An Early Look at the Impact of Express Lane Eligibility on Medicaid and Children’s Health Insurance Program Enrollment: An Analysis of the Statistical Enrollment Data System June 4, 2012 By Fredric Blavin, Genevieve M. Kenney, and Michael Huntress Acknowledgements: This study was supported by contract number HSP23320095642WC /HHSP23337026T from the Assistant Secretary for Planning and Evaluation, Department of Health and Human Services. The opinions and conclusions expressed in this article are those of the authors and do not necessarily represent the views of the funder, the Urban Institute, or its sponsors or trustees. This research benefited from the helpful insights of Lisa Dubay, Douglas Wissoker, Timothy Waidmann, Rick Kronick, Ben Sommers, Sheila Hoag, and Christopher Trenholm; assistance from Stan Dorn, Sean Orzol, and Maggie Colby in understanding the specifics of the Express Lane Eligibility Policies that were implemented in each state; assistance from Matthew Hodges at Mathematica and Jeffrey Silverman at CMS who helped with the cleaning of the SEDS data; and Jenny Haley and Christine Coyer for verifying state information for other policy variables. Abstract 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. This report uses 2007 to 2011 quarterly data from the Statistical Enrollment Data System (SEDS) to measure the effects of ELE on total Medicaid and CHIP enrollment. SEDS is a web-based system maintained by CMS to collect Medicaid and CHIP enrollment data from states on a quarterly basis since 2000. During the period of analysis, eight states implemented ELE, ranging from policies that coordinate eligibility with the Supplemental Nutritional Assistance Program (Alabama, Iowa, Louisiana, Oregon, South Carolina), the National School Lunch Program (Louisiana, New Jersey, Oregon), or the Special Supplemental Nutrition Program for Women, Infants and Children (Georgia) to targeted outreach policies through income tax returns (Maryland, New Jersey). We estimate difference-in-difference equations (separate models for total Medicaid/CHIP enrollment and Medicaid only) with quarter and state fixed effects to measure the effect of ELE implementation on child enrollment, where the dependent variable is the natural log of enrollment in each state and quarter. The key independent variable is an indicator for whether or not the state had ELE in place in the given quarter, allowing the experience of matched non-ELE states to serve as a formal counterfactual against which to assess the changes in the ELE states. The model also controls for time varying factors within each state, such as the unemployment rate, population levels, Medicaid/CHIP eligibility changes, and the implementation of other state-level Medicaid/CHIP enrollment simplification policies. We estimate alternative model specifications to assess the robustness of the estimated ELE impacts and find significant evidence that ELE implementation increased Medicaid and Medicaid/CHIP enrollment. The estimated impacts of ELE on Medicaid enrollment were consistently positive, ranging between 4.0 and 7.3 percent, with most estimates statistically significant at the 5 percent level. Overall, these estimates had a central tendency of about 5.5 percent. The analyses also find evidence that ELE increased Medicaid/CHIP enrollment. Across a series of models, estimated impacts were again consistently positive, though less often statistically significant, with a central tendency of about 4.2 percent. Our results imply that ELE has been an effective way for states to increase new enrollment or improve the ease of retaining coverage among children eligible for Medicaid or CHIP. Overview Nearly 4.7 million uninsured children are eligible for Medicaid or the Children’s Health Insurance Program (CHIP) (Kenney et al. 2010). Prior research attributes non-participation in Medicaid and CHIP—through low take-up or poor retention—to a host of factors, including lack of information about program eligibility, administrative hassle, and policy design complexities (Currie 2006; Remler and Glied 2003). To address some of these barriers, the Children’s Health Insurance Program Reauthorization Act of 2009 (CHIPRA) gave states the option to implement Express Lane