Factors Contributing to High Estimated SNAP Participation Rates: 

 

Insights from Microsimulation Model Comparisons and 

Analysis of CPS-Linked SNAP Administrative Records Data 

Final Report 

April 30, 2021 

Laura Wheaton 

Nancy Wemmerus 

Thomas Godfrey 

Submitted to: 

U.S. Bureau of the Census, 

Data Acquisitions Branch, Economic Reimbursable 

Surveys Division 

4600 Silver Hill Rd, Suitland, MD 20746 

Submitted by: 

Decision Demographics, LLC 

5217 Shawano Terrace 

Madison, WI 53705 

Project Officer: 

 Joey Morales 

Project Director: 

 Nancy Wemmerus 

Purchase Order 333LB18P00000060 
Factors Contributing to High Estimated SNAP Participation Rates

i

Acknowledgements 

This report was prepared for the U.S. Bureau of the Census, Data Acquisitions Branch, Economic 

Reimbursable Surveys Division by Laura Wheaton of the Urban Institute and Nancy Wemmerus 

and Thomas Godfrey of Decision Demographics, LLC with quality assurance review by Stephen 

Tordella of Decision Demographics and Douglas Wissoker of the Urban Institute. 

The authors would like to acknowledge invaluable contributions to this study from Kameron Burt 

and Barbara Murphy of the Food and Nutrition Service, U.S. Department of Agriculture; Karen 

Cunnyngham, John Czajka, Joel Smith, and Alma Vigil of Mathematica; Kevin Werner, Douglas 

Wissoker, Robert Santos, Sarah Knowles, Danielle Kwon, and Silke Taylor of the Urban Institute; 

Stephen Tordella and Priscilla Foran of Decision Demographics; and Joey Morales, Denise 

Flanagan Doyle, Renuka Bhaskar, Nikolas Pharris-Ciurej, Erik Vickstrom, Jonathan Eggleston, 

John Voorheis, Greg Weyland, Tara Jones, and Lori Fox of the U.S. Census Bureau. 

This study was conducted under purchase order number 333LB18P00000060. 

All non-public data presented in text, tables, and figures have been reviewed and authorized for 

inclusion by a U.S. Census Bureau Disclosure Review Board with DRB Delegated Authority 

Approval Numbers CBDRB-FY21-CES014-027, CBDRB-FY21-CES014-028, CBDRB-FY21-

CES014-029, and CBDRB-FY21-CES014-032. 
Factors Contributing to High Estimated SNAP Participation Rates

ii

Table of Contents 

1. Introduction ................................................................................................................................ 1 

Microsimulation Models ......................................................................................................... 1 

Background and Motivation for the Study .............................................................................. 2 

Analyses and Key Findings ..................................................................................................... 3 

Conclusion and Recommendations for Future Research ........................................................ 4 

Definitions Used in this Report ............................................................................................... 5 

2. SNAP Participation Rate Estimates by Model and Data Source ................................................ 6 

Models, Surveys, and Data Years ........................................................................................... 6 

Methodology ........................................................................................................................... 7 

Participation Rate Estimates for Key Subgroups .................................................................... 8 

Subgroups with Participation Rates above 90 Percent .......................................................... 11 

Subgroups with Participation Rates above 90 Percent in 2016, including State Results ...... 15 

Chapter 2 Summary and Recommendations ......................................................................... 19 

3. Representation of SNAP Cases in the CPS ASEC ................................................................... 21 

Interview Rate Analysis ........................................................................................................ 21 

SNAP Subgroup Representation in the ASEC ...................................................................... 31 

Chapter 3 Summary and Recommendations ......................................................................... 36 

4. TRIM3 Simulated Eligibility of SNAP Cases in Linked Data ................................................. 40 

Data for the Eligibility Analysis ............................................................................................ 40 

Results ................................................................................................................................... 43 

Chapter 4 Summary and Recommendations ......................................................................... 66 

5. Conclusion and Recommendations for Further Research ........................................................ 71 

References ..................................................................................................................................... 73 

Appendices 
Factors Contributing to High Estimated SNAP Participation Rates

1 

1. Introduction 

The Supplemental Nutrition Assistance Program (SNAP) is the nation’s largest nutrition assistance 

program; in the average month of federal fiscal year 2019, SNAP helped 36 million poor and lowincome Americans in 18 million households purchase food, at a total annual federal cost of $60.4 

billion. The program is administered by the Food and Nutrition Service (FNS) of the U.S. 

Department of Agriculture (USDA) in cooperation with states (USDA, 2021). 

Each year, FNS publishes SNAP participation rates, defined as the number of individuals and cases 

who receive SNAP under federal income and asset rules, divided by the number who are eligible 

for assistance (Cunnyngham, 2018). The number of participants is obtained from state 

administrative data. However, administrative data do not contain information on people who are 

eligible but do not apply for SNAP, so the number of individuals and cases eligible for SNAP is 

estimated by applying microsimulation models to household survey data. 

Microsimulation Models 

Microsimulation models apply SNAP eligibility rules to households in survey data to determine 

if, based on the demographic and income information reported in the survey, the people in the 

household are eligible for benefits. If a survey household contains one person, a married couple, 

or parents with minor children, the household is treated as a single “unit” for SNAP purposes. 

More complex households containing multiple families or unrelated individuals may be divided 

into more than one unit, with each treated separately for eligibility determination. 

The FNS participation rate estimates are developed by Mathematica, using the MATH CPS-based 

eligibility model. The model produces SNAP eligibility estimates using data from the Current 

Population Survey Annual Social and Economic Supplement (CPS ASEC). The CPS ASEC is a 

nationally representative survey of households administered by the Census Bureau in February, 

March, and April of each year. The survey gathers detailed income and demographic information 

and is the source of the Census Bureau’s official poverty estimates (Semega et al., 2020). 

SNAP participation rates and other program participation rates are also generated by the Transfer 

Income Model Version 3 (TRIM3), a microsimulation model that is developed and maintained by 

the Urban Institute with funding from the U.S. Department of Health and Human Services, Office 

of the Assistant Secretary for Planning and Evaluation (HHS ASPE) (Zedlewski & Giannarelli, 

2015). TRIM3 provides HHS ASPE with Supplemental Security Income (SSI) and Temporary 

Assistance for Needy Family (TANF) participation rate estimates for use in an annual report to 

Congress (HHS, 2018) and performs detailed simulations of SNAP and other means-tested benefit 

programs. The TRIM3 project team provides SNAP eligibility and participation rate estimates to 

HHS ASPE in annual unpublished baseline reports. 
Factors Contributing to High Estimated SNAP Participation Rates

2 

Background and Motivation for the Study 

For several years, the FNS and TRIM3 participation rate methodologies have generated SNAP 

participation rates that are above 100 percent for some demographic subgroups. SNAP 

participation rates exceeding 100 percent are unexpected. Even with substantial program outreach, 

some eligible families will elect not to participate, so participation rate estimates should be below 

100 percent. A participation rate above 100 percent does not necessarily indicate that ineligible 

people are receiving assistance—it could arise from any number of issues related to the 

administrative data, survey data, both, and/or the microsimulation methods used to produce the 

eligibility estimates. 

In 2014, FNS and Mathematica convened an expert panel to discuss the issue of the unexpectedly 

high participation rates for certain population subgroups. The panel considered various 

methodological changes, some of which have been implemented; none appear to have the potential 

to bring participation rates for single-parent families below 100 percent.1

 The panel convened 

again in 2017, and panelists submitted recommendations for research. An internal memorandum 

to FNS summarizes the panel’s recommendations.2

In this report, we follow up on some of the expert panel’s research recommendations.3 We first 

compare SNAP participation rate estimates across microsimulation models and data sources to 

determine if the high participation rates are unique to the FNS estimates or are also observed in 

other models and data sources. We then analyze SNAP administrative data that have been linked 

with the CPS ASEC to explore data and modeling issues that might explain the high participation 

rate estimates. We merge TRIM3 eligibility flags with the linked data to investigate the 

characteristics of SNAP cases simulated as eligible and ineligible in TRIM3. 

The linked data analysis builds upon a concerted multiple-year effort by the Census Bureau in 

cooperation with the USDA to obtain SNAP administrative data from individual states to support 

research combining administrative and survey data.4 Our analyses of linked SNAP administrative 

and survey data use data files produced by Mathematica under contract to FNS, in which the SNAP 

administrative data for Illinois, Mississippi, and Tennessee were cleaned, standardized, and linked 

with the CPS ASEC.5

 This report also incorporates unpublished microsimulation model estimates 

made available to us by Mathematica and FNS. 

                                                            

1

 See Leftin, Smith, & Cunnyngham (2015) for a summary of the expert panel’s recommendations. 

2

 See Cunnyngham, Gray, & Lauffer (2017) for a summary of the expert panel’s recommendations from the 2017 

panel. 

3

 Laura Wheaton, the lead author for this report, served as a member of both expert panels and offered many of the 

recommendations pursued here. She co-directs the TRIM3 microsimulation project at the Urban Institute. 

4https://www.ers.usda.gov/topics/food-nutrition-assistance/food-assistance-data-collaborative-researchprograms/census-fns-ers-joint-project/ 

5

 Czajka & Cunnyngham (2021). 
Factors Contributing to High Estimated SNAP Participation Rates

3 

Analyses and Key Findings 

We present findings from our analyses in the next three chapters and conclude with a summary of 

our findings and implications for future research. 

SNAP Participation Rate Estimates by Model and Data Source 

Chapter 2 presents the findings of our comparisons of microsimulation models and data sources. 

We compare the FNS participation rate estimates developed using the MATH CPS-based 

eligibility model with estimates produced by other microsimulation models developed by 

Mathematica and the Urban Institute. These models can also produce eligibility and participation 

rate estimates, though their primary purpose is to estimate the effects of potential SNAP policy 

changes. 

We find that certain subgroups have participation rates of 100 percent or more across all 

microsimulation models and data sources examined. These subgroups include SNAP cases 

consisting of a single adult with children, people in SNAP cases with countable income below 50 

percent of the poverty guideline, and people in SNAP cases eligible for between 76 and 99 percent 

of the maximum benefit for their case size. Other subgroups, such as one-person SNAP cases, 

child-only SNAP cases, and cases with adults age 18 to 49 without disabilities in childless 

households have high participation rates in the MATH CPS estimates, but not in the other models 

or data sources examined, suggesting that differences in modeling approach may play a role. We 

examine results both at the national level and in the three states included in the linked data 

analysis—Illinois, Mississippi, and Tennessee. 

Representation of SNAP Cases in the CPS ASEC 

In Chapter 3, we analyze linked SNAP administrative data and CPS ASEC data for Illinois, 

Mississippi, and Tennessee to see if there is evidence of survey under-coverage of SNAP 

participants overall or for key subgroups. If SNAP participants, or certain subgroups of SNAP 

participants, are underrepresented in the survey data, then this could contribute to eligibility 

estimates that are below the actual levels of participation according to administrative data. 

We find that the three states differ in whether households with SNAP recipients are more likely, 

less likely, or about equally likely to respond to the ASEC than are households without SNAP 

recipients. Combining results for the three states, we find that SNAP cases with one adult and one 

or more children are less likely to be in households with a CPS ASEC interview than are SNAP 

cases with multiple adults and children. We find evidence to suggest that SNAP cases with one 

adult and one or more children may be underrepresented in the final CPS ASEC, though further 

work is needed to confirm these findings. 

TRIM3 Simulated Eligibility of SNAP Cases in Linked Data 

In Chapter 4, we present the results of an analysis that merges TRIM3 SNAP unit identifiers and 

eligibility flags with the linked CPS ASEC and administrative data. We examine SNAP cases in 

the linked data to see if they are in a TRIM3 unit simulated as eligible or ineligible for SNAP. We 
Factors Contributing to High Estimated SNAP Participation Rates

4 

observe the extent to which differences in TRIM3 unit and SNAP case membership affect the 

TRIM3 eligibility estimates and explore the extent to which imputation of CPS ASEC variables 

and other factors affect simulated eligibility. We find that differences in TRIM3 unit and SNAP 

case membership are much more common among TRIM3 units found ineligible for SNAP than 

among those found eligible. SNAP cases simulated as ineligible in TRIM3 are also much more 

likely than those simulated as eligible to be “whole imputes” in which the ASEC portion of the 

survey is imputed, or to have imputed income amounts. 

We then focus on a key subgroup—SNAP cases with one adult and one or more children—and 

find that just 52 percent are identified as eligible one-adult plus child units within TRIM3. 

Common reasons for this discrepancy include the absence of the SNAP case’s children from the 

ASEC household, the absence of the case adult from the ASEC household (with children living 

instead with multiple adults, such as grandparents and other relatives), and the inclusion in the 

TRIM3 unit of a spouse, partner, or other adults. We consider possible explanations for these 

discrepancies, including movement of children and adults between households and definitional 

differences regarding how adults who are ineligible due to immigrant status or for other reasons 

are counted in the administrative data and survey data. 

Conclusion and Recommendations for Future Research 

In Chapter 5, we conclude with a discussion that highlights the key findings of the analyses and 

considers their implications for microsimulation modeling and linked administrative data analysis. 

 
Factors Contributing to High Estimated SNAP Participation Rates

5 

Definitions Used in this Report 

FNS publications typically use the term “household” to refer to the group of people who apply for 

and receive SNAP together. We use the term “case” for this concept and reserve the term 

household to describe the group of people who share a residence. A residence can have more than 

one SNAP case if individuals or groups of people within the shared residence purchase and prepare 

food separately. 

A TRIM3 “unit” is a “case” as defined by the TRIM3 microsimulation model. Much of our analysis 

focuses on differences in case, household, and TRIM3 unit membership. We use the term case to 

refer to the group of people who apply for and receive SNAP together to help keep these concepts 

distinct. 

 

Key Definitions 

Housing Unit: A house, apartment, group of rooms, or single room intended as separate living quarters 

Household: The individual or group of people who occupy a housing unit 

Microsimulation Model: A sophisticated computer program that applies detailed eligibility rules to 

individual people and households in household survey data to simulate eligibility and 

participation in government programs 

SNAP Case: The individual or group of people who apply for SNAP together 

• Includes people who live together and customarily buy and prepare food together 

• There can be more than one “case” in a “household” as defined above 

SNAP Participant: A person or case receiving SNAP benefits 

SNAP Participation Rate: The number of SNAP participants divided by the number eligible for SNAP 

• The number of participants is obtained from FNS administrative data. 

• The number eligible for SNAP is estimated using microsimulation models. 

• Participation rates are estimated at both the person level and case level. 

TRIM3 Unit: The “case” as defined by the TRIM3 microsimulation model 
Factors Contributing to High Estimated SNAP Participation Rates

6 

2. SNAP Participation Rate Estimates by Model and Data Source 

In this chapter, we examine the extent to which participation rate estimates vary by the 

microsimulation model and survey used to produce the estimate, and also by whether eligibility is 

determined under federal rules alone or when also including participants made eligible through 

state broad based categorical eligibility rules (BBCE). 

If a demographic subgroup has an unexpectedly high participation rate according to one model but 

not another, it might be useful to explore how different modeling approaches affect the estimate. 

But if a demographic subgroup has an unexpectedly high participation rate across models, it might 

be best to focus on issues in the underlying survey data and SNAP administrative data, while also 

considering how commonalities in approach across models might influence the result. 

Although we are primarily concerned with groups that have estimated participation rates above 

100 percent, we expand the analysis to include groups with participation rates above 90 percent. 

While it is possible that a subgroup truly has a participation rate above 90 percent but below 100 

percent, it is also possible that the participation rate is overstated. 

We begin by describing the models, surveys, and data years included in the analysis. We then 

briefly describe the methodology used to produce participation rate estimates, eligibility estimates, 

and information about participants. Next, we summarize participation rates for key subgroups 

across models, surveys, and data years—highlighting key differences by source. We then turn our 

focus to subgroups with estimated participation rates exceeding 90 percent, according to 2016 

participation rates developed by Mathematica for FNS. We examine how results for these 

subgroups compare across models, data sources, and data years at the national level and for the 

three states included in the linked data analysis. We conclude by discussing the implications of the 

findings for the remainder of our analysis and for future research. 

Models, Surveys, and Data Years 

Our analysis examines participation rates in 2016 (the most recent available estimate across models 

at the time work began) and 2011 (selected to allow comparison with results generated from 

Survey of Income and Program Participation (SIPP) data). 

We obtained results from the following models, surveys, and data years:

CPS-based models

• MATH CPS-based eligibility model (developed by Mathematica under contract with 

FNS). This model provides the eligibility estimates used to develop the participation rates 

released annually by FNS (Cunnyngham, 2018). The model operates on data from the CPS 

ASEC. 

We obtained 2011 and 2016 estimates from published data, where available. With 

permission from FNS, Mathematica provided us with estimates for subgroups with 
Factors Contributing to High Estimated SNAP Participation Rates

7 

participation rates above 100 percent (which are masked in the published data), eligibility 

estimates for the three linked data study states, and eligibility estimates including BBCE. 

• TRIM3 model (developed by the Urban Institute under contract with HHS ASPE). We 

generated 2011 and 2016 TRIM3 eligibility estimates from publicly available TRIM3 

baseline data at the national level and for the three states. TRIM3 uses data from the CPS 

ASEC. 

SIPP–based model

• MATH SIPP+ (developed by Mathematica under contract with FNS). The MATH SIPP+ 

model operates on SIPP data. With permission from FNS, Mathematica provided national 

level eligibility estimates for 2011 from the MATH SIPP+ model. We calculated 

participation rates from these eligibility estimates using the same numerator as in the CPSbased estimates. Due to sample size limitations in the SIPP, we do not include state 

estimates. 

ACS-based model 

• Urban Institute ATTIS model (developed by the Urban Institute with foundation 

funding). The Analysis of Transfers, Taxes, and Income Security (ATTIS) Model adapts 

TRIM3 simulation methods to the American Community Survey (ACS).6 We prepared the 

2016 eligibility estimates presented here, at the national level and for the three states. 

Methodology 

The participation rates presented here are calculated by dividing the number of people or cases 

that participate in SNAP according to SNAP administrative data by the number that are eligible 

for SNAP. Mathematica estimates the number of participants using data from the SNAP QC data 

file, an edited version of a raw data file generated by the SNAP Quality Control System, based on 

a sample of participating cases (Cunnyngham, 2018). The eligibility estimates are obtained by 

applying microsimulation models to survey data. The SNAP QC estimates reflect participation in 

the “average month” of the federal fiscal year. The eligibility estimates are also designed to reflect 

an “average month,” although the models vary in their definition.7

Because the eligibility estimates and participant counts are drawn from different sources, it is 

possible for the estimated participation rate to exceed 100 percent. This occurs if there are more 

participants, according to the administrative data, than there are people eligible, according to the 

                                                            

6

 ATTIS uses an augmented version of the ACS produced by the University of Minnesota’s Integrated Public Use 

Microdata Series project (Ruggles et al., 2020); (ATTIS stands for Analysis of Transfers, Taxes, and Income Security; 

TRIM3 is the Transfer Income Model, version 3). See Pyati (2020) for an overview of the ATTIS model. 

7

 The MATH CPS model combines data from two consecutive years of CPS ASEC data to construct an average 

monthly eligibility estimate representing the federal fiscal year. The TRIM3 and ATTIS average monthly estimates 

reflect the calendar year. The MATH SIPP+ model uses eligibility estimates from a single month of SIPP data. 
Factors Contributing to High Estimated SNAP Participation Rates

8 

microsimulation estimates. As noted above, this is not necessarily due to ineligible people 

receiving SNAP; various data and methodological issues could contribute to the result. 

Rates are Shown Under Federal Rules Alone and then Including BBCE 

We provide two different sets of participation rate estimates. We first provide estimates calculated 

according to the methodology used to produce the official FNS participation rate estimates (e.g., 

Cunnyngham, 2018). These estimates reflect eligibility and participation among cases that are 

eligible for SNAP under federal rules. The estimates exclude additional cases made eligible 

through state BBCE rules.8

 In addition to showing participation rate estimates under this primary 

definition, we also show participation rate estimates when BBCE policies are included. 

The models estimate eligibility by first determining which members of a survey household apply 

together for SNAP. This creates one or more potential SNAP units within a survey household. The 

models then use SNAP eligibility and benefit rules to determine the eligibility and benefits of each 

potential SNAP unit, based on the income and demographic information of the unit members. 

When simulating eligibility under the federal SNAP rules, the simulation models “turn off” 

eligibility through state BBCE policies. The state BBCE eligibility rules are then “turned on” to 

produce eligibility estimates including BBCE. 

When calculating participation rates under the first definition (federal rules) we use participation 

counts from the published participation rate estimates (Cunnyngham, 2018). These estimates are 

derived from SNAP Quality Control (QC) data, but are adjusted to exclude cases that would be 

ineligible for SNAP under federal rules.9 With permission from FNS, Mathematica provided 

additional unpublished estimates for the states included in this analysis. When calculating 

participation rates under the broad definition of eligibility (including BBCE) we use data for all 

SNAP participants based on SNAP QC data. If the required participant count for the subgroup is 

available in the 2011 or 2016 Characteristics of Supplemental Nutrition Assistance Program 

Households report, we use the published figure.10 Otherwise, we calculate the number of 

participants from the SNAP QC data. 

Participation Rate Estimates for Key Subgroups 

We present key findings from the analysis in Tables 2.1, 2.2, and 2.3 (below). Appendix Tables 

A.1 and A.2 provide the underlying eligibility and participation numbers used to calculate the 

national participation rate estimates, as well as results for additional subgroups. Detailed findings 

for Illinois, Mississippi, and Tennessee are provided in Appendix Tables A.3 through A.8. 

                                                            

8

 BBCE provides states the flexibility to waive assets tests or increase the asset limit. States can also increase the gross 

income eligibility limit as high as 200 percent of the poverty guideline, making additional households eligible for 

assistance. However, states cannot change the phase-out of the SNAP benefit, and so households may have their 

benefit phased out or reduced to a small benefit amount before the higher eligibility limit is reached. 

9

 The adjustment involves imputation, because states that have opted to waive the assets test lack data on assets that 

would be needed to determine if a household is eligible according to federal rules. 

10 See USDA 2012 and 2017. 
Factors Contributing to High Estimated SNAP Participation Rates

9 

Table 2.1 shows participation rates for cases, individuals, individuals by age, and adults age 18 to 

49 without disabilities in childless households. The top panel shows participation rates estimated 

under federal rules (excluding the effect of BBCE), and the bottom panel shows results for all 

participants and eligible people, including those made eligible through state BBCE policies. We 

show results for 2016 for the MATH CPS-based eligibility model (referred to here as MATH CPS), 

TRIM3, and ATTIS. We show results for 2011 for MATH CPS, TRIM3, and the MATH SIPP+ 

model. 

Key findings from Table 2.1 include the following: 

• Participation rates are higher in 2016 than 2011 for both TRIM3 and the MATH CPS model 

and for both sets of eligibility rules. The case participation rates are 7 percentage points 

higher in 2016 than in 2011 according to MATH CPS and 8 percentage points higher in 

2016 according to TRIM3. 

• The overall 2011 MATH SIPP+ case and individual participation rate estimates are 

approximately halfway between the MATH CPS and TRIM3 estimates. 

• The 2016 ATTIS participation rate estimates are generally lower than the participation rate 

estimates for the other two models. This may be because the ACS does not capture as much 

“other income” (income types not specifically addressed in the questions) as is captured in 

the CPS, so fewer cases are found ineligible based on aggregated sources of income. 

• MATH CPS participation rates are higher than TRIM3 when modeling federal rules, but 

closer to TRIM3 for most key subgroups when modeling BBCE rules. A possible 

explanation is that some parts of each model requiring greater imputation (i.e., asset values 

and net income amounts) are not as critical when simulating BBCE, so are less likely to 

contribute to variation in the estimates. 

• Participation rate estimates vary more for cases than for individuals under federal rules, 

but not under BBCE rules. The 2016 case participation rate is 14 points higher in MATH 

CPS than TRIM3 under federal rules, but just 2 points higher with BBCE. The MATH CPS 

individual participation rate is 5 points higher than TRIM3 under federal rules, and 4 points 

lower than TRIM3 under BBCE. 

• The 2016 child participation rates are at least 100 percent for both TRIM3 and the MATH 

CPS model when simulating federal rules and are 90 percent or more when simulating 

BBCE rules. 

• The participation rate for adults age 18 to 49 without disabilities in childless households is 

97 percent for the MATH CPS model (in 2016 under federal rules), but much lower in 

TRIM3 (63 percent). This gap is also large under BBCE estimates. 

 
Factors Contributing to High Estimated SNAP Participation Rates

10 

Table 2.1 National SNAP Participation Rates, as Defined by Number Participating According to SNAP QC 

Based Estimates Divided by Number Eligible, by Data Source, Model, Year, and whether Estimate 

includes State BBCE 

 2016 2011 

 CPS ACS CPS SIPP 

 MATH TRIM3 ATTIS MATH TRIM3 MATH 

Federal Rules 

All cases 89 75 66 82 67 74 

All individuals 85 80 72 78 72 75 

Age 

 

 

Children (17 or younger) 104 100 91 96 89 84 

Pre-school age (0-4) 105 103 91 101 96 95 

School-age (5-17) 104 98 91 93 86 79 

Nonelderly adults (18 to 59) 88 80 70 79 71 76 

Elderly individuals (60+) 45 43 41 38 38 44 

Adults age 18 to 49 without disabilities in 

childless households 97 63 47 83 54 77 

 

Including Broad Based Categorical Eligibility (BBCE) 

 

 

 

All cases 63 61 58 58 57 58 

All individuals 62 66 63 58 63 60 

Age 

 

 

Children (17 or younger) 90 91 85 82 83 75 

Pre-school age (0-4) 92 94 85 89 92 87 

School-age (5-17) 89 90 85 79 79 70 

Nonelderly adults (18 to 59) 70 70 63 63 64 63 

Elderly individuals (60+) 25 30 32 19 26 27 

Adults age 18 to 49 without disabilities in 

childless households 70 52 41 63 48 62 

 

Source: Developed from eligibility estimates from the MATH CPS-Eligibility model, MATH SIPP+ model, TRIM3, and ATTIS 

combined with participation estimates based on SNAP QC data (as processed by Mathematica) 
Factors Contributing to High Estimated SNAP Participation Rates

11 

Subgroups with Participation Rates above 90 Percent 

Table 2.2 presents participation rate estimates for subgroups with MATH CPS participation rates 

above 90 percent in 2016 according to published estimates (Cunnyngham, 2018). Although our 

primary focus is on subgroups with participation rates above 100 percent, we are also interested in 

subgroups with participation rates above 90 percent. As noted in the introduction, while it is 

possible that a subgroup truly has a participation rate above 90 percent but below 100 percent, it 

is also possible that the participation rate is overstated. 

