CHIPRA Mandated Evaluation of Express Lane Eligibility: First Year Findings

Publication Date

Nov 30, 2012

By:
Sheila Hoag, Sean Orzol, Margaret Colby, Adam Swinburn, Fredric Blavin, Genevieve M. Kenney, Michael Hutnress
Mathematica Policy Research, Inc.

Abstract

This evaluation describes existing state Express Lane Eligibility (ELE) programs including the administrative costs and ELE enrollment trends, estimates the impact of ELE adoption on total enrollment, and previews the issues that will be examined through future evaluation activities.

Disclaimer

This study was conducted by the Urban Institute under contract number [CONTRACT_NUMBER], task order number [TASK_ORDER_NUMBER], with the HHS's Office of Assistant Secretary for Planning and Evaluation. The authors take full responsibility for the accuracy of material presented herein. The views expressed are those of the authors and should not be attributed to ASPE or HHS.

Executive Summary

The Children’s Health Insurance Program (CHIP), a landmark initiative to help close the health insurance coverage gap for low-income children, celebrated its 15th anniversary in August 2012. Together with Medicaid, CHIP has helped fuel a decline in the number of uninsured children, which has fallen from 11.4 million (15.1 percent of children) in 1997 when CHIP was enacted to 8.0 million (10.0 percent of children) in 2010 (Current Population Survey, Annual Social and Economic Supplement 2011). In February 2009, the Children’s Health Insurance Program Reauthorization Act of 2009 (CHIPRA) reauthorized CHIP and funded it through 2013. Funding for CHIP was further extended to 2015 by the Patient Protection and Affordable Care Act and the Health Care and Education Reconciliation Act of 2010 (collectively referred to as the Affordable Care Act).

Congress provided states with new tools and new funds through CHIPRA to address shortfalls in enrollment, as well as in access to, and quality of, care (Harrington et al. 2011). One of these new options is a policy that implements CHIPRA section 203 called Express Lane Eligibility (ELE). With ELE, a state’s Medicaid and/or CHIP program can rely on another agency’s eligibility findings to qualify children for public health coverage, despite their different methods of assessing income or otherwise determining eligibility. ELE thus gives states another way to try to identify, enroll, and retain children who are eligible for Medicaid or CHIP but who remain uninsured. The concept of using data from existing government databases and other means-tested programs to expedite and simplify enrollment in CHIP and Medicaid has been promoted for more than a decade; before CHIPRA, however, Federal law limited state reliance on information from other agencies (Families USA 2010; The Children’s Partnership 2012). CHIPRA also gave states an incentive to implement ELE by making it one of the eight policies states could adopt to qualify for performance bonus payments (CHIPRA section 104).

The ELE option is intended to simplify the identification, enrollment, and retention of uninsured children eligible for CHIP or Medicaid. In turn, this simplification process may produce gains in coverage, as families who might otherwise not apply for (or renew) coverage for their eligible children, or might not complete this process successfully, are able to do so. Although prior research on ELE’s possible coverage effects is limited, the available evidence supports the potential of the policy to produce meaningful gains in coverage. For example, using ELE to qualify children for health coverage based on their participation in the Supplemental Nutrition Assistance Program (SNAP), Kenney et al. (2010) estimate that ELE could reach 15.4 percent of eligible, uninsured children. Using ELE to qualify children for health coverage based on state income tax records could reach even more children: an estimated 89 percent of uninsured children who qualify for Medicaid or CHIP live in families who file Federal income tax returns (Dorn 2009). Presumably, a large proportion of these families file state tax returns as well, particularly in states that supplement the Federal Earned Income Tax Credit. A recently published descriptive analysis of the ELE program in Louisiana reported that state officials attribute the decline in the percentage of uninsured children who qualified for Medicaid from 5.3 percent in 2009 to 2.9 percent in 2011 to the state’s ELE policy, implemented in February 2010 (Dorn et al. 2012).

CHIPRA requires an evaluation of ELE, with reports of evaluation findings to be submitted to Congress. In September 2011, a contract was awarded to Mathematica Policy Research and its subcontractors, the Urban Institute and Health Management Associates, to conduct the evaluation, which is being overseen by the Office of the Assistant Secretary for Planning and Evaluation (ASPE).

CHIPRA specifies that the ELE evaluation include four components:

An evaluation of the administrative costs or savings related to identifying and enrolling children through ELE methods compared to the costs of identifying and enrolling eligible but unenrolled children through the state’s regular methods
An assessment of whether ELE improves a state’s ability to identify and enroll eligible but unenrolled children
Recommendations for legislative or administrative changes that would improve ELE’s effectiveness as a method for enrolling or retaining children in Medicaid or CHIP
A report on the percentage of children erroneously enrolled in Medicaid or CHIP based on the Express Lane agency findings

This interim report is the first of two reports to Congress that will fulfill the statutory requirements; it addresses the first of the four CHIPRA-specified components, with the latter three components to be addressed in the second report to Congress, due in September 2013. This report has three purposes:

To describe existing ELE programs, including the costs and new enrollment trends associated with ELE implementation
To estimate the impact of ELE adoption on total enrollment
To preview the issues that will be examined through future evaluation activities and presented in the final evaluation report, due to Congress in September 2013

This report describes the nine programs approved for ELE as of April 2012, providing a descriptive analysis of the costs associated with ELE implementation in six of these states (Alabama, Iowa, Louisiana, Maryland, New Jersey, and Oregon); it also provides a descriptive analysis of the new enrollment trends in Alabama, Iowa, Louisiana, and New Jersey; it estimates the impact of ELE adoption on total enrollment in eight ELE states (Alabama, Georgia, Iowa, Louisiana, Maryland, New Jersey, Oregon, and South Carolina); and it previews issues that will be examined through future evaluation activities and presented in the final evaluation Report to Congress to be submitted in September 2013. This report draws on primary and secondary data sources, including qualitative data collected through interviews with program administrators in six states, and quantitative (enrollment) data obtained both directly from selected ELE states and from the CHIP Statistical Enrollment Data System (SEDS), a reporting system maintained by the Centers for Medicare & Medicaid Services (CMS) since 2000 that collects new and total Medicaid and CHIP enrollment data from all states on a quarterly basis. The report also draws on other secondary data to support the qualitative analysis, including published and unpublished literature on CHIP, CHIPRA, and ELE, to supplement the main data sources and to provide motivation and context for the findings in each chapter. Sources include state plan amendments, ELE and standard application forms, and state budget and performance reports. And, likewise, to support the analysis of the SEDS data, the report draws on several sources, including data from the Bureau of Labor Statistics, U.S. Census Bureau, and Kaiser Family Foundation.

Ele Implementation Options for States

Section 203 of CHIPRA authorizes ELE and permits states to rely on findings of other public agencies to determine whether a child satisfies one or more requirements for Medicaid or CHIP eligibility. In doing so, states can disregard technical differences in how these programs define the household members whose earnings are considered in determining eligibility, as well as other methodological differences in assessing whether children meet applicable requirements (Dorn et al. 2012). ELE can be used to meet any eligibility criterion except U.S. citizenship.

Under the statute, states can choose to partner with any of 11 specific types of state agencies; states also can select an unlisted agency that fits the definition of an Express Lane agency; and if they choose, states can obtain and use information directly from state income tax records or returns (CHIPRA section 203; Center for Medicaid and State Operations 2010). With ELE, not only can the Express Lane agency vary, but so can ELE features. For example, states can apply ELE to CHIP and/or Medicaid, with a focus on enrollment, renewal, or both. states also can use traditional approaches to CHIP “screen and enroll requirements” or they can choose one of two alternative approaches for meeting these requirements. In addition, in pursuing ELE, states can choose to include or exclude an “automatic enrollment” option, possible when states have all the information they need from the Express Lane agency findings to make an eligibility determination or renew coverage, without the need for an application for coverage.

CHIPRA included several protections for families who might be subject to ELE. For example, Express Lane agencies must notify families that their information will be shared with the Medicaid or CHIP agencies, solely to determine Medicaid or CHIP eligibility, and families must be able to opt out of sharing this information (CHIPRA section 203). To use the automatic enrollment option, states must obtain the family’s consent to enroll the child, and the family must be informed about the available services, how to access them, if there is cost sharing, how to maintain the coverage, etc. In addition, for children subject to premiums or cost sharing (common in CHIP programs), the state must provide notice to the family that the child might qualify for lower premiums or cost sharing if the child were evaluated for eligibility using “regular” procedures (Center for Medicaid and State Operations 2010). ELE cannot be used to deny coverage; CHIPRA requires states to initiate a standard eligibility determination for Medicaid and CHIP for any child found ineligible through the use of ELE.

Findings

States have implemented diverse ELE programs.

As of April 1, 2012, CMS had approved nine states (Alabama, Colorado, Georgia, Iowa, Louisiana, Maryland, New Jersey, Oregon, and South Carolina) to implement ELE. Five of the states (Alabama, Colorado, Louisiana, Maryland, and South Carolina) are approved for ELE in their Medicaid programs, while four (Georgia, Iowa, New Jersey, and Oregon) are approved for ELE in both Medicaid and their separate CHIP programs. Even among this small number of states, the striking variation in ELE designs reflects the flexibility that CHIPRA afforded. For example, states’ partner agencies range from those administering other public benefits—such as the National School Lunch Program (NSLP) (New Jersey and Colorado), SNAP (Alabama, Iowa, Louisiana, Oregon, and South Carolina), Temporary Assistance for Needy Families (TANF) (Alabama and South Carolina), and the Special Supplemental Nutrition Program for Women, Infants, and Children (WIC) (Georgia)—to those responsible for aspects of state revenue, including the Division of Taxation (New Jersey) and the Office of the Comptroller (Maryland). Tables 1 and 2 summarize the key features and aims of the ELE programs in the six states included in the descriptive analysis of the costs and new enrollment associated with ELE implementation.

Table 1. Features and Implementation Status in Six States with Approved ELE Programs as of December 2010

State Program Using ELE	Alabama Medicaid	Iowa Medicaid	Iowa Separate CHIP	Louisiana Medicaid	Maryland Medicaid	New Jersey Medicaid/CHIP	Oregon Medicaid/CHIP
Partner Agency	SNAP and TANF	SNAP	Medicaid	SNAP	State tax agency	State tax agency^a	SNAP^b
Program Strategy	SNAP and TANF income findings used to establish income after consumer declarations at application and renewal.	Data match to identify potentially eligible children; shortened application form mailed out; SNAP findings establish income eligibility.	Medicaid applications and redeterminations showing CHIP-level income automatically referred to CHIP for processing using Medicaid eligibility findings.	Automated eligibility, enrollment, and renewal based on SNAP findings.	Potentially eligible children identified; shortened application form mailed out. State income tax returns used in process to establish state residence.	Data match to identify potentially eligible children; shortened application form mailed out. State income tax returns establish income eligibility.	Data match to identify potentially eligible children; shortened application form mailed out. SNAP findings establish income eligibility.
Prior Data Sharing with Partner Agency	X	X	X	X		X	X
State Plan Amendment Approval Date: Applications	Jun-10	Jun-10	Jun-11	Jan-10	Sep-10	Jun-09	Oct-10
Implemented for Applications^c	Apr-10	Jun-10	Jul-04	Feb-10	Sep-08	May-09	Aug-10
State Plan Amendment Approval Date: Renewals	Nov-09		^d	Jan-10
Implemented for Renewals^c	Oct-09		^d	Nov-10
Implementation Status as of December 2011	Partial	Full	Full	Full	Partial	Full	Full
Enrollments as of December 2011^e	50,257	2,065	33,427^f	20,240	Unknown	5,321	4,632^g
Renewals as of December 2011^e	150,000			200,000

Source: Mathematica interviews with state staff conducted between January and May 2012 and analysis of state administrative data provided by states between January and June 2012.

^aNew Jersey also has an NSLP ELE program, which has been implemented in all counties as of October 2011. Because it was not in place as of December 2010, the NSLP partnership is excluded from this initial analysis.

^b As of December 2011, Oregon was also piloting an NSLP ELE program in four school districts.

^c Implementation dates are those recognized by CMS as the program’s “Effective Date” unless available information suggests that a different date more accurately reflects the timing of meaningful changes. Implementation dates for Alabama, Iowa Medicaid, New Jersey, and Oregon are the CMS effective dates. The date for Iowa CHIP was given by state staff in an interview. For Louisiana, the date reflects when children were first enrolled via ELE. For Maryland, the 2008 date reflects the first outreach mailings on the basis of tax return information, which is the event considered by state staff to be the start of ELE in that state; this date also is referenced in Idala et al. (2011). Maryland’s process was not recognized as ELE by CMS until 2010, when the state started using the mailing process to establish in-state residency.

^eEnrollment counts were computed from state administrative data and reflect the number of enrollments resulting from ELE application processes. Renewal counts reflect estimates provided by state staff during interviews of the approximate number of renewals resulting from ELE renewal processes.

^f We requested enrollment data from states for the period beginning one year prior to the ELE program effective date. Because CMS recognizes the effective date for Iowa CHIP’s ELE process as June 2010, we present data on enrollments from June 2009 through December 2011. The number of enrollees since the ELE process began in 2004 is likely much higher.

^g As of November 2011.

CHIP = Children’s Health Insurance Program; CMS = Centers for Medicare & Medicaid Services; ELE = Express Lane Eligibility; NSLP = National School Lunch Program; SNAP = Supplemental Nutrition Assistance Program; TANF = Temporary Assistance for Needy Families.

Table 2. Aims of ELE in Six States with Approved ELE Programs as of December 2010

State Program Using ELE	Alabama Medicaid	Iowa Medicaid	Iowa Separate CHIP	Louisiana Medicaid	Maryland Medicaid	New Jersey Medicaid/CHIP	Oregon Medicaid/CHIP
Reduce staff time	X	X	X	X		X	X
Improve outreach		X		X	X	X	X
Simplify application experience	^a	X	X	X	X	X	X
Simplify renewal	^a			X
Smooth transitions between Medicaid and CHIP			X

Source: Mathematica interviews with state staff conducted between January and May 2012.

^aSince April 2010, self-declaration of income has been accepted for most ELE and non-ELE children, if income cannot be verified through databases accessible to state eligibility staff. This is not the case for children of self-employed parents, for whom income verification is required. Thus, ELE reduces the documentation burden for children with self-employed parents whose income cannot be verified via other databases but can be verified via SNAP or TANF databases.

CHIP = Children’s Health Insurance Program; ELE = Express Lane Eligibility; SNAP = Supplemental Nutrition Assistance Program; TANF = Temporary Assistance for Needy Families.

ELE has benefited applicants, reducing documentation requirements and expediting the eligibility determination process for families.

Across the seven ELE programs implemented as of December 2010, Medicaid and CHIP staff cited a number of potential benefits to applicants from their ELE programs. In five of the seven ELE programs (all except Alabama and Maryland), ELE required that applicants submit fewer documents in support of their ELE applications compared to standard applications, and required fewer interactions with state staff (Table 3). In addition, in six programs (all but Maryland), ELE applications were processed more quickly than standard applications, which resulted in expedited eligibility determinations for families. These expedited processes shortened the typical time from application to enrollment for an eligible child from roughly three or four weeks to one week or less.

Some states also noted that ELE has improved the application experience of non-ELE applicants. For example, in New Jersey, the use of tax information in ELE has led to more extensive use of tax information in standard application processing. Specifically, people applying via the standard route are permitted to attest to income and are not required to provide pay stubs if their attestation matches tax return data. Also, in Alabama and Louisiana, state staff suggested that the time saved by diverting some applications to ELE routes has meant that standard processing times for non-ELE applications are quicker than they would be otherwise.

Administrative savings or costs of ELE varied widely. States that used ELE to process large numbers of children were better able to generate net savings quickly.

Net administrative costs or savings differed widely across the six early states adopting ELE (Figure 1) (although these findings should be reviewed with caution given methodological challenges computing administrative costs and savings, including recall bias, incomplete data, and other process changes in the study period). For example, the ELE programs in Alabama and Louisiana yielded large net annual savings, driven by the staff time saved through processing large numbers of ELE applications and renewals in a more efficient manner than possible through traditional means. By contrast, in Maryland, ELE did not generate annual savings (for example, in staff time) that could offset new mailing costs associated with the policy, resulting in a net cost to the state from the policy. And, in New Jersey, targeted mailings cost the state about $250,000 per year, but no savings from ELE accrues to the state; any savings generated from more efficient ELE application processing are absorbed by a third-party administrator. Finally, ELE programs in Oregon and Iowa Medicaid were essentially “cost neutral” from an administrative perspective, as added mailing costs and the time spent processing unsuccessful ELE applications offset the time savings from processing successful ELE applications more efficiently than by traditional means.

Notably, in no case did program staff describe ELE as directly resulting in the need to hire additional staff or the ability to eliminate staff positions. Any time savings resulting from ELE were instead used to address other program needs. For example, Louisiana saved about 69,000 staff hours per year from ELE—the equivalent of 33 full-time positions—but it used the savings to make up for staffing reductions caused by state budget cuts. Staff described ELE as enabling Medicaid to stay on top of its workload in the face of reduced staffing. Similarly, Alabama noted that staff time saved through ELE may have enabled staff to process the traditional applications more quickly than would otherwise have been possible.

Table 3. Perceived Benefits of ELE for Applicants

State Program Using ELE	Alabama Medicaid	Iowa Medicaid	Iowa Separate CHIP	Louisiana Medicaid	Maryland Medicaid	New Jersey Medicaid/CHIP	Oregon Medicaid/CHIP
Supporting Documentation Requested of Applicants Through ELE Method	Identity Citizenship Social Security Medical coverage (if applicable)	None	Medical coverage (if applicable)	None	Income Citizenship/ immigration status Social Security Expenses	None	Medical coverage (if applicable)
Supporting Documentation Requested of Applicants Through Standard Method	Identity Citizenship Social Security Medical coverage (if applicable)	Identity Income Citizenship Social Security	Identity Income Citizenship/immigration status Social Security Medical coverage (if applicable)	Income Citizenship/ immigration status Bank accounts Medical coverage (if applicable)	Income Citizenship/ immigration status Social Security Expenses	Income Citizenship/ immigration status Medical coverage (if applicable)	Income Citizenship/ immigration status (in some cases) Social Security Employment status Medical coverage (if applicable)
Time from Application Receipt to Coverage (Days) Through ELE Method	<6	2	5^a	<1	No difference	7	3
Time from Application Receipt to Coverage (Days) Through Standard Method	<25	<30	20	<30	No difference	30	9
Interactions with the State	No difference	Fewer	Fewer	Fewer	No difference	Fewer	Fewer

Source: Mathematica analysis of interviews with state staff. Information about documentation requirements reflects interviews with state staff supplemented by a review of Medicaid and CHIP websites and application materials.

^aELE enrollees to the separate CHIP program are enrolled roughly five days after the referral from Medicaid; however, coverage is retroactive to the Medicaid filing date.

CHIP = Children’s Health Insurance Program; ELE = Express Lane Eligibility.

Figure 1. Net Annual Administrative Savings and (Costs) from ELE

Figure 1. Net Annual Administrative Savings and (Costs) from ELE

Source: Mathematica analysis of Interviews and follow-up correspondence with state staff between January and June 2012.

ELE = Express Lane Eligibility.

ELE appears to have increased enrollment, at least in some states, but the magnitude of the increase is uncertain.

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 (although among children who were added, we observe sizable differences in the distribution of age by enrollment pathway, suggesting that ELE may have been successful at enrolling some traditionally hard-to-reach older children). 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. However, 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 have adopted ELE for renewals) 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 these descriptive findings, 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, the analysis finds 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 with 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.

Any administrative savings or enrollment gains achieved through ELE appear less related to the type of partner agency and more related to the processes in place to use partner agency information and whether the partner agency is likely to have data on chi

In our discussions with administrators, several themes emerged about what factors facilitated ELE process improvements. Among them, states should think carefully about their selection of a viable Express Lane agency partner: it is a critical first step in the ELE process. First-year findings do not suggest that partnering with a certain type of agency—such as SNAP, TANF, Medicaid, or state tax agency—makes cost savings or enrollment gains more likely. Rather, it is likely tied to the processes for using partner agency information and whether that agency has data on large numbers of eligible-but-uninsured children or enrolled children up for renewal. States might consider “test” data matches with potential partners to understand how ELE might function in practice. For example, besides partnering with SNAP and TANF, Alabama considered an ELE partnership for its CHIP program with the state’s child care subsidy program because the income eligibility levels for both programs are similar. Through a test data match, Alabama administrators found that few eligible children were identified through the child care subsidy program, and that, given its older information systems, the costs of the systems changes that would be needed far outweighed the potential gains to coverage.

State administrators also said it was easier to implement ELE processes when they could build upon existing relationships, because of the familiarity with agency staff and their operations, as well as the existence of data use agreements that could easily be modified to accommodate ELE. Of the seven ELE programs studied as part of the qualitative analysis, six chose an Express Lane partner with which they had a prior data-sharing relationship.

Finally, in states where the ELE process uses partner agencies’ findings as a means to target application mailings, we find far fewer ELE-linked enrollments. These more tepid findings suggest that this outreach-focused approach to ELE, which requires parents to respond to mailings, may offer less promise as a means for enrolling large numbers of new children.

Future evaluation work will extend the first-year findings and inform how ELE policies can best be designed at the Federal and state levels.

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.

I. the Federal Evaluation of Express Lane Eligibility

Initially authorized with bipartisan support through the Balanced Budget Act of 1997, CHIP was set to expire in 2007 unless reauthorized by Congress. Congress gave CHIP a temporary reprieve in December 2007: the Medicare, Medicaid, and SCHIP Extension Act of 2007 extended the program and funded it through March 2009 (Kaiser Commission on Medicaid and the Uninsured 2009). In February 2009, the Children’s Health Insurance Program Reauthorization Act of 2009 (CHIPRA) reauthorized CHIP and funded it through 2013. Funding for CHIP was further extended to 2015 by the Patient Protection and Affordable Care Act and the Health Care and Education Reconciliation Act of 2010 (collectively referred to as the Affordable Care Act).

Concerned about the many children eligible but not enrolled in public coverage—estimated at 4.7 million children as of 2008 (Kenney et al. 2010)—Congress provided states with new tools and new funds through CHIPRA to address shortfalls in enrollment, as well as in access to, and quality of, care (Harrington et al. 2011). One of these new options is a policy called Express Lane Eligibility (ELE). With ELE, a state’s Medicaid and/or CHIP program can rely on another agency’s eligibility findings to qualify children for public health coverage, despite their different methods of assessing income or otherwise determining eligibility. ELE thus gives states another way to try to identify, enroll, and retain children who are eligible for Medicaid or CHIP but who remain uninsured. The concept of using data from existing government databases and other means-tested programs to expedite and simplify enrollment in CHIP and Medicaid has been promoted for more than a decade; before CHIPRA, however, Federal law limited state reliance on information from other agencies by requiring such information to be cross-walked into the Medicaid and CHIP eligibility methodologies (Families USA 2010; The Children’s Partnership 2012).

Although the Affordable Care Act extended CHIP funding to 2015, CHIPRA slated the ELE policy option to end September 30, 2013, unless Congress acts to extend or modify it. Despite this possible end to the policy option, CHIPRA also gave states an incentive to adopt ELE by making it one of the eight policies states could adopt to qualify for performance bonus payments (CHIPRA section 104).¹

¹ Section 104 of CHIPRA specifies that the performance bonus money is intended to offset the additional costs resulting from enrollment and retention efforts. To qualify for CHIPRA performance bonuses, states must increase Medicaid enrollment beyond a specified target and implement at least five of eight administrative policies considered best practices for simplifying enrollment and renewal. See http://insurekidsnow.gov/ (2011) for more information on the bonus criteria.

A. Purpose of This Report

CHIPRA further authorized a comprehensive, independent, and rigorous evaluation of ELE, with reports of evaluation findings to be submitted to Congress; this is the first of two reports that will fulfill the mandate. This report has three purposes:

To describe existing ELE programs, including the costs and new enrollment trends associated with ELE implementation
To estimate the impact of ELE adoption on total enrollment
To preview the issues that will be examined through future evaluation activities and presented in the final evaluation report, due to Congress in September 2013

Federal and state policymakers are keenly interested in understanding the full implications of ELE as a route to enrolling children, or keeping them enrolled, in public coverage. This evaluation provides an important opportunity to document ELE implementation and understand the implications of adopting the policy. In its later phases, the study will also provide an opportunity to understand other methods of simplified or streamlined enrollment that states have pursued and to assess the benefits and potential costs of these methods compared with those of ELE. Taken together, findings from the study will help Congress and the nation better understand ELE and assess its value.

