Changes in Joint Medicaid/CHIP and SNAP Participation Rates, 2011 to 2013: Findings from the Work Support Strategies Evaluation

Brief

Changes in Joint Medicaid/CHIP and SNAP Participation Rates, 2011 to 2013: Findings from the Work Support Strategies Evaluation

Abstract

As part of the Work Support Strategies initiative, states took steps to improve access to multiple public benefits. This brief examines one measure of these efforts, changes over time in the joint participation rate—the extent to which individuals who are eligible for the Supplemental Nutrition Assistance Program (SNAP) and public health insurance coverage (Medicaid or the Children's Health Insurance Program) receive both. We find that four out of five states had substantial increases in multiple benefit receipt among eligible individuals from 2011 to 2013. These findings demonstrate states can implement changes to improve access to the both SNAP and Medicaid.

Full Publication




Changes in Joint Medicaid/CHIP and SNAP Participation Rates, 2011 to 2013

Public food and medical assistance programs for low-income families are intended to address different needs, but often for the same families. But are low-income families receiving the combined set of public programs intended to assist them? Tracking the joint participation rate—a measure of the extent to which individuals eligible for the Supplemental Nutrition Assistance Program (SNAP) and health insurance coverage through Medicaid or the Children’s Health Insurance Program (CHIP) receive both benefits—is an important step in assessing whether these public programs are meeting families’ needs.1 This brief examines changes over time in the joint participation rate for five states. As part of the Work Support Strategies (WSS) initiative, these five states took steps to improve access to multiple benefits for eligible individuals and families. We find that four of these states made substantial strides toward increasing multiple benefit receipt between 2011 and 2013. These findings show that states can implement changes to improve access to the combined set of SNAP and Medicaid benefits.

 

Why Joint Participation?

 

Federal and state food and medical assistance programs aim to improve the circumstances of low-income families and individuals. To achieve this goal, benefits must reach those in need. Monitoring the program participation rate—the percentage of those eligible for programs who actually receive benefits—can demonstrate how well programs are reaching those in need, as defined by program eligibility rules. Participation rates are regularly estimated and tracked for individual programs such as SNAP and Medicaid/CHIP (Cunnyngham 2016; Kenney et al. 2012, 2016).2

 

Because public programs generally address a specific need, and many low-income families have multiple needs, it is important to evaluate whether public assistance programs are reaching families in need. One way to do this is to evaluate joint participation rates. Tracking joint participation rates over time provides data states can use to examine the effects of existing efforts and the need for additional efforts to improve access to multiple benefits. However, there is currently no regular source of information on joint food and medical assistance program participation.

 

States that participated in the WSS initiative (see box 1) committed to improving access to multiple benefits through a range of efforts, including increased coordination of eligibility processes and policies across programs and reduction of bureaucratic redundancies that posed barriers to multiple-program receipt. Five WSS states (Colorado, Idaho, Illinois, North Carolina, and South Carolina) provided data that allow us to calculate and examine changes in joint participation rates for SNAP and Medicaid/CHIP from 2011 to 2013. We start with 2011, the first year of the WSS project, and measure change through 2013 because this was the last year before implementation of some of the biggest changes included in the Patient Protection and Affordable Care Act. Effects of the Affordable Care Act on joint participation rates could be significant given major increases in Medicaid eligibility in many states and the implications for program operations of implementing the law’s many requirements. Focusing on 2011–13 allows us to track whether WSS activities were associated with increases in joint participation rates.

 

This brief builds on previous work (Wheaton et al. 2014) that presented SNAP and Medicaid/CHIP joint eligibility estimates for all 50 states and joint participation rates for 5 WSS states for 2011.3 We first present information on joint eligibility for children and adults in these 5 states. These estimates show how many individuals qualify and could be served by both programs. They also show the variability across states. We then report changes in joint participation rates in these states from 2011 to 2013 for children, adults under 65, and the combined population under 65.

