Discussion Paper Series

University of Kentucky

Center for

Poverty Research

DP 2013-02

UKCPR

University of Kentucky Center for Poverty Research, 302D Mathews Building, Lexington, KY, 40506-0047

Phone: 859-257-7641; Fax: 859-257-6959; E-mail: ukcpr@uky.edu

www.ukcpr.org

ISSN: 1936-9379

EO/AA

SNAP and Obesity

Craig Gundersen

Department of Agricultural and Consumer Economics

University of Illinois

Preferred citation

Gundersen, Craig. SNAP and Obesity. University of Kentucky Center for Poverty Research Discussion Paper

Series, DP2013-02.

Author correspondence

Craig Gundersen, Department of Agricultural and Consumer Economics, 324 Mumford Hall, 1301 West

Gregory Drive, University of Illinois, Urbana, IL 61801-3605; Email: cgg[email protected]; Phone: 217-

333-2857.

SNAP and Obesity

Craig Gundersen

Soybean Industry Endowed Professor of Agricultural Strategy, Department of Agricultural and

Consumer Economics

Executive Director, National Soybean Research Laboratory

University of Illinois

Introduction

Hunger and its accordant consequences were serious problems in the United States 50

years ago. In response, the U.S. government established the Supplemental Nutrition Assistance

Program (SNAP, then known as the Food Stamp Program). Over time, the program expanded

extensively such that today it serves approximately 47 million people in 2012, with total benefits

of almost $74 billion. In the aggregate the program is large; in addition, the benefits received by

individuals can be quite large. For example, the maximum benefit level was $668 for a family of

four in 2012. Due to its total size and the importance to individual households, SNAP has

become a central component of the social safety net and ensuing discussion in the United States.

The central goal of SNAP is to alleviate hunger and, as part of this, SNAP is designed to

increase the food expenditures of low-income Americans. A body of research has examined

whether or not SNAP has achieved these goals. Early work demonstrated that the receipt of

SNAP in the U.S. leads to increases in food consumption (in comparison to equivalent amounts

of cash) and nutrient intakes of SNAP recipients were higher than eligible non-recipients.

After

the advent of various measures of food insecurity, research further demonstrated that SNAP

recipients are less likely to be food insecure than eligible non-recipients and, as found in some

studies, substantially less likely to be food insecure.

Along with these direct impacts on food

intakes, SNAP has also been found to improve well-being over other dimensions including

reductions in poverty (e.g., Bishop et al., 1996; Tiehen et al., 2012), improvements in birth

outcomes (Almond et al., 2011), lower mortality (Krueger et al., 2004), and better general health

Examples of this work includes Basiotis et al. (1983), Basiotis et al. (1987), Chavas and Yeung (1982), Devaney

and Fraker (1989), Devaney and Moffitt (1991), Hama and Chern (1988), Johnson et al. (1981), Levedahl (1995),

Salathe (1980), Senauer and Young (1986), and Smallwood and Blaylock (1985). More recent work on this topic

includes Hoynes and Schanzenbach (2009).

Recent work includes DePolt et al., 2009; Kreider et al., 2013 Mykerezi and Mills, 2010; Nord and Golla, 2009;

and Ratcliffe et al., 2011.

(Kreider et al., 2012). Moreover, by reducing food insecurity, the negative impacts of food

insecurity on various health outcomes are diminished.

It is perhaps relatively non-controversial to state that SNAP has been successful at

improving the well-being of low-income Americans. In recent years, though, there have been

some proposals that have sought to change the structure of SNAP such that it becomes, at least in

part, an “anti-obesity” program. These proposals have emerged due to a perception that SNAP

leads recipients to have higher weights than non-recipients.

In this chapter, I begin with a theoretical consideration of whether SNAP benefits lead to

increases in food consumption that could lead to increases in weight status. Theoretically, the

effect of SNAP is ambiguous. Insofar as SNAP is a near-cash program, I next consider whether,

empirically, higher incomes are associated with higher rates of obesity. This is done for both

children and for adults. I show, consistent with previous work, that income is, in general,

inversely related to rates of obesity.

