Would You Like Paper or Plastic? Food StampsDisbursement Method and Take-up∗

Steven Bednar†

March 7, 2011

Abstract

The Food Stamp Program (FSP) is a federal program whose purpose is toprovide a safety net so that households with low incomes can maintain a nutritiousdiet. In the 1990s, every state and the District of Columbia switched from issuingpaper stamps to using an Electronic Benefit Transfer (EBT) debit card to disbursebenefits. It is possible that individuals are less stigmatized using EBT than paperstamps. If this is true, then controlling for other factors and conditioning oneligibility, there should be a change in food stamp take up rates when states adoptthis change. Using yearly data from the Panel Study of Income Dynamics (PSID),I find that when states switch to EBT there is a positive, yet insignificant effecton take up rates for single mothers and a negative but insignificant effect for nonsingle mothers. It appears as though stigma is not an important factor in take uprates or that the decrease in food stamp trafficking opportunities due to the nowindivisible nature of benefits offsets any decreased stigma effects.

∗Preliminary and incomplete; comments welcome. Please do not quote without permission. I thankEbonya Washington, Hanming Fang and Matthew Johnson for helpful discussions. All remaining errorsare my own.†Department of Economics, Elon University. E-mail: sbednar@elon.edu

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1 Introduction

The Food Stamp Program (FSP) provided more than $25 billion worth of benefits in

2005 to over 21 million individuals per month. The FSP is the largest federal food safety

net program, yet as Figure 1 shows, estimated take up rates are below 60% for those

who are eligible.1 An eligible household might not participate in FSP for several reasons.

Bartlett and Burstein (2004) estimate that most eligible households know about FSP,

but half of the non-participants do not think they are eligible. These families tend to

have higher incomes. Alternatively, the benefits from taking up food stamps might be

less than the transaction costs. For example, some families are only eligible for $10 a

month in food stamp benefits while Ponza et al. (1999) estimates that the average out-of-

pocket application expense is $10.31 in 1999. Daponte, Sanders and Taylor (1999) note

that the median benefits of the eligible non-participating households is well below that of

participating households. Finally, stigma associated with using food stamps might deter

participation.2 This paper asks whether an exogenous change to the benefit disbursement

method which likely decreases stigma increases take-up rates of eligible individuals. That

is, does stigma keep some individuals from utilizing government assistance.

There are at least two types of stigma. Stuber and Kronebusch (2004) describes

them as identity and treatment stigma. Some individuals may not want to be identified

with being a user of a welfare programs. Others might seek to avoid negative treatment

that is caused directly by participation in a welfare program. When paying for groceries

using paper food stamps, the cashier and those in line at the grocery store will see that

the person uses a welfare program and might treat them with disrespect. A household

member might be embarrassed to be seen using food stamps even though they do not

1Blank and Ruggles (1996) find that there are many short spells of eligibility that do not result inparticipation.

2Moffitt (1983) first models the idea that there are psychological costs, or stigma, associated withwelfare.

2

find anything wrong with using government assistance to purchase food (Breunig and

Dasgupta 2003).

Two major policy changes relating to the food stamp program occur during the

1990s. The first is the change from issuing paper stamps to an electronic benefits transfer

(EBT) card to deliver food stamps benefits. Between 1993 and 2004, every state and

the District of Columbia implemented the change. The second is the passage of the

Personal Responsibility and Work Opportunity Reconciliation Act of 1996 (PRWORA).

This major welfare reform affected the FSP and its consequences are detailed below.

The introduction of EBT could directly decrease or eliminate treatment stigma.

EBT is similar to a debit style gift certificate. The user swipes a card through the same

machine at the grocery store as those who pay with a credit or debit card, enters a

personal identification number, and benefits are reduced from the card by the amount of

the purchase. The card is automatically credited with benefits each month and can be

used to purchase the same items as paper food stamps3. Transactions made using EBT

are nearly indistinguishable from those made using a debit or credit card. If treatment

stigma is a reason why some eligible households do not take up food stamps and they live

in a state that adopts EBT, then the change could induce them to participate. According

to the Food and Nutrition Service (FNS) of the U.S. Department of Agriculture (USDA),

“surveys have shown that most participants prefer an EBT system to the paper coupon

system it replaced. Many recipients have said that EBT reduces the stigma associated

with food stamp coupon use.” However, if a household does not know about the switch

to EBT or that they are eligible for food stamps to begin with, then this change should

have no effect.

