and may reduce trust in the Exchange

The proposed default into zero-APTC plans seems unfair. A zero-APTC amount does not reflect the Exchange’s best estimate of the APTC that these enrollees are eligible for. Moreover, it treats similar individuals quite differently: consumers with zero-premium plans get a default into a zero-APTC plan, while consumers with near zero plans continue with their current default. Arguably, many consumers with a zero-premium plan would be better off in a $1 premium plan so that they would not be subject to the proposed default. The proposed rule’s default is likely to diminish trust in the Exchange, trust in recommendations, and trust that its defaults are well-chosen.

  1. The proposed rule’s default is unlikely to pass a cost-benefit test

The proposed default into zero-APTC plans has large costs and few benefits. It is therefore unlikely to pass a cost-benefit test. The only benefit mentioned from this proposed default into zero premium plans is that “automatic re-enrollment may lead to incorrect expenditures of APTC, some of which cannot be recovered through the reconciliation process due to statutory caps.” No estimate of this benefit has been made public.

However, the proposed default into zero-APTC plans has clear costs. First, there is a cost in time and effort to the enrollees who must complete the eligibility redetermination process to receive their APTC. Second, because individuals are likely to take the proposed default due to behavioral frictions and then drop coverage, this policy is likely to increase the number of the uninsured. Being uninsured has negative consequences on the uninsured individuals themselves, such as increased mortality [10] and financial stress. Moreover, becoming uninsured also has negative externalities on others, including on the uncompensated/charity care and lack of access to vaccines and other treatments that would prevent the transmission of infectious disease. The total of these costs likely outweigh any benefits from the reduction of incorrect APTC expenditures.

  1. Alternatives to the proposed rule’s default, including the status quo, are better

The current automatic reenrollment default is rational, in that it assumes that enrollees have not had a large positive change in income in the absence of any new information. The current automatic reenrollment default for those with zero-premium plans also minimizes administrative costs of billing and collecting small premiums.  In the absence of clear evidence that there are large costs to the current automatic reenrollment default, I believe the current default is best.

However, if a change to the automatic reenrollment process needed to be made, there are alternatives to the proposed default into zero-APTC that are superior. I outline these alternative defaults for enrollees who previously received zero-premium plans and briefly discuss their pros and cons.

i. A default into a $1 premium plan.: The proposed rule mentions the concern that there is a “particular risk associated with enrollees who are automatically re-enrolled with APTC that cover the entire plan premium.” A logical alternative is to default them into a plan that requires a small token payment, which would still require action on the part of enrollees to continue coverage.

The cons of this policy are that it would entail administrative costs to collect a small premium, and that some individuals actually eligible for a zero-subsidy plan would drop coverage altogether because of inattention to small bills. I do not believe that the default into a $1 premium plan would pass a cost-benefit test compared to the status quo default, but I do believe that this default would have benefits far exceeding costs compared to the proposed default into zero-APTC plans.

ii. A default into the Exchange’s best estimate of the enrollees’ APTC eligibility. A zero-APTC amount does not reflect the Exchange’s best estimate of the APTC that these enrollees are eligible for. The Exchange could develop an estimate of what enrollees are eligible for given data sources or a statistical model.

Compared to the status quo default, this default would require developing such an infrastructure to forecast eligibility, adding additional complexity and costs for the Exchange. However, it would be better than the proposed zero-APTC default because it would reduce overall administrative burden and the consequences of costly mistakes due to dropped coverage.

  1. State Exchanges should be given the freedom to define their own reenrollment procedures.

The proposed rule solicited “comment on whether the approaches discussed above should be adopted only for Exchanges using the Federal platform, or whether they should also be required for State Exchanges that operate their own eligibility and enrollment platforms.”

Allowing State Exchanges to determine their own automatic reenrollment procedures is consistent with the value of federalism. State Exchanges may experiment with different approaches that inform optimal policy for automatic reenrollment. Allowing flexibility to State Exchanges also allows automatic reenrollment procedures to be tailored to state market conditions.

Finally, State Exchanges that operate their own eligibility and enrollment platforms might find it costly or difficult to modify those platforms to comply with the proposed rule.

Thank you for considering these comments.

Sincerely,

Keith Marzilli Ericson
Associate Professor of Markets, Public Policy, and Law
Questrom School of Business
Boston University

(This comment represents my views as an academic and not the views of Boston University as an institution.)

