Effect of Supplemental Nutrition Assistance Program on Food Consumption among Low-Income Workforce in the United States

Executive Summary
In order to understand the effect of social programs on the target population it is often crucial to
determine the effect of the program on the population before and after administration of the program.
Using a Regression Discontinuity Design, this research explores the effect of Supplemental Nutrition
Assistance Program (SNAP) on weekly food consumption in the United States’ low-income workforce.
The research aims to answer the question as to whether there exists a relationship between the treatment
and control variables as well as the interaction effect and if it does, what kind of relationship
From the results, at 0.05 level of significance, we reject the null hypothesis of no difference between the
means of expenditure on food between the beneficiaries of SNAP and Non-SNAP beneficiaries and
conclude that there is significant relationship between the overall effect of SNAP in the United States and
when using the multiple regression model we realize that there exists a positive relationship where an
increase in $1 million expenditure in the SANP program leads to an increase weekly food consumption
by approximately $1914.36 implying that the SNAP program influences both the taste of food
consumption among recipients of the social program and amount of food consumed from the consumer
stores. However, when analyzing the effect of SNAP state wise, some states food consumption is not
influenced by SNAP participation.

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 RDD...............................................................................................................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
Among the priority objectives by the United States Department of Agriculture (USDA) are: increment of
food security, reduction of hunger though enabling more access to food, promoting consumption of
healthy diets, as well as 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: Supplemental Nutrition Assistance Program (SNAP), Special Supplemental Nutrition
Program for Women, Infants, and Children (WIC); the National School Lunch and School Breakfast
(School Meals) Programs, etcetera (Fraker and Devaney, 2013). Over the recent past, in the analysis of
the performance of the programs for instance when considering the Supplemental Nutrition Assistance
Program during the financial year 2011, the program served approximately over 46 million Americans
with a cost of approximately $75 billion (Senal and West, 2015). In comparison with the WIC and NLSP
programs, the WIC program has enabled approximately 8.2 million families to access healthy foods,
health care, nutrition education and advice (Schmier, 2015) whereas the NLSP in 2014 alone served about
30 million school children with the government allocating approximately $12.7 billion (CBO, 2015).
Today, the SNAP program is implemented through issuance of monthly benefits to beneficiaries i.e. in
the form of Electronic Benefit Transfer (EBT) which are used when making purchases at selected
consumer stores (Carlson et al., 2017).
In this section, an overview of the SNAP program, eligibility, foods included in the program and the
researches objective are explored.

SNAP
According to Hoynes et al. (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
Falk (2014) on the study of categorical eligibility of SNAP notes that, for one to be eligible for the food
stamp they must fall into the following criterion, i.e.: their maximum gross income per month should be
130% that of the federal poverty level, their resource count should not exceed $2,250 unless there is a
disabled or elderly person in which case the countable resources should not exceed $3, 500, the
employment status is also a consideration except for the pregnant, elderly and the disabled who are
exempted from the employment requirement. Other participants who contribute to the workforce are
lawfully admitted non-citizens (Rosenbaum, 2013) hence are suitable for the study’s analysis.
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 historical data,
a regression model is used to examine whether there exists a relationship between the expenditure of
SNAP program and expenditure of food consumption by persons eligible for the SNAP program.
Moreover, the research adopts a Regression Discontinuity Design Methodology (RDD) which upon
proper design, implementation and analysis, provides an unbiased estimate of the local treatment effect
thence it is approximately as good as a randomized experiment when trying to measure a treatment effect.