Elsevier

Journal of Health Economics

Volume 56, December 2017, Pages 292-316
Journal of Health Economics

Medicaid program choice, inertia and adverse selection

https://doi.org/10.1016/j.jhealeco.2017.04.006Get rights and content

Abstract

In 2012, Kentucky implemented Medicaid managed care statewide, auto-assigned enrollees to three plans, and allowed switching. Using administrative data, we find that the state’s auto-assignment algorithm most heavily weighted cost-minimization and plan balancing, and placed little weight on the quality of the enrollee-plan match. Immobility − apparently driven by health plan inertia − contributed to the success of the cost-minimization strategy, as more than half of enrollees auto-assigned to even the lowest quality plans did not opt-out. High-cost enrollees were more likely to opt-out of their auto-assigned plan, creating adverse selection. The plan with arguably the highest quality incurred the largest initial profit margin reduction due to adverse selection prior to risk adjustment, as it attracted a disproportionate share of high-cost enrollees. The presence of such selection, caused by differential degrees of mobility, raises concerns about the long run viability of the Medicaid managed care market without such risk adjustment.

Introduction

Between 2002 and 2014, the share of the Medicaid population enrolled in managed care grew from 58 percent to 77 percent (CMS, 2011, Mathematica Policy Research, 2016). By 2014, 61 percent of the 71.7 million Medicaid recipients nationwide were enrolled in comprehensive managed care plans, a sharp increase from the 56 percent just one year earlier (Mathematica Policy Research, 2015, Mathematica Policy Research, 2016). As of July 2015, 48 states use managed care for at least some Medicaid recipients, 39 states contract with managed care organizations (MCOs) and 29 of them (including DC) use MCOs exclusively (Smith et al., 2015).

In many instances, consumers in a health insurance market face many choices between different plans. Even with a fully-binding individual mandate that compels health insurance coverage, offering choice between different health insurance plans during open enrollment periods − either through Medicaid MCOs, Qualified Health Plans (QHPs) in the Marketplace, in Medicare Part D, or elsewhere − raises the possibility of adverse selection and consequently economic losses for insurers. This has been seen recently in private Marketplace plans with major insurers − UnitedHealth Group, Humana Inc., and most recently Aetna − withdrawing completely, scaling back, or cancelling expansions, citing large losses on Marketplace plans (Matthews, 2016). Such adverse selection “death spirals” have been demonstrated in some other health insurance contexts (Cutler and Reber, 1998).

Compared with either Marketplace QHPs or Medicare Part D, analysis of coverage choices in Medicaid MCOs allows us to investigate the consequences of inertia, adverse selection, and plan payment design on insurance market stability in a completely new setting. In QHPs, consumers typically face multiple bronze, silver, gold and platinum plans, with different subsidized premiums, copayments or coinsurance rates, deductibles, out-of-pocket maximums, and network coverage. Given this complexity, recent work has argued for personalized decision support and smart defaults (Handel and Kolstad, 2015b). In Medicare Part D − which only focuses on the prescription drug portion of the healthcare package for the elderly − recent work has noted that the financial complexity of the plans appears to lead to “choice inconsistencies” and little learning on the part of consumers (Abaluck and Gruber, 2011, Abaluck and Gruber, 2016, Ketcham et al., 2012, Ketcham et al., 2015). In both contexts, the consumer must compare both financial implications (making a forecast of future distribution health care use) and benefit generosity across multiple plans. In contrast, the choice problem for Medicaid MCOs is simpler because the financial implications from different plans are minimal. Recipients with income under 150 percent of the FPL generally cannot be charged premiums for any plan. They also pay nominal amounts for drugs, and face more limited copayments for non-emergency use of emergency departments.1 Thus, with zero premiums and negligible out-of-pocket cost differences across plans, recipients should choose plans based on benefit quality in the absence of inertia.

