CHIPRA Mandated Evaluation of Express Lane Eligibility: First Year Findings. 2. Additional Data Sources

12/01/2012

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.51 We 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]).

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