Impact of the SNAP Program on Food Consumption

This study investigates the impact of the Supplemental Nutrition Assistance Program (SNAP) on food consumption habits of low-income Americans. It examines the relationship between receiving SNAP benefits and weekly household expenditures on food. The study finds that participation in the SNAP program impacts household expenditures allocated to food, with SNAP recipients spending about $10 less per week on food compared to non-recipients. However, household food expenditures were about $1.50 higher among SNAP recipients from 2009-2018.

George Emmanuel Kalamotousakis
EC344 Semester Project
Professor Rodrigo Schneider
George Emmanuel Kalamotousakis
16-04-2019
IMPACT OF THE SNAP PROGRAM ON FOOD CONSUMPTION
Statement of Responsibility:
“I have not witnessed any wrongdoing, nor have I personally violated any conditions of the
Skidmore Honor Code while taking this examination.”
Signature:
George Emmanuel Kalamotousakis
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George Emmanuel Kalamotousakis
Executive Summary
Hunger, food insecurity and malnutrition are major public health challenges affecting
many low-income Americans. The Supplemental Nutrition Assistance Program (SNAP) serves
as the US agency to assist low-income sections of the US population towards improving food
security and improving nutrition. SNAP has grown rapidly over the past few years, with the
number of participants increasing by roughly 300% between 2000 to 2015. The dramatic
increase has led to a series of studies being conducted to investigate its impact on recipients’
food consumption habits.
Various researchers have proved that SNAP is highly effective at alleviating food
insecurity and improving nutrition. “SNAP has improved food security and reduced poverty for
millions of Americans” (U.S. Department of Agriculture, 2015). SNAP has helped “an average
of 45.8 million individuals per month in the year 2015” (U.S. Department of Agriculture, 2015).
The program has been found to have far-reaching short-term and long-term benefits on
participants from low-income households. A recent study suggests that “The ARRA (American
Recovery and Reinvestment Act of 2009) led to roughly a 12 percent increase in benefits for the
typical SNAP recipient and lifted roughly 8 percent, or 530,000 households, out of food
insecurity” (U.S. Departmentt of Agriculture, 2019). Food assistance to the low-income earners
“leads to a reduction in hunger rates and improvements in health and academic performance
among beneficiary children” (U.S. Department of Agriculture, 2019).
The primary aim of the current study was to investigate the impact of receiving SNAP
benefits on weekly household food expenditures. The study used the difference in differences
technique where subjects were studied for the differential impacts of SNAP program benefits on
food expenditure. The study examined the impact of SNAP benefits on food expenditures from
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George Emmanuel Kalamotousakis
2000-2018, using an interaction variable that compares expenditures before 2009 with
expenditures from 2009 to 2018 (after an expansion of SNAP benefits took place). Regression
analysis (using OLS regression) was conducted for purposes of analyzing the data. The study
made use of nutrition survey data available at https://cps.ipums.org/cps/index.shtml.
A key finding of the study is that participation in the SNAP program impacts household
expenditures allocated to food. In general, respondents who received SNAP benefits spent about
$10 less per week on food than households that did not receive SNAP benefits. However,
household food expenditures were about $1.50 higher among respondents who received SNAP
benefits from 2009-2018. This appears to show that people who participate in the SNAP program
end up increasing resource allocation to food expenditure during the time period studied.
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Table of Contents
Executive Summary 1
Contents 2
Introduction 3
SNAP 4
Eligibility 4
Research Objective 4
Research questions 5
Limitations 5
Methodology 5
Regression Discontinuity Design 5
Assumptions of DID 6
Instruments 7
Data 7
Hypotheses 8
Null Hypothesis 1 8
Alternative Hypothesis 8
Results and Discussion 9
Results 9
Regression Discontinuity 9
Discussion 11
Conclusion 11
References 12
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Introduction
The primary objectives of the United States Department of Agriculture (USDA) are
improvement of food security, reduction of hunger through enabling more access to food,
promoting consumption of healthy diets, and conducting nutrition education among low-income
Americans (Ratcliffe, McKernan, & Zhang, 2011). To realize the department’s objective, some
of the programs designed by USDA include the “Supplemental Nutrition Assistance Program
(SNAP), Special Supplemental Nutrition Program for Women, Infants, and Children (WIC), and
the National School Lunch and School Breakfast (School Meals) Programs” (Fraker and
Devaney, 2013).