Key findings from Table 2.2 include the following (for 2016 under federal rules): 

• Participation rates for cases consisting of a single adult with children are far above 100 

percent for MATH CPS (133 percent) and TRIM3 (129 percent). ATTIS also has a 109 

percent participation rate for this group. 

• Both TRIM3 and MATH CPS have participation rates of at least 100 percent for the 

following groups: 

o Children 

o People in cases with no countable income 

o People in cases with income between 1 and 50 percent of the poverty guideline for a 

case of their size 

o People in cases eligible for benefits equal to 76 to 99 percent of the maximum benefit 

for a case of their size, and people in cases eligible for the maximum benefit 

o Cases composed of a single adult with children (as noted above) 

• The MATH CPS participation rate is at least 100 percent but TRIM3 is below 100 percent 

for the following subgroups: 

o Child-only cases (135 percent MATH CPS, 85 percent TRIM3) 

o Cases with SSI (102 percent MATH CPS, 94 percent TRIM3) 

o Cases with no countable income or income below 100 percent of the poverty 

guideline 

• The MATH CPS participation rate is between 91 and 99 percent but TRIM3 is below 91 

percent for the following subgroups: 

o Adults age 18 to 49 without disabilities in childless households (as noted for Table 

2.1) 

o People in one-person cases (98 percent MATH CPS, 67 percent TRIM3) 

o People in cases without earned income (94 percent MATH CPS, 86 percent TRIM3) 

o People in cases with income between 51 percent and 100 percent of the poverty 

guideline (95 percent MATH CPS, 80 percent TRIM3) 
Factors Contributing to High Estimated SNAP Participation Rates

12 

Participation rate estimates are also high for some subgroups when simulating BBCE rules, 

although BBCE does attenuate overall participation levels. Subgroups with high participation rates 

include (for 2016): 

• People in cases with no income (98 percent MATH CPS, 112 percent TRIM3) 

• People in cases with countable income between 1 and 50 percent of the poverty guideline 

(104 percent MATH CPS, 99 percent TRIM3) 

• People in cases eligible for 76 to 99 percent of the maximum benefit (115 percent MATH 

CPS, 111 percent TRIM3) 

• People in cases eligible for the maximum benefit (99 percent for both MATH CPS and 

TRIM3) 

• Cases with a single adult and children (120 percent MATH CPS and 118 percent TRIM3) 

In addition, the MATH CPS participation rate estimates for child-only cases remain high under 

BBCE: 123 percent in MATH CPS under BBCE rules in 2016, compared with 74 percent for 

TRIM3. 

 
Factors Contributing to High Estimated SNAP Participation Rates

13 

Table 2.2 Subgroups with Estimated Participation Rates above 90 Percent According to 2016 FNS Mathematica 

Estimates, by Data Source, Model, Year, and whether Estimate Includes State BBCE 

 2016 2011 

 CPS ACS CPS SIPP 

 MATH TRIM3 ATTIS MATH TRIM3 MATH 

Federal Rules 

Individual Characteristics 

Age 

 

 

Children (17 or younger) 104 100 91 96 89 84 

Pre-school age (0-4) 105 103 91 101 96 95 

School-age (5-17) 104 98 91 93 86 79 

Adults age 18 to 49 without disabilities in childless households 97 63 47 83 54 77 

In one-person cases 98 67 58 90 58 73 

In cases without earned income 94 86 83 88 81 90 

Countable income as a percentage of poverty guidelines 

No income 102 105 86 75 78 90 

1 to 50 percent 118 105 104 119 97 108 

51 to 100 percent 95 80 75 87 75 73 

Benefit as a percentage of maximum benefit 

76 to 99 percent 124 113 109 109 95 106 

Maximum benefit 111 101 86 90 89 97 

 

Case Characteristics 

Case composition 

Single-adult with children 133 129 109 125 114 99 

Child only 135 85 78 136 68 83 

Cases containing 

Non-elderly individuals with disabilities 93 93 96 84 83 90 

Adults age 18 to 49 without disabilities in childless 

households 115 71 54 100 61 79 

Case countable income source 

SSI 102 94 95 93 86 90 

Countable income as a percentage of poverty guidelines 

No income 118 99 81 91 74 102 

1 to 50 percent 123 95 91 131 93 109 

51 to 100 percent 100 77 72 92 72 74 

 

(Table continues) 
Factors Contributing to High Estimated SNAP Participation Rates

14 

Table 2.2 (continued)

 2016 2011 

 CPS ACS CPS SIPP 

 MATH TRIM3 ATTIS MATH TRIM3 MATH 

Including Broad Based Categorical Eligibility (BBCE) 

Individual Characteristics 

Age 

 

 

Children (17 or younger) 90 91 85 82 83 75 

Pre-school age (0-4) 92 94 85 89 92 87 

School-age (5-17) 89 90 85 79 79 70 

Adults age 18 to 49 without disabilities in childless households 70 52 41 63 48 62 

In one-person cases 72 56 52 64 50 55 

In cases without earned income 70 72 74 66 69 72 

Countable income as a percentage of poverty guidelines 

No income 98 112 91 71 81 91 

1 to 50 percent 104 99 103 107 94 99 

51 to 100 percent 88 79 75 80 75 70 

Benefit as a percentage of maximum benefit 

76 to 99 percent 115 111 110 100 94 102 

Maximum benefit 99 99 87 82 88 90 

 

 

Case Characteristics 

Case composition 

Single-adult with children 120 118 103 113 108 92 

Child only 123 74 73 128 66 80 

Cases containing 

Non-elderly individuals with disabilities 80 86 92 71 76 81 

Adults age 18 to 49 without disabilities in 

childless households 84 60 47 76 56 64 

Case countable income source 

SSI 99 91 93 87 83 86 

Countable income as a percentage of poverty guidelines 

No income 115 107 87 87 77 102 

1 to 50 percent 102 88 90 111 88 96 

51 to 100 percent 93 76 72 85 71 71 

 

Source: Developed from eligibility estimates from the MATH CPS-Eligibility model, MATH SIPP+ model, TRIM3, and ATTIS 

combined with participation estimates based on SNAP QC data (as processed by Mathematica) 

Subgroups defined based on "Trends in Supplemental Nutrition Assistance Program Participation rates: Fiscal Year 2010 

to Fiscal Year 2016" 

 
Factors Contributing to High Estimated SNAP Participation Rates

15 

Subgroups with Participation Rates above 90 Percent in 2016, including State Results 

Table 2.3 provides Illinois, Mississippi, and Tennessee estimates under federal rules and BBCE in 

2016 for the subgroups of interest. We present estimates from the MATH CPS and TRIM3 models, 

omitting some subgroups due to low sample sizes. 

All the state estimates should be interpreted with caution due to sample size limitations.11 These 

are not the official FNS state participation estimates. The official estimates use shrinkage 

methodology to overcome the limitations of small sample sizes (Cunnyngham, 2019). 

Nevertheless, the MATH CPS estimates presented here are close to the official estimates. The 

estimate for Illinois is 103 percent (the published estimate indicates Illinois has a participation rate 

of at least 100 percent), the estimate for Mississippi (78 percent) is lower than the point estimate 

in the published results (83 percent) but is within the confidence interval in the published results. 

The estimate for Tennessee (93 percent) matches the point estimate in the published data. 

We provide the state estimates so that we can see if subgroups with unexpectedly high participation 

rates in the national data have similarly high participation rates in the state data. If so, analysis of 

the linked survey and administrative data for the state might offer helpful insights into the reasons 

for high participation rates at the national level. But if the participation rate for the subgroup is 

much lower than at the national level, analysis of the state may not be as informative. We describe 

results for each state, after first giving a brief description of key policies in effect in the state in 

2016. 

Illinois 

Illinois had BBCE in 2016—there was no asset test and the gross income limit was 165 percent of 

poverty (relative to 130 percent under federal rules for cases without an elderly member or person 

with disabilities). 

Another area of state policy variation involves whether a state has a waiver from the 3-month time 

limit for able-bodied adults without children (ABAWDs) who do not meet work requirements. 

States can apply for waivers from the ABAWD time limit for the full state or for parts of the state 

due to high unemployment or insufficient jobs.12 Eligibility estimates for adults age 18 to 49 

without disabilities in childless households are likely to be affected by whether the state has an 

ABAWD waiver; Illinois had an ABAWD waiver covering the entire state in 2016. 

Key findings for Illinois include the following: 

• The MATH CPS estimates for Illinois (under federal rules) exceed 100 percent in all 

subgroups of interest. The estimates from Illinois exceed the national estimates for all 

subgroups. 

                                                            

11 We have shaded cells in the table that have 50 or fewer observations, to identify cells where sample sizes are 

particularly low. 

12 FNS issued revised regulations in 2019. The policy described here reflects the rules in effect in 2016. 
Factors Contributing to High Estimated SNAP Participation Rates

16 

• The TRIM3 estimates for Illinois also equal or exceed the national TRIM3 estimates for 

all subgroups, though remain below 100 percent for some. 

• Estimated participation rates are substantially lower in Illinois when BBCE eligibility is 

simulated, but still substantially exceed 100 percent for some subgroups, suggesting that it 

is not just the additional imputations needed to estimate participation rates under federal 

rules that causes the high participation rates. The overall reduction in participation rates 

with BBCE is not surprising, since the cases that become eligible for SNAP due to Illinois’ 

higher income limit likely qualify for smaller benefits than other participants and may 

therefore be less likely to participate. 

Mississippi 

Asset limits were waived in 2016 under Mississippi’s BBCE policy, but income eligibility limits 

were the same as federal rules. Mississippi did not have a waiver from ABAWD time limits in 

2016. 

Key findings for Mississippi (under the federal rules) include the following: 

• Mississippi has lower estimated participation rates than the national estimates; this holds 

true across most of the subgroups shown in Table 2.3, for both MATH CPS and TRIM3. 

• However, participation rates in Mississippi are particularly high for single-adult cases with 

children (142 percent in MATH CPS and 129 percent in TRIM3). 

• The participation rate estimates for adults age 18 to 49 without disabilities in childless 

households exceed the national rate in both MATH CPS and TRIM3 and is above 100 

percent in MATH CPS. 

• In contrast, the participation rate for child-only cases is very low in Mississippi in both 

models (20 percent in MATH CPS and 11 percent in TRIM3). 

Tennessee 

Tennessee did not have BBCE in 2016. Therefore, we provide results only for eligibility under the 

federal rules. Tennessee reintroduced ABAWD time limits in 2016, though it did have waivers 

covering counties with the highest unemployment rates. 

Key findings for Tennessee include the following: 

• The Tennessee participation rate is higher than the national estimates for both models, and 

this holds true for most of the subgroups shown in Table 2.3. However, the participation 

rates for most subgroups are not quite as high as in Illinois. 

• Most of the subgroups have MATH CPS participation rate estimates close to or exceeding 

100 percent. Child-only cases are an exception, with an estimated participation rate equal 

to 72 percent in Tennessee, compared with 135 percent in the national MATH CPS 

estimate. 
Factors Contributing to High Estimated SNAP Participation Rates

17 

Table 2.3 All Participating Individuals and Cases and Subgroups with Estimated Participation Rates 

above 90 percent According to 2016 FNS Mathematica National Estimate, 2016 CPS 

Estimates for MATH CPS and TRIM3, Nationally and for Three States 

(Data cells shaded in grey indicate sample size less than 50) 

 MATH CPS TRIM3 

 National IL MS TN National IL MS TN 

Federal Rules 

 

Individual Characteristics 

All individuals 85 103 78 93 80 94 79 89 

Age 

 

 

Children (17 or younger) 104 114 99 112 100 103 97 108 

Adults age 18 to 49 without disabilities in 

childless households 97 160 105 163 63 80 79 88 

In one-person cases 98 124 79 113 67 89 57 74 

In cases without earned income 94 133 90 106 86 123 87 99 

Countable income as a percentage of 

poverty guidelines 

No income 102 172 94 156 105 178 106 125 

1 to 50 percent 118 137 106 111 105 109 89 111 

51 to 100 percent 95 116 91 102 80 95 79 83 

Benefit as a percentage of maximum 

benefit 

76 to 99 percent 124 129 92 104 113 125 105 122 

Maximum benefit 111 146 86 114 101 111 96 108 

 

Case Characteristics 

All cases 89 115 78 97 75 95 71 81 

Case composition 

Single-adult with children 133 174 142 136 129 149 129 120 

Child only 135 186 20 72 85 110 11 53 

Cases containing 

Adults age 18 to 49 without 

disabilities in childless households 115 191 123 209 71 94 82 96 

Countable income as a percentage of 

poverty guidelines 

No income 118 234 107 185 99 183 93 127 

1 to 50 percent 123 144 113 108 95 95 80 82 

51 to 100 percent 100 117 89 102 77 95 71 78 

 

 

(Table continues) 
Factors Contributing to High Estimated SNAP Participation Rates

18 

Table 2.3 (continued) 

 MATH CPS TRIM3 

 National IL MS TN National IL MS TN 

Including Broad Based Categorical Eligibility (BBCE) 

Individual Characteristics 

All Individuals 62 72 71 66 74 79 

Age 

 

 

Children (17 or younger) 90 89 92 91 90 98 

Adults age 18 to 49 without disabilities in 

childless households 70 109 100 52 71 83 

In one-person cases 72 84 74 56 69 57 

In cases without earned income 70 90 81 72 96 87 

Countable income as a percentage of 

poverty guidelines 

No income 98 153 89 112 192 110 

1 to 50 percent 104 105 96 99 101 87 

51 to 100 percent 88 102 84 79 94 80 

Benefit as a percentage of maximum 

benefit 

76 to 99 percent 115 111 84 111 121 105 

Maximum benefit 99 116 80 99 111 97 

 

 

Case Characteristics 

 

 

All cases 63 78 72 61 72 71 

Case composition 

Single-adult with children 120 144 137 118 125 128 

Child only 123 172 20 74 101 11 

Cases containing 

Adults age 18 to 49 without 

disabilities in childless households 84 135 122 60 84 86 

Countable income as a percentage of 

poverty guidelines 

No income 115 212 102 107 199 96 

1 to 50 percent 102 99 98 88 84 75 

51 to 100 percent 93 103 85 76 93 72 

 

Source: Developed from eligibility estimates from the MATH CPS-Eligibility model and TRIM3 combined with 

participation estimates based on SNAP QC data (as processed by Mathematica). 

Subgroups defined based on "Trends in Supplemental Nutrition Assistance Program Participation rates: Fiscal Year 

2010 to Fiscal Year 2016" 

IL: Illinois; MS: Mississippi; TN: Tennessee 

Light shading reflects cells with 50 or fewer observations. Tennessee did not have BBCE in 2016. 

 
Factors Contributing to High Estimated SNAP Participation Rates

19 

Chapter 2 Summary and Recommendations

The comparisons provided here helped inform the linked data analyses to be described in Chapters 

3 and 4 and also have implications for future research. 

Implications for Linked Data Analysis

Based on the findings presented here, we conclude that single adult cases with children are an 

important subgroup to investigate using the linked data. Single adult cases with children have 

estimated participation rates above 100 for all models and data years, except for MATH SIPP+ 

(which had a participation rate of 99 percent in 2011). 

The results for the three states suggest that they are all appropriate states to include in the linked 

administrative and CPS data analysis. 

• Illinois is a good candidate because its participation rate issues seem even more pronounced 

than at the national level. 

• Tennessee is a good state to include for policy reasons—because it does not have BBCE, 

its participation rate estimates are not affected by the assumptions needed to “back out” 

participants who would have failed the federal assets test. 

• Participation for some subgroups of interest nationally are below 100 percent in 

Mississippi. Nevertheless, Mississippi, like the other states, has participation rates far 

above 100 percent for single adult cases with children. Mississippi is also of interest for 

policy reasons, as it is the only one of the three states that had ABAWD time limits in place 

for the full state for the entire year. Mississippi had BBCE in 2016, but unlike Illinois, the 

BBCE rules were only used to waive the asset test. 

Implications for Future Research on Microsimulation Modeling Methodology

The findings presented here can also inform future work beyond what could be accomplished in 

this study.

Examining high participation rates in MATH CPS 

Certain population subgroups have unexpectedly high participation rates in the MATH CPS 

estimates but not in TRIM3 and MATH SIPP+. These subgroups may benefit from analyzing 

differences in modeling approaches. For example: 

• Differences in methods to divide survey households into potential SNAP units could be 

contributing to fewer eligible cases (and higher participation rates) in MATH CPS for 

adults age 18 to 49 without disabilities in childless households and for one-person cases. It 

is worth noting that the 2011 MATH CPS participation rate for one-person cases (90 

percent) is well above the 2011 MATH SIPP estimate (73 percent) which is itself 

substantially above the TRIM3 estimate (58 percent). 
Factors Contributing to High Estimated SNAP Participation Rates

20 

• Differences across models in participation rates for adults age 18 to 49 without disabilities 

in childless households could be affected by methods to simulate ABAWD time limits. 

However, this is probably not the main factor behind the differences. The gap between the 

MATH CPS and TRIM3 participation rates is only slightly larger in 2016 (when over half 

of states had ABAWD time limits) than in 2011 (when almost all states had waivers). 

• The 2016 MATH CPS participation rate for child-only cases (135 percent) is much higher 

than the 2016 TRIM3 rate (85 percent). The 2011 MATH CPS estimate (136) is also much 

higher than the 2011 TRIM3 estimate (68 percent), as well as the 2011 MATH SIPP 

estimate (83 percent). More detailed analyses of the modeling approaches affecting this 

group might provide insight. 

Correcting for SSI Underreporting 

It is not surprising to see high SNAP participation rates (above 90 percent) for cases receiving SSI. 

SSI recipients have low income and states are likely to encourage or facilitate their application to 

SNAP; joint processing allows states to do so automatically. SSI is underreported in the CPS 

ASEC (Meyer, Mok, & Sullivan, 2009) and both MATH CPS and TRIM3 correct for this known 

underreporting. The MATH CPS model has a 102 percent participation rate for cases with SSI in 

2016, compared with 94 percent in TRIM3. It might be helpful to compare how the TRIM3 and 

MATH CPS methodologies correct for underreporting of SSI, to see if changes to the MATH CPS 

methodology might reduce estimated participation rates below 100 percent. 

Exploring Differences in MATH CPS and TRIM3 Estimates 

Although we have pointed to instances where TRIM3 participation rates are below 100 percent 

while MATH CPS estimates are above 100 percent, this does not mean that the TRIM3 modeling 

approach is necessarily “better.” For example, it might be the case that an underlying issue in the 

survey data explains the high MATH CPS participation rate estimate for a subgroup; perhaps the 

same result does not appear in TRIM3 because the model is not capturing a particular rule at the 

same level of detail as the MATH CPS, and thus overstates eligibility for the subgroup. Further 

investigation would be needed to shed additional light on these issues. Such research could 

improve estimates produced by both models. 

 
Factors Contributing to High Estimated SNAP Participation Rates

21 

3. Representation of SNAP Cases in the CPS ASEC 

In this chapter, we analyze linked CPS and SNAP administrative case record data for Illinois, 

Mississippi, and Tennessee to explore whether SNAP cases overall or for key subgroups are 

underrepresented in the CPS. If SNAP cases are underrepresented, this could contribute to the high 

participation rate estimates for certain subgroups described in Chapter 2. 

We first examine CPS response rates of sampled households with and without a SNAP case. We 

find that the three states differ in whether households with a SNAP case are more likely, less likely, 

or about equally likely to respond to the CPS survey as are those without a SNAP case, given that 

the household has been found eligible for an interview 

We next examine differences in interview rates by type of SNAP case. Combining data for the 

three states, we find that housing units with a one-adult plus child case are less likely to have a 

CPS interview than are housing units with a multiple-adult plus child case. We also observe lower 

interview rates for SNAP cases with income up to 50 percent of the poverty guideline than for 

those with income above 50 percent of poverty. 

Finally, we compare the weighted number of SNAP cases in the linked CPS ASEC data with the 

actual number of SNAP cases in the administrative data. We find that multiple-adult plus child 

cases are overrepresented in the linked data for each year between 2012 and 2016, whereas childonly cases and one-adult plus child cases are underrepresented. In addition, one-person cases with 

a member between 18 and 59 are substantially underrepresented in the linked data. Our estimates 

do not adjust for match error, so further research is needed to confirm the findings for 

underrepresented groups. We provide implications and suggestions for future research at the end 

of this chapter. 

Interview Rate Analysis 

We begin by investigating whether CPS response rates differ for households with and without a 

SNAP case and whether interview rates vary for different types of SNAP cases. We use SNAP 

receipt as obtained from state SNAP administrative case records. 

Some of our results include all sampled housing units, including those that are found ineligible for 

the CPS ASEC interview—for example, because the housing unit is found by the CPS interviewer 

to be vacant or demolished. We also present response rates for the sampled households that are 

found eligible for the interview. 

We use the term “housing unit” to refer to the broader concept that includes both ineligible and 

eligible units, and “household” when referring to the housing units found eligible for the interview. 

Similarly, we use the term “interview rate” when describing all sampled units, and “response rate” 

when describing responses to the interview among housing units found eligible for an interview. 
Factors Contributing to High Estimated SNAP Participation Rates

22 

Data for Interview Rate Analysis 

We analyze interview rates using a dataset that links SNAP cases in the standardized Illinois, 

Mississippi, and Tennessee administrative SNAP data prepared by Mathematica with housing 

units in the 2016 basic March CPS sample. The SNAP data include all SNAP cases in the 

standardized administrative data for the three states in March 2016. By selecting the same month 

for the CPS and administrative data, we hope to achieve the greatest possible consistency between 

the two data sources. However, if a SNAP participant has moved and not notified the SNAP agency 

about the change of address, it is possible that the SNAP participant no longer resides at that 

address. 

Our interview rate estimates are based on the March CPS sample, rather than the full CPS ASEC. 

The CPS ASEC is a supplement to the basic CPS—a monthly survey sponsored by the Census 

Bureau and the Bureau of Labor Statistics to collect data for unemployment statistics. Households 

selected for the CPS are interviewed in four consecutive months, are then out of the survey for 

eight months, and are then interviewed for another four consecutive months. The ASEC interview 

is administered to all households in the basic March CPS sample plus additional households 

identified in other months that meet designated criteria for Hispanic ethnicity, race, and presence 

of children. These additional households receive the ASEC interview in February, April, and (for 

one group of Hispanic households) in March, thus expanding the size of the CPS ASEC sample 

(U.S. Census Bureau, 2019). 

We exclude the groups interviewed in February and April and the additional Hispanic households 

interviewed in March to avoid overstating the response rate. These additional groups have already 

shown a willingness to be interviewed. Otherwise, the Census Bureau would not know their race, 

ethnicity, and whether children are present. Including these households, without also including 

corresponding non-interview households from the same months, would likely overstate the 

response rate estimates. 
Factors Contributing to High Estimated SNAP Participation Rates

23 

We use the Master Address File Identifier (MAFID) to link the SNAP administrative data and CPS 

data. The MAFID is a unique housing unit identifier assigned by the Census Bureau to both the 

SNAP administrative data and CPS data to facilitate address-level matches without revealing 

personally identifying information. We use MAFID for this part of the analysis because it allows 

us to match the SNAP administrative data with all sampled housing units, including those that are 

found ineligible for the interview or are unable to be interviewed. MAFID is available for 88 

percent of the SNAP administrative case records13 and 100 percent of CPS housing units in the 

March 2016 data.14

The interview rate estimates use the CPS base weight.15 Most housing units within a state have the 

same probability of selection and the same base weight, and the Census Bureau advises using 

unweighted data or the base weight to analyze interview rates.16 Additional adjustments are applied 

to the base weight to produce the final survey weights (U.S. Census Bureau, 2019). 

                                                            

13 SNAP administrative data for March 2016 include a MAFID for 86 percent of cases in Illinois, 85 percent in 

Mississippi, and 92 in Tennessee.

14 We focus on 2016 for this analysis because MAFIDs are missing for all or some non-interviewed households in 

earlier year internal Census Bureau CPS data files available to us at the time of our analysis, and because we did not 

have access to standardized SNAP administrative data for Illinois for 2017. 

15 We use the GESBWGT weight variable available on internal Census Bureau files. GESBWGT includes housing 

units that were added to the basic CPS beginning in 2001 to meet the requirements of the State Children’s Health 

Insurance Program (SCHIP, now CHIP) legislation. The CHIP expansion increased sample size in many states to 

better support estimates of low-income children without health insurance. The Census Bureau also maintains a 

GEBWGT basic weight in internal files that excludes the expanded CHIP sample (personal communication, Greg 

Weyland, Census Bureau). Our analysis pertains to all housing units in the basic March sample, and so we use 

GESBWGT when analyzing interview rates. 

16 See discussion in Chapter 4-1 Nonsampling Error, U.S. Census Bureau (2019).

Data Linkage Identifiers 

The Census Bureau provides housing identifiers and person-level identifiers for use in linking survey 

data with administrative data files. The identifiers are unique and enable matches between different 

data sources without revealing personally identifying information. We use the following variables to link 

data files for this analysis. 

Master Address File Identifier (MAFID) 

• Unique housing unit identifier for address-level matches 

• Allows address-level matches even when no information is collected about the people at the 

address 

• Used for the interview rate analysis 

Protected Identification Key (PIK) 

• Unique person-level identifier 

• Allows information about an individual in the administrative data to be attached to the same 

individual in the survey data 

• Used for the Subgroup Representation Analysis 

• Used for the TRIM3 Simulated Eligibility Analysis in Chapter 4 
Factors Contributing to High Estimated SNAP Participation Rates

24 

Interview Status 

We present results for housing units with and without a MAFID-linked SNAP case by interview 

status, including interview or Type A, B, or C non-interview. “Type A” non-interviews refer to 

households that are eligible for an interview but could not be contacted or refused to participate. 

