Simulated Versus Actual SNAP Unit Composition 

in Survey Households in Two States 

John L. Czajka 

Karen Cunnyngham 

Randy Rosso 

Mathematica Policy Research 

1100 1st Street NE, 12th Floor 

Washington, DC 20002-4221

Proceedings of the 2015 Federal Committee on Statistical Methodology (FCSM) Research Conference 

I. Background and Overview1

Studies linking state administrative records for the Supplemental Nutrition Assistance Program (SNAP) to 

household survey data have focused almost exclusively on estimating the underreporting of SNAP participation and 

benefits in the surveys and on investigating the characteristics of households and individuals associated with the 

misreporting of participation (see, for example, Bollinger and David 1997; Meyer and Goerge 2011; Harris 2014). 

Such studies have been few in number because of the infrequency with which state agencies that administer SNAP 

have shared their data with external entities for research purposes. 

A recent exception to the general focus of these studies used SNAP records from Texas linked to survey data from 

the American Community Survey (ACS) to estimate and analyze access to SNAP at both the state and county levels 

(Newman and Scherpf 2013). A key component of this study involved the simulation of eligibility for SNAP 

benefits in order to estimate SNAP access among households explicitly targeted by SNAP—that is, those individuals 

and families who appeared to be eligible for SNAP benefits based on the income and other characteristics reported 

in the survey. 

The research described in this paper focuses on an aspect of eligibility simulation that has challenged researchers for 

decades—namely, the grouping of household members into the units whose eligibility is jointly determined. The 

research expands upon previous efforts to link SNAP administrative records to household survey data by including 

records from the administrative data that do not match directly to individuals in the survey data. With these 

additions, the data allow a fuller assessment of SNAP unit formation. Furthermore, our research generates findings 

that extend beyond SNAP eligibility simulation to the potential use of SNAP and other administrative records to 

improve the efficiency and accuracy of decennial census operations. Section A provides important background for 

this research. Section B gives an overview of the research goals and an outline of this paper. 

A. Background

Unlike household surveys (but like other administrative data), SNAP administrative records are compiled and 

maintained at the program unit level—that is, the level at which SNAP eligibility is assessed and benefits are 

determined. A SNAP “household,” or unit, is defined as “individuals who share a residential unit and customarily 

purchase and prepare food together” (U.S. Department of Agriculture 2014). Individuals who live together but do 

not purchase and prepare food together generally may apply as separate SNAP units. However, spouses must apply 

together and parents must apply with their children under age 22 who reside with them—even if those children have 

spouses or children of their own. Minor children may be eligible as “child-only” units if their parents do not meet 

the U.S. residency requirement or are otherwise categorically ineligible for SNAP. Elderly individuals who are 

unable to purchase and prepare food because of a substantial and permanent disability and their spouses may apply 

 

1

 This work was conducted under a project cosponsored by the Census Bureau’s Center for Administrative Records 

Research and Applications (CARRA) and the Economic Research Service (ERS) of the U.S. Department of 

Agriculture. The views expressed here are the authors’ and not necessarily the sponsors. 
as a separate unit if the gross monthly income of remaining household members is below 165 percent of the federal 

poverty guideline. 

When a household survey is used to simulate eligibility for SNAP, a key step is to identify the SNAP unit(s) within 

each sample household. Researchers customarily rely on reported family and partner relationships for this. 

Countable income and resources, if the latter is available on the survey or imputed, are then aggregated across the 

members of each unit and compared to SNAP income and resource screens to determine if a unit is eligible for 

SNAP. 

Typically, household surveys that collect data on SNAP participation do not identify the members of the SNAP unit. 

Consequently, comparisons of aggregate characteristics between SNAP units in administrative data and survey 

households that report receiving SNAP benefits show large differences. Even when the surveys identify the 

members of the SNAP unit, differences persist. Part of the reason is that the approach to identifying SNAP 

participants in most surveys does not lend itself very well to identifying child beneficiaries when their parents are 

not participants themselves. In the Survey of Income and Program Participation (SIPP), for example, a respondent 

cannot report that he or she receives SNAP benefits on behalf of a child and also report not being covered by those 

benefits. As a result, child-only units are rarely reported as such. Either no child participants are reported or they are 

reported with the parent incorrectly included as a member of the unit as well. 

SNAP quality control (QC) data show that nearly half (46.7 percent) of the average monthly SNAP units in 2009 

consisted of a single person; about a third contained two or three persons; and just 3.2 percent had six or more 

members (Czajka et al. 2012).2

In the SIPP for that year, one-person units (based on reported unit composition and 

average monthly participation) represented 35.1 percent of the total; two- and three-person units were 39.4 percent, 

and units with six or more persons were 5.8 percent of the total. Substituting the SIPP household size for reported 

SNAP unit composition produces substantially greater discrepancies. Only 22.6 percent of the SIPP households 

reporting SNAP benefits were one-person households, 39.5 percent had two or three members, and 10.8 percent had 

six or more members. Households that reported receiving SNAP benefits in the Current Population Survey Annual 

Social and Economic Supplement (CPS ASEC) and the ACS resembled SNAP households in the SIPP. For 

example, 22.9 percent of average monthly SNAP households in the CPS ASEC and 21.6 percent of the ACS 

households with any SNAP participation in the prior 12 months had a single person. Similarly, 40.6 percent of 

SNAP households in the CPS ASEC and 39.7 percent of those in the ACS had two or three members. Likewise, 9.4 

percent of the CPS ASEC households and 11.0 percent of ACS households with SNAP benefits had six or more 

members.

B. Overview 

The objective of the larger project from which this paper was derived was to use linked New York State and 

Colorado administrative and survey data to conduct within-household comparisons of administrative SNAP units 

with full households and simulated SNAP units in the ACS, the CPS ASEC, and the SIPP. This paper describes the 

linkage process for all three surveys but focuses on the ACS in its presentation of findings from the comparative 

analysis. For the ACS analysis, we used the simulated SNAP units developed by Decision Demographics based on 

specifications provided by the Economic Research Service (ERS), which cosponsored this research with the U.S. 

Census Bureau. 

In contrast to previous studies of linked survey and SNAP administrative records, this analysis takes account of 

unmatched SNAP recipients in the administrative units that are matched to survey households. The unmatched 

recipients are in SNAP units from which at least one person was matched to a survey household member. Including 

the unmatched SNAP recipients enables us to calculate the size and demographic composition of the matched 

administrative units and to compare the incidence of unmatched persons in both the survey and administrative data. 