EBT will not necessarily affect identity stigma. Even though payments are made

using something similar to a debit card, the individual still relies on government as-

3Food stamp benefits may not be used to purchase alcohol, tobacco, or pre-made hot meals.

3

sistance. Any personal feelings about being a food stamp user should not change no

matter how technologically updated the payment procedure becomes. While others in

line will no longer know that a payment is made using food stamps, the user will still

know. Identity stigma may change during especially difficult economic times or when

the participation rate is higher. I account for both of these pieces of information in my

analysis. All else equal, higher unemployment and participation rates are associated

with a greater probability of take-up.

Welfare reform in 1996 also plays an important role in food stamps usage. Many

households that previously received cash assistance became ineligible under the new

regime, Temporary Assistance for Needy Families (TANF). Food stamps were bundled

together with other government assistance, so when TANF was introduced many house-

holds that no longer qualified for welfare were dropped from the food stamp rolls as well.

Many of these households were still eligible for food stamps even though they were not

eligible for TANF, but were not informed of their eligibility (Heflin 2004). Zedlewski

and Brauner (1999) find that declining participation in welfare programs directly con-

tributed to the fall in FSP participation among families with gross income below 130%

of the federal poverty level. The effect was particularly noticeable at levels of income

less than 50% of the poverty level. I include recertification intervals as an explanatory

variable as these change with the introduction of TANF.

Figure 2 shows individual participation in FSP for the period 1990-2004 as well as

the national unemployment rate over these years. FSP participation fell after welfare

reform in 1996, but the unemployment rate had been falling steadily since 1992. While

some eligible individuals were dropped from the rolls, the improving economy was a

large factor in the declining participation (Cunnyngham 2004).

There are three main strategies in the literature for examining why seemingly eligi-

ble households do not take up food stamps. The first is to survey potentially eligible

4

households and ask questions geared towards stigma and information effects. Bartlett

and Burstein (2004) among others suggests that stigma plays an important role in the

take up decision. In 1980 and 1987 the Panel Study of Income Dynamics (PSID) de-

termined which individuals were eligible for food stamps but did not participate. Most

of the nonparticipants did not think that they were eligible for food stamps. Of those

that did, the most common reasons for nonparticipation were desiring independence,

administrative hassles and stigma (Coe 1983, Coe and Hill 1998).

The second strategy is to run an experiment. Daponte et al. (1999) advised a group

of non-participants about their eligibility and potential benefits then observed differences

in the behavior from a control group that was not given any information. They conclude

that there are two major reasons why seemingly eligible families do not take up food

stamps. The first is that many are actually ineligible. The second is that families simply

do not know that they are eligible and for how much they are eligible. They find no

correlation between perceived and actual eligibility in their data, so asking a family why

they do not take up food stamps could be misinterpreted.

The third strategy is to test empirically for the effects of exogenous changes on take up

rates. Currie and Grogger (2001) examine take up decisions for households with incomes

less than 300% of the poverty level during the period 1990 to 1998. They find that the

introduction of TANF has a significant negative impact on food stamp take up. The

adoption of EBT has only marginally significantly positive effects for married households

without children and in rural areas. Manchester and Mumford (2009) builds a structural

model in the style of Keane and Moffitt (1998) to tease out the psychological and time

costs of welfare participation. They find that psychological costs are much greater than

time costs for the FSP. In monetized terms, individuals face roughly $19 in psychological

costs a week from participating in the FSP. They use the 1997 sample of the SIPP and

do not differentiate between paper stamps and EBT disbursement methods.

5

This paper tests empirically whether stigma plays an important role in why some

eligible households do not participate in the FSP. I use the variation in implementation

of EBT across states to test whether the reduced stigma increases participation rates.

I argue that the timing of the change is an exogenous process. This paper extends the

literature in several ways. First, I use the actual rules of the FSP to compute eligibility.

When I construct a sample in the manner of Currie and Grogger (2001), the number

of seemingly eligible households triples yet only 3% of these additional households ever

report usage. It is possible, then, that Currie and Grogger (2001) find no effect of EBT

on take-up because most of their sample is never eligible so they cannot use food stamps

even when EBT is introduced. Second, conditional on eligibility I estimate the amount of

benefits that a household will be eligible for, once again based on the actual rules which

vary by state, year and household size. Currie and Grogger (2001) assign the maximum

value to each household based on the state, year and household size. My sample runs

from 1996-2003, when nearly all of the states switch to EBT. I find that the probability

of take-up is unchanged with the introduction of EBT. I argue that either stigma is not

reduced as claimed or that the decrease in food stamp trafficking opportunities due to

the now indivisible nature of benefits offsets any decreased stigma effects.