Given the inverse relationship between income and obesity, one would anticipate that

SNAP receipt would lead to reductions in obesity. A series of recent papers has considered

whether, in fact, SNAP does lead to reductions in obesity. I next review the results of these

papers. The evidence there is mixed – two have shown slightly elevated probabilities of obesity

Among children, the effects of food insecurity includes higher risks of some birth defects (Carmichael et al., 2007,

anemia (Eicher-Miller et al., 2009, Skalicky et al., 2006), lower nutrient intakes (Cook et al., 2004), greater

cognitive problems (Howard, 2011), higher levels of aggression and anxiety (Whitaker et al., 2006), higher

probabilities of being hospitalized (Cook et al., 2006), poorer general health (Cook et al., 2006; Gundersen and

Kreider, 2009), higher probabilities of asthma (Kirkpatrick et al., 2010), higher probabilities of behavioral problems

(Huang et al., 2010), and more instances of oral health problems (Muirhead et al., 2009). Among adults,

consequences include lower nutrient intakes (Kirkpatrick and Tarasuk, 2007; McIntyre et al., 2003), mental health

problems (Heflin et al., 2005), physical health problems (Tarasuk , 2001), depression (Whitaker et al., 2006),

diabetes (Seligman et al., 2007), higher levels of chronic disease (Seligman et al., 2009), and worse outcomes on

health exams (Stuff et al., 2004). Food insecure seniors have lower nutrient intakes (Lee and Frongillo, 2001; Ziliak

et al., 2008), are more likely to be in poor or fair health (Lee and Frongillo, 2001; Ziliak et al., 2008), and are more

likely to have limitations in activities of daily living (ADL) (Ziliak et al., 2008).

for at least a subset of recipients, while most have shown no impact or reductions in the

probability of obesity.

Despite the lack of convincing evidence that SNAP leads to increases in obesity, several

proposals have emerged to restrict what SNAP participants can purchase with their benefits. I

review these proposals. I then conclude with a discussion of some of the potential consequences

for the well-being of low-income households due to these restrictions.

Theoretical Consideration of the Impact of SNAP Benefits on Obesity

A household eligible for SNAP must make a decision about whether or not to receive

benefits. The two primary costs associated with participating in SNAP are stigma and

transactions costs. The stigma associated with receiving SNAP can arise due to a person’s own

distaste for receiving SNAP, the fear of disapproval from others when redeeming SNAP, and/or

the possible negative reaction of caseworkers.

Second, transaction costs can diminish the

attractiveness of participation, including travel time to, and time spent in, a SNAP office; the

burden of transporting children to the office or paying for child-care services; and the direct costs

of paying for transportation. A household faces these costs on a repeated basis because it must

recertify its eligibility.

The amount of SNAP benefits a household receives is decreasing in

income and increasing in family size. If the benefits exceed the costs, a household will decide to

enter the program.

For a discussion of stigma associated with participation in assistance programs see, e.g., Moffitt (1983), Rainwater

(1982), Ranney and Kushman (1987), Stuber and Kronebusch (2004), Stuber and Schlesinger (2006), and Wu and

Eamon (2010).

See, e.g., Ponza et al. (1999) for evidence of the impact of transactions costs from household surveys and, e.g.,

Ziliak et al. (2003) for evidence of the impact of transactions costs from data on state caseloads.

While transactions costs can play an effective targeting role by discouraging those in less need from applying for a

program, in the United States it often appears that transactions costs are often actually discouraging those most in

need. In SNAP, this can occur when the application process is too difficult for those with limited education levels

and these with limited education levels also have lower incomes. For more on this see Currie and Gahvari, 2008.

The solid line in Figure 1 displays the budget constraint for a household prior to receiving

SNAP.

This is expressed in terms of the choices available to a household between food and

other goods. The asterisk on the line represents the choice made by a household. The dashed

line represents the choices available to a household due to the receipt of SNAP (denoted as S in

the figure).

While, technically, a household could reduce its expenditures on food, there is no

empirical evidence of a negative marginal propensity to consume food out of SNAP benefits.

So, we would anticipate that due to receiving SNAP, households would increase their food

expenditures (i.e., move in a northeastern direction). One should note that this is akin to a

similar increase in income – if a household saw an increase in income due to, say, an increase in

wages for a household member, one would see an increase in food expenditures.