Unmatched recipients may reflect match failures (when there are also unmatched survey household members), but 

they may also represent household members who were not enumerated with the survey household. 

 

2

 SNAP QC data are monthly samples of SNAP case files drawn by each state to assess the accuracy of eligibility 

and benefit determination. Original and corrected values of selected fields are entered into a database and 

transmitted to the Food and Nutrition Service (FNS), which calculates and publishes error rates and produces an 

annual database of QC sample cases containing information on unit characteristics as well as benefit amounts. 
Chapter II discusses the data sources used in this research, explains how we constructed the analysis files, and 

provides counts of survey households that were matched to one or more administrative records for each pairing of a 

survey with a state administrative file. Chapter III reviews our findings from the linked ACS and New York SNAP 

data, while Chapter IV discusses our findings from the linked ACS and Colorado SNAP data. Finally, Chapter V 

summarizes our key findings and discusses their implications. 

II. Data and Methods 

This chapter provides an overview of the three household surveys and the New York and Colorado administrative 

data to which they were linked. We then describe the construction of the analysis files, concluding with a summary 

of the matched households by survey. 

A. Overview of the Three Surveys 

The ACS is a continuous survey, with independent monthly samples of households. Sample households are first 

contacted by mail and asked to complete and mail back their survey forms. On average, roughly half do so. A 

sample of the households that do not respond by mail is selected for intensive follow-up, starting with telephone 

calls and progressing if necessary to in-person visits. The data collected by these follow-up methods are typically 

obtained two to three months after the sample month. Because the ACS reference period is defined relative to the 

interview month, the survey data are organized by the month in which the data were collected, then assembled into 

calendar year files for processing and analytical use. Although the survey month is not identified on public use files, 

it is identified on the Census Bureau’s internal files. We used the survey month in linking the ACS data to the SNAP 

administrative data, as explained below. 

The CPS is a monthly survey to which the Census Bureau appends a number of supplements each year to collect 

additional data on a variety of topics. The ASEC collects data on household demographic composition, income 

during the prior calendar year, health insurance coverage, and migration. The ASEC is conducted primarily in 

March of each year, with a relatively small number of additional interviews conducted in February and April.3 As 

with the ACS, the Census Bureau does not release the interview month on the public use file—however, that 

information is available on the internal file. Because household composition is defined as of the interview month, we 

used the interview month in linking the CPS ASEC data to the SNAP administrative data, as explained below. 

Unlike the ACS and CPS ASEC, the SIPP is a longitudinal survey. Members of households interviewed at the start 

of the survey are reinterviewed every four months over a period of a few years. At each interview, they are asked 

detailed questions about each of the four preceding months, yielding monthly data on household composition, 

income, participation in numerous programs, health insurance coverage, and other characteristics. Modules attached 

to most of the interviews collect additional information on specialized topics such as assets, retirement plans, and 

child care expenses, which vary by wave. Some of these topical modules are repeated at annual intervals; others are 

administered less often. Because the SIPP data are reported by reference month rather than interview month, we 

used the reference month in linking the SIPP data to the SNAP administrative data. Details are provided below. 

The three surveys differ substantially in the number of households from which they collect data. In 2012, the ACS 

collected data from more than 2 million households. For the CPS ASEC, the interviewed sample size in 2012 was 

74,000 households. For the SIPP, 2012 represented the end of the fourth year and the start of the fifth year of data 

collection for the 2008 panel. Although the 2008 panel started with about 40,000 interviewed households, the 

number of households responding to each of the three waves in 2012 was around 30,000. 

Another way in which the surveys differ is in the amount of information they collect that is relevant to simulating 

SNAP unit composition. The ACS collects the most limited data. The questionnaire asks how each household 

member is related to the household reference person, or householder, but it does not ask how household members 

are related to each other. Some relationships can be inferred. For example, the Census Bureau reconstructs 

subfamilies among persons related to the householder. This effort is assisted by subsequent questions on marital 

 

3

 The February and April interviews are part of a congressionally mandated sample expansion that was introduced in 

2001 to improve the precision of state-specific estimates of uninsured children. The ASEC supplement became the 

new name for what was previously known as the March supplement. 
status, whether a woman has given birth in the past 12 months, and whether an individual’s grandchildren are in the 

household. Clearly, the limited relationship data has implications for the simulation of SNAP units, particularly 

among persons unrelated to the householder. The Census Bureau does not attempt to infer relationships among such 

persons. Users who wish to do so must base their inferences on age and gender. 

The CPS ASEC collects sufficient relationship data to identify subfamilies directly; this extends to persons unrelated 

to the householder. However, subfamily identification among the latter is less complete than among persons related 

to the householder. For example, parent-child relationships among persons unrelated to the householder are 

established only for children under 18. No other relationships among adults are identified, besides spouses. 

The SIPP collects full relationship information among all household members in one of its early topical modules, 

but that information is not maintained over time. Nevertheless, even the monthly relationship data collected in the 

SIPP are more complete than those collected in the CPS ASEC. In addition, a periodic module collects information 

on who shares food expenses, which is relevant to SNAP unit composition. Moreover, while all three surveys collect 

data on SNAP participation, only the SIPP determines which household members were covered by SNAP—and it

does so for every month. The SIPP also collects information on which reported SNAP beneficiaries in each month 

formed SNAP units together. Although SNAP participation is underreported in the SIPP, the underreporting is far 

lower than in either of the two other surveys (Czajka et al. 2012). Given this, reported unit composition is available 

for a substantial majority of the actual SNAP participants who were included in the SIPP households. 

B. Overview of New York and Colorado SNAP Administrative Data 

SNAP administrative data are compiled and maintained at the state level. Although the systems that states use to 

administer SNAP vary, state administrative records generally (1) identify who received SNAP benefits in a given 

month and (2) contain a variety of information on the individual recipients and the unit as a whole. The household is 

not a relevant unit for SNAP administration—states prepare statistics on the numbers of units receiving SNAP 

benefits, not the number of households. Although address information, theoretically, may link units residing in the 

same household in a given month, the accuracy of addresses from month to month is not as important 

administratively as the accuracy of unit identifiers. 

Under agreements between the state SNAP agencies and the Census Bureau, New York and Colorado supplied 

SNAP administrative data to the bureau for research purposes. New York provided data for multiple years. Colorado 

initially supplied data for just 2012 and subsequently added 2013. The New York and Colorado data were delivered 

in the form of person-months or pay-months, meaning that there is a record for every month that a person was a 

beneficiary. 