The rest of the paper is organized as follows; Section 3 describes the data, Section 4

details the methodology, Section 5 gives the results and Section 6 concludes.

2 Introduction of EBT

In November 1989, the state of Maryland launched its EBT pilot program. In 1993 it

had become the first state to use EBT exclusively. As part of welfare reform in 1996,

every state was mandated to issue benefits via EBT state-wide by 2002. A few states

applied for and received extensions until 2004. The FSP was renamed Supplemental

6

Nutrition Assistance Program in 2008 and official documents no longer refer to stamps

when describing the program. Figure 3 shows the percent of benefits that are disbursed

using EBT from 1991-2003. Between 1996 and 2003 the program went from issuing

12% to 95% of all benefits with EBT. Timing of the introduction of EBT is taken to

be exogenous in this study. To test this assumption, I follow Hoynes and Schanzenbach

(2010) and use state-wide characteristics from the 1990 census to predict the timing of

the adoption of EBT. The dependent variable is the number of days since Maryland

began its pilot program until the given state began its pilot. I also track the number

of days it took the state to implement the program state-wide. I regress these variables

on median income, the poverty rate, the unemployment rate and the percent of the

state that is black. Results are displayed in Table 1. Positive coefficients indicate later

predicted implementation dates. None of the coefficients are significant and as a group

explain very little of the variation. While the standard errors are large, possibly due to

the small sample size, I take this as evidence that state demographics and underlying

economic conditions did not affect implementation of EBT.

3 Data

I use data from the Panel Study of Income Dynamics (PSID) for the years 1997, 1999,

2001, and 2003.4 The survey is conducted biannually but contains information for the

current and previous year. The PSID over samples low income households so my results

are weighted to make the sample representative of the population. I also use data from

the US Department of Agriculture to define when each state begins the pilot program

of EBT and when each state uses EBT exclusively. It is not possible to know for sure

whether a household has access to EBT in the period between the pilot program initiation

4The PSID does not have data necessary to calculate potential benefits before 1997. Other notesabout the data can be found in Appendix 6.1

7

and when the program is implemented statewide. I report results only for when EBT

has gone statewide by the beginning of the year. The main results are unchanged if I

instead assume that individuals have access to EBT if the pilot program was initiated

by the beginning of the year.

By focusing on 1997-2003 I potentially ignore the information effects of welfare re-

form5. Households dropped from food stamp rolls due to the implementation of TANF

should have had enough time to find out if they are indeed still eligible for food stamps.

I include year fixed effects to capture lingering unobserved effects. I exploit more vari-

ation of the change from paper stamps to EBT than any of the previous studies. Only

five of the states had implemented EBT by the beginning of 1997. Figure 3 shows the

percent of food stamp benefits delivered with EBT from 1991 to 2003. Before 1997, only

15% of benefits were delivered with EBT. By 2004 paper stamps had been completely

eliminated. So, there is a considerable amount of variation with which to identify the

effect of EBT on take up.

It is also possible that there are network effects. A household might be less stigma-

tized if there are many other households that use food stamps and the cost of receiving

information about FSP will likely be smaller if there are more households in the neigh-

borhood that use food stamps. The PSID reports location at the state level. I include

estimated participation rates at the state level from the USDA. These are estimates

because the number of eligible individuals is calculated from the Current Population

Survey.

One limitation of the data is that annual income is reported while food stamp eligi-

bility is calculated using monthly income. I divide annual income by 12 to get a measure

of monthly income. It is therefore possible that I incorrectly classify some households

5In Appendix 6.2 I discuss how my results vary if I omit data from 1997, creating further distancefrom welfare reform.

8

as ineligible. There are data sets that have information on income at the monthly level,

but individuals are not followed for as many years as those in the PSID.

4 Methodology

4.1 Estimating Eligibility and Calculating Benefits

I calculate eligibility based on two income tests and an asset test as described in Ap-

pendix 6.3. Table 2 provides eligibility checks on the data. The estimated take up rates

for the full sample are on the low end of the spectrum in the literature. This could be

due to using only information provided by the household head. If some members of the

household other than the household head use food stamps then I underreport take up.

