CHIPRA Mandated Evaluation of Express Lane Eligibility: First Year Findings. A. Background and Motivation


Express Lane Eligibility (ELE) has the potential to efficiently increase enrollment in Medicaid and the Children’s Health Insurance Program (CHIP) by allowing state Medicaid and CHIP agencies to use data already acquired by other agencies to determine program eligibility. In contrast to other enrollment and retention policies that have common structural features across states (for example, presumptive eligibility, continuous eligibility, and elimination of asset requirements), ELE programs have additional features that vary across states: they can apply to initial eligibility determination or redetermination; they can apply to Medicaid alone, CHIP alone, or both programs; they can apply to any Medicaid/CHIP eligibility factor other than citizenship; they can include or dispense with the need to submit a separate application for health coverage; and they can utilize different levels of technology and automation.

To understand ELE’s overall effect on enrollment, we analyzed 2007 to 2011 Medicaid and CHIP quarterly enrollment data available through the Statistical Enrollment Data System (SEDS) to assess changes in Medicaid and CHIP enrollment in states after ELE implementation, using changes occurring over the same period in other states as a counterfactual. This impact analysis relies on multivariate models to account for possible confounding policy, demographic, and economic changes, and time-invariant differences between ELE and non-ELE comparison states that might be driving Medicaid/CHIP enrollment changes and might otherwise be incorrectly attributed to ELE adoption or mask the effects of ELE. This is the first analysis of which we are aware that quantifies the impact of ELE policies adopted by eight states (Alabama, Georgia, Iowa, Louisiana, Maryland, New Jersey, Oregon, and South Carolina) under the Children’s Health Insurance Program Reauthorization Act of 2009 (CHIPRA).44

The multivariate analysis presented in this chapter accounts for changes in economic conditions and Medicaid and CHIP policies outside ELE that might otherwise bias estimates of the ELE effect. A recession that began in 2007 dominated the main period of analysis, the first fiscal quarter of 2007 to the fourth quarter of 2011, when unemployment rose, real personal income fell, and more people lived in families without a full-time worker. Economic conditions between 2009 and 2011 stabilized but remained depressed relative to conditions before the recession (Holahan and Chen 2011). The loss of coverage during economic downturns, such as during the most recent recession, is linked to declines in employment, and thus loss of employer-sponsored coverage. Not surprisingly, prior research has found strong links between the unemployment rate and the overall loss of coverage (Cawley and Simon 2003; Cawley et al. 2011; Holahan and Garret 2009). However, Medicaid and CHIP enrollment increases offset some losses in private coverage. In fact, the uninsured rate among children has declined slightly in recent years due to increased enrollment in Medicaid and CHIP (Blavin et al. 2012; Holahan and Chen 2011).

From 2007 to 2011, several states expanded Medicaid/CHIP eligibility to children from families with higher income and introduced changes to their enrollment and renewal processes, mostly aimed at reducing the number of children eligible for Medicaid and CHIP but who remain uninsured (Heberlein et al. 2012).45 Our main analysis controls for Medicaid/CHIP eligibility changes, joint application for Medicaid and CHIP, presumptive eligibility, administrative verification of income, elimination of in-person interviews, elimination of asset test requirements, and continuous eligibility. Prior research findings conclude that these enrollment and renewal simplifications can promote enrollment and continuous coverage (Wachino and Weiss 2009). Without controlling for changes in these policies, Medicaid/CHIP enrollment increases during the period of analysis might be incorrectly attributed to ELE. Appendix A of the detailed report to the Office of the Assistant Secretary for Planning and Evaluation (ASPE) on this issue describes the aggregate changes to these Medicaid/CHIP policies among ELE and non-ELE states (Blavin et al. 2012).

This chapter addresses the following questions:

  1. Does the implementation of ELE have a positive effect on combined Medicaid/CHIP or Medicaid-only enrollment? If so, how large are the enrollment gains?
  2. Are enrollment effects similar across different types of ELE programs?
  3. To what extent are enrollment effects robust within the subset of states that implemented ELE?
  4. If there are positive enrollment impacts, do they appear to be sustained over time?

The next sections describe the data, methodological approach, and results. The concluding section summarizes the key findings, discusses the policy implications, and describes the limitations of this analysis.

44 Prior studies have used descriptive or qualitative methods to examine the experiences of a single state (for example, Louisiana in Dorn et al. [2012]) or the experiences of early adopting ELE states (for example, reviews of ELE policies in Alabama, Iowa, Louisiana, and New Jersey in Families USA [2011]).

45 Although the Balanced Budget Act that authorized CHIP defined very narrow CHIP income limits—not more than 50 percentage points above the state’s Medicaid income-eligibility threshold as of March 31, 1997, and not more than 200 percent of the FPL—a state can exceed these rules by disregarding certain income, thus permitting states to expand eligibility to children of higher income (Balanced Budget Act of 1997 Section 2110; Hess et al. 2011).

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