As part of its standard preprocessing of administrative data, the Census Bureau adds a Master Address File Identifier 

and Protected Identification Key (PIK), where possible, and strips off direct personal identifiers. The Master 

Address File is a database of unique household addresses that the Census Bureau develops and maintains for 

household survey sampling and decennial census enumeration. The PIK is a unique personal identifier generated by 

the Census Bureau to facilitate the linking of records across databases while preserving the confidentiality of 

individual records. Each PIK corresponds to a social security number (SSN), but the SSN cannot be derived from 

the PIK. 

The Census Bureau assigns PIKs to the records in each survey or administrative database by using either an exact or 

a probabilistic match between that database and an enhanced version of the Numident database, in which the Social 

Security Administration maintains data collected from applications for SSNs. Many administrative databases include 

SSNs, which are first validated against the Numident database; PIKs can then be assigned to records with validated 

SSNs by exact match. For probabilistic matches the match variables include full names, demographic information, 

and addresses. Once PIKs are assigned, files can be linked by an exact match on PIK—yet, the removal of direct 

identifiers prevents users with access to the data from identifying individual records. The fact that PIKs do not exist 

for persons without SSNs (or, recently, the alternate taxpayer identification numbers that the Internal Revenue 

Service assigns to individuals who do not qualify to receive SSNs) excludes a small percentage of the population. 

However, a bigger source of missing or erroneous PIK assignments is incomplete or incorrect identifying 

information. Widely shared surnames and varied spellings also contribute to PIK assignment failures. In household 

surveys, typically 9 to 10 percent of the sample cannot be assigned PIKs. Furthermore, because the match is 
probabilistic, some of the PIK assignments may be incorrect, which in very large files can result in duplicate PIK 

assignments. Statistics on erroneous PIK assignments are not available. For the New York and Colorado SNAP files, 

the bureau was able to assign PIKs to 99.2 percent of the records, which meant that virtually all of the records were 

potentially eligible to be linked to household survey data from the two states (Tordella et al. 2014). 

C. Construction of the Analysis Files 

Each of the analysis files that we created combines one of the state SNAP administrative files with one of the three 

surveys. We constructed analysis files linking the New York SNAP administrative records to the ACS, CPS ASEC, 

and SIPP, and an analysis file linking the Colorado SNAP administrative records to the ACS. Linkages between the 

Colorado SNAP records and both the CPS ASEC and the SIPP would have produced too few records for useful

analysis. 

1. General Approach 

Creation of each analysis file involved the following six steps: 

1. Preprocessing the administrative data for the selected state (New York or Colorado) 

2. Preprocessing the survey data (the ACS, CPS ASEC, or SIPP) for that state 

3. Linking the administrative records to the survey records by PIK 

4. Adding the unmatched members of each survey household with one or more matched members 

5. Adding the unmatched members of each administrative SNAP unit with one or more matched members 

6. Appending the ERS simulated unit ID to the survey records (for the ACS only)

Steps 3 through 5 constitute the main components to the construction of each analysis file. After we linked state 

SNAP records and survey person records by PIK, every survey household in which one or more members was 

matched to a state SNAP record was included in the analysis file. To these households, we added the survey records 

of any unmatched household members. This ensured that every survey household in the analysis file included all of 

its members as identified in the survey household roster. Next, we added state SNAP person records of unmatched 

SNAP participants who shared SNAP unit membership with a matched state SNAP person. This component, which 

was by far the most complex of the three steps, created an “enhanced survey household.” We use this term to 

describe the analysis file households because some of the households contain more persons than were identified by 

the survey.4

Originally, we intended to include all unmatched persons in the state data who resided at the same address as the 

matched persons. This would have enabled us to add SNAP units with no members who matched to the survey 

records. Given that about 10 percent of the survey records lack PIKs, this could have been particularly useful for 

capturing small SNAP units—especially one-person units—that were excluded by the limitations of the matching 

process. These units might include survey household members who lacked PIKs and therefore could not be matched. 

However, we could not add units that had no unmatched members at the same address because the address 

information in both states was deficient. 

The analysis files we constructed are at the person level and contain three types of records. Using the ACS–New 

York analysis file as an example, the file includes person records drawn from the ACS, the New York SNAP data, 

or both. For the ACS household members who were matched to New York SNAP participants, the person record 

contains data from both the ACS and the New York SNAP data. For the ACS household members who were not 

matched to New York SNAP participants, the person record contains only the ACS data. For members of New York 

 

4

 As we will discuss later, when there are at least as many unmatched survey household members as unmatched state 

SNAP unit members, we cannot be sure that the unmatched SNAP members are truly additions to the survey 

household. Rather, it could be that all of the unmatched SNAP unit members would have been matched to the 

unmatched survey household members if PIKs had been assigned correctly to all members of the survey household 

and SNAP administrative unit. 
SNAP units who were not matched to ACS records but whose units were matched, the person record contains only 

New York SNAP data. Therefore, for each ACS household, there is a record for every ACS household member, plus 

any additional unmatched New York SNAP participants whose SNAP units include at least one member who 

matched a member of the ACS household. This is the enhanced survey household. 

2. Survey-Specific Procedures 

The procedures that we followed in constructing an analysis file of linked survey and administrative data differed 

somewhat across the three surveys. The survey-specific procedures are outlined below. 

a. The ACS

Although the reference period for reporting SNAP participation in the ACS is the past 12 months, we chose to 

restrict our matches to the survey month and the two preceding months. Specifically, for each New York ACS 

sample member in the 2012 annual sample, we searched the New York SNAP administrative file for a match on the 

PIK and the survey month. For records with no match, we searched the New York SNAP file for a second time for a 

match on the PIK but in the month prior to the interview month. Finally, for the remaining unmatched New York 

ACS records, we searched the New York SNAP file for a third time for a match on PIK two months prior to the 

interview month. As we will show in the next chapter, the number of matches dropped sharply between the survey 

month and each prior month. That SNAP enrollment was still rising in 2012 may have contributed to this outcome. 

Had our matches been performed in a period of declining enrollment, the falloff in matches between one month and 

the prior month may have been less precipitous. As it was, the pattern of declining matches provided strong support 

for our decision to focus on the survey month. 

b. The CPS ASEC 

Although the bulk of the CPS ASEC interviews are conducted in March, some are conducted in February and April. 