When I restrict my sample to single mothers the take up rates across all years is 62%,

which is closer to what is found in the literature.6

Given a household is eligible for food stamps, benefits are calculated by subtracting

30% of net income from a maximum benefit level which depends on household size.

Benefits are provided in order that every household can spend at least 30% of the

poverty level of income on food.

4.2 Estimation Technique

I restrict my sample to households that were eligible in at least one of the sample years:

1997, 1999, 2001, and 20037. This leaves me with 1970 observations. My empirical

model takes the following form:

6Blank and Ruggles (1996) estimate take up rates around 55% for single mothers between 1996 and1999.

7Results from using a balanced panel are nearly identical.

9

Takeupist = γEBTst +Xistβ + STstθ + Statesα1 + Y eartα2 + εist (1)

where EBT is a dummy variable that equals one if the EBT had been implemented

statewide by the beginning of the year.8 X is a vector of demographic variables. It

includes amount of estimated benefits, age, gender, race and education. ST includes the

statewide participation rate, recertification interval in months and outreach spending

measured in dollars per eligible non-participant per year. Individuals might have more

access to information and feel less stigma through network effects the greater the par-

ticipation rate. The discrete and differential changes in disbursement method allow for

the inclusion of state effects. Finally I include dummy variables for the years to control

for lingering effects of welfare reform.

5 Results

Table 3 displays the regression results. The top panel does not include state or year

effects while the bottom panel does include them. All regressions are weighted by the

PSID family weights and the standard errors are clustered at the state-year level. I

split the sample into several groups that may be differentially affected by the switch to

EBT. The groups are single mothers, singles without children, married couples with and

without children and households where the head is aged sixty or older. Single mothers

with low income are often eligible for other welfare programs. There is evidence that the

stigma cost is only paid once (Moffitt 1983, Keane and Moffitt 1998), so it is possible

that changes in disbursement method or certification periods will have less of an effect

8Tables where EBT equals one if the pilot program had begun by the beginning of the year areavailable upon request. The substantive results are unchanged.

10

on this group.

EBT is negative and statistically significant at the 5% level for households headed

by an individual older than age sixty. It is possible that older food stamps users prefer

the paper payment method to a more technological based payment method. While

the coefficient on EBT is not statistically significant for any of the other subgroups,

it is positive when children are present and negative when there are no children in

the household. It is possible that stigma plays a larger role for individuals or families

with children. As I discuss below, EBT decreases black market resale opportunities,

which may be more of a motivating factor for groups without children. It has been

documented that take-up is positively associated with the amount of benefits (e.g. Dahan

and Nisan 2010, Daponte et al. 1999). I find this to be consistent across all groups.

Higher statewide participation rates could lead to even greater take-up for several

reasons. First, the larger the network of individuals taking benefits the more likely it

is that individuals will have the necessary information to apply to program. Secondly,

individuals might feel less stigmatized if there are many others who take food stamps

benefits. I find that singles without children are the only group that is positively and

significantly affected by greater statewide participation. Outreach spending per person

only has a positive impact on married households without children. Estimated take-up

rates for married households without children is extremely low at 12%. While it is not

clear what specific outreach programs were funded, encouraging participation by a few

members of this group could have a large impact on take-up rates. Other than EBT

implementation dates, states control recertification intervals. These are typically shorter

for individuals with positive earnings (seven months on average across my sample) and

longest for individuals aged sixty or older (thirteen months on average). Longer recerti-

fication intervals do not seem to influence take-up decisions. It is likely that individuals

do not learn about these requirements until after they have decided to apply for benefits

11

in the first place.

Female and black heads of households are also more likely to take-up food stamps

across all groups and as a whole, take-up rates are lower for older individuals. This has

been documented before (Haider, Jacknowitz and Schoeni 2003). Throughout all of the

specifications, high school dropouts are more likely to take-up food stamps than those

who have received at minimum a high school diploma. It is possible that high school

dropouts have lower incomes on average which would lead to greater benefits.