B. What Is Express Lane Eligibility (ELE)?

Section 203 of CHIPRA authorizes ELE and permits states to rely on findings of other public agencies to determine whether a child satisfies one or more requirements for Medicaid or CHIP eligibility. In doing so, states can disregard technical differences in how these programs define the household members whose earnings are considered in determining eligibility, as well as other methodological differences in assessing whether children meet applicable requirements (Dorn et al. 2012). The criteria for Medicaid and CHIP eligibility include income, age, residency, and immigration status or U.S. citizenship; ELE can be used to meet any of these except U.S. citizenship.²

In adopting ELE, states can choose to partner with any of 11 listed state agencies; states also can select an unlisted agency that fits the definition of an Express Lane agency; and states also can obtain and use information directly from state income tax records or returns. Based on guidance from the Centers for Medicare & Medicaid Services (CMS), this definition includes an agency determining eligibility for assistance through any of the following programs: Temporary Assistance for Needy Families (TANF); child support enforcement; Medicaid; CHIP; Supplemental Nutrition Assistance Program (SNAP); the National School Lunch Program (NSLP); the Special Supplemental Nutrition Program for Women, Infants, and Children (WIC); the United States Housing Act of 1937; Head Start; child care under the Child Care and Development Block Grant Act of 1990; or the Native American Housing Assistance and Self-Determination Act of 1996 (Center for Medicaid and State Operations 2010). Unlisted agencies include another state government agency that has fiscal liability or legal responsibility for the accuracy of the eligibility determination findings relied on by the state; or a public agency subject to an interagency agreement limiting the disclosure and use of information shared for determining Medicaid or CHIP eligibility (the public agency may be an agency administered by an Indian tribe recognized by the state or Federal government that determines eligibility for any of the programs listed). Express Lane agencies cannot be private for-profit organizations or agencies that only determine eligibility for programs funded under the Title XX Social Services Block Grant.³

With ELE, not only can the Express Lane agency vary, but so can ELE features. For example, states can apply ELE to CHIP and/or Medicaid, with a focus on enrollment, renewal, or both. In addition, in pursuing ELE, states can choose to include or exclude an “automatic enrollment” option that avoids the need for an application, possible when states have all the information they need from the Express Lane agency findings to make an eligibility determination or renew coverage.

To satisfy the CHIP “screen and enroll” requirements, which dictate that children do not qualify for CHIP unless they have been screened for Medicaid and found ineligible, states adopting ELE can either use traditional approaches or apply one of two new methods. In the first method, states can set a screening threshold 30 percentage points (or more) above the highest Medicaid eligibility threshold. Children with family income at or below the threshold, as found by the Express Lane agency, are considered to have met the Medicaid eligibility income test for the purpose of complying with the Title XXI screen and enroll requirements. For children with family income above this threshold, states must assess whether these children are income-eligible for CHIP, based on the Express Lane agency findings, but they need not be screened for Medicaid eligibility (Center for Medicaid and State Operations 2010). Using the second method, states can temporarily enroll children in CHIP if the child appears CHIP-eligible using the Express Lane agency findings; however, during the temporary enrollment period, states must conduct a full eligibility determination to establish either Medicaid or CHIP eligibility.⁴Even for children ultimately found Medicaid-eligible, states can claim Title XXI matching funds for the temporary CHIP enrollment period; this is an advantage for states, because the Federal government matching rate is higher in CHIP than in Medicaid.

Federal rules offer several protections for families who might be subject to ELE. For example, Express Lane agencies must notify families that their information will be shared with the Medicaid or CHIP agencies, solely to determine Medicaid or CHIP eligibility, and families must be able to opt out of sharing this information. To use the automatic enrollment option, states must obtain the family’s consent to enroll the child, and the family must be informed about the available services, how to access them, if there is cost sharing, how to maintain the coverage, etc.⁵ In addition, whether or not a state implements the automatic enrollment option, for children subject to premiums or cost sharing (common in CHIP programs), the state must provide notice to the family that the child might qualify for lower premiums or cost sharing if the child were evaluated for eligibility using “regular” procedures (Center for Medicaid and State Operations 2010). ELE cannot be used to deny coverage; CHIPRA requires states to initiate a standard eligibility determination for Medicaid and CHIP for any child found ineligible through the use of ELE.

To offset concerns that ELE could introduce enrollment errors—that is, that children who are ineligible might be enrolled in Medicaid or CHIP—CHIPRA also requires states to implement systems to track ELE enrollment, which permits states to calculate error rates. If auditing reveals error rates of 3 percent or more in the first two years of ELE operations, states must identify actions to reduce the error rate; rates in excess of 3 percent will result in reduced Federal payments to states for the amount above 3 percent.

As with other options, CMS approval is required to implement ELE as defined in the statute.⁶ States submit a state plan amendment (SPA) to CMS specifying the state’s ELE plans. The SPA must provide detail on how the ELE option will operate, which partner agency (or agencies) were selected, how the screen and enroll requirements will be satisfied, and how the Express Lane agency differs with regard to income eligibility determination. ELE SPAs are required for both Medicaid and CHIP, depending on the health program to which ELE applies.

² CHIPRA extended the citizenship verification requirements already used in Medicaid to CHIP effective January 1, 2010. CHIPRA also permits a new option for states to meet this requirement through a data-matching process with the Social Security Administration (CHIPRA 111-3 section 211).

³ Title XX block grants cover programs to prevent child abuse, increase the availability of child care, and provide community-based care for the elderly and disabled (Social Security Administration 2012).

⁴ States must use simplified procedures to minimize the family burden for this full eligibility determination. For example, the State cannot require the family to submit or verify information already provided by the Express Lane partner agency or available to the state from another source, unless the State believes that information to be false (Centers for Medicaid and State Operations 2010).

⁵ CMS permits a wide range of consent methods for states using automatic enrollment, including oral, written, electronic signature, signature on an Express Lane agency application, or other means that CMS approves (Centers for Medicaid and State Operations 2010).

⁶ Arizona, California, and Illinois report an ELE partnership in their Federal fiscal year (FFY) 2011 annual CHIP reports, although these partnerships are not approved by CMS (Mathematica analysis of FFY 2011 CHIP Annual Report Template Reports, June 11, 2012). States that are approved can use ELE as one of the five of eight qualifying simplifications for a CHIPRA performance bonus, so it can be financially advantageous to a state to seek approval.

C. Potential Benefits of ELE

Although ELE’s primary goal is to facilitate eligible children’s participation in Medicaid or CHIP, a number of other benefits could accrue to states from adopting the policy. Here, we review these potential benefits.

1. Potential Coverage Gains

The ELE option is intended to simplify the identification, enrollment, and retention of uninsured children eligible for Medicaid or CHIP. In turn, this simplification process may produce gains in coverage, as families who might otherwise not apply for (or renew) coverage for their eligible children, or might not complete this process successfully, are able to do so.

Although prior research on ELE’s possible coverage effects is limited, the available evidence supports the potential of the policy to produce meaningful gains in coverage. For example, using ELE to qualify children for health coverage based on their participation in SNAP, Kenney et al. (2010) estimate that ELE could reach 15.4 percent of eligible, uninsured children. Using ELE to qualify children for health coverage based on state income tax records could reach even more children: an estimated 89 percent of uninsured children who qualify for Medicaid or CHIP live in families who file Federal income tax returns (Dorn 2009). Presumably, a large proportion of these families file state tax returns as well, particularly in states that supplement the Federal Earned Income Tax Credit. A recently published descriptive analysis of the ELE program in Louisiana reported that state officials attribute the decline in the percentage of uninsured children who qualified for Medicaid from 5.3 percent in 2009 to 2.9 percent in 2011 to the state’s ELE policy, implemented in February 2010 (Dorn et al. 2012).

Research on other administrative simplification policies likewise offers evidence on the potential of ELE to produce coverage gains. For example, Bansak and Raphael (2006) used a pre-post design on the 1998 and 2002 March Current Population Surveys (CPS) and found that policy changes aimed at making it easier for families to enroll and retain coverage for their children (such as eliminating the asset test, offering continuous eligibility and coverage, and simplifying the application and renewal processes) had large, statistically significant positive effect on CHIP take-up. Similarly, Kronebusch and Elbel (2004) analyzed the CPS and found that certain administrative simplification policies, such as presumptive eligibility and self-declaration of income, had a positive effect on Medicaid and CHIP enrollment (although these findings have to be considered cautiously because of methodological limitations associated with estimating enrollment rates using a survey sample). In addition, Wolfe and Scrivner (2005) obtained similar results and also found evidence suggesting that specific outreach activities (such as dedicating a telephone line to help people complete applications and using community groups to do outreach and application assistance) can have a positive effect on CHIP take-up. In a more targeted and econometrically rigorous analysis, Aizer (2003) examined the impact of community-based application assistance programs in California on Medicaid enrollment using data from 1996 to 2000; like ELE, application assistance programs can lower the cost of enrollment from the family’s perspective. Overall, Aizer found that application assistance programs had a large impact on Medicaid enrollment, particularly among Hispanic (4.6 percent) and Asian (6 percent) children relative to other children in the same community.

2. Potential Savings Resulting from Administrative Efficiencies

States may consider adopting ELE primarily because of its promise of expanding coverage of eligible children, or as a way to qualify for CHIPRA performance bonus funds. However, ELE may have additional, perhaps less well recognized, benefits for states, most of which center on administrative efficiencies and the savings they may create.

First, ELE can increase the percentage of program expenditures spent on benefits, rather than on administration. By relying on determinations that other programs have already made, Medicaid and CHIP can reduce the administrative costs of enrollment or renewal. Although states will likely need to make expenditures to create the infrastructure and make policy decisions regarding ELE’s implementation, the end result can be an operationally more efficient system that can reduce ongoing administrative costs. In determining the impact of ELE on administrative efficiency, one must compare and assess start-up costs from ongoing savings resulting from ELE implementation.

ELE also holds significant promise for helping states do more with less. In much of the country, the worst state budget crises in decades have led to staff cutbacks and hiring freezes, shrinking the number of staff who take applications and determine eligibility, even as the ongoing economic downturn prolongs an elevated demand for services. Strategies like ELE, which can lower administrative costs of enrollment and renewal through the use of automated data matches (versus manual processing of forms), are particularly appealing to state officials with limited administrative resources.

Using ELE for renewal also could diminish churning (when children cycle on and off of public coverage, experiencing breaks in coverage) of eligible children in Medicaid and CHIP, which also could lead to administrative savings. Churning is a nontrivial problem among children eligible for public coverage: in 2008, a quarter of all uninsured children had been enrolled in Medicaid or CHIP the year before (Sommers 2010). Sommers (2005) also found that a state’s decision to implement a separate CHIP program raises by 45 percent the likelihood that eligible children lose coverage at renewal. Churning leads to real costs for families and states: children experience periods without coverage, despite their ongoing eligibility, and states incur costs to re-process eligibility for children already on their programs. These costs can be significant: studies of administrative changes in Washington state showed that reducing eligibility periods from 12 to 6 months increased administrative costs by $5 million, while, at the same time, enrollment fell by more than 30,000 children (Ku et al. 2009; Wachino and Weiss 2009). Moreover, because Medicaid and CHIP agencies can be Express Lane agencies, ELE could also ease transitions between the two programs at renewal, eliminating the churning sometimes triggered when family income changes.

3. Other Potential Benefits

Several other potential benefits might accrue to states and beneficiaries as a result of implementing ELE. First, ELE can address the problem of siloed public benefits. Low-income families seeking several forms of assistance must typically provide the same information to more than one program, each of which pays its staff to process that information. This creates needless administrative costs for government agencies, as well as more demanding application procedures for families that reduce participation levels. One reason for such redundancy involves technical differences between program eligibility rules. For example, SNAP generally limits benefits to households with “net income” at or below 100 percent of the Federal poverty level (for comparison, Medicaid minimum thresholds are 133 percent of the Federal poverty level for children from birth through age 5 and 100 percent of the Federal poverty level for children ages 6 to 19). One might think that SNAP-recipient children are thus necessarily income-eligible for Medicaid and that, when families seek Medicaid on their behalf, they could be relieved of the need to document income. However, SNAP determines “net income” by applying excess shelter cost deductions that Medicaid does not use. SNAP and Medicaid also use different definitions of the household members whose needs and earnings count in determining income. Therefore, SNAP eligibility determinations typically cannot qualify children for Medicaid without families providing additional information or Medicaid staff meticulously “cross-walking” information from SNAP records into the categories established by Medicaid eligibility rules.

A traditional approach to breaking down these silos involves modifying different programs’ rules and procedures so they align. Although this approach has considerable merit, it is not easy to change the rules of multiple programs. ELE takes a different approach that may, under some circumstances, be easier to implement. In this approach, each program continues to apply its own eligibility methodologies, but one agency uses the other agency’s findings to qualify people for subsidies, despite their methodological differences.

Second, ELE also provides a new way to address the challenges created by delinking Medicaid and cash assistance. Before the Personal Responsibility and Work Opportunity Reconciliation Act (PRWORA) passed in 1996, the typical route to Medicaid ran through applications for cash assistance, which represented a higher priority than health coverage for many low-income families. With ELE, a state can once again connect health coverage to other benefits, like SNAP, that many families perceive as higher priority.

It is true that public benefit programs have long offered application forms that permit families to simultaneously seek SNAP, Medicaid, cash assistance, and sometimes other benefits (such as energy assistance). However, such forms request the information needed by all these programs, a burden that defeats many applicants. To help families avoid long forms, some states have encouraged families to file SNAP-only applications or Medicaid/CHIP-only applications. ELE offers states a way to reduce the burden of multi-program enrollment: a family filing a SNAP-only application, for example, can receive Medicaid or CHIP as well.

Finally, ELE might help states implement the Affordable Care Act. The Affordable Care Act requires states to transition to data-based eligibility methods that will qualify people for Medicaid, CHIP, and subsidies in the Affordable Insurance Exchanges (a new marketplace for obtaining coverage, often referred to as “the exchanges”).⁷ Because of the central role of modified adjusted gross income (MAGI) and tax return information under the Affordable Care Act, this progress could be particularly meaningful for states that use ELE to grant eligibility based on data matches with tax agencies. ELE might also preserve linkages between programs like SNAP, NSLP, Medicaid, and CHIP, assuring that eligible children remain insured as states focus on systems issues and covering newly eligible individuals in 2014. In addition, SNAP linkages may be useful for states seeking to reduce their administrative burdens by prequalifying low-income adults who will be newly eligible for Medicaid in 2014. More fundamentally, the Affordable Care Act requires that, whenever possible, eligibility for Medicaid, CHIP, and subsidies in the exchange must be established, verified, and renewed based on matches with reliable data sources. With ELE, states can begin making the challenging shift from traditional, manual methods to data-based routines for processing applications and renewals.

⁷ The development of Affordable Insurance Exchanges (operated by states or the Federal government) through which certified health plans and subsidies would be made available to eligible individuals was legislated as part of the Affordable Care Act. The exchanges must coordinate eligibility determinations with Medicaid and CHIP so that eligibility for the appropriate program is established based on a single application that a person can submit online, over the telephone, by mail, or in person.

D. Overview of the ELE Evaluation

In September 2011, Mathematica Policy Research and its subcontractors, the Urban Institute and Health Management Associates, were awarded the contract to conduct the congressionally mandated evaluation of ELE. The contract runs through September 2013. The Office of the Assistant Secretary for Planning and Evaluation (ASPE) is overseeing this contract.

1. Evaluation Goals

Section 203 of CHIPRA requires that the ELE evaluation include four components:

An evaluation of the administrative costs or savings related to identifying and enrolling children through ELE methods compared to the costs of identifying and enrolling eligible but unenrolled children through the state’s regular methods
An assessment of whether ELE improves a state’s ability to identify and enroll eligible but unenrolled children
Recommendations for legislative or administrative changes that would improve ELE’s effectiveness as a method for enrolling or retaining children in Medicaid or CHIP
A report on the percentage of children erroneously enrolled in Medicaid or CHIP based on the Express Lane agency findings⁸

Table I.1 summarizes how the evaluation will address each of the four components. Key methods include a descriptive study of program costs, enrollment, and utilization; an impact analysis, using data from the CHIP Statistical Enrollment Data System (SEDS); case studies; and an assessment of the state policy context, through an information review, a 51-state survey, and quarterly interviews with non-case study states. Two cross-cutting reports implementing the statutory requirements will synthesize these findings: (1) this Interim Report to Congress; and (2) the Final Report to Congress.

Table I.1. Evaluation Components Mandated by Congress and Methods Planned to Address Them

	Methods to Address Evaluation Components				Reporting
Evaluation Components Mandated by Congress	Program Cost, Enrollment, and Utilization Study	SEDS Enrollment Impact Analysis	ELE and Non-ELE Case Studies	Information Review, Quarterly Interviews, 51-State Survey	Interim Report to Congress (2012)	Final Report to Congress (2013)
1. An evaluation of the administrative costs or savings related to identifying and enrolling children through ELE methods compared to the costs of identifying and enrolling eligible but unenrolled children through the state’s regular methods	X		X		X	X
2. An assessment of whether ELE improves a state’s ability to identify and enroll eligible but unenrolled children	X	X	X	X	X	X
3. Recommendations for legislative or administrative changes that would improve ELE’s effectiveness as a method for enrolling or retaining children in Medicaid or CHIP	X	X	X	X		X
4. A report on the percentage of children erroneously enrolled in Medicaid or CHIP based on the Express Lane agency findings				X^a		X

^a The Final Report to Congress that is part of this evaluation will report ELE error rates, but these will not be calculated as part of the evaluation; per CMS’ instruction to states, states will report those to CMS, and we will report those in the Final report to Congress in 2013 if they are available in time to include them (Center for Medicaid and State Operations 2010).

CHIP = Children’s Health Insurance Program; CMS = Centers for Medicare & Medicaid Services;

ELE = Express Lane Eligibility; SEDS = Statistical Enrollment Data System.

Table I.2 reviews the purpose of each of the four main study methods and the planned timing of each component. The first study component, the program cost, enrollment, and utilization study, aims to understand the administrative costs or savings in states that have implemented ELE, as well as a study of states that have implemented other simplifications (the “non-ELE” states). In the evaluation’s first year, we conducted a descriptive analysis of costs in the six states that had implemented ELE as of December 2010, and a descriptive analysis of enrollment in four of the six states (limited to those states that could identify those who had enrolled or renewed through ELE). The second study component, the analysis of SEDS data, uses state-reported data to assess ELE’s impact on enrollment. Both components will be assessed again in the study’s second year, with the descriptive study of costs and enrollment extended to all case study states (including non-ELE states) and a study of utilization differences in a subset of ELE states also occurring.

Table I.2. Methods Planned for Evaluation of ELE, Their Purpose, and Timing

Method	Purpose	Year 1	Year 2
Program Cost, Enrollment, and Utilization Study	Understand administrative costs or savings from ELE/non-ELE programs Understand descriptive trends in new enrollment in ELE/non-ELE states Assess whether utilization differs between ELE enrollees and those who enroll through traditional enrollment approaches in states where individual-level data with an indicator for ELE enrollment pathway are available	X	X
Analysis of SEDS Data	Assess ELE’s effect on enrollment	X	X
ELE and Non-ELE Case Studies, Including Key Informant Interviews and Focus Groups	Describe ELE implementation, evaluate its benefits, assess best practices in ELE states and in states that have adopted other simplification approaches (key informant interviews) Hear family experiences about ELE and non-ELE simplification approaches (focus groups)		X
Information Review, Quarterly Interviews, 51-State Survey	Provide comprehensive review of publicly available information on adopted simplifications (information review) Provide periodic review focusing on state-specific sources and issues (quarterly interviews) Internet survey with open- and closed-ended items about state simplifications and barriers and challenges to adopting ELE (51-state survey)	X	X

ELE = Express Lane Eligibility; SEDS = Statistical Enrollment Data System.

Case studies will be conducted in 14 states: 9 states that had implemented ELE as of July 2012, and 5 non-ELE states that have implemented other enrollment or retention simplifications. These non-ELE case studies will be used to document, assess, and compare ELE with other approaches to identifying potentially eligible children and to streamline eligibility for Medicaid and CHIP. In addition, to gain lessons from states outside the 14 targeted for case studies, an informant from all the remaining states will be recruited for the quarterly monitoring calls, and Medicaid and CHIP directors in all states will be invited to participate in an online survey.⁹

⁸ This evaluation will report ELE error rates but will not calculate them; per CMS’ instruction to states, states will report those to CMS, and we will report those in the Final Report to Congress in 2013.

⁹ For a full description of the study methods, see Wrobel et al. (2012).

2. Research Questions Addressed by This Report and Data Sources Used to Answer Them

This interim Report to Congress details findings from the first two components of the evaluation completed in 2012: (1) the descriptive analysis of costs in six ELE states and the descriptive analysis of enrollment in four of those ELE states, and (2) the analysis of the impact of ELE on enrollment in Medicaid and CHIP programs. Table I.3 describes the key research questions each of these components addresses.

Table I.3. Key Research Questions Addressed in This Report

Study Component	Key Research Questions Addressed Through First-Year Evaluation Activities
Descriptive Study of Administrative Costs and Enrollment	How has ELE been implemented? How are ELE programs similar and different across states? What are the up-front investment costs associated with implementing ELE? What are the marginal savings or costs to the state from processing an application or renewal using ELE, rather than the traditional mechanism? How many children are enrolled through ELE – both upon initial implementation and on an ongoing basis? Do children who enter Medicaid and CHIP through ELE stay enrolled as long as children who enroll through standard pathways? Within a state, how do the demographic characteristics of enrollees who enter through ELE compare with those of children who enroll through standard pathways? Have enrollees who enter through simplified approaches to enrollment ever been Medicaid or CHIP beneficiaries in the past?
Impact Analysis Using SEDS Data	Does the implementation of ELE have a positive effect on Medicaid/CHIP enrollment? If so, how large are the enrollment gains? Are enrollment effects similar across different types of ELE programs? To what extent are enrollment effects robust within the subset of states that implemented ELE? If there are positive enrollment impacts, do they appear to be sustained over time?

Study Component

Key Research Questions Addressed Through First-Year Evaluation Activities

Descriptive Study of Administrative Costs and Enrollment

How has ELE been implemented?
How are ELE programs similar and different across states?
What are the up-front investment costs associated with implementing ELE?
What are the marginal savings or costs to the state from processing an application or renewal using ELE, rather than the traditional mechanism?
How many children are enrolled through ELE – both upon initial implementation and on an ongoing basis?
Do children who enter Medicaid and CHIP through ELE stay enrolled as long as children who enroll through standard pathways?
Within a state, how do the demographic characteristics of enrollees who enter through ELE compare with those of children who enroll through standard pathways?
Have enrollees who enter through simplified approaches to enrollment ever been Medicaid or CHIP beneficiaries in the past?

Impact Analysis Using SEDS Data

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

CHIP = Children’s Health Insurance Program; ELE = Express Lane Eligibility; SEDS = Statistical Enrollment Data System.

This report includes data collected through primary data collection efforts and from secondary sources. As described in more detail in Chapters II and III, researchers interviewed state staff in six ELE states (Alabama, Iowa, Louisiana, Maryland, New Jersey, and Oregon) between January and March 2012 to collect information on their ELE programs and processes and to assess the administrative costs of ELE implementation. For the descriptive analysis of new enrollment in these states (conducted in all of the states except Maryland and Oregon), state staff populated aggregate table shells (although in two states, Alabama and Louisiana, we were able to obtain access to individual-level data on which to base the analyses). For the analysis of the impact of ELE on enrollment, we used data from SEDS, a reporting system maintained by CMS since 2000 that collects new and total Medicaid and CHIP enrollment data from states on a quarterly basis, as well as other secondary data sources to support the multivariate analysis, including data from the Bureau of Labor Statistics, U.S. Census Bureau, and Kaiser Family Foundation. Chapter IV further describes methods and includes a complete list of secondary sources for the impact analysis.

Finally, we used published and unpublished literature on CHIP, CHIPRA, and ELE to supplement the main data sources and to provide motivation and context for the findings in each chapter. Sources include SPAs, ELE and standard application forms, and state budget and performance reports.

3. Evaluation States

To varying degrees, every state and the District of Columbia will be included in the evaluation. All states are in the SEDS analysis, and all states will be in the assessment of the state policy context, which includes an information review of published data, a 51-state survey, and quarterly interviews (limited to non-case study states).

