The Correlation between Free and Reduced-Price Lunch and
High School Academic Performance in North Carolina
Danilo Morales, Western Vance High School
Matt Charles, Mitchell High School
Research Experience for Teachers, Computer Science Department, Appalachian State University
Abstract
Poverty affects the growth, development, and educational outcomes of many young people across North
Carolina. It is a critical element that needs to be supported by other components of poverty alleviation strategies
such as the free and reduced-priced lunches in school to improve students’ outcome. This paper uses the percentage
of free and reduced-priced lunches as a measure of poverty in schools to examine the performance of a county’s
high schools based on multiple forms of standardized assessments, graduation rates, and dropout rates under a
comparative and established assumption that poverty plays a significant role in the academic achievements of
schools across North Carolina.
I. INTRODUCTION
Intellectual development covers human
perception, thinking, and learning. There are many
factors that contribute to the learning development of
a human being. Some of these are the genes,
environment and the socio-economic status.
However, the role of good nutrition in human growth
and learning is very important. Nutrition during the
early years of a human’s life may be linked to
performance in later years. There is substantial proof
that proper nutrition plays an important role for
cognitive functioning of the human being and to the
brain’s development, which begins even before
birth1. In addition, the economic status, primarily the
household income, of every family takes a big part in
achieving this objective.
As a way to provide nutrition to
impoverished students, the federal government began
subsidizing school lunches with the passage of the
National School Lunch Act in 1946. This law
established the National School Lunch Program
(NSLP). In fiscal year 2014, federal school nutrition
programs spent nearly $12.6 billion to provide 30.3
million low cost meals daily to students across the
United States. The total funding for all nutrition
programs sums up to $16.3 billion in both cash and
commodity payments in fiscal year 2014 2. According
to the North Carolina Department of Public
Instruction (NCDPI) , during school year 2007-08,
there were about 48.4 percent of students in North
Carolina who were enrolled in the Federal Nutrition
Program, and it increased to 56 percent for school
year 2011-123. In spite of the governmental effort, a
modest participation in the program of Free and
Reduced-Price Meals in some school districts were
observed.
Eligibility for the free and reduced-price
lunch program is based on the federally defined
poverty level. According to the Federal Government,
the poverty level was defined in 2012 as an annual
income of $23,283 or less for a family of four, or
$11,945 for an individual4. In North Carolina, the
overall poverty rate for 2012 was 17.2 percent,
according to the U.S. Census Bureau's American
Community Survey5. Conceivably, this information
explains the increase on the number of students who
qualified for the Free and Reduced-Price Meal
Program of the government for the past years.
The following sections state the overview on
the data gathered from North Carolina Department of
Public Instruction from 2006-20136,7. The
information was used to examine whether the
program also contributed a positive impact on
students’ academic performance for high school
students in North Carolina and to analyze other
factors attributed to it. This study is not design to be
absolute nor to be a comprehensive analysis of all the
possible determinant factors of the students’
academic achievements, but rather it is intended to
add on to the existing research and other related
studies. Furthermore, this research cannot hope to
generalize the findings beyond the data used in the
analysis due to other factors and variables that affects
students’ performance.
The body of this research will examine the
trends that might be supported or contradicted by the
data gathered. Since this report is solely based on
information collected from NCDPI and U.S. Census
Bureau, there is no attempt to use the information
acquired to change or to make recommendations on
the existing guidelines of the state and of the federal
government for the education and health programs.
This is only a simple report on what was discovered
on the existing data and in the hope that the possible
findings will clarify factors that affect the academic
achievements of the high school students across the
state.
II. METHODOLOGY
The analyses presented on this report were
based on the collected information from school year
2006-07 to school year 2013-14 obtained from North
Carolina Department of Public Instructions and the
U.S. Census Bureau. Charter schools were excluded
from the analyses and the study focuses only on
traditional public high schools. The process of the
analyses involves the examination on the relationship
among poverty rate, percentage of the free and
reduced-price meals, multiple forms of standardized
assessments (EOC, ACT, and SAT results),
graduation rates and the dropout rates for each of the
100 counties in North Carolina.
The Federal Government sets the standard to
identify the recipient of the National Lunch Program.
Students from families with an income at or below
130 percent of the federally defined poverty level are
eligible for free meals. Those students with family
income that is between 130 percent and 180 percent
of the federally defined poverty level are eligible for
reduced-price meals in which they will pay no more
than $0.40 for a meal. The local school food
authorities are responsible to set the prices for paid
meals but they are required to operate their services
as a non-profit program. Figure 1 illustrates the
guidelines for the Federal Poverty Level.
Figure 1: 2015 Federal Poverty Level (FPL)
Guidelines by Family Size All Programs (excluding
SNAP)
In this research, the Pearson productmoment correlation coefficient was used to assess the
relationship between the percent of participants of the
free and reduced-price meal program and the
students’ academic achievements. This statistical
standard is designed to identify the linear correlation
between two variables, which gives emphasis on the
degree to which a linear model may describe the
relationships between two components. However, the
association between the two variables in person’s
correlation does not imply causation since there are
other factors that affect the acquired data. The
variable x and y are commonly used to represent two
different components in correlation.
The Correlation Coefficient that is
represented by variable (r) may range from -1.00 to
+1.00. The values closer to 1.00 represent stronger
relationships while values closer to zero (0) represent
weak relationship. A zero (0) coefficient indicates no
relationships between variables. The relative strength
of this relationship is defined in Table 1. The
positive or negative sign in the correlation sets the
direction of the relationship. The straight line as seen
in the graphs in Figure 3, represents the prediction
line or the fitted line. This is also the line, which is
closest to the most points in the graph. The
correlation coefficient is calculated using the
equation shown in Figure 2, where n is the number of
data points.