Box 1. Work Support Strategies

 

Joint Eligibility

 

In 2013, 42 million individuals, over half of them children, were eligible for both SNAP and Medicaid/CHIP in the United States (Wheaton et al. 2016). Individuals may not be aware of their eligibility because of a lack of information or the complexity of program rules, so we cannot rely on survey reports of eligibility to determine the number of people eligible for each program or combinations of programs. To estimate SNAP and Medicaid/CHIP eligibility (for each program and jointly) we use microsimulation models. These models use information on family income, size, and other circumstances from household survey data together with SNAP and Medicaid/CHIP program rules to determine how many individuals are eligible for each program and for both programs.4

 

Figure 1 shows 2013 joint eligibility rates for children (under age 19) and nonelderly adults (ages 19 to 64) in the five WSS states and nationwide. Nationwide, 35 percent of all children and 8 percent of adults were eligible for SNAP and Medicaid/CHIP. Similarly, a substantially higher percentage of children were eligible compared to adults in each state. The joint eligibility rate for children is higher than for adults because a greater share of children are poor (DeNavas-Walt and Proctor 2015) and because children remain eligible for health coverage under Medicaid/CHIP at higher income levels. Future estimates should show an increase in eligibility among adults in states that expand Medicaid eligibility under the ACA.

Figure 1. Children and Adults Eligible for Both SNAP and Medicaid/CHIP in WSS States and Nationwide, 2013

The percentage of children eligible for both programs varied considerably across states, from 25 percent in Colorado to 44 percent in North Carolina. Adult eligibility varied to a lesser extent, from 5 percent in Idaho to 10 percent in South Carolina. This variation across states is caused by the same factors mentioned earlier: differences in eligibility rules and population characteristics.

 

State poverty rates and program rules for SNAP and Medicaid/CHIP for 2013 are listed in table 1.5 North Carolina has extended eligibility for SNAP to households with incomes up to 200 percent of the federal poverty level (FPL), compared to 130 percent of FPL in the other states. The Medicaid/CHIP eligibly threshold for children is also 200 percent of FPL. Illinois has the highest income limit among these states for child eligibility for Medicaid/CHIP, 300 percent of FPL, but has not raised the SNAP eligibility limit above 130 percent of FPL. In addition, North Carolina and South Carolina have higher poverty rates than the other three states, resulting in more individuals eligible for benefits, all else equal. For adults, Idaho has the lowest Medicaid eligibility threshold among these states for parents and caretakers of dependent children, 20 percent of FPL for jobless adults and 37 percent of FPL for working adults. All of these factors affect the share of the population eligible for both programs.

Table 1. State Poverty Rates and Program Rules, 2013

Changes in joint program eligibility rates between 2011 and 2013 were, as expected, small given no substantial changes in rules or poverty levels. Table 2 shows that changes in all states were of less than half a percentage point among adults. Colorado and Idaho experienced somewhat greater changes among children: nearly a percentage point decrease in Colorado and a 1.6 point increase in Idaho.

Table 2. Change in Children and Adults Eligible for Both SNAP and Medicaid/CHIP in WSS States and Nationwide, 2011 to 2013

 

Joint Participation

 

There are a number of reasons eligible individuals may not participate in programs. These include lack of knowledge or misinformation about the program or their eligibility; not needing or wanting to accept public benefits; difficulty getting to the correct office, communicating with staff, completing applications, or supplying necessary documentation; long waiting times at each step; or judging the costs of accessing the program to be higher than the benefit.6

 

Over the course of the WSS initiative, states took several steps to address program access issues:

  • Shorter applications and online applications.
  • Screeners that assess eligibility of new clients for multiple programs.
  • Sharing of applicant documentation across programs to determine eligibility and/or automatically enroll clients.
  • Electronic verification of income and citizenships.
  • Integrated computer systems that determine eligibility.
  • New ways for applicants to find information about the status of their benefits.
  • Colocated program offices.
  • Shorter wait times in offices and overall time between application and receipt of benefits.

The specific activities a state carried out during WSS depended on their individual goals, broader context factors, and status when the initiative began.7 We expect to see increases in joint participation rates for these states based on WSS efforts.

