Impact of the SNAP Program on Food Consumption

This study investigates the impact of the SNAP program on food expenditure and consumption habits of low-income Americans. The study found that participation in the SNAP program positively impacts expenditure allocated to food items. The study made use of nutrition survey data available at https://cps.ipums.org/cps/index.shtml.

IMPACT OF THE SNAP PROGRAM ON FOOD CONSUMPTION

Executive Summary
Hunger, food insecurity and malnutrition is a major public health challenge affecting many low-
income Americans. The Supplemental Assistance Program serves as the US agency to assists
such low income sections of the US population towards improving food security and nutrition.
The Supplemental Assistance Program(SNAP) has rapidly grown over the past few years, with
participants increasing by about 300% between 2000 to 2015. The dramatic increase has led to a
series of research being conducted to investigate its impact on a people’s food consumption
habits.
Various researchers have proved that SNAP is highly effective at alleviating food insecurity and
improving nutrition. The program has been found to have far-reaching short-term and long-term
benefits on low –income household participants. Food assistance to the low-income people lead
to reduction in hunger rates and improvements in health and academic performance among
beneficiary children.
The primary aim of the current study was to investigate the impact of the SNAP program on food
expenditure. The study was conducted following the difference in difference technique where subjects
were studied for the differential impacts of SNAP program on food expenditure. Regression analysis 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 positively impacts on expenditure
allocated to food items. In a quest to fight food insecurity and improve nutrition, people who participate
in the SNAP program end up increasing resource allocation to food expenditure. An interaction with other
factors that influence food expenditure increases the effect that SNAP has on food expenditure.

Contents
Executive Summary.........................................................................................................................1
Contents...........................................................................................................................................2
Introduction......................................................................................................................................3
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

Introduction
The primary aim of the current study was to investigate the impact of the SNAP program on food
expenditure. The study was conducted following the difference in difference technique where subjects
were studied for the differential impacts of SNAP program on food expenditure.
According to (Hoynes, 2014) SNAP is, “…one of the largest cash or near-cash means tested, universal
safety net programs in the United States.” In particular, in 2013 there was an estimated $275 per benefits
for each household per month, that is $133 per person (Schmier, 2015). However, over time the
program’s implementation and benefits have changed a little relatively over time with just the same
framework adopted approximately 50 years ago being in 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 the household consumption.
Eligibility
The SNAP program is funded by the federal government and eligibility criteria are determined
by the federal government. For households to be eligible to be part of the SNAP program, 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 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 (Findeis, 2011). 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). The SNAP benefits are intended to help improve nutrition among
low-income households in the United States.

Research Objective
The objective of this study is to examine the impact of the SNAP program on food expenditure.
Research questions
After addressing the research objective, we hope to answer the following questions:
i. Is there a difference between the expenditure of the SNAP work force and the Non-SNAP work
force?
ii. Does SNAP affect the beneficiaries’ food consumption?
Limitations
Our research was prone with a number of limitations which include the credibility of the data
since it was collected from secondary sources. The model used in this study has estimated effects
which are particularly only unbiased as long as the functional form of the relationship between
the treatment and outcome is correctly modelled otherwise the results are prone to biasness.
Moreover, other treatments might contaminate the chosen treatment variable if different
treatments occur on the same cut-off points of the original assignment variables.
Methodology
Difference in Difference
The difference in difference (DID) is a statistical analysis technique used in quantitative
research. It tries to emulate the experimental design technique of research. Using the Difference
in difference 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.

Contrary to time series analysis which analyzes trends over time or cross-sectional analysis, the
difference in difference technique makes use of panel data to estimate the differences of the
changes that are observed in the response variable over time as a result of the treatment effect,
between the experimental and control groups.
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 difference
technique makes use of randomization in the selection of groups and subjects. However, the
difference in difference method may still be subject to some biases, for instance regression bias,
reverse causality bias and the omitted variable bias.
The DID technique requires that data is measured at two or more time periods for both the
experimental and control groups. Comparison is then made on the average change in the
dependent variable over time on the experimental group against the average change in the
dependent variable over time on the control group.
The difference in difference 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. That is, λ2-λ1 are equal in both s=1 and s=2. 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.
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.
The DID model is applied in this research in order to assess the impact of the SNAP program on
food expenditure. The treatment for this case is the admission of eligible
Instruments
For this assignment the STATA statistical software will be used for analysis. To perform DID in STATA
one can use just a normal difference in difference 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:
yit= γs(i)+ λt+δI+ έt,
where s(i) is the treatment group, yit is the dependent variable for subjects i and t and I(…)
represents the dummy variable equal to 1 when the event under topic is true, and 0 otherwise.
In a plot of time vs y by group, γs represents the vertical intercept and λt is the time trend shared
by both groups according to the parallel trend assumption. δ is the treatment effect, and έt is the
residual term.
Data
Given the research design which requires the use of historical data, secondary data is collected from
https://cps.ipums.org/cps/index.shtml (IPUMS). The website provides census and survey data
around the world. Data integration and documentation enhanced by IPUMS makes it easy to
study change and conduct comparative research on variables contained in a data set. Data is