Six states (Alabama, Iowa, Louisiana, Maryland, New Jersey, and Oregon) implemented ELE before 2011 and have already begun to participate in the evaluation (as part of the descriptive study of enrollment and costs); we also plan to conduct case studies in these states. Colorado, Georgia and South Carolina received their ELE approval later, in mid-2011, and are expected to participate in evaluation case studies beginning later this year. In addition to being the focus of multiday site visits, all these states will be part of the study of enrollment and costs.

States will be recruited for the study of non-ELE simplifications later this year. Following the design for the evaluation, the recruited states will feature best practice simplifications, such as those simplifications identified in the CHIPRA performance bonus criteria (for example, presumptive eligibility or auto-renewal procedures). In addition, like the ELE states, selected states must be able to identify children who entered through the particular non-ELE pathway being studied in their administrative data, both to track enrollment and to permit recruiting for focus groups. Therefore, as part of the non-ELE state recruiting process, we will speak with state officials to understand the feasibility of inclusion given the study’s goals. Finally, states will be chosen to reflect diversity along several dimensions, including geography, percentage of uninsured children, and participation rates in public coverage.¹⁰

¹⁰ See Wrobel et al. (2012) for more information on state selection criteria.

E. Road Map for the Report

Chapter II describes the designs of the ELE programs approved as of April 1, 2012, discussing states’ design approaches, presenting a detailed review of the ELE processes of the six states in the cost and enrollment study, and reviewing the potential benefits of ELE for beneficiaries. Chapter III presents the descriptive analysis of administrative costs and new enrollment and retention in six states. Chapter IV presents findings from the analysis of SEDS data on ELE’s impact on enrollment. Chapter V summarizes and synthesizes findings from the evaluation’s first year, discusses the implications of these findings, and reviews future work to be conducted in the second year of the evaluation, to be reported to Congress in 2013.

II. Design Features of Approved Ele Programs

A. Background and Motivation

As of April 1, 2012, the Centers for Medicare & Medicaid Services (CMS) had approved nine states–Alabama, Colorado, Georgia, Iowa, Louisiana, Maryland, New Jersey, Oregon, and South Carolina–to implement Express Lane Eligibility (ELE). Even among this small number of states, the striking variation in ELE designs reflects the flexibility that the Children’s Health Insurance Program Reauthorization Act (CHIPRA) affords. For example, states’ partner agencies range from those administering other public benefits—such as the National School Lunch Program (NSLP) (New Jersey and Colorado), the Supplemental Nutrition Assistance Program (SNAP) (Alabama, Iowa, Louisiana, and South Carolina), Temporary Assistance for Needy Families (TANF) (Alabama and South Carolina), and the Special Supplemental Nutrition Program for Women, Infants, and Children (WIC) (Georgia)—to those responsible for aspects of state revenue, including the Division of Taxation (New Jersey) and the Office of the Comptroller (Maryland). To inform our analysis of the administrative costs and savings associated with ELE, as well as our interpretation of enrollment trends, this chapter documents the similarities and differences between ELE programs as they have been implemented.

B. Data and Methods

For the six states with ELE programs in effect as of December 2010 (Alabama, Iowa, Louisiana, Maryland, New Jersey, and Oregon), the primary data source used in this chapter is interviews with state staff, conducted between January and March 2012. Initial questions about ELE design and current implementation status were included along with questions about the administrative costs of ELE and the perceived benefits for applicants in a structured discussion guide (which can be found in Appendix A). From March through June 2012, interview notes were submitted to state staff for review and verification, and we sought follow-up to outstanding questions that were not resolved during the initial interview. We also held brief discussions with ELE partner agency staff where possible.

To supplement the interviews, we reviewed publicly available documents, including state plan amendments, ELE and standard application forms, and state budget and performance reports. We compared staff interviews with program descriptions available through these secondary documents and resolved any apparent conflicts concerning implementation dates and current operational status.

For Colorado, Georgia, and South Carolina, states whose ELE programs were approved after December 2010 and thus were not included in the first year of the administrative cost and enrollment study, ELE program design descriptions in this chapter are based only on secondary data sources. These states are expected to participate in case studies planned for 2012-2013, which will gather further information on the implementation status of ELE programs in these states for inclusion in the Final Report to Congress.

C. ELE Programs Implemented as of December 2010

ELE program designs varied widely across the six states we interviewed. This section describes ELE programs in each of the six states. Table II.1 provides an overview of the ELE program features described below, while Table II.2 summarizes the aims of each of the approved ELE programs. Table II.3 summarizes the application and documentation requirements for beneficiaries entering coverage through ELE.

1. Alabama

As of April 2012, Alabama’s ELE program has been used primarily to streamline Medicaid administrative processing. Alabama’s Express Lane partner agency is the Department of Human Resources, which provides information for ELE processing from SNAP and TANF. Families must still initiate a coverage application or respond to a renewal notice. Most families must also submit the same forms and information as they would without the ELE program in place. However, when state eligibility workers are able to confirm income in SNAP or TANF databases, this finding automatically satisfies Medicaid income eligibility requirements. Further checking of other databases is not done, which saves time.¹¹ State staff estimate that ELE has been used to assess eligibility (on either initial applications or renewals) for more than 200,000 children.

The Alabama Medicaid Agency (AMA) is implementing its ELE program in five phases, the first two of which are already in place. In October 2009, the program began using income eligibility determinations from SNAP and TANF to process Medicaid renewals for children, and in April 2010 it began using ELE to process initial Medicaid applications for children. Currently, eligibility workers must manually check SNAP and TANF databases to establish income after it has been declared on Medicaid application and renewal forms. In later phases, the state plans to automate enrollment and renewal by matching data with SNAP and TANF records. Later phases also call for extending ELE enrollment and renewal processes to women who are pregnant or accessing the family planning benefit, and for establishing additional agencies as ELE partners, such as WIC.

Table II.1. Features and Implementation Status in Six States with Approved ELE Programs as of December 2010

State Program Using ELE	Alabama Medicaid	Iowa Medicaid	Iowa Separate CHIP	Louisiana Medicaid	Maryland Medicaid	New Jersey Medicaid/CHIP	Oregon Medicaid/CHIP
Partner Agency	SNAP and TANF	SNAP	Medicaid	SNAP	State tax agency	State tax agency^a	SNAP^b
Program Strategy	SNAP and TANF income findings used to establish income after consumer declarations at application and renewal.	Data match to identify potentially eligible children; shortened application form mailed out; SNAP findings establish income eligibility.	Medicaid applications and redeterminations showing CHIP-level income automatically referred to CHIP for processing using Medicaid eligibility findings.	Automated eligibility, enrollment, and renewal based on SNAP findings.	Potentially eligible children identified; shortened application form mailed out. State income tax returns used in process to establish state residence.	Data match to identify potentially eligible children; shortened application form mailed out. State income tax returns establish income eligibility.	Data match to identify potentially eligible children; shortened application form mailed out. SNAP findings establish income eligibility.
Prior Data Sharing with Partner Agency	X	X	X	X		X	X
State Plan Amendment Approval Date: Applications	June 2010	June 2010	June 2011	January 2010	September 2010	June 2009	October 2010
Implemented for Applicationsc	April 2010	June 2010	July 2004	February 2010	September 2008	May 2009	August 2010
State Plan Amendment Approval Date: Renewals	November 2009		^d	January 2010
Implemented for Renewals^c	October 2009		^d	November 2010
Implementation Status as of December 2011	Partial	Full	Full	Full	Partial	Full	Full
Enrollments as of December 2011^e	50,257	2,065	33,427^f	20,240	Unknown	5,321	4,632^g
Renewals as of December 2011^e	150,000			200,000

Source: Mathematica interviews with state staff conducted between January and May 2012 and analysis of state administrative data provided by states between January and June 2012.

^a New Jersey also has an NSLP ELE program, which has been implemented in all counties as of October 2011. Because it was not in place as of December 2010, the NSLP partnership is excluded from this initial analysis.

^b As of December 2011, Oregon was also piloting an NSLP ELE program in four school districts.

^d Iowa’s separate CHIP ELE process does not include renewals. However, children whose eligibility is redetermined by Medicaid, resulting in an ineligibility finding, may be ELE-referred to CHIP, with income-eligibility for CHIP established based on Medicaid’s income findings. This constitutes an ELE transfer, rather than a renewal. ^eEnrollment counts were computed from state administrative data and reflect the number of enrollments resulting from ELE application processes. Renewal counts reflect estimates provided by state staff during interviews of the approximate number of renewals resulting from ELE renewal processes.
^f We requested enrollment data from states for the period beginning one year prior to the ELE program effective date. Because CMS recognizes the effective date for Iowa CHIP’s ELE process as June 2010, we present data on enrollments from June 2009 through December 2011. The number of enrollees since the ELE process began in 2004 is likely much higher.
^g As of November 2011.

Table II.2. Aims of ELE in Six States with Approved ELE Programs as of December 2010

State Program Using ELE	Alabama Medicaid	Iowa Medicaid	Iowa Separate CHIP	Louisiana Medicaid	Maryland Medicaid	New Jersey Medicaid/CHIP	Oregon Medicaid/CHIP
Reduce staff time	X	X	X	X		X	X
Improve outreach		X		X	X	X	X
Simplify application experience	^a	X	X	X	X	X	X
Simplify renewal	^a			X
Smooth transitions between Medicaid and CHIP			X

Source: Mathematica interviews with state staff conducted between January and May 2012.

^a Since April 2010, self-declaration of income has been accepted for most ELE and non-ELE children, if income cannot be verified through databases accessible to state eligibility staff. This is not the case for children of self-employed parents, for whom income verification is required. Thus, ELE reduces the documentation burden for children with self-employed parents whose income cannot be verified via other databases but can be verified via SNAP or TANF databases.

CHIP = Children’s Health Insurance Program; ELE = Express Lane Eligibility

Table II.3. Applications and Documentation That Applicants Entering Through ELE Route Are Required to Submit

State Program Using ELE	Alabama Medicaid	Iowa Medicaid	Iowa Separate CHIP	Louisiana Medicaid	Maryland Medicaid	New Jersey Medicaid/CHIP	Oregon Medicaid/CHIP
Standard application	X
Shortened application		X			X	X	X
No application			X	X
Standard documentation	X				X
Reduced documentation			X				X
No documentation		X		X		X

Source: Mathematica interviews with state staff conducted between January and May 2012.

CHIP = Children’s Health Insurance Program; ELE = Express Lane Eligibility.

¹¹ Since April 2010, self-declaration of income has been accepted for most ELE and non-ELE children if income cannot be verified through databases accessible to state eligibility staff. This is not the case for children of self-employed parents, for whom income verification is required. Thus, ELE reduces the documentation burden for children with self-employed parents whose income cannot be verified via other databases but can be verified via SNAP or TANF databases.

2. Iowa

Iowa has two distinct ELE processes: one for children enrolling in Medicaid, and one that facilitates automatic referrals from Medicaid to the state’s separate CHIP program.

a. Medicaid

Iowa Medicaid’s ELE process was introduced in June 2010. Using an automated data-matching procedure, families enrolling in SNAP are screened for Medicaid eligibility and receipt by the Department of Human Services, which administers both programs. Shortened application forms—with no questions about income and resources—are automatically mailed to families of children on SNAP who are not publicly insured. To try to make clear to parents the link between SNAP enrollment and contact from Medicaid, matches are conducted and applications are sent daily, thus preventing a lengthy gap between completing the SNAP application and receiving the follow-up form for health coverage. Typically, parents are required only to sign and return the form they receive; in rare cases where the Social Security Administration cannot verify citizenship, additional documentation must also be submitted. Upon receipt of a completed application form, state eligibility workers enter the family’s information and finalize Medicaid enrollment. From June 2010 through December 2011, about 23,000 applications were mailed out to potential enrollees, and just over 2,000 children were enrolled via ELE.

b. Separate CHIP Program

What later became Iowa’s separate CHIP’s ELE process was initially implemented in July 2004 to address concerns about delayed or incomplete applications that occurred when cases were referred between Medicaid and the state’s separate CHIP program, hawk-i. Prior to July 2004, case files for children who were income-ineligible for Medicaid were manually transferred to the separate CHIP program, and CHIP staff independently conducted another eligibility assessment using their own income calculation rules. Under the automated referral process that Iowa developed, children who are assessed for Medicaid eligibility (either at initial application or at redetermination) and found ineligible due to income are electronically referred to CHIP. CHIP staff then accept the Medicaid income finding, verify insurance status (usually without applicant involvement), and enroll the children in CHIP.¹² Enhanced coordination between the two agencies helps ensure that children eligible for public coverage do not “fall through the cracks” and remain or become uninsured. CMS recognized this automated referral mechanism as an ELE process as of July 2010. From June 2009 through December 2011, more than 1,000 children a month have been enrolled in hawk-i through it.¹³

¹² CHIP staff check to see whether records indicate that a child has private health insurance, and if the records do so indicate, applicants must provide evidence to the contrary.

¹³ For this study, we requested enrollment data from states for the period beginning one year prior to the ELE program effective date. Because CMS recognizes the effective date as July 1, 2010, we requested and present data on enrollments since June 2009.

3. Louisiana

Louisiana’s Department of Health and Hospitals, which administers Medicaid, chose the Department of Children and Family Services, which administers SNAP, as its Express Lane partner agency. The ELE program was initially implemented for new Medicaid enrollments. Officials first conducted a one-time data match between the two programs in December 2009 to identify families receiving SNAP but not Medicaid. SNAP families whose children were not enrolled in Medicaid received a letter informing them of their children’s eligibility for it and explaining how they could opt out. Children identified from the initial data match who did not opt out were automatically found eligible for Medicaid in February 2010. Their families were mailed a Medicaid card and a letter informing them that use of the card would constitute consent to the child’s enrollment in Medicaid. At the point of renewal, children who had never used the card and did not take advantage of a final opportunity to consent to enrollment were disenrolled, in keeping with CMS rules (Dorn et al. 2012).

Since initial implementation, ELE in Louisiana has undergone multiple adjustments. In November 2010, the program expanded to include redeterminations. In January 2011, the SNAP application form was changed to include a check box that allowed families to opt in to the data match and consent to enrollment into coverage. Relative to the prior opt-out procedure, this simplified the administrative process somewhat, because state officials did not need to monitor card usage to confirm consent to enrollment. Among children who opt in, those found eligible for and not already in Medicaid are automatically enrolled, and citizenship status is verified via the Social Security Administration.¹⁴ Data matches and enrollments are now processed daily, and Medicaid staff are not involved unless the Medicaid and SNAP information shows a mismatch (when a child has previously received Medicaid, the matching process sometimes flags a mismatched data element, such as date of birth).¹⁵ ELE renewals in Louisiana also happen automatically, without requiring staff time, using the same matching mechanism to establish income eligibility based on SNAP receipt at redetermination. Because ELE is fully automated, Louisiana’s process does not require beneficiaries to submit an application form or any documentation specifically for Medicaid. As of December 2011, more than 20,000 enrollments and 200,000 renewals had been processed via ELE.

¹⁴ According to the state, the citizenship status of a small number of children cannot be verified automatically, but this is usually resolved by correcting errors in information submitted to the Social Security Administration about the children to be checked.

¹⁵ When the data-matching process was first introduced and business rules for matching were being refined, more frequent intervention by staff was required to adjudicate potential duplicate enrollees. For example, if SNAP records showed a child who could not be found in prior Medicaid records, state staff manually intervened to ensure that, in fact, the records involved a new child rather than a duplicate enrollee (Dorn et al. 2012).

4. Maryland

Since 2008, the Maryland Department of Health and Mental Hygiene (DHMH), which administers the Medicaid program, has partnered with the Office of the Comptroller (the state taxation agency) to conduct outreach to tax filers whose children are potentially eligible for Medicaid. The state income tax form includes a box for families to check to indicate whether their dependents have health insurance. The Comptroller’s office sends a streamlined Medicaid application form—with fewer reference pages and no questions on immigration status—to families with incomes below 300 percent of the Federal poverty level (FPL), as shown on their tax returns, who indicate they have uninsured dependents.

CMS recognized Maryland’s process as an ELE program effective April 2010, after the state modified its procedures to use tax filings to establish state residency. Specifically, returning the streamlined application mailed by the Comptroller is accepted as evidence of state residency, as it signifies the family’s prior filing of a state income tax return; for standard applications, residency is confirmed by consulting a state database. Otherwise, application processing for ELE and standard routes is currently the same. Through the use of 2009 tax return data, in 2010 just over 150,000 letters were sent to potentially eligible families, yielding an unknown number of enrollments.¹⁶

Maryland has plans for data-sharing and enhanced data-matching processes that will change the ELE process. A recent Memorandum of Understanding between the Comptroller and DHMH will allow the Comptroller to provide tax return information to DHMH starting in 2012. DHMH will match the Comptroller’s database against its own database of existing Medicaid recipients, enabling more targeted mailings. DHMH is also considering the use of income information provided on tax returns for determining Medicaid income eligibility.

¹⁶ The state does not track which enrollments resulted from ELE compared to standard applications.

5. New Jersey

Since May 2009, the New Jersey Department of Human Services (DHS), which administers Medicaid and CHIP, has partnered with the Division of Taxation to implement ELE. The state income tax form includes a box that families can check to indicate whether dependents under age 19 have health insurance. The Division of Taxation provides tax return data to DHS for families who indicate uninsured dependents, and DHS then matches the information with Medicaid and CHIP records to create a targeted mailing list for outreach. DHS sends targeted families an abbreviated public health insurance application form that does not require parents’ income or employment status information. If the form is returned, information obtained from the tax return, including income, social security number, and address, is used to process the ELE application. Importantly, when adjusted gross income from the state tax form is being used to determine eligibility, parents are not required to provide documentation of income or report income a second time when filing the Medicaid application. As of December 2011, about 5,300 enrollments have been processed via ELE.

New Jersey also launched a statewide ELE process with NSLP in October 2011. Under this process, schools provide the third-party Medicaid and CHIP administrator with a list of uninsured children, with an indicator for those children who receive free or reduced-price school lunches. Families of children with such an indicator are sent ELE application packets. Because this partnership began relatively recently, we have not evaluated it this year; however, findings on the NSLP ELE program will be included in the final Report to Congress.

6. Oregon

Oregon’s ELE program was effective as of September 2010. Each month, the Office of Healthy Kids (OHK), which administers the Medicaid and CHIP programs, screens children enrolled in SNAP against its database of publicly insured children. Families of SNAP children who lack public insurance are sent a shortened application form (with no questions about income and employment). Parents can return the form by mail or can contact OHK by telephone to have their children’s public health insurance eligibility assessed. Income reported to SNAP is used to establish eligibility and enroll children in Medicaid or CHIP. Occasionally, eligibility staff must obtain additional information, such as proof of medical insurance expiration or cancellation if a child is found to have private health insurance but appears to be otherwise CHIP-eligible. As of November 2011, about 4,600 enrollments had been processed through ELE.

D. ELE Programs Implemented Since January 2011

In addition to New Jersey’s second ELE partnership with the NSLP, which had been implemented in all counties by October 2011, three new states—Colorado, Georgia, and South Carolina—have received approval from CMS to implement ELE programs since January 2011. Below are brief descriptions of these states’ ELE processes based on our review of secondary sources.

Colorado’s ELE program began implementation in October 2011 and features a partnership with Colorado Department of Education-approved school districts that administer the NSLP and Colorado’s Medicaid program. At the time the state plan amendment was approved in March 2012, 24 of 176 school districts were participating. The program aims to simplify the initial application process by using eligibility information from the school lunch program to determine state residency, household size, and gross household income. Remaining requirements, such as citizenship and identity, are verified through the standard process.

Georgia’s ELE program began in 2011 and features a partnership with the state’s WIC program to establish eligibility for Medicaid and PeachCare for Kids (the state CHIP program). Families who apply to WIC are asked if they would like to be referred to Medicaid and PeachCare for Kids. If they consent, WIC information for the eligible child (including identity, income, household size, age, and residency) is shared with the state’s eligibility contractor, who pre-populates a joint Medicaid/PeachCare application and sends it to the family, asking for the child’s social security number (SSN) and proof of immigration status and including WIC-provided information for confirmation. The application also requests information for other children in the family, and WIC income information is used to determine eligibility for all children in the household. This process enables Georgia’s ELE partnership to reach to older children, even though WIC eligibility is limited to children under age 5. In October 2011, Georgia reported more than 1,000 Medicaid enrollments and 900 PeachCare enrollments through its partnership with WIC.

South Carolina’s ELE program, effective April 2011, uses income data from the TANF and SNAP programs to complete renewals for its Healthy Connection program (the state’s Medicaid and Medicaid-expansion CHIP program). The process requires no involvement by families or eligibility staff. As of October 2011, the state reports that eligibility for 65,000 children had been renewed through the streamlined program. South Carolina is also exploring additional data-matching opportunities with the Department of Education and NSLP.

E. Potential Benefits to Applicants

In the early-implementation (pre-December 2010) states, Medicaid and CHIP staff cited a number of potential benefits to applicants from their ELE programs (Table II.4). In five of the seven ELE programs (all but Alabama and Maryland), ELE required that applicants submit fewer documents in support of their ELE applications compared to standard applications, and required fewer interactions with state staff.¹⁷ In addition, in six programs (all but Maryland), ELE applications were processed more quickly than standard applications, which resulted in expedited eligibility determinations for families. Indeed, these expedited processes shortened the typical time from application to enrollment for an eligible child from roughly three or four weeks to one week or less.

Some states also noted that ELE has improved the application experience of non-ELE applicants. For example, in New Jersey, the use of tax information in ELE has led to more extensive use of tax information in standard application processing. Specifically, people applying via the standard route are permitted to attest to income and are not required to provide pay stubs if their attestation matches tax return data.¹⁸ Simplifications made to the application materials in support of ELE in New Jersey, such as a streamlined presentation of managed care plan options, were also carried over to standard applications. In Alabama and Louisiana, state staff suggested that the time saved by diverting some applications to ELE routes has meant that standard processing times for non-ELE applications are shorter than they would be otherwise.

Table II.4. Perceived Benefits of ELE for Applicants

State Program Using ELE	Alabama Medicaid	Iowa Medicaid	Iowa Separate CHIP	Louisiana Medicaid	Maryland Medicaid	New Jersey Medicaid/CHIP	Oregon Medicaid/CHIP
Supporting Documentation Requested of Applicants Through ELE Method	Identity Citizenship Social Security Medical coverage (if applicable)	None	Medical coverage (if applicable)	None	Income Citizenship/ immigration status Social Security Expenses	None	Medical coverage (if applicable)
Supporting Documentation Requested of Applicants Through Standard Method	Identity Citizenship Social Security Medical coverage (if applicable)	Identity Income Citizenship Social Security	Identity Income Citizenship/ immigration status Social Security Medical coverage (if applicable)	Income Citizenship/ immigration status Bank accounts Medical coverage (if applicable)	Income Citizenship/ immigration status Social Security Expenses	Income Citizenship/ immigration status Medical coverage (if applicable)	Income Citizenship/ immigration status (in some cases) Social Security Employment status Medical coverage (if applicable)
Time from Application Receipt to Coverage (Days) Through ELE Method	<6	2	5a	<1	No difference	7	3
Time from Application Receipt to Coverage (Days) Through Standard Method	<25	<30	20	<30	No difference	30	9
Interactions with the State	No difference	Fewer	Fewer	Fewer	No difference	Fewer	Fewer

^a ELE enrollees to the separate CHIP program are enrolled roughly five days after the referral from Medicaid; however, coverage is retroactive to the Medicaid filing date.

CHIP = Children’s Health Insurance Program; ELE = Express Lane Eligibility.

¹⁷ In Alabama, self-declaration of income has been accepted for most ELE and non-ELE children since April 2010. This is not the case for children of self-employed parents, for whom income verification is required. Thus, ELE reduces the documentation burden for children with self-employed parents whose income cannot be verified via other databases but can be verified via SNAP or TANF databases. However, for most families, documentation requirements under ELE remain the same as those under the traditional application process.

¹⁸ State staff do not recognize this use of tax return information in processing standard applications as ELE; we will use the case study in the evaluation’s second year to further understand this ELE-like process.