I do not find any evidence that the introduction of EBT increased take-up rates in

the FSP. One consequence of EBT is that it is more difficult to sell benefits on the black

market. Swiping the card leaves an electronic mark for how much food is purchased which

increases the effectiveness in auditing store owners. Secondly, benefits are less divisible

than they are in paper stamp form. If an individual were to sell twenty percent of their

benefits, they would either need to accompany the person with which the trade is made

to the store or trust them with the card and pin number. Benefits are automatically

added each month which means that once someone has control of a card and the pin

number they can receive all of the benefits until recertification is required. According

to estimates from The Office of Analysis, Nutrition and Evaluation, trafficking declined

as more states adopted EBT. Some households might find the recertification process no

longer worth the trouble if they cannot resell their stamps for cash. Whitmore (2002)

presents survey estimates that food stamps can trade on the black market for about

65% of their face value, but also notes that it is much more difficult to do so with EBT.

Evidence is also presented that on average individuals only value their benefits at 80%.

That is, they would prefer to consume less food than they have benefits. It is possible,

therefore, that any decrease in stigma is offset by an increase in the difficulty to redeem

the benefits for cash.

In Table 4 I show the results of the following aggregate participation model:

12

ln(Participation)st = α1Uratest +Wstα2 + εist (2)

where Participation is the statewide participation rate (number of individuals par-

ticipating divided by estimated number of eligible individuals), Urate is the state-year

unemployment rate and W is a vector of time-varying aspects of the welfare policies,

including whether the EBT pilot program has begun, outreach spending per eligible

non-participant and recertification intervals.9

I find that the introduction of EBT has a small negative but significant impact on

statewide participation rates. Outreach spending and longer recertification periods have

modest positive effects on participation while a higher unemployment rate also leads

to greater participation. The effect of EBT is much smaller but positive, and only

significant at the 10% level when state and year effects are included.

Finally, I check for a mobility effect. Meyer (2000) finds modest effects of welfare

recipients moving to states that offer greater benefits. If many households move from

states that use paper stamps to those that use EBT, this would imply that stigma is

vastly important. However, of those eligible for food stamps only four households move

across states during my entire sample and only one of them moves from a paper stamp

state to an EBT state. The four households move to states where relatives reside. I

cannot, however, say how attrition from the would affect this analysis.

9(Currie and Grogger 2001) reports similar results and notes that this is what is mainly done in theliterature.

13

6 Conclusion

If stigma is a reason why some households do not take up food stamps and EBT re-

duces stigma, then conditioning on eligibility, EBT should have a positive effect on the

probability of take up. I do not find this to be the case. There are two interpretations

for why EBT does not have a significant effect empirically. The first is that treatment

stigma does not affect participation decisions. If this is the case then reducing stigma

should not necessarily be listed as something positive that has come from the introduc-

tion of EBT as it does not increase participation rates of low income households. The

second interpretation is that any decrease in stigma is offset by a decrease in trafficking

opportunities as benefits are less divisible with a card than with stamps.

This paper conditions on eligibility instead of just assuming that all families with

gross income below 300% or even 130% of the poverty level are eligible for food stamps.

When I repeat the analysis by assuming that all households with income less than 300%

of the poverty level are eligible for the maximum amount of benefits, the coefficient on

EBT is closer to zero and the standard errors are smaller. This is due to the larger sample

size and the fact that two-thirds of the households are not eligible for foodstamps. More

variation in the change from paper stamps to EBT is exploited than any other paper in

the literature. I also addresses possible endogeneity of the introduction of EBT and find

no evidence that EBT is implemented based on state characteristics.

The empirical nature of this paper could benefit from using data collected with

higher frequency, such as the SIPP. The SIPP, however, covers a much shorter time

period. Income needs to be measured accurately in order for eligibility and benefits to

be calculated correctly. Any over reporting of income will result in under calculating

eligibility and benefits. It would be nice to control for neighborhood effects. It is

possible that information problems will be less prevalent in neighborhoods where many

14

households use food stamps. Stigma could be less of a problem as well. In this study I

can only control for participation at the state level.

15

References

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2004,” Mathematical Policy Research, Inc. for the Food and Nutrition Service, U.S.

Department of Agriculture, 2004.

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Nonparticipants,” Abt Associates, Inc. for the Economic Research Service, U.S.

Department of Agriculture, 2004.

Blank, Rebecca and Patricia Ruggles, “When do women use AFDC & food stamps?

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Breunig, Robert and Indraneel Dasgupta, “Are People Ashamed of Paying with

Food Stamps?,” Journal of Agricultural Economics, 2003, 54 (2), 203–225.