“Type B” housing units are temporarily ineligible for an interview, such as those that are vacant 

or temporarily occupied by people with another permanent address. “Type C” housing units are 

permanently ineligible for interview, such as housing units that have been demolished. The Census 

Bureau drops housing units identified as Type C from subsequent interview months but continues 

to contact Type A and Type B housing units for each month that they remain in sample. 

Whole Imputation 

We disaggregate interviewed households into those with an ASEC interview and “whole imputes.” 

A whole impute refers to a household that responded to the basic CPS interview but did not 

complete the ASEC portion of the interview. When this occurs, the Census Bureau keeps the 

information from the basic CPS interview but fills in the ASEC variables with values from similar 

surveyed respondents. 

Whole impute households are likely to have accurate information for basic demographic 

information like age, relationship, race, and ethnicity (which are available in the basic CPS). 

However, microsimulation model estimates rely on detailed income variables and other data 

provided by the ASEC portion of the interview. No imputation method can assign values perfectly 

to each respondent, and so it is likely that the Census Bureau’s procedures assign some SNAP 

households to have income and other characteristics that are inconsistent with SNAP eligibility. 

We identify a household as a whole impute if at least one household member is a whole impute—

as any one member’s income could be enough to raise the household’s income above the SNAP 

eligibility limit. 

CPS Interview Status for Households with and without SNAP 

Table 3.1 shows the interview status of March 2016 CPS housing units in the combined data for 

Illinois, Mississippi, and Tennessee.17 A housing unit is counted as having a SNAP case if the 

unit’s MAFID matches the MAFID of at least one SNAP case in the state administrative data.18

The combined weighted results are affected by the relative population sizes of the three states. 

Illinois represents 56 percent of the weighted total of March sampled housing units, Tennessee 

represents 30 percent, and Mississippi represents 14 percent (not shown).19 

                                                            

17 Results by state are provided in Appendix Table B.1. 

18 We use “housing unit” instead of “household” to describe interview rates involving Type B and Type C housing 

units. “Household” refers to the group of people living in a housing unit and some Type B and all Type C units are 

uninhabited. 

19 Among sampled housing units with a matched SNAP case, 49 percent of the weighted total reflects Illinois, 38 

percent reflects Tennessee, and 13 percent reflects Mississippi. 
Factors Contributing to High Estimated SNAP Participation Rates

25 

Overall, 72 percent of housing units in the basic March CPS sample for the three combined states 

are interviewed; 12 percent are type A non-interviews (eligible for interview, but not interviewed); 

16 percent are type B non-interviews (temporarily ineligible, such as vacant or temporarily 

occupied by members whose usual residence is elsewhere); and 1 percent are type C noninterviews (permanently ineligible, such as demolished). 

Table 3.1 Interview Status by Whether SNAP Case at Housing Unit Address, 20161

All CPS sampled 

housing units 

SNAP case at CPS address? 

 Yes No 

N 4,600 650 4,000 

Weighted (thousands) 9,132 1,220 7,912 

Interview status 

Percentage distribution 100% 100% 100% 

Interview 72% 79% 70% 

Non-interview (Type A/B/C) 28% 21% 30% 

Detailed interview status 

Percentage distribution 100% 100% 100% 

Interview 72% 79% 70% 

Non-interview: Type A 12% 14% 11% 

Type B: Usual residence elsewhere 4% 1% 4% 

Type B: Vacant 11% 5% 12% 

Type B: All other 1% 2% 

Type C Housing units 1% 1% 

Type B: All other and Type C housing units 1% 

 

Sources: Decision Demographics & Urban Institute tabulations of linked 2016 CPS ASEC and SNAP 

administrative data for Illinois, Mississippi, and Tennessee 

Universe: Housing units in the basic March CPS sample (excluding Hispanic oversample) 

1Bottom row combines cells to avoid disclosure. Addresses are matched by MAFID to preserve confidentiality. 

DRB Delegated Authority Approval Number CBDRB-FY21-CES014-028 (Ns are rounded to meet disclosure 

avoidance requirements). 

Interview rates are higher for housing units with a SNAP case, but this primarily reflects lower 

rates of Type B and Type C non-interviews among housing units with SNAP participants. Type B 

and C housing units comprise 7 percent of housing units with a SNAP case and 19 percent of 

housing units without a SNAP case. The SNAP administrative data should reflect the current 

address of a SNAP case, unless the members have moved and not informed the SNAP agency of 

the change in address. Therefore, we would expect that housing units with SNAP participants 

would be less likely to be identified as Type B or Type C than other sampled CPS housing units. 

Table 3.2 excludes Type B and C non-interviews and disaggregates the interviewed households 

into those that responded to the ASEC portion of the interview and those where at least one 

household member is a whole impute, for whom all ASEC variables are obtained from another 
Factors Contributing to High Estimated SNAP Participation Rates

26 

household’s response. We also refer to whole impute households as households that responded to 

the Basic CPS but not the ASEC supplement. 

We find that when the three states are combined, the rates of interview, whole imputation, and 

Type A non-interview are very similar for households with and without a SNAP case. About 70 

percent of households have ASEC interviews, 16 percent are wholly imputed, and 14 percent are 

Type A non-interviews. 

Table 3.2 Interview Status and Whole Imputation by Whether SNAP Case at 

Household Address, 20161

 

CPS Sampled 

households 

SNAP case at CPS address? 

Yes No 

ILLINOIS, MISSISSIPPI, & TENNESSEE 

N 3,800 600 3,200 

Weighted (thousands) 7,613 1,141 6,472 

Interview status 

Percentage distribution 100% 100% 100% 

Interview, not whole impute 70% 69% 70% 

Basic CPS interview, whole 

impute to ASEC 16% 15% 17% 

Non-Interview: Type A 14% 15% 14% 

 

ILLINOIS 

N 1,600 200 1,400 

Weighted (thousands) 4,334 562 3,771 

Interview status 

Percentage distribution 100% 100% 100% 

Interview, not whole impute 71% 69% 71% 

Basic CPS interview, whole 

impute to ASEC 17% 18% 16% 

Non-Interview: Type A 13% 13% 12% 

 

MISSISSIPPI 

N 1,100 150 900 

Weighted (thousands) 984 146 838 

Interview status 

Percentage distribution 100% 100% 100% 

Interview, not whole impute 75% 84% 73% 

Basic CPS interview, whole 

impute to ASEC 10% 6% 11% 

Non-Interview: Type A 15% 10% 16% 

 

(Table continues) 

 
Factors Contributing to High Estimated SNAP Participation Rates

27 

Table 3.2 (continued)

CPS Sampled 

households 

SNAP case at CPS address? 

Yes No 

TENNESSEE 

N 1,100 200 900 

Weighted (thousands) 2,295 433 1,863 

Interview status 

Percentage distribution 100% 100% 100% 

Interview, not whole impute 65% 65% 65% 

Basic CPS interview, whole 

impute to ASEC 19% 15% 19% 

Non-Interview: Type A 17% 20% 16% 

 

Sources: Decision Demographics & Urban Institute tabulations of linked 2016 CPS ASEC and 

SNAP administrative data for Illinois, Mississippi, and Tennessee 

Universe: Households in the basic March CPS sample (excluding Hispanic oversample). 

Excludes Type B and Type C non-interview housing units. 

1Addresses are matched by MAFID to preserve confidentiality. 

DRB Delegated Authority Approval Numbers CBDRB-FY21-CES014-028 and 

CBDRB-FY21-CES014-029 (Ns are rounded to meet disclosure avoidance requirements). 

This overall finding masks differences by state, with Illinois having the greatest representation in 

the combined results due to its larger population.20 Households with a SNAP recipient have similar 

Type A nonresponse rates to households without a SNAP recipient in Illinois, lower nonresponse 

rates in Mississippi, and higher nonresponse rates in Tennessee. In Illinois, 13 percent of 

households with a SNAP recipient and 12 percent of non-SNAP households eligible for the CPS 

interview are Type A non-interview, compared with 10 percent and 16 percent in Mississippi and 

20 and 16 percent in Tennessee, respectively. We see similar patterns of Type A nonresponse for 

Mississippi and Tennessee in 2017 (Appendix Table B.2), though the difference between 

households with a SNAP recipient and non-SNAP households is somewhat larger for Mississippi 

and smaller for Tennessee than in 2016.21 Given these differing results by state, the implications 

for national estimates are unclear and further investigation may be warranted. 

 

                                                            

20 Approximately 57 percent of the weighted total for March sample households excluding Type B and C is for 

Illinois, 30 percent is for Tennessee, and 13 percent is for Mississippi based on the weighted totals shown in Table 

3.2.

21 2017 administrative SNAP case record data for Illinois were not available for inclusion in the study. 
Factors Contributing to High Estimated SNAP Participation Rates

28 

CPS Interview Status by SNAP Case Type

We next examine CPS interview status by SNAP case type, focusing on four key subgroups with 

sufficient sample size to support the analysis: 

1) cases with at least one person age 60 or older without children; 

2) cases with one adult and one or more children; 

3) cases with multiple adults and one or more children; and 

4) one-person cases with an adult between the ages of 18 and 59.22 

Two other subgroups are included in the total but not shown separately. They include child-only 

cases (in which only the children in the family qualify for SNAP) and cases with multiple adults 

and no member younger than 18 or older than 59. Case characteristics are defined using the 

information recorded in the SNAP administrative data. 

Whereas Tables 3.1 and 3.2 focused on housing units and households, Table 3.3 focuses on SNAP 

cases. A CPS household with multiple families or individuals may have more than one SNAP case. 

For example, a household containing a family and an unrelated individual might have two SNAP 

cases—one for the family and one for the unrelated individual. Each case would be counted 

separately in Table 3.3 and tabulated according to the interview status of the CPS housing unit. 

Although response rates are often estimated for households found eligible for the interview, we 

show the interview status for all SNAP cases with a MAFID that matches a sampled housing unit 

(including the housing units found ineligible for the interview). If addresses in the SNAP 

administrative data are up to date, we would expect few SNAP cases to be present in type B and 

type C non-interviewed housing units. Overall, we find that 8 percent of the cases are in type B or 

C units (Table 3.3). Future research could examine whether such cases appear in the SNAP 

administrative data at a different address in a subsequent month (suggesting delayed reporting of 

a change of address to the agency), reflect inaccurate CPS classification of the address as type B 

or type C, or reflect errors in assignment of MAFID. 

 

                                                            

22 We define “child” as anyone under age 18. Examples of cases with multiple adults and children include married 

parent families, cohabiting couples with children, a parent with a child under 18 and another child aged 18 or above, 

and cases involving extended families, such as grandparents and adult siblings.
Factors Contributing to High Estimated SNAP Participation Rates

29 

Table 3.3 Interview Status by SNAP Case Type, 20161

 SNAP case type 

Total2

At least one 

member 60+, 

without 

children 

One adult 

with 

child(ren) 

Multiple 

adults with 

child(ren) 

One person 

case, age 18 

to 59 

N 750 150 200 100 200 

Weighted (thousands) 1,426 266 392 219 428 

Interview status 

Percentage distribution 100% 100% 100% 100% 100% 

Interview, not whole impute 64% 66% 64% 72% 63% 

Basic CPS interview, whole impute to ASEC 14% 17% 9% 18% 9% 

Non-interview: Type A 14% 12% 19% 13% 

Non-interview: Type BC 8% 5% 8% 15% 

Non-interview: Type ABC 10% 

 

Sources: Decision Demographics & Urban Institute tabulations of linked 2016 CPS ASEC and SNAP administrative data 

for Illinois, Mississippi, and Tennessee 

Universe: Housing units in the basic March CPS sample (excluding Hispanic oversample) that match the address of a 

SNAP case. Addresses are matched by MAFID to preserve confidentiality. 

1Bottom row combines cells to avoid disclosure. 

2The total includes child-only cases and cases with multiple adults without members younger than 18 or 60 or above, not 

shown separately. 

DRB Delegated Authority Approval Number CBDRB-FY21-CES014-028 (Ns are rounded to meet disclosure 

avoidance requirements). 

We find that a higher share of multiple-adult plus child cases linked with the CPS are in housing 

units with ASEC interviews (72 percent) than are one-adult plus child cases (64 percent), cases 

with a person aged 60 or above without children (66 percent), and one-person cases aged 18 to 59 

(63 percent). Multiple-adult plus child cases are much less likely to be in Type A, B, or C housing 

units (not interviewed for the basic CPS). Ten percent of multiple-adult plus child cases are noninterviews, compared with 27 percent of one-adult plus child cases, 17 percent of cases with a 

member aged 60 or above without children, and 28 percent of one-person cases between the ages 

of 18 and 59. 

We find similar patterns for 2017 when looking at combined results for Mississippi and Tennessee, 

though the overall interview rate is higher (Appendix Table B.3). Multiple-adult plus child cases 

linked with the CPS are much more likely to be in housing units with ASEC interviews than are 

one-adult plus child cases. Eighty-three percent of multiple-adult plus child cases are in housing 

units with ASEC interviews, compared with 69 percent of one-adult plus child cases. Nine percent 

of multiple-adult plus child cases are not interviewed for the basic CPS, compared with 19 percent 

of one-adult plus child cases. 

 
Factors Contributing to High Estimated SNAP Participation Rates

30 

CPS Interview Status by Presence of Earnings and Poverty Level

Table 3.4 examines interview rates of SNAP cases by presence of earnings and poverty level, for 

the combined three states in March 2016. Earnings status and poverty level are obtained from the 

administrative data and reflect the SNAP agency’s most recent information about the case’s 

monthly income The poverty level reflects the SNAP case’s countable gross income as a 

percentage of the poverty guideline used for eligibility determination. 

Table 3.4 Interview Status by Earnings Status and Poverty Level of SNAP Case, 2016 

 

Case earnings and poverty status 

Total 

Does not 

have 

earnings 

Has 

earnings 

50% of 

poverty or 

below 

Above 50% 

of poverty 

N 750 500 200 300 400 

Weighted (thousands) 1,426 1,020 405 613 812 

Interview status 

Percentage distribution 100% 100% 100% 100% 100% 

Interview, not whole impute 64% 66% 61% 60% 68% 

Basic CPS interview, whole impute to ASEC 14% 13% 16% 15% 13% 

Non-Interview: Type A 14% 14% 13% 16% 12% 

Non-Interview: Type BC 8% 7% 10% 9% 7% 

Cases with at least one adult and one child and no member aged 60 or above 

N 300 150 150 150 150 

Weighted (thousands) 610 325 286 317 293 

Interview status 

Percentage distribution 100% 100% 100% 100% 100% 

Interview, not whole impute 67% 67% 66% 64% 70% 

Basic CPS interview, whole impute to ASEC 13% 10% 16% 15% 10% 

Non-Interview: Type A 15% 16% 14% 14% 16% 

Non-Interview: Type BC 6% 7% 4% 7% 4% 

One person case, age 18 to 591

 

N 200 200 100 100 

Weighted (thousands) 428 351 223 205 

Interview status 

Percentage distribution 100% 100% 100% 100% 

Interview, not whole impute 63% 66% 56% 72% 

Basic CPS interview, whole impute to ASEC 9% 9% 12% 6% 

Non-Interview: Type A 13% 13% 18% 8% 

Non-Interview: Type BC 15% 12% 14% 15% 

 

Sources: Decision Demographics & Urban Institute tabulations of linked 2016 CPS ASEC and SNAP administrative data 

for Illinois, Mississippi, and Tennessee 

Universe: Housing units in the basic March CPS sample (excluding Hispanic oversample) that match the address of a 

SNAP case. Addresses are matched by MAFID to preserve confidentiality. 
Factors Contributing to High Estimated SNAP Participation Rates

31 

1Data for “One person case, age 18 to 59 with earnings” have been withheld due to small sample size and to avoid 

disclosure for some cells. 

DRB Delegated Authority Approval Number CBDRB-FY21-CES014-028 (Ns are rounded to meet disclosure avoidance 

requirements). 

Overall, cases with earnings are about 5 percentage points less likely to have an ASEC interview 

than are those that do not have earnings. Cases with income above 50 percent of the poverty 

guideline are about 8 percentage points more likely to have an ASEC interview. 

It may seem counterintuitive that interview rates would be lower for cases with earnings yet higher 

for cases above 50 percent of poverty, as we might expect that a case with earnings would also be 

more likely to have income above 50 percent of poverty. Household composition likely contributes 

to this effect. Cases with older members are less likely to have earned income than other types of 

SNAP cases and are more likely to have income above 50 percent of poverty.23 To provide further 

insight, we show results separately for cases with at least one adult and one child and no member 

aged 60 or above, and for one-person cases between the ages of 18 and 59. 

Focusing first on cases with at least one adult and one child and no member aged 60 or older, we 

see little difference in ASEC interview rates for cases with and without earnings. Cases without 

earnings are somewhat more likely to be Type A, B, or C non-interview (23 percent relative to 18 

percent). ASEC interview rates are higher for cases with income above 50 percent of the poverty 

guideline than for the poorest cases (70 percent relative to 64 percent) and rates of whole 

imputation are lower (10 percent relative to 15 percent). 

Relatively few one-person cases between the ages of 18 and 59 have earned income, and we lack 

sufficient sample size to show interview rates separately for this group.24 The ASEC interview rate 

is substantially higher for one-person cases with income above 50 percent of the poverty guideline 

than for the poorest cases (72 relative to 56 percent), the rate of whole imputation is lower (6 

percent relative to 12 percent), and the combined type A, B, and C non-interview rate is lower (23 

percent relative to 32 percent). 

SNAP Subgroup Representation in the ASEC 

In this section we expand our focus to the full CPS ASEC for Illinois, Mississippi, and Tennessee. 

We examine the representation of SNAP cases in the final linked data to see if differences in 

interview rates among SNAP subgroups result in differences in the extent to which these subgroups 

are represented in the final CPS ASEC data. 

                                                            

23 According to national estimates, 6.7 percent of SNAP cases with members aged 60 or more had earnings in 2016, 

compared with 55.3 percent of cases with children. Eighty-six percent of SNAP cases with members aged 60 and 

above had gross countable income above 50 percent of the poverty guideline, compared with 54.6 percent of cases 

with children. See tables A.3 and A.6, USDA (2017). 

24 Nationally, 38 percent of one-person cases (excluding people aged 60 and over) had a disability and another 16 

percent had earned income in 2016. These estimates are calculated from Table 3.2 in USDA 2017. 
Factors Contributing to High Estimated SNAP Participation Rates

32 

The interview rate analysis presented above uses the Census Bureau’s base weights, before 

adjustment for nonresponse. The Census Bureau adjusts for household nonresponse by increasing 

the weights of interviewed households that are in sample areas like those of the non-interviewed 

households. Nonresponse bias will remain if interviewed households differ with respect to the 

income and demographic characteristics of non-interviewed households (Census Bureau, 2019). 

Bias may also be introduced through whole imputation, if households that do not respond to the 

ASEC portion of the interview differ from those who respond in ways that are not controlled for 

by the imputation procedures. 

In addition to adjusting for household nonresponse, the Census Bureau adjusts the basic CPS 

weights to reach population targets at the state level and by race, ethnicity, sex, and age. The ASEC 

weighting procedure includes various adjustments beyond those required for the basic CPS. These 

include adjustments needed to incorporate the additional samples from the ASEC interviews 

conducted in February, April, and for the additional Hispanic households in March; to account for 

certain armed forces members who are excluded from the basic CPS but included in the ASEC;25

and to equalize weights of husbands and wives (Census Bureau, 2019).26 

Although weights are adjusted for race, ethnicity, sex, and age, they are not adjusted for family 

composition—such as one-adult with child and multiple-adult with child family status. If one-adult 

with child families are less likely to respond to the basic CPS than multiple-adult families, then it 

is possible that they will be underrepresented in the basic CPS and CPS ASEC. 

Data for SNAP Subgroup Representation Analysis 

For this analysis, we use data sets for 2012 through 2016 constructed by Mathematica.27 The 

Mathematica data sets are constructed by linking the CPS ASEC data with SNAP administrative 

data for Illinois, Mississippi, and Tennessee by Protected Identification Key (PIK). PIKs are 

unique identifiers created by the Census Bureau to facilitate matches between survey and 

administrative data without revealing personally identifying information. The match is performed 

at the person level. SNAP administrative data for the linked participant and information about his 

or her case are appended to the individual’s record in the CPS ASEC. People in the CPS ASEC 

are matched with the SNAP administrative data month corresponding to their interview month—

February, March, or April. 

Starting from the linked datasets prepared by Mathematica, we tabulate cases where at least one 

case member in the SNAP administrative data has a PIK match with a person in the CPS ASEC. 

We weight each SNAP case using the ASEC supplement weight of the case head. If the case head 

is not matched with a person in the CPS ASEC, we use the weight of the oldest case member who 

                                                            

25 Armed forces members are excluded from the basic CPS but are included in the ASEC if they live off post or on 

post with family members and have at least one civilian adult in the household. 

26 More recent years of CPS ASEC data also equalize weights for cohabiting partners, but that does not affect the data 

years presented here. 

27 Czajka & Cunnyngham (2021).
Factors Contributing to High Estimated SNAP Participation Rates

33 

has a PIK match with a person in the CPS ASEC.28 We classify cases according to their 

composition in the administrative data. 

Although nearly all administrative data records have a PIK, PIKs are missing for between 14 and 

16 percent of people in the 2012 to 2016 CPS ASEC data for Illinois, between 6 and 8 percent in 

Mississippi, and between 9 and 15 percent in Tennessee (Czajka, 2021). Our estimates do not 

adjust for missing PIKs and so our weighted counts of SNAP cases should be somewhat below 

those in the SNAP administrative data.29 The SNAP administrative data contain some types of 

cases (such as for homeless individuals) that are outside the scope of the CPS ASEC. This should 

also lower our weighted results somewhat, relative to the administrative data. 

As expected, the overall weighted number of SNAP cases in the linked data is lower than the actual 

number of cases in the administrative data. The weighted linked data represent 74 percent of SNAP 

administrative data cases in 2016 (Figure 3.1 and Appendix Table B.4). A somewhat higher share 

(between 81 and 83 percent) is represented in the 2013 to 2015 linked data. The 2012 linked data 

capture 86 percent of the SNAP administrative data total. Some of the reduction in represented 

cases between 2012 and 2016 is likely attributable to declining CPS ASEC PIK rates in Mississippi 

and Tennessee over these years. PIK rates fluctuated within a 2-percentage point range in Illinois 

during this time period. 

Figure 3.1 Share of SNAP Cases Represented in the Linked Data, by Year 

Sources: Decision Demographics & Urban Institute tabulations of 2012–2016 linked CPS ASEC 

and SNAP administrative data for Illinois, Mississippi, and Tennessee. 

DRB Delegated Authority Approval Number CBDRB-FY21-CES014-028 

                                                            

28 The Mississippi and Tennessee state SNAP administrative data identify the head of the case. If the case head is 

linked to the CPS ASEC, we use the weight of the case head to weight the results. If the case head is not linked to the 

CPS ASEC, we use the weight of the oldest linked case member. We use the weight of the oldest linked member for 

all cases in Illinois because case head is not identified in the SNAP administrative data. 

29 This may affect some subgroups more than others, as a Census Bureau study using the 2009 ACS found that 

young children, minorities, immigrants, recent movers, low-income individuals, and non-employed individuals are 

less likely to receive a PIK. However, changes to the PIK assignment process in 2010 did significantly address the 

PIK deficit among young children (Bond et al., 2014). 

74%

83%

81%

82%

86%

0% 20% 40% 60% 80% 100%

2016

2015

2014

2013

2012
Factors Contributing to High Estimated SNAP Participation Rates

34 

We next examine the representation of SNAP cases with children in the linked CPS ASEC data. 

We show results for the one-adult plus child and multiple-adult plus child subgroups included in 

our interview rate analysis, and also examine “child-only” cases. Child-only cases are cases in 

which only the children in the SNAP case are eligible for benefits. For example, under SNAP 

rules, a citizen child can be eligible for SNAP even if the child’s parents are ineligible due to their 

immigrant status. All or a portion of the parents’ income would be deemed to the child when 

determining eligibility, but they would not be taken into consideration when determining the 

household’s size for eligibility and benefit calculation. Child-only cases represent a relatively 

small share of all cases. They are excluded from other tables due to sample size limitations, but 

are included in the totals. 

We find that the linked data represent between 64 and 79 percent of child-only cases, between 82 

and 94 percent of one-adult plus child cases, and between 105 and 121 percent of multiple-adult 

plus child cases (Figure 3.2). Multiple-adult plus child cases are most overrepresented and childonly and one-adult plus child cases are most underrepresented in the 2016 data. 

Figure 3.2 Share of SNAP Cases with Children Represented in the Linked Data, by Year 

Sources: Decision Demographics & Urban Institute tabulations of 2012–2016 linked CPS ASEC and SNAP 

administrative data for Illinois, Mississippi, and Tennessee. 

DRB Delegated Authority Approval Number CBDRB-FY21-CES014-028 

121%

109%

105%

111%

110%

82%

91%

94%

85%

94%

64%

77%

69%

79%

71%

0% 20% 40% 60% 80% 100% 120% 140%

2016 Multiple adults

2015 Multiple adults

2014 Multiple adults

2013 Multiple adults

2012 Multiple adults

2016 One adult

2015 One adult

2014 One adult

2013 One adult

2012 One adult

2016 Child only

2015 Child only

2014 Child only

2013 Child only

2012 Child only
Factors Contributing to High Estimated SNAP Participation Rates

35 

Finally, we examine cases without children or people over 59. In addition to showing results for 

one-person cases, we provide results for cases with multiple adults and no members below 18 or 

above 59. This latter group was not shown separately in the interview rate analysis due to sample 

size limitations, though was included in the totals. 

One-person cases are the least represented group among the subgroups examined in all years but 

2012, when multiple-adult cases had the least representation (Appendix Table B.4). The linked 

data represent between 49 and 76 percent of the SNAP administrative data cases for one-person 

cases, with the lowest share in 2016 and the highest share in 2012 (Figure 3.3). The data represent 

between 66 percent and 89 percent of cases with multiple adults. 

Figure 3.3 Share of SNAP Cases with Adults between 18 and 59 Represented in the Linked Data, by Year 

Sources: Decision Demographics & Urban Institute tabulations of 2012–2016 linked CPS ASEC and SNAP 

administrative data for Illinois, Mississippi, and Tennessee. 