III. Analysis of Costs, New Enrollment, and Retention Associated with Ele

A. Analysis of Administrative Costs and Savings Associated with Express Lane Eligibility

Although Express Lane Eligibility (ELE) has significant potential to increase administrative efficiencies, the net effect of the policy on administrative costs is uncertain. On the one hand, using eligibility findings or other data from partner agencies may enable a state to lower per-enrollee costs by reducing the number of staff hours needed to process an ELE-based application or renewal. On the other hand, ELE may introduce new costs, both for public health insurance agencies and the agencies with which they partner, as the information systems required to support ELE are developed, built, and maintained. Depending on the design of their program, states may also incur new ongoing costs associated with outreach, such as mailing applications to individuals who report uninsured dependents on their state tax forms.

To evaluate the balance of costs and savings associated with establishing and continuing to operate ELE programs, we gathered data focused on two questions: (1) what are the up-front investment costs associated with implementing ELE? and (2) what are the marginal savings or costs to the state from processing an application or renewal using ELE, rather than the traditional mechanism? In addressing these questions, we considered the potential costs and savings not only for the state Medicaid and Children’s Health Insurance Program (CHIP) agencies, but also for their partner agencies.

1. Data and Methods

a. Data Collection

Data on the administrative costs of ELE were primarily obtained through telephone consultations with state staff. Initial conversations were conducted between January and March 2012, following a structured discussion guide (found in Appendix A). From March through June 2012, we submitted notes from these discussions to state staff for review and verification, and we sought follow up to any questions that had not been resolved during the initial conversation. The discussion guide solicited detailed information on: the traditional enrollment process; the steps that are abbreviated, omitted, or added through the ELE process; the marginal costs associated with ELE and traditional enrollment processes; and the costs of initial ELE implementation. Cost domains that we considered included the salary and benefits of state staff; contractor reimbursements; modification of information management systems; and other direct costs, such as printing or mailing applications. We also sought to understand contextual factors that might affect estimates of costs, such as other concurrent changes in enrollment processes and prior data-sharing relationships with partner agencies that may have impacted the relative cost and ease of formalizing ELE data-sharing arrangements.

To assess changes to these cost domains, we sought information from policy, program, information systems, and front-line eligibility workers at state Medicaid and/or CHIP agencies. We also held brief discussions with ELE partner agency staff where possible. For Alabama and Louisiana, which have implemented ELE renewal processes, we explored both initial and renewal ELE processes, relative to the traditional processes. To supplement information obtained through discussions with state staff, we reviewed publicly available documents including state plan amendments, ELE and standard application forms, and state budget and performance reports.

b. Data Analysis

Combining data from all discussions with staff in a given state, we calculated three types of ELE-related costs and savings: (1) the initial costs of implementation, (2) the savings or costs per marginal ELE application, and (3) the total savings or costs on an annual basis. Because states vary in the way they have chosen to divide ELE tasks between the state Medicaid or CHIP agency and ELE partner agency, we calculated all three of these measures as the costs or savings to the public sector, regardless of the original funding source. If certain costs or savings were absorbed by private contractors, they are not included in our analysis because they did not affect public sector finances in the short run.¹⁹ Below we provide additional details on these measures.

Initial costs of implementation.

The initial costs of implementation primarily reflect eligibility and policy staff training and information systems modifications needed to implement data-sharing arrangements with ELE partner agencies. Because they were often working on multiple initiatives and did not contemporaneously document time dedicated specifically to ELE, policy staff struggled to estimate their efforts on ELE design and implementation; however, they were able to characterize the opportunity costs states incurred by prioritizing ELE over other policy development initiatives. Staff training costs are presented in person-hours spent, noting the range of the duration of training sessions and the number of staff who attended them. In some states, such as Louisiana and Iowa, ELE was covered during a regularly scheduled staff training session that would still have focused on another topic in the absence of ELE. In other states, new training sessions were created specifically for ELE. Estimates of information systems modifications and computer programming were obtained from public health insurance and/or partner agency staff. Because programming costs were often documented on a “per job” basis, states were generally able to supply reliable estimates.

Savings and costs for the marginal application.

Our calculation of savings or costs per marginal application assumes that applications being processed via ELE that resulted in an ELE enrollment would otherwise have been processed the traditional way. This calculation fulfills the Children’s Health Insurance Program Reauthorization Act (CHIPRA) mandate that the evaluation compare costs through ELE versus costs of the traditional application process, and it is a useful way to examine how ELE costs differ from those of traditional enrollment methods. However, this method of calculating and comparing marginal administrative costs does not capture additional administrative costs that states may incur (such as hiring additional case managers or recruiting additional providers) if ELE significantly increases enrollment. To the extent that ELE significantly increases enrollment, generating these types of additional administrative costs, our analysis may overstate this component of marginal cost savings to a state.²⁰

For Oregon and Iowa Medicaid, we also assume that staff time spent on ELE applications that do not result in ELE enrollments represents new costs to these states (costs they would not have incurred in the absence of ELE). This assumption does not affect costs estimates for the other ELE programs examined. For example, in Louisiana, the automated ELE data matching process means essentially no staff time is spent on ELE applications that do not result in enrollment. In Alabama and Maryland, the ELE process costs no additional staff time; and in New Jersey, extra time spent on ELE is absorbed by the third-party contractor, whose contracts were not adjusted to account for changes introduced by ELE.

Given these assumptions, we began by subtracting the minutes taken by each type of staff member to process a typical application via ELE from the minutes taken by each type of staff member to process a typical application via the traditional route. Some states provided a range of times for the ELE process, the traditional process, or both, since time per application depends on factors such as the number of individuals per application and the complexity of household relationships. If the state provided a range, we took the midpoint as our estimate. We multiplied the time saved for a typical ELE application by the proportional salary and benefits for the relevant members of the application processing staff. If the state provided a range of salaries, we again used the midpoint. The calculation does not include possible overhead costs (including any managerial staff time or non-payroll costs such as rent or utility costs), nor does it include other possible savings, such as avoided outreach costs or the avoided costs of individuals seeking application assistance.²¹

The calculation described above yields an estimate of the amount of staff time saved by processing successful applications via ELE rather than the traditional route. From this estimate, we subtracted the cost of processing unsuccessful ELE applications (where applicable) and the cost of other new ongoing expenses associated with ELE—primarily mailing costs per successful ELE enrollment. The difference between staff time saved and new costs reflects the change in administrative expenses associated with the marginal ELE application.

Net annual savings or costs.

We multiplied the average number of successful applications per year by our estimate of the savings and costs for the marginal application. The average number of successful applications per year is based on data from program start through December 2011. This assumption evens out month-to-month fluctuations in enrollment; however, we note that some states enrolled many more people in the first months of ELE than in later months (see Section B later in this chapter).

¹⁹ New Jersey and Iowa CHIP both use private contractors for eligibility determinations. Both programs confirmed that contracts were not amended to account for any increase in the volume of applications or any time savings per application, resulting from ELE.

²⁰ On the other hand, some children who enrolled in public health insurance via ELE when they were healthy might have signed up later, in a worse state of health, if ELE were not in place. Enrolling when sick might entail greater administrative costs associated with expedited eligibility determination (versus the traditional enrollment method) and retroactive payment of Medicaid claims. The potential savings of enrolling children when they are healthy, rather than in response to a health crisis, are not taken into account in this analysis.

²¹ Managerial staff time was excluded from calculations because comparable data were not available across states. The amount of managerial time spent per application is also typically small, since direct managerial oversight is not required in most cases.

c. Limitations

Data for this analysis may be subject to significant recall bias, as they are based on individuals’ recollection of complex events and activities, most of which occurred more than a year prior to our data collection. This is particularly true in states where ELE was adopted as part of a broader initiative. For example, in Oregon, requirements for income documentation were simplified, 12-month continuous eligibility was introduced, and the mailing of redetermination notices was automated around the same time that ELE was introduced. With the exception of programming costs, which many states document on a “per-job” basis, staff members were unlikely to have documented their time spent on ELE implementation. In short, although we have made estimates using the best available information, we acknowledge that in every state, some information is likely to be missing or inaccurate, which may in turn lead our estimates to overstate or understate the true costs of ELE.

For two ELE programs, Maryland and Iowa CHIP, concerns about missing data have led us to exclude certain estimates from our findings. In Maryland, the Centers for Medicare & Medicaid Services (CMS)-mandated markers for identifying ELE children were not available, preventing a count of ELE enrollees and hence an analysis of costs or savings per enrollee. Additionally, the Office of the Comptroller—Maryland Medicaid’s ELE partner agency—has played a large role in ELE, but staff were not available to answer some key questions. Accordingly, Maryland’s initial programming costs are not presented here since they were all borne by the Office of the Comptroller. For Iowa CHIP, the process now called ELE has been in place since 2004, and has completely replaced the most relevant counterfactual process—a manual referral procedure—against which ELE costs and savings would ideally be measured. Consequently, information about savings and costs per application for Iowa CHIP could not be calculated and are not presented.

Lastly, in a number of states, traditional enrollment processes have changed as a result of ELE, complicating the comparison and possibly understating the savings associated with ELE. For example, in Alabama, eligibility staff now look at the Supplemental Nutrition Assistance Program (SNAP) and Temporary Assistance for Needy Families (TANF) databases for all children at initial application to establish income-eligibility for Medicaid based on net income as found by TANF or SNAP; this was not the case before ELE was implemented. Differences between ELE and non-ELE enrollment processes are still apparent, but it is important to recognize that, in some cases, ELE and the traditional enrollment pathways are now more similar than ELE and the pathways in place before ELE. We expect that site visits in the second year will provide more in-depth information about the ELE policies pursued in each state. As with other aspects of our future research, the additional information obtained through site visits could require us to revisit some of the interim conclusions articulated in this report.

2. Findings: Implementation Costs of ELE

The costs of implementing ELE varied widely among states across all three main categories: (1) staff time spent programming information technology (IT) systems; (2) staff time spent in training; and (3) staff time spent on other activities, such as designing policies and procedures, designing ELE application forms, or changing Express Lane partner agency forms to allow information sharing with Medicaid and CHIP agencies. No state reported that ELE required new IT hardware or new staff.²²

²² Implementation costs are summarized in this section. Detailed data on implementation costs are available in Appendix Table A.1.

a. Programming and Related Costs

Figure III.1 shows the estimated costs of staff or contractor time needed to program IT systems for ELE. In Louisiana, the state Medicaid agency spent $83,000 on up-front programming costs, and its partner agency spent a further $22,500. Notably, Louisiana also spent approximately $310,000 on staff time to troubleshoot the automated data-matching mechanism in the six months following ELE implementation. This amount represents the cost of 4 to 10 Medicaid program staff members working to resolve matching problems, including manual processing of any ELE cases that could not be processed automatically and continually updating the matching algorithms to reduce the number of cases needing manual review. Although other programs with automated data matches also experienced a start-up period during which processes were refined, they did not report staff time expenditures of the same magnitude as those experienced by Louisiana.

Iowa Medicaid’s ELE strategy also required extensive programming work, costing $84,000 at implementation. Oregon and Alabama spent $1,600 and $6,300, respectively. New Jersey’s partner agency, the Division of Taxation, spent approximately $40,000 at implementation, and a small amount of additional funding after the first year of the program to amend the ELE question on tax return forms.²³

Maryland’s Medicaid agency did not spend anything on programming at implementation. Its Express Lane partner agency, the Office of the Comptroller, incurred programming costs, but was not available to answer questions about its ELE implementation experience. Maryland’s program has continued to evolve since initial implementation, and in early 2012 the state was preparing to implement more targeted outreach processes enabled by a recently negotiated data-sharing arrangement. The state will now screen the list of tax filers under 300 percent of the Federal poverty level (FPL) who indicate uninsured dependents against the database of individuals already enrolled in Medicaid or CHIP, and will mail ELE applications only to households that do not already receive public insurance. Programming work to enable these more targeted mailings is estimated to cost approximately $25,000, although this cost may be absorbed by the Medicaid agency’s regularly contracted IT programmer.

Figure III.1. Programming Costs at Implementation, by ELE Program

Figure III.1. Programming Costs at Implementation, by ELE Program

Notes: Costs shown here include Medicaid and/or CHIP agency costs, and ELE partner agency costs, as appropriate. Maryland’s programming costs are not shown because we were unable to obtain data from the partner agency, which bore all the programming costs at initial implementation.

CHIP = Children’s Health Insurance Program; ELE = Express Lane Eligibility.

There are two explanations for the variation in IT costs. First, Iowa Medicaid and Louisiana have implemented ELE programs with a higher degree of automation than other ELE programs. Louisiana’s ELE program is fully automated: in 90 percent of cases, staff spend no time processing applications for ELE enrollees, and in the remaining 10 percent, they spend around five minutes per application. For renewals, which affect many more children, Louisiana’s process is completely automated. Iowa Medicaid’s ELE program is less automated, but the data match between SNAP and Medicaid data and the mailing of an ELE application form are automated. Staff only become involved when application forms are received. Second, programming costs appear to be related to the IT systems in use. Louisiana and Iowa Medicaid both use legacy Medicaid Management Information Systems (MMIS), which makes programming more cumbersome than with newer systems. In contrast, Oregon’s MMIS was implemented in December 2008, and the state’s very low programming cost for ELE reflects the relative simplicity of making changes to this modern system.

Qualitatively, states also perceived that prior data-sharing relationships made ELE easier to implement. Six of the seven programs studied in the first year of the evaluation built on existing data-sharing relationships with their partner agencies. In the case of all four SNAP partnerships, Medicaid already benefitted from a shared information technology infrastructure, client database, or other data-sharing arrangement. States uniformly highlighted these relationships as a key factor in the initial selection of partner agencies for ELE implementation. As states consider extending ELE programs to additional agencies where data-sharing partnerships must be newly forged, they may face more significant logistical and administrative hurdles, and consequently higher initial implementation costs to establish data-sharing procedures.

²³ New Jersey’s third-party contractor also spent a small amount on programming, but these costs are not shown because they were absorbed by the contractor.

b. Staff Training

Another significant implementation cost of ELE was staff training. Figure III.2 shows that the estimated number of staff training hours ranged from 55 in Maryland to 700 in Louisiana. These totals were driven by both the length of training and the number of staff involved. In New Jersey, only a select team of 15 contractor staff were trained to handle ELE applications, while for Iowa Medicaid’s ELE program, all Income Maintenance Workers (650) were trained. Iowa Medicaid’s training session lasted only 20 minutes, while New Jersey’s lasted half a day.

In addition to these meetings, all states produced training materials such as “frequently asked questions” and updated staff manuals, or offered telephone or email “hotlines” for ELE questions. New Jersey’s ELE implementation also involved a significant outreach effort to inform volunteer income tax assistance preparers and tax preparation companies such as H&R Block and Turbo Tax about correctly answering the ELE question on tax returns. This effort was initiated in ELE’s second year after the state learned that many tax preparers incorrectly answered the insurance status question on behalf of their clients in ELE’s first year.

Figure III.2. Time Invested in Training at Implementation, by ELE Program

Figure III.2. Time Invested in Training at Implementation, by ELE Program

Source: Interviews and follow-up correspondence with state staff between January and June 2012.

Notes: Maryland’s staff time shown here is that of the Medicaid agency; we believe the partner agency’s staff training time would have been little or none, but could not verify this with the partner agency. Time shown here for other states includes Medicaid and/or CHIP agency staff time, and ELE partner agency time, as appropriate. The time spent on New Jersey’s tax preparer outreach effort is not included in Figure III.2. In addition to what is shown here, an unknown number of low-income tax preparers received about 15 minutes of training per person. Tax-preparation companies primarily received guidance through an update to the state’s tax-preparation guidance booklet.

CHIP = Children’s Health Insurance Program; ELE = Express Lane Eligibility.

c. Opportunity Costs

In each state, we asked whether implementing ELE took resources away from other activities, or whether other activities were postponed or deprioritized to accommodate ELE. For most ELE programs, the answer was ‘no;’ staff could not recall whether there were opportunity costs for Iowa CHIP’s ELE program. However, Louisiana and Iowa Medicaid reported substantial opportunity costs; these are the same states that spent the most on programming and implemented the most automated ELE mechanisms. For example, Louisiana delayed streamlining its online application process and delayed increased collaboration with the state’s Department of Corrections because of ELE. Both of these activities might have resulted in increased Medicaid enrollments.

3. Marginal Costs and Savings of ELE 24

²⁴ Marginal costs and savings of ELE are summarized in this section. Detailed data on marginal costs are available in Appendix Table A.2.

a. Time Savings for Initial Applications and Renewals

ELE applications for initial enrollments take less staff time than the traditional application process in all states except Maryland, where ELE saves no time. Figure III.3 shows that the time savings range from fewer than 5 minutes per application in Alabama, to more than 30 minutes in Louisiana. For Iowa Medicaid, Louisiana, and New Jersey, ELE reduces the time to process an application by more than half relative to the traditional process. These time savings translate into cost savings to the public sector of $1 per successful ELE application for Alabama, $8 for Oregon, $15 for Louisiana, and $16 for Iowa Medicaid.

Figure III.3. Time Saved Processing an ELE Application Compared to a Standard Application

Figure III.3. Time Saved Processing an ELE Application Compared to a Standard Application

Source: Mathematica analysis of interviews and follow-up correspondence with state staff between January and June 2012.

ELE = Express Lane Eligibility.

Alabama and Louisiana also save time on renewals processed via ELE. In Louisiana, ELE renewals are fully automated, so each ELE renewal saves 21 minutes. This translates to a saving of approximately $10 per renewal. In Alabama, each renewal done via ELE saves fewer than five minutes, translating to a saving of just under $1 per renewal.

Figure III.4 shows the value of annualized time savings from processing applications and renewals through ELE rather than the traditional routes. Louisiana saves much more than Oregon and Iowa Medicaid—nearly $2,000,000 per year—primarily because of the volume of cases it processes using ELE (Louisiana processes 11,000 applications and 180,000 renewals compared to 3,800 applications for Oregon and 1,300 applications for Iowa Medicaid). Alabama processes approximately 27,000 ELE applications and 67,000 renewals per year, but because the ELE process currently saves staff little time, Alabama’s annual savings from ELE applications and renewals are much lower than Louisiana’s.²⁵ In New Jersey, the state’s third-party contractor absorbs the value of the time savings, since their contract was not modified to account for ELE. Thus, the value of time saved is shown as zero dollars.

²⁵ State administrative data were not able to capture the number of ELE renewals that have taken place in Alabama. State staff estimated 200,000 children had been processed using ELE from October 2009 through December 2011, and administrative data analyzed by Mathematica showed that approximately 50,000 of these were initial applications. We have, therefore, assumed that 150,000 renewals were processed using ELE in Alabama up to December 2011.

b. New Ongoing Costs Associated with ELE

Most ELE programs incur new ongoing costs (Figure III.5). For example, ELE programs for Iowa Medicaid, Maryland, New Jersey, and Oregon include mailings to potential ELE enrollees that would otherwise not be sent.²⁶ These mailings cost the public sector approximately $12,000 per year for Iowa, $24,000 per year for Oregon, $75,000 per year for Maryland, and $251,000 per year for New Jersey.^{27, 28} Data available for Iowa suggest the response rate for this mailing is about 10 percent. Given the rate at which mailings are successfully converted to enrollments, mailing costs per ELE enrollment ranged from $6 for Oregon and $9 for Iowa Medicaid to $122 for New Jersey (data for Maryland were not available).

Figure III.4. Value of Time Saved from Processing ELE Applications and Renewals, Per Year

Figure III.4. Value of Time Saved from Processing ELE Applications and Renewals, Per Year

Source: Mathematica analysis of Interviews and follow-up correspondence with state staff between January and June 2012.

Notes: Maryland’s current ELE process saves no time compared to the traditional application. For New Jersey, the value of time savings accrues to the third-party contractor, since contracts were not revised to account for ELE. Thus, savings to the public sector are shown here as zero. As explained earlier in the chapter, these estimates assume no impact on total enrollment—any increase in enrollment that might be caused by ELE would result in reduced time savings, meaning the total administrative savings would be overstated here.

ELE = Express Lane Eligibility.

In Oregon and Iowa Medicaid, ELE also results in time being spent on unsuccessful ELE applications, which cost staff time that would not be spent if applications were submitted only through the traditional route. For Oregon, around 23 percent of ELE applications do not result in enrollments, and for Iowa Medicaid around 11 percent of ELE applications do not result in enrollments. The time spent on these unsuccessful applications costs about $8 per application in Oregon and about $7 per application for Iowa Medicaid, resulting in annual costs of $9,000 and $1,000 respectively.

Figure III.5. New Ongoing Costs Associated with ELE, Per Year

Figure III.5. New Ongoing Costs Associated with ELE, Per Year

²⁶ In some states, outreach mailings that are associated with ELE would probably have been sent even if ELE had not been implemented. For example, in Maryland, tax outreach mailings were done before the process evolved into ELE in 2010. However, since mailings are an intrinsic part of ELE in these states, and since we do not know the extent to which mailings would or would not have happened without ELE, we have included the costs of all ELE mailings in our estimates of ELE costs.

²⁷ For Iowa Medicaid and Maryland, regular mailing costs include printing, materials, and postage. For New Jersey, regular mailing costs include printing, materials, postage, and mailing assembly. For Oregon, regular mailing costs include printing, materials, postage, and staff time, including programming. Maryland’s partner agency may also incur monthly staff costs; however, the Office of the Comptroller did not participate in the first year of this evaluation, so these data are unavailable.

²⁸ The number of ELE packets that New Jersey mails out has steadily declined over time. The estimates of $251,000 per year and $122 per successful application reflect the average since program implementation. In 2009, 300,000 packets were mailed, but in 2011 only 41,000 packets were sent. Thus, by 2011 ELE mailing costs had declined to $76,000 per year. However, since response rates in 2011 were lower than in 2009, the mailing cost per successful ELE enrollment was actually higher by 2011, at $150 per successful ELE enrollment.

c. Net Marginal Administrative Costs and Savings of ELE

As Figure III.6 shows, ELE programs have generated a range of net costs and savings. The ELE programs in Alabama and Louisiana yielded large net savings, driven by the staff time saved through processing relatively efficient ELE applications and renewals instead of traditional applications and renewals.

In Maryland, ELE did not save any staff time that would offset new mailing costs. In New Jersey, ELE has added costs (for new mailings) but it has not generated any savings to offset those costs. The time savings from more efficient ELE application processing in New Jersey is absorbed by the state’s third-party administrator. The existing contract was not revised to account for either an increased number of applications resulting from ELE or the increased simplicity of those applications relative to traditional applications.

Programs in Oregon and Iowa Medicaid were essentially cost neutral from an administrative perspective. Mailing costs and the time spent processing unsuccessful ELE applications balanced time savings from successful ELE applications.

Figure III.6. Net Annual Administrative Savings and (Costs) from ELE

Figure III.6. Net Annual Administrative Savings and (Costs) from ELE

Medicaid to stay on top of its workload in the face of reduced staffing. Similarly, Alabama noted that staff time saved through ELE may have enabled staff to process the traditional applications more quickly than would otherwise have been possible.

4. Discussion

The heterogeneity of the seven programs considered in this study explains the wide range of estimates for administrative costs and savings. Findings indicate that implementation costs are highest when states aggressively pursue automated strategies, but if these strategies reach large numbers of applicants and reduce per capita eligibility determination costs, states can expect to recoup their investment, in some cases quite quickly. Alternatively, states that primarily pursue ELE as an outreach strategy, to engage historically difficult-to-enroll populations, may incur upfront and ongoing costs that are difficult to recapture, particularly if administrative processing efficiencies do not result or are absorbed by contractors. However, even in these states, program administrators uniformly viewed ELE as a worthwhile strategy because it facilitated the goal of coverage expansion.

For example, although Louisiana’s ELE process was the most expensive to implement, it is delivering notable savings in terms of staff time. More than 90 percent of these savings are attributable to renewals; Louisiana’s automated ELE process negates the need for staff to spend any time on approximately 15,000 Medicaid renewals each month. These represent 30 percent of all children’s Medicaid renewals. Louisiana also avoids most new ongoing costs, because its strategy does not involve mailings to potentially eligible applicants who may not enroll. Given the magnitude of these savings, Louisiana offset its initial implementation costs and even realized net administrative savings within the first year of ELE program operations, although its up-front costs were the highest reported among states included in the first year of this evaluation.

Apart from Louisiana, Alabama has processed far more children than any other state, resulting in the second-largest net administrative savings from ELE. These savings accrue even though the current ELE process—with eligibility workers manually checking SNAP and TANF databases— saves staff fewer than five minutes per ELE application or renewal. Again, savings are primarily attributable to renewals—approximately 70 percent of Alabama’s ELE cases in a given year are renewals rather than new applications.