 

Joint Participation Rate Outcomes

 

Figures 3A, 3B, and 3C show our calculated joint participation rate estimates for five WSS states in 2011 and 2013 for the total nonelderly population, children, and nonelderly adults, respectively.8 We exclude eligible persons not covered by Medicaid/CHIP who have health insurance from another source (such as through their employer or a spouse’s or parent’s employer). Although these individuals are eligible for public health coverage programs, they are less likely to take up these benefits. Restrictions exist to discourage switching from private to public coverage, such as requiring people be uninsured for several months before they can enroll in public coverage. We exclude them so as to best reflect the joint participation rates of persons in need of Medicaid/CHIP.

Figure 3. Joint SNAP and Medicaid/CHIP Participation Rates

The results show that four of the five states (Colorado, Idaho, Illinois, and South Carolina) had increases in joint participation rates from 2011 to 2013 in almost all age groups. Increases were substantial in three states: from 73 percent to 87 percent in Colorado, 70 percent to 78 percent in Illinois, and 73 percent to 81 percent in South Carolina. The increase in Idaho was smaller (from 93 percent to 96 percent) likely because the state’s initial rates were already high. North Carolina’s joint participation rates were fairly stable, with a slight decrease among adults and children.

 

We cannot definitively attribute changes (or lack thereof) in joint participation rates to WSS activities. In addition to differences in program operations and activities, variation across states could also reflect factors such as state culture, attitudes toward benefit receipt, differences in awareness about Medicaid/CHIP from public discussion and the ACA, and so on.9

 

Isolating results for children (figure 3B) and for adults (figure 3C) shows that increases in joint participation rates were not confined to one age group but occurred for both. Colorado had substantial increases among both children (77 percent to 90 percent) and adults (65 percent to 81 percent), as did Illinois (85 percent to 94 percent among children and 47 percent to 55 percent among adults). Illinois may have lower overall joint participation rates among adults because of its relatively higher income eligibility criteria for Medicaid/CHIP (up to 139 percent of FPL). Eligible individuals with relatively higher incomes may not know they are eligible or be as familiar with public programs as lower-income individuals. They are also more likely to be working, which can cause challenges for application, and could associate a greater stigma with public benefits.10 They may also have less incentive to enroll because they are only eligible for a relatively small SNAP benefit.

 

Idaho had a substantial increase among adults (78 percent to 90 percent) and a decline among children, although the rate remained very high at 95 percent. Idaho had higher 2011 joint participation rates than the other states, possibly reflecting efforts the state made prior to the WSS initiative to integrate programs and make it easier for eligible families to access state self-reliance programs. South Carolina had a substantial increase among children (80 percent to 92 percent) and a small increase among adults (62 percent to 64 percent). During this time, South Carolina began using electronic data on SNAP eligibility to automatically enroll children in Medicaid (Edwards and Kellenberg 2013). North Carolina had small declines among both children and adults. North Carolina’s relatively lower joint participation rate among children in both years may be explained in part by the state’s extension of SNAP eligibility to 200 percent of FPL, making it one of just five states to do so. Higher-income eligible individuals may be less likely to take up benefits for the reasons stated above. In addition, the state began implementing a new eligibility system for SNAP in 2013, delaying processing of SNAP applications and benefit renewals and possibly affecting joint participation numbers.

 

Conclusions

 

Different public programs for low-income families address different needs, but often for the same families. Over 42 million individuals nationwide, more than half of them children, are eligible for SNAP and Medicaid/CHIP services to help them meet their nutrition and health care needs. Program participation rates— the percentage of those eligible for programs who actually receive benefits—are an important measure of progress toward meeting the needs of low-income families. Monitoring the rate of joint participation in SNAP and Medicaid/CHIP allows policymakers to track whether public programs are reaching those they intend to assist.