Note: This figure reflects cases consisting of at least one adult aged 18 to 59 and no younger or older members. 

DRB Delegated Authority Approval Number CBDRB-FY21-CES014-028 

 

86%

81%

70%

89%

66%

49%

68%

64%

68%

76%

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

2016 Multiple adults

2015 Multiple adults

2014 Multiple adults

2013 Multiple adults

2012 Multiple adults

2016 One adult

2015 One adult

2014 One adult

2013 One adult

2012 One adult
Factors Contributing to High Estimated SNAP Participation Rates

36 

These findings suggest that a contributing factor to the high estimated participation rates for oneadult plus child cases and possibly child-only cases may be their underrepresentation in the CPS 

ASEC. Although we have not adjusted for missing PIKs, multiple-adult plus child cases are 

consistently overrepresented in the linked data for the combined states of Illinois, Mississippi, and 

Tennessee across five years of CPS ASEC data. Because the CPS ASEC is weighted to control 

totals by age, this consistent overrepresentation of multiple-adult plus child cases suggests that 

other types of cases with children may be underrepresented.30

The underrepresentation of one-person cases aged 18 to 59 is also noteworthy, particularly the low 

level of representation in 2016 (49 percent). This finding differs from the interview rate analysis, 

in which the interview rate for one-person cases aged 18 to 59 is close to the overall interview rate 

for SNAP cases. Missing PIKs likely contribute to the lower rate observed here. Cases with more 

than one member may be more likely to be represented in the linked data because there is a greater 

chance that at least one member will match by PIK. One-person adult cases may also be more 

likely to be homeless and outside the scope of the CPS ASEC. Another possibility is that the 

incorporation of other samples into the final ASEC increases the representation of SNAP cases 

with children more so than for one-person cases aged 18 to 59. 

Chapter 3 Summary and Recommendations 

Our goal in investigating SNAP case interview rates and representation in the CPS ASEC is to see 

if there is evidence to suggest that SNAP cases are underrepresented in the survey data. If 

households with SNAP recipients are less likely to respond to the CPS ASEC than households 

without SNAP recipients, and if subsequent weighting steps do not account for these differences, 

then the weighted SNAP eligibility estimates produced by microsimulation models may be too 

low. SNAP participation rates are calculated by dividing the number of participants according to 

administrative data by the number simulated as eligible by microsimulation models. If eligibility 

estimates are too low, then estimated participation rates will be too high, particularly for some 

subgroups. 

The interview rate analysis links sampled March CPS housing units with SNAP cases in the 

Illinois, Mississippi, and Tennessee SNAP administrative data by MAFID. The results reflect 

findings for these three states for 2012 to 2016. Sample sizes are modest for some of our subgroup 

analyses, rounding to as few as 100 unweighted cases. Further work with additional states and 

years of data would be needed to confirm these findings and address their implications for national 

estimates. Nevertheless, these results do provide evidence suggesting that certain key subgroups 

are underrepresented in the CPS ASEC. 

 

                                                            

30 We refer to this as suggestive rather than conclusive because population controls are at the individual level whereas 

we examine results at the case level. 
Factors Contributing to High Estimated SNAP Participation Rates

37 

Summary of Findings 

Results from this analysis show: 

• Differing results by state as to whether a sampled household with a SNAP case is more 

likely or less likely to complete the CPS ASEC interview than a sampled household without 

a SNAP case 

• Lower CPS ASEC interview rates for sampled housing units containing a one-adult plus 

child case than for sampled housing units containing a multiple-adult plus child case 

• Substantially lower CPS ASEC interview rates among one-member cases aged 18 to 59 

with income up to 50 percent of the poverty guideline relative to those with income above 

50 percent of the poverty guideline 

• Somewhat lower CPS ASEC interview rates for one-adult plus child cases with income up 

to 50 percent of the poverty guideline relative to those above 50 percent of the poverty 

guideline 

The representation analysis links the CPS ASEC with SNAP cases from the state administrative 

data by PIK and compares the resulting weighted number of SNAP cases with the total number 

from the administrative data. The analysis finds: 

• Generally declining representation of SNAP cases in the CPS ASEC data from 2012 to 

2016 

• Overrepresentation of multiple-adult plus child cases in each year examined and 

underrepresentation of other cases with children 

• Declining representation of one-member cases aged 18 to 59, with just 49 percent 

represented in 2016 

Implications for Participation Rate Estimates 

The three states differ in whether response rates among households with SNAP are higher or lower 

than among households without SNAP. Thus, it is difficult to draw conclusions at the national 

level as to whether SNAP cases are overrepresented or underrepresented. Nevertheless, we do find 

evidence of underrepresentation of some subgroups relative to others, potentially contributing to 

the high participation rate estimates for the underrepresented groups. 

Initial findings from another ongoing study support many of the findings we present here. The 

study examines the representation of SNAP cases overall and for different subgroups using SNAP 

administrative case records for 21 states and 10 years that have been linked with the CPS ASEC 

and ACS (Meyer and Wu 2021). The estimates adjust for missing PIK and find a 95 percent 

coverage rate for SNAP individuals in the CPS ASEC, with variations by subgroup as noted below. 

 
Factors Contributing to High Estimated SNAP Participation Rates

38 

One-Adult Plus Child Cases 

The lower interview rates for one-adult plus child cases relative to multiple-adult plus child cases, 

combined with the overrepresentation of multiple-adult plus child cases across five years of CPS 

ASEC data suggest that one-adult plus child cases are underrepresented and multiple-adult plus 

child cases are overrepresented in the CPS ASEC for these states and data years. The CPS is 

weighted to population totals for age, sex, race, and ethnicity—but not for family composition, so 

lower interview rates for one-adult plus child cases can lead to lower representation of these cases 

in the final CPS ASEC data. Meyer and Wu (2021) also find evidence of overrepresentation of 

married parent cases in the CPS and underrepresentation of single parent cases.31

Underrepresentation of one-adult plus child cases may therefore contribute to unrealistically high 

participation rate estimates for this subgroup. 

SNAP Cases Below 50 Percent of Poverty 

SNAP cases with income below 50 percent of the poverty guideline are another subgroup with 

unrealistically high participation rate estimates (Chapter 2). The relatively lower CPS interview 

rates observed for this group among one-member cases aged 18 to 59 and among cases with 

children and adults aged 18 to 59 may suggest that they are underrepresented in the CPS ASEC, 

contributing to the higher than expected participation rates. 

One-Member Cases Aged 18 to 59 

One-member cases have high participation rates according to the MATH CPS+ model, though not 

for other microsimulation models (Chapter 2). We do not look at all one-member cases but do 

examine those with members aged 18 to 59. We find that, although interview rates for this group 

do not differ much from interview rates of SNAP cases on average, their representation in the CPS 

ASEC has been generally falling across the five years examined. Of particular concern, just 49 

percent of one adult cases aged 18 to 59 are represented in the data for these three states in 2016. 

Further investigation could confirm whether this pattern is observed in other states and years, 

determine whether these patterns persist when adjusting for missing PIKs, and consider the extent 

to which some of the people in the administrative data (such as homeless individuals) are outside 

the scope of the CPS ASEC. Initial findings from Meyer and Wu (2021) find that one-person 

SNAP cases are underrepresented in the CPS ASEC to a greater extent than are other groups.32

Eligible Nonparticipants

It is possible that the underrepresentation of key subgroups of SNAP cases observed in the linked 

administrative and survey data also occurs for families and individuals in these subgroups who are 

eligible for SNAP but do not participate. We are not able to investigate this question using the 

methods and data available for this study. If eligible nonparticipants are also underrepresented, 

                                                            

31 Meyer and Wu estimate a CPS coverage rate of 92 percent for single parent SNAP cases and 103 percent for 

married parent SNAP cases. 

32 The single person estimates by Meyer and Wu are for all individuals, not just those aged 18 to 59, and show a 

CPS coverage rate of 86 percent.
Factors Contributing to High Estimated SNAP Participation Rates

39 

then this would further bias eligibility estimates downward for these subgroups and contribute to 

higher than expected participation rates.

Future Research to Address Underrepresentation 

If findings from this analysis are supported by subsequent research spanning a larger number of 

states and including more recent data years, approaches could be developed to reweight the CPS 

ASEC data used as input to the microsimulation models to compensate for the underrepresentation 

of affected groups. 

 
Factors Contributing to High Estimated SNAP Participation Rates

40 

4. TRIM3 Simulated Eligibility of SNAP Cases in Linked Data 

In this chapter, we investigate how differences in simulated and actual case membership, 

imputation, and other factors contribute to SNAP cases being simulated as ineligible or eligible by 

the TRIM3 microsimulation model. We find that differences in TRIM3 unit and SNAP case 

membership are much more common among SNAP cases simulated as ineligible for SNAP than 

among those simulated as eligible. SNAP cases simulated as ineligible by TRIM3 are also much 

more likely to be “whole imputes” in which the ASEC portion of the survey is imputed, or to have 

imputed income amounts. We examine other factors that might complicate eligibility 

determination, including recent job loss, noncitizen status, and mover status, but find that these are 

much less common than differences in SNAP case and TRIM3 unit membership and imputation. 

We then focus on a key subgroup—cases identified as one-adult plus child in the SNAP 

administrative data—and find that only about half are identified as an eligible one-adult plus child 

unit within TRIM3. Common reasons for discrepancies include the absence of the SNAP case’s 

children from the ASEC household, the absence of the case adult from the ASEC household (with 

children living instead with multiple adults, such as grandparents and other relatives), and the 

inclusion in the TRIM3 unit of a spouse, partner, or other adults. We explore potential contributing 

factors, including movement of children and adults between households, and definitional 

differences regarding how adults who are ineligible due to immigrant status or for other reasons 

are counted in the administrative data and survey data. We provide suggestions for future research 

regarding this question in the concluding section of this chapter. 

Data for the Eligibility Analysis 

The eligibility analysis uses linked CPS ASEC and SNAP administrative data for Illinois, 

Mississippi, and Tennessee prepared by Mathematica.33 People in the CPS ASEC are linked by 

PIK with SNAP administrative data for the month corresponding to their interview month, for each 

year of CPS ASEC data from 2012 to 2016. We augment the data by attaching simulated TRIM3 

SNAP unit identifiers and eligibility flags to the linked data sets. The TRIM3 eligibility flags 

indicate the number of months a person was found eligible in the prior calendar year according to 

the TRIM3 simulation. 

The data available to this study do not enable analysis of SNAP participation and simulated 

eligibility in the same month. Instead, SNAP participation reflects the CPS ASEC interview 

month, and TRIM3 eligibility reflects eligibility in at least one month of the prior calendar year.34

Some people who were participating in SNAP in February, March, or April may be new entrants 

                                                            

33 These are the same data used for the SNAP case representation analysis discussed in Chapter 3. 

34 Mathematica linked SNAP participant information to the CPS ASEC based on the CPS ASEC interview month 

(February, March, or April) in order to reduce the likelihood that a member of a SNAP case is no longer present 

within the CPS ASEC household at the time of the CPS ASEC interview or that another person has joined the CPS 

ASEC household since the case was established. The linked data do not include information about whether the 

SNAP case was participating in SNAP in the prior calendar year, and so we are not able to control for that in this 

analysis but could do so in future work. 
Factors Contributing to High Estimated SNAP Participation Rates

41 

who were ineligible in the prior calendar year. However, based on the amount of time a case 

typically remains on SNAP, we expect that most cases participating in SNAP in February, March, 

or April would also have participated in at least some months of the prior calendar year.35

We describe the TRIM3 SNAP simulation methodology below and then describe the steps taken 

to incorporate the TRIM3 data into the linked data. 

TRIM3 SNAP Simulation Methodology 

TRIM3 follows the steps a caseworker would perform when determining eligibility: identifying 

whether people meet categorical eligibility requirements, performing assets tests, calculating 

income and deductions, determining income eligibility, and calculating the benefit amount. The 

model captures state variation in program rules, including BBCE rules.36

The model operates on a monthly basis, simulating eligibility and benefits in each month of the 

calendar year covered by the survey. TRIM3 allocates the annual income amounts reported in the 

CPS ASEC across the months of the year, factoring in reported weeks of employment and 

unemployment, and allocating spells of unemployment across the year to match real-world trends 

in unemployment. 

The TRIM3 eligibility rules reflect the year of the annual income data collected in the CPS ASEC. 

For example, the 2016 CPS ASEC collects information about annual income for 2015.37 TRIM3 

uses this information and the 2015 eligibility rules to assign eligibility.38

SNAP eligibility estimates are sensitive to the methods used to construct SNAP units or “cases” 

from the members of the CPS ASEC household. The TRIM3 “unit” represents the group of people 

within the CPS household simulated to apply for SNAP together. This might include everyone in 

the household, or a subset. For example, a household with a single parent and her child and an 

unrelated roommate might apply as two SNAP units—one containing the single parent and child 

and the other including the roommate. 

Much of our analysis focuses on the extent to which TRIM3 unit membership matches the 

membership of the SNAP case. When presenting the results, we use the term “unit” to describe the 

simulated TRIM3 unit, “case” to describe members of the case according to the SNAP 

                                                            

35 An analysis of SNAP participants in the 2008 Survey of Income and Program Participation finds that 92 percent 

of people receiving SNAP in December 2008 received SNAP for more than six months and 86 percent received 

SNAP for more than a year. The estimate includes time already spent on SNAP as of December 2008 plus 

subsequent months on SNAP (Leftin et al., 2014). 

36 TRIM3 documentation is available at trim3.urban.org. For additional background, see Zedlewski & Giannarelli 

(2015). 

37 TRIM3 results are typically presented and described according to the calendar year represented by the data. For 

ease of presentation, we use the CPS ASEC data year in this chapter and describe the results as showing eligibility in 

the prior calendar year. 

38 SNAP cost of living adjustments are released at the beginning of each federal fiscal year (October). When eligibility 

rules and benefit amounts change during a calendar year, the TRIM3 SNAP simulation typically uses a weighted 

calendar year average. For some state level rules, TRIM3 uses the value that was in effect for the majority of the year. 
Factors Contributing to High Estimated SNAP Participation Rates

42 

administrative data, and “household” to refer to the CPS ASEC household. We define “case type” 

based on the characteristics of case members in the SNAP administrative data and TRIM3 “unit 

type” based on the characteristics of the people in the TRIM3 unit (also see Definitions, page 5). 

For example, if a case in the SNAP administrative data consists of a mother and child, we would 

describe this as a “one-adult plus child” case. If the mother and child are linked with the CPS 

ASEC data and are part of a TRIM3 unit with the mother’s cohabiting partner, then the TRIM3 

unit would be described as a “multiple-adult plus child” unit. 

According to SNAP regulations, a SNAP case (or, in TRIM3 terms a unit) is comprised of all 

people in a household who “customarily purchase and prepare meals together for home 

consumption.”39 Married couples are required to apply for SNAP together, and minor children 

living at home are required to apply with their parents or guardians. Because the CPS does not 

report information on food purchasing habits, TRIM3 must make assumptions regarding which 

household members apply together as a single SNAP unit. TRIM3 splits households that receive 

TANF and households in which fewer people are reported to receive SNAP than are in the 

household into as many units as possible subject to FNS regulations.40 TRIM3 then uses logit 

models to estimate whether two additional types of households are split into multiple units: (1) 

non-TANF households in which there are multiple potential units and all members are reported to 

receive SNAP; and (2) non-TANF households that do not report receiving SNAP and have 

multiple potential units.41

 

Incorporating TRIM3 Variables into the Linked Data 

TRIM3 is continuously updated and improved. To provide consistency in the years examined, we 

reran the SNAP eligibility simulations performed on the 2012 through 2015 CPS ASEC using the 

methods used for the 2016 CPS ASEC and used the updated estimates for this analysis.

The TRIM3 model “clones” households containing noncitizens and also clones certain highincome households, dividing the weight across the cloned households. Cloning of noncitizen 

households supports the model’s detailed assignment of immigrant status; cloning of high-income 

households supports the model’s tax estimates. For this analysis, we identified the version of each 

cloned household with the most people simulated as eligible for SNAP and merged the TRIM3 

unit and eligibility information from that household to the linked data file.42 

 

                                                            

39 7 CFR § 273.1 

40 The CPS ASEC asks about SNAP receipt in the prior calendar year, including how many people in the household 

received SNAP. TANF status is obtained from the TRIM3 TANF simulation, which corrects for underreporting of 

TANF in the CPS ASEC data. 

41 The logit models were estimated using data from the 2008 SIPP panel. 

42 We use the original CPS ASEC weight for our estimates rather than the partial cloned weight used for these 

households in TRIM3. 
Factors Contributing to High Estimated SNAP Participation Rates

43 

Results 

In the analysis below, we classify SNAP cases that were receiving SNAP at the time of the CPS 

ASEC survey according to the SNAP administrative data by their simulated TRIM3 eligibility 

status in the prior calendar year. Some cases found ineligible by TRIM3 may have been truly 

ineligible in the prior calendar year, and only just recently started SNAP due to a job loss or for 

other reasons.43 

We first present the overall TRIM3 eligibility estimates of SNAP cases in the linked data by year 

and state. We then show the extent to which SNAP cases simulated as eligible and ineligible by 

TRIM3 have matching administrative data case and TRIM3 unit membership, have whole ASEC 

imputation or income imputation, or have other factors that might complicate eligibility 

determination, including recent job loss, noncitizen status, and mover status. 

We use the SNAP case as the basis for analysis. We count the case as “eligible” according to 

TRIM3 if at least one member of the SNAP case is in a TRIM3 unit simulated to be eligible in at 

least one month of the prior calendar year. 

Due to sample size limitations, we pool the results for Illinois, Mississippi, and Tennessee when 

showing results by year, combine the five years of data when showing results by state, and combine 

data across the three states and five years when examining results by detailed characteristic. The 

combined results are affected by the relative population sizes of the three states; approximately 47 

percent of the weighted total for the combined states and years is for cases in Illinois, 35 percent 

is for Tennessee, and 17 percent is for Mississippi.44 

 

                                                            

43 The linked ASEC-SNAP administrative data files available to our study do not provide information on prior year 

SNAP participation and so we are not able to quantify this effect. (See Data for the Eligibility Analysis, above). 

44 The weighted shares are calculated from the weighted state totals shown in Appendix Table C.2. 
Factors Contributing to High Estimated SNAP Participation Rates

44 

TRIM3 Simulated Eligibility 

Figure 4.1 presents TRIM3 simulated eligibility estimates for SNAP cases where at least one 

member is matched by PIK with a person in the CPS ASEC data.45 TRIM3 simulates a higher 

share of SNAP cases to be eligible in the 2012 through 2014 CPS ASEC data than in the 2015 and 

2016 data. Between 75 and 77 percent of cases receiving SNAP at the time of the 2012 to 2014 

CPS ASEC surveys are simulated to be eligible in at least one month of the prior calendar year, 

compared with 72 percent in 2015 and 71 percent in 2016. 

Figure 4.1 Simulated Eligibility of SNAP Cases in ASEC, by Year 

Sources: Decision Demographics & Urban Institute tabulations of 2012–2016 linked CPS ASEC, TRIM3, and SNAP 

administrative data for Illinois, Mississippi, and Tennessee 

Note: SNAP case status reflects the CPS ASEC survey month. TRIM3 simulated eligibility reflects eligibility in the 

prior calendar year. 

DRB Delegated Authority Approval Number CBDRB-FY21-CES014-027 

 

                                                            

45 We weight the results using the March supplement weight for the oldest case member matched to the CPS ASEC. 

71%

72%

75%

77%

75%

29%

28%

25%

23%

25%

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

2016

2015

2014

2013

2012

Eligible in 1+ months Ineligible in all months
Factors Contributing to High Estimated SNAP Participation Rates

45 

Combining data across the five years, we find that 78 percent of SNAP cases in Mississippi are 

simulated to be eligible for SNAP, compared with 74 percent in Tennessee and 73 percent in 

Illinois (Figure 4.2). 

Figure 4.2 Simulated Eligibility of SNAP Cases in ASEC, Combined 2012 to 2016 data, by State 

Sources: Decision Demographics & Urban Institute tabulations of combined 2012–2016 linked CPS ASEC, TRIM3, 

and SNAP administrative data for Illinois, Mississippi, and Tennessee 

Note: SNAP case status reflects the CPS ASEC survey month. TRIM3 simulated eligibility reflects eligibility in the 

prior calendar year. 

DRB Delegated Authority Approval Number CBDRB-FY21-CES014-027 

Case and Unit Consistency 

Ideally, the TRIM3 unit and SNAP case would have exactly the same members. Eligibility limits 

and maximum benefit amounts rise as the case or unit size increases. The inclusion or exclusion 

of an adult from a case or unit increases or decreases the income of the case or unit. If unit 

membership does not match case membership then both size and income may differ, affecting 

eligibility. Unit assignment is therefore a critical first step of SNAP eligibility modeling. 

We define a TRIM3 unit and SNAP case to have matching membership if each person in the SNAP 

case is linked by PIK with a member of the same TRIM3 unit, and the TRIM3 unit does not include 

any member not linked by PIK to the SNAP case. This approach will overstate the extent of unit 

and case mismatches to the extent that some people have missing or erroneous PIKs. 

We find that cases simulated as eligible in TRIM3 are much more likely to have matching case 

and TRIM3 unit membership than are those simulated as ineligible (Figure 4.3 and Appendix Table 

C.1). In 2016, 63 percent of the cases simulated as eligible have membership matching the TRIM3 

unit. In other words, each case member is linked by PIK with a member of the same TRIM3 unit, 

and no non-case members are included in the unit. In contrast, just 31 percent of cases simulated 

as ineligible have matching case and unit membership in 2016. 

74%

78%

73%

25%

22%

27%

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

Tennessee

Missisisippi

Illinois

Percent eligible in 1+ months Percent ineligible in all months
Factors Contributing to High Estimated SNAP Participation Rates

46 

Figure 4.3 SNAP Case and TRIM3 Unit Consistency by Simulated Eligibility and Year 

Sources: Decision Demographics & Urban Institute tabulations of 2012–2016 linked CPS ASEC, TRIM3, and SNAP 

administrative data for Illinois, Mississippi, and Tennessee 

Note: SNAP case status reflects the CPS ASEC survey month. TRIM3 simulated eligibility reflects eligibility in at least 

one month of the prior calendar year. 

DRB Delegated Authority Approval Number CBDRB-FY21-CES014-027 

Some case and unit mismatches are unavoidable, because one or more of the case members does 

not appear in the CPS ASEC. This affects between 14 and 21 percent of the cases simulated as 

eligible by TRIM3 in the 2012 to 2016 data, and between 22 percent and 31 percent of cases 

simulated as ineligible. These estimates may be somewhat overstated, if some case members are 

in the CPS ASEC but are not matched due to missing PIK or other PIK error.46 

Case and unit membership mismatches also occur when all case members are in the household, 

usually because a household member who is not a member of the case is assigned to the TRIM3 

                                                            

46 We perform a sensitivity test for missing PIK when presenting results for one-adult plus child cases. We find that 

if we exclude cases where the PIK is missing for a member of the SNAP case or CPS ASEC household, we reduce 

the total number of one-adult plus child SNAP cases with a different TRIM3 unit type by nearly a third. 

31%

22%

28%

28%

23%

63%

65%

65%

67%

65%

28%

27%

31%

30%

22%

21%

17%

18%

14%

17%

41%

51%

41%

42%

55%

16%

17%

18%

19%

18%

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

2016 ineligible

2015 ineligible

2014 ineligible

2013 ineligible

2012 ineligible

2016 eligible

2015 eligible

2014 eligible

2013 eligible

2012 eligible

Unit and case match 1+ Case member not in ASEC Other mismatch
Factors Contributing to High Estimated SNAP Participation Rates

47 

unit. These mismatches account for between 41 percent and 55 percent of cases simulated as 

ineligible by TRIM3 in the 2012 to 2016 data. 

We find similar patterns when we combine data across years and review results by state (Figure 

4.4 and Appendix Table C.2). Unit and case matches are much more common among cases 

simulated as eligible by TRIM3 than among those simulated as ineligible. Tennessee has slightly 

greater consistency between case and unit membership than the other two states, regardless of 

simulated eligibility status. 

Figure 4.4 SNAP Case and TRIM3 Unit Consistency by Simulated Eligibility; 

Combined 2012 to 2016 data, by state 

Sources: Decision Demographics & Urban Institute tabulations of combined 2012–2016 linked CPS ASEC, TRIM3, 

and SNAP administrative data for Illinois, Mississippi, and Tennessee 

Note: SNAP case status reflects the CPS ASEC survey month. TRIM3 simulated eligibility reflects eligibility in at least 

one month of the prior calendar year. 

DRB Delegated Authority Approval Number CBDRB-FY21-CES014-027 

 

29%

25%

25%

71%

65%

61%

30%

25%

27%

15%

21%

17%

41%

50%

49%

14%

14%

22%

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

Tennessee ineligible

Mississippi ineligible

Illinois ineligible

Tennessee eligible

Mississippi eligible

Illinois eligible

Unit and case match 1+ case member not in ASEC Other mismatch
Factors Contributing to High Estimated SNAP Participation Rates

48 

Figure 4.5 aggregates results across the years and the three states and shows results by whether the 

case is a one-adult plus child case or another type of case. We classify case type by the case 

characteristics according to the administrative data. 

Figure 4.5 SNAP Case and TRIM3 Unit Consistency by Simulated Eligibility and Case Type; 

Combined 2012 to 2016 data 

Sources: Decision Demographics & Urban Institute tabulations of combined 2012–2016 linked CPS ASEC, TRIM3, 

and SNAP administrative data for Illinois, Mississippi, and Tennessee 

Note: SNAP case status reflects the CPS ASEC survey month. TRIM3 simulated eligibility reflects eligibility in at least 

one month of the prior calendar year. 

DRB Delegated Authority Approval Number CBDRB-FY21-CES014-027 

One-adult plus child cases are less likely to have matching unit and case membership than are 

other types of cases, regardless of simulated eligibility status. Fifty-four percent of one-adult plus 

child cases simulated as eligible by TRIM3 have matching unit and case membership, compared 

with 70 percent of other eligible cases. Just 18 percent of one-adult plus child cases simulated as 

ineligible by TRIM3 have matching unit and case membership, compared with 30 percent of other 

cases simulated as ineligible. Among ineligible one-adult plus child cases, 45 percent have at least 

one case member who does not have a PIK match with a member of the CPS ASEC household. 