Given the results in Louisiana and Alabama, using ELE for renewals has clear potential; however, other states did not uniformly perceive ELE renewals as an advantage over existing renewal facilitation techniques. For example, New Jersey and Maryland already have processes for administrative and ex parte renewals, respectively, allowing them to automatically renew coverage or send pre-populated forms to parents to sign and return, and they did not believe ELE renewals would produce additional efficiencies.

In Oregon and Iowa Medicaid—states whose ELE programs reach relatively few families—ELE processes are essentially cost neutral from an administrative perspective. Substantial up-front investments made by Iowa Medicaid may or may not be recovered over the long term, depending on the trend in ELE-facilitated enrollments over the coming years.

Maryland and New Jersey, which have partnered with their state tax agencies, spend more on doing tax-based ELE mailings than they save in administrative costs. In New Jersey, only about 1 percent of these mailings result directly in enrollments, and savings from processing an application accrue to a contractor. Maryland does not track ELE enrollments so we cannot estimate a conversion rate. However, even if Maryland’s conversion rate were significantly higher than New Jersey’s, the fact that its ELE process saves eligibility staff no time compared to the traditional application process means that, as it currently operates, it has no potential to reduce administrative costs.

Although the estimates in this chapter reflect the best available information, we recognize that, in each state, some of the costs of ELE have been omitted due to challenges in recall or missing data. Accordingly, we interpret the final calculations as generally indicative of the direction and magnitude of the likely savings or costs that states considering ELE might expect to accrue. In the second year of the evaluation, we will revisit these estimates to assess whether states’ early cost experiences with ELE have persisted, as well as the impact of anticipated changes in ELE programs, such as Alabama’s plans for enhanced automation and Maryland’s improved application targeting.

Finally, we note that, in this study, administrative costs were narrowly defined to focus on expenses associated with eligibility processing. Our estimates, therefore, do not include the costs and savings of covering children in public health insurance programs who otherwise would not be insured. Capitated premiums or payments for services used by children who are newly enrolled in public insurance could easily outweigh any administrative savings from their expedited enrollment or renewal; in 2006, nationwide average Medicaid costs for non-disabled children were $249 per member per month (Lipson et al. 2010).

B. Enrollment and Retention Study

1. Background and Motivation

The primary goal of ELE is to increase enrollment among eligible children. In this section, we present findings from an analysis of descriptive data on enrollment and retention in four states that have adopted ELE in order to examine the extent to which states implementing ELE may be achieving this goal.²⁹ Findings from the analysis address the following four questions:³⁰

How many children are enrolled through ELE—both upon initial implementation and on an ongoing basis?
Do children who enter Medicaid and CHIP through ELE stay enrolled as long as children who enroll through standard pathways?
Within a state, how do the demographic characteristics of enrollees who enter through ELE compare with those of children who enroll through standard pathways?
Have enrollees who enter through ELE processes ever been Medicaid or CHIP beneficiaries in the past?

²⁹ As an extension to this descriptive analysis, Chapter IV analyzes more formally the impact of ELE on total program enrollment, exploiting the “natural experiment” that arises from selected states adopting ELE at different points in time.

³⁰ In the second year of the evaluation, we will extend this analysis to explore the effect of ELE on renewals, focusing on two states (Alabama and Louisiana) that authorized ELE for renewal.

2. Data and Methods

To optimize the quantity and quality of data available to support the analyses for this report, data were obtained in two ways. First, in Alabama and Louisiana, we analyzed individual-level data that Mathematica had already acquired for the Robert Wood Johnson Foundation MaxEnroll project. In both administrative files, the data contain a monthly eligibility code that allows us to identify children who enrolled through ELE versus those who enrolled through standard processes. Using these data, we answered the questions above by, first, identifying the number of new enrollees who obtained coverage under ELE and under standard enrollment pathways in the two states and, second, comparing the available demographic characteristics and durations of coverage of these two groups of new enrollees.

Second, in two other ELE states—Iowa and New Jersey—we carried out a similar analysis by using aggregate data tables requested from each state’s Medicaid and (as necessary) CHIP data administrator.³¹ We submitted shells for these tables, shown in Appendix B, to states along with information about how to populate them.

On the first data table, we asked the states to provide counts of monthly new enrollments processed by ELE and by traditional methods for children who qualified for Medicaid or CHIP on the basis of income (rather than a reason such as disability or foster care status).³² The requested data ranged from the year before the state adopted ELE (as determined by CMS) to the most recent month available. Next, we asked states to provide the monthly enrollment counts disaggregated by several demographic characteristics. For example, we requested data on a child’s age, primary language, citizenship status, household income, and urban/rural status. We also asked states to review past enrollment records to look for a recent period of prior public coverage in Medicaid or CHIP, to help address whether ELE enrollees are truly new to the system and how recently they might have had contact.

The second data table requested information on continuous coverage, disenrollments, and transfers, stratified by ELE status. For example, for each monthly cohort of new enrollees in the specified program (CHIP or Medicaid), we requested data on the number of beneficiaries who remained enrolled in the program 6, 12, 13, 18, and 24 months after initial enrollment, how many had disenrolled from the program at those time points, and how many had transferred to the other program at those time points. To assess the extent to which ELE enrollees “churn” back to the same program after disenrolling, among those individuals who disenrolled fewer than 13 months after initially enrolling in the program, we requested data on the number who reenrolled (via ELE or traditional routes) within 3, 6, or 12 months.³³

To simplify the data request and ensure that we would be able to provide quality assurance by reviewing the spreadsheets for internal consistency, we chose not to request data on several more complex measures of interest such as the average length of continuous enrollment, average gap in enrollment, or churning rate. In the second year of the evaluation, we plan to analyze these and several other retention outcomes using individual-level administrative data for the six states with ELE programs effective as of December 2010.

We shared the table shells with states in January 2012 and scheduled an orientation call, including both policy staff and the information systems staff directly responsible for compiling the data request, to walk through the table shells and guidebook and to answer any immediate questions. We had further follow-up with the states as needed, and periodically reached out to states to assess progress and provide support until the populated tables had been submitted in a usable format. We also conducted follow-up calls with states as needed to ensure that we understood the data that states had reported.

Some data elements in the tables could not be populated by states because of limitations in their data systems, limiting the extent of certain comparisons. Most notably, in New Jersey, the state data system only maintains new enrollment counts for applications processed by the state’s vendor and so does not include counts for children who enrolled at county Medicaid offices (approximately two-thirds of new enrollments).³⁴ This limitation does not affect the counts of new enrollments linked to ELE in the state, which are all processed centrally; however, it does substantially understate the count of “non-ELE” new enrollees, in turn undermining comparisons between the two groups. In addition, Iowa’s separate CHIP program and New Jersey were not able to include information on enrollees’ prior public coverage. Each state was missing some important demographic and family-level characteristic or had a high proportion of missing values; this was particularly true in Alabama and Louisiana, where the data files were not created for this project but for another purpose (as discussed above).

Because ELE is being used to enroll Medicaid- or CHIP-eligible but uninsured children who participate in other public programs or who have been identified using partner agency data, this analysis focuses on assessing ELE’s impact on new enrollment. We also anticipate that new enrollee counts will be more sensitive to ELE as opposed to total counts. For Alabama and Louisiana, where we had access to individual-level enrollment data, we defined a new enrollment as a two-month spell of coverage preceded by a gap in public coverage of at least two months. We asked states completing the aggregate enrollment tables to mimic this definition, to the best of their ability given data constraints.

³¹ Ideally, we would have conducted the analysis using individual-level data for all ELE states, but the timeline for this study was too short to establish new data use agreements. Thus, for these four states, we obtained only aggregate data, specified in a series of table shells. Two other ELE States, Maryland and Oregon, could not respond to our request; Oregon lacked sufficient staff resources to populate the tables and Maryland’s data systems lack a marker for ELE to populate the tables successfully.

³² The tables are structured to collect Medicaid and CHIP data separately, recognizing that the two programs’ data systems and availability of variables might differ in some States.

³³ We requested data on churning; however, the follow-up periods are currently too short to provide an accurate picture in most states. Therefore, we have not included this in the first-year evaluation, but will assess differences in churn rates in the second year of the evaluation.

³⁴ New Jersey Department of Human Services, personal communication, April 2, 2012.

3. Findings

Below we discuss the findings of this analysis. We begin by addressing the most basic question: How many children have entered through ELE? To establish how many children are directly enrolled through ELE both at initial implementation and on an ongoing basis, we compared monthly Medicaid and CHIP trends to determine whether new enrollments were facilitated by ELE or non-ELE processes. Second, we consider whether children enrolled through ELE are more difficult to retain in the system at renewal. Comparing retention outcomes of ELE and non-ELE enrollees will inform whether it is easier or more difficult for ELE enrollees to retain coverage. We can balance this finding against the relative ease of initial enrollment via ELE to determine the overall effectiveness of this mechanism. We next compare the demographic and enrollment history profile of ELE enrollees to those of individuals who enrolled over the same time period through standard enrollment processes. To the extent that ELE reaches new families, those families are likely to differ from those that can be reached by other means. These findings will help policymakers understand whether ELE brings unique value as an outreach or enrollment tool. At the end of this chapter, we present an overall discussion of our findings and the lessons that can be taken from the experiences of these early adopting states.

a. New Enrollment

Alabama

Alabama Medicaid began using SNAP and TANF databases to establish income on initial applications in April 2010. During the first 23 months of this policy, ELE was used to process 56,673 initial Medicaid applications. Use of ELE increased steadily during the first few months the program was in place, with the number of ELE enrollees increasing from fewer than 1,000 in April to 2,257 in July 2010.³⁵Starting in August 2010, use of ELE in the processing of initial enrollments appeared to reach a steady level, with the state averaging 2,588 ELE enrollments per month through December 2011. In the last two months for which we have data (January and February 2012) we note an uptick in ELE use, with approximately 3,500 ELE processed new enrollments in February 2012.

In the two years ELE has been used for initial applications, the number of children newly enrolling in Medicaid through standard processes has steadily declined (Figure III.7). New enrollments averaged approximately 13,500 per month in the 12 months before ELE, reached just over 15,000 in April 2010, the month ELE began, and then began to decline as ELE use picked up. Total new Medicaid enrollments have been flat or slightly declining over the period—averaging 13,651 per month in calendar year 2009, 13,518 in 2010, and 11,152 in 2011.

Figure III.7. New Medicaid Enrollment, ELE and Non-ELE, Alabama

Figure III.7. New Medicaid Enrollment, ELE and Non-ELE, Alabama

Source: Mathematica analysis of Maximizing Enrollment grantee state data, 2012.

ELE = Express Lane Eligibility.

Iowa Medicaid

ELE-generated new enrollments from Iowa Medicaid’s partnership with the state SNAP agency were modest over the first 21 months of the program. From June 2010 through February 2012, 2,333 children were newly enrolled in Iowa’s Medicaid program via ELE.³⁶Over the same period, 128,507 children were newly enrolled in Medicaid through standard enrollment processes (Figure III.8). As a percentage of overall new enrollments during this period, less than 2 percent of all new Medicaid enrollments occurred via ELE applications.

Figure III.8. New Medicaid and Medicaid Expansion CHIP Enrollment, ELE and Non-ELE, Iowa

Figure III.8. New Medicaid and Medicaid Expansion CHIP Enrollment, ELE and Non-ELE, Iowa

Iowa’s Separate CHIP Program

Iowa’s program of referring unsuccessful Medicaid applications to the separate CHIP program for auto-enrollment using Medicaid’s income eligibility findings has been in place since 2004, although our data start in June 2009.³⁷ During the 31 months examined, Iowa’s automatic Medicaid to separate CHIP ELE referral program facilitated 33,472 new enrollments. During the same period, 40,590 new enrollments were processed using standard enrollment processes.

As a tool for enrolling children, ELE has been growing in importance for Iowa’s separate CHIP program during the period examined. In December 2011, 54 percent of all new enrollments were processed by Iowa separate CHIP program’s ELE component, up from 41 percent in June 2009, the first month for which we have data (Figure III.9). This trend may merely reflect growth in the share of children who are newly enrolling in the separate CHIP program as a result of a Medicaid transfer, regardless of the existence of an ELE policy. Thus, it can offer little information on whether ELE has affected the numbers of children covered by insurance in the state. However, given the number of applications processed using ELE during the period, there is strong evidence that ELE has been an important part of the separate CHIP program’s eligibility and enrollment system in Iowa.

Figure III.9. New Separate CHIP Program Enrollment, ELE and Non-ELE, Iowa

Figure III.9. New Separate CHIP Program Enrollment, ELE and Non-ELE, Iowa

Louisiana

Using ELE to auto-enroll eligible children based on determination from the state SNAP agency added 22,430 new enrollments in the Louisiana Medicaid program (which includes both Medicaid and the state’s Medicaid expansion CHIP program) from February 2010 to March 2012. As discussed in Chapter II, Louisiana’s ELE program has evolved during the period examined, and the trends in enrollments processed by ELE coincide with those changes.

The initial data match with SNAP coupled with a data match “opt-out” policy extended coverage to a substantial number of children in the state, as evidenced by the big spikes in new enrollment in the first half of 2010 (see Figure III.10): 16,993 children were newly enrolled in Medicaid via ELE between February 2010 and July 2010.³⁸ Many of the children enrolling through ELE in the initial months of the policy remained in public coverage for only a short time. Nevertheless, this initial influx of new enrollees coincided with a shift in the intercept for total enrollment. Total enrollment in Medicaid and the state’s Medicaid expansion CHIP program increased from 646,963 in January 2010, the month prior to the first ELE enrollments, to 669,321 in January 2011 (data not shown).

Figure III.10. New Medicaid and Medicaid Expansion CHIP Enrollment, ELE and Non-ELE, Louisiana

Figure III.10. New Medicaid and Medicaid Expansion CHIP Enrollment, ELE and Non-ELE, Louisiana

Source: Mathematica analysis of Maximizing Enrollment grantee state data, 2012.

ELE = Express Lane Eligibility

In January 2011, Louisiana changed the SNAP application to allow families to opt in to ELE data sharing and enrollment. Additionally, in early 2011 the state moved from monthly to daily data matching across the two agencies. We find essentially no ELE-generated new enrollments from November 2010 through July 2011, as state officials reported that ELE was “turned off” while the state implemented these new policies.

In the subsequent eight months—from August 2011 to March 2012 (the last month for which we have new enrollments)—4,511 new enrollees entered via ELE. These figures suggest some continued new enrollment gains through the ELE mechanism, and give us some indication of the number of monthly new enrollees in SNAP that are found to be eligible for and not already enrolled in Medicaid and auto-enrolled through ELE (500 to 800 per month).

New Jersey

Overall, New Jersey’s ELE program has led to 5,321 children enrolling in Medicaid or CHIP via ELE processes. However, after an initial uptick in new enrollment via ELE in the months following implementation, New Jersey’s “in-reach” partnership with the state Division of Taxation has not led to meaningful numbers of children directly enrolling in Medicaid or CHIP through ELE.

During the first calendar year of the program, approximately 300,000 ELE applications were mailed to all households that checked the box on the tax form indicating that they had uninsured dependents. In response to this outreach, the data show new enrollment of 2,642 children via ELE in Medicaid and 1,762 via ELE in CHIP from June 2009 through December 2009 (see Figure III.11). In these initial seven months, approximately 8 percent of all new CHIP and Medicaid enrollments processed by the state’s vendor were ELE applications.³⁹

In the subsequent two years, far fewer applications were mailed due to changes to the insurance question on the tax form and other screening guidelines about which families were eligible to receive a mailing. A likely consequence of these targeting decisions was that very few children were directly enrolled through the ELE pathway, according to the data. In calendar years 2010 and 2011, only 917 children were enrolled in Medicaid and CHIP via ELE. Further research will be needed to explore other potential causes for this drop in enrollment through ELE.

Figure III.11. New Medicaid and CHIP Enrollment, ELE and Non-ELE, New Jersey

Figure III.11. New Medicaid and CHIP Enrollment, ELE and Non-ELE, New Jersey

Source: Mathematica analysis of state-reported data, May 2008 – December 2011.

ELE = Express Lane Eligibility.

³⁵ Based on the individual-level data, the first new ELE enrollments started in March 2010, likely due to retroactively applying a start date for some children who were enrolled in April.

³⁶This includes children eligible for either the state’s traditional Medicaid or the Medicaid expansion CHIP program. Of the 2,333 new enrollments processed by ELE, 2,141 of these children were eligible under Medicaid eligibility criteria and 192 were eligible under the Medicaid expansion.

³⁷ Iowa’s separate CHIP program’s ELE process was officially recognized in June 2010, even though the state had this process in place since 2004. Given that there were no changes in policy when the program was recognized as ELE, we asked the state to provide data starting in June 2009 to match what we requested from Iowa Medicaid.

³⁸ According to our conversations with state staff, the processing of individuals found eligible but not enrolled in Medicaid in the initial data match took place over several months. In particular, manual review was required to work through data mismatches between the two administrative databases—for example, when the two agencies had different Social Security numbers or dates of birth for the same individual.

³⁹ As mentioned above, Medicaid enrollments processed by the counties are not included in this analysis.

b. Retention

Alabama

In Alabama, retention rates for ELE new enrollments and non-ELE new enrollments appear similar. In both groups, 12-month continuous coverage rates are quite high, about 90 percent (Figure III.12). In addition to ELE, Alabama has enacted other enrollment and renewal simplification polices, such as 12-month continuous coverage and self-declaration of income, which apply to the vast majority of applicants. At 18 months, we likewise see similar rates of continuous coverage for both groups, about 70 percent of enrollees, suggesting little difference in the rate of successful redeterminations between the two groups.⁴⁰

Figure III.12. Retention Rate: Months from New Enrollment, ELE Versus Non-ELE, Alabama Medicaid

Figure III.12. Retention Rate: Months from New Enrollment, ELE Versus Non-ELE, Alabama Medicaid

_{Source: Mathematica analysis of Maximizing Enrollment grantee state data, 2012.}

_{ELE = Express Lane Eligibility}

Iowa Medicaid

Children enrolling in Iowa’s Medicaid program through ELE have rates of retention that are similar to or slightly higher than those of non-ELE new enrollees (Figure III.13), starting with the 12-month measure. For example, approximately 68 percent of ELE enrollees remained continuously covered in the Medicaid program 13 months after their initial enrollment, compared to 61 percent of children enrolled through standard processes. The data show a similar 7-8 percentage point difference at 18-months; however the sample of ELE children with enough follow-up to assess retention at this time point is only 538 individuals, so these numbers should be interpreted with caution.

Figure III.13. Retention Rate: Months from New Enrollment, ELE Versus Non-ELE, Iowa Medicaid

Figure III.13. Retention Rate: Months from New Enrollment, ELE Versus Non-ELE, Iowa Medicaid

_{Source: Mathematica analysis of state-reported data, June 2009 — February 2012.}

_{Note: Because the program began in June 2010, data are not yet available to present the 24-month retention rate statistics.}

_{ELE = Express Lane Eligibility.}

Iowa’s Separate CHIP Program

Figure III.14 shows retention rates in Iowa’s separate CHIP program for ELE and non-ELE enrollees. At each of the retention points examined, children enrolled via ELE were less likely to have remained continuously enrolled than children entering through the standard enrollment process. For example, at 12 months after initial enrollment, 68 percent of non-ELE enrollees are still enrolled in Iowa’s separate CHIP program compared to 55 percent for ELE enrollees. It is likely that this disparity in retention rates is due partly to cost-sharing requirements in the separate CHIP program; non-ELE enrollees are likely to expect cost-sharing requirements, but ELE enrollees are not, since they apply to Medicaid rather than to the separate CHIP program. Put differently, ELE children enter CHIP through Medicaid, whereas other children enter CHIP “off the street.” Those different routes into the program may be associated with characteristics that lead to different retention rates, such as frequently fluctuating household circumstances. It is not clear how much of the difference in retention is due to ELE, compared to these other factors.

Figure III.14. Retention Rate: Months from New Enrollment, ELE Versus Non-ELE, Iowa’s Separate CHIP Program

Figure III.14. Retention Rate: Months from New Enrollment, ELE Versus Non-ELE, Iowa’s Separate CHIP Program

Louisiana

ELE enrollees in Louisiana are less likely to remain enrolled as long as children who enroll through standard enrollment processes (Figure III.15). However, the difference is likely due to the state’s initial approach to ELE, which was an opt-out approach but required affirmative consent—which was the family’s use of Medicaid coverage. At renewal, children who had completed the enrollment process by using their Medicaid cards were renewed automatically if they continued to receive SNAP. By contrast, the children who had never consented to enrollment were terminated unless they took advantage of a final opportunity to consent, as part of the renewal process. Largely due to this approach, the initial cohort of ELE enrollees experienced high early attrition; of the 17,917 children enrolled via ELE under the “opt-out” policy, only 7,863 (less than 45 percent) remained continuously enrolled for 12 months^.41 Louisiana has since changed its SNAP application form to allow an opt-in policy for data matching, replacing the two-step process described above. However, not enough time has elapsed since implementation to allow for a comparison of retention of children enrolling under this approach. We plan to update this analysis to address that question in the second year of the evaluation.

Figure III.15. Retention Rate: Months from New Enrollment, ELE Versus Non-ELE, Louisiana

Figure III.15. Retention Rate: Months from New Enrollment, ELE Versus Non-ELE, Louisiana

_{Source: Mathematica analysis of Maximizing Enrollment grantee state data, 2012.}

_{ELE = Express Lane Eligibility.}

⁴⁰ Using regression models that exploit the individual-level data available in Alabama, we continue to see only small (2-4 percentage point) differences between the two groups in retention rates after controlling for possible differences in age, gender, and month of enrollment.

⁴¹ In regressions that control for age, gender, and month of enrollment, the differences in retention rates between ELE and non-ELE fall by about one-third, a result driven by the much older age distribution of ELE enrollees (discussed below).

c. Demographic Characteristics of New Enrollees

Children enrolling through ELE appear to be similar to other Medicaid and CHIP enrollees in most respects except for one: we observe sizable differences in the distribution of age by enrollment pathway, suggesting that ELE may have been successful at enrolling some traditionally hard-to-reach older children. Additionally, there are smaller but notable differences in family size in the two programs that provided this data. And, finally, in the two Iowa programs, we find ELE enrollees have substantially lower incomes than non-ELE enrollees. Table III.1 compares ELE and non-ELE children in terms of demographic characteristics.

Age

For every state, a larger proportion of ELE enrollees than non-ELE enrollees are teenagers (13 to 18 years old) than non-ELE enrollees (Figure III.16). This difference is greatest in Louisiana, mostly driven by the fact that a sizable proportion of non-ELE enrollees are under age one, while less than 5 percent of ELE enrollees are. However, even if infants are excluded from the analysis, the average age of ELE enrollees in Louisiana is higher than that of non-ELE enrollees. Other states show smaller, but still sizeable differences between the two age groups. For example, in Iowa Medicaid, 31 percent of ELE enrollees are ages 13-18, compared to 23 percent of non-ELE enrollees; and there is a similar 8-percentage point difference by group among new enrollees in New Jersey Medicaid (35 to 27 percent). Given that teenagers are traditionally the most likely age group of children to be uninsured, this finding may speak to the promise of using ELE as a means of reaching and enrolling older children.