 

This brief uses data from five states that participated in the WSS initiative to calculate changes in joint participation rates from 2011 to 2013. Because these states aimed to improve the extent to which individuals receive the benefits for which they are eligible, we expected to see rates increase. We find that four of the five WSS states made substantial progress toward increasing joint participation in SNAP and Medicaid/CHIP over this time. As we stated earlier, we cannot definitively attribute these changes (or lack thereof) to WSS activities. However, given the stated goals of WSS states, these results suggest state efforts helped improve access to the combined set of benefits and are a bellwether to other states that such improvement is possible.

 

This method of calculating joint participation rates could be extended to other states with the necessary state administrative data. Together with current information on individual program participation rates, these data offer states additional ways of measuring their progress toward meeting the needs of low-income individuals.

Methodology

 

The joint participation rate estimates presented in this brief were developed by dividing the number of individuals participating in both SNAP and Medicaid/CHIP, according to state administrative data, by the number estimated to be eligible for both programs according to microsimulation estimates developed from the American Community Survey (ACS).

 

Eligibility

 

The joint eligibility estimates are prepared using 2011 and 2013 ACS data as processed by TRIM3 and the Urban Institute’s Medicaid/CHIP Eligibility Simulation Model.11 Both models capture detailed, state-level variation in program eligibility rules. The ACS is a nationwide survey that provides yearly estimates of demographic, housing, social, and economic characteristics for all states as well as smaller geographic areas.12 Residents of group quarters and institutions are excluded from our estimates, as are members of the military and persons age 65 and above.

 

Joint eligibility is determined by merging the TRIM3 SNAP and Medicaid/CHIP Simulation Model eligibility estimates at the individual level. TRIM3 SNAP estimates are generated at the monthly level, and we assume that a person found to be eligible for SNAP in a given month who is also found eligible for Medicaid according to our model’s estimate will be eligible for Medicaid in any months in which he or she is eligible for SNAP.13 Results are presented as average monthly estimates and reflect the number of people eligible for both SNAP and Medicaid in the average month of the year. Additional details on the methodology for estimating joint eligibility are presented in Wheaton, Lynch, and Johnson (2016).

 

Participation

 

The average number of individuals receiving both SNAP and Medicaid each month is obtained from tabulations of administrative data provided to us by Colorado, Idaho, Illinois, North Carolina, and South Carolina. South Carolina’s data reflect the counts for April 2011 and April 2013. Other states provided counts for each month, which we then used to calculate an average monthly estimate for each year. North Carolina’s 2011 numbers exclude September and October because of emergency SNAP benefits issued in response to Hurricane Irene. Participation numbers for North Carolina in 2011 differ from those included in Wheaton et al. (2014) because of revised administrative numbers provided by the state.

 

To maintain consistency with eligibility estimates, we asked states to exclude, to the extent possible, individuals enrolled in both Medicaid and Medicare, persons only eligible for Medicaid family planning services, and persons receiving medical assistance from state-funded programs other than Medicaid/CHIP. People in the sample identified as undocumented immigrants, who may receive emergency Medicaid, are not included in joint eligibility or participation estimates because they are not eligible for SNAP.

 

Joint Participation Rates

 

Joint participation rates for the five states are calculated by dividing the average number of nonelderly persons receiving both SNAP and Medicaid each month in 2011 or 2013 by the average number found eligible for both each month in the respective year. The joint participation rate estimates exclude eligible persons not covered by Medicaid/CHIP who have health insurance from another source (such as through their employer or a spouse’s or parent’s employer). Health insurance coverage is based on ACS data as edited by the Urban Institute (Lynch, Haley, and Kenney 2014).

 

The 2011 joint participation rates presented here differ somewhat from our prior analysis (Wheaton et al. 2014) because of methodological changes to improve consistency between Medicaid/CHIP and SNAP eligibility estimates and to incorporate revised 2011 administrative data estimates for North Carolina.