𝑟=
𝑛(∑ 𝑥𝑦) − (∑ 𝑥)(∑ 𝑦)
√[𝑛 ∑ 𝑥 2 − (∑ 𝑥)2 ][𝑛 ∑ 𝑦 2 − (∑ 𝑦)2 ]
Figure 2: The equation used to calculate the Pearson
Correlation Coefficient
Absolute Value of
Strength of
Correlation Coefficient
Relationship
(r)
1
Perfect
Over 0.7
Strong
0.50 - 0.69
Moderate
0.30 - 0.49
Low
Less than 0.30
Weak
0
No Correlation
Table 1: Correlation of Coefficient and Strength of
Relationship
Note: This categorization shall be used as broad
guideline.
On the other hand, R2 in the correlation
(expressed in decimals) is the percent of difference in
the variability of the model. In other words, it
represents how much the change in y is affected by
the change in x.
The different scatterplots and the associated
correlation coefficients are presented in the following
data. Figure 3 illustrates the differences among the
graphs.
% Free and Reduced vs
Poverty Rate
100.0
80.0
60.0
40.0
20.0
0.0
-5
5
15
25
35
Figure 4b: Scatterplot of Percent Participation in Free
and Reduced-Price Meal versus the Poverty Rate for
School Year 2008-2009
Figure 3: Scatterplots and the Associated Correlation
III. RESULT
Starting from school year 2006-07 to school
year 2013-14, it is observed that the percent of
students who participated in the National School
Lunch Program have gone up from 37.88 percent to
47.92 percent (See figure 4a and 4b). In addition, the
percentages of the recipient for free lunch went up
from 30.81 percent to 41.42 percent while the
percentages that were enrolled for reduced-price meal
went down from 7.19 percent to 6.5 percent.
60.00%
Percent Participants in Free and
Reduced-Price Meal
There was a very strong correlation
established between the Poverty Rate and the percent
of participants in the Free and Reduced-Price Meal
for all school years 2006-07 to 2013-14. In fact the
highest correlation was established in school year
2008-09 with a correlation coefficient (r) of 0.839. It
was also observed that about 62.85 percent of the
variability in poverty rate was affected by the
variability of the percent participants in the free and
reduced-priced lunch program. Table 2 below,
identifies the different correlation coefficients in 8
different school years.
Taking the correlation
between all 8 years’ worth of data points into
consideration, it was established that the free and
reduced lunch participation is a suitable stand-in as a
measure of poverty in schools. Henceforth, poverty
rate will be represented as the percentage of
participation in the free and reduced-price lunch
program.
50.00%
40.00%
Subject
School
Year
Poverty Rate
2006-07
2007-08
2008-09
2009-10
2010-11
2011-12
2012-13
2013-14
30.00%
20.00%
10.00%
0.00%
Figure 4a: Percent Participants in Free and ReducedPrice Meal (SY 2006-07 – 2012-13)
% Participants in
Free and
Reduced-Price
Meal
0.758
0.758
0.839
0.765
0.755
0.693
0.735
0.705
Table 2: Correlation between the Poverty Rate and
the Percent Participants of Free and Reduced- Price
Lunch
Another factor that was examined in this
paper was the correlation between the percent
participants in the free and reduced-price meal over
the multiple forms of standardized assessment,
graduation rates, and dropout rates. Table 3 and
Figure 6 illustrate the trend of the coefficients in 6
school years for English 1 EOC Percent of Proficient.
There is a moderate negative correlation observed in
this table.
Subject
School Year
English I
EOC %
Proficient
2006-07
2007-08
2008-09
2009-10
2010-11
2011-12
2012-13
2013-14
% Participants in
Free and
Reduced-Price
Meal
-0.722
-0.690
-0.723
-0.532
-0.636
-0.519
-
Table 3: Correlation between English 1 EOC Percent
Proficient and the Percent of Free and Reduced-Price
Meals.
EOC Eng 1 Proficiencty vs. %
Free and Reduced Luch
100
The same observation was seen for both Mathematics
and Biology subjects.
Table 4 reports the trend of the correlation
coefficient in the Biology Percent Proficiency and
Mathematics Percent of proficiency. The correlations
were not very consistent. In school year 2009-10 to
school year 2011-12 the correlations were weak. It
was an outstanding data since those school years
were the years when the economic recession
happened.
% Participants in
Subjects
School Year
Free and ReducedPrice Meal
2006-07
-0.564
2007-08
-0.570
2008-09
-0.650
Biology
2009-10
-0.486
EOC %
2010-11
-0.451
Proficient
2011-12
-0.434
2012-13
-0.708
2013-14
-0.726
2006-07
-0.656
2007-08
-0.651
2008-09
-0.619
2009-10
-0.498
Math %
Proficient
2010-11
-0.477
2011-12
-0.414
2012-13
-0.620
2013-14
-0.630
Table 4: Correlation in Biology and Math EOC
Proficiency and the Participation in Free and
Reduced-Price Meals
80
60
40
20
0
0.0
20.0
40.0
60.0
80.0
100.0
Figure 6: Scatterplot of English 1 EOC vs Percent
Participation Free and Reduced Lunch School Year
2006-07
Figure 6, shows that about 52.09 percent
difference in variability on the English 1 