This shift out in the budget constraint could lead to an increase in calories which could

lead to an increase in weight for individuals in the household. In addition, a household could

purchase goods which would lead to an increase in sedentary activities which, again, could lead

to an increase in weight. So, in theory, receiving SNAP, just like any increase in income, could

lead to an increase in weight. The converse could also hold. Considering other goods in

isolation, an increase in income could enable a household to engage in activities that would lead

to less sedentary activities (e.g., the purchase of a membership at a YMCA).

At least implicitly, the central argument used by those who believe SNAP leads to

increases in obesity is that recipients will choose to purchase more products that lead to weight

gain. Suppose households allocate consumption to a combination of at-home purchases of

In this figure, p

denotes the price of food, p

denotes the price of other goods, Y denotes income, and S denotes

SNAP benefits.

Because SNAP can only be spent on food purchased from approved retail food outlets, it differs from the shift out

that would occur if a household received the same amount in income.

“healthy food” and “unhealthy food”.

Like in Figure 1, the household’s pre-SNAP choices

are represented by a solid line (and the choice of food types by the asterisk) in Figure 2 and the

post-SNAP choices are represented by a dashed line. Without more information about the

preferences of the household, it is not clear what will happen to the consumption of “healthy”

and “unhealthy” foods. If “unhealthy foods” (“healthy foods”) are an inferior good, then the

total consumption of “unhealthy foods” (“healthy foods”) would fall resulting in a proportional

increase in “healthy foods” (“unhealthy foods”). If both “unhealthy foods” and “healthy foods”

are normal goods than consumption of both would increase. In any case, the combined influence

of increased income due to SNAP receipt is theoretically ambiguous.

Empirical Consideration of the Impact of Income on Obesity

I now consider some empirical evidence regarding rates of obesity at various income

levels.

If there are differences in obesity rates at different levels of income, it provides some

empirical evidence regarding the theoretically ambiguous effect of SNAP discussed above.

To examine the relationship between income and obesity, I use data from the 2001-2010

waves of the National Health and Nutrition Examination Survey (NHANES). The NHANES,

conducted by the National Center for Health Statistics, Centers for Disease Control

These terms are in quotes because virtually no food is completely healthy and no food is completely unhealthy. In

terms of the connection to obesity, the consumption of more “unhealthy foods” is generally seen as associated with

increases in the probability of obesity but there are obviously many other factors that influence a person’s weight

status.

Of course, due to the receipt of SNAP (or any other increase in income) households will do other things in terms

of food consumption including, for example, purchasing food that has been prepared by others. I concentrate on the

“healthy” and “unhealthy” foods since this seems like the main potential consequence for those who believe SNAP

participation leads to increases in obesity.

Along with influencing food choices, SNAP can have other effects on obesity. One key area is with respect to

stress. The stress experienced by households has been associated with higher probabilities of obesity, especially

among children. (See Gundersen et al. (2011) for a review.) If receiving SNAP reduces stress this could be an

indirect avenue through which SNAP participation can lead to reductions in obesity.

This is intended as purely a descriptive exercise. As such, I’m overlooking numerous issues including, e.g., the

potential effect of weight status on wages (see Brendan and Kline (2008) for more on this issue).

(NCHS/CDC), is a program of surveys designed to collect information about the health and

nutritional status of adults and children in the United States through interviews and direct

physical examinations. The survey currently includes a national sample of about 5,000 persons

each year, about half of whom are children. Vulnerable groups, including Hispanics and African-

Americans, are oversampled.

Given the problems associated with self-reports of heights and weights in surveys (see

Connor Gorber (2007) for a review), a key advantage of the NHANES is that heights and

weights were measured with an automated data-collection system by a trained technician in the

NHANES mobile examination center. With these heights and weights, one can calculate the

BMI (kg/m2). For adults I consider two categories - obese (BMI above 30) and, as a subset of

obese, those who are severely or very severely obese (BMI above 35).

For children, these

heights and weights are mapped into a percentile by using age- and gender-specific reference

values of the CDC growth charts for the United States (Ogden et al., 2002). To somewhat mimic

the categories for adults, I consider two BMI percentile cutoffs – at or above the 95

percentile

and at or above the 99

percentile.

In each of the following figures we show the proportion of persons who are obese, broken

down by income categories defined with respect to the poverty line (less than the poverty line,

between 100 and 200% of the poverty line, between 200 and 300% of the poverty line, between

300 and 400% of the poverty line, above 400% of the poverty line). In Figure 3 I begin with a

comparison for all adults (i.e., those 18 and over) in the sample.