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Case of the Food Stamp Pro,” Journal of Economics Issues, 1983, 17 (4), 1035–

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and Daniel H. Hill, “Food Stamp Participation and Reasons for Nonparticipa-

tion: 1986,” Journal of Family and Economics Issues, 1998, 19 (2), 107–130.

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2002,” 2004.

Currie, Janet and Jeffrey Grogger, “Explaining Recent Declines in Food Stamp

Program Participation,” Brookings Papers on Urban Affairs, 2001, pp. 203–244.

16

Dahan, Momi and Udi Nisan, “The Effect of Benefits Level on Take-Up Rates:

Evidence from a Natural Experiment,” International Tax and Public Finance, 2010,

12 (2), 151–173.

Daponte, Beth Osbourne, Seth Sanders, and Lowell Taylor, “Why Do Low In-

come Households Not Use Food Stamps?: Evidence From an Experiment,” Journal

of Human Resources, 1999, 34 (3), 612–628.

Haider, Steven J., Alison Jacknowitz, and Robert F. Schoeni, “Food Stamps

and the Elderly Why is Participation so Low?,” The Journal of Human Resources,

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the Food Stamp Program,” NBER Working Paper 16198, 2010.

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2000. Working Paper.

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view, 1983, 73 (5), 1023–1035.

17

Ponza, Michael, James C. Ohls, Lorenzo Moreno, Amy Zambrowski, and

Rhoda Cohen, “Customer Service in the Food Stamp Program: Final Report,”

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icy Research, Princeton, NJ, 1999.

Stuber, Jennifer and Karl Kronebusch, “Stigma and Other Determinants of Par-

ticipation in TANF and Medicaid,” Journal of Policy Analysis and Management,

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Whitmore, Diane, “What Are Food Stamps Worth?,” Princeton University Industrial

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Federalism. Washington: Urban Institute, 1999.

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Figure 1: Participation Rates of Eligible Individuals in the Food Stamps Program

50

55

60

65

70

75

80

1994

1995

1996

1997

1998

1999

1999

2000

2001

2002

2003

2004

Part

icip

atio

n ra

te (%

)

National FSP Participation Rate

Source: Barrett and Poikolainen (2004)Usage taken from administrative rolls. Number of eligible individuals estimated from the CurrentPopulation Survey (CPS). Household participation rates follow the same trend but are roughly fivepercentage points below individual participation rates in each year.

19

Figure 2: Food Stamps Program Participation

3%

4%

5%

6%

7%

8%

15

17

19

21

23

25

27

29

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

Food

Sta

mp

Part

icip

atio

n (m

illio

ns o

f ind

ivid

uals

)

Food Stamp Usage and Unemployment

Food Stamp Participation Unemployment Rate

FSP participation from FNS National Level Annual Survey. National unemployment data is fromFred2 database at the St. Louis Fed.

20

Figure 3: Percent of Food Stamp Benefits Disbursed Using EBT

FNS report to congress, October 2003 http://www.fns.usda.gov/fsp/ebt/pdfs/2003_congress.pdf

21

Table 1: Determinants of EBT Pilot Start Date and State-wide Implementation

Pilot Implementation State-wide Usage

Median Income -0.033 -0.034(0.073) (0.056)

Poverty Rate -7176.376 -9110.839(16009.344) (12266.517)

% Rural 1374.852 677.267(1562.715) (1197.368)

% Black 188.675 -109.726(1874.655) (1436.379)

Unemployment Rate 146.411 171.761(241.052) (184.696)

Constant 4128.943 3749.045(4326.317) (3314.867)

R2 0.061 0.051N 51 51Note: Independent variable is the number of days to implement the pilot programor to use EBT state wide.

