The findings presented in this report should be considered tentative; because ELE is so new and so varied, in its potential uses, and its implementation has been limited to a handful of States, it is too soon to draw conclusions about its effects on administrative costs or enrollment. In many instances, we need the second-year evaluation activities to assess the robustness of findings from this first year and to further elucidate the reasons behind, and meaning of, most of them. In addition, although this report looks at ELE’s implementation, ELE might have differential longer-term effects. For example, as ELE processes mature, the costs and savings that accrue to States could change. The second-year evaluation activities will provide a longer post-implementation period and offer more extensive data to assess both short- and longer-term policy effects. We also will be better able to distinguish between inherent features of ELE and issues that arise based on particular State choices about how to implement this new option.
Future analyses will both extend and assess the robustness of the first-year findings on administrative costs and enrollment by including additional states in these analyses and focusing on a longer period post-ELE implementation. First-year findings regarding retention rates are particularly limited and also mixed; for example in Iowa Medicaid, retention rates were higher for those entering through ELE, but in Louisiana and Iowa’s separate CHIP program, retention rates were lower. In Alabama, there was no discernible difference in retention between ELE and those who entered through the standard route. It is likely that, in Iowa’s separate CHIP program, the cost-sharing policy in CHIP is affecting retention rates. Because Louisiana changed its policy regarding ELE renewal, moving from affirmative consent to an opt-in policy for data matching, we are eager to study the effect of this state policy change on retention rates there, and will be able to do so in the second year.
In addition, at the Federal level, future analyses will examine how the CHIPRA performance bonuses may have influenced ELE adoption and whether and how this policy might be modified in the future. Congress specified that states that implemented at least five out of eight simplifications (one of which was ELE) and that increased Medicaid enrollment by a specified threshold could qualify for bonus funds. These funds represent a significant Federal investment: more than $500 million has been awarded in the first three years (2009 through 2011) to 23 states. All 9 ELE states are among these 23 states; 3 of them (Georgia, Maryland, and South Carolina) needed ELE to meet the 5 of 8 threshold (the other states would have met the 5 of 8 policy criteria without having ELE in place). The case studies will help us better understand whether the availability of funds acted as an incentive to implement ELE, and further assess whether this new investment in states that implemented ELE is warranted or needs adjustment, given enrollment outcomes using ELE methods.
A further important question for future evaluation work regards ELE’s potential value following implementation of the Affordable Care Act in 2014. States have a compressed timeline with which to prepare to enroll, by Congressional Budget Office estimates, approximately 9 million subsidized individuals through the Affordable Insurance Exchanges and 7 million individuals into Medicaid or CHIP as of 2014, rising to 23 and 10 million, respectively, by 2016 (Congressional Budget Office 2012). Asked about the potential for ELE to benefit states in meetings these targets, administrators who had implemented ELE felt it was too soon to answer this question, but that ELE programs and experiences likely would be useful in the context of Affordable Care Act implementation.
Finally, as the research on this project unfolds, we expect to learn more about whether ELE supports or compromises program integrity. Program integrity involves the incorrect application of a program’s eligibility rules to a particular household. As this is an important policy concern, states will report their error rates to CMS, and these will be included in the final Report to Congress on ELE submitted in September 2013.