References:

  1. Bernheim, Douglas, Andrey Fradkin, and Igor Popov. 2015. “The welfare economics of default options in 401 (k) plans.” American Economic Review, 105(9), pp.2798-2837.
  2. Carroll, Gabriel, James Choi, David Laibson, Brigitte Madrian, and Andrew Metrick. 2009. “Optimal Defaults and Active Decisions.” Quarterly Journal of Economics 124,1639-1674.
  3. Ericson, Keith M. 2017. “On the interaction of memory and procrastination: Implications for reminders, deadlines, and empirical estimation.” Journal of the European Economic Association, 15(3), 692-719.
  4. Ericson, Keith M. Marzilli. 2014. “When consumers do not make an active decision: Dynamic default rules and their equilibrium effects.” National Bureau of Economic Research Working Paper 20127.
  5. Johnson, Eric J., and Daniel Goldstein. 2003. “Do Defaults Save Lives?” Science 302 (5649): 1338–39.
  6. Madrian, Brigitte and Dennis Shea. 2001. “The Power of Suggestion: Inertia in 401(k) Participation and Savings Behavior.” Quarterly Journal of Economics 116, 1149-1187.
  7. Handel, B.R., 2013. Adverse selection and inertia in health insurance markets: When nudging hurts. American Economic Review, 103(7), pp.2643-82.
  8. Ericson, Keith M. 2014. “Consumer Inertia and Firm Pricing in the Medicare Part D Prescription Drug Insurance Exchange”. American Economic Journal: Economic Policy 6, 38-64.
  9. Ericson, K.M., Kingsdale, J., Layton, T. and Sacarny, A., 2017. “Nudging leads consumers in Colorado to shop but not switch ACA marketplace plans.” Health Affairs, 36(2), pp.311-319.
  10. Goldin, J., Lurie, I.Z. and McCubbin, J., 2019. “Health Insurance and Mortality: Experimental Evidence from Taxpayer Outreach.” National Bureau of Economic Research Working Paper 26533.
  11. Domurat, R., Menashe, I. and Yin, W., 2019. “The Role of Behavioral Frictions in Health Insurance Marketplace Enrollment and Risk: Evidence from a Field Experiment.” National Bureau of Economic Research Working Paper 26153.
  12. Ericson, K.M. and Sydnor, J.R., 2018. “Liquidity constraints and the value of insurance.”National Bureau of Economic Research Working Paper 24993.
Nudging Leads to Consumers In Colorado To Shop But Not Switch ACA Marketplace Plans

Nudging Leads to Consumers In Colorado To Shop But Not Switch ACA Marketplace Plans

Nudging Leads to Consumers In Colorado To Shop But Not Switch ACA Marketplace Plans (Health Affairs 2017open access version here, joint with Jon Kingsdale, Timothy Layton, and Adam Sacarny)

The Affordable Care Act (ACA) dramatically expanded the use of regulated marketplaces in health insurance, but consumers often fail to shop for plans during open enrollment periods. Typically these consumers are automatically reenrolled in their old plans, which potentially exposes them to unexpected increases in their insurance premiums and cost sharing. We conducted a randomized intervention to encourage enrollees in an ACA Marketplace to shop for plans. We tested the effect of letters and e-mails with personalized information about the savings on insurance premiums that they could realize from switching plans and the effect of generic communications that simply emphasized the possibility of saving. The personalized and generic messages both increased shopping on the Marketplace’s website by 23 percent, but neither type of message had a significant effect on plan switching. These findings show that simple “nudges” with even generic information can promote shopping in health insurance marketplaces, but whether they can lead to switching remains an open question.

Design Issues in Economics Lab Experiments: Randomization

I’ve seen a lot of experimental economics papers as a coeditor of the Journal of Public Economics and a frequent reviewer for many journals. There are some recurring design and analysis decisions that lead authors astray. I’ll discuss a series of them. The first is Not Randomizing Treatment. It’s more common than you might think!

Not randomizing treatment. Randomly assigning participants to treatment is one of the key benefits of lab-based economics experiments. When we want to test the effect of a treatment, we want treatment to be orthogonal to everything else. It’s pretty clear how to do this with participant-level randomization—a random number generator assigns each participant to a treatment.

Things often go awry when we move to group-level randomization. For instance, you want to run a public goods game under one set of rules (“Basic Rules”), and then see how contributions change when it is run under another set of rules (“Enhanced Rules”).

Ideally, for each session, you would randomly assign half of the participants who show up to play Basic Rules (and interact with other participants in the Basic Rules condition), and half to play Enhanced Rules (and interact with other participants in the Enhanced Rules condition). This is great, you’ve actually achieved participant-level randomization.