30%

18%

26%

70%

54%

65%

21%

45%

28%

14%

27%

17%

50%

38%

46%

17%

19%

17%

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

Other ineligible

One adult plus child(ren) ineligible

Total ineligible

Other eligible

One adult plus child(ren) eligible

Total eligible

Unit and case match 1+ Case member not in ASEC Other mismatch
Factors Contributing to High Estimated SNAP Participation Rates

49 

Whole ASEC Imputation and Income Imputation

We next examine cases simulated as eligible or ineligible by TRIM3 by whether they have whole 

imputation of the ASEC supplement, do not have whole ASEC imputation but did not respond to 

an income item question and have at least one imputed income value, or have neither whole 

imputation nor income item imputation. We count a case as a whole impute if at least one case 

member is a whole impute. If no member is a whole impute we classify the case as an “income 

item impute” if at least one case member has an imputed income value.47

If a SNAP case is linked with a CPS ASEC household that has whole imputation or income item 

imputation, then it is possible that the income or other characteristics imputed to the household 

will be inconsistent with SNAP eligibility. For example, if the CPS ASEC respondent did not 

report the amount of their earnings, he or she might be imputed an earnings amount that places the 

person over the SNAP eligibility limit. Errors in CPS ASEC reported income could also cause a 

SNAP participant to be simulated as ineligible by TRIM3, though we do not explore that 

possibility here. 

Although whole imputation and income item imputation can help explain why TRIM3 finds a 

linked SNAP recipient case to be ineligible for SNAP, imputation does not necessarily affect 

overall eligibility estimates. The goal of imputation is to assign the right distribution of income or 

other characteristics to people with missing data. Following up on the example above, if another 

(non-SNAP) household contained a person with earnings above the SNAP eligibility limit, who 

did not report their earnings, and this person was imputed earnings below the SNAP eligibility 

limit, then this household would now represent the “eligible” household in the estimates. If 

imputations are distributionally accurate, they should not affect eligibility estimates. Even so, they 

will cause some households in linked administrative and survey data to have income and other 

characteristics that appear inconsistent with SNAP eligibility. 

We find that imputation levels are lowest in 2012 and 2013 regardless of eligibility status and that 

imputation is much more common among cases simulated as ineligible than among those simulated 

as eligible (Figure 4.6). In 2012, 81 percent of cases simulated as eligible by TRIM3 and 60 percent 

of cases simulated as ineligible had neither whole ASEC imputation nor income item imputation. 

As of 2016, 69 percent of cases simulated as eligible by TRIM3 and 33 percent of cases simulated 

as ineligible had neither whole imputation nor income item imputation. Thirty-five percent of the 

cases simulated as ineligible in the 2016 data had whole ASEC imputation, and another 32 percent 

had income item imputation. 

 

                                                            

47 TRIM3 unit eligibility may also be affected by imputation among unit members who are not also members of the 

SNAP case. We do not identify this effect in this analysis. 
Factors Contributing to High Estimated SNAP Participation Rates

50 

Figure 4.6 Imputation Status of SNAP Cases by Simulated Eligibility and Year 

Sources: Decision Demographics & Urban Institute tabulations of 2012–2016 linked CPS ASEC, TRIM3, and SNAP 

administrative data for Illinois, Mississippi, and Tennessee 

Note: Cases identified as “No impute” correspond to a TRIM3 unit in which no member has imputation of the entire 

ASEC supplement (“Whole impute”) or any income value (“Income item impute”). 

Note: SNAP case status reflects the CPS ASEC survey month. TRIM3 simulated eligibility reflects eligibility in at least 

one month of the prior calendar year. 

DRB Delegated Authority Approval Number CBDRB-FY21-CES014-027 

 

33%

46%

44%

56%

60%

69%

66%

71%

77%

81%

35%

28%

26%

15%

18%

9%

8%

8%

8%

7%

32%

26%

30%

29%

22%

22%

25%

21%

15%

12%

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

2016 ineligible

2015 ineligible

2014 ineligible

2013 ineligible

2012 ineligible

2016 eligible

2015 eligible

2014 eligible

2013 eligible

2012 eligible

No impute Whole impute Income item impute
Factors Contributing to High Estimated SNAP Participation Rates

51 

Pooling data across the five years, we find that imputation rates were higher among SNAP cases 

in Illinois and Tennessee than among SNAP cases in Mississippi. Eighteen percent of SNAP cases 

simulated as eligible in Mississippi and 38 percent of those simulated as ineligible had whole 

imputation or income item imputation, compared with roughly 30 percent of cases found eligible 

and 55 percent of cases found ineligible in the other two states (Figure 4.7). 

Figure 4.7 Imputation Status of SNAP Cases by Simulated Eligibility; 

Combined 2012 to 2016 data, by state 

Sources: Decision Demographics & Urban Institute tabulations of combined 2012–2016 linked CPS ASEC, TRIM3, 

and SNAP administrative data for Illinois, Mississippi, and Tennessee 

Note: Cases identified as “No impute” correspond to a TRIM3 unit in which no member has imputation of the entire 

ASEC supplement (“Whole impute”) or any income value (“Income item impute”). 

Note: SNAP case status reflects the CPS ASEC survey month. TRIM3 simulated eligibility reflects eligibility in at least 

one month of the prior calendar year. 

DRB Delegated Authority Approval Number CBDRB-FY21-CES014-027 

 

45%

62%

46%

73%

82%

69%

27%

16%

25%

9%

3%

9%

28%

22%

29%

18%

15%

21%

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

Tennessee ineligible

Mississippi ineligible

Illinois ineligible

Tennessee eligible

Mississippi eligible

Illinois eligible

No impute Whole impute Income item impute
Factors Contributing to High Estimated SNAP Participation Rates

52 

Imputation rates are similar for one-adult plus child cases and other types of cases simulated as 

eligible by TRIM3 (Figure 4.8). Among cases simulated as ineligible, 46 percent of one-adult plus 

child cases and 55 percent of other cases have whole imputation or income item imputation. 

Figure 4.8 Imputation Status of SNAP Cases by Simulated Eligibility and Case Type; 

Combined 2012 to 2016 data 

Sources: Decision Demographics & Urban Institute tabulations of combined 2012–2016 linked CPS ASEC, TRIM3, 

and SNAP administrative data for Illinois, Mississippi, and Tennessee 

Note: Cases identified as “No impute” correspond to a TRIM3 unit in which no member has imputation of the entire 

ASEC supplement (“Whole impute”) or any income value (“Income item impute”). 

Note: SNAP case status reflects the CPS ASEC survey month. TRIM3 simulated eligibility reflects eligibility in at least 

one month of the prior calendar year. 

DRB Delegated Authority Approval Number CBDRB-FY21-CES014-027 

Match and Imputation Details and Other Factors by Simulated Eligibility 

Table 4.1 presents additional details about case and unit mismatches, types of income imputation, 

and other factors that might affect simulated eligibility. Results are pooled for the three states 

across the five years of data and are shown separately for one-adult plus child cases and other types 

of cases. 

45%

54%

48%

74%

72%

73%

27%

18%

25%

7%

9%

8%

28%

28%

28%

19%

19%

19%

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

Other ineligible

One adult plus child(ren) ineligible

Total ineligible

Other eligible

One adult plus child(ren) eligible

Total eligible

No impute Whole impute Income item impute
Factors Contributing to High Estimated SNAP Participation Rates

53 

Table 4.1 ASEC Characteristics of SNAP Cases by SNAP Case Type and TRIM3 Eligibility Status; 

 Combined 2012 to 2016 data 

Total One-adult plus child(ren) Other case types 

Eligible 1+ 

months 

Ineligible 

in all 

months Total 

Eligible 1+ 

months 

Ineligible 

in all 

months Total 

Eligible 1+ 

months 

Ineligible 

in all 

months Total 

Total unweighted counts 3,100 1,000 4,100 900 300 1,200 2,200 750 2,900 

Weighted total (in thousands) 7,742 2,190 5,553 

 

TRIM3 unit and case match status 

Percentage distribution 100% 100% 100% 100% 100% 100% 100% 100% 100% 

TRIM3 unit matches SNAP case 65% 26% 55% 54% 18% 45% 70% 30% 59% 

1+ members not in ASEC household 17% 28% 20% 27% 45% 31% 14% 21% 16% 

Other case and unit mismatch 17% 46% 25% 19% 38% 24% 17% 50% 25% 

 

Imputation 

Percentage distribution 100% 100% 100% 100% 100% 100% 100% 100% 100% 

No imputation 73% 48% 67% 72% 54% 67% 74% 45% 66% 

Whole impute 8% 25% 12% 9% 18% 12% 7% 27% 12% 

Not whole impute, any income item imputed 19% 28% 21% 19% 28% 21% 19% 28% 21% 

 

Details on match status1 

Percentage distribution 100% 100% 100% 100% 100% 100% 100% 100% 100% 

Match: One person household 23% 10% 20% 0% 0% 0% 32% 13% 27% 

Match: 2+ person household 42% 17% 36% 54% 18% 45% 38% 16% 32% 

1+ case members not in ASEC household 17% 28% 20% 27% 45% 31% 14% 21% 16% 

1+ TRIM3 unit members not on case 15% 46% 23% 17% 37% 22% 15% 50% 24% 

1+ case members in different TRIM3 unit 2% 2% 2% 2% 2% 0% 2% 

(Table continues) 

 
Factors Contributing to High Estimated SNAP Participation Rates

54 

Table 4.1, continued 

Total One-adult plus child(ren) Other case types 

Eligible 1+ 

months 

Ineligible 

in all 

months Total 

Eligible 1+ 

months 

Ineligible 

in all 

months Total 

Eligible 1+ 

months 

Ineligible 

in all 

months Total 

Whole imputation or imputation of income 

type 

Whole impute or earnings imputation 16% 40% 23% 20% 39% 25% 15% 41% 22% 

Whole impute or unemployment/workers 

comp imputation 9% 26% 14% 11% 20% 13% 8% 29% 14% 

Whole impute or social security, retirement, 

disability, or survivor income imputation 12% 31% 17% 11% 19% 13% 12% 36% 18% 

Whole impute or asset income imputation 12% 32% 17% 14% 25% 17% 12% 35% 18% 

Whole impute or child support or other 

financial assistance imputation 10% 27% 14% 13% 22% 15% 9% 29% 14% 

Whole impute or means-tested assistance 

imputation 11% 26% 15% 12% 19% 14% 11% 30% 15% 

 

Other characteristics 

Recent job loss by case member 14% 14% 14% 15% 13% 15% 14% 14% 14% 

Case has at least one non-citizen 3% 4% 4% 3% 3% 3% 4% 5% 4% 

Household has at least one non-citizen 8% 9% 8% 7% 8% 7% 8% 10% 9% 

At least one case member moved in last 

year 19% 14% 18% 27% 17% 24% 16% 13% 16% 

 

Case has any of above factors2 

Percentage distribution 100% 100% 100% 100% 100% 100% 100% 100% 100% 

Case and unit don’t match 35% 74% 45% 46% 82% 56% 31% 71% 41% 

Case and unit match, complicating factor 30% 19% 27% 30% 13% 26% 30% 22% 28% 

Case and unit match, no complicating factor 36% 7% 28% 24% 5% 19% 40% 7% 32% 

Sources: Decision Demographics & Urban Institute tabulations of combined 2012–2016 linked CPS ASEC, TRIM3, and SNAP administrative data for Illinois, Mississippi, and 

Tennessee. 

Note: Cases identified as “No impute” correspond to a TRIM3 unit in which no member has imputation of the entire ASEC supplement (“Whole impute”) or any income value 

(“Income item impute”). SNAP case status reflects the CPS ASEC survey month. TRIM3 simulated eligibility reflects eligibility in at least one month of the prior calendar year. 

1 Each case is placed in the first row that describes it. 

2 Complicating factors include whole imputation, imputation of any income amount, recent job loss by case member, case has at least one non-citizen, and/or at least one 

case member who is in the ASEC household moved in the last year. 

DRB Delegated Authority Approval Number CBDRB-FY21-CES014-027 (Ns are rounded to meet disclosure avoidance requirements). 
Factors Contributing to High Estimated SNAP Participation Rates

55 

Case and Unit Mismatches 

Among one-adult plus child cases simulated as ineligible by TRIM3, 18 percent have matching 

case and unit membership, 45 percent have a case member that is not matched with a household 

member in the ASEC, and 38 percent have a TRIM3 unit member who is not a member of the case 

(we examine these cases in greater detail in a later section). 

Among other types of cases simulated as ineligible by TRIM3, 30 percent have matching case and 

unit membership, 21 percent have a case member that is not matched with a household member in 

the ASEC, and 50 percent have a TRIM3 unit member who is not a member of the case. 

TRIM3 rarely splits members of the same case across different TRIM3 units. Just 2 percent of 

cases in which case and unit membership do not match involve situations where members of the 

same case have been allocated across more than one TRIM3 unit. 

Types of Income Imputation 

Turning to imputation, we find that 39 percent of one-adult plus child cases simulated as ineligible 

by TRIM3 either have whole imputation or earnings imputation, compared with 20 percent of 

those simulated as eligible. Asset income (income from interest, dividends, and rents or royalties) 

is the next most common type of income imputation for one-adult plus child cases simulated as 

ineligible. Twenty-five percent of one-adult plus child cases simulated as ineligible by TRIM3 

have whole imputation or asset income imputation, compared with 14 percent of cases simulated 

as eligible. 

Among other types of cases simulated as ineligible by TRIM3, imputation of Social Security, 

retirement, disability, or survivor income is the second most common type of imputation after 

earnings. Forty-one percent of other types of cases simulated as ineligible by TRIM3 have whole 

imputation or earnings imputation; 36 percent have whole imputation or imputation of Social 

Security, retirement, disability, or survivor income, and 35 percent have whole imputation or 

imputation of asset income. The corresponding figures for other types of cases simulated as eligible 

for SNAP are 15 percent, 12 percent, and 12 percent respectively. 

Other Factors that Might Affect Eligibility 

In addition to examining case and unit consistency and imputation status, we review three other 

factors that might affect eligibility estimates. First, we examine whether the SNAP case has at least 

one member who had worked in the last year but was not working in the interview month according 

to the CPS ASEC. If so, then they might recently have lost a job and have truly been ineligible in 

the prior calendar year.48 We find that although 14 percent of the linked SNAP cases have such a 

member, they make up about the same share of TRIM3 units regardless of simulated eligibility 

status. 

                                                            

48 The linked SNAP administrative and ASEC data available to this analysis do not identify SNAP participation in 

the prior calendar year and so we are not able to determine if the case participated in the prior year. 
Factors Contributing to High Estimated SNAP Participation Rates

56 

Next, we review noncitizen status. Unauthorized immigrants are ineligible for SNAP and other 

noncitizens have restricted eligibility. Although native, naturalized, and noncitizen status is 

reported on the CPS ASEC, other details (such as whether a noncitizen is unauthorized) must be 

imputed, complicating eligibility determination. 

We find little difference in noncitizen status among cases simulated as eligible or ineligible in 

TRIM3. About 3 to 4 percent of SNAP cases include a noncitizen member regardless of TRIM3 

simulated eligibility status, and 8 to 9 percent are in a CPS ASEC household with a noncitizen.49

It is possible that the approach we use to handle cloned TRIM3 households mitigates differences 

arising from noncitizen status.50

Finally, we examine whether any SNAP case member moved in the last year according to the CPS 

ASEC.51 We speculate that simulation estimates might be more reliable for people with stable 

living conditions. For example, a SNAP recipient might have only recently moved in with other 

family members whose combined income for the prior year make the simulated TRIM3 unit appear 

ineligible. However, we find that cases simulated as eligible by TRIM3 are more likely to include 

a case member who moved in the prior year (19 percent) than are those simulated as ineligible (14 

percent). 

Combined Effect of Various Factors 

Taking case and unit mismatch, imputation, and the other factors into consideration, we find that 

just 7 percent of cases simulated as ineligible in TRIM3 have none of these complicating factors, 

compared with 36 percent of those simulated as eligible. Among one-adult plus child cases, 5 

percent of cases simulated as ineligible in TRIM3 lack any of these complicating factors, compared 

with 24 percent of those simulated as eligible. 

Role of Imputation and Other Factors when Case and Unit Membership Match 

If most cases simulated as ineligible in TRIM3 do not have matching SNAP case and TRIM3 unit 

membership, what role do imputation and other factors play when case and TRIM3 membership 

match? 

Focusing just on cases where SNAP case and TRIM3 unit membership match, we find that 75 

percent of cases simulated as eligible and 43 percent of cases simulated as ineligible have neither 

whole imputation nor income item imputation (Figure 4.9). Of the cases simulated as eligible, 5 

percent have whole imputation and 20 percent have income imputation. In contrast, 19 percent of 

the cases simulated as ineligible have whole imputation and another 37 percent have imputation 

of at least one income amount. As with the overall results, earnings imputation continues to be the 

                                                            

49 We defined a case as having a noncitizen member if any case member was a noncitizen according to the reported 

information in the CPS ASEC. 

50 Cloned immigrant households differ in their assignment of immigrant status. Because we select the clone with the 

most eligible members for inclusion in this analysis, we reduce the situations in which the imputed immigrant status 

would cause the case to be simulated as ineligible in TRIM3. 

51 We classify a mover as someone who reports that they were not living in the same household one year ago. 
Factors Contributing to High Estimated SNAP Participation Rates

57 

most common type of income item imputation among cases simulated as ineligible, followed by 

Social Security, retirement, disability, or survivor income, and then by asset income (Table 4.2) 

Figure 4.9 Imputation Status of SNAP Cases by Simulated Eligibility; 

Cases with matching case and TRIM3 unit membership, combined 2012 to 2016 data 

Sources: Decision Demographics & Urban Institute tabulations of combined 2012–2016 linked CPS ASEC, TRIM3, 

and SNAP administrative data for Illinois, Mississippi, and Tennessee 

Note: Cases identified as “No impute” correspond to a TRIM3 unit in which no member has imputation of the entire 

ASEC supplement (“Whole impute”) or any income value (“Income item impute”). 

Note: SNAP case status reflects the CPS ASEC survey month. TRIM3 simulated eligibility reflects eligibility in at least 

one month of the prior calendar year. 

DRB Delegated Authority Approval Number CBDRB-FY21-CES014-027 

 

43%

75%

19%

5%

37%

20%

0% 20% 40% 60% 80% 100%

Ineligible

Eligible

No impute Whole impute Income item impute
Factors Contributing to High Estimated SNAP Participation Rates

58 

Table 4.2 ASEC Characteristics of SNAP Cases by Simulated Eligibility; 

 Cases with matching case and TRIM3 unit membership, combined 2012 to 2016 data 

Eligible 1+ 

months 

Ineligible in all 

months Total 

Total unweighted counts 2,000 250 2,200 

Weighted total (thousands) 4,266 

Imputation 

Percentage distribution 100% 100% 100% 

No imputation 75% 43% 71% 

Whole impute 5% 19% 7% 

Not whole impute, any income item imputed 20% 37% 22% 

Whole imputation or imputation of income type 

Whole impute or earnings imputation 12% 41% 16% 

Whole impute or unemployment/workers comp 

imputation 6% 22% 8% 

Whole impute or social security, retirement, 

disability, or survivor income imputation 10% 31% 13% 

Whole impute or asset income imputation 10% 29% 12% 

Whole impute or child support or other financial 

assistance imputation 7% 22% 9% 

Whole impute or means-tested assistance 

imputation 8% 21% 10% 

Other characteristics 

Recent job loss by case member 13% 19% 14% 

Case has at least one non-citizen 2% 6% 3% 

Household has at least one non-citizen 3% 6% 3% 

At least one case member moved in last year 18% 16% 17% 

Case has any of above factors1 

Case has at least one of the above factors 46% 74% 49% 

Case has none of the above factors 54% 26% 51% 

 

Sources: Decision Demographics & Urban Institute tabulations of combined 2012–2016 linked CPS ASEC, 

TRIM3, and SNAP administrative data for Illinois, Mississippi, and Tennessee 

Note: Cases identified as “No impute” correspond to a TRIM3 unit in which no member has imputation of the 

entire ASEC supplement (“Whole impute”) or any income value (“Income item impute”). 

Note: SNAP case status reflects the CPS ASEC survey month. TRIM3 simulated eligibility reflects eligibility 

in at least one month of the prior calendar year. 

1Complicating factors include whole imputation, imputation of any income amount, recent job loss by case 

member, case has at least one non-citizen, and/or at least one case member who is in the ASEC household 

moved in the last year. 

DRB Delegated Authority Approval Number CBDRB-FY21-CES014-027 (Ns are rounded to meet disclosure 

avoidance requirements). 

 
Factors Contributing to High Estimated SNAP Participation Rates

59 

Among the cases with matching SNAP case and TRIM3 unit membership, those simulated as 

ineligible by TRIM3 are more likely to have a case member with a recent job loss, a case member 

who is a noncitizen, or to be in a household with at least one noncitizen (regardless of whether the 

noncitizen is a member of the case). These findings are consistent with our expectation but were 

not apparent when viewing the results in the context of all cases in Table 4.1. Cases with a member 

who moved in the last year continue to make up a higher share of cases simulated as eligible (18 

percent) than of those simulated as ineligible (16 percent) although the difference is smaller than 

when viewed in the context of all cases. 

Considering these factors in combination, we find that 74 percent of the cases with matching SNAP 

case and TRIM3 unit membership that are simulated as ineligible in TRIM3 have whole 

imputation, income item imputation, or one of the other complicating factors compared with 46 

percent of those simulated as eligible. 

Investigating the Shortfall in One-Adult Plus Child Cases 

We use combined linked data for all three states for 2012 to 2016 to investigate factors that might 

explain the apparent shortfall of one-adult plus child cases in the TRIM3 eligibility estimates. Oneadult plus child cases have estimated participation rates well above 100 percent across 

microsimulation models and data sources (Chapter 2), so factors explaining simulated ineligibility 

are of key interest. 

Ideally, a one-adult plus child SNAP case from the administrative data that is linked with the CPS 

ASEC would also be a one-adult plus child TRIM3 unit. However, we find that just over half (52 

percent) of one-adult plus child SNAP cases in the linked data are one-adult plus child units in 

TRIM3 that are simulated to be eligible in at least one month of the prior calendar year (Table 4.3). 

Another 21 percent are a different type of eligible unit, and 20 percent are a different type of unit 

that is simulated to be ineligible. Just 7 percent are one-adult plus child units simulated to be 

ineligible in the prior calendar year. 

Table 4.3 One-Adult Plus Child Cases by TRIM3 Unit Type and 

 Simulated Eligibility Status; Combined 2012 to 2016 data 

N 

Weighted 

(thousands) 

Weighted 

percentage 

Total 1,200 2,190 100% 

Eligible 1 adult + child(ren) 52% 

Ineligible 1 adult + child(ren) 7% 

Eligible other TRIM3 unit type 21% 

Ineligible other TRIM3 unit type 20% 

 

Sources: Decision Demographics & Urban Institute tabulations of combined 2012–2016 

linked CPS ASEC, TRIM3, and SNAP administrative data for Illinois, Mississippi, and 

Tennessee 

Note: TRIM3 simulated eligibility reflects eligibility in at least one month of the prior 

calendar year. 

DRB Delegated Authority Approval Number CBDRB-FY21-CES014-029 (Ns are rounded 

to meet disclosure avoidance requirements). 
Factors Contributing to High Estimated SNAP Participation Rates

60 

While a substantial number of one-adult plus child SNAP cases in the linked data are not simulated 

to be one-adult plus child TRIM3 units, the reverse situation can also occur. Some SNAP cases in 

the linked data that are not one-adult plus child cases according to the SNAP administrative data 

are simulated to be one-adult plus child TRIM3 units. Among SNAP cases linked with a TRIM3 

one-adult plus child unit, 14 percent are another type of SNAP case simulated to be eligible by 

TRIM3 (Table 4.4).52

Table 4.4 SNAP Cases that Correspond to a TRIM3 1 Adult + Child Unit, by SNAP 

Case Type and TRIM3 Unit Eligibility; Combined 2012 to 2016 data 

N 

Total 

(thousands) Percentage 

Total 900 1,545 100% 

Eligible 1 adult + child(ren) 74% 

Ineligible 1 adult + child(ren) 10% 

Eligible other admin case type 14% 

Ineligible other admin case type 2% 

 

Sources: Decision Demographics & Urban Institute tabulations of combined 2012–2016 

linked CPS ASEC, TRIM3, and SNAP administrative data for Illinois, Mississippi, and 

Tennessee 

Note: TRIM3 simulated eligibility reflects eligibility in at least one month of the prior 

calendar year. 

DRB Delegated Authority Approval Number CBDRB-FY21-CES014-029 (Ns are rounded 

to meet disclosure avoidance requirements). 

Table 4.5 lists the reasons that one-adult plus child SNAP cases do not meet the one-adult plus 

child definition in TRIM3. The most common reason, accounting for 28 percent of the cases, is 

that the SNAP case’s children are not present in the corresponding CPS ASEC household. In 

another 15 percent of cases, the SNAP case’s adult is not in the CPS ASEC household and the case 

children are in a TRIM3 unit with more than one adult. The case adult’s spouse is included in the 

TRIM3 unit along with the case adult in 20 percent of cases, and 18 percent of the cases incorporate 

the case adult’s cohabiting partner into the TRIM3 unit. The remaining 19 percent of the cases are 

in a TRIM3 unit that does not include the spouse or partner of the case adult but does include some 

other adult—such as an older relative or adult child of the case adult.53

 

 

                                                            

52 Some of these other case types are child-only SNAP cases in which the adults are ineligible for SNAP. The 

TRIM3 unit classification used here does not check the eligibility status of the adults on the case and so does not 

identify “child-only” cases. TRIM3 units consisting of an ineligible adult and a child would be counted as “oneadult plus child” here, but as “child-only” in the SNAP administrative data. Therefore, Table 4.4 probably overstates 

the extent to which a one-adult plus child unit in TRIM3 has some other case type in the SNAP administrative data.