As seen there, obesity rates

Recent studies have demonstrated that while those with BMIs above 35 have higher mortality risks than those

who are “normal weight” (BMIs between 20 and 25) but there is less evidence that those who have BMIs above 30

(but below 35) have higher mortality risks (Flegal et al., 2013; Gronninger, 2006; Mehta and Chang, 2009).

Nevertheless, because the obesity category is often invoked in research on weight status, and, in particular, on

studies examining the impact of SNAP, it is included here.

Those who are pregnant are not included in the adult or child analyses.

fall from the lowest income category to the highest income category – from 36.3% for the

highest income category to 31.3% for the lowest. For the above 35 BMI category, there is a

secular decline with respect to income with a similar absolute decline from the highest to the

lowest income categories (19.1% to 13.0%).

In Figure 4, the relationship between income and obesity is broken down by gender. For

males, the proportion of those in the obese category is actually higher for those with incomes

above 400% of the poverty line in comparison to those with incomes below the poverty line.

When the breakdown is for over 35 BMI, the two lowest levels are in the ends of the income

distribution. For females, the story is markedly different. There is a steady decline in the

proportion of those who are obese and severely or very severely obese as income increases. A

comparison between the highest and lowest income categories for both obesity categories

demonstrates a very large decline - 42.3% to 30.4% and 24.4% to 15.5%. Based on these gender

comparisons, one can state that the inverse relationship between income and the probability of

obesity is primarily due to the relationship for women.

Figure 5 is for all children between the ages of 3 and 17.

There is a steady decline in

probabilities of obesity as income increases when all children are included. For example, from

the lowest to the highest income spectrum there is a decline in the probability of being in the 95

percentile of higher from 20.4% to 13.2% and for being in the 99

percentile or higher from

6.1% to 2.6%. Unlike for adults, the results are similar in terms of increasing income being

associated with lower probabilities of obesity for both boys and girls (see Figure 6).

Children under the age of three are not included in the sample because there is no commonly accepted way to

establish body mass index (BMI) percentiles for children this young.

Along with BMI, there are other measures of obesity that can be examined when

considering the relationship between income and obesity.

For adults I consider waist

circumference combined with BMI as a measure of obesity. Based on the criterion from

http://www.nhlbi.nih.gov/health/public/heart/obesity/lose_wt/bmi_dis.htm , persons with BMIs

above 25 and waist circumferences above 102 cm (men) or 88 cm (women) are considered at a

similar level of risk for negative health outcomes as those with BMIs above 30 but without waist

circumferences above those critical values. A similar story holds for persons with BMIs above

30 and waist circumferences above the critical values listed above – they are considered at a

similar level of risk as those with BMIs above 30 but without a waist circumference above those

critical values. As seen in Figure 7, there is not the decline in obesity as incomes increase one

sees when just BMI is used. While those with incomes above 400% of the poverty line have

lower rates of obesity under either measure than those with incomes below the poverty line, the

intermediate income categories, in the main, have higher rates of obesity than those found among

the poor. In Figure 8, breakdowns by gender over these measures are displayed. Two things are

worth emphasizing. First, for men, the extent of obesity is actually higher for those with

incomes over 400% of the poverty line in comparison to those with incomes under the poverty

line. This is similar to the result found for the over 30 BMI measure found in Figure 4. In

contrast, for women, there is a secular decline as income increases, similar to Figure 4. Second,

while the proportions of men found to be obese in Figure 8 are slightly higher than in Figure 4,

the proportions of women found to be obese in Figure 8 are substantially higher than in Figure 4.

There have been several other studies that have examined the relationship between obesity and income including,

for adults, e.g., Chang and Lauderdale, 2005; Garcia Vilar and Quintana-Domeque, 2009; Jolliffe, 2011; Mclaren,

2007; Reynolds and Himes, 2007; Wardle et al., 2002; and Zhang and Wang, 2004 and, for children, e.g.,

Margerison-Zilko and Cubbin, 2013; Phipps et al., 2006; Shrewsbury and Wardle, 2008; To the best of my

knowledge, this is the first time the relationship between income and non-BMI measures of obesity has been

examined.