But it’s logistically complicated to have different rules going on simultaneously (and perhaps the lab cannot handle enough people). So instead, you do session-level randomization. You run a “large-enough” number of sessions. You create a random order of sessions, so you might run Basic-Enhanced-Basic-Basic-Enhanced etc. If the number of sessions is large enough, you cluster standard errors at the session level and proceed.

But running different sessions with different rules in a random order is complicated. Plus, you might get an idea about what rules to run after running a few sessions. So what you do is run a few Basic Rules sessions, and then run a few Enhanced Rules sessions. This is where randomization has failed. Your subject population could be changing over time (perhaps early subjects are more eager, or have lower value of time). Or news events could change beliefs and preferences. The list of potential stories can be long; some can be ruled out, others cannot. But because your session order is not random, you are not guaranteed to have your treatment be orthogonal to everything else. As a result, you’ve missed out on the benefit of randomized experiments, and it’s unclear what to conclude from comparing your treatments.

 

Inferring Risk Perceptions and Preferences using Choice from Insurance Menus: Theory and Evidence

Inferring Risk Perceptions and Preferences using Choice from Insurance Menus: Theory and Evidence

New working paper:

Inferring Risk Perceptions and Preferences using Choice from Insurance Menus: Theory and Evidence (joint with Philipp Kircher, Johannes Spinnewijn, and Amanda Starc)

Demand for insurance can be driven by high risk aversion or high risk. We show how to separately identify risk preferences and risk types using only choices from menus of insurance plans. Our revealed preference approach does not rely on rational expectations, nor does it require access to claims data. We show what can be learned non-parametrically from variation in insurance plans, offered separately to random cross-sections or offered as part of the same menu to one cross-section. We prove that our approach allows for full identification in the textbook model with binary risks and extend our results to continuous risks. We illustrate our approach using the Massachusetts Health Insurance Exchange, where choices provide informative bounds on the type distributions, especially for risks, but do not allow us to reject homogeneity in preferences.

Measuring Consumer Valuation of Limited Provider Networks

Measuring Consumer Valuation of Limited Provider Networks

Measuring Consumer Valuation of Limited Provider Networks

Published: American Economic Review, Papers and Proceedings, 2015.

Longer version: NBER Working Paper 20812. (Joint with Amanda Starc)

WTP for Network Breadth

We measure provider coverage networks for plans on the Massachusetts health insurance exchange using a two measures: consumer surplus from a hospital demand system and the fraction of population hospital admissions that would be covered by the network. The two measures are highly correlated, and show a wide range of networks available to consumers. We then estimate consumer willingness-to-pay for network breadth, which varies by age. 60-year-olds value the broadest network approximately $1200-1400/year more than the narrowest network, while 30-year-olds value it about half as much. Consumers place additional value on star hospitals, and there is significant geographic heterogeneity in the value of network breadth.

Measuring Consumer Valuation of Limited Provider Networks

Measuring Consumer Valuation of Limited Provider Networks

Published: American Economic Review, Papers and Proceedings, 2015.

Longer version: NBER Working Paper 20812. (Joint with Amanda Starc)

WTP for Network Breadth

 

We measure provider coverage networks for plans on the Massachusetts health insurance exchange using a two measures: consumer surplus from a hospital demand system and the fraction of population hospital admissions that would be covered by the network. The two measures are highly correlated, and show a wide range of networks available to consumers. We then estimate consumer willingness-to-pay for network breadth, which varies by age. 60-year-olds value the broadest network approximately $1200-1400/year more than the narrowest network, while 30-year-olds value it about half as much. Consumers place additional value on star hospitals, and there is significant geographic heterogeneity in the value of network breadth.

Memory and Procrastination

MemoryForWeb

I have two papers examining limited memory.  Most recently:

 On the Interaction of Memory and Procrastination: Implications for Reminders 

Abstract: I examine the interaction between present-bias and limited memory. Individuals in the model must choose when and whether to complete a task, but may forget or procrastinate. Present-bias expands the effect of memory: it induces delay and limits take-up of reminders. Cheap reminder technology can bound the cost of limited memory for time-consistent individuals but not for present-biased individuals, who procrastinate on setting up reminders. Moreover, while improving memory increases welfare for time-consistent individuals, it may harm present-biased individuals because limited memory can function as a commitment device. Thus, present-biased individuals may be better off with reminders that are unanticipated. Finally, I show how to optimally time the delivery of reminders to present-biased individuals.