53 We find that for 10 percent of these cases, the other adults are the case adult’s children between the ages of 18 and 

21. These children are included in the TRIM3 unit in accordance with SNAP regulations. 
Factors Contributing to High Estimated SNAP Participation Rates

61 

Table 4.5 Reason One-Adult Plus Child Case is not One-Adult Plus Child in TRIM3; 

 Combined 2012 to 2016 data 

N 

Weighted 

(thousands) 

Weighted 

percentage 

All units 

Case adult not in ASEC 15% 

Case children are not in ASEC 28% 

ASEC spouse in TRIM3 unit 20% 

ASEC partner in TRIM3 unit 18% 

No spouse/partner; other adult in TRIM3 unit 19% 

Total 500 898 100% 

 

Excluding units where member is missing PIK 

Case adult not in ASEC 11% 

Case children are not in ASEC 25% 

ASEC spouse in TRIM3 unit 22% 

ASEC partner in TRIM3 unit 22% 

No spouse/partner; other adult in TRIM3 unit 20% 

Total 350 613 100% 

 

Sources: Decision Demographics & Urban Institute tabulations of combined 2012–2016 linked 

CPS ASEC, TRIM3, and SNAP administrative data for Illinois, Mississippi, and Tennessee 

Note: Each case is placed in the first row that describes it. 

Note: TRIM3 simulated eligibility reflects eligibility in at least one month of the prior calendar year. 

DRB Delegated Authority Approval Number CBDRB-FY21-CES014-029 (Ns are rounded to meet 

disclosure avoidance requirements). 

These estimates may overstate the extent to which the case adult and case children are absent from 

the ASEC household. People lacking a PIK in the administrative data or CPS ASEC data are not 

linked and likely account for some of the case members identified as absent from the household 

(Czajka, 2021). PIK errors may also prevent some matches. When viewing the data, we observed 

that some cases have nonmatched members who are the same age and sex as a nonmatched member 

of the CPS ASEC household but have different PIKs. These could represent different people—

such as the current and former partner of the household reference person—but could also reflect 

errors in PIK assignment in either data source. 

As a sensitivity test, we present results at the bottom of Table 4.5 that exclude cases where the PIK 

is missing for a member of the SNAP case or CPS ASEC household. This reduces the total number 

of one-adult plus child SNAP cases with a different TRIM3 unit type by nearly a third. The share 

of cases where the SNAP case’s children are not in the CPS ASEC household falls from 28 percent 

to 25 percent and the share where the case adult is not in the CPS ASEC household falls from 15 

percent to 11 percent. This test does not account for possible errors in PIK assignment. 
Factors Contributing to High Estimated SNAP Participation Rates

62 

The absence of case members from the CPS ASEC household and the inclusion of a spouse or 

partner in the TRIM3 unit explain most of the one-adult plus child SNAP cases that are some other 

type of TRIM3 unit. We take a closer look at each of these groups below. 

Case Member Absent from the ASEC household 

We examine family relationships and mover status to obtain insight into the characteristics of oneadult plus child cases where the children or adult are missing from the ASEC household. If a 

child’s recent move from one household (where they lived with a parent) to another household 

explains the discrepancy, we might expect to see that the children are living with grandparents or 

other relatives and have moved within the last year. 

We measure the characteristics of the case children in the CPS ASEC household using the 

characteristics of the youngest SNAP case child linked with the CPS ASEC data. The results are 

based on 80 unweighted cases and so should be considered exploratory. We find that just 12 

percent of the youngest children in one-adult plus child cases where the case adult is missing from 

the CPS ASEC household have moved in the prior year (Table 4.6). This is lower than the average 

for SNAP cases linked with the CPS ASEC. According to Table 4.1, 18 percent of all SNAP cases 

linked with the CPS ASEC have at least one member who has moved in the prior year. 

Table 4.6 ASEC Characteristics of Youngest Case Child; One-adult plus child cases 

where adult is not in the ASEC, combined 2012 to 2016 data 

N 80 

Weighted total (in thousands) 133 

Weighted 

percentage 

Presence of parents in ASEC household of youngest case child 

Two parents present 36% 

One parent present 24% 

No parents present 40% 

Relationship to household reference person of youngest case child 

Child 49% 

Grandchild 33% 

Other Relative 12% 

Nonrelative 6% 

Youngest case child in same household 1 year ago? 

Under age 1 3% 

Yes (non-mover) 85% 

No (mover) 12% 

Sources: Decision Demographics & Urban Institute tabulations of combined 2012–2016 linked 

CPS ASEC, TRIM3, and SNAP administrative data for Illinois, Mississippi, and Tennessee 

Note: TRIM3 simulated eligibility reflects eligibility in at least one month of the prior calendar year. 

DRB Delegated Authority Approval Number CBDRB-FY21-CES014-029 (Ns are rounded to meet 

disclosure avoidance requirements). 
Factors Contributing to High Estimated SNAP Participation Rates

63 

In over a third of cases (36 percent) involving a one-adult plus child case where the adult is missing 

from the CPS ASEC household, the youngest SNAP case child has two parents present within the 

household. This might seem surprising, if we assume that the absent case adult is also a parent. 

However, it is possible that the absent case adult is a guardian or caretaker, or that a parent in the 

household is a stepparent. In some cases, one of the parents may actually be the case adult, but not 

linked with the SNAP administrative data due to missing PIK or PIK error.54 In about a quarter of 

cases (24 percent) the youngest child on the SNAP case has one parent present within the ASEC 

household and in the remaining 40 percent the youngest child has no parents present. 

Another possibility is that the case adult recently moved out of a household containing the case 

children and other family members. This might apply to some of the 33 percent of the children 

living with a grandparent, for example.55 

If we turn the focus to case adults who are in CPS ASEC households without case children, we 

find that 23 percent moved within the last year (Table 4.7). This is a higher share than is observed 

for children in Table 4.6 and is somewhat higher than the overall average for all SNAP cases linked 

with the CPS ASEC shown in Table 4.1. 

 

                                                            

54 When reviewing example households, we identified cases and households that appeared consistent with each of 

these explanations. 

55 A child who is the grandchild of the household reference person might also have one or both parents in the 

household. The table simply shows the relationship of the youngest child to the household reference person. 
Factors Contributing to High Estimated SNAP Participation Rates

64 

Table 4.7 ASEC Characteristics of Case Adult; One-adult plus child cases where 

case children are not in the ASEC, combined 2012 to 2016 data

N 100 

Weighted total (in thousands) 248 

Weighted 

percentage 

Marital status of case adult 

Married spouse present or married spouse absent or widowed 24% 

Divorced 19% 

Separated 3% 

Never married 55% 

Case adult in same household 1 year ago? 

Yes (non-mover) 77% 

No (mover) 23% 

Relationship of case adult to household reference person 

Reference person with relatives 28% 

Reference person without relatives 30% 

Spouse of reference person 4% 

Child of Reference person 19% 

Other Relative of reference person 7% 

Unmarried partner or nonrelative of household reference person 13% 

 

Sources: Decision Demographics & Urban Institute tabulations of combined 2012–2016 

linked CPS ASEC, TRIM3, and SNAP administrative data for Illinois, Mississippi, and 

Tennessee 

Note: TRIM3 simulated eligibility reflects eligibility in at least one month of the prior calendar 

year. 

DRB Delegated Authority Approval Number CBDRB-FY21-CES014-029 (Ns are rounded to 

meet disclosure avoidance requirements). 

Over half of the case adults who are in ASEC households without case children have never been 

married (55 percent), 24 percent are married or widowed, 19 percent are divorced, and 3 percent 

are separated. Most (62 percent) are a household reference or the spouse of a reference person, 19 

percent are the child of a household reference person, and 13 percent are a partner or other 

nonrelative of the household reference person. 

Spouse or Partner Included in the TRIM3 Unit 

According to SNAP regulations, married couples who live together must apply for SNAP together. 

Cohabiting partners are not required to apply together, but children are required to apply with their 

parents. Therefore, cohabiting parents of the same child should apply together. We find that the 

cohabiting partner is the parent of at least one of the case adult’s children in nearly all (95 percent) 

of the one-adult plus child SNAP cases with a cohabiting partner in the TRIM3 unit. We 
Factors Contributing to High Estimated SNAP Participation Rates

65 

investigated the extent of imputation of married couple and parent-child relationships but found it 

was rare enough to not affect the estimates.56

Different treatment of ineligible unit members in the SNAP administrative data and TRIM3 may 

explain some cases that appear to have one adult and children in the SNAP administrative data but 

include a spouse or partner in the TRIM3 unit. If one spouse or partner is ineligible for SNAP—

for example, due to immigrant status or a program violation—all or a portion of the income of the 

ineligible person may be deemed available to other family members, but the person is not taken 

into consideration when determining the family’s benefit and is not considered a SNAP 

participant.57 

TRIM3 counts the ineligible spouse or partner as part of the “unit” and applies appropriate income 

deeming and benefit calculation rules. The model captures restrictions based on immigrant status 

and student status, but lacks data to simulate other types of ineligibility, such as failure to comply 

with work requirements or sanctions for drug violations. 

It is not clear from the SNAP administrative data used for this study whether ineligible spouses 

and partners are included in the data.58 If they are included, we count them as members of the case 

and we classify the case as having multiple adults and children. If they are not included, then some 

of the one-adult plus child SNAP cases with a spouse or partner in the TRIM3 unit might be cases 

where the spouse or partner is ineligible for SNAP. 

Even if the SNAP administrative data exclude ineligible spouses and partners, we would not 

expect that to have much effect on the estimates presented here. According to our tabulations of 

the 2016 SNAP QC data, just four percent of one-adult plus child cases in the combined data for 

Illinois, Mississippi, and Tennessee have a nonparticipating adult member. 

The 2016 SNAP QC data also show that ineligibility due to immigrant status is the most 

common reason that a married parent family has only one eligible parent, accounting for 78 

percent of these cases.59 However, ineligibility due to immigrant status appears to explain few of 

the one-adult plus child cases that include a spouse or partner in the TRIM3 unit. We find that 

just 29 percent of one-adult plus child SNAP cases in which a spouse is included in the TRIM3 

unit involve a spouse who might be ineligible due to immigrant status (data not shown).60

                                                            

56 Information about marital status and parent-child relationships is collected in the basic CPS interview and so is not 

affected by whole imputation. 

57 Deeming rules vary depending on the reason for a person’s ineligibility. Depending on the reason, all, some, or 

none of the income may be deemed available to other case members.

58 To obtain greater insight, we tabulated participation status variables for each state’s administrative data. We find 

that in Mississippi, about 2 to 3 percent of all people represented in the data are nonparticipants. Nobody is identified 

as a nonparticipant in the Illinois data, and less than 1 percent are identified as nonparticipants in the Tennessee data 

(Appendix Table C.3). Based on these results, it appears likely that the Mississippi data include ineligible case 

members, the Illinois data do not, and the Tennessee data may contain some but not all ineligible members.

59 These are national estimates. The QC sample for this subgroup is too small to support reliable estimates for 

Illinois, Mississippi, and Tennessee.

60We use the reported CPS ASEC information on citizenship and immigrant status to determine the immigrant status 

of the people in the TRIM3 unit and count any person who is not native-born as potentially ineligible due to immigrant 
Factors Contributing to High Estimated SNAP Participation Rates

66 

Among one-adult plus child SNAP cases where the case adult has a cohabiting partner in the 

TRIM3 unit, nearly all (almost 100 percent) consist of native-born partners. Thus, ineligibility of 

the spouse or partner is unlikely to explain most of the cases that appear as a one-adult plus child 

case in the administrative data but include a spouse or partner in the TRIM3 unit. 

We also investigated whether one member of the couple had moved within the last 12 months and 

the other had not. If a spouse or partner had only recently joined the ASEC household, the family 

might not yet have informed the SNAP agency. However, we find virtually no instances among 

the cases under consideration where one spouse or partner had reported to the CPS that they had 

moved in the last 12 months and the other had not. 

Chapter 4 Summary and Recommendations 

Our goal in linking the simulated TRIM3 unit identifiers and eligibility flags to the linked CPS 

ASEC and SNAP administrative data is to better understand the various factors that explain why 

a case that receives SNAP according to state administrative data is simulated as ineligible for 

SNAP in TRIM3. This can provide insight regarding unexpectedly high participation rates for 

some subgroups and can potentially inform improvements in microsimulation modeling. 

The eligibility analysis builds on datasets created by Mathematica that link the CPS ASEC and 

SNAP administrative data for Illinois, Mississippi, and Tennessee. People in the CPS ASEC are 

linked by PIK with SNAP administrative data for the month corresponding to their interview 

month, for each year of CPS ASEC data from 2012 to 2016. We augment the data by attaching 

simulated TRIM3 SNAP unit identifiers and eligibility flags to the linked data sets. 

The available data do not align perfectly with respect to the timing of SNAP participation and 

eligibility. SNAP participation reflects participation at the time of the CPS ASEC interview, and 

simulated eligibility reflects eligibility in at least one month of the prior calendar year. Thus, some 

level of ineligibility among participants would be expected, even with perfect data and simulation 

techniques. 

 

                                                            

status. We group naturalized citizens with noncitizens because, even though they are eligible for SNAP, there is 

evidence that some noncitizens report themselves as naturalized in survey data (Passel, Clark, & Fix, 1997, Van Hook 

& Bachmeier, 2013; Brown et al., 2018). The results provide an upper bound estimate of the extent that ineligibility 

due to immigrant status might affect the findings, since naturalized citizens and certain categories of legally present 

noncitizens are eligible for SNAP. 
Factors Contributing to High Estimated SNAP Participation Rates

67 

Summary of Findings: All Cases 

Results from this analysis show: 

• The share of SNAP cases in the linked data that are simulated as eligible by TRIM3 

declined from between 75 and 77 percent in the 2012 to 2014 CPS ASEC data to 72 percent 

in 2015 and 71 percent in 2016. 

• Cases simulated as eligible are much more likely to have matching SNAP administrative 

data case membership and TRIM3 unit membership than those simulated as ineligible. 

• Some case members do not appear in the CPS ASEC, and so it is not possible for TRIM3 

SNAP unit and administrative case membership to match. 

• Whole imputation and income item imputation for linked SNAP cases steadily increased 

from 2012 to 2016. 

• Imputation rates are higher for cases simulated as ineligible than for those simulated as 

eligible. In 2016, 67 percent of cases simulated as ineligible and 31 percent simulated as 

eligible had whole imputation or income item imputation. 

• Among cases where the SNAP case and TRIM3 unit membership match, 56 percent of 

SNAP cases simulated as ineligible and 25 percent of those simulated as eligible have 

whole imputation or income item imputation. 

Summary of Findings: One-adult plus child cases 

We use combined linked data for all three states for 2012 to 2016 to investigate factors that might 

explain the apparent shortfall of one-adult plus child cases in the TRIM3 eligibility estimates. We 

start with a sample of approximately 1,200 linked one-adult plus child SNAP cases, of which about 

300 are simulated as ineligible in TRIM3. Some of our estimates come from subgroups of oneadult child SNAP cases with sample sizes as low as 80. In general, results should be regarded as 

exploratory and confirmed by extending the analysis to additional years and states. 

Focusing on one-adult plus child cases, we find that much of the shortfall in eligible one-adult plus 

child cases is that they do not appear to be one-adult plus child units in TRIM3. 

Of one-adult plus child cases in the linked data: 

• 52 percent are one-adult plus child units in TRIM3 simulated as eligible 

• 21 percent are a different type of TRIM3 unit simulated as eligible 

• 20 percent are a different type of TRIM3 unit simulated as ineligible 

• 7 percent are one-adult plus child units in TRIM3 simulated as ineligible 

Thus, a major part of the shortfall in one-adult plus child cases is that TRIM3 does not identify 

them as one-adult plus child units. In most cases, this is unavoidable. TRIM3 can’t recreate the 

SNAP case membership and remain consistent with eligibility rules because the case children or 

the case adult do not appear in the ASEC household, or the TRIM3 unit includes the case head’s 

spouse or cohabiting partner (who is the parent of at least one of the case head’s children in 95 
Factors Contributing to High Estimated SNAP Participation Rates

68 

percent of the cases). Married couples and cohabiting parents who are both the parents of the same 

child are required to apply for SNAP together, and so alternative unit types cannot be constructed 

for these units while remaining consistent with program rules. 

Missing PIKs, PIK match error, and definitional issues regarding whether an ineligible spouse is 

or is not included in the administrative data may cause these issues to be overstated to some extent. 

Even so, these unit and case definition inconsistencies, combined with the potential 

underrepresentation of one-adult plus child cases observed in Chapter 3, may explain much of the 

shortfall in one-adult plus child cases in this combined group of states and data years. 

These apparent inconsistencies raise questions about inaccurate reporting of household 

membership to the SNAP agency, errors in survey reporting of CPS ASEC household membership 

and relationships, and differences in the residence of children according to survey and 

administrative data. Further investigation would be needed to explore these issues, but we offer 

some initial thoughts here. 

Ideas for Future Research Arising from Eligibility Analysis 

Missing PIK and PIK Error 

PIK errors and missing PIKs can make it appear that a case member is not present within the ASEC 

household even when the case member is included in the data. This can lead the number of 

participants in linked data to be undercounted and likely causes us to overstate the extent to which 

there are discrepancies in SNAP case and TRIM3 unit membership. However, this is unlikely to 

have much effect on the extent to which one-adult plus child cases are identified as one-adult plus 

child units in TRIM3. For example, if the case adult’s children are in the household but PIK errors 

prevent them from being identified as members of the SNAP case, the case would still be classified 

in our analysis as a one-adult plus child case (assuming no other adult is included in the unit). It is 

only when the case adult does not have any children within the household that the case would be 

classified as one in which the children are absent from the household. 

Nevertheless, overcoming the limitations of missing PIK and PIK error—for example by allowing 

matches between additional case members and CPS household members based on sex and age—

would be very beneficial to future research using the linked data files. 

Inclusion of Ineligible Spouse or Partner 

It was not clear from the data available to us whether a spouse or partner (who was also the parent 

of a child) was not known to the SNAP agency or was known but not included in the administrative 

data because the spouse or partner was ineligible for SNAP (for example, due to immigrant status 

or because of a program violation). If an ineligible spouse or partner was known to the agency but 

not included in the administrative data files (for example, because the ineligible spouse or partner 

was not considered a “participant”) then the case would be appropriately classified as a one-adult 

plus child case in the SNAP administrative data but would have been classified as a two adult plus 

child case in the TRIM3 estimates. However, if a spouse or partner is not known to the agency, 

then the presence of the spouse or partner in the CPS ASEC data suggests error of some sort—
Factors Contributing to High Estimated SNAP Participation Rates

69 

either in the reporting of relationship information to the SNAP agency or in the reporting of CPS 

ASEC family relationships. 

A suggestion for improvement to the SNAP administrative data files at the Census Bureau would 

be to document whether ineligible case members are included, and if so, the variables that should 

be used to identify them. This will differ substantially by state and may not be adequately reflected 

in state metadata documentation; conversations with state database administrators and case worker 

supervisors could be revealing in understanding elements of the administrative record data to be 

considered when expanding this analysis. 

Ideally, the SNAP administrative data files collected by the Census Bureau would include data for 

both the participating and nonparticipating members of a case, with variables that clearly indicate 

whether a person is a participating member who is taken into consideration when calculating the 

case’s benefit, or a nonparticipating member whose income may be deemed available to the case. 

This would provide the greatest flexibility in the use of these data. For example, an analysis that 

focused strictly on people eligible for and receiving SNAP would exclude the ineligible case 

members. But an analysis that focused on the living arrangements of children receiving SNAP 

would appropriately identify those living with both parents—even when one or both parents is 

ineligible for SNAP due to their immigrant status or for some other reason. 

Inaccurate Reporting to the SNAP Agency 

SNAP agencies perform monthly reviews of sampled cases as part of the QC system. QC reviewers 

review data on file about the sampled cases and then visit the household to re-interview participants 

to verify eligibility and the benefit amount. Error rates are generally considered to be low. 

Ineligible SNAP cases accounted for about 1 percent of participant cases in the average month of 

2016.61 A recommendation for future research would be to assess the extent of error specific to 

family relationship and residence issues that could affect whether a case is classified as having one 

or multiple adults with children. Linking SNAP QC data to the PIK-linked datasets could be further 

revealing. 

CPS ASEC Respondent Error 

The absence of a SNAP case’s children from the household could be due to survey response error. 

Children, particularly young children, are undercounted in the decennial Census and in household 

survey data (Jensen & Hogan, 2017). Children in complex households or households with a single 

parent were most likely to be missed by the 2010 Census (Jensen et al., 2018). In some cases, the 

child’s household was included in the Census, but the child was omitted. In other cases, the entire 

household was missing (Fernandez, Shattuck, & Noon, 2018). Future research could consider the 

extent to which under coverage and missed children within the CPS ASEC contribute to the 

shortfall in one-adult plus child cases and investigate the possibility of reweighting the data to 

compensate for the shortfall. 

                                                            

61 This estimate is calculated from Table II.2 in Vigil et al., 2017. 
Factors Contributing to High Estimated SNAP Participation Rates

70 

Errors in reporting of family relationships can also occur in survey data. Although we observed 

negligible imputation rates for marital status and for the variables linking children with parents, 

future research could consider if and how much misreported relationships might affect results. For 

example, a survey respondent might report an unrelated couple living in the household as married, 

not knowing in fact that they are cohabiting. 

Movers 

Another possibility is that the members of a SNAP case have only recently moved into separate 

households and have not yet informed the SNAP agency at the time of the CPS ASEC interview. 

For example, a child might have moved from living with one parent to living with grandparents or 

with another parent and stepparent. 

We investigated movers to some extent in our analysis. When reviewing SNAP cases where the 

children were in the household, but the case head was missing, we found that the children were 

less likely to have moved in the prior year than was true for SNAP cases overall. In cases where 

the case head was in the CPS ASEC household, but the children were not, we found that a higher 

share (23 percent) had moved within the last 12 months than was found on average for SNAP 

cases. These findings are based on CPS ASEC responses to questions about whether a person was 

living in the same household a year ago. Future research could examine prior and subsequent 

months of administrative data, to see if changes in case membership are revealed that might explain 

the apparent inconsistency in residency of the case adult and children. Researchers might also look 

over prior or subsequent months of CPS data to observe if household membership had changed 

according to the CPS data. 

 
Factors Contributing to High Estimated SNAP Participation Rates

71 

5. Conclusion and Recommendations for Further Research 

Developing SNAP eligibility and participation rate estimates is a highly complex task involving 

two distinct data sources—the household survey data used to develop the eligibility estimates and 

the SNAP administrative data that provide information on program participants. Participation rate 

estimates above 100 percent occur when microsimulation models find fewer people eligible for 

SNAP in survey data than receive SNAP according to administrative data. Understanding these 

high participation rates is complicated by the fact that the eligibility estimates and counts of 

participants are drawn from different sources. By analyzing linked SNAP administrative data, CPS 

ASEC data, and TRIM3 eligibility data for three states, we are able to provide insight into possible 

explanations for high participation rates in some population subgroups and reasons for 

inconsistencies in simulated TRIM3 eligibility. 

The issues that contribute to participation rates above 100 percent for some subgroups may also 

affect participation rate estimates for subgroups with lower participation rates. The participation 

rate for a subgroup could be overstated, despite the fact that the estimate has not yet exceeded 100 

percent. Alternatively, the estimated participation rate for a subgroup could be too low, if the 

subgroup is overrepresented in the CPS ASEC. 

At the end of each chapter we have offered summaries and chapter focus-specific 

recommendations; here we conclude by drawing attention to three key issues—not only relevant 

to microsimulation modeling and participation rate estimates, but also germane to the use of linked 

SNAP administrative data files and CPS ASEC files for other research purposes. 

Our findings are based on data from three states and five years of data; we recommend that the 

research be extended to additional states and data years. If our findings hold when analysis is thus 

extended, the following implications emerge: 

1) One-adult plus child SNAP cases are underrepresented and multiple-adult plus child cases 

are overrepresented in the CPS ASEC due to the lower interview rates for one-adult plus 

child cases and the higher interview rates of households with multiple-adult plus child 

cases. 

2) Among one-adult plus child SNAP cases identified in the linked SNAP administrative data 

and CPS data, 41 percent are identified as some other unit type in TRIM3. In most cases 

this is unavoidable because the case children are not in the CPS household, a spouse or 

partner (and co-parent of the case adult’s child) is present in the household, or the case 

adult is missing from the household. 

3) Imputation rates are much higher for SNAP cases in the linked data that are simulated as 

ineligible by TRIM3 than for those simulated as eligible. In 2016, 67 percent of cases 

simulated as ineligible and 31 percent simulated as eligible had whole imputation or 

income item imputation. 

The inconsistencies in household and case membership help to explain why a SNAP case linked 

with the CPS ASEC data might be simulated as ineligible by TRIM3 and other microsimulation 
Factors Contributing to High Estimated SNAP Participation Rates

72 

models. Eligibility limits and maximum benefit amounts increase with case size. Income rises or 

falls with the addition or subtraction of adults (and their associated income) from the case. 

Whole imputation of the CPS ASEC and income item imputation also help explain why a SNAP 

case linked with CPS ASEC data might be simulated as ineligible by TRIM3 or other 

microsimulation models. The results show that most linked data cases simulated as ineligible by 

TRIM3 have whole imputation or income item imputation, suggesting that the income or other 

characteristics imputed to these cases is inconsistent with SNAP eligibility. 

Whole imputation and income item imputation do not necessarily present a problem for 

microsimulation model estimates or contribute to unrealistically high participation rates. No 

imputation method is perfect at the individual level. Rather, imputation methods seek to achieve 

an appropriate distribution of income or other characteristics across individuals with missing data. 

The challenge arises when analyzing the simulated eligibility of people who receive SNAP 

according to linked SNAP administrative data and CPS ASEC data. In this report, we have 

documented that most of the linked SNAP administrative data cases simulated as ineligible by 

TRIM3 are whole imputes or have income item imputation. Future research could look more 

closely at SNAP cases linked with TRIM3 units that lack whole imputation and income item 

imputation, to identify areas for improvement in microsimulation methods. 

Our findings also have implications for the use of linked administrative data and survey data to 

correct for underreporting of program benefits in the survey data (Fox et al., 2017; Shantz & Fox, 

2018; Mittag, 2019; Fox, Rothbaum & Shantz, 2021). 