freely available and obtained from the website. Food security data is extracted for purposes of
this research.
The food security survey is designed to capture the impact of SNAP on food expenditures. Apart
from food expenditures, the survey also records information on household SNAP benefits, family
income, number of family members, labor force status, and relationship to family head.
The sample dataset was cleaned to contain 12 variables i.e.:
Variables Included in Dataset
Primary Dependent Variable:
FSTOTXPN Total expenditures on food last week
Independent Variables (FSFDSTMP is primary IV):
FSFDSTMP Receive SNAP (food stamps in past year)
FSSTMPVAL Value of SNAP/food stamp benefits
Controls:
RELATE Relationship to household head
EMPSTAT Employment status
LABFORCE Labor force status
STATEFIP State (FIPS code)
FAMINC Family income of householder
FAMSIZE Number of own family members in household
NCHILD Number of own children in household
EDUC Educational attainment (recode)
The research data has 1,897,845 entries obtained from the 50 States in the US. For further documentation
on the dataset go to data documentation
Hypotheses
Two hypotheses were formulated in order to enable us to address the research question that is:
Null Hypothesis 1

There is no relationship between SNAP and food expenditures
Alternative Hypothesis
There is a relationship between SNAP and food expenditures
Null Hypothesis 2
There is no relationship between the interaction term and expenditure on food
Alternative hypothesis
There is a relationship between the interaction term and expenditure on food
Null Hypothesis 3
There is no relationship between, educational level, number of children, employment status and
participation in SNAP and food expenditure.
Alternative Hypothesis
There is a relationship between, educational level, number of children, employment status and
participation in SNAP and food expenditure.

Results and Discussion
Results
Relationship between SNAP and food expenditure
The results found a p-value of 0.0000 which is less than 0.05. This implies that at the 5% level of
significance, we reject the null hypothesis 1 that there is no relationship between SNAP and food
expenditure. We thus conclude that a relationship between SNAP and food expenditure indeed
exists.
The regression model for this relationship is represented by;
Food expenditure=145.0945−9.6448∗SNAP

The model implies that SNAP has a negative effect on food expenditure. An increase in SNAP
by one unit would result to a decrease in food expenditure by about 9.7 units.
Relationship between interaction and expenditure on food
The results on regressing expenditures on interaction of factors present a p-value of 0.04 which is
less than 005. Therefore, at the 5% level of significance, we reject the null hypothesis that there
is no relationship between interaction and expenditure on food.
The regression model for this relationship is given by;
Food expenditure=88.95388+1.509753∗interaction
The model implies that interaction between factors has a positive effect on food expenditure. 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 units.

Discussion
SNAP is designed to improve nutrition status among low income households in the United states.
Participants o the SNAP program are expected to improve food security and nutrition status in
their households by allocating more resources to food expenditure. However, the current study
found that participation in the SNAP program reduces food expenditure. This could be attributed
to the fact that the households participating in the program are offered free food items.
The effect of SNAP alone on food expenditure is found to be negative. In this case, other factors
that may influence expenditure on foods are controlled. 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 explaining the differences in the expenditure allocation to food.
It therefore follows that interaction has a positive relationship with food expenditure. Increasing
interaction among control factors results to an increase in food expenditure.
Conclusion
The main objective of this study was to examine the impact of the SNAP program on food
expenditure. The effect of participation in the SNAP program in association with other factors is
found to increase expenditures allocated to food. Previous research has demonstrated that
participation in the SNAP program increases food expenditure share by about 15 %. It is also
estimated that participation in the SNAP program increases utility share by about 5 %.
The results of this study provide knowledge and contribute to past research and existing literature
on the impacts of SNAP participation on food expenditure. This study did not, however,
investigate the impact that participation in the SNAP program has on revenue allocation to non-

food commodities. Research ought to be done on this as some non-food commodities such as
medication help improve nutrition.
Our research was prone with a number of limitations which include the credibility of the data
since it was collected from secondary sources. The model used in this study has estimated effects
which are particularly only unbiased as long as the functional form of the relationship between
the treatment and outcome is correctly modelled otherwise the results are prone to biasness.
Moreover, other treatments might contaminate the chosen treatment variable if different
treatments occur on the same cut-off points of the original assignment variables.
Further, the difference in difference method may still be subject to some biases, for instance
regression bias, reverse causality bias and the omitted variable bias.
A better analysis of the impacts of SNAP participation on food security and nutrition could be
arrived at given some innovations. First, various differential policies should be implemented at
state levels since different states have different population characteristics such as family and
poverty distribution. These differential policies ensure that the SNAP program is evaluated at
specific levels thus the impacts of the program can better be visualized. Second, nutritional
awareness ought to be conducted in order to have more people participating in the SNAP
program.

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