SNAP is the largest program that assists people and families in the United States who are
suffering from hunger issues. “It offers nutrition assistance to millions of eligible, low-income
individuals and families” who are in need of it and “provides economic benefits to communities”
(U.S. Department of Agriculture, 2015). It has “helped more than 40 million Americans” (Center
on Budget and Policy Priorities, 2018). SNAP is very well known for the Food Stamp Program
as well. SNAP is funded by the federal government and the different states who use it. The total
cost of SNAP sums up to approximately $70 billion USD per fiscal year (Nord & Prell, 2011).
After the 2009 expansion that occurred “food expenditures increased by 5.4% and food
insecurity declined by 2.2%” (Nord & Prell, 2011).
SNAP benefits can be enjoyed by almost all households with low incomes. Who receives
SNAP benefits is determined by the government and the eligibility criteria are the same across
the nation, although some states can alter specific rules of the program. An example is that in
some states have different qualifications of who can get SNAP depending on the “value of a
vehicle or a household they might own.” In most cases in order to be able to get SNAP one must
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be making under 1,287 dollars per year. There are also specific “categories of people who are not
eligible for SNAP regardless of how small their income or assets may be” (U.S. Department of
Agriculture, 2015). These categories of people include “strikers, college students, legal
immigrants and illegal immigrants also are not eligible for SNAP” (U.S. Department of
Agriculture, 2015).
According to the Center on Budget and Policy Priorities an “average SNAP recipient
receives $126 a month which equals $4.20 a day or $1.40 per meal” (Center on Budget and
Policy Priorities, 2018). Depending on the specific income of a household SNAP benefits
received are a bit different and offer a more suitable food program which is more appropriate to
it. This means that in some cases poorer households enjoy more benefits than wealthier
households (Center on Budget and Policy Priorities, 2018).
Today, the SNAP program is implemented through issuance of monthly benefits to
beneficiaries in the form of Electronic Benefit Transfer (EBT) which are used when making
purchases at selected stores (Carlson et al., 2017). In 2013 there was an estimated $275 per
benefits for each household per month, that is $133 per person (Schmier, 2015). However, the
program’s implementation and benefits have changed very little over time with just the same
framework adopted approximately 50 years ago being adopted in the program today (Kim,
2015). The foods included in the SNAP program include breads and cereals, fruits and
vegetables, meats, fish and poultry, dairy products, seeds and plants that produce food for
household consumption.
Many people across the country believe that SNAP has had positive effects, including
decreasing the hunger rate in the United States (Center on Budget and Policy Priorities, 2018).
SNAP had more than 42 million participants in the fiscal year 2017 which amounts to 13% of the
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American population (Center on Budget and Policy Priorities, 2018). More than “68% of SNAP
participants were families with children.” SNAP has benefited also a lot of people who are
currently not in the workforce or are unemployed. SNAP helps the unemployed buy groceries so
they can support their family (Center on Budget and Policy Priorities, 2018).
Research Objective
The objective of this study is to examine how the Supplemental Nutrition Assistance
Program influences food consumption among the low-income work force in the United States.
As such, using data from 2000-2018, a regression model is used to examine whether a
relationship exists between receipt of SNAP benefits and weekly household expenditures on food
after the expansion of the SNAP program that happened in 2009. In this particular research 2009
was identified as the cutoff point for performing this experiment. The year 2009 was selected
because that was the year that the expansion happened in the SNAP program and it would be an
ideal cutoff point because results would show clear differences of household expenditures on
food consumption for families that received SNAP before and after that point. Moreover, the
research adopts a DID (difference in differences) design which upon proper design,
implementation and analysis, provides an unbiased estimate of the local treatment effect, making
it approximately as good as a randomized experiment when trying to measure a treatment effect.