22

Table 2: Eligibility Calculations

All Single Mothers

Ineligible, no reported benefits (%) 87.7 59.2Ineligible, reported benefits (%) 1.0 4.6Eligible, no reported benefits (%) 8.3 15.4Eligible, reported benefits (%) 3.1 20.8Take up rate 1 (%) 33.0 62.2(2 + 4)/(2 + 3 + 4)Take up rate 2 (%) 27.0 57.44/(3+4)

23

Table 3: Effects on Food Stamp Take-up by Groups

No State or Year Trends

All Single Mothers Single, No Kids Married, No Kids Married Parents Age > 60EBT -0.004 0.056 -0.018 -0.052 0.004 0.022

(0.024) (0.065) (0.031) (0.067) (0.063) (0.052)Recertification 0.001 0.006 -0.003 -0.002 -0.004 -0.001

(0.004) (0.010) (0.005) (0.006) (0.006) (0.007)Outreach Spending per Person -0.006 0.054 -0.010 -0.012 0.010 -0.012

(0.020) (0.105) (0.030) (0.011) (0.026) (0.024)Participation Rate 0.413*** -0.190 0.750*** 0.129 -0.007 0.496

(0.136) (0.287) (0.169) (0.441) (0.293) (0.400)Unemployment Rate 3.513*** 5.712** 2.685 1.148 4.380* 1.417

(1.261) (2.658) (1.752) (3.373) (2.166) (2.709)Potential Benefits 0.024** 0.031* -0.013 -0.003 0.046** 0.015

(0.009) (0.018) (0.012) (0.030) (0.020) (0.027)Age -0.003** -0.007** -0.000 -0.004 0.000 -0.002

(0.002) (0.003) (0.002) (0.003) (0.004) (0.005)High School Dropout 0.194*** 0.316*** 0.185*** -0.061 0.195*** 0.237***

(0.031) (0.069) (0.046) (0.071) (0.041) (0.062)Young Child 0.155*** 0.090 0.020 0.166

(0.045) (0.058) (0.058) (0.217)Married -0.009 -0.102

(0.053) (0.090)Female 0.156*** 0.064 0.066 -0.171 0.054

(0.049) (0.040) (0.202) (0.188) (0.080)Black 0.116*** 0.172* 0.121*** 0.117 0.042 0.076

(0.032) (0.092) (0.037) (0.071) (0.080) (0.075)Constant -0.267* 0.221 -0.485*** 0.320 -0.149 -0.259

(0.133) (0.243) (0.169) (0.337) (0.309) (0.417)

R2 0.182 0.245 0.125 0.077 0.104 0.138N 1970 445 946 170 332 616Note: Standard errors clustered at state level in parenthesis.

Includes State and Year Trends

All Single Mothers Single, No Kids Married, No Kids Married Parents Age > 60EBT -0.052* 0.018 -0.068 0.042 0.040 -0.061**

(0.029) (0.057) (0.046) (0.120) (0.123) (0.030)Recertification 0.000 -0.027** -0.003 0.008 0.006 -0.006

(0.005) (0.011) (0.007) (0.010) (0.012) (0.007)Outreach Spending per Person 0.010 -0.107* 0.019 0.094 0.011 -0.012

(0.035) (0.061) (0.041) (0.131) (0.092) (0.034)Participation Rate 0.390 0.245 1.022*** -0.217 -0.977 0.228

(0.274) (0.442) (0.262) (0.655) (0.717) (0.380)Unemployment Rate 1.895 4.903 0.049 16.602* -3.721 3.178

(3.010) (3.722) (4.213) (9.659) (6.711) (4.025)Potential Benefits 0.026*** 0.047** -0.012 0.042 0.056** 0.021

(0.009) (0.018) (0.013) (0.029) (0.022) (0.027)Age -0.004* -0.007* -0.000 -0.001 -0.002 -0.000

(0.002) (0.004) (0.002) (0.002) (0.005) (0.006)High School Dropout 0.193*** 0.366*** 0.190*** 0.023 0.186*** 0.195***

(0.033) (0.065) (0.054) (0.048) (0.056) (0.068)Young Child 0.153*** 0.057 0.002 0.170

(0.044) (0.058) (0.061) (0.257)Married -0.030 -0.064

(0.056) (0.080)Female 0.165*** 0.062 0.091 -0.146 0.073

(0.050) (0.040) (0.165) (0.289) (0.073)Black 0.132*** 0.150** 0.151*** 0.135 -0.002 0.101

(0.037) (0.062) (0.040) (0.087) (0.117) (0.079)Constant -0.171 0.217 -0.553** -0.546 0.802 -0.223

(0.245) (0.423) (0.251) (0.522) (0.701) (0.505)

R2 0.223 0.429 0.187 0.435 0.212 0.314N 1970 445 946 170 332 616Note: Standard errors clustered at state level in parenthesis. All regressions are weighted by the PSID family weights.EBT equals one if the state used EBT statewide. Recertification is the number of months that an individual receives benefitsbefore they must recertify. Outreach spending is measured in dollars per eligible non-participant. Participation rate is astate-wide estimate. Benefits are in hundreds of dollars and are potential because they are estimated. Young child meansthe youngest child in the household is aged five or younger.

