CHIPRA Mandated Evaluation of Express Lane Eligibility: First Year Findings. B. Implications of First-Year Findings Related to Enrollment


A second main policy inquiry regarding ELE is whether it increases enrollment. Because no two ELE programs operate in the same way, the analysis of enrollment is challenging. In the descriptive analysis of new enrollment through ELE (discussed in Chapter III), we find wide variation in the numbers of enrollments processed through ELE; ELE programs that automatically enroll children they find likely eligible (as in Louisiana) process a much larger fraction of their children through ELE than states using the policy mainly as a tool for targeting outreach but still require the family identified to send in an application for coverage (as in Iowa Medicaid and New Jersey). Interestingly, children enrolled via ELE were older and, in the case of Iowa Medicaid, less likely to have had a recent spell of coverage than non-ELE children, suggesting that the policy may be picking up some children who are traditionally harder to reach.

Except for Louisiana, where we observe a noticeable spike in both overall new enrollments and ELE-based new enrollments around the adoption of the policy, we see little descriptive evidence from the new enrollment analysis that ELE increased the number of children added to coverage. We likewise see little evidence of differences in retention between ELE and non-ELE new enrollees, again except for Louisiana, where roughly half of all ELE new enrollees exited coverage within 12 months due to a temporary policy requiring them to use their Medicaid card within 12 months as a form of “opt-in” consent. Because the descriptive analysis can provide no estimate of the counterfactual—that is, what enrollment in ELE states would have been without the policy—it may significantly underestimate the effects of the policy on coverage. Moreover, because the descriptive analysis focused only on new enrollments tied directly to ELE, it could not account for enrollment gains arising indirectly from the policy, nor could it fully assess those affected by ELE renewal processes. These limitations are relevant for several states, including Alabama and Louisiana (both of which recognized time savings for non-ELE processes as a result of ELE) and New Jersey (where ELE has led to more extensive use of tax information, a “spillover” that cannot be identified in our data).

In contrast to the descriptive new enrollment data from the ELE states, the multivariate analysis, which combines aggregate total enrollment data for both ELE and non-ELE states, finds significant evidence of an ELE effect on total enrollment. Drawing on a range of models, we find that ELE increased total Medicaid enrollment by an estimated 5.5 percent and Medicaid and CHIP enrollment combined by an estimated 4.2 percent. Findings for Medicaid were notably robust, showing little variation in magnitude and remaining statistically significant across many sensitivity tests. However, as in all impact studies using regression methods to estimate the counterfactual, our multivariate findings might be biased from unobserved factors that differ between ELE and non-ELE states over time.

Taken together, these first-year findings suggest that ELE can be an effective tool for enrolling children into coverage. However, because evidence that ELE significantly increased enrollment is stronger in the multivariate findings than in the descriptive findings, it will be critical to revisit both analyses in the evaluation’s second year, as more post-ELE enrollment data become available. The case studies will further support these second-year analyses, permitting a more thorough understanding of other simplifications and the trends in enrollment under ELE. Overall, there is some uncertainty about the magnitude of the ELE effect due to potential biases in the descriptive and multivariate analyses, but both first-year studies independently find evidence that ELE had a positive effect on enrollment.

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