Research questions
After addressing the research objective, we hope to answer the following questions:
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 Is there a difference between the household food expenditures of SNAP beneficiaries and
those who do not receive SNAP benefits?
 Does receiving SNAP benefits affect the beneficiaries’ food consumption?
Eligibility for SNAP Benefits
The SNAP program is funded by the federal government and eligibility criteria are
determined by the federal government. For households to be eligible for SNAP benefits, they
must have a monthly income less that 130 percent of the official poverty line. The eligible
households receive SNAP benefits every month. Since 2004, eligible households have benefited
in form of Electronic Benefit Transfer cards, which they can use to purchase food items.
The sample in the data which was used for this study was restricted to a “subsample of
the data with a higher probability of being eligible for participation in the SNAP program. Only
households with gross income below 130% of the poverty line were considered for the analyses”
(Caprio, & Boonsaeng & Zhen & Okrent, 2014). “The calculation of the poverty line was
conducted using the 1998-2009 poverty guidelines issued by the U.S. Department of Health &
Human Services (HHS)” (Caprio, & Boonsaeng & Zhen & Okrent, 2014). The SNAP benefits
are intended to “help improve nutrition among low-income households in the U.S” (Caprio, &
Boonsaeng & Zhen & Okrent, 2014).
.
Limitations
The model used in this study has estimated effects which are only unbiased as long as the
functional form of the relationship between the treatment and outcome is correctly modeled
otherwise the results are prone to bias. Moreover, other treatments might contaminate the chosen
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t variable if different treatments occur on the same cut-off points of the original assignment
variables.
Limitations that are identified in this research is that the treated group differs from
untreated group in important ways and so interaction is correlated with error. Additionally, there
are time trends such as the expenditures that were already rising faster among the newly eligible
group. In the research conducted something changed at the same time so one can’t tell what is
responsible for the results obtained.
Methodology
Difference in Differences
The difference in differences (DID) design is a statistical analysis technique used in
quantitative research. It tries to emulate the experimental design technique of research. Using the
difference in differences technique, we investigate the differential impact of a treatment on an
experimental group in comparison with the control group in a natural experiment. The impact of
treatment on the response variables is calculated. That is, the average change in the response
variable over time on the experimental group is compared to the average change in the response
variable over time for the control group. In this particular research, they DID method is useful
because the SNAP program changed in 2009. Testing the effects of the SNAP expansion is
similar to studying the results of an experiment by comparing the experimental group – SNAP
recipients after 2009 – to the control group households before 2009.
The essence of this technique of research is that it helps in mitigating some effects of
extraneous variables and bias as a result of group selection. This is because the difference in
differences technique makes use of randomization in the selection of groups and subjects.
However, the difference in differences method may still be subject to some biases, for instance
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regression bias, reverse causality bias and the omitted variable bias, which I discuss in greater
detail in the “Discussion” section.
To guarantee the accuracy of the DID model, the characteristics of subjects of the
experimental and control groups are expected to remain unchanged over periods. This is because
extraneous factors such as change in subject characteristics over time may adversely compromise
results of the model.
Instruments
For this assignment, the STATA statistical software will be utilized for analysis. To
perform DID in STATA one can use just a normal difference in differences regression model
which in our case will be regressing expenditure against the treatment variable, post-treatment
variable and the interaction variable together and independently to examine their relationship
with expenditure as well as develop a predictive model. After defining all of our variables, our
multiple regression model will be:
Food expendituresit = 𝛼 + 𝛽1𝑃𝑜𝑠𝑡𝑡t + 𝛽2𝑃𝑜𝑠𝑡𝑡t ∗ (𝑇𝑟𝑒𝑎𝑡𝑚𝑒𝑛𝑡𝑖) + 𝛽3Interaction𝑖t +
𝛽4𝑋𝑖𝑡 + 𝛬𝑖 + 𝜖𝑖t
Where a represents a constant/y intercept which are numbers you are going to estimate.