Medicaid beneficiaries who are required to enroll in MCOs must be offered a choice of at least two plans, and those who do not select a plan are auto-enrolled in one. All but one of the 39 states with MCOs have an auto-enrollment process.2 The median state typically auto-enrolls 45 percent of new recipients, defaulting them into a particular MCO. Besides the universal goal of lowering program costs, states typically include factors such as past provider relationships, geographic location, and continuity with other family members into their auto-assignment process. In addition, 23 of the 38 states with auto-assignment attempt to balance enrollments among plans, while 15 states consider plan capacity. Only 8 states’ auto-assignment algorithms account for quality rankings. Auto-assignment, and the likelihood of at least some degree of inertia from such defaults, has important implications for the impact of adverse selection on MCO profitability and, thus market stability. In economic terms, one can think of the state’s objective as choosing an auto-assignment strategy as one of several policy tools that strikes some balance between promoting the stability of the Medicaid managed care market (plan balancing) and matching enrollees with the highest quality plans while at the same time minimizing costs.

In this paper we examine the impact of auto-assignment and health plan inertia (i.e. the extent to which auto-assignment predicts enrollment) on adverse selection and the subsequent impact of selection and plan payment on the stability of the Medicaid managed care market in Kentucky, which introduced statewide Medicaid managed care in 2012.3 The state auto-assigned enrollees to one of three plans then established a 90-day open enrollment period in which enrollees could switch. Using rich administrative data on all Medicaid enrollees in Kentucky, we analyze the impact of the auto-assignment algorithm selected by the state and enrollee responses to auto-assignment during open enrollment on the state’s Medicaid budget, the quality of the match between enrollees and the plan in which they ultimately enroll, and the profitability of the plans. Plan profitability is a key determinant of how well the market functions. Given this background, we attempt to answer the following specific questions: first, what weight does the state’s auto-assignment algorithm give to the competing objectives of plan balancing, maximizing enrollee-plan match, and minimizing costs? Second, to what extent do individual enrollees remain in their auto-assigned plan? Third, do differential degrees of mobility, or lack thereof, lead to adverse selection? Finally, with the inertia from auto-assignment, does selection threaten the stability of the market both before and after risk adjusted plan payments are implemented?4

Our analysis produces several strong conclusions. First, we find evidence that the state’s auto-assignment algorithm most heavily weighted cost considerations (i.e. lower capitation rates) and plan balancing, and placed less weight on quality of the enrollee-plan match. That is, instead of producing a “smart individual default” by maximizing quality of care for enrollees, the algorithm largely attempted to minimize costs, in a sense producing a “smart societal default” (from the point of view of taxpayers). For example, our simulation suggests that the algorithm selected by the state saved them over $31 million annually (approximately $200 per enrollee) as compared to the “smart individual default” algorithm. Second, from the state’s perspective, the presence of inertia contributed to the success of their cost-minimization strategy. Even in the lowest quality plans, more than half of auto-assigned enrollees did not opt-out, and the percentage was greater in the highest quality plans.5 Third, we observe a considerable degree of adverse selection, caused by lower levels of inertia among high cost enrollees. Although the share of enrollees that switched plans during open enrollment was small, the share of prior health care spending associated with those enrollees was large. Among individuals in the top 10 percent of the prior spending distribution, mobility across plans was dramatically higher regardless of initial plan assignment.

Given that such high-cost individuals comprise nearly 50 percent of all prior spending, such movements have important implications for the financial stability of the three plans. Our simulations suggest that the plan generally considered to be the highest quality incurred the largest initial reduction in profit margin due to adverse selection prior to risk adjustment, as it attracted a disproportionate share of high-cost enrollees during open enrollment. In addition, the plan considered to be lowest quality saw a large increase in profit margins, while a third plan lost money. The presence of such selection, caused by differential degrees of inertia between the healthy and the sick, raises concerns about the long run viability of the Medicaid managed care market in this context. The inertia from auto-assignment alone was clearly insufficient to ensure market stability. The state attempted to address these stability concerns with a subsequent round of budget-neutral risk adjustment to the capitation rates. Our simulations show risk adjustment did improve stability, as profits were non-negative for all three plans afterwards.

The rest of the paper is arranged as follows: Section 2 reviews the literature on inertia, with a focus on insurance markets, then Section 3 provides an institutional background on the transition to statewide Medicaid managed care in Kentucky. Section 4 presents an economic model of the choices faced by enrollees, the state, and the MCOs. Section 5 lays out our empirical strategy and Section 6 describes the administrative data we use to implement this strategy. Our results, including a series of policy simulations, are presented in Section 7. Section 8 discusses auto-assignment strategies and welfare implications, and Section 9 concludes the paper.