Table III.1. Characteristics of ELE and Non-ELE New Enrollees (Percentages)

	Alabama Medicaid		Iowa Medicaid		Iowa’s CHIP Program		Louisiana Medicaid		New Jersey Medicaid		New Jersey CHIP (Medicaid and Separate CHIP Programs)
Characteristic	ELE	Non-ELE	ELE	Non-ELE	ELE	Non-ELE	ELE	Non-ELE	ELE	Non-ELE	ELE	Non-ELE
Age
< 1 year	4	36	3	8	0	0	2	52	5	10	2	2
1-5 years	36	25	27	31	36	33	22	17	25	25	22	23
6-12 years	37	22	40	29	36	40	39	18	35	35	27	29
13-18 years	22	17	31	23	28	27	38	14	35	27	36	22
19-21 years	0	0	0	10	-	-	0	0	0	3	0	0
Unknown	0	0	0	0			0	0	0	0	4	24
Gender
Female	50	50	49	52	49	49	19	7	48	50	50	53
Male	50	50	51	48	51	52	22	6	52	50	50	47
Unknown	0	0	0	0			60	87	0	0	0	0
Primary Language
English	NA	NA	98	95	94	97	40	13	100	100	100	97
Spanish	NA	NA	2	5	2	2	0	1	0	0	0	3
Other	NA	NA	0	0	0	0	0	0	0	0	0	0
Unknown	NA	NA	0	0	5	1	60	87	0	0	0	0
Citizenship
Citizen	NA	NA	97	96	100	100	65	31	97	89	94	91
Qualified non-citizen	NA	NA	1	2	0	0	0	0	3	10	6	9
Other	NA	NA	0	0	-	-	0	0	0	0	0	0
Unknown	NA	NA	2	2	0	0	34	69	0	0	0	0
Income
< 100% FPL	NA	NA	88	80	3	5	NA	NA	8	7	0	0
>100 to 133% FPL	NA	NA	11	17	5	6	NA	NA	91	89	2	10
>133 to 185% FPL	NA	NA	0	2	73	53	NA	NA	1	5	57	44
>185 to 200% FPL	NA	NA	0	0	8	13	NA	NA	0	0	13	19
>200 to 300% FPL	NA	NA	0	1	11	23	NA	NA	0	0	24	24
>300% FPL	NA	NA	0	0	0	0	NA	NA	0	0	4	4
Unknown	NA	NA	0	0	0	0	NA	NA	0	0	0	0
Family Size
2-3	NA	NA	NA	NA	36	31	NA	NA	44	38	45	40
4	NA	NA	NA	NA	30	32	NA	NA	29	29	31	32
5+	NA	NA	NA	NA	33	37	NA	NA	27	32	25	28
Other	NA	NA	NA	NA	0	0	NA	NA	0	0	0	0
Unknown	NA	NA
Location
Metropolitan	NA	NA	58	54	49	51	33	14	-	-	-	-
Urban	NA	NA	27	28	30	27	17	7	87	85	88	79
Rural	NA	NA	15	18	21	23	14	7	13	15	12	21
Unknown	NA	NA	0	0	0	0	36	72	0	0	0	0

Sample: Children newly enrolled in coverage; data for Iowa’s separate CHIP program and New Jersey Medicaid and CHIP are for June 2009–December 2011; data for Louisiana Medicaid and Medicaid expansion CHIP are for February 2010–November 2011.

Note: Iowa’s separate CHIP program income and family size numbers included some (fewer than 50) individuals counted twice.

CHIP = Children’s Health Insurance Program; ELE = Express Lane Eligibility; FPL = Federal poverty level; NA = not available.

Figure III.16. Percentage of New Enrollees Ages 13–18, ELE and Non-ELE

Figure III.16. Percentage of New Enrollees Ages 13–18, ELE and Non-ELE

Source: Mathematica analysis of Maximizing Enrollment grantee state data, 2012 for Alabama and Louisiana, and Mathematica analysis of state-reported data, May 2008 – December 2011 for New Jersey, June 2009 – February 2012 for Iowa Medicaid, and June 2009 – December 2011 for Iowa’s separate CHIP program.

CHIP = Children’s Health Insurance Program; ELE = Express Lane Eligibility.

Household Income

In Iowa’s separate CHIP and Medicaid programs, children enrolling through ELE are from families with lower incomes. In the separate CHIP program, 81 percent of ELE enrollees are below 185 percent of the FPL, compared to 64 percent of non-ELE enrollees. In Iowa Medicaid, enrollees below 100 percent of the FPL make up 88 percent of all new ELE enrollees, while the corresponding percentage for the non-ELE group is 80. In Iowa’s separate CHIP ELE program, because ELE enrollees are by definition applicants who have first applied to Medicaid, they are more likely to be closer to the Medicaid income eligibility threshold than those entering through standard enrollment pathways. Iowa Medicaid’s ELE program has a different program design (matching to SNAP data). Here, it may be the case that ELE mailings are more successful at reaching potential enrollees below 100 percent of the FPL than standard forms of outreach. However, so few children have been enrolled through the ELE pathway in Iowa Medicaid that it is difficult to draw any solid conclusions from these observed differences.

Family Size

ELE-enrolled children in Iowa’s separate CHIP program and New Jersey are more likely to be from families comprising two or three people, and less likely to be from families comprising four or more people, compared to non-ELE enrolled children. However, the observed differences are small (5 to 6 percentage points) and information on family size is only available from two of the four states, making it difficult to conclude much at this early stage.

Prior Public Coverage

Because of data availability, we can only review data on prior public coverage in three ELE programs: Alabama, Iowa Medicaid, and Louisiana. In both Iowa Medicaid and Louisiana, we find that children who enrolled via ELE were less likely than non-ELE enrollees to have a spell of public coverage in the 12-months prior to enrollment (Figure III.17). For example, ELE enrollees in Louisiana were 12 to 15 percentage points less likely than children who enrolled through standard processes to have a prior spell of public coverage in the 12 months before enrolling.

We find the opposite relationship in Alabama, with ELE enrollees more likely to have a recent spell of public coverage than non-ELE enrollees, with a difference of approximately 10 percentage points. However, whereas Iowa Medicaid and Louisiana used ELE as a mechanism to identify and enroll eligible but uninsured children, Alabama’s ELE program does not include an outreach component.⁴²Because families of children still need to apply for coverage as they would if ELE were not in place, regardless of whether state workers use TANF and SNAP income determinations in establishing Medicaid eligibility, we would not necessarily expect to find children in the ELE group more likely to be disconnected from public coverage than the non-ELE children.

Figure III.17. Public Coverage in 12 Months Prior to Enrollment, ELE and Non-ELE, Alabama, Iowa Medicaid, and Louisiana

Figure III.17. Public Coverage in 12 Months Prior to Enrollment, ELE and Non-ELE, Alabama, Iowa Medicaid, and Louisiana

Mathematica analysis of Maximizing Enrollment grantee State data, 2012 for Alabama and Louisiana, and Mathematica analysis of state-reported data, June 2009 – February 2012 for Iowa Medicaid.

ELE = Express Lane Eligibility

⁴² Alabama has plans to expand ELE to auto-enroll children who apply to TANF or SNAP and are found to be eligible for and not already enrolled in Medicaid (a process similar to Louisiana’s ELE program). However, this phase has not yet been implemented.

4. Discussion

Across the five state ELE programs examined here, the number of new enrollees associated with ELE varied widely. Results from Louisiana, which showed a substantial spike in ELE-related enrollments in the early months of the policy, suggest that using income determination findings from other agencies can, at least with the initial data match, potentially add a sizeable number of new children to coverage. In Iowa Medicaid and New Jersey, states that use partner agencies findings as a means to target application mailings, we find far fewer ELE-linked enrollments. These more tepid findings suggest that this outreach-focused approach to ELE, which requires parents to respond to mailings, may offer less promise as a means for enrolling large numbers of new children. However, as in Louisiana, we find that children enrolled via ELE in these states 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 from families that are traditionally hard to reach.

Alabama Medicaid’s and Iowa’s separate CHIP program both used ELE for a large proportion of the new enrollments they processed during the time period we examined. However, in Alabama there is no difference from the beneficiary’s viewpoint between entering through ELE or through the standard route (same forms, same documentation required).⁴³ In Iowa’s separate CHIP program, ELE enrollees are drawn from families that applied to Medicaid. ELE does reduce the steps and paperwork required of applicants referred from Medicaid to the separate CHIP program, but it is not being used as a mechanism to target and enroll eligible but uninsured children. Although we do not have the evidence to verify this, it is likely that the children who enrolled through ELE might have enrolled anyway. Therefore, in these states the value of ELE may be mostly the administrative savings and efficiencies it creates.

Similarly, the findings on retention of ELE enrollees vary across states. When compared to non-ELE enrollees, ELE enrollees have higher rates of retention in Iowa Medicaid, lower rates of retention in Louisiana and Iowa’s separate CHIP program, and no difference in retention in Alabama. This is likely due to the design of these programs; for example, the low retention rate for ELE enrollees in Louisiana is almost certainly tied to the use of a temporary opt-out policy by the state. However, given the relatively short time since implementation of most of the programs, the question of retention under ELE will need to be revisited.

This initial descriptive analysis of ELE enrollments gives a sense of the impact of ELE on enrollment growth and which populations may particularly benefit from ELE. During the second year of the evaluation, we will update the analyses conducted in the first year using individual-level data for all six states. This will allow us to examine ELE’s impact on renewals in Alabama and Louisiana and observe longer-run trends in new enrollments and retention in all six states, including outcomes such as churning on and off public coverage that were not possible to document during the relatively short, first-year evaluation observation period.

⁴³ This is true as long as the applicant does not report self-employment income that cannot be verified via other available databases.

IV. Assessing ELE’s Impact on Enrollment: An Analysis of the Statistical Enrollment Data System

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:

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?
Are enrollment effects similar across different types of ELE programs?
To what extent are enrollment effects robust within the subset of states that implemented ELE?
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).

B. Data

As described in Chapter II, as of January 2012, eight states had received Centers for Medicare & Medicaid Services (CMS) approval of ELE state plan amendments (SPAs). Table IV.1 summarizes the programs and the implementation date assumptions used for the empirical analysis. This chapter combines this information with Medicaid/CHIP enrollment data from SEDS and various policy and economic indicators from additional data sources.

Table IV.1. States with Approved State Amendments for CHIPRA Express Lane Eligibility as of January

State	Health Program	Eligibility Factors	Express Lane Program(s)	ELE Function	Implementation Date	SEDS Implementation Date (Fiscal Year)	# of Post-ELE Quarters
Alabama I	Medicaid	Income	SNAP; TANF	Renewal	10/1/2009	Q1 2010	8
Alabama II	Medicaid	Income	SNAP; TANF	Initial determination and renewal	4/1/2010	Q3 2010	6
Georgia	Medicaid & CHIP	Income, identity, age, state residence	WIC	Initial determination	1/1/2011	Q2 2011	3
Iowa	Medicaid & CHIP	All eligibility factors except immigration status and citizenship from SNAP; Income from Medicaid	SNAP; Medicaid^a	Initial determination (SNAP & Medicaid); redetermination (Medicaid)	6/1/2010 (SNAP); 7/1/2010 (Medicaid)	Q4 2010	5
Louisiana I	Medicaid	Income, state residence, SSN, identity	SNAP	Initial determination and renewal	2/10 for applications; 11/10 for renewals	Q2 2010	7
Louisiana II	Medicaid	Income, state residence, SSN, identity	SNAP	Initial determination and renewal	12/09 for applications; 11/10 renewals	Q1 2010	8
Oregon	Medicaid & CHIP	Income and state residence (and SSN, from SNAP)	SNAP; NSLP (pilot)	Initial determination	8/1/2010 for SNAP^b; 11/11 for NSLP	Q4 2010	5
South Carolina	Medicaid	Income and assets	SNAP; TANF	Renewal	4/1/2011	Q3 2011	2
New Jersey	Medicaid & CHIP	Income and identity	State income tax; NSLP	Initial determination^c	5/1/2009	Q3 2009	10
Maryland I	Medicaid	State residence	State income tax	Initial determination	5/1/2008 (tax-based outreach)	Q1 2009^d	12
Maryland II	Medicaid	State residence	State income tax	Initial determination	4/1/2010 (tax-based ELE)	Q3 2010	6

Source: Urban Institute analysis of CHIP and Medicaid state plan amendments, Centers for Medicare & Medicaid Services.

Notes: 1) For states with two rows, the first row corresponds to the implementation date used for the main analysis, and the second row corresponds to the sensitivity analysis date. (2) Federal fiscal year quarters are as follows: first quarter, October 1 through December 31; second quarter, January 1 through March 31; third quarter, April 1 through June 30; and fourth quarter, July 1 to September 30.

^a This program uses one-way Medicaid-to-CHIP ELE referrals. There are no CHIP-to-Medicaid ELE referrals. ELE is used for redeterminations that result in a child being transferred from Medicaid to CHIP.

^b Enrollments under the SNAP ELE initiative began in September 2010, but CMS recognizes the state plan amendment effective date as August 2010.

^c New Jersey’s ELE program is authorized for applications and renewals, but officials report ELE has only been used for initial applications at this point.

^d Maryland's tax-based outreach program was implemented in May 2008, but applications were not sent out until September.

CHIP = Children's Health Insurance Program; CHIPRA = Children's Health Insurance Program Reauthorization Act; CMS = Centers for Medicare & Medicaid Services; ELE = Express Lane Eligibility; NSLP = National School Lunch Program; SNAP = Supplemental Nutrition Assistance Program; TANF = Temporary Assistance for Needy Families; WIC = Special Supplemental Nutrition Program for Women, Infants and Children.

1. SEDS Data

SEDS is a web-based system maintained by CMS since 2000 that collects new and total Medicaid and CHIP enrollment data from states on a quarterly basis. States must submit quarterly enrollment data within 30 days after the end of the fiscal quarter and aggregate annual data within 30 days after the end of the fourth quarter.⁴⁶ This analysis uses quarterly and annual total enrollment data from three of the SEDS reporting forms and, to our knowledge, is the first analysis to do so.⁴⁷

This report focuses on the 2007 to 2011 quarterly SEDS data on total enrollment (the unduplicated number of children ever enrolled during the quarter). Throughout the analysis, we define Medicaid enrollment to include both traditional Medicaid and Title XXI CHIP-funded Medicaid expansions. We define total Medicaid/CHIP enrollment to include enrollment in traditional Medicaid, CHIP-funded Medicaid expansions, and separate CHIP programs. Quarterly data before 2007 are excluded due to reporting errors and high item nonresponse rates.

Some quality issues are evident in the total enrollment data, including missing observations and likely reporting errors. We addressed quality issues in the quarterly data by imputing missing values and repairing reporting errors on a case-by-case basis. Our imputation strategy, which uses interpolation in most instances, is consistent with procedures that Mathematica developed while working with the annual SEDS data (Ellwood et al. 2003).⁴⁸ Data points were also cross-validated using the Medicaid Statistical Information System and monthly Medicaid/CHIP enrollment data reports from the Kaiser Commission on Medicaid and the Uninsured (Kaiser 2011a; Kaiser 2011b). We made imputations on fewer than 5 percent of state-quarter observations and the final analysis file, with the imputations, was approved by CMS/SEDS analysts on March 30, 2012.

Two non-ELE states, Maine and Montana, are excluded from this analysis due to concerns about data reliability. Maine implemented a new Medicaid Management Information System in 2011 and identified problems in its enrollment data caused by reporting errors. It is currently working on this problem, but is not expected to submit revised data any time soon.⁴⁹ Similarly, we found substantial variation in the Montana data from 2007 to 2011, although patterns in the 2009 to 2011 data could be partially explained by Medicaid/CHIP expansions and changes in economic conditions, according to the CMS Regional Office.⁵⁰ We also conducted several statistical tests (for example, difference in fit, a diagnostic meant to show how influential a point is in a statistical regression) and determined that Montana was an outlier state, which indicated that it might not serve as an accurate counterfactual to ELE states. Although Montana had some influence on the regression model in the multivariate analysis, we found that our main results did not substantially change by its inclusion or exclusion. However, given the outlier tests and uncertainty over the validity of the state’s data, we excluded Montana from the descriptive and multivariate analyses.

Appendix C, Table C.1 contains the Medicaid and CHIP quarterly enrollment data for all ELE states from 2007 to 2011. Imputed enrollment values are noted.

⁴⁶ Federal fiscal year quarters are as follows: first quarter, October 1 through December 31; second quarter, January 1 through March 31; third quarter, April 1 through June 30; and fourth quarter, July 1 to September 30.

⁴⁷ All data files were downloaded in January 2012. There were also smaller state-specific downloads through March 2012, because some states revised their enrollment data.

⁴⁸ For instance, if data from a particular quarter were missing or inconsistent, we averaged data from the previous and subsequent quarters. If states had more than one quarter of missing data, we allocated the difference between the last- and the next-reported quarter evenly over the missing quarters. Edited cases were cross-validated with other data sources when possible.

⁴⁹ Email correspondence with CMS contact person for SEDS, on March 30, 2012.

⁵⁰ Email correspondence with CMS contact person for SEDS, on March 30, 2012.

2. Additional Data Sources

The multivariate analysis accounts for many variables, such as changes in economic conditions and in various non-ELE enrollment policies that might otherwise bias the estimates of ELE’s effects. To construct these variables, we draw on a number of data sources:

Quarterly state unemployment rate data from the Bureau of Labor Statistics (Bureau of Labor Statistics 2012)
Child state population estimates from the U.S. Census Bureau (U.S. Census Bureau 2012)
Annual state Medicaid and CHIP eligibility rules for parents and children from the Urban Institute’s Medicaid eligibility simulation model and the Kaiser Family Foundation
Implementation dates of various state policies that influence the ease of new enrollment into Medicaid or CHIP, from publications from the Kaiser Commission on Medicaid and the Uninsured and the Georgetown Center for Children and Families (Cohen Ross et al. 2007; Cohen Ross et al. 2008; Cohen Ross et al. 2009a; Cohen Ross et al. 2009b; Heberlein et al. 2011; Heberlein 2012). We assumed implementation during the second quarter of the Federal fiscal year when we could not find the exact implementation date for a given policy. We selected the following Medicaid and CHIP policy covariates: joint application for Medicaid and CHIP, presumptive eligibility, administrative verification of income, no in-person interview, elimination of asset test, and continuous eligibility.⁵¹We did not include the elimination of an asset test in Medicaid because no state in our sample made changes to this policy during the period of analysis. Table IV.2 highlights aggregate changes in these policies during the period of analysis.

Table IV.2. Number of States with Medicaid/CHIP Administrative Simplification Policies and Average Eligibility Thresholds by Year, ELE vs. Non-ELE States

	2007 ELE	2007 Non-ELE	2008 ELE	2008 Non-ELE	2009 ELE	2009 Non-ELE	2010 ELE	2010 Non-ELE	2011 ELE	2012 Non-ELE
Medicaid Program	8	41	8	41	8	41	8	41	8	41
Presumptive Eligibility, Medicaid	1	8	2	12	2	12	1	12	2	13
Administrative Verification of Income, Medicaid	1	8	1	9	2	9	2	12	1	12
No In-Person Interview, Medicaid	8	36	8	36	8	38	8	38	8	39
Continuous Eligibility, Medicaid	4	11	4	11	6	12	6	14	6	15
Average Child Medicaid Income Eligibility Threshold (% FPL)	145	161	157	161	157	164	158	164	164	161
Average Parent Medicaid Income Eligibility Threshold (% FPL)	66	91	70	91	87	92	78	88	78	91
Separate CHIP	6	28	5	30	7	30	7	30	6	31
Medicaid/CHIP Joint Application	6	26	5	27	6	28	6	28	6	29
Presumptive Eligibility, CHIP	1	5	1	8	2	7	1	7	2	8
Administrative Verification of Income, CHIP	3	5	1	6	2	6	2	9	1	10
No In-Person interview, CHIP	6	25	5	27	7	29	7	29	6	30
No Asset Test, CHIP	5	27	4	29	5	29	6	29	6	29
Continuous Eligibility, CHIP	3	20	3	22	6	22	6	22	5	21
Average CHIP Income Eligibility Threshold (% FPL)	245	218	234	225	231	232	276	235	289	241

Source: 2007 to 2012 publications from the Kaiser Commission on Medicaid and the Uninsured and the Georgetown Center for Children and Families.

Notes: (1) Policies in place during first fiscal quarter, except in 2011. In 2011, policies in place during the fourth fiscal quarter are shown. (2) ELE states are Alabama, Georgia, Iowa, Louisiana, Maryland, New Jersey, Oregon, and South Carolina. Non-ELE states include all other states except Maine and Montana. (3) CHIP thresholds are estimated among states with separate CHIP programs. (4) Medicaid counts include Title XIX (Medicaid) or Title XXI Medicaid expansion CHIP programs.

CHIP = Children's Health Insurance Program; ELE = Express Lane Eligibility; FPL = Federal poverty level.

Finally, we use the 2011 Current Population Survey to create simulated adult and child eligibility variables consistent with the method developed by Cutler and Gruber (1996). This method applies each state’s eligibility thresholds to a standardized national sample of parents and children, as opposed to a particular state’s own population, removing time-variant factors and differences in the income distribution across states. The derived eligibility variables capture the generosity of each state’s eligibility criteria and are not confounded by varying conditions across or within states over time.

⁵¹ We selected these variables based on data quality, the ability to characterize the policy change in a quantitative analysis, the number of program changes observed during the period of analysis to ensure sufficient degrees of freedom, and prior evidence on the policy’s potential impact on Medicaid/CHIP enrollment (for example, policies documented in Wachino and Weiss [2009]).

C. Methods

Our study includes both a descriptive analysis of the SEDS data and a multivariate (regression-based) analysis. The main descriptive analysis examines enrollment trends in Medicaid and CHIP drawing on the SEDS quarterly data from 2007 onward. Through this analysis, we compare Medicaid and CHIP enrollment trends between ELE and non-ELE states and identify any noticeable spikes in enrollment in ELE states following their implementation of the policy. The multivariate analysis extends the descriptive analysis, using regression modeling to control for the counterfactual—that is, what the trend in Medicaid and CHIP enrollment would have been in ELE states in absence of the policy. Drawing on this estimate, the multivariate analysis provides a causal estimate of the impact of ELE on Medicaid/CHIP enrollment, offering arguably the most robust evidence to date on how and whether ELE expands coverage to children who would otherwise be uninsured.⁵²

⁵² However, given the nature of the data, we are unable to determine actual coverage status before enrollment in Medicaid/CHIP. For instance, some of the estimated enrollment gains through ELE could be attributable to children who were previously uninsured or had private health insurance.

1. Multivariate Analysis: The Main Model

Using 2007 to 2011 quarterly SEDS data, we estimate separate regression models for total Medicaid/CHIP enrollment and for Medicaid enrollment only, where the dependent variable is the log transformation of children’s enrollment in each state and quarter. We estimate two-way fixed effect difference-in-difference equations with balanced panels as our main models for this analysis, where the eight ELE states constitute the treatment group (with the intervention occurring at different points in time) and matched non-ELE states with similar pre-2009 enrollment trends comprise the comparison group. The main estimation equations are the following:

(1) Log(McaidCHIP)_i,t = ∝ + β₁ELE_i,t+ β₂OTHERPOLICY_i,t + β₃COVARIATES_i,t + υ_i + δ_t + ∈_i.t

(2) Log(Medicaid)_i,t = ∝ + β₁ELE_i,t+ β₂OTHERPOLICY_i,t + β₃COVARIATES_i,t + υ_i + δ_t + ∈_i.t

where ∝ is the intercept term, i is an index for state, t is an index for unique quarter, υ is a set of state dummy variables (state fixed effects), δ_t is a set of quarter-specific dummy variables (quarter fixed effects), and isin;_i,t is a random error term. The dependent variable, Log(McaidCHIP)_i,t, is the log of the number of children ever enrolled in Medicaid or CHIP in state i during quarter t, and corresponds Log(McaidCHIP)_i,t to the number of children ever enrolled in Medicaid. We log transform enrollment so that the dependent variable has a normal distribution; otherwise the distribution of the untransformed variable is heavily skewed. We report robust standard errors clustered at the state level to correct for possible heteroskedasticity and autocorrelation (White 1980; Bertrand et al. 2004).

The key independent variable of interest is ELT_i,t, which is set to one when the observation is an ELE state and the quarter either contains the month when ELE was implemented or is after ELE implementation. This variable measures the effects of ELE on Medicaid/CHIP or on Medicaid-only enrollment, depending on the model. With a log-transformed dependent variable, the estimated ELE coefficient reflects the percentage change in total enrollment associated with ELE implementation. We anticipate that ELE will have a positive impact on Medicaid/CHIP enrollment—that is, β₁ is greater than zero.

Compared with the simple descriptive comparisons, findings from this model offer more rigorous evidence of the effects of ELE because they control for many sources of potential confounding factors. The state fixed effects ϒ_i help control time-invariant differences across states that could be correlated with the ELE variable, such as inherent differences between ELE and non-ELE states, for example, potential differences in reporting accuracy of the SEDS data. The quarter fixed effects β_t control for factors common to all states that vary from quarter to quarter.