𝛽1 is coefficient which shows the relationship of how much savings change for people who did
not have their food expenditures affected. In addition to this 𝛽2 showed how different were the
treatment and control before the expansion and shows 𝛽3 how much more did the savings of the
treatment group change than the savings of the control group. 𝛽4 represents all the other controls
that you want. Λi in the equation represents the time invariant εi = time variant.
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Post is a dummy variable equal to 1 after 2009 which is the cut-off point in our
hypothesis and Interaction is the product of the two other dummy variables. Moreover, 𝛬𝑖 is a
year fixed effect 𝑋𝑖𝑡 is a vector of controls (number of children, family size).
t represents could be years from 2000 until 2018. Sub i = means that is varies across
observations and sub t it means that is varies across time
Food expenditures it which includes sub i + sub t, varies for individuals and time t is the
amount saved by an individual I in year t. If the individual received SNAP benefits or not after
2009.
Post does not vary among individuals, but varies over time, dummy variable equal to 1 if
for years after 2009. Treatment varies for individuals but does not vary over time, a dummy
variable equal to 1 if an individual received SNAP benefits, and 0 if he is not.
Interaction changes through time and for individuals. It is dummy variable to the product
of post and treatment of the dummy variable post and treatment. X varies among people and time
and a only changes individuals. ε which is the Error must have an i because it is individual
component and It changes for individuals in that specific situation.
Furthermore, fixed effects are a way of controlling characteristic about individuals that
do not changed over time and their race, gender do not change as well. Fixed effects are
variables that do no change over time. Location is also very common fixed effect.
The difference in differences technique operates on a set of assumptions, similar to the
Ordinary Least Squares (OLS) technique. In addition to the assumptions shared with the OLS
model, DID assumes a parallel trend. The parallel trend assumption requires that in the absence
of treatment, the difference between the experimental and control groups remains constant over
time. Violation of this assumption shall lead to biased estimation of the treatment effect.
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Data
For this study, I used data from the IPUMS’ Current Population Survey (CPS), which can
be found at https://cps.ipums.org/cps/index.shtml. The website consolidates and harmonizes
census and survey data acquired by the monthly U.S. labor force survey, called the Current
Population Survey (CPS). Each month, the Census Bureau and the Bureau of Labor Statistics
survey approximately 60,000 households across the United States, gathering demographic and
employment data and providing an estimate of the unemployment rate. IPUMS data is available
for free and can be obtained from the website. For this study, I used data on household food
expenditures and food security, which is released in December of each year.
The dataset used for this study contained 2,524,267 observations (of individual
households) and 12 variables. The dependent variable was a self-reported measure of weekly
household food expenditures. The main independent variable was a binary variable that
measured whether or not a household receives SNAP benefits. Other variables included the
respondent’s state of residence, family income, number of children in the household, educational
attainment, and labor force status.
Research Questions/Hypotheses
Over the course of this study, I tested three research questions about the relationship
between receiving SNAP benefits and household food expenditures.
First, I examined the relationship between receiving SNAP benefits and weekly
household food expenditures using basic linear (OLS) regression. Second, I examined the same
relationship using the DID method and an interaction variable that combined the SNAP benefits
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variable with the time period 2009-2018 (after the 2009 expansion of benefits to jobless
individuals without children). Finally, I examined the effect of controlling for the respondents’
education level and the number of children in the household on the DID estimate.
These research questions can also be framed as the following hypotheses:
1) Null Hypothesis 1
There is no relationship between SNAP and weekly household food expenditures.
Alternative Hypothesis
There is a relationship between SNAP and weekly household food expenditures.
2) Null Hypothesis 2
There is no relationship between the interaction term and weekly household food
expenditures.
Alternative hypothesis
There is a relationship between the interaction term and weekly household food
expenditures.