Turning to children, tricep skinfold and subscapular skinfold are two ways of measuring

obesity in children. Here we use the 95 percentile thresholds established in Addo and Himes

(2010). In Figure 9 the results are displayed in a manner akin to Figure 5. The patterns are

similar in the two figures insofar as increased incomes are associated with lower levels of obesity

whether measured by BMI, tricep skinfold, or subscapular skinfold. In Figure 10 the results are

broken down by gender. For girls, the patterns with both skinfold measures are similar to those

with BMI – a steady decline in obesity as income increases. For boys, the pattern is slightly

different between Figures 6 and 10 – there is a slight increase in obesity rates for skinfold

measures as income increases (in contrast to BMI) and the drop is especially marked in Figure 10

for children in households with incomes between 3 and 4 times the poverty line to those with

incomes above 4 times the poverty.

In general, empirical evidence points to higher incomes being associated with lower

probabilities of obesity and severe obesity. And, this, in general, holds with alternative measures

of obesity. As a consequence, one’s prior, with the exception of adult men, is that mechanisms

like SNAP that would increase the ability to purchase food would lead to declines in the

probability of being obese.

Findings Regarding the Effect of SNAP on Obesity

Given the empirical evidence presented above, it may be unlikely that receiving more

money to purchase food will lead to increased probabilities of obesity. As such, we would

anticipate that SNAP recipients are less likely to be obese than eligible non-recipients. It may be

the case, though, that SNAP recipients are more likely to be obese than non-recipients. This

could occur if, for example, households that choose to enter the program are those that are more

prone towards higher weights. A third possibility is that SNAP has no impact on the probability

of obesity of SNAP participants. This could occur if, for example, the extra money received by

participating in SNAP is not enough to change outcomes.

The literature looking at SNAP and obesity has found evidence of each of these three

possibilities. I now turn to a selection of these studies. Insofar as SNAP participants are

unlikely to be similar to eligible SNAP non-participants over unobserved characteristics, I limit

myself to studies which address this selection issue. In discussing each of these, I concentrate on

what the authors perceive to be the central findings from their work.

Two studies have found positive effects of SNAP on obesity for at least a subset of the

population. Meyerhoefer and Pylypchuk (2008) used data from the combined cross-sections of

the 2000-2003 Medical Expenditure Survey (MEPS). They find that female SNAP participants

are 5.9% more likely to be overweight or obese than eligible female non-participants. For men,

however, there is no statistically significant impact of SNAP participation on weight status. The

second study to find a positive impact of SNAP on obesity is Baum (2011). Like Meyerhoefer

and Pylypchuk, he finds a positive impact for women but not for men. While the result is

statistically significant, Baum notes that the “…effects are relatively small…”

The majority of studies have found that SNAP has no effect on obesity for any subset of

the population. Using longitudinal data from the National Longitudinal Study of Youth (NSLY)

1979 restricted to adult women, Fan (2010) finds no evidence of a relationship between SNAP

participation and obesity. This result is for both short-term and long-term participation and after

performing several robustness checks. Baum (2012) considers whether or not SNAP promotes

excessive weight gain during pregnancy with data from the NSLY 1979. He finds that while

SNAP does decrease the probability of gaining too little weight during pregnancy, SNAP

participation does not lead to increases in the probability of gaining too much weight. Some

have argued that the pattern of higher rates of obesity among low-income households is due to

SNAP participation. If this were the case then the gap between low-income Americans and non-

low-income Americans would persist over time. Ver Ploeg et al. (2007) find, using a series of

cross-sections from the National Health and Nutrition Examination Survey (NHANES) find that

this gap has not persisted and, in fact, has narrowed over time. Also using the NHANES, Kreider

et al. (2012) find that SNAP participants are between 3.3 percentage points and 47.4 percentage

points less likely to be food insecure than SNAP non-participants but the result is not statistically

significantly different from zero.