Section snippets

Literature review

There is an established inertia literature with studies on retirement plans (Madrian and Shea, 2001, Choi et al., 2002, Choi et al., 2004, Chetty et al., 2014, Messacar, 2014), organ donation (Johnson and Goldstein, 2003, Abadie and Gay, 2006), life insurance (Harris and Yelowitz, 2017), and income tax refunds (Jones, 2012). More closely related to this study, there has been evidence of inertia in health insurance decisions including Medicare Part D (Ketcham et al., 2012, Ericson, 2014, Ho et

Institutional background

The introduction of the Passport Health Plan (Passport) in November 1997 marked Kentucky Medicaid’s first major attempt to transition its enrollees into managed care coverage. Passport is a local non-profit MCO anchored by the University of Louisville hospital network. All Medicaid enrollees that live in the Louisville area (Region 3 in Fig. 1) were required to enroll in Passport.7

An economic model of insurance choice

Here we describe an economic model of insurance choice that borrows heavily from choice models presented in Handel (2013) and Handel and Kolstad (2015a), two papers that examine the employer-provided health insurance market. Like an employer, in our context the state/taxpayers serve as an intermediary between insurance providers/managed care organizations (MCOs) and those being covered (employees/Medicaid recipients). As mentioned in the previous section, the state contracted with MCOs and

Methods and identification strategy

We estimate models examining inertia from auto-assignment in the first year of open enrollment, and how differential degrees of inertia lead to adverse selection. We estimate linear probability models estimating inertia of the form:ENROLLipr=β0+β1ASSIGNipr+β2Xipr+δr+εipr.where ENROLLipr is an indicator for whether individual i in region r ultimately enrolled in plan p and ASSIGNipr indicates whether that individual was initially assigned to that plan. As discussed, there are three plans

Data

Given that the MMC auto-assignment process started in November 2011, we pulled from the Kentucky Medicaid administrative database all records for each enrollee continuously enrolled between January 2010 and March 2012 not living in region 3 of the state.29 This allows us to observe their pre-managed care (i.e.

Basic inertia results and adverse selection results

Table 7A provides the first pass at examining inertia, by estimating Eq. (4).34 In the full sample, it is clear that initial assignment matters for enrollment. Assignment to Wellcare increases the likelihood of enrollment in Wellcare by 83 percentage points, assignment in Coventry raises enrollment by 78 percentage points, and assignment in Spirit raises enrollment by 57 percentage points. Note

Auto-assignment strategies and welfare implications

In this section, we discuss how the state’s auto-assignment strategy might vary under different assumptions about the presence of mobility/inertia, the observability of mobility type, and the degree to which the state’s Medicaid budget is fixed. This discussion is based on our economic model and our empirical results. While identification issues prevent us from estimating the structural parameters of our economic model in the same fashion as Handel (2013), we believe our model does allow for a

Conclusions

In this paper we examine the impact of auto-assignment, adverse selection, risk adjustment, and health plan inertia on the functioning of the Medicaid managed care market in Kentucky. We find evidence that the state’s auto-assignment algorithm most heavily weighted on cost and plan balancing, and placed less weight on the quality of the enrollee-plan match. The presence of inertia contributed to the success of the state’s cost-minimization strategy, as more than half of enrollees assigned to

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  • Cited by (0)

    We would like to thank Tim Harris, Makayla Palmer, Minji Sohn, and Jaesang Sung for their research assistance. We would also like to thank David Agrawal, Amy Burke, Julia Costich, Embry Howell, Inas Rashad Kelly, Jenny Kenney, Ashley Palmer, Ben Ukert, Laura Wherry, the staff of the Foundation of Healthy Kentucky, the staff of the University of Kentucky Institute for Pharmaceutical Outcomes & Policy, and seminar participants at the University of Kentucky, University of Louisville, the Association for Public Policy Analysis and Management, the American Society of Health Economists, and the Southern Economic Association for their helpful advice and assistance. We are also grateful for comments from the referees and editor. Any errors are, of course, our own.

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