3) Null Hypothesis 3
Including controls for educational level and number of children in the household has no
significant effect on the relationship between the interaction term and food expenditures.
Alternative Hypothesis
Including controls for educational level and number of children in the household has a
significant effect on the relationship between the interaction term and food expenditures.
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Results and Discussion
Results
Relationship between SNAP and food expenditures
VARIABLE
S
expenditure
s
Standard errors in
parentheses
*** p<0.01, ** p<0.05, * p<0.1
snap -9.645***
-0.307
Constant 145.1***
-0.0923
Observations 1,561,308
R-squared 0.001
According to the first regression, there is a statistically significant relationship between
receiving SNAP benefits and weekly household food expenditures. Overall, during the time
period from 2000-2018, households that received SNAP benefits spent about $9.64 less per week
on food than households that did not receive SNAP benefits. The “SNAP” variable has a p-value
of 0.000 which is less than 0.05. This implies that at the 5% level of significance, we reject null
hypothesis 1 that there is no relationship between SNAP and food expenditures.
The model implies that receiving SNAP benefits has a negative effect on weekly
household food expenditures. Since receiving SNAP benefits is a binary variable, an increase in
SNAP by one unit (ie, a household receiving SNAP benefits) results in a decrease in food
expenditures by about $9.64 per week.
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Relationship between interaction variable and expenditure on food, controlling for education
and number of children in household
VARIABLES expenditures Standard errors in parentheses
-1
*** p<0.01, ** p<0.05, * p<0.1
interaction 1.629***
-0.615
snap -12.48***
-0.449
post 13.46***
-0.188
Constant 139.7***
-0.119
Observations 1561308
R-squared 0.004
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According to the regression in the table above (which tests hypothesis 2), there is a
statistically significant relationship between the interaction variable and weekly household food
expenditures (without including controls for education level and the number of children in the
household). Households that received SNAP benefits during the time period 2009-2018 spent
about $1.63 more per week than other households. The interaction variable has a p-value of
0.008 which is less than 0.05. This implies that at the 5% level of significance, we reject null
hypothesis 2 that there is no relationship between the interaction term and weekly household
food expenditures.
According to the regression in the table below (which tests hypothesis 3), there is a
statistically significant relationship between the interaction variable and weekly household food
expenditures when controlling for education level and the number of children in the household.
Households that received SNAP benefits during the time period 2009-2018 spent about $1.51
more per week than other households. The interaction variable has a p-value of 0.04 which is less
than 0.05, so this result remains statistically significant even after including the controls.
However, these results are slightly different from the previous regression (which did not include
any controls). After controlling for education and the number of children in a household,
receiving SNAP benefits increases weekly expenditures on food.
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Relationship between interaction variable and expenditure on food, controlling for education
and number of children in household
VARIABLE
S
expenditure
s
Standard errors in
parentheses
-1
*** p<0.01, ** p<0.05, *
p<0.1
snap -17.78***
-0.553
nchild 19.55***
-0.0881
educ 0.383***
-0.00388
interaction 1.510**
-0.736
o.snap -
post 12.41***
-0.2
o.interaction -
Constant 88.95***
-0.345
Observations 1,236,707
R-squared 0.053
The model implies that interaction between factors has a positive effect on weekly
household food expenditures, when controlling for these other factors. The factors jointly
influence food expenditure positively. An increase in interaction effect by 1 unit would result to
a corresponding increase in food expenditure by about $1.51. Additionally, while there is a
relationship between receiving SNAP benefits and lower food expenditures, the post variable
shows that food expenditures in general were higher after 2009 than before 2009. This may help
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to explain why the interaction variable shows an increase in food expenditures of $1.51 among
SNAP beneficiaries after 2009 compared to before that date.
Discussion
SNAP is designed to improve food security and nutrition among low income households
in the United States. Participants in the SNAP program are expected to experience improved
food security and nutrition in their households as a result of spending more money on food.