In linking the New York SNAP data to the CPS ASEC, we followed the same procedures used with the ACS. That 

is, we first linked SNAP records to New York records in the CPS ASEC by PIK and interview month. For 

unmatched New York CPS ASEC records, we then searched the New York SNAP file for matches in the month 

prior to the interview month. Finally, for the remaining unmatched New York CPS ASEC records, we searched the 

New York SNAP file for matches two months prior to the interview month. 

c. The SIPP 

Because the SIPP is longitudinal, with monthly data for every interviewed sample member, we employed a different 

linkage strategy than we did with the ACS and CPS ASEC. The SIPP interviews for a survey wave are conducted 

over four consecutive months. The sample is divided into four “rotation groups” that are equally sized. The members 

of each rotation group are interviewed in the same month and asked about their activities over the same previous 

four months. With this design, there is one month that appears in the reference period for all four rotation groups in a 

given wave. We used the SIPP data from waves 12, 13, and 14, which have as their common reference months 

April, August, and December 2012, respectively. 

For each of the three months, we searched the New York SNAP data for matches to New York SIPP records in that 

month. We then added unmatched SIPP household members and unmatched SNAP unit members to create a set of 

enhanced SIPP households in each month. Because of the panel nature of the survey and the continuity of SNAP 

participation, a given household might appear in all three of the match months. However, its membership might not 

be the same in all three months and its SNAP participation might be different. Furthermore, a given household in 

April might be two households in December if one or more members left to form a new household. Limiting 

households to one appearance in the data set is not only challenging—requiring us to determine which of two or 

three versions of a household is best—it also reduces the representativeness of the final data set. In other words, 

although multiple appearances of the same household overstate the effective sample size of the data set based on raw 

sample counts, they reduce bias relative to a data set that limits each household to one appearance. Given these 

considerations, we opted to pool the April, August, and December data sets without removing any SNAP 

households. This effectively tripled the size of the SIPP analysis file. 
 
3. The Possibility of Missed Matches 

Primarily because of missing PIKs in the survey data, it is theoretically possible that there were SNAP 

administrative units that we were unable to add to the survey households because they contained no matched 

members. If we missed some units that should have been included, they were most likely to have contained only one 

or two members, as the likelihood that a SNAP administrative unit will contain at least one matching member rises 

with the unit size. Similarly, it is highly likely that we missed some SNAP households entirely. If the sole member 

of a one-person SNAP household in the survey data lacked a PIK, then a match was not possible. With a 90 percent 

PIK rate for the ACS and CPS ASEC (the SIPP is somewhat lower), 10 percent of the one-person SNAP households 

would be missed if successful PIK assignment were independent of SNAP participation. Because SNAP participants 

are generally required to have SSNs, their PIK rate should be higher than the average survey respondent. Furthermore, some of the findings we present below suggest that survey PIK assignment is more successful for householders than for other household members. For these reasons, the fraction of one-person SNAP households missed 

by our linkages is probably well below 10 percent. Nevertheless, small SNAP households are likely to be

underrepresented in our analysis files. 

Unidentified SNAP units in households with one or more matched units will lead us to underestimate the average 

number of SNAP units among our matched households. At the same time, if unidentified SNAP households are 

disproportionately one-unit households, their exclusion from our analysis files will contribute a bias in the opposite 

direction to our estimates of the average number of SNAP units per household. We cannot predict the net effect of 

these opposing biases. 

4. Summary of Match Results by Survey 

Actual and projected match results for the six survey and administrative data linkages for 2012 are reported in Table 

II.1. Linking the largest of the three surveys, the ACS, with administrative data from New York yielded nearly 

20,000 households with matched data. Linking the same state administrative data to the CPS ASEC produced a 

small fraction of matched households—just over 600—while the SIPP match produced an average of about 290 

matched households over the three months. Combining the matches from the three months gave us 860 matched 

households in our analysis file. 

Table II.1. Number of Enhanced Survey Households in Each Analysis File, Actual and Projected 

Analysis File Actual Size Projected Size 

ACS–New York SNAP 19,856 

CPS ASEC–New York SNAP 604

SIPP–New York SNAPa 1,274 

ACS–Colorado SNAP 2,899 

CPS ASEC–Colorado SNAP 90

SIPP–Colorado SNAPa 190

Source: Mathematica Policy Research, from linked 2012 survey and SNAP administrative data. 

a File combines households from three waves containing many of the same households interviewed in 

different months. 

In terms of total participants, the Colorado SNAP program is one-sixth the size of New York’s, so we would expect 

the matching process for each of the three surveys to yield roughly one-sixth the number of matched sample 

households as New York. For the ACS, we obtained close to 3,000 matched Colorado households—a little under 

one-sixth the number we obtained in New York but almost five times as many as in the CPS ASEC–New York 

SNAP file. However, linking the Colorado SNAP data with the CPS ASEC and the SIPP would appear likely to 

yield too few matched households to support very useful analysis in isolation from other data. The projected counts 

of 90 for the CPS ASEC and 130 for the SIPP would severely limit the types of analysis that could be conducted on 

the matched households. 
III. Findings from the ACS: New York 

We present findings from our analysis of the ACS data matched to New York and Colorado SNAP records, 

beginning with the former. In discussing the findings from our analysis of the ACS data linked to New York SNAP 

administrative data, we begin by examining the results of the linkage at the person level. Following that, we review 

key findings at the level of the enhanced ACS household, then we turn to an examination of the comparative 

characteristics of ACS households, simulated SNAP units, and actual SNAP units. 

A. Linkage Results at the Person Level

We examine the linkage results at the person level beginning with the ACS and then turning to the New York SNAP 

data. In all, the 19,586 ACS households to which at least one New York SNAP record was matched contain 57,442 

individuals (Table III.1). Of these, 39,371 or 68.5 percent were matched to New York SNAP records. Of those who 

were not matched, 15,260 or 26.6 percent of the total had a PIK, implying that they would have been matched if they 

had participated in SNAP during the survey month or one of the two preceding months. The remaining 2,811 or 4.9 

percent of the total had no PIK, so they could not have been matched to the New York SNAP data even if they had 

participated in SNAP in New York during the three-month reference period. The fraction lacking a PIK is less than 

half the proportion of all 2012 ACS person records that could not be assigned a PIK (Tordella et al. 2014). New 

York could be above the ACS average with respect to PIK success, but we suspect that most of the difference is due 

to a higher than average PIK rate among households in which at least one member could be linked to a SNAP 

participant record. 

Table III.1. Distribution of ACS Household Members by Match Result to New York SNAP 

Administrative Data 

Match Result Number Percentage 

All ACS household members 57,442 100.0 

Matched a New York SNAP administrative record 39,371 68.5 

Did not match a New York SNAP administrative record 18,071 31.5 

Has a PIK 15,260 26.6 

Does not have a PIK 2,811 4.9 

Source: Mathematica Policy Research, from linked 2012 ACS and New York SNAP administrative data. 