A third set of studies find that SNAP participation leads to reductions in the probability

of obesity. In a sample of boys and girls between the ages of 5 and 18 from the NSLY 1979,

Schmeiser (2011) finds that boys and girls between the ages of 5 and 11 and boys between the

ages of 12 and 18 who participate in SNAP have lower probabilities of overweight and obesity

than eligible non-participants. Among girls between the ages of 12 and 18, SNAP participation

has no statistically significant effect on the obesity or overweight. Other studies have examined

the contemporaneous impact of SNAP participation but Hoynes et al. (2012) use data from the

Panel Study of Income Dynamics and information on the roll-out of food stamps from 1961 to

1975 to consider the effect of participation in SNAP in childhood on adult obesity. They find

that participation in SNAP in childhood leads to reductions in the probability of obesity in

adulthood. Burgstahler et al. (2012) used a data set composed of households with children in

counties with poverty rates over 20% from three states –Illinois, Iowa, and Michigan. They find,

that children in SNAP participating households are less likely to be overweight than children in

non-participating eligible households. This effect is strong – for each 10% increase in SNAP

participation rates, there would be a 5.7% decrease in rates. These three studies demonstrate

that, even though SNAP is not designed to reduce someone’s probability of obesity, there is

evidence that this is occurring as one of the indirect effects of SNAP.

Potential Consequences of Proposals to Limit Purchases with SNAP

The above discussion provides some context about the role SNAP may play, in its current

configuration, in addressing the issue of obesity in the United States. There have been several

recent proposals that have sought to fundamentally change the structure of SNAP with the stated

goal of reducing obesity among low-income Americans. I now cover these proposals.

Following that, I consider the potential consequences of these proposals.

Proposals to limit SNAP purchases

Over the past 50 years, there have been multiple attempts to restrict purchases of certain

food items. These attempts arise from desires to “improve the nutrient intake of recipients,”

prohibit recipients from purchasing “luxury items”, or stigmatize certain food products.

The

most recent proposals have generally concentrated on restricting specific categories of foods

deemed as “unhealthy foods” or “junk foods”.

The most highly publicized and discussed effort to restrict SNAP purchases was

contained in a waiver request to USDA by the New York Department of Health and Mental

Hygiene and Human Resources Administration (2010). This waiver request, which was turned

down by USDA, would have banned SNAP recipients from using SNAP benefits to purchase

most any beverage with more than 10 calories per 8-ounce servings. This ban would have

Some have also argued that, irrespective of the impact purchases of certain products may have on recipients, the

government should not be involved in promoting certain food items.

included things such as sports drinks (e.g., Gatorade, Powerade), soda (e.g., Coca-Cola,

Mountain Dew), vegetable drinks (e.g., V8), and iced tea drinks. Other products with more than

10 calories per 8-ounce serving would still have been allowed, though, including milk, milk

substitutes, and 100% fruit juices.

There have been other state-level efforts. In Maine, a proposal by Governor LePage (LD

1411) would not allow SNAP benefits to be used to purchase any product that is subject to the

state sales tax (Stone, 2013).

This would primarily rule out the purchase of certain beverages

and snack items with SNAP benefits. In Wisconsin, a proposal by Representative Dean Kaufert

would not restrict the purchase of any individual items but would instead impose limits on the

proportion of SNAP purchases that could be made (Clark, 2013). A SNAP recipient would have

to use two-thirds of their SNAP benefits to purchase “healthy foods” where this list is taken from

a list of foods approved for purchases with WIC benefits and then some other foods are added.

The other one-third of SNAP benefits could then be used to purchase whatever foods a recipient

chooses. This proposal differs from the other proposals covered above as eligible purchases

would be defined by (a) a list of approved items (rather than restricted items) and (b) what has

already been purchased by a recipient. How exactly this would be implemented was never

articulated in the proposal but it was approved by the Wisconsin State Assembly (Marley and

Stein, 2013). Governor Haley in South Carolina has also proposed making restrictions on what

can be purchased (Holleman, 2013).

These proposals for restrictions mirror previous state proposals. For example, in 2004,

Minnesota requested a waiver from the USDA to prevent SNAP recipients from using SNAP

benefits to purchase certain food items. These items generally fell under the category of “candy”

In Maine, unless otherwise taxed, there is no sales tax for food items.

Some foods that are often considered healthy – for example, organic foods – would generally be banned for

purchase under the “2/3 of healthy foods rule” because most organic foods cannot be purchased with WIC benefits.

but some items commonly thought of as “candy” were exempted from this restrictions if they

contained flour. For example, Kit Kat bars were exempt from this restriction but Hershey bars

were not. Like other waiver requests, this was not approved by the USDA (Holden, 2004).