By including indicators for other state policy changes and time-varying covariates, we control for other factors that change over time, which could also contribute to differences in aggregate Medicaid and CHIP enrollment numbers. OTHERPOLICY is a series of state policy variables and COVARIATES is a series of other state-level controls that vary over time and that could influence Medicaid/CHIP enrollment. In the combined Medicaid/CHIP model—Equation (1)—OTHERPOLICY includes the simulated Medicaid/CHIP eligibility threshold for children;⁵³ the simulated Medicaid eligibility threshold for parents; and dummy indicators for the presence of a separate CHIP program, joint applications for Medicaid and CHIP, presumptive eligibility for Medicaid, administrative verification of income for Medicaid, no in-person interview for Medicaid, continuous eligibility for Medicaid, presumptive eligibility for CHIP, administrative verification of income for CHIP, no in-person interview for CHIP, elimination of asset test for CHIP, and continuous eligibility for CHIP. In the Medicaid-only model—Equation (2)—we use the simulated child Medicaid eligibility threshold and do not include the CHIP-specific policy dummy variables. In the main specification, COVARIATES includes the state quarter-specific unemployment rate and year-state child population estimates that are log transformed.

⁵³ The simulated CHIP eligibility threshold is used for states with separate CHIP programs and the simulated child Medicaid eligibility threshold is used for all other states. In sensitivity models in which we focus on separate CHIP only, COVARIATES includes the CHIP eligibility threshold and CHIP-specific administrative simplification dummy variables.

a. Choosing Comparison States

Difference-in-difference models provide consistent estimates of the treatment effect only if, in the absence of the policy intervention, the time path in the outcome is the same for both the treatment and comparison states (Meyer 1995). For example, if Medicaid enrollment trends upward (downward) at a faster rate within the comparison group relative to the ELE states, the difference-in-difference model will understate (overstate) the benefits of ELE implementation. Given the widespread variation in Medicaid/CHIP participation, enrollment, and policies across states, we anticipate that some non-ELE states will have similar trends in enrollment compared with ELE states, whereas others will have dissimilar trends.

Using a method similar to that employed by Lien and Evans (2005), we chose comparison states that had similar pre-ELE trends in Medicaid and Medicaid/CHIP enrollment as the ELE states. Because the first ELE program was implemented in 2009, we focus on trends in the 2007 and 2008 quarters before adoption of ELE. To select the comparison states, we estimate models similar to Equations (1) and (2) that include a time trend interacted with an ELE state indicator. We include one non-ELE state at a time and test if the average trend among ELE states differs from the trend for that non-ELE state. If we reject the hypothesis at the 5 percent level that the coefficient associated with the interaction term equals zero, we exclude the non-ELE state from the sample, thus increasing the likelihood of choosing comparison states that possess a similar trend in Medicaid or Medicaid/CHIP enrollment as the average treatment state before ELE implementation.

The final Medicaid model includes 33 comparison states and the final Medicaid/CHIP model includes 25 comparison states. In the Medicaid model, we exclude Colorado, and Wyoming from the comparison group. In the combined Medicaid/CHIP model, we exclude California, Connecticut, Florida, Indiana, Kentucky, Missouri, North Dakota, Ohio, Tennessee, and Texas. We excluded Arizona, Illinois, Maine, Montana, Nevada, New Mexico, Virginia, and Washington from both models.

b. Sensitivity Tests

We conduct a series of robustness checks to explore the consistency of the ELE parameter estimates. To the extent that these estimates display consistency, it strengthens the evidence provided by the original model specification and, thereby, the conclusions that can be drawn from the analysis. These robustness checks include reestimating the main model with the following variants:

Alternative specifications of the control variables to determine the source of the ELE effect:
- To start, we remove the policy variables, unemployment rate, and child population from the main model specification (that is, this model includes only state and quarter fixed effects). This simple unadjusted difference-in-difference model removes all time-varying covariates and approximates the average ELE treatment effect from the descriptive data, relative to the chosen set of comparison states (alternative 1).
- We then add the policy variables to the simple model (all at once and each individually) to determine if their inclusion alters the magnitude and significance of the ELE variable (alternative 2).
- We also add the unemployment rate and child population variables to the simple model to determine if their inclusion alters the magnitude and significance of the coefficient on the ELE variable (alternative 3).
- We replace all of the administrative simplification dummy variables with a policy index, ranging from 0 to 5 in the Medicaid model and 0 to 10 in the Medicaid/CHIP model (alternative 4).
Alternative specifications with respect to how the comparison group is defined, excluding non-ELE states in a systematic manner to determine if specific control states drive the main results. These tests are important because the non-ELE states control for what the baseline trend in Medicaid/CHIP enrollment would have been in the absence of ELE.
- We include all 41 non-ELE states as the comparison group in the Medicaid/CHIP and Medicaid models (alternative 5).
- We use the same methodology from the main model to select comparison states, but exclude non-ELE states in which the time trend interaction term is statistically significant at the 10 percent level (alternative 6) and at the 1 percent level (alternative 7). In the Medicaid/CHIP model, there are 22 comparison states in alternative 6 and 35 in alternative 7, compared with 25 in the main model. In the Medicaid-only model, there are 30 and 36 comparison states in alternatives 6 and 7, respectively, compared with 33 in the main model.
- We use a similar but more restrictive method to select comparison states (alternative 8). Instead of interacting the ELE indicator with the time trend, we interact each quarter dummy with the ELE variable and exclude non-ELE states in which we reject the null hypothesis that the joint interaction terms are zero at the 5 percent level. This method increases the likelihood of choosing comparison states that have the same quarter-to-quarter pattern in enrollment before 2009 and excludes more comparisons states relative to the main model scenario. Under this alternative, there are 15 comparison states in the Medicaid/CHIP model and 19 comparison states in the Medicaid model.
- We exclude non-ELE states that are statistical outliers and might not serve as ideal comparison states. For this exercise, we remove 8 non-ELE states from the Medicaid/CHIP model and 9 non-ELE states from the Medicaid-only model that had observations with studentized residuals greater than 2.5 and less than –2.5 in the main model specification (alternative 9).
- Similarly, we reestimate the simple unadjusted Medicaid/CHIP and Medicaid-only difference-in-difference models, including one non-ELE state at time to determine which comparison states have the strongest influence on the ELE coefficient magnitude. We then rank the states based on the estimated ELE coefficient when they are included in the model and reestimate the main model, excluding the comparison states that resulted in the 5 highest and the 5 lowest ELE effects, respectively (alternative 10). We also estimate a variant that excludes comparison states with the 10 highest and 10 lowest ELE effects (alternative 11).

We also estimate several other alternative models to support the robustness of the ELE variable, but the results are not included here.⁵⁴ For instance, we include a control for whether the state expanded coverage to children who have lawfully resided in the United States for fewer than five years under the new CHIPRA option.⁵⁵ We also add controls for the receipt of Cycle I (awarded September 2009) or Cycle II (awarded August 2011) CHIPRA outreach grants.

⁵⁴ These results are discussed in the final report to ASPE (Blavin et al 2012) and are available upon request.These results are discussed in the final report to ASPE (Blavin et al 2012) and are available upon request.
⁵⁵ We also created different ELE policy variables—“ELE through SNAP” and “ELE through tax returns”—to explore whether there appeared to be a differential effect based on the type of ELE program implemented, but the limited experience with ELE to date constrains our ability to make such an assessment.

D. Characterizing ELE Effects

Any attempt to characterize the effects of ELE must be seen in the context of a policy that can vary widely in both its implementation and target population. This underscores the importance of assessing the effects of ELE within individual or small groups of states, as a way to best understand the ELE models that might be most effective. In order to do so, we reestimate the main model excluding one ELE state at a time to determine if the overall effect is primarily driven by the ELE experience in single state or if the ELE effect seems to vary across states.⁵⁶ Similarly, we estimate state-specific models in which we define a unique set of comparison states with similar pre-ELE enrollment trends. We used the same method to select the comparison states as we did in the main model, but each model is estimated on the pre-ELE period specific to each ELE state, providing a more accurate reflection of enrollment trends before ELE implementation within that state. Although we do not place much emphasis on the individual impact estimates derived for each state, these models help validate the robustness of the main results and use a more accurate set of comparison states specific to each ELE state.

We also assess whether ELE works instantaneously or gradually by estimating a model that interacts the main ELE variable with a “number of quarters since ELE adoption” variable (set to zero for pre-ELE implementation and for non-ELE states). However, such assessments are challenging because of the limited sample of ELE states and post-implementation periods, which reduces the degrees of freedom for detecting differences between pre- and post-ELE enrollment for different ELE approaches and different post-ELE time periods.

⁵⁶ The simple difference-in-difference model, which includes only state and quarter dummy variables (described in the multivariate results section) indicates that ELE states had higher enrollment growth after they implemented ELE relative to before they implemented it, compared with the changes in enrollment growth found in the non-ELE comparison states.

E. Results

1. Descriptive Findings

From 2007 through 2011, total Medicaid/CHIP enrollment increased substantially among both ELE and the 41 non-ELE states (data not shown). Total Medicaid/CHIP enrollment among all 8 ELE states increased from 4.18 million to 5.15 million from the first fiscal quarter of 2007 to the last fiscal quarter of 2011, representing an increase of 23 percent. The remaining 41 non-ELE states experienced a fairly comparable percentage increase in Medicaid/CHIP enrollment over the same period, with aggregate enrollment increasing by 24 percent, from 25.0 million to 31.1 million. Medicaid enrollment growth from 2007 to 2011 among ELE states was 2 percentage points higher than the growth rate among non-ELE states. Separate CHIP enrollment among non-ELE states steadily increased over the period as well, but CHIP trends among ELE states varied, as described in the following pages.

Figures IV.1 and IV.2 show the trends in average Medicaid/CHIP and Medicaid enrollment among the eight ELE states, the chosen comparison states, and the excluded non-ELE states. Both figures show that ELE and comparison States had comparable enrollment trends before 2009; the average 2007–2008 quarterly growth rate was approximately 0.4 percent among the ELE and comparison states in the Medicaid model (Figure IV.1) and 0.3 percent in the Medicaid/CHIP model (Figure IV.2). Figures IV.1 and IV.2 also show that, from 2009 to 2011, there were no noticeable differences in average enrollment among the ELE and comparison states. However, the descriptive data highlight quarter-to-quarter changes in enrollment only among ELE and non-ELE states and do not provide an estimate of the causal effect of ELE. By taking into account the scattered implementation of ELE policies and controlling for prevailing trends, fixed differences across states, and time-varying effects such as changes in the economy or state policy, the multivariate analysis provides a more accurate characterization of the overall effects of ELE on Medicaid/CHIP enrollment.⁵⁷

⁵⁷ The simple difference-in-difference model, which includes only state and quarter dummy variables
(described in the multivariate results section) indicates that ELE states had higher enrollment growth after they
implemented ELE relative to before they implemented it, compared with the changes in enrollment growth found in
the non-ELE comparison states.

2. Multivariate Findings

Findings from the main multivariate difference-in-difference models show statistically significant evidence of a positive effect of ELE on enrollment (Table IV.3). On average, the main model, which uses the designated sets of comparison states described earlier, indicates that ELE implementation increased combined Medicaid/CHIP enrollment by 4.2 percent (statistically significant at the 10 percent level) and Medicaid enrollment by 5.6 percent (statistically significant at the 5 percent level), holding all other observed policy and economic changes constant.

Figure IV.1. Average Medicaid Enrollment Among ELE States and Comparison States, 2007-2011

Figure IV.1. Average Medicaid Enrollment Among ELE States and Comparison States, 2007-2011

Figure IV.2. Average Medicaid/CHIP Enrollment Among ELE States and Comparison States, 2007-2011

Figure IV.2. Average Medicaid/CHIP Enrollment Among ELE States and Comparison States, 2007-2011

Source: CMS Statistical Enrollment Data System (SEDS) as of March 30, 2012, verified and provided by CMS.

Notes: (1) Total enrollment includes children who were ever enrolled in Medicaid or CHIP during the fiscal quarter. (2) ELE states are Alabama, Georgia, Iowa, Louisiana, Maryland, New Jersey, Oregon, and South Carolina. Maine and Montana are excluded from all samples.

CHIP = Children's Health Insurance Program; CMS = Centers for Medicare & Medicaid Services; ELE = Express Lane Eligibility.

Table IV.3. Results for Main Multivariate Regression Models, 2007-2011 Quarterly SEDS Data

	Dependent Variable (log transformed)
	Total Medicaid/CHIP Enrollment	Medicaid Enrollment Only
R-squared	0.99	0.99
Sample size	660	820
Express Lane Eligibility	0.0420* (0.024)	0.0562** (0.026)
Unemployment Rate	0.0067 (0.006)	0.0055 (0.006)
Log (Child Population)	0.8550** (0.381)	1.209*** (0.414)
Separate CHIP	0.0120 (0.023)	-0.0104 (0.017)
Simulated Eligibility Threshold for Children	0.0003 (0.001)	0.0005 (0.001)
Simulated Eligibility Threshold for Parents	-0.0024 (0.002)	-0.0037 (0.002)
Joint Application	-0.0331 (0.027)	-0.0279 (0.027)
Presumptive Eligibility-Medicaid	0.0589 (0.042)	0.0192 (0.026)
Admin. Verification of Income-Medicaid	0.0222 (0.050)	0.0635*** (0.023)
No In-Person Interviews-Medicaid	0.0390 (0.061)	0.0254 (0.042)
Continuous Eligibility-Medicaid	0.0443 (0.049)	0.0375 (0.028)
Presumptive Eligibility-CHIP	-0.0153 (0.044)	N/A
Admin. Verification of Income-CHIP	-0.0108 (0.053)	N/A
No In-Person Interviews-CHIP	0.0281 (0.052)	N/A
No Asset Test-CHIP	0.0273 (0.061)	N/A
Continuous Eligibility-CHIP	0.0120 (0.051)	N/A
Constant	1.005 (5.295)	-3.995 (5.776)

Source: CMS Statistical Enrollment Data System (SEDS) as of March 30, 2012, verified and provided by CMS

Notes:(1) Robust standard errors clustered at the state level are in parentheses. (2) *p<.1, **p<.05, ***p<.01. (3) All models include state and quarter fixed effects (coefficients not shown). (4) Total enrollment includes children who were ever enrolled in Medicaid or CHIP during the fiscal quarter. Medicaid enrollment only includes children who were ever enrolled in Title XIX (Medicaid) or Title XXI Medicaid expansion CHIP programs during the fiscal quarter.

CHIP = Children's Health Insurance Program; CMS = Centers for Medicare & Medicaid Services; SEDS = Statistical Enrollment Data System.

a. Sensitivity Analyses

Across a series of alternative models that address different potential sources of specification error and bias (Table IV.4), we consistently find a positive estimated ELE effect, supporting the findings from the main model. In all of the alternative models in Table IV.4, the ELE coefficient remains positive with a central tendency that is close to what we find in the main model; we find that the magnitude associated with the ELE variable in the total Medicaid/CHIP alternative models ranges from 2.4 to 4.8 percent and in the Medicaid-only alternative models ranges from 4.0 to 7.3 percent. For all other models in which the results are not shown, we find that the ELE effect is also close to what we find in the main model.

Although remaining consistently positive, however, we find that the statistical significance of the estimated ELE effect varies across the model specifications. The estimated ELE coefficient in the basic unadjusted difference-in-difference model (alternative 1) is still similar in magnitude to the main fully adjusted model result, but is not statistically significant (p-value = .11 in the Medicaid model and .20 in the Medicaid/CHIP model). Alternatives 2 and 4 show that controlling for differential policy changes among ELE states and the comparison group strengthens the precision of the estimated effect, but that the inclusion or exclusion of the policy variables do not drive the magnitude and direction of the ELE variable in the main model.

We also find that the ELE effect is slightly smaller in magnitude and statistically insignificant (p-value = .12 in the Medicaid model and .14 in the Medicaid/CHIP model) when we use all 41 non-ELE states as the comparison group, as opposed to using states with similar pre-ELE enrollment trends (alternative 5). However, the estimates of the ELE effect from this model could be biased downward because they include comparison states with quarterly enrollment levels trending upward relative to ELE states during the pre-implementation period. We also find that the ELE effect in the Medicaid model is statistically significant in all of the remaining comparison group sensitivity models (alternatives 6 through 11), whereas the ELE coefficient in the Medicaid/CHIP model is statistically significant in only two of these six alternatives.

Appendix C contains a more detailed discussion of each alternative and describes how these results raise confidence in the direction of the ELE effects found in the main Medicaid model, but introduces some uncertainty about the underlying impact of ELE on separate CHIP enrollment based on the findings in the combined Medicaid/CHIP model.

b. Findings on Other Variables

According to the results in the main models, the log transformation of the child population has a positive and statistically significant effect on enrollment as expected (Table IV.3). These results imply that a 1 percent increase in a state’s total child population would yield a 0.86 percent increase in quarterly Medicaid/CHIP enrollment and a 1.21 percent increase in Medicaid enrollment on average, holding all else constant. The coefficient on the unemployment variable is 0.007 in the Medicaid/CHIP enrollment model and 0.005 in the Medicaid-only model, but is statistically insignificant.

Table IV.4. Estimated ELE Effects for Alternative Models, 2007-2011 Quarterly SEDS Data

	Dependent Variable (log transformed)
	Total Medicaid/CHIP Enrollment	Medicaid Enrollment Only
Main Regression Model	0.0420* (0.024)	0.0562** (0.026)
Alternative Specification of Control Variables (1) State and quarter fixed effects only (unadjusted model)	0.0349 (0.028)	0.0406 (0.025)
(2) Unadjusted model+policy variables	0.0471* (0.024)	0.0587** (0.026)
(3) Unadjusted model +unemployment rate and child population	0.0346 (0.028)	0.0401 (0.025)
(4) Policy index instead of dummy variables	0.0478* (0.0280)	0.0518** (0.025)
Alternative Specification of Comparison States (5) Include all 41 non-ELE states as comparison states	0.0335 (0.022)	0.0422 (0.026)
(6) 10% significance threshold for dropping comparison state	0.0360 (0.022)	0.0595** (0.026)
(7) 1% significance threshold for dropping comparison state	0.0377 (0.024)	0.0565** (0.025)
(8) Excluding states based on joint test	0.0244 (0.026)	0.0551* (0.029)
(9) Excluding outlier comparison states	0.0425** (0.020)	0.0726*** (0.023)
(10) Excluding top 5 and bottom 5 comparison states in terms of ELE effect	0.0364* (0.020)	0.0552** (0.024)
(11) Excluding top 10 and bottom 10 comparison states in terms of ELE effect	0.0277 (0.018)	0.0506* (0.025)

Source: CMS Statistical Enrollment Data System (SEDS) as of March 30, 2012, verified and provided by CMS.

CHIP = Children’s Health Insurance Program; CMS = Centers for Medicare & Medicaid Services; ELE = Express Lane Eligibility; SEDS = Statistical Enrollment Data System.

The results in Table IV.5 suggest that the ELE effect on Medicaid/CHIP and Medicaid enrollment varies across states. When we re-estimate each of the main models excluding one ELE state at a time, we find that the coefficient on the ELE variable is smaller in magnitude (compared with the main effect) when Iowa, Maryland, New Jersey, and Oregon are excluded, suggesting that the ELE effect might have been stronger in these four states. The ELE effect is no longer statistically significant at conventional levels when any one of these four states are individually removed from the Medicaid/CHIP model, whereas in the Medicaid model, only the exclusion of Oregon eliminates the statistical significance associated with the ELE coefficient (p-value = .12). However, the ELE effect in the Medicaid model remains statistically significant when Oregon is removed from some of the alternative model specifications that alter the composition of comparison states, such as alternatives 9 and 10. Altogether, this suggests that no single state’s experience drives the average effect in the Medicaid model.

Similarly, the ELE coefficients are positive and statistically significant in both the Medicaid-only and combined Medicaid/CHIP models, with magnitudes exceeding the average effect, when Iowa, Maryland, New Jersey, and Oregon are included in the sample one at a time. We also find a smaller but statistically significant effect for Alabama in the Medicaid-only model. In contrast, there is no evidence in favor of a positive ELE effect on enrollment when the other ELE states are included one at a time. These results hold when we use the main model comparison states and the comparison states specific to each ELE state. We also grouped states by type of ELE program (for example, ELE through income tax returns and ELE through the Supplemental Nutrition Assistance Program [SNAP]), but found inconsistent results across model specifications (results not shown).

The results in Table IV.6 suggest that ELE implementation had a sustained impact on Medicaid enrollment over the period of analysis. We explored this by including a continuous variable that measures the number of quarters since ELE was implemented in the state, along with an interaction term with the ELE dummy variable. We find that the interaction is positive and statistically significant at the 10 percent level in the Medicaid enrollment model only. This result suggests that the ELE effect on enrollment could be stronger the longer states have had ELE in place. However, given the limited number of post-ELE implementation quarters, the sensitivity of this result across model specifications, and the discontinuous nature of ELE implementation in some states, we will provide more confident estimates of the pattern of ELE effects over time in our subsequent analyses, described next.

Table IV.5. Estimated ELE Effect for Models on Different Subsets of ELE States, 2007-2011 Quarterly SEDS Data

	Dependent Variable (log transformed)
	Total Medicaid/CHIP Enrollment	Medicaid Enrollment Only
Main Regression Model	0.0420* (0.024)	0.0562** (0.026)
Models Excluding Individual States:
Alabama	0.0509* (0.028)	0.0625** (0.030)
Georgia	0.0527** (0.024)	0.0642** (0.027)
Iowa	0.0295 (0.024)	0.0480* (0.028)
Louisiana	0.0554** (0.026)	0.0739*** (0.024)
Maryland	0.0325 (0.024)	0.0515* (0.026)
New Jersey	0.0382 (0.026)	0.0514* (0.027)
Oregon	0.0390 (0.024)	0.0344 (0.022)
South Carolina	0.0494* (0.026)	0.0636** (0.026)

Source: CMS Statistical Enrollment Data System (SEDS) as of March 30, 2012, verified and provided by CMS.

Notes: (1) Robust standard errors clustered at the state level are in parentheses. (2) *p<.1, **p<.05, ***p<.01. (3) All models include state and quarter fixed effects (coefficients not shown). All other right-hand side variables are the same as those in the Table 3 main results. (4) Total enrollment includes children who were ever enrolled in Medicaid or CHIP during the fiscal quarter. Medicaid enrollment only includes children who were ever enrolled in Title XIX (Medicaid) or TItle XXI Medicaid expansion CHIP programs during the fiscal quarter. (5) The Medicaid/CHIP models include 660 and the Medicaid model includes 820 state-quarter observations

CMS = Centers for Medicare & Medicaid Services; SEDS = Statistical Enrollment Data System

c. Characterizing the ELE Effects

Table IV.5. Estimated ELE Effect for Models on Different Subsets of ELE States, 2007-2011 Quarterly SEDS Data

	Dependent Variable (log transformed)
	Total Medicaid/CHIP Enrollment	Medicaid Enrollment Only
Main Regression Model	0.0420* (0.024)	0.0562** (0.026)
Models Excluding Individual States: Alabama	0.0509* (0.028)	0.0625** (0.030)
Georgia	0.0527** (0.024)	0.0642** (0.027)
Iowa	0.0295 (0.024)	0.0480* (0.028)
Louisiana	0.0554** (0.026)	0.0739*** (0.024)
Maryland	0.0325 (0.024)	0.0515* (0.026)
New Jersey	0.0382 (0.026)	0.0514* (0.027)
Oregon	0.0390 (0.024)	0.0344 (0.022)
South Carolina	0.0494* (0.026)	0.0636** (0.026)

Source: CMS Statistical Enrollment Data System (SEDS) as of March 30, 2012, verified and provided by CMS.

Notes: (1) Robust standard errors clustered at the state level are in parentheses. (2) *p<.1, **p<.05, ***p<.01. (3) All models include state and quarter fixed effects (coefficients not shown). All other right-hand side variables are the same as those in the Table 3 main results. (4) Total enrollment includes children who were ever enrolled in Medicaid or CHIP during the fiscal quarter. Medicaid enrollment only includes children who were ever enrolled in Title XIX (Medicaid) or TItle XXI Medicaid expansion CHIP programs during the fiscal quarter. (5) The Medicaid/CHIP models include 660 and the Medicaid model includes 820 state-quarter observations

CMS = Centers for Medicare & Medicaid Services; SEDS = Statistical Enrollment Data System.

F. Discussion

Our impact analysis finds significant evidence that ELE implementation increased Medicaid enrollment. Across a series of model specifications, estimated impacts of ELE 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. Although the multivariate analysis finds consistent evidence that ELE had a positive effect on enrollment, some questions remain about the magnitude of the impact.