 

Notes

  1. Though Medicaid and CHIP are two different programs, we group them together for the purpose of studying access to work support benefits.
  2. Because the methods used in this brief differ from those used in the USDA-published SNAP participation rates (Cunnyngham 2016) and Medicaid/CHIP rates reported in Kenney et al. (2016), we cannot make direct comparisons to individual program participation rates.
  3. Rhode Island was also part of the WSS initiative but was unable to provide joint administrative data in the format necessary for this study.
  4. A summary description of methods and assumptions used to create these eligibility numbers is provided at the end of this brief.
  5. Federally set eligibility for SNAP is a net income limit of 100 percent of FPL, a gross income limit of 130 percent of FPL (for units without an elderly or disabled member), and a liquid asset limit of $2,000 ($3,000 for units with an elderly or disabled member). Since 1999, states have been allowed to expand eligibility by adopting broad-based categorical eligibility policies, which make households that receive services funded by Temporary Assistance for Needy Families categorically eligible for SNAP. Under these policies, states are able to increase federal SNAP limits on household income and remove limits on assets.
  6. Some of these can occur at the time of benefit renewal as well so that individuals who previously received benefits do not continue to receive them.
  7. For more information about WSS initiative activities, see Hahn et al. (2016); Isaacs, Katz, and Kassabian (2016); and Loprest, Gearing, and Kassabian (2016). Additional outcomes of these activities will be available in Isaacs, Katz, and Amin (forthcoming).
  8. States provided administrative data on the number of individuals under age 19, ages 19 to 64, and the total combined population under 65 receiving both SNAP and Medicaid/CHIP in 2011 and 2013.
  9. Although considerable care is taken in the eligibility simulation and use of administrative data for this analysis, methodological and sampling issues may have different effects in different states.
  10. Lower participation among relatively higher-income people is also observed in studies of Medicaid/CHIP (Kenney et al. 2012).
  11. TRIM3 is funded primarily by the Department of Health and Human Services, Office of the Assistant Secretary for Planning and Evaluation. Documentation is available at http://trim3.urban.org. The adaptation of TRIM3 methods to the ACS data was funded by the Annie E. Casey Foundation and the MacArthur Foundation.
  12. We use an augmented version of the ACS developed by researchers at the University of Minnesota as part of their Integrated Public Use Microdata Series project because it includes imputations that provide additional detail on the relationships of individuals in ACS households. See Ruggles et al. (2010).
  13. The Medicaid/CHIP eligibility estimate is obtained by dividing annual income by 12 and comparing to the relevant income threshold.

 

References

 

Cunnyngham, Karen E. 2016. “Reaching Those in Need: Estimates of State Supplemental Nutrition Assistance Program Participation Rates in 2013.” Alexandria, VA: US Department of Agriculture.

DeNavas-Walt, Carmen, and Bernadette D. Proctor. 2015. Income and Poverty in the United States: 2014. Washington, DC: US Government Printing Office.

Edwards, Jennifer, and Rebecca Kellenberg. 2013. CHIPRA Express Lane Eligibility Evaluation: Case Study of South Carolina's Express Lane Eligibility Processes. Washington, DC: Mathematica Policy Research.

Hahn, Heather, Ria Amin, David Kassabian, and Maeve Gearing. 2016. Improving Business Processes for Delivering Work Supports for Low-Income Families: Findings from the Work Support Strategies Evaluation. Washington, DC: Urban Institute.

Heberlein, Martha, Tricia Brooks, Joan Alker, Samantha Artiga, Jessica Stephens. 2013. Getting into Gear for 2014: Findings from a 50-State Survey of Eligibility, Enrollment, Renewal, and Cost-Sharing Policies in Medicaid and CHIP, 2012–2013. Washington, DC: Kaiser Commission on Medicaid and the Uninsured.

Isaacs, Julia B., Michael Katz, and David Kassabian. 2016. Changing Policies to Streamline Access to Medicaid, SNAP, and Child Care Assistance: Findings from the Work Support Strategies Evaluation. Washington, DC: Urban Institute.

Isaacs, Julia B., Michael Katz, and Ria Amin. Forthcoming. Improving the Efficiency of Benefit Delivery: Outcomes from the Work Support Strategies Evaluation. Washington, DC: Urban Institute.

Kenney, Genevieve M., Victoria Lynch, Michael Huntress, Jennifer M. Haley, and Nathaniel Anderson. 2012 “Medicaid/CHIP Participation among Children and Parents.” Washington, DC: Urban Institute.