It is significant that most of the unmatched ACS records have PIKs. These records could not be matched to New

York SNAP administrative records, on which PIKs were absent less than 1 percent of the time. It is highly likely, 

therefore, that these individuals did not participate in SNAP during the three-month reference period. We discuss the 

implications at the household level below. In addition to the 39,371 New York SNAP administrative records that 

were matched to the ACS records, another 3,766 New York SNAP records were added to the enhanced ACS 

households as unmatched members of the New York SNAP units of the matched persons (Table III.2). All but 19 of 

these records had PIKs. That a record with a PIK failed to match anyone in the ACS household to which another 

member of the SNAP unit was successfully matched could have one of several explanations: 

 The New York SNAP record’s ACS counterpart did not have a PIK. 

 One of the two records had an incorrect PIK. 

 The person represented by the New York SNAP record did not live in the ACS household during the 

survey month. 

 The person represented by the New York SNAP record lived in the ACS household during the survey 

month but was not reported. 

The last possibility implies a deficiency in the identification of household members in the ACS. Such cases have 

direct implications for the decennial census and how administrative records from SNAP might be helpful in

improving the quality of the census count. Our analysis did not attempt to explain the missed matches, however. 

 
Table III.2. Distribution of New York SNAP Administrative Records by Match Result to the ACS 

and by Match Month 

Match Status Number Percentage 

New York SNAP unit members in enhanced ACS households 43,137 100.0 

Matched an ACS record 39,371 91.3 

Did not match an ACS record 3,766 8.7 

Has a PIK 3,747 8.7 

Does not have a PIK 19 0.0 

Records matched to ACS records 39,371 100.0 

Matched in ACS interview month 37,707 95.8 

Matched one month prior to ACS interview month 1,023 2.6 

Matched two months prior to ACS interview month 641 1.6 

Source: Mathematica Policy Research, from linked 2012 ACS and New York SNAP administrative data. 

The lower portion of Table III.2 shows how often the New York SNAP administrative records that matched ACS 

records were drawn from either the survey interview month, one month earlier, or two months earlier. The vast 

majority of matches—95.8 percent—occurred in the survey interview month. Another 2.6 percent occurred one 

month earlier; the remaining 1.6 percent occurred two months earlier.5

 Adding matches from the two previous 

months does not expand the survey household; rather, it expands the SNAP unit to include more members of the 

survey household. The small numbers added from the two months preceding the interview month support our

strategy of limiting the search for matches to just three months. 

B. Linkage Results at the Household Level 

In turning from the person level to the household, we examine first the prevalence of SNAP units—both simulated 

and actual—within households participating in SNAP. We then look at the combined incidence of unmatched 

individuals from the survey and administrative data and the alignment of simulated and actual units within the same 

household. 

1. SNAP Units in Households 

Table III.3 directly addresses the important issue of the prevalence of multiple SNAP units within the same 

household. The table provides a cross-tabulation of the number of administrative SNAP units by the number of 

simulated SNAP units in New York ACS households with at least one member linked to a SNAP administrative 

record. The unit simulations presented in this table do not make use of the ERS eligibility simulation or a 

participation imputation, so we are comparing participating administrative units with simulated units that could be 

(1) ineligible or eligible and nonparticipating as well as (2) eligible and participating. Of course, all of the 

households have at least one member participating in SNAP, which may have implications for the eligibility and 

participant status of the other members. 

Of the 19,586 ACS households, only 7.2 percent had more than one SNAP administrative unit, while 33.9 percent 

had more than one simulated unit. Thus, the SNAP unit simulation creates multiple SNAP units more frequently 

than participating units exist in the New York data. The numbers of units of each type are related, however. Nearly 

two-thirds (64.8 percent) of the households had only one of each type of unit. Another 3.0 percent had two of each 

type of unit and another 0.6 percent had three or more of each type of unit. Furthermore, if there was only one 

simulated unit, there was nearly always only one administrative unit. On the other hand, if there was only one 

administrative unit, then 30 percent of the time there was more than one simulated unit. And if there were exactly 

 

5

 Not shown in the table: all 3,766 nonmatching NY SNAP records, which were added to the analysis file because 

they shared SNAP unit membership with one or more matched records, were drawn from the survey interview 

month. Limiting these additions to the survey month increases the likelihood that the persons in these units were in 

the household at the time of the survey. 
two administrative units, then more than half the time there was a different number of simulated units, with one 

simulated unit being as likely as three simulated units. 

Table III.3. Prevalence of Multiple SNAP Units in New York ACS Households: All Simulated Units Included 

Administrative SNAP Units 

with Matches to the Same 

ACS Household 

Total ACS 

Households 

Simulated SNAP Units Within the Same ACS Household 

One Two Three Four 

Five or 

More 

Number 19,586 12,946 4,282 1,555 533 270

One 18,178 12,688 3,668 1,246 413 161

Two 1,267 253 596 267 94 59

Three or more 141 5 18 42 26 50

Percentage of Total ACS 

Households 100.0 66.1 21.9 7.9 2.7 1.4 

One 92.8 64.8 18.7 6.4 2.1 0.8 

Two 6.5 1.3 3.0 1.4 0.5 0.3 

Three or more 0.7 0.0 0.1 0.2 0.1 0.3 

Source: Mathematica Policy Research, from linked 2012 ACS and New York SNAP administrative data. 

When we restrict the simulated units to those that the ERS program simulates to be eligible financially, we find that 

23.0 percent of the ACS households have no simulated units, while only 13.7 percent have more than one simulated 

unit (Table III.4). This latter figure contrasts with 33.9 percent when all simulated units are included. Below, we 

compare the overall number of simulated SNAP units with and without the eligibility screen and contrast these totals 

with the number of actual SNAP units in these households. 

Table III.4. Prevalence of Multiple SNAP Units in New York ACS Households: Only Eligible Simulated Units 

Included 

Administrative SNAP Units 

with Matches to the Same 

ACS Household 

Total ACS 

Households 

Simulated SNAP Units Within the Same ACS Household 

None One Two Three Four 

Five or 

More 

Number 19,586 4,510 12,393 2,150 413 85 35

One 18,178 4,316 11,947 1,617 249 41 6 

Two 1,267 179 429 497 124 28 12

Three or more 141 15 17 36 40 16 17

Percentage of Total ACS 

Households 100.0 23.0 63.3 11.0 2.1 0.4 0.2 

One 92.8 22.0 61.0 8.3 1.3 0.2 0.0 

Two 6.5 0.9 2.2 2.5 0.6 0.1 0.1 

Three or more 0.7 0.1 0.1 0.2 0.2 0.1 0.1 

Source: Mathematica Policy Research, from linked 2012 ACS and New York SNAP administrative data. 