1) Case membership according to the administrative data may be inconsistent with 

information about the people and relationships among people within a survey household, 

particularly for cases identified as having one-adult plus children according to the 

administrative data. The level of inconsistency observed here suggests the need for careful 

consideration about how such inconsistencies should be handled in analyses that involve 

linked survey and administrative data. 

2) It is important to account for the fact that whole imputation and income item imputation 

can cause some actual SNAP participants (based on linked administrative data) to have 

income data in the survey that is too high for program eligibility. This should be taken into 

consideration when analyzing program receipt (according to administrative data) by 

income level in the CPS ASEC. Possible approaches to avoiding this problem are to 

develop estimates that exclude households with imputed data (reweighting appropriately) 

or to control for SNAP receipt from linked data when performing whole imputation and 

income item imputation. 

Analyses of linked administrative data and survey data offer many opportunities but also raise new 

questions and challenges. We hope that the information provided here will provide ideas to 

motivate future research in the areas of microsimulation modeling and linked data analysis. 

 
Factors Contributing to High Estimated SNAP Participation Rates

73 

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Factors Contributing to High Estimated SNAP Participation Rates

Appendices 

Appendix A SNAP Participation Rate Estimates 

Appendix B CPS ASEC Interview Rate and Representation Analysis 

Appendix C Eligibility Analysis 
Factors Contributing to High Estimated SNAP Participation Rates

Appendix A: SNAP Participation Rate Estimates 

List of Tables 

A.1: National Estimated SNAP Individual Participation Rates in 2011 and 2016 by Data 

Source, Model, and Whether Estimate Includes State Broad Based Categorical Eligibility 

(BBCE) 

A.2: National Estimated SNAP Case Participation Rates in 2011 and 2016 by Data Source, 

Model, and Whether Estimate Includes State Broad Based Categorical Eligibility (BBCE) 

A.3: Estimated State-Level SNAP Individual Participation Rates for State Illinois in Year 

2016 by Data Source, Model, and Whether Estimate Includes State Broad Based 

Categorical Eligibility (BBCE) 

A.4: Estimated State-Level SNAP Case Participation Rates in State Illinois in Year 2016 by 

Data Source, Model, and Whether Estimate Includes State Broad Based Categorical 

Eligibility (BBCE) 

A.5: Estimated State-Level SNAP Individual Participation Rates for State Mississippi in Year 

2016 by Data Source, Model, and Whether Estimate Includes State Broad Based 

Categorical Eligibility (BBCE) 

A.6: Estimated State-Level SNAP Case Participation Rates in State Mississippi in Year 2016 

by Data Source, Model, and Whether Estimate Includes State Broad Based Categorical 

Eligibility (BBCE) 

A.7: Estimated State-Level SNAP Individual Participation Rates for State Tennessee in Year 

2016 by Data Source, Model, and Whether Estimate Includes State Broad Based 

Categorical Eligibility (BBCE) 

A.8: Estimated State-Level SNAP Case Participation Rates in State Tennessee in Year 2016 

by Data Source, Model, and Whether Estimate Includes State Broad Based Categorical 

Eligibility (BBCE) 
Table A.1: National Estimated SNAP Individual Participation Rates in 2011 and 2016

By Data Source, Model, and Whether Estimate Includes State Broad Based Categorical Eligibility (BBCE)

(Numbers in thousands)

SIPP ACS SIPP ACS

Individual Characteristic Participating MATH TRIM3 MATH ATTIS MATH TRIM3 MATH ATTIS

2016 (Federal Rules)

All Individuals 39,904 47,070 50,023 55,678 85 80 72

Age

Children (17 or younger) 17,874 17,193 17,931 19,739 104 100 91

Pre-school age (0-4) 5,430 5,194 5,295 5,995 105 103 91

School-age (5-17) 12,443 11,999 12,635 13,745 104 98 91

Nonelderly adults (18 to 59) 17,613 19,986 21,890 25,071 88 80 70

Elderly Individuals (60+) 4,417 9,890 10,201 10,867 45 43 41

Adults age 18 to 49 without disabilities in childless households 3,452 3,544 5,487 7,280 97 63 47

Noncitizens 1,766 2,788 2,720 2,601 63 65 68

Citizen children living with noncitizen adults 3,556 4,382 5,058 5,072 81 70 70

Case size

1 person 10,263 10,487 15,243 17,641 98 67 58

2 people 7,175 11,053 9,841 10,847 65 73 66

3 people 7,703 8,673 8,143 8,844 89 95 87

4 or more people 14,764 16,858 16,796 18,345 88 88 80

Case countable income sources

No Earned income 22,621 23,953 26,165 27,104 94 86 83

Earned income 17,283 23,117 23,857 28,574 75 72 60

TANF 3,092 4,519 3,658 3,573 68 85 87

SSI 7,686 8,934 8,092 7,949 86 95 97

Social Security 7,446 11,332 11,338 12,715 66 66 59

Countable Income as a Percentage of Poverty Guidelines

No Income 6,450 6,330 6,120 7,528 102 105 86

1 to 50 percent 10,605 8,960 10,081 10,227 118 105 104

51 to 100 percent 17,059 18,044 21,366 22,789 95 80 75

101 percent or more 5,791 13,736 12,455 15,133 42 46 38

Benefit as a percentage of maximum benefit

low benefits (1 to 50 percent of max) 9,554 18,702 19,446 21,703 51 49 44

high benefits (51 to 99 percent of max) 16,845 16,204 17,237 18,291 104 98 92

51 to 75 percent 8,126 9,200 9,510 10,310 88 85 79

76 to 99 percent 8,719 7,004 7,727 7,981 124 113 109

maximum benefit 13,505 12,164 13,340 15,685 111 101 86

CPS

Eligible

CPS

Participation Rate (Participants/Eligibles)

Factors Contributing to High Estimated SNAP Participation Rates

A-1
Table A.1: National Estimates (continued)

SIPP ACS SIPP ACS

Individual Characteristic Participating MATH TRIM3 MATH ATTIS MATH TRIM3 MATH ATTIS

2016 Including State Broad Based Categorical Eligibility (BBCE)

All Individuals 43,465 69,611 65,424 68,920 62 66 63

Age

Children (17 or younger) 19,174 21,368 21,105 22,572 90 91 85

Pre-school age (0-4) 5,818 6,326 6,202 6,817 92 94 85

School-age (5-17) 13,356 15,043 14,903 15,755 89 90 85

Nonelderly adults (18 to 59) 19,180 27,397 27,496 30,427 70 70 63

Elderly Individuals (60+) 5,112 20,845 16,823 15,921 25 30 32

Adults age 18 to 49 without disabilities in childless households 3,825 5,433 7,378 9,316 70 52 41

Noncitizens 1,964 3,752 3,453 3,120 52 57 63

Citizen children living with noncitizen adults 3,824 5,336 6,003 5,845 72 64 65

Case size

1 person 11,342 15,814 20,230 22,020 72 56 52

2 people 8,043 21,442 15,915 15,793 38 51 51

3 people 8,252 10,574 9,455 9,945 78 87 83

4 or more people 15,828 21,781 19,824 21,162 73 80 75

Case countable income sources

No Earned income 24,118 34,597 33,653 32,722 70 72 74

Earned income 19,347 35,014 31,771 36,199 55 61 53

TANF 3,123 4,583 3,787 3,661 68 82 85

SSI 7,796 9,482 8,577 8,312 82 91 94

Social Security 8,311 21,820 18,411 18,225 38 45 46

Countable Income as a Percentage of Poverty Guidelines

No Income 6,877 6,994 6,165 7,595 98 112 91

1 to 50 percent 11,008 10,615 11,069 10,643 104 99 103

51 to 100 percent 17,658 20,151 22,467 23,464 88 79 75

101 percent or more 7,922 31,851 25,723 27,219 25 31 29

Benefit as a percentage of maximum benefit

low benefits (1 to 50 percent of max) 11,790 36,979 32,809 33,746 32 36 35

high benefits (51 to 99 percent of max) 17,551 18,300 18,313 18,869 96 96 93

51 to 75 percent 8,515 10,463 10,142 10,658 81 84 80

76 to 99 percent 9,035 7,837 8,171 8,211 115 111 110

maximum benefit 14,124 14,332 14,302 16,305 99 99 87

CPS CPS

Eligible Participation Rate (Participants/Eligibles)

Factors Contributing to High Estimated SNAP Participation Rates

A-2
Table A.1: National Estimates (continued)

SIPP ACS SIPP ACS

Individual Characteristic Participating MATH TRIM3 MATH ATTIS MATH TRIM3 MATH ATTIS

2011 (Federal Rules)

All Individuals 40,694 52,161 56,146 54,413 78 72 75 

Age

Children (17 or younger) 18,455 19,303 20,748 22,101 96 89 84 

Pre-school age (0-4) 6,302 6,258 6,546 6,657 101 96 95 

School-age (5-17) 12,153 13,046 14,202 15,444 93 86 79 

Nonelderly adults (18 to 59) 18,844 23,985 26,529 24,677 79 71 76 

Elderly Individuals (60+) 3,395 8,872 8,869 7,635 38 38 44 

Adults age 18 to 49 without disabilities in childless households 4,132 4,975 7,643 5,349 83 54 77 

Noncitizens 1,604 3,108 2,754 4,116 52 58 39 

Citizen children living with noncitizen adults 3,433 4,831 5,680 3,114 71 60 110 

Case size

1 or 2 people 16,906 22,447 26,881 23,545 75 63 72 

1 person 9,429 10,509 16,147 12,967 90 58 73 

2 people 7,477 11,938 10,734 10,578 63 70 71 

3 people 8,288 9,968 9,577 9,629 83 87 86 

4 or more people 15,500 19,746 19,688 21,240 78 79 73 

Case countable income sources

No Earned income 24,565 27,975 30,500 27,324 88 81 90 

Earned income 16,128 24,186 25,646 27,089 67 63 60 

TANF 4,657 5,007 5,438 4,983 93 86 93 

SSI 7,620 9,514 8,980 8,829 80 85 86 

Social Security 6,790 11,515 11,147 11,905 59 61 57 

Countable Income as a Percentage of Poverty Guidelines

No Income 6,221 8,310 7,945 6,887 75 78 90 

1 to 50 percent 11,718 9,816 12,137 10,897 119 97 108 

51 to 100 percent 16,808 19,410 22,343 22,911 87 75 73 

101 percent or more 5,946 14,624 13,721 13,717 41 43 43 

Benefit as a percentage of maximum benefit

low benefits (1 to 50 percent of max) 7,834 15,852 17,594 19,066 49 45 41 

high benefits (51 to 99 percent of max) 18,898 20,863 22,808 20,958 91 83 90 

51 to 75 percent 8,806 11,614 12,157 11,430 76 72 77 

76 to 99 percent 10,092 9,249 10,652 9,528 109 95 106 

maximum benefit 13,962 15,445 15,743 14,388 90 89 97 

Eligible Participation Rate (Participants/Eligibles)

CPS CPS

Factors Contributing to High Estimated SNAP Participation Rates

A-3
Table A.1: National Estimates (continued)

SIPP ACS SIPP ACS

Individual Characteristic Participating MATH TRIM3 MATH ATTIS MATH TRIM3 MATH ATTIS

2011 Including State Broad Based Categorical Eligibility (BBCE)

All Individuals 44,087 76,285 70,474 72,893 58 63 60 

Age

Children (17 or younger) 19,891 24,196 23,928 26,617 82 83 75 

Pre-school age (0-4) 6,767 7,588 7,395 7,809 89 92 87 

School-age (5-17) 13,124 16,608 16,533 18,808 79 79 70 

Nonelderly adults (18 to 59) 20,430 32,517 31,950 32,303 63 64 63 

Elderly Individuals (60+) 3,765 19,571 14,596 13,973 19 26 27 

Adults age 18 to 49 without disabilities in childless households 4,491 7,146 9,307 7,243 63 48 62 

Noncitizens 1,758 4,001 3,238 5,080 44 54 35 

Citizen children living with noncitizen adults 3,731 5,776 6,359 3,630 65 59 103 

Case size

1 or 2 people 18,348 37,786 36,136 34,268 49 51 54 

1 person 10,118 15,700 20,254 18,300 64 50 55 

2 people 8,230 22,086 15,882 15,968 37 52 52 

3 people 8,902 12,570 11,159 11,715 71 80 76 

4 or more people 16,837 25,929 23,179 26,911 65 73 63 

Case countable income sources

No Earned income 25,874 39,296 37,625 35,717 66 69 72 

Earned income 18,213 36,990 32,849 37,177 49 55 49 

TANF 4,700 5,268 5,579 5,132 89 84 92 

SSI 7,684 10,441 9,631 9,744 74 80 79 

Social Security 7,393 22,242 17,607 19,105 33 42 39 

Countable Income as a Percentage of Poverty Guidelines

No Income 6,529 9,196 8,034 7,167 71 81 91 

1 to 50 percent 12,203 11,436 12,942 12,294 107 94 99 

51 to 100 percent 17,450 21,940 23,383 25,016 80 75 70 

101 percent or more 7,905 33,713 26,115 28,416 23 30 28 

Benefit as a percentage of maximum benefit

low benefits (1 to 50 percent of max) 9,701 34,373 29,958 33,486 28 32 29 

high benefits (51 to 99 percent of max) 19,756 24,098 23,956 23,152 82 82 85 

51 to 75 percent 9,246 13,543 12,816 12,816 68 72 72 

76 to 99 percent 10,510 10,555 11,140 10,336 100 94 102 

maximum benefit 14,630 17,814 16,560 16,255 82 88 90 

Subgroups defined based on "Trends in Supplemental Nutrition Assistance Program Participation rates: Fiscal Year 2010 to Fiscal Year 2016"

Source: Developed from eligibility estimates from the MATH CPS-Eligibility model, MATH SIPP+ model, TRIM3, and ATTIS combined with participation estimates based 

 on SNAP QC data, as processed by Mathematica

Eligible

CPS CPS

Participation Rate (Participants/Eligibles)

Factors Contributing to High Estimated SNAP Participation Rates

A-4
Table A.2: National Estimated SNAP Case Participation Rates in 2011 and 2016

By Data Source, Model, and Whether Estimate Includes State Broad Based Categorical Eligibility (BBCE)

(Numbers in thousands)

SIPP ACS SIPP ACS

Case Characteristic Participating MATH TRIM3 MATH ATTIS MATH TRIM3 MATH ATTIS

2016 (Federal Rules)

All Cases 19,556 21,982 26,159 29,509 89 75 66

Case composition

Children (17 or younger) 8,524 8,183 8,527 9,402 104 100 91

Single-adult 5,106 3,832 3,971 4,680 133 129 109

Married-head 1,443 2,319 2,277 2,359 62 63 61

Other 1,976 2,031 2,279 2,363 97 87 84

Multiple-Adult 932 1,259 1,047 1,020 74 89 91

Child Only 1,044 772 1,231 1,343 135 85 78

No Children 11,032 13,799 17,632 20,107 80 63 55

Cases Containing

Elderly individuals 4,057 8,532 8,999 9,458 48 45 43

Non-elderly individuals with disabilities 4,172 4,486 4,467 4,358 93 93 96

Adults age 18 to 49 without disabilities in childless households 3,187 2,771 4,469 5,913 115 71 54

Noncitizens 1,195 2,032 1,900 1,933 59 63 62

Case countable income source

Earned Income 6,005 7,898 9,057 11,442 76 66 52

TANF 1,026 1,272 1,192 1,178 81 86 87

SSI 4,546 4,439 4,843 4,794 102 94 95

Social Security 5,070 7,187 7,805 8,825 71 65 57

Countable Income as a Percentage of Poverty Guidelines

No Income 4,056 3,448 4,083 5,017 118 99 81

1 to 50 percent 4,074 3,307 4,300 4,488 123 95 91

51 to 100 percent 8,710 8,713 11,348 12,086 100 77 72

101 percent or more 2,716 6,514 6,428 7,919 42 42 34

Eligible

CPS CPS

Participation Rate (Participants/Eligibles)

Factors Contributing to High Estimated SNAP Participation Rates

A-5
Table A.2: National Estimates (continued)

SIPP ACS SIPP ACS

Case Characteristic Participating MATH TRIM3 MATH ATTIS MATH TRIM3 MATH ATTIS

2016 Including State Broad Based Categorical Eligibility (BBCE)

All Cases 21,483 34,011 35,197 37,179 63 61 58

Case composition

Children (17 or younger) 9,209 10,292 10,212 10,872 89 90 85

Single-adult 5,492 4,588 4,653 5,313 120 118 103

Married-head 1,593 3,266 2,862 2,872 49 56 55

Other 2,123 2,438 2,697 2,688 87 79 79

Multiple-Adult 1,008 1,532 1,194 1,165 66 84 87

Child Only 1,115 906 1,503 1,523 123 74 73

No Children 12,274 23,718 24,985 26,307 52 49 47

Cases Containing

Elderly individuals 4,676 16,605 14,087 13,342 28 33 35

Non-elderly individuals with disabilities 4,366 5,437 5,059 4,759 80 86 92

Adults age 18 to 49 without disabilities in childless households 3,524 4,218 5,895 7,438 84 60 47

Noncitizens 1,341 2,769 2,442 2,347 48 55 57

Case countable income source

Earned Income 6,865 12,912 12,797 15,114 53 54 45

TANF 1,036 1,286 1,230 1,210 81 84 86

SSI 4,584 4,645 5,037 4,917 99 91 93

Social Security 5,709 13,918 12,731 12,619 41 45 45

Countable Income as a Percentage of Poverty Guidelines

No Income 4,389 3,802 4,113 5,061 115 107 87

1 to 50 percent 4,250 4,153 4,842 4,741 102 88 90

51 to 100 percent 8,990 9,671 11,887 12,427 93 76 72

101 percent or more 3,853 16,384 14,354 14,950 24 27 26

Eligible Participation Rate (Participants/Eligibles)

CPS CPS

Factors Contributing to High Estimated SNAP Participation Rates

A-6
Table A.2: National Estimates (continued)

SIPP ACS SIPP ACS

Case Characteristic Participating MATH TRIM3 MATH ATTIS MATH TRIM3 MATH ATTIS

2011 (Federal Rules)

All Cases 19,221 23,495 28,680 25,916 82 67 74

Case composition

Children (17 or younger) 9,047 9,325 10,102 10,800 97 90 84

Single-adult 5,227 4,187 4,569 5,277 125 114 99

Married-head 1,675 2,831 2,764 272 59 61 616

Other 2,145 2,307 2,769 5,250 93 77 41

Multiple-Adult 1,094 1,534 1,225 3,978 71 89 28

Child Only 1,051 773 1,544 1,272 136 68 83

No Children 10,175 14,170 18,578 15,117 72 55 67

Cases Containing

Elderly individuals 3,108 7,666 7,861 6,852 41 40 45

Non-elderly individuals with disabilities 4,051 4,825 4,880 4,505 84 83 90

Adults age 18 to 49 without disabilities in childless households 3,773 3,777 6,153 4,775 100 61 79

Noncitizens 1,107 2,161 1,897 2,855 51 58 39

Case countable income source

Earned Income 5,593 8,113 9,756 9,890 69 57 57

TANF 1,536 1,485 1,769 1,637 103 87 94

SSI 4,180 4,504 4,855 4,625 93 86 90

Social Security 4,250 7,076 7,553 7,637 60 56 56

Countable Income as a Percentage of Poverty Guidelines

No Income 3,940 4,327 5,334 3,873 91 74 102

1 to 50 percent 4,514 3,447 4,871 4,145 131 93 109

51 to 100 percent 8,188 8,920 11,424 11,050 92 72 74

101 percent or more 2,579 6,802 7,050 6,848 38 37 38

Eligible Participation Rate (Participants/Eligibles)

CPS CPS

Factors Contributing to High Estimated SNAP Participation Rates

A-7
Table A.2: National Estimates (continued)

SIPP ACS SIPP ACS

Case Characteristic Participating MATH TRIM3 MATH ATTIS MATH TRIM3 MATH ATTIS

2011 Including State Broad Based Categorical Eligibility (BBCE)

All Cases 20,782 35,793 36,574 35,827 58 57 58

Case composition

Children (17 or younger) 9,788 11,759 11,749 13,097 83 83 75

Single-adult 5,587 4,953 5,169 6,051 113 108 92

Married-head 1,870 4,006 3,462 348 47 54 537

Other 2,331 2,801 3,117 6,698 83 75 35

Multiple-Adult 1,200 1,917 1,413 5,289 63 85 23

Child Only 1,131 884 1,705 1,409 128 66 80

No Children 10,993 24,034 24,825 22,730 46 44 48

Cases Containing

Elderly individuals 3,422 15,593 12,218 11,923 22 28 29

Non-elderly individuals with disabilities 4,198 5,886 5,504 5,168 71 76 81

Adults age 18 to 49 without disabilities in childless households 4,088 5,411 7,326 6,364 76 56 64

Noncitizens 1,211 2,803 2,252 3,551 43 54 34

Case countable income source

Earned Income 6,356 13,214 12,781 14,018 48 50 45

TANF 1,546 1,533 1,818 1,678 101 85 92

SSI 4,195 4,819 5,073 4,871 87 83 86

Social Security 4,657 13,770 11,907 12,635 34 39 37

Countable Income as a Percentage of Poverty Guidelines

No Income 4,148 4,769 5,390 4,066 87 77 102

1 to 50 percent 4,710 4,233 5,333 4,922 111 88 96

51 to 100 percent 8,466 10,001 11,910 11,994 85 71 71

101 percent or more 3,458 16,790 13,941 14,845 21 25 23

Subgroups defined based on "Trends in Supplemental Nutrition Assistance Program Participation rates: Fiscal Year 2010 to Fiscal Year 2016"

Source: Developed from eligibility estimates from the MATH CPS-Eligibility model, MATH SIPP+ model, TRIM3, and ATTIS combined with participation estimates based 

 on SNAP QC data, as processed by Mathematica

CPS CPS

Eligible Participation Rate (Participants/Eligibles)

Factors Contributing to High Estimated SNAP Participation Rates

A-8
Table A.3: Estimated State-Level SNAP Individual Participation Rates for Illinois in 2016

By Data Source, Model, and Whether Estimate Includes State Broad Based Categorical Eligibility (BBCE)

(Numbers in thousands)

ACS ACS

Individual Characteristic Participating MATH TRIM3 ATTIS MATH TRIM3 ATTIS

2016 (Federal Rules)

All Individuals 1,745 1,698 1,865 2,120 103 94 82

Age

Children (17 or younger) 737 648 717 742 114 103 99

Nonelderly adults (18 to 59) 835 714 808 961 117 103 87

Elderly Individuals (60+) 174 335 340 417 52 51 42

Adults age 18 to 49 without disabilities in childless households 207 129 260 298 160 80 70

Case size

1 person 500 402 560 684 124 89 73

2 or more people 1,245 1,296 1,305 1,436 96 95 87

Case earned income status

No Earned income 1,006 754 816 992 133 123 101

Earned income 739 944 1,049 1,128 78 70 66

Countable Income as a Percentage of Poverty Guidelines

No Income 372 216 209 304 172 178 122

1 to 50 percent 428 312 392 384 137 109 111

51 to 100 percent 755 652 791 858 116 95 88

101 percent or more 190 517 472 573 37 40 33

Benefit as a percentage of maximum benefit

low benefits (1 to 50 percent of max) 355 672 699 854 53 51 42

high benefits (51 to 99 percent of max) 826 642 659 652 129 125 127

maximum benefit 564 385 506 614 146 111 92

Participation Rate (Participants/Eligibles)

sample size < 50

Eligible

CPS CPS

Factors Contributing to High Estimated SNAP Participation Rates

A-9
Table A.3: Illinois (continued)

ACS ACS

Individual Characteristic Participating MATH TRIM3 ATTIS MATH TRIM3 ATTIS

2016 Including State Broad Based Categorical Eligibility (BBCE)

All Individuals 1,894 2,614 2,558 2,780 72 74 68

Age

Children (17 or younger) 783 876 867 884 89 90 89

Nonelderly adults (18 to 59) 910 1,042 1,026 1,188 87 89 77

Elderly Individuals (60+) 201 696 666 708 29 30 28

Adults age 18 to 49 without disabilities in childless households 228 210 322 375 109 71 61

Case size

1 person 553 661 800 907 84 69 61

2 or more people 1,341 1,953 1,758 1,873 69 76 72

Case earned income status

No Earned income 1,086 1,204 1,134 1,301 90 96 83

Earned income 808 1,409 1,425 1,478 57 57 55

Countable Income as a Percentage of Poverty Guidelines

No Income 402 262 209 304 153 192 132

1 to 50 percent 445 425 442 405 105 101 110

51 to 100 percent 794 781 848 886 102 94 90

101 percent or more 253 1,147 1,059 1,184 22 24 21

Benefit as a percentage of maximum benefit

low benefits (1 to 50 percent of max) 426 1,313 1,301 1,467 32 33 29

high benefits (51 to 99 percent of max) 863 779 713 673 111 121 128

maximum benefit 605 523 544 640 116 111 95

Subgroups defined based on "Trends in Supplemental Nutrition Assistance Program Participation rates: Fiscal Year 2010 to Fiscal Year 2016"

Source: Developed from eligibility estimates from the MATH CPS-Eligibility model, MATH SIPP+ model, TRIM3, and ATTIS combined with participation estimates based 

 on SNAP QC data, as processed by Mathematica

CPS CPS

Eligible Participation Rate (Participants/Eligibles)

Factors Contributing to High Estimated SNAP Participation Rates

A-10
Table A.4: Estimated State-Level SNAP Case Participation Rates in Illinois in 2016

By Data Source, Model, and Whether Estimate Includes State Broad Based Categorical Eligibility (BBCE)

(Numbers in thousands)

ACS ACS

Case Characteristic Participating MATH TRIM3 ATTIS MATH TRIM3 ATTIS

2016 (Federal Rules)

All cases 902 785 953 1,140 115 95 79

Case composition

Children (17 or younger) 366 282 310 357 130 118 102

Single-adult 224 129 150 180 174 149 124

Married-head 51 78 75 82 65 68 62

Other 91 75 85 95 121 108 95

Multiple-adult 39 47 37 40 83 104 98

Child Only 52 28 47 55 186 110 94

No Children 536 503 643 782 107 83 69

Adults age 18 to 49 without disabilities in childless households 195 102 208 234 191 94 83