The effect of receiving SNAP benefits alone on household food expenditures is found to
be negative. In this case, other factors that may influence expenditure on foods are not controlled
for. When other factors are added, the magnitude of the effect of SNAP on food expenditure
changes. The effect of interaction between SNAP and other factors is positive, which is
indicative of the significance of the control variables (and the post variable, which compares
household food expenditures before 2009 with expenditures after 2009) to explaining the
differences in the expenditures allocated to food. It therefore follows that interaction has a
positive relationship with food expenditure. Household food expenditures were higher in the
period after 2009, even for households that received SNAP benefits.
The fact that the results differ between the first regression and the later regressions
reveals why it is better to use the DID method. The first regression looks at the effects of
receiving SNAP benefits in general across the entire time period from 2000-2018, and finds that
receiving SNAP benefits reduces weekly household food expenditures by an average of about
$9.64. But this simple regression does not take into account the expansion of the SNAP program
that happened in 2009. This is why it makes more sense to use the DID method, which compares
the effects of receiving SNAP benefits during and after 2009 with the effects before 2009. When
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we include the interaction term, we see that receiving SNAP benefits actually increases weekly
household food expenditures by about $1.51 (when controlling for other factors).
Still, this study has several limitations. For one thing, an increase of $1.51 may not seem
like much. However, this depends on what a given household’s typical weekly food expenditures
are. An increase in spending of $1.51 is much more significant for a poor household that only
spends $40 or $50 per week on food, and in that case receiving SNAP benefits may make a
difference to that family’s nutrition. So, the effect of such an increase depends on how much a
household was spending on food before receiving SNAP benefits.
Additionally, just because household food expenditures are slightly higher does not mean
that the SNAP program has necessarily achieved its goals. For example, maybe people still aren’t
eating nutritious food for some reason. Further research will be necessary to determine what
types of people SNAP recipients are buying and whether or not receiving SNAP benefits leads to
an increase in nutrition and overall health.
Conclusion
The main objective of this study was to examine the impact of the SNAP program on weekly
household food expenditures. Among participants in the SNAP program after 2009, receiving
SNAP benefits is found to increase household expenditures allocated to food by about $1.51 per
week. Participation in the SNAP program increases food expenditure share by about 15% and it
is also increases utility share by about 5% according to the United States Department of
Agriculture (U.S. Department of Agriculture, 2015). Food expenditure is “data set that measures
the U.S. food system, quantifying the value of food acquired in the United States by type of
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product, outlet, and purchaser” (United States Department of Agriculture, 2019). Utility share is
the “share of total household expenditure (as a proxy of income) spent on food which indicates
that the poorer and more vulnerable a household, the larger the share of household income spent
on food” (Data4Diets, 2014).
The results of this study contribute to past research and existing literature on the impacts
of SNAP participation on food expenditures. This study did not, however, investigate the impact
that participation in the SNAP program has on household spending on non-food commodities.
Research ought to be done on this as some non-food commodities such as medication help
improve nutrition.
A better analysis of the impacts of SNAP participation on food security and nutrition
could be arrived at given some innovations. First, while SNAP is a national program, studying its
effects in individual states would provide greater insights into its strengths and weaknesses.
Since different states have different industries and natural resources, as well as different levels of
poverty, unemployment, and education, it would be useful to study the effects of a program like
SNAP on a state or even local level. Finally, nutritional awareness ought to be conducted in
order to have more people participating in the SNAP program, which may help researchers study
the impact of the program in the future.
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References
 Carlson, A., Lino, M., Juan, W., Hanson, K., and Basiotis, P. (2017). Thrifty Food Plan.
Retrieved from:
http://www.cnpp.usda.gov/Publications/FoodPlans/MiscPubs/TFP2006Report.pdf
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Supplemental Nutrition Assistance Program on Food and Nonfood Spending Among
Low-Income Households. Retrieved April 03, 2019, from
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 Nord, M., & Prell, M. (2011, April). Food Security Improved Following the 2009 ARRA
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