Forthcoming, Journal of the European Economic Association. Latest version here, with results on empirical estimation. Older version: NBER Working Paper 20381

This paper built on my previous work on memory, showing that people are overconfident about the probability they will remember:

Forgetting We Forget: Overconfidence and Memory

Abstract:  Do individuals have unbiased beliefs, or are they over- or underconfident? Overconfident individuals may fail to prepare optimally for the future, and economists who infer preferences from behavior under the assumption of unbiased beliefs will make mistaken inferences. This paper documents overconfidence in a new domain, prospective memory, using an experimental design that is more robust to potential confounds than previous research. Subjects chose between smaller automatic payments and larger payments they had to remember to claim at a six-month delay. In a large sample of college and MBA students at two different universities, subjects make choices that imply a forecast of a 76% claim rate, but only 53% of subjects actually claimed the payment.

Published 2011 in the Journal of the European Economic Association; Ungated working paper available at SSRN.

Press Coverage:

 

Memory and Procrastination

Memory and Procrastination

MemoryForWeb

I have two papers examining limited memory.  Most recently:

 On the Interaction of Memory and Procrastination: Implications for Reminders 

Abstract: I examine the interaction between present-bias and limited memory. Individuals in the model must choose when and whether to complete a task, but may forget or procrastinate. Present-bias expands the effect of memory: it induces delay and limits take-up of reminders. Cheap reminder technology can bound the cost of limited memory for time-consistent individuals but not for present-biased individuals, who procrastinate on setting up reminders. Moreover, while improving memory increases welfare for time-consistent individuals, it may harm present-biased individuals because limited memory can function as a commitment device. Thus, present-biased individuals may be better off with reminders that are unanticipated. Finally, I show how to optimally time the delivery of reminders to present-biased individuals.

Forthcoming, Journal of the European Economic Association. Latest version here, with results on empirical estimation. Older version: NBER Working Paper 20381

This paper built on my previous work on memory, showing that people are overconfidence about the probability they will remember:

Forgetting We Forget: Overconfidence and Memory

Abstract:  Do individuals have unbiased beliefs, or are they over- or underconfident? Overconfident individuals may fail to prepare optimally for the future, and economists who infer preferences from behavior under the assumption of unbiased beliefs will make mistaken inferences. This paper documents overconfidence in a new domain, prospective memory, using an experimental design that is more robust to potential confounds than previous research. Subjects chose between smaller automatic payments and larger payments they had to remember to claim at a six-month delay. In a large sample of college and MBA students at two different universities, subjects make choices that imply a forecast of a 76% claim rate, but only 53% of subjects actually claimed the payment.

Published 2011 in the Journal of the European Economic Association; Ungated working paper available at SSRN.

 

Press Coverage:

When Consumers Do Not Make an Active Decision: Dynamic Default Rules and their Equilibrium Effects

Dynamic Defaults

Dynamic defaults for recurring purchases determine what happens to consumers enrolled in a product or service who take no action at a decision point. Consumers may face automatic renewal, automatic switching, or non-purchase defaults. Privately optimal dynamic defaults depend on the contributions of adjustment costs versus psychological factors leading to inaction: both produce inertia under renewal defaults, but differ under non-renewal defaults. Defaults have equilibrium effects on pricing by changing the elasticity of repeat demand. Socially optimal defaults depend on firms’ pricing responses as well; more elastic repeat demand restrains price increases on repeat customers and can reduce inefficient switching.

(Latest draft here. Older: NBER Working Paper 20127).

 

See also discussion in The Incidental Economist.

 

 

When Consumers Do Not Make an Active Decision: Dynamic Default Rules and their Equilibrium Effects

When Consumers Do Not Make an Active Decision: Dynamic Default Rules and their Equilibrium Effects

Dynamic Defaults

Dynamic defaults for recurring purchases determine what happens to consumers enrolled in a product or service who take no action at a decision point. Consumers may face automatic renewal, automatic switching, or non-purchase defaults. Privately optimal dynamic defaults depend on the contributions of adjustment costs versus psychological factors leading to inaction: both produce inertia under renewal defaults, but differ under non-renewal defaults. Defaults have equilibrium effects on pricing by changing the elasticity of repeat demand. Socially optimal defaults depend on firms’ pricing responses as well; more elastic repeat demand restrains price increases on repeat customers and can reduce inefficient switching.

(Latest draft here. Older: NBER Working Paper 20127).

See also discussion in The Incidental Economist.