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Table 4: Effects on Aggregate FSP Participation

Participation Rate

EBT -0.073*** 0.024*(0.017) (0.014)

Unemployment Rate 2.463*** 2.851***(0.706) (0.893)

Outreach Spending per Person 0.011*** 0.005(0.003) (0.005)

Recertification for Earners -0.000 0.010***(0.003) (0.003)

Recertification for non-earners 0.011** -0.004(0.004) (0.004)

Recertification for Elderly 0.007*** 0.004**(0.002) (0.002)

State and Year Trends No YesR2 0.153 0.842N 406 406Note: Robust standard errors in parenthesis. State participationrates from USDA. Annual outreach spending per eligiblenon-participant in dollars. Recertification periods are oftendifferent by group, with shorter times for earners and longerfor the elderly.

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6.1 Notes on the data

After estimating eligibility and calculating potential benefits, I find that a few house-

holds that receive SSI or TANF benefits are calculated to be ineligible for food stamps.

These households are normally automatically eligible for food stamp benefits, so I change

their eligibility and assign them the minimum benefits for their household size. I drop

the twelve households with negative income. They report large negative income is some

years and large positive income in others. Most have large savings accounts that prevent

them from being eligible for food stamps and none of them participate. A few house-

holds are eligible for food stamps but their benefits are negative. This can occur for

households with three or fewer members with net income close to the poverty level10.

The minimum amount of benefits is $10 per month, so I assign this amount to these

households. Finally, a few households report not knowing whether they received food

stamps benefits. They report not knowing the answers to other questions as well. There

are 10 of these households, so I drop them.

6.2 Results when omitting 1997 data

The PSID became a biannual survey after 1997. Some questions were dropped and

others added stating with the 1999 survey, so I lost information by using the entire

panel. It might be possible to avoid complications of welfare reform by omitting 1997

data as well. Households dropped from food stamp rolls because of welfare reform should

have enough time to find out that they are still eligible for food stamps. I include 1997

information in the paper to exploit more variation in my independent variable of interest.

When I restrict the sample to 1999-2003 the results are largely unchanged. A variable

10For example, in 2001 a household of size two must have net income below $922 while the maximumbenefits for a household of this size is $234. Benefits equal max benefits ($234) minus 30% of net income($276.6), so a two person family with net income of $922 is eligible for -$42.

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for whether the PSID interview was conducted in English (not asked in 1997) comes in

negative and significant. This could hint at information effects. All tables are available

upon request.

6.3 Estimating Eligibility and Calculating Benefits

To calculate whether a household is eligible for the FSP, I use the methods outlined in

the Green Books of the U.S. Committee on Ways and Means. Two income requirements

must be passed, gross and net. Gross income includes all cash income except for certain

exclusions including loans, income received for care of someone outside the household,

income tax refunds and retroactive social security payments (counted as assets), Federal

energy assistance, and income earned by school children 17 or younger. Gross household

income must be below 130% of the poverty level, which is different for households of

different size and is slightly higher in Hawaii and Alaska than the 48 continental states.11

Net income is calculated by subtracting from gross income a standard deduction, 20%

of earned income to account for taxes, any child support payments, out of pocket day

care expenses up to $175 per child or $200 if the child is under two years old, and

shelter expenses that exceed 50% of net income after all other deductions have been

made, up to $250. Net income must fall below 100% of the poverty level. An asset

test must be passed as well. The household cannot have more than $2000 in liquid

assets (including savings and checking accounts, stocks, and IRAs) or $3000 if there is

an elderly dependent living in the household.

A complication arises in that while typically the entire household applies for food

stamps together, it is possible that fewer members of the household apply together if

they purchase food and prepare meals separately. A few restrictions apply, such as

spouses cannot apply separately, nor can parents (legal guardians) and their children

11These are based on higher poverty guidelines in these states.

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(dependents). This complication presents itself in my data, in that it is not always the

case that the number of members of the household who use food stamps is the same as

the total number of people in the household. I have data on total household income, not

income on each individual who lives in the household. Even then, it would be difficult

to determine all the combinations of people who might potentially apply together.

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