2. Match Status Within Households 

Table III.5 breaks down the 19,586 enhanced ACS households by the match status of their members and those of the 

linked SNAP units. In 55.6 percent of the households, all members of the ACS household were matched to New 

York SNAP administrative unit members. For most of these households, or 52.2 percent of all households with one 

or more matches, there were no unmatched SNAP administrative unit members. Of the 10,892 households in which 

all ACS members were matched, there were 660 households (3.4 percent of the total) in which one or more

administrative unit members were not matched (that is, we added an unmatched administrative unit member to the 

ACS household.) 

 
Table III.5. Match Status of ACS Household Members and New York SNAP Administrative Unit Members 

Match Status Summary Number Percentage 

ACS households containing matched person 19,586 100

All ACS household members matched 10,892 55.6 

All administrative unit members matched 10,232 52.2 

Some administrative unit members not matched 660 3.4 

Some ACS household members not matched 8,694 44.4 

All administrative unit members matched 7,284 37.2 

Some administrative unit members not matched 1,410 7.2 

Source: Mathematica Policy Research, from linked 2012 ACS and New York SNAP administrative data. 

Among the 8,694 households (44.4 percent of the total) in which there was at least one unmatched member of the 

ACS household, about three-quarters (7,284 or 37.2 percent of the total) had no unmatched administrative SNAP 

unit members. A small subset of the matched households with one or more unmatched ACS members (1,410 

households or 7.2 percent of the total) also had unmatched SNAP administrative unit members. 

C. Characteristics of Simulated Versus Administrative SNAP Units 

To better understand what the SNAP unit simulation does, we compare the characteristics of entire ACS households 

with (1) those of SNAP units simulated using the ERS algorithm, (2) the subset of these that were also simulated to 

be eligible, and (3) the actual SNAP units matched to the ACS households from the New York administrative data. 

1. Demographic Characteristics 

Table III.6 compares the full households and both simulated and actual SNAP units with respect to size (number of 

persons) and other demographic characteristics. The ERS simulation algorithm yielded 29,852 simulated SNAP 

units across the 19,586 ACS households, or about 1.52 units per household. Of these, 18,463 simulated units or

fewer than 1 unit per household, were judged to be eligible to receive SNAP benefits. The number of New York 

SNAP administrative units that linked to the ACS households through one or more members numbered 21,159, or 

about 1.08 units per household. The excessive simulated units when eligibility is not considered could be the result 

of the simulation creating too many small units. As described by Newman and Scherpf (2013), the simulation 

assumes that, subject to certain mandatory rules of unit formation, “households will form the smallest units 

possible.” This would lead us to expect a greater proportion of small units among the simulated units than among the 

actual SNAP administrative units.6

FMWWith the focus on creating small units, one-person units are more common among the simulated units—both 

the total units and eligible units—than among the actual units, accounting for 58.1 percent of both sets of simulated 

units versus 52.9 percent among SNAP administrative units.7 Both two- and three-person units are more common 

 

6

 Simulations of SNAP-eligible units have tended to produce too few one-person units compared to what is observed 

in actual caseload statistics. Designing a simulation to produce the smallest units consistent with the mandatory rules 

would seem to be an appropriate strategy to address this chronic limitation of past simulations. 

7

 In Chapter II, we explained that if a one-person SNAP household had a missing or incorrect PIK in the ACS, then 

that household would not have been matched and, therefore, would not be included in our analysis file. Larger 

households could have been excluded as well, but the number of missing SNAP households is probably greatest for 

one-person households, given that a single missing PIK would prevent a match. If the number of one-person 

households is understated in the analysis file, this would imply that the numbers of simulated and actual one-person 

units are also understated. In Table III.1, we saw that the percentage of ACS household members who were missing 

PIKs (in households with one or more matched members) was 4.9 percent. We suggest that this is a reasonable rate 

of missing PIKs to assume for one-person households participating in SNAP, as their missing PIKs would be due to 

problems in assigning a PIK rather than their lack of an SSN. If we apply this rate of “missingness” to the number of 

matched one-person households, we find that we would be missing fewer than 300 households. This would raise the 
among SNAP administrative units than among simulated units, offsetting the difference in one-person units. Units of 

size four or larger are about equally common among simulated and actual SNAP units. 

Table III.6. All Matched New York ACS Households Compared to Simulated SNAP Units and 

Administrative SNAP Units 

Household/Unit Characteristic 

ACS 

Households 

Matched 

with New 

York Data 

All SNAP 

Units 

Simulated 

with ERS 

Algorithm 

Eligible 

SNAP Units 

Simulated 

with ERS 

Algorithm 

SNAP Units 

from New 

York 

Administrative 

Data 

Total 19,586 29,852 18,463 21,159 

Household/SNAP unit size 

1 28.4 58.1 58.1 52.9 

2 21.7 18.1 16.8 19.7 

3 17.2 9.7 9.9 12.6 

4 13.8 7.4 7.6 7.6 

5 9.1 3.9 4.1 3.7 

6 or more 9.8 2.9 3.5 3.4 

Household/unit contains: 

Children (birth to 17) 43.9 29.7 32.0 38.3 

Age: birth to 4 19.8 13.1 14.8 17.8 

Age: 5 to 17 36.3 24.3 26.3 30.8 

Children only 0.0 0.1 0.1 4.4 

Non-elderly adults (age 18 to 59) 77.5 72.5 69.6 65.8 

Non-elderly disabled members (birth to 59) 23.8 17.1 21.4 19.7 

Elderly adults (age 60 or older) 41.7 29.9 32.4 32.9 

Living alone 17.1 22.2 25.6 25.1 

Source: Mathematica Policy Research, from linked 2012 ACS and New York SNAP administrative data. 

Demographic composition is of particular interest given that simulated units have tended to differ from SNAP 

quality control units in being more likely to have children and more likely to have elderly members (60 or older), but 

less likely to consist of an elderly person living alone. With respect to children, we find the opposite, with 

administrative units being more likely to have both preschool and school-age children than simulated units—

although, less likely than the ACS household. Consistent with prior findings, however, there are virtually no childonly units among the simulated SNAP units and effectively no child-only households—yet, child-only units 

represent 4.4 percent of all SNAP administrative units in New York State. 