The less robust evidence of an effect of ELE on combined Medicaid/CHIP enrollment is not surprising given how modestly ELE has been implemented for CHIP. Indeed, at the time of this analysis, only four states implemented ELE through CHIP, one of which (Iowa) had an ELE-like policy in effect before the period of analysis. We would also expect the effects from Oregon and Georgia’s ELE programs would be heavily weighted toward Medicaid, because each state’s Express Lane agency—the Special Supplemental Nutrition Program for Women, Infants and Children (WIC) and SNAP, respectively—has income eligibility levels that encompass the Medicaid threshold but are below the CHIP threshold. In other words, these findings do not mean that ELE policies cannot affect CHIP enrollment, but rather that the existing ELE programs are targeted more toward Medicaid than to CHIP enrollment.

Table IV.6. Estimated ELE Effect for Regressions That Model the ELE Effect over Time, 2007-2011 Quarterly SEDS Data

	Dependent Variable (log transformed)
	Total Medicaid/CHIP Enrollment	Medicaid Enrollment Only
Main Regression Model	0.0420* (0.024)	0.0562** (0.026)
Number of Quarters Since ELE Implementation
ELE	0.0279 (0.024)	0.0374 (0.024)
ELE*Number of Quarters Since ELE Implementation	0.00401 (0.003)	0.00509* (0.003)

Source: CMS Statistical Enrollment Data System (SEDS) as of March 30, 2012, verified and provided by CMS

Notes: (1) Robust standard errors clustered at the state level are in parentheses. (2) *p<.1, **p<.05, ***p<.01. (3) All models include state and quarter fixed effects (coefficients not shown). All other right-hand side variables are the same as those in the Table 3 main results. (4) Total enrollment includes children who were ever enrolled in Medicaid or CHIP during the fiscal quarter. Medicaid enrollment only includes children who were ever enrolled in Title XIX (Medicaid) or TItle XXI Medicaid expansion CHIP programs during the fiscal quarter. (5) The Medicaid/CHIP models include 660 and the Medicaid model includes 820 state-quarter observations.
CHIP = Children's Health Insurance Program; CMS = Centers for Medicare & Medicaid Services; ELE = Express Lane Eligibility; SEDS = Statistical Enrollment Data System.

Although our results suggest that ELE can have a positive effect on Medicaid enrollment, it is uncertain how this finding might generalize to a particular state or state program. We find that ELE had an above-average effect on enrollment in Iowa and Oregon, where ELE primarily functioned through SNAP, and in Maryland and New Jersey, where ELE functioned through the tax system as an outreach tool. However, differences across states or ELE approaches are not statistically significant and the experience for any individual state could vary widely due to differences in policy design, implementation, or its target population.

As with any quasi-experimental impact analysis, unobservable factors might bias our estimated ELE effects. Specifically, unless accounted for in our models, any factors correlated with the timing of ELE adoption that also affect enrollment might bias our estimates of ELE effects. For example, some states might have upgraded their information technology systems or implemented targeted outreach programs, subsequently increasing enrollment, at the same time they carried out ELE⁵⁸. Should such factors increase enrollment in ELE and not be accounted for in our set of policy covariates, it could upwardly bias our estimates. Alternatively, should non-ELE states pursue such unmeasured initiatives, it could bias our impact estimates toward zero.

Acknowledging this bias risk, we have conducted a series of robustness checks that raise confidence in our findings. The estimated effects of ELE vary only slightly across sensitivity tests and are not driven by the inclusion of a single variable (or set of variables) or the inclusion of a single ELE state. We also find that the average ELE effect remains statistically significant and similar in magnitude to what we find in the main regression model when we exclude different sets of comparison states.

Our findings further suggest that ELE might have an extended effect over time (rather than a one-time increase), though this finding should be viewed with caution given the short post-ELE period available at the time of this analysis. (Most of the ELE policies were approved in 2010 or later and this analysis of the SEDS data was finalized in May 2012.) Unlike other eligibility and enrollment simplification strategies that might diffuse slowly, ELE policies were implemented quickly and it is possible that the effect could phase out over time, depending on the details of state policy. We rely on quarterly data to obtain the longest possible window of post-ELE data over the analysis period and will reassess impacts in 2013, when a longer post-ELE experience will be available.

Finally, more research is needed to assess the effects of the non-ELE policy variables on Medicaid or Medicaid/CHIP enrollment. Although we included several of these variables in our models, this analysis cannot conclude whether they had an effect on enrollment because we did not subject them to robustness analyses. Also, a number of them showed little variation in the analysis period, leading to imprecisely estimated effects. A separate and more extensive analysis, focused on individual non-ELE policy variables, is needed to assess their effects rigorously.

⁵⁸ For example, in New Jersey, ELE was the centerpiece of a broader initiative to increase coverage of uninsured children eligible for Medicaid/CHIP and to ensure retention of enrollees in these programs (State of New Jersey 2009). The initiative included broader changes to information technology, staffing, public awareness and media outreach, and application simplification.

V. Summary of First-year Evaluation Findings

Building on research efforts demonstrating that enrollment and retention simplification policies could lead to coverage gains for children and potential efficiency gains for states and the Federal government, the Children’s Health Insurance Program Reauthorization Act of 2009 (CHIPRA) offered states new policy options, and new incentives, to try to find, enroll, and retain eligible children. This report has focused on one of the new options CHIPRA afforded to states—Express Lane Eligibility (ELE)—which permits states to use findings from other public agencies to determine Medicaid or Children’s Health Insurance Program (CHIP) eligibility.

In this chapter, we discuss interim results from the evaluation’s first year. 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 long-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 long-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.

Next, we discuss first-year findings and their implications for administrative costs, enrollment, processes, and policies. We finish with a discussion of future evaluation work and how it will extend the findings from the first year.

A. Implications of First-Year Findings Related to Administrative Costs of ELE

A variety of factors influenced whether states incurred net administrative costs or savings as a result of ELE implementation. Per capita operational savings are realized to the extent that automation and data-sharing arrangements substitute for manual procedures, thereby reducing the resources required to process a single application or renewal. The extent of such savings depends on the number of cases that are processed more efficiently. Simply put, should a state gain operational savings through ELE, the more children it can process through the policy, the quicker it will recoup its up-front administrative investments.

For example, Louisiana’s highly automated ELE program was the most expensive to implement among all the states we examined, involving both programming costs and staff effort to define new policies. However, because the capture is so high—15,000 automated renewals per month—Louisiana offset the implementation costs we have been able to quantify within the first year ELE was in effect for renewals. At the same time, Iowa’s Medicaid ELE program, which is also partly automated and leads to an estimated 28 minutes in savings over processing an application through traditional means, is essentially cost-neutral to the state. This is because the savings in processing are offset by new mailing costs but more so because of low capture—only about 1,300 children are enrolled annually through Iowa Medicaid’s ELE methods. Automation did not result in any staffing reductions in the states we examined. However, staff were freed up to work on other projects or to process applications and renewals that came in through regular channels. More broadly, ELE helped states use their existing staff to handle an increased caseload.

For states considering automated ELE processes, the up-front cost of automation was lower in states with newer information technology systems in place, as expected. Louisiana and Iowa Medicaid were both using legacy information systems that date to the 1970s, resulting in significant programming costs to implement the automation needed for their respective ELE designs (State of Louisiana 2011; Iowa Department of Human Services 2012). In contrast, Oregon implemented a new MMIS in December 2008, and its programming costs of $1,600 to implement ELE reflect the greater simplicity of making changes to such a modern system. Moreover, states with newer information systems are likely to be able to execute the needed programming for ELE much faster, letting them operationalize ELE more rapidly. States with legacy systems should consider these factors when evaluating potential automation for ELE; however, as noted above, Louisiana realized considerable savings, despite its older system.

States also should carefully consider who will bear the administrative costs of ELE implementation. States may be able to find different funding sources to support the implementation of ELE—Louisiana’s implementation was costly, but most of the funds to support it came from a foundation grant. However, states looking to automate processes through ELE in the future may be able to take advantage of the Affordable Care Act’s 90 percent matching rate for eligibility and enrollment systems to help support the investment in automation (76 FR 21590). One source that none of the ELE states evaluated used was amending their third party administrator contracts to extract savings accruing from the new ELE processes, such as faster processing time for applications under ELE, although this could be considered in the future or by other states.

Finally, in determining the total cost impact of ELE, one must consider the cost of providing health care to newly enrolled or renewed children through Medicaid or CHIP along with potential administrative savings. This balance will vary based on each state’s circumstances and approach to ELE; for example, ELE renewals may involve a greater balance of administrative savings relative to health care costs. states should likewise consider the opportunity costs of implementing ELE: Louisiana and Iowa Medicaid, the states that invested the most in ELE programming and implemented the most automated ELE programs among the study states, both deferred work on other projects because of ELE, including work on other simplifications that might also have led to increased enrollment. Another factor to consider is that ELE can, in conjunction with other simplifications, qualify states for CHIPRA performance bonuses through federal fiscal year 2013 to help support the costs of new enrollment. Moreover, state administrators uniformly viewed ELE as a worthwhile strategy to support coverage expansion goals despite expenses (expected or not) they incurred.

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.

C. Implications of First-Year Findings on Processes

At its core, ELE is intended to simplify the processes used to find eligible children, then enroll and retain them in Medicaid and CHIP. As discussed, benefits from more efficient processes can accrue to states in administrative savings—permitting states to do more with less—but also to beneficiaries, by removing barriers to application or renewal processes that historically have prevented many families from insuring their children.

Although the study of ELE impacts on processes was not a focus of first-year evaluation activities, in our discussions with administrators for the descriptive analysis of costs and enrollment, several themes emerged about what factors facilitated ELE process improvements. Among them, states should think carefully about their selection of a viable Express Lane agency partner: it is a critical first step in the ELE process. First-year findings do not suggest that a single type of agency, whether the Supplemental Nutrition Assistance Program (SNAP), Temporary Assistance for Needy Families (TANF), Medicaid or tax agency, assures a certain outcome. Rather, the processes in place for how the ELE program uses partner agency information and whether that partner agency is likely to have data either on many children not already enrolled in public coverage or data on enrolled children (in the case of renewals) seem to affect ELE outcomes. States might consider “test” data matches with potential partners to understand how ELE might function in practice. For example, Alabama considered an ELE partnership for its CHIP program with the state’s child care subsidy program because the income eligibility levels for both programs are similar. Through a test data match, Alabama administrators found that few eligible children were identified through the match with the child care subsidy program, and that, given its older information systems, the costs of the systems changes that would be needed far outweighed the potential gains to coverage.

State administrators also said it was easier to implement ELE processes when they could build upon existing relationships, because of the familiarity with agency staff and their operations, as well as the existence of data use agreements that could easily be modified to accommodate ELE. Of the seven ELE programs studied in the qualitative analysis, six chose an Express Lane partner with which they had a prior data-sharing relationship—only Maryland did not.

Some state administrators said that ELE did not need to be implemented all at once, and that there could be advantages, particularly for ironing out processes, to implementing ELE gradually. For example, Alabama’s ELE, currently a manual process, plans to automate ELE in future phases. Alabama can incorporate lessons from its current ELE processes in its future plans, such as identifying which processes would save the most time through automation, and try to focus resources on those areas. Similarly, two states implemented ELE with either applications or renewals before moving forward on both fronts, or began with supervisors examining a few test cases before resolving policy and operational issues that would later be implemented statewide.

Not every ELE program studied included traditional outreach components as part of its ELE process, such as mailings to those identified through the Express Lane agency findings, but several did. Findings about outreach processes are mixed. ELE may have led to enrollment of children not reached by traditional outreach methods. However, mailing costs can be significant, and states’ use of application mailings have shown only modest enrollments according to the descriptive analysis presented in Chapter III. The one state that used automatic data matches to identify eligible but uninsured children and begin enrolling them into coverage—Louisiana—reached many more children at a much lower marginal cost, compared to states that used traditional mailings. Future work through this evaluation will help interpret these mixed findings, identifying the kinds of state policy choices that maximize enrollment of eligible children while achieving administrative savings.

Improvements in enrollment and retention processes achieved through ELE do appear to remove barriers for beneficiaries in most states studied, which is an important procedural achievement. As discussed in Chapter II, five of the seven ELE programs studied (all except Alabama and Maryland) required less documentation from applicants to process ELE applications compared to standard applications, and required fewer interactions with state staff. In six of seven programs (all except Maryland), ELE applications were processed more quickly than standard ones: in these six states, enrollment through ELE takes about one week or less, compared to up to 30 days using standard processes.

Finally, ELE processes have sometimes had helpful effects on standard processing procedures. For example, in New Jersey, the use of tax information in ELE has led to more extensive use of tax information in standard application processing, and its application simplifications made in support of ELE were also carried over to standard applications in the state. In Alabama and Louisiana, state staff suggested that the time saved by diverting some applications to ELE routes has meant that standard processing times for non-ELE applications are quicker than they would otherwise have been.

Identification of process lessons is a key focus of the second-year evaluation activities, both through case studies and the 51-state survey. Recent literature has identified other potential process lessons, such as minimizing demands on the partner agency and clarifying that differences in program rules are legitimate when training caseworkers, which we can investigate through future evaluation work (Dorn et al. 2012).

The enormous differences among state ELE approaches mean that it is difficult to generalize about ELE effects. As this evaluation continues, we hope to identify state approaches that appear more and less promising as strategies to increase enrollment of eligible children and improve administrative efficiency while safeguarding program integrity. Distinguishing between inherent effects of ELE and the effects that result from particular state choices involving policy and operational approach will be an important contribution of future work.

D. Implications of First-Year Findings for State and Federal Policy

Although first-year findings are tentative, they have helped identify several important policy issues that future evaluation activities can address. For example, we need to better understand ELE’s effects on retention outcomes and whether state policy or process modifications could affect retention through ELE versus standard routes. First-year descriptive findings about retention are mixed: in Iowa Medicaid, retention rates were higher for those entering through ELE, but in Louisiana and in 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.

Another policy of interest at the Federal level is assessing the effects, if any, of linking the CHIPRA performance bonuses to ELE. As noted earlier, 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 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 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 meeting 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 be reporting their error rates to CMS, and if these are available when the final Report to Congress on ELE is submitted in September 2013, we will report them.

E. Future Work: The Second Year of the ELE Evaluation

As described in Chapter I, many new primary data collection activities are planned for the second evaluation year, including a 51-state survey investigating state simplifications and administrator views on ELE, case studies in ELE and non-ELE states including key informant interviews and focus groups, and a utilization analysis in a subset of ELE states where individual-level data are available. These data collection activities will help us interpret first-year findings, understand the longer-term effects of ELE on costs and enrollment, and shed light on new issues (such as whether utilization differs between those entering through ELE versus traditional routes). We are also eager to understand whether early findings about agency choice being less important than other factors persist in states that have partnered with agencies not yet studied, including the newly added ELE program in New Jersey (with NSLP) and Georgia’s ELE, which is partnering with WIC. Another important new contribution will be the qualitative assessment in non-ELE states, which will allow us to understand and compare alternative simplifications and their potential to contribute to administrative savings, enrollment or retention gains, or both, as well as whether the alternative simplifications might help states simplify adults’ enrollment in 2014. This qualitative assessment will also help us understand more about opportunity costs related to investment in simplification strategies besides ELE.

Key first-year activities will be continued in the second year. For example, we will continue to study administrative cost and enrollment outcomes linked to ELE to understand whether and how they change as ELE programs and processes mature. Another year’s worth of data will help us revisit our multivariate analysis and assess the extent to which ELE significantly increases Medicaid and CHIP enrollment. Where data are available, it will also help us investigate whether ELE continues to enroll relatively large numbers of older children, an eligible population that has been traditionally harder to reach than younger children. Similarly, we will update the SEDS analysis. By doing so, we can assess a much more substantial period of ELE performance in most states, enriching the statistical precision of the analysis and providing more detail on the effects of ELE, particularly those that might be lagged or time-limited. We will also use SEDS data on new Medicaid/CHIP enrollment to tease out ELE’s effect on the flow of new enrollees, provided the data quality is sufficiently improved. This could provide additional information related to the effects of ELE on new Medicaid/CHIP enrollment as opposed to retention, as well as lead to additional policy recommendations. For instance, if the ELE effect from this study is primarily driven by new enrollment but not retention, it’s possible that ELE’s impact on enrollment will decrease over time if it is associated with high levels of Medicaid/CHIP churning.

This collection of activities will inform two key deliverables: (1) recommendations on possible ELE improvements, at the state and Federal levels; and (2) a second Report to Congress. The recommendations report will focus on two topics central to maximizing ELE’s effectiveness: (1) administrative and legislative changes to improve ELE’s effectiveness; and (2) best outreach and streamlined enrollment/renewal practices under Medicaid and CHIP, whether through ELE or other simplifications. The audiences for these recommendations are Federal policymakers with the authority to reshape ELE, as well as Federal and state officials seeking guidance on how best to enroll eligible but uninsured children in Medicaid and CHIP.

Given the findings to date and the current policy environment, we anticipate that the recommendations also might include several other topics. For example, we discussed above the importance of considering lessons from ELE for Affordable Care Act implementation. As noted in Chapter I, we are also interested in understanding whether ELE might potentially alleviate churning issues. Moreover, because Federal lawmakers must decide whether to continue ELE’s statutory authority beyond FY 2013, we could identify the gains and risks of ending, continuing, or modifying this option. Included in that assessment would be a review of whether the evidence suggests that ELE is being used for fraudulent enrollment, based on the separate work to calculate ELE error rates, as well as our case studies; this was a concern voiced as ELE was being debated by Congress in authorizing CHIPRA, so it will be an important consideration in the policy recommendation on ELE’s continuation (Congressional Record 2009 [Grassley]; Leavitt 2007). Finally, because ELE might break down silos between public benefit programs, we could identify lessons learned about bridging program gaps through strategies like ELE. As the second-year research activities occur, we expect there also may be additional topics identified that could be included in the recommendations related to ELE.

The final Report to Congress, due in 2013, will synthesize the findings of all data collection and analysis activities from the evaluation’s first and second years, summarizing and incorporating themes that emerged across the different evaluation components.

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Appendix A: Supporting Documentation for the Study of Administrative Costs Associated with Ele

Discussion Guide for Study of Administrative Costs Associated with Ele

A. State Planning for ELE

What planning activities occurred in your state before deciding to implement ELE?
1. How did your state determine the agencies with which you would partner?
2. Did the state complete any cost-benefit analyses before implementing ELE? If so, are you able to share those with us?

B. Understanding State Enrollment Processes

Do you have a flow chart of the standard enrollment process that you could share with us? By standard enrollment process, we mean the one that most children who qualify primarily on the basis of income would complete.
1. How long has this standard enrollment pathway been established?
2. Have there been any major changes in this pathway in the past three years? If so, please describe these changes, when they occurred, and why they were implemented.
If no flow chart is available, please talk us through the different steps that someone must complete to enroll in your state’s Medicaid and/or CHIP program through standard pathways.
1. Please think about the different people who must review an application, enter or verify data in a computer system, or have contact with an applicant. If your state uses both electronic and paper application pathways, we are interested in developing (or obtaining) a process flow chart for both methods.
2. Please include any steps that are completed by contracted vendors, as well as those completed by state staff.
3. Please also consider any steps that occur in passing an application between the Medicaid and CHIP programs.
Please walk us through the steps of the standard enrollment pathway(s) and indicate which steps are different for beneficiaries who enroll through the ELE pathway. In what ways do these steps differ?
1. Has the ELE enrollment process changed since ELE was first implemented? If so, please describe the ways this process has changed.
For children who primarily qualify on the basis of income, does your state offer any other facilitated enrollment pathways, aside from ELE, that would follow a different set of steps and processes?
1. What are these alternate enrollment pathways, and how do they differ from the standard route? When were these alternate pathways implemented?
If your state has also implemented ELE for renewals, please talk us through the standard renewal pathway, and any differences for an ELE renewal.

C. Marginal Impacts on Contracted Costs and Staff Time

Which of the steps in the standard enrollment process (if any) are completed by a contracted vendor?
1. Which steps in the ELE enrollment process are completed by a contracted vendor?
How are those contractors reimbursed? Some potential examples include—on a per-application basis, a fixed contract price, or a cost-plus-fee basis.
1. What changes in contractor reimbursement have occurred, if any, as a result of ELE?
About how much time do staff spend completing each step in the standard application process?
For steps that are unique to ELE, about how much time do staff spend completing each step for a new ELE application?
What is the salary range for a staff member who processes enrollment applications (standard or ELE)?
How many staff members are dedicated to managing ELE applications full time?
1. How many staff members spend part of their time managing ELE applications? About what percentage of their time would you estimate is spent in managing ELE applications?
2. Were any staff members newly hired to support ELE? Are there plans to hire anyone for such a position?
Are there any new ongoing direct expenses associated with ELE enrollment? Some potential examples include new mailing expenses or printing costs for customized enrollment forms.

D. Implementation Costs

Thinking back to when ELE was first implemented, what data system changes, if any, were needed to implement ELE?
1. About how many staff days (or what contractor costs) were required to make those changes?
2. What is the salary range for a staff member responsible for data systems management?
3. Are there any other reasons why these systems changes were made, or were they done solely to support ELE?
4. Would any of these data systems changes be necessary to implement provisions of the Affordable Care Act?
Please describe any training concerning ELE that occurred at implementation.
1. How many people were involved in the training? What types of staff?
2. How long did the training take to complete?
Aside from staff training and data system enhancements, did any other major activities or processes occur in order to implement ELE?
Please describe other eligibility or enrollment process initiatives (if any) that were implemented concurrently with or around the same time as ELE.
Did ELE implementation take resources away from other activities? If so, what types of resources were diverted and which activities were postponed or deprioritized?

E. ELE Impacts on Partner Agencies

Please describe any data-sharing or outreach coordination that you conducted with your ELE partner agency before implementing ELE.
Have your data-sharing and outreach coordination activities changed because of the implementation of ELE? If so, how?
Are you aware of any process or staffing changes your partner agency has made to enable ELE?
Who should we contact at your partner agency to better understand how its costs or staffing might have changed?

F. Overall Enrollment and Outreach Costs

What is your annual budget for all enrollment activities for the current year? What is the size of that budget in comparison to the last two years?
Please tell us about your major outreach strategies (aside from ELE) to enroll children who would qualify primarily on the basis of income.
What is your annual budget for these activities for the current year? What is the size of that budget in comparison to the last two years?
How do you evaluate the success of those investments in outreach? Do you know or have an estimate of how many children are contacted and/or enrolled through direct outreach?

G. Impacts for Beneficiaries

From the beneficiary perspective, how does the application experience change when they enroll through ELE, rather than through standard enrollment routes?
1. Are there changes in the total days necessary to process an application?
2. Does the number of interactions with state staff or contractors that are required change?
3. Does the type of documentation or number of different documents that they must provide change?
From your observations, how easy is the ELE enrollment process relative to the standard enrollment process?
Do you have a sense of how many children have been successfully enrolled in Medicaid or CHIP as a result of ELE?

H. Concluding Thoughts

Are there other aspects of your ELE program that we should know about to understand how financial and staff time costs differ under ELE?
Do you anticipate any changes to the cost of ELE in the next year? For example, because fixed-price contracts will be renegotiated to accommodate changes in work flow, because new staff will need to be hired, or because some aspects of ELE will become newly automated?
Are there additional staff members within your agency with whom we should follow up for additional detail on any of the topics we have discussed?

Appendix Table A.1. Estimated Implementation Costs of ELE

State Program Using ELE	Alabama	Iowa Medicaid	Iowa CHIP	Louisiana	Maryland	New Jersey	Oregon
Labor Costs to the State for Programming^a	$6,300	$84,000	$15,000	$106,000	Not available	$40,000	$1,600
Number of Staff Trained	250	650	600	350	55	15^b	60
Length of Training	2 hours	20 minutes	1 hour	2 hours	1 hour	4 hours	2 hours
Other Tangible Costs	None	Shorter application form developed Cost: unknown	None	Up to 10 program staff worked to resolve technical problems for six months Cost: 310,000	Shorter application form developed Cost: unknown	Shorter application form developed Tax filing instructions changed Cost: unknown	Shorter application form developed Cost: $2,050
Opportunity Costs	None	Staff diverted from other projects	Not available^c	Staff diverted from other projects	None^d	None	None

Files

rpt.pdf (pdf, 1.1 MB)

Topics

Healthcare Coverage & Access | Access to Services and Benefits & Services Integration

Populations

Children

Location- & Geography-Based Data

State Data

Program

Medicaid | Children's Health Insurance Program (CHIP)