Kenney, Genevieve M., Jennifer M. Haley, Clare Wang Pan, Victoria Lynch, and Matthew Buettgens. 2016. Children's Coverage Climb Continues: Uninsurance and Medicaid/ CHIP Eligibility and Participation under the ACA. Washington, DC: Urban Institute.

Laird, Elizabeth, and Carole Trippe. 2014. Programs Conferring Categorical Eligibility for SNAP: State Policies and the Number and Characteristics of Households Affected. Washington, DC: Mathematica Policy Research.

Loprest, Pamela, Maeve Gearing, and David Kassabian. 2016. States’ Use of Technology to Improve Delivery of Benefits: Findings from the Work Support Strategies Evaluation. Washington, DC: Urban Institute.

Lynch, Victoria, Jennifer Haley, and Genevieve M. Kenney. 2014. “The Urban Institute Health Policy Center’s Medicaid/CHIP Eligibility Simulation Model.” Washington, DC: Urban Institute.

Ruggles, Steven, J. Trent Alexander, Katie Genadek, Ronald Goeken, Matthew B. Schroeder, and Matthew Sobek. 2010. Integrated Public Use Microdata Series: Version 5.0 (Machine-readable database). Minneapolis: University of Minnesota.

Wheaton, Laura, Victoria Lynch, Pamela Loprest, Erika Huber. 2014. Joint SNAP and Medicaid/CHIP Program Eligibility and Participation in 2011. Washington, DC: Urban Institute.

Wheaton, Laura, Victoria Lynch, and Martha Johnson. 2016. “The Overlap in SNAP and Medicaid/CHIP Eligibility, 2013.” Washington, DC: Urban Institute.

 

About the Authors

 

Pamela Loprest is a senior fellow and labor economist in the Income and Benefits Policy Center at the Urban Institute. Loprest studies how to structure programs and policies to better support work among low-income families, especially those with work-related challenges, including research on families disconnected from work and welfare and persons with disabilities. She co-led the Work Support Strategies evaluation.

 

Victoria Lynch is a research associate in the Health Policy Center at the Urban Institute. She is a survey methodologist with in-depth understanding of public policy on Medicaid, the Children’s Health Insurance Program (CHIP), and other health insurance.

 

Laura Wheaton is a senior fellow in the Urban Institute’s Income and Benefits Policy Center specializing in the analysis of government safety-net programs, poverty estimation, and the microsimulation modeling of tax and transfer programs. Wheaton codirects the TRIM3 microsimulation model project. Her recent projects include analyses of the antipoverty effects of nutrition assistance programs, the effects of SNAP asset limits, and SNAP churn.

 

Acknowledgments

 

The Ford Foundation has provided generous lead funding for the Work Support Strategies initiative, including its evaluation, by committing $21 million over five years. The Open Society Foundations, Annie E. Casey Foundation, Kresge Foundation, and JPMorgan Chase also gave crucial support. We are grateful to them and to all our funders, who make it possible for Urban to advance its mission.

 

The views expressed are those of the authors and should not be attributed to the Urban Institute, its trustees, or its funders. Funders do not determine research findings or the insights and recommendations of Urban experts. Further information on the Urban Institute’s funding principles is available at www.urban.org/support.

 

The authors would like to acknowledge the other members of the Work Support Strategies evaluation team who helped collect the data on which this report is based, including Heather Hahn, Julia B. Isaacs, Monica Rohacek, Ria Amin, David Kassabian, and Michael Katz. We thank Joyce Morton and Silke Taylor for programming support and Martha Johnson for research assistance. We also would like to thank Stacey Dean, Olivia Golden, Genevieve Kenney, and Elizabeth Lower-Basch for their reviews of earlier drafts and helpful insights. We would also like to thank the many state and local staff members in Colorado, Idaho, Illinois, North Carolina, and South Carolina who provided us with and helped us to understand their administrative data.

Copyright June 2016. Urban Institute


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