National-level proposals have surfaced as well. One from Senator Coburn (OK), in

“Coburn Amendment number 421”

to the Senate Budget, proposed that no junk foods be

allowed for purchase with SNAP benefits. A later proposal by Senator Coburn (OK), “Coburn

Amendment number 1000”

was added to the Farm Bill.

Potential consequences of imposing restrictions

The stated goal underlying each of the proposals to restrict SNAP purchases is that these

restrictions will lead to reductions in obesity among low-income Americans. As with many new

policies, it is unclear whether or not this goal will be achieved.

The goal may be achieved if

households do decide to substitute consumption of “unhealthy foods” for “healthy foods” and, as

a further step, this substitution leads to reductions in weight. In theory, there is no guarantee this

will hold insofar as virtually all SNAP recipients are inframarginal (see, e.g., Breunig and

Dasgupta, 2005) and, therefore, recipients may just reallocate the distribution of food purchases

out of SNAP and out of cash. Even if there was a total reallocation of purchases of “healthy

foods”, for reasons discussed below, it is not clear whether or not this policy would lead to

reductions in obesity. An indirect way that these restrictions may lead to reductions in obesity is

http://www.coburn.senate.gov/public//index.cfm?a=Files.Serve&File_id=b542226b-c7d9-4206-9f05-

25b647742ca6

http://www.coburn.senate.gov/public//index.cfm?a=Files.Serve&File_id=ef26c43b-db74-4a86-8d44-

4a545a731673

Even if persons believe that SNAP leads to increases in the probability of obesity, the reasons for why

government policy should be directed towards changing people’s weight status are not altogether clear. One reason

that is sometimes given is that there are negative externalities associated with obesity. However, there is no clear

evidence of these negative externalities. For more discussion about why government should or should not be

involved in this realm see Bhattacharya and Sood (2011) and Lusk (2013).

via the decline in the number of SNAP recipients. While, as covered above, there is no clear

evidence that SNAP leads to increases in obesity, some in favor of restrictions on purchases

implicitly believe that there is evidence of this.

For reasons discussed in what follows, the

imposition of SNAP restrictions will lead to a decline in the number of participants – according

to those who believe that SNAP is associated with increases in obesity, even if there is no net

change in “healthy” and “unhealthy” foods, the decline in SNAP participation will lead to

reductions in obesity.

The restrictions on SNAP purchases may also lead to an increase in obesity in the United

States. Most of the evidence shows that SNAP (as currently constructed) leads to reductions in

obesity or, at the very least, no change in obesity. As a consequence, making changes to SNAP

which lead to declines in participation (see below) could lead to increases in obesity. Or, to put

this a different way, this will lead to reductions in income which, as seen in Figures 3 through 6,

generally leads to increases in obesity.

While the potential effect of restrictions on SNAP purchases on obesity in the United

States is ambiguous, the effect of these restrictions on the overall well-being of low-income

Americans is relatively clear. I now turn to the negative impact these restrictions would have on

low-income Americans.

As covered above, SNAP participants have been shown, among other things, to have

lower probabilities of food insecurity, poverty, and low birth weights and higher levels of food

expenditures and nutrient intakes. If the participation rate among SNAP eligible households was

to decline, in the aggregate, the well-being of low-income Americans would decline. These

Other proponents of imposing restrictions may acknowledge that research demonstrates SNAP does not lead to

increases in obesity but, nevertheless, think that SNAP can do more in efforts to reduce obesity in the United States.

Irrespective of the reasons for wanting to change the structure of SNAP, the consequences due to restrictions

discussed in this section remain the same.

restrictions would lead to declines in participation insofar as they would lead to increases in

stigma and transactions costs. I now turn to each.

Increases in stigma Restrictions on SNAP benefits would increase the stigma associated

with SNAP, as participants would feel singled out as being irresponsible and incapable of

making well-informed food purchases. Participants also would be worried when making

purchases that some of what they have purchased is not eligible for SNAP. This information,

which can be stigmatizing, would then be revealed to others in the check-out line either through

a request by the cashier to provide cash or other funds to make the purchase of those items or by

having to make a request of the cashier to remove the items from the purchase. SNAP

restrictions also send a negative message about the program in general, by implicitly assuming

that SNAP recipients have worse diets and are more likely to be obese.