IV. Additional Findings from the ACS: Colorado

In reviewing results from the ACS Colorado SNAP match, we were particularly interested in how they differ, if at 

all, from those obtained with the ACS New York SNAP match. One difference that we observed immediately is that 

matched ACS household members made up a smaller share of all ACS household members in the households with 

matches than they did in New York. Specifically, 61.5 percent of the ACS household members in Colorado were 

matched (Table IV.1). In New York, this figure was 68.5 percent. The fraction of household members without a 

PIK, 5.3 percent, was essentially the same as in New York, while the overall PIK rates for New York and Colorado 

SNAP administrative records were about the same (Tordella et al. 2014). Therefore, the lower fraction of matches 

cannot be attributed to a lower PIK rate. 

The proportion of SNAP records added through direct matches to ACS records rather than through a SNAP case 

affiliation—89.0 percent—was also lower than in New York, but by only 2 percentage points (Table IV.2). We also 

 

percentage share of one-person households to 29.5 percent while raising the percentage shares of simulated and 

actual SNAP units by somewhat smaller fractions, given their larger totals. 
found that a slightly smaller fraction of administrative records matched in the interview month versus the two prior 

months: 93.2 percent in Colorado and 95.8 percent in New York. Compared to the direct matches, a smaller 

fraction—89.4 percent—of the SNAP records added through case affiliations came from the interview month.

Table IV.1. Distribution of ACS Household Members by Match Result to Colorado SNAP Administrative 

Data 

Match Result Number Percentage 

All ACS household members 9,214 100.0 

Matched a Colorado SNAP administrative record 5,666 61.5 

Did not match a Colorado SNAP administrative record 3,548 38.5 

Has a PIK 3,064 33.3 

Does not have a PIK 3 5.3 

Source: Mathematica Policy Research, from linked 2012 ACS and Colorado SNAP administrative data. 

Table IV.2. Distribution of Colorado SNAP Administrative Records by Match Result to the ACS and by 

Match Month 

Match Status Number Percentage 

Colorado SNAP unit members in enhanced ACS households 6,367 100.0 

Matched an ACS record 5,666 89.0 

Did not match an ACS record 701 11.0 

Has a PIK 696 10.9 

Does not have a PIK 5 0.1 

Records matched to ACS records 5,666 100.0 

Matched in ACS interview month 5,282 93.2 

Matched one month prior to ACS interview month 247 4.4 

Matched two months prior to ACS interview month 137 2.4 

Records not matched to ACS records, in same Colorado SNAP unit 701 11.0 

Same SNAP unit in ACS interview month 627 89.4 

Same SNAP unit one month prior to ACS interview month 45 6.4 

Same SNAP unit two months prior to ACS interview month 29 4.1 

Source: Mathematica Policy Research, from linked 2012 ACS and Colorado SNAP administrative data. 

Distributions of simulated and actual SNAP units in Colorado households were similar to what we found in New 

York. Households with a single SNAP administrative unit and a single simulated SNAP unit were slightly more 

common in Colorado than they were in New York: 94.5 percent of households in Colorado had a single SNAP 

administrative unit and 68.3 percent had a single simulated unit (Table IV.3). In New York, these two figures were 

92.8 percent and 66.1 percent, respectively (Table III.3). When we restricted the simulated units to those that were 

eligible, the percentage of households with no simulated units was about 9 percentage points higher than New York: 

31.9 percent in Colorado versus 23.0 percent in New York (Table IV.4). Likewise, the fraction of households with 

one unit was 6 percentage points lower than in New York, and the fraction with two or more simulated units was 3 

percentage points lower.

The distribution of matched sample households by the match results for their members reveals some notable 

differences from New York. In Colorado, 42.7 percent of the matched households had all of their ACS members 

matched, while 40.1 percent of matched households also had all of their SNAP administrative unit members 

matched (Table IV.5). The comparable figures for New York were 55.6 percent and 52.2 percent, respectively—

substantially higher. Among the Colorado households with at least one unmatched ACS member (57.3 percent of the 

total), four-fifths (or 46.3 percent of the total) had no unmatched administrative SNAP unit members. The 

corresponding figures for New York were 44.4 percent and 37.2 percent, respectively. Overall, 13.7 percent of the 

SNAP unit members in Colorado were unmatched compared to 10.6 percent in New York. 
Table IV.3. Prevalence of Multiple SNAP Units in Colorado ACS Households: All Simulated Units Included 

Administrative SNAP Units with 

Matches to the Same ACS 

Household 

Total ACS 

Households 

Simulated SNAP Units Within the Same ACS Household 

One Two Three Four 

Five or 

More 

Number 2,899 1,979 642 200 56 22

One 2,741 1,944 572 167 43 15

Two or more 158 35 70 33 13 7 

Percentage of total ACS households 100.0 68.3 22.1 6.9 1.9 0.8 

One 94.5 67.1 19.7 5.8 1.5 0.5 

Two or more 5.5 1.2 2.4 1.1 0.4 0.2 

Source: Mathematica Policy Research, from linked 2012 ACS and Colorado SNAP administrative data. 

Table IV.4. Prevalence of Multiple SNAP Units in Colorado ACS Households: Only Eligible Simulated Units 

Included 

Administrative SNAP Units with 

Matches to the Same ACS 

Household 

Total ACS 

Households 

Simulated SNAP Units Within the Same ACS Household 

None One Two Three Four 

Five or 

More 

Number 2,899 926 1,660 262 37 14 2,899 

One 2,741 877 1,621 211 22 10 2,741 

Two or more 158 49 39 51 15 4 158

Percentage of total ACS households 100.0 31.9 57.3 9.0 1.3 0.5 100.0 

One 94.5 30.3 55.9 7.3 0.8 0.3 94.5 

Two or more 5.5 1.7 1.3 1.8 0.5 0.1 5.5 

Source: Mathematica Policy Research, from linked 2012 ACS and Colorado SNAP administrative data. 

Table IV.5. Match Status of ACS Household Members and Colorado SNAP Administrative Unit Members 

Match Status Summary Number Percentage 

ACS households containing matched person 2,889 100

All ACS household members matched 1,237 42.7 

All administrative unit members matched 1,163 40.1 

Some administrative unit members not matched 74 2.6 

Some ACS household members not matched 1,662 57.3 

All administrative unit members matched 1,342 46.3 

Some administrative unit members not matched 320 11.0 

Source: Mathematica Policy Research, from linked 2012 ACS and Colorado SNAP administrative data. 