Case earned income status

No Earned income 628 470 567 686 134 111 92

Earned income 274 315 386 453 87 71 60

Countable Income as a Percentage of Poverty Guidelines

No Income 248 106 135 206 234 183 120

1 to 50 percent 161 112 170 179 144 95 90

51 to 100 percent 391 333 411 459 117 95 85

101 percent or more 102 234 237 295 44 43 35

Participation Rate (Participants/Eligibles)

sample size < 50

Eligible

CPS CPS

Factors Contributing to High Estimated SNAP Participation Rates

A-11
Table A.4: Illinois (continued)

ACS ACS

Case Characteristic Participating MATH TRIM3 ATTIS MATH TRIM3 ATTIS

2016 Including State Broad Based Categorical Eligibility (BBCE)

All cases 987 1,264 1,366 1,526 78 72 65

Case composition

Children (17 or younger) 390 386 390 427 101 100 91

Single-adult 236 164 189 208 144 125 114

Married-head 57 124 100 107 46 57 53

Other 97 99 101 113 98 96 86

Multiple-adult 42 67 46 46 63 90 92

Child Only 55 32 55 67 172 101 82

No Children 597 878 975 1,099 68 61 54

Adults age 18 to 49 without disabilities in childless households 214 158 254 285 135 84 75

Case earned income status

No Earned income 687 789 816 916 87 84 75

Earned income 301 475 549 610 63 55 49

Countable Income as a Percentage of Poverty Guidelines

No Income 269 127 135 207 212 199 130

1 to 50 percent 169 170 201 193 99 84 88

51 to 100 percent 410 397 441 474 103 93 87

101 percent or more 139 571 588 652 24 24 21

Subgroups defined based on "Trends in Supplemental Nutrition Assistance Program Participation rates: Fiscal Year 2010 to Fiscal Year 2016"

Source: Developed from eligibility estimates from the MATH CPS-Eligibility model, MATH SIPP+ model, TRIM3, and ATTIS combined with participation estimates based 

 on SNAP QC data, as processed by Mathematica

CPS CPS

Eligible Participation Rate (Participants/Eligibles)

Factors Contributing to High Estimated SNAP Participation Rates

A-12
Table A.5: Estimated State-Level SNAP Individual Participation Rates for Mississippi in 2016

By Data Source, Model, and Whether Estimate Includes State Broad Based Categorical Eligibility (BBCE)

(Numbers in thousands)

ACS ACS

Individual Characteristic Participating MATH TRIM3 ATTIS MATH TRIM3 ATTIS

2016 (Federal Rules)

All Individuals 562 724 708 747 78 79 75

Age

Children (17 or younger) 253 256 260 259 99 97 98

Nonelderly adults (18 to 59) 260 313 320 355 83 81 73

Elderly Individuals (60+) 49 154 129 132 32 38 37

Adults age 18 to 49 without disabilities in childless households 43 41 54 86 105 79 50

Case size

1 person 124 157 218 227 79 57 55

2 or more people 439 566 490 520 78 90 85

Case earned income status

No Earned income 329 367 379 404 90 87 81

Earned income 233 357 330 342 65 71 68

Countable Income as a Percentage of Poverty Guidelines

No Income 101 108 96 118 94 106 86

1 to 50 percent 135 127 152 140 106 89 96

51 to 100 percent 270 298 340 327 91 79 83

101 percent or more 56 190 121 161 29 46 35

Benefit as a percentage of maximum benefit

low benefits (1 to 50 percent of max) 172 289 322 347 60 53 50

high benefits (51 to 99 percent of max) 234 253 222 208 92 105 112

maximum benefit 157 182 164 192 86 96 82

Participation Rate (Participants/Eligibles)

sample size < 50

Eligible

CPS CPS

Factors Contributing to High Estimated SNAP Participation Rates

A-13
Table A.5: Mississippi (continued)

ACS ACS

Individual Characteristic Participating MATH TRIM3 ATTIS MATH TRIM3 ATTIS

2016 Including State Broad Based Categorical Eligibility (BBCE)

All Individuals 579 819 730 757 71 79 76

Age

Children (17 or younger) 260 283 265 261 92 98 100

Nonelderly adults (18 to 59) 268 350 329 358 77 81 75

Elderly Individuals (60+) 51 187 136 138 27 38 37

Adults age 18 to 49 without disabilities in childless households 45 45 54 86 100 83 52

Case size

1 person 128 174 223 231 74 57 55

2 or more people 451 646 507 526 70 89 86

Case earned income status

No Earned income 339 420 391 411 81 87 82

Earned income 240 399 338 346 60 71 69

Countable Income as a Percentage of Poverty Guidelines

No Income 105 118 96 118 89 110 89

1 to 50 percent 138 144 159 143 96 87 97

51 to 100 percent 277 328 345 333 84 80 83

101 percent or more 58 229 130 164 25 45 35

Benefit as a percentage of maximum benefit

low benefits (1 to 50 percent of max) 177 333 335 353 53 53 50

high benefits (51 to 99 percent of max) 239 283 227 211 84 105 113

maximum benefit 163 204 168 194 80 97 84

Subgroups defined based on "Trends in Supplemental Nutrition Assistance Program Participation rates: Fiscal Year 2010 to Fiscal Year 2016"

Source: Developed from eligibility estimates from the MATH CPS-Eligibility model, MATH SIPP+ model, TRIM3, and ATTIS combined with participation estimates based 

 on SNAP QC data, as processed by Mathematica

Eligible

CPS CPS

Participation Rate (Participants/Eligibles)

A-14

Factors Contributing to High Estimated SNAP Participation Rates
Table A.6: Estimated State-Level SNAP Case Participation Rates in Mississippi in 2016

By Data Source, Model, and Whether Estimate Includes State Broad Based Categorical Eligibility (BBCE)

(Numbers in thousands)

ACS ACS

Case Characteristic Participating MATH TRIM3 ATTIS MATH TRIM3 ATTIS

2016 (Federal Rules)

All cases 259 330 367 390 78 71 66

Case composition

Children (17 or younger) 123 120 126 127 103 98 97

Single-adult 84 59 65 81 142 129 103

Married-head 19 32 34 27 59 56 70

Other 20 29 27 18 69 75 109

Multiple-Adult 19 24 18 15 79 107 127

Child Only 1593 20 11 29

No Children 137 210 241 264 65 57 52

Adults age 18 to 49 without disabilities in childless households 37 30 45 68 123 82 55

Case earned income status

No Earned income 186 222 249 257 84 75 72

Earned income 73 108 118 134 68 62 55

Countable Income as a Percentage of Poverty Guidelines

No Income 59 55 63 69 107 93 85

1 to 50 percent 43 38 53 55 113 80 78

51 to 100 percent 134 150 188 182 89 71 74

101 percent or more 23 87 63 84 26 37 27

Participation Rate (Participants/Eligibles)

sample size < 50

Eligible

CPS CPS

Factors Contributing to High Estimated SNAP Participation Rates

A-15
Table A.6: Mississippi (continued)

ACS ACS

Case Characteristic Participating MATH TRIM3 ATTIS MATH TRIM3 ATTIS

2016 Including State Broad Based Categorical Eligibility (BBCE)

All cases 268 372 378 397 72 71 68

Case composition

Children (17 or younger) 126 132 129 128 95 98 99

Single-adult 86 63 67 82 137 128 105

Married-head 20 38 35 28 53 57 73

Other 20 30 27 18 67 74 108

Multiple-adult 19 25 18 15 76 107 127

Child only 1593 20 11 29

No Children 142 240 250 269 59 57 53

Adults age 18 to 49 without disabilities in childless households 39 32 45 68 122 86 57

Case earned income status

No Earned income 192 252 256 262 76 75 73

Earned income 75 120 122 135 63 61 56

Countable Income as a Percentage of Poverty Guidelines

No Income 61 60 63 69 102 96 88

1 to 50 percent 44 45 59 57 98 75 78

51 to 100 percent 137 162 190 185 85 72 74

101 percent or more 25 104 66 86 24 38 29

Subgroups defined based on "Trends in Supplemental Nutrition Assistance Program Participation rates: Fiscal Year 2010 to Fiscal Year 2016"

Source: Developed from eligibility estimates from the MATH CPS-Eligibility model, MATH SIPP+ model, TRIM3, and ATTIS combined with participation estimates based 

 on SNAP QC data, as processed by Mathematica

CPS CPS

Eligible Participation Rate (Participants/Eligibles)

Factors Contributing to High Estimated SNAP Participation Rates

A-16
Table A.7: Estimated State-Level SNAP Individual Participation Rates for Tennessee in 2016

By Data Source, Model, and Whether Estimate Includes State Broad Based Categorical Eligibility (BBCE)

(Numbers in thousands)

ACS ACS

Individual Characteristic Participating MATH TRIM3 ATTIS MATH TRIM3 ATTIS

2016 (Tennessee does not have BBCE so results reflect eligibility under federal rules)

All Individuals 1,100 1,184 1,238 1,353 93 89 81

Age

Children (17 or younger) 467 417 432 461 112 108 101

Nonelderly adults (18 to 59) 526 509 587 651 103 90 81

Elderly Individuals (60+) 107 257 219 241 42 49 44

Adults age 18 to 49 without disabilities in childless households 119 73 136 198 163 88 60

Case size

1 person 294 261 399 422 113 74 70

2 or more people 806 923 840 932 87 96 87

Case earned income status

No Earned income 691 652 699 695 106 99 99

Earned income 409 532 540 658 77 76 62

Countable Income as a Percentage of Poverty Guidelines

No Income 257 165 205 199 156 125 129

1 to 50 percent 285 257 257 249 111 111 115

51 to 100 percent 444 434 535 583 102 83 76

101 percent or more 114 328 242 323 35 47 35

Benefit as a percentage of maximum benefit

low benefits (1 to 50 percent of max) 291 439 532 617 66 55 47

high benefits (51 to 99 percent of max) 411 395 337 397 104 122 104

maximum benefit 399 350 370 339 114 108 118

Subgroups defined based on "Trends in Supplemental Nutrition Assistance Program Participation rates: Fiscal Year 2010 to Fiscal Year 2016"

sample size < 50

Source: Developed from eligibility estimates from the MATH CPS-Eligibility model, MATH SIPP+ model, TRIM3, and ATTIS combined with participation estimates based 

 on SNAP QC data, as processed by Mathematica

Eligible

CPS CPS

Participation Rate (Participants/Eligibles)

Factors Contributing to High Estimated SNAP Participation Rates

A-17
Table A.8: Estimated State-Level SNAP Case Participation Rates in Tennessee in 2016

By Data Source, Model, and Whether Estimate Includes State Broad Based Categorical Eligibility (BBCE)

(Numbers in thousands)

ACS ACS

Case Characteristic Participating MATH TRIM3 ATTIS MATH TRIM3 ATTIS

2016 (Tennessee does not have BBCE so results reflect eligibility under federal rules)

All cases 543 562 667 717 97 81 76

Case composition

Children (17 or younger) 221 206 219 226 107 101 98

Single-adult 141 104 117 128 136 120 110

Married-head 40 55 51 53 73 78 75

Other 41 47 51 44 87 81 94

Multiple-adult 27 29 26 25 93 103 106

Child only 13 18 25 18 72 53 71

No Children 321 356 447 492 90 72 65

Adults age 18 to 49 without disabilities in childless households 111 53 115 156 209 96 71

Case earned income status

No Earned income 399 375 454 455 106 88 88

Earned income 144 186 213 262 77 68 55

Countable Income as a Percentage of Poverty Guidelines

No Income 159 86 125 131 185 127 122

1 to 50 percent 99 92 121 107 108 82 93

51 to 100 percent 227 223 293 305 102 78 74

101 percent or more 58 161 127 175 36 46 33

Subgroups defined based on "Trends in Supplemental Nutrition Assistance Program Participation rates: Fiscal Year 2010 to Fiscal Year 2016"

sample size < 50

Source: Developed from eligibility estimates from the MATH CPS-Eligibility model, MATH SIPP+ model, TRIM3, and ATTIS combined with participation estimates based 

 on SNAP QC data, as processed by Mathematica

Eligible

CPS CPS

Participation Rate (Participants/Eligibles)

Factors Contributing to High Estimated SNAP Participation Rates

A-18
Factors Contributing to High Estimated SNAP Participation Rates

Appendix B: CPS ASEC Interview Rate and Representation Analysis 

List of Tables 

B.1: Interview Status by Whether SNAP Case at Housing Unit Address, by State, 2016 

B.2: Interview Status and Whole Imputation by Whether SNAP Case at Household Address, 

by State, 2017 

B.3: Interview Status by SNAP Case Type, 2017 

B.4: Share of SNAP Cases Represented in the Linked Data, by Case Type and Year 
Table B.1

All CPS sampled 

housing units Yes No

N 200 1,900 1,700 

Weighted (thousands) 600 5,108 4,508 

Interview status

Percentage distribution 100% 100% 100%

Interview 74% 82% 73%

Non interview: Type A 11% 12% 10%

Non interview: Type B/C 15% 6% 16%

N 200 1,400 1,200 

Weighted (thousands) 161 1,243 1,082 

Interview status

Percentage distribution 100% 100% 100%

Interview 67% 82% 65%

Non interview: Type A 12% 9% 12%

Non interview: Type B/C 21% 9% 23%

N 250 1,400 1,100 

Weighted (thousands) 459 2,780 2,321 

Interview status

Percentage distribution 100% 100% 100%

Interview 69% 75% 68%

Non interview: Type A 14% 19% 13%

Non interview: Type B/C 18% 6% 20%

Universe: Housing units in the basic March CPS sample (excluding Hispanic oversample) 

DRB Delegated Authority Approval Number CBDRB-FY21-CES014-029 (Ns are rounded to meet 

disclosure avoidance requirements).

Interview Status by Whether SNAP Case at Housing Unit Address, by State, 2016

SNAP case at CPS address?

Illinois

Mississippi

Tennessee

Sources: Decision Demographics & Urban Institute tabulations of linked 2016 CPS ASEC and 

SNAP administrative data for Illinois, Mississippi, and Tennessee

Factors Contributing to High Estimated SNAP Participation Rates

B-1
Table B.2

Yes No

N 350 2,200 1,900 

Weighted (thousands) 519 3,338 2,818 

Interview status

Percentage distribution 100% 100% 100%

Interview, not whole impute 71% 79% 70%

Basic CPS interview, whole impute to ASEC 14% 7% 15%

Non-Interview: Type A 15% 14% 15%

N 150 1,100 950 

Weighted (thousands) 136 985 850 

Interview status

Percentage distribution 100% 100% 100%

Interview, not whole impute 77% 86% 75%

Basic CPS interview, whole impute to ASEC 9% 6% 9%

Non-Interview: Type A 15% 8% 16%

N 200 1,200 950 

Weighted (thousands) 383 2,352 1,969 

Interview status

Percentage distribution 100% 100% 100%

Interview, not whole impute 69% 77% 68%

Basic CPS interview, whole impute to ASEC 16% 7% 18%

Non-Interview: Type A 15% 16% 14%

DRB Delegated Authority Approval Number CBDRB-FY21-CES014-028 and CBDRB-FY21-CES014-029

 (Ns are rounded to meet disclosure avoidance requirements).

MISSISSIPPI

TENNESSEE

Universe: Households in the basic March CPS sample (excluding Hispanic oversample). Excludes type B and type C 

non-interview housing units.

Interview Status and Whole Imputation by Whether SNAP Case at Household Address, by State, 2017

CPS sampled 

households

SNAP case at CPS address?

Sources: Decision Demographics & Urban Institute tabulations of linked 2017 CPS ASEC and SNAP administrative 

data for Mississippi and Tennessee

Factors Contributing to High Estimated SNAP Participation Rates

B-2
Table B.3

Interview Status by SNAP Case Type, 20171

Total2

At least one 

member 60+, 

without children

One adult with 

child(ren)

Multiple adults 

with child(ren)

One person case, 

age 18 to 59

N 450 70 100 70 150

Weighted (thousands) 668 95 187 103 245

Percentage distribution 100% 100% 100% 100% 100%

Interview, not whole impute 74% 75% 69% 83% 75%

Basic CPS Interview, whole impute to ASEC 8% 4% 11% 9% 4%

Non-interview: Type A 11% 16% 13% 10%

Non-interview: Type BC 7% 5% 6% 11%

Non-interview: Type ABC 9%

1Bottom row combines cells to avoid disclosure. 

2

The total includes child only cases and cases with multiple adults without members younger than 18 or 60 or above, not shown 

separately.

SNAP case type

Sources: Decision Demographics & Urban Institute tabulations of linked 2017 CPS ASEC and SNAP administrative data for 

Mississippi and Tennessee

DRB Delegated Authority Approval Number CBDRB-FY21-CES014-028 (Ns are rounded and some cells are collapsed to meet 

disclosure avoidance requirements).

Universe: Housing units in the basic March CPS sample (excluding Hispanic oversample) that match the address of a SNAP case. 

Addresses are matched by MAFID to preserve confidentiality.

Factors Contributing to High Estimated SNAP Participation Rates

B-3
Table B.4

Cases in SNAP 

administrative data 

(thousands)

Weighted SNAP 

cases in linked data 

(thousands)

Percentage of SNAP 

cases represented in 

linked data

2012

Total 1,565 1,819 86%

At least one member 60+ without children 272 232 85%

Child only 44 62 71%

One adult with child(ren) 456 483 94%

Multiple adults with child(ren) 278 253 110%

One person case, age 18 to 59 501 663 76%

Multiple person case, all age 18 to 59 55 84 66%

2013

Total 1,616 1,978 82%

At least one member 60+ without children 297 252 85%

Child only 54 68 79%

One adult with child(ren) 435 513 85%

Multiple adults with child(ren) 293 263 111%

One person case, age 18 to 59 504 747 68%

Multiple person case, all age 18 to 59 79 89 89%

2014

Total 1,585 1,969 81%

At least one member 60+ without children 311 262 84%

Child only 46 67 69%

One adult with child(ren) 475 506 94%

Multiple adults with child(ren) 267 254 105%

One person case, age 18 to 59 474 743 64%

Multiple person case, all age 18 to 59 61 87 70%

2015

Total 1,651 1,986 83%

At least one member 60+ without children 328 293 89%

Child only 53 69 77%

One adult with child(ren) 452 497 91%

Multiple adults with child(ren) 259 238 109%

One person case, age 18 to 59 527 770 68%

Multiple person case, all age 18 to 59 67 83 81%

Share of SNAP Cases Represented in the Linked Data, by Case Type and Year

Factors Contributing to High Estimated SNAP Participation Rates

B-4
Cases in SNAP 

administrative data 

(thousands)

Weighted SNAP 

cases in linked data 

(thousands)

Percentage of SNAP 

cases represented in 

linked data

2016

Total 1,326 1,783 74%

At least one member 60+ without children 328 281 86%

Child only 40 63 64%

One adult with child(ren) 370 454 82%

Multiple adults with child(ren) 253 209 121%

One person case, age 18 to 59 322 661 49%

Multiple person case, all age 18 to 59 57 66 86%

Sources: Decision Demographics & Urban Institute tabulations of 2012–2016 linked CPS ASEC and SNAP administrative data 

for Illinois, Mississippi, and Tennessee

DRB Delegated Authority Approval Number CBDRB-FY21-CES014-028 (Ns are rounded to meet disclosure avoidance 

requirements).

Table B.4, continued

Factors Contributing to High Estimated SNAP Participation Rates

B-5
Factors Contributing to High Estimated SNAP Participation Rates

Appendix C: Eligibility Analysis 

List of Tables 

C.1: ASEC Characteristics of SNAP Cases by TRIM3 Eligibility Status, by Year 

C.2: ASEC Characteristics of SNAP Cases by TRIM3 Eligibility Status; Combined 2012 to 

2016 data, by state 

C.3: Percentage of SNAP Case Members in State Ad Rec Data that appear to be Nonparticipating Based on State Codes 

 

 
Table C.1

ASEC Characteristics of SNAP Cases by TRIM3 Eligibility Status, by Year

Eligible 1+

months

Ineligible in

all months Total

N 600 200 800

Weighted total (in thousands) 1,564

TRIM3 unit and case match status

Percentage distribution 100% 100% 100%

TRIM3 unit matches SNAP case 65% 23% 54%

Mismatch: at least one case member not in ASEC household 17% 22% 18%

Mismatch: all case members are in ASEC household 18% 55% 27%

Imputation

No imputation 81% 60% 76%

Whole impute 7% 18% 10%

Not whole impute, any income item imputed 12% 22% 14%

N 600 200 800

Weighted total (in thousands) 1,617

TRIM3 unit and case match status

Percentage distribution 100% 100% 100%

TRIM3 unit matches SNAP case 67% 28% 58%

Mismatch: at least one case member not in ASEC household 14% 30% 18%

Mismatch: all case members are in ASEC household 19% 42% 24%

Imputation

No imputation 77% 56% 72%

Whole impute 8% 15% 9%

Not whole impute, any income item imputed 15% 29% 18%

2012

2013

Factors Contributing to High Estimated SNAP Participation Rates

C-1
Eligible 1+

months

Ineligible in

all months Total

N 600 200 800

Weighted total (in thousands) 1,586

TRIM3 unit and case match status

Percentage distribution 100% 100% 100%

TRIM3 unit matches SNAP case 65% 28% 56%

Mismatch: at least one case member not in ASEC household 18% 31% 21%

Mismatch: all case members are in ASEC household 18% 41% 23%

Imputation

No imputation 71% 44% 65%

Whole impute 8% 26% 12%

Not whole impute, any income item imputed 21% 30% 23%

N 700 250 950

Weighted total (in thousands) 1,651

TRIM3 unit and case match status

Percentage distribution 100% 100% 100%

TRIM3 unit matches SNAP case 65% 22% 53%

Mismatch: at least one case member not in ASEC household 17% 27% 20%

Mismatch: all case members are in ASEC household 17% 51% 27%

Imputation

No imputation 66% 46% 61%

Whole impute 8% 28% 14%

Not whole impute, any income item imputed 25% 26% 26%

2014

2015

Table C.1, continued

Factors Contributing to High Estimated SNAP Participation Rates

C-2
Eligible 1+

months

Ineligible in

all months Total

N 600 200 800

Weighted total (in thousands) 1,326

TRIM3 unit and case match status

Percentage distribution 100% 100% 100%

TRIM3 unit matches SNAP case 63% 31% 54%

Mismatch: at least one case member not in ASEC household 21% 28% 23%

Mismatch: all case members are in ASEC household 16% 41% 23%

Imputation

No imputation 69% 33% 58%

Whole impute 9% 35% 17%

Not whole impute, any income item imputed 22% 32% 25%

Sources: Decision Demographics & Urban Institute tabulations of combined 2012–2016 linked CPS ASEC, TRIM3, and SNAP 

administrative data for Illinois, Mississippi, and Tennessee 

Note: Cases identified as “No impute” correspond to a TRIM3 unit in which no member has imputation of the entire ASEC supplement 

(“Whole impute”) or any income value (“Income item impute”). SNAP case status reflects the CPS ASEC survey month. TRIM3 simulated 

eligibility reflects eligibility in at least one month of the prior calendar year.

DRB Delegated Authority Approval Number CBDRB-FY21-CES014-027

2016

Table C.1, continued

Factors Contributing to High Estimated SNAP Participation Rates

C-3
Table C.2

Eligible 1+ 

months

Ineligible in 

all months Total

N 1,300 450 1,700

Weighted total (in thousands) 3,665

Percentage distribution 100% 100% 100%

TRIM3 unit matches SNAP Case 61% 25% 51%

Mismatch: at least one case member not in ASEC household 17% 27% 20%

Mismatch: all case members are in ASEC household 22% 49% 29%

Imputation

No imputation 69% 46% 63%

Whole impute 9% 25% 14%

Not whole impute, any income item imputed 21% 29% 24%

N 950 250 1,200

Weighted total (in thousands) 1,345

Percentage distribution 100% 100% 100%

TRIM3 unit matches SNAP Case 65% 25% 56%

Mismatch: at least one case member not in ASEC household 21% 25% 22%

Mismatch: all case members are in ASEC household 14% 50% 22%

Imputation

No imputation 82% 62% 78%

Whole impute 3% 16% 6%

Not whole impute, any income item imputed 15% 22% 17%

N 850 300 1,200

Weighted total (in thousands) 2,732

Percentage distribution 100% 100% 100%

TRIM3 unit matches SNAP Case 71% 29% 60%

Mismatch: at least one case member not in ASEC household 15% 30% 19%

Mismatch: all case members are in ASEC household 14% 41% 21%

Imputation

No imputation 73% 45% 66%

Whole impute 9% 27% 14%

Not whole impute, any income item imputed 18% 28% 20%

DRB Delegated Authority Approval Number CBDRB-FY21-CES014-027

ASEC Characteristics of SNAP Cases by TRIM3 Eligibility Status; Combined 2012 to 2016 data, by state

ILLINOIS

MISSISSIPPI

TENNESSEE

Sources: Decision Demographics & Urban Institute tabulations of combined 2012–2016 linked CPS ASEC, TRIM3, and SNAP 

administrative data for Illinois, Mississippi, and Tennessee 

Note: Cases identified as “No impute” correspond to a TRIM3 unit in which no member has imputation of the entire ASEC supplement 

(“Whole impute”) or any income value (“Income item impute”). SNAP case status reflects the CPS ASEC survey month. TRIM3 simulated 

eligibility reflects eligibility in at least one month of the prior calendar year.

Factors Contributing to High Estimated SNAP Participation Rates

C-4
Table C.3 

Illinois Mississippi Tennessee

2012 0.0 1.5 suppressed

2013 0.0 3.3 0.6

2014 0.0 2.5 suppressed

2015 0.0 2.8 0.6

2016 0.0 3.2 suppressed

2017 na 3.3 0.9

DRB Delegated Authority Approval Number CBDRB-FY21-CES014-028 

Percentage of SNAP case members 

that are non-participating

Sources: Decision Demographics & Urban Institute tabulations of 2012–2017 

State SNAP administrative data

Universe: SNAP administrative case persons in Illinois, Mississippi, and 

Tennessee

Percentage of SNAP Case Members in State Ad Rec Data that 

appear to be Non-participating Based on State Codes

Factors Contributing to High Estimated SNAP Participation Rates

C-5