That restrictions would lead to increases in stigma has also been recognized by the

USDA. To use their words, in response to a request for restrictions by Minnesota, “…such a

program change could add confusion and embarrassment at the point of sale when program

recipients attempt to purchase food items once allowable but now deemed ineligible. Moreover,

implementation of this waiver would perpetuate the myth that FSP participants do not make wise

food purchasing decisions. (Holden, 2004)”

Increases in transaction costs In deciding whether or not to receive SNAP, there are

several costs that recipients need to incur. Restrictions on SNAP purchases would further

increase these transaction costs over two main dimensions.

First, SNAP recipients will need to spend more time figuring out which food items are

eligible for purchase with SNAP benefits and which are not. In stores where “SNAP eligible” or

“SNAP ineligible” is clearly and correctly displayed, ascertaining which are eligible would be

straightforward upon arriving at the food retailer. But in stores without such displays, SNAP

recipients would need to ascertain this information on their own (i.e., the opportunity cost of

shopping with SNAP is higher).

Second, the number of stores accepting SNAP benefits would be likely to decline if

restrictions were put into place. This is due to the higher costs to stores associated with

implementing these restrictions and, in response, many stores will simply choose not to accept

SNAP benefits rather than incur those higher costs. This would raise the transaction costs to

SNAP recipients because they would have to go further to use their SNAP benefits.

Conclusion

SNAP has proven to be one of the most successful safety net programs since its

implementation 50 years ago. This program has often come under attack throughout its history

for many perceived problems (e.g., that it discourages labor force participation). Most recently,

SNAP has come under attack for being perceived as one of the causes of the current rates of

obesity found in the U.S. One response that has gained some traction is to restrict what can and

cannot be purchased with SNAP.

As covered here, there is very little evidence that SNAP is associated with higher

probabilities of obesity among participants in comparison to eligible non-participants. In

contrast, there is clear evidence that (a) SNAP improves the well-being of recipients over

numerous dimensions and (b) imposing restrictions will lead to declines in participation. In light

of this evidence, policymakers and program administrators should be reluctant to make

fundamental changes to a program as successful as SNAP.

Figure 1

Food

Other Goods

Y/p

(Y+S)/p

Figure 2

“Unhealthy

Foods”

“Healthy Foods”

Y/p

(Y+S)/p

0 10 20 30 40

Percent

<1 1-2 2-3 3-4 >4

Figure 3: Obesity Status of Adults by Income

to Poverty Line Ratio, 2001-2010

>30 BMI >35 BMI

0 10 20 30 40

Percent

male female

<1 1-2 2-3 3-4 >4 <1 1-2 2-3 3-4 >4

Figure 4: Obesity Status of Adults by Income

to Poverty Line Ratio, 2001-2010: By Gender

>30 BMI >35 BMI

0 5 10 15 20

Percent

<1 1-2 2-3 3-4 >4

Figure 5: Obesity Status of Children by Income

to Poverty Line Ratio 2001-2010

>95th %tile >99th %tile

0 5 10 15 20 25

Percent

male female

<1 1-2 2-3 3-4 >4 <1 1-2 2-3 3-4 >4

Figure 6: Obesity Status of Children by Income

to Poverty Line Ratio 2001-2010: By Gender

>95th %tile >99th %tile

0 10 20 30 40 50

Percent

<1 1-2 2-3 3-4 >4

Figure 7: BMI and Waist Circ. Status of Adults by Income

to Poverty Line Ratio, 2001-2010

>25 BMI and high WC >30 BMI and high WC

0 10 20 30 40 50 60

Percent

male female

<1 1-2 2-3 3-4 >4 <1 1-2 2-3 3-4 >4

Figure 8: BMI and Waist Circ. Status of Adults by Income

to Poverty Line Ratio, 2001-2010: By Gender

>25 BMI and high WC >30 BMI and high WC

0 5 10 15 20

Percent

<1 1-2 2-3 3-4 >4

Figure 9: Direct Indicators of Obesity for Children by Income

to Poverty Line Ratio 2001-2010

tricep skinfold subscapular skinfold

0 5 10 15 20 25

Percent

male female

<1 1-2 2-3 3-4 >4 <1 1-2 2-3 3-4 >4

Figure 10: Direct Indicators of Obesity for Children by Income

to Poverty Line Ratio 2001-2010: By Gender

tricep skinfold subscapular skinfold

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