Compared to New York, SNAP households and simulated units in Colorado are larger. Also, SNAP administrative 

units have a slightly higher proportion of larger units than in New York, but the proportion of units that consist of 

just one person is slightly larger than in New York as well, at 55.7 percent versus 52.9 percent (Table IV.6). Also, 

unlike New York, one-person units are more common among SNAP administrative units than among simulated

units in Colorado, where they represent 50.9 percent of all simulated units and 52.9 percent of simulated eligible 

units. 

Because of Colorado’s generally younger population, there are notable differences in the age distribution of SNAP 

beneficiaries and the members of SNAP households. Differences between SNAP households, simulated units, and 

SNAP administrative units are similar to what we saw in New York, however. Compared to New York, SNAP 

households and units are more likely to include children and much less likely to include elderly adults. For example, 

54.0 percent of Colorado ACS households and 44.7 percent of SNAP administrative units have children under 18. In 

New York, these fractions are 43.9 percent and 38.3 percent, respectively. Similarly, child-only units represent 9.3 
percent of all SNAP administrative units in Colorado, compared to 4.4 percent in New York.8

 Units with elderly 

adults make up only 18.2 percent of the administrative units in Colorado, compared to 32.9 percent in New York. 

Elderly adults living alone are 15.6 percent of the SNAP administrative units in Colorado versus 25.1 percent in 

New York. Within Colorado as in New York, the SNAP administrative units are more likely to contain school-age 

children and, very slightly, younger children than the simulated units. However, the SNAP administrative units are 

less likely to contain school-age children than SNAP households. In Colorado, elderly adults living alone are most 

common among simulated eligible units at 17.8 percent, compared to 15.6 percent among actual SNAP units. 

Table IV.6. All Matched Colorado ACS Households Compared to Simulated SNAP Units and Administrative 

SNAP Units 

Household/Unit Characteristic 

ACS 

Households 

Matched with 

Colorado 

Data 

All SNAP 

Units 

Simulated 

with ERS 

Algorithm 

Eligible 

SNAP Units 

Simulated 

with ERS 

Algorithm 

SNAP Units 

from Colorado 

Administrative 

Data 

Total 2,899 4,221 2,359 3,075 

Household/SNAP unit size 

1 23.2 50.9 52.9 55.7 

2 20.0 17.6 15.7 14.3 

3 18.0 11.5 11.4 12.3 

4 15.7 9.5 8.8 8.7 

5 11.1 6.2 6.8 5.5 

6 or more 12.0 4.3 4.5 3.4 

Household/unit contains: 

Children (birth to 17) 54.0 38.1 38.9 44.7 

Age: birth to 4 26.6 18.4 20.0 21.3 

Age: 5 to 17 43.4 30.4 30.8 35.6 

Children only 0.0 0.1 0.0 9.3 

Non-elderly adults (age 18 to 59) 86.5 81.5 79.4 73.8 

Non-elderly disabled members (birth to 59) 28.7 21.9 26.9 22.5 

Elderly adults (age 60 or older) 28.0 20.7 22.3 18.2 

Living alone 10.5 14.8 17.8 15.6 

Source: Mathematica Policy Research, from linked 2012 ACS and Colorado SNAP administrative data. 

V. Summary and Implications

In this final chapter, we summarize our principal findings and discuss their implications for SNAP unit simulation 

and the potential use of SNAP administrative records in decennial census enumeration. 

A. Summary of Findings 

Our principal findings from the analysis of linked survey and administrative SNAP data are the following: 

 A substantial fraction of households receiving SNAP benefits—between 44 percent and 47 percent in New 

York State and 57 percent in Colorado—included persons who were not recipients. 

 More than 1 in 10 households receiving SNAP benefits had participants identified through administrative 

records who were not identified as members of the survey household. Most of these households had 

unmatched survey household members as well, suggesting that incomplete matches may have contributed 

to these first two findings. 

 

8

 This striking difference is similar to what was recorded in the 2013 SNAP QC data, where child-only units were 

8.2 percent of the total in Colorado and 5.4 percent of the total in New York.
 A very substantial majority of households receiving SNAP benefits in New York—93 percent—contained 

only one SNAP unit, while nearly all of the remaining SNAP households included only two SNAP units. 

 Simulation of SNAP units in households receiving SNAP benefits produced a greater incidence of multiple 

SNAP units—nearly a third of all SNAP households—than was observed with linked administrative data. 

 In nearly a quarter—23 percent—of the households receiving SNAP benefits in New York and nearly a 

third—32 percent—in Colorado, no one was simulated to be eligible for SNAP. 

 Simulated SNAP units were very similar to actual SNAP units in their distribution by size. In New York, 

however, one-person units were more common among simulated than actual SNAP units, while the reverse 

was true in Colorado. 

 SNAP units containing elderly adults, including those living alone, were about equally common among 

administrative and simulated eligible units in New York but somewhat more common among simulated 

eligible units in Colorado. 

 Using the entire household as a proxy for the SNAP unit yields too few units overall and far too few oneperson units. 

These findings carry implications for the simulation of SNAP units and also for ways in which SNAP administrative 

data might be useful in improving decennial census enumeration. These implications are discussed below. 

B. Implications 

This research has focused on SNAP unit composition and the correspondence between actual and simulated SNAP 

units, so the primary implications involve ways to improve the simulation of SNAP units. However, findings 

regarding SNAP members who do not appear to have been counted in the survey household have implications for 

household rostering in both surveys and the decennial census. We consider such implications after discussing how 

our findings bear on SNAP simulation. 

1. SNAP Simulation 

A widespread perception among those modeling SNAP eligibility has been that SNAP households often contain 

multiple units. This appeared the most plausible way to reconcile the numbers of one-person SNAP units with the 

numbers of eligible one-person households. In linking SNAP units to survey households, however, we found that 

households with a single SNAP unit accounted for the vast majority of households receiving SNAP but that SNAP 

households often have members who are not receiving SNAP benefits. These two findings suggest a change in 

tactics toward finding single eligible subunits in more households and away from searching for multiple eligible 

subunits within each household. 

2. Census Enumeration 

Most of the implications that we see for census enumeration bear further research. These implications depend upon 

researchers first establishing whether or not the New York and Colorado SNAP data correctly identify household 

residents missed by major household surveys conducted by the Census Bureau. If omissions from survey household 

rosters can be confirmed, then whatever can be learned about the causes may suggest ways to reduce such omissions 

in the future. Because the SIPP follows sample members over time and collects data on all of the persons with whom 

they share households, further research with the linked SIPP and SNAP administrative data will be useful in 

determining what role timing may play in the correspondence between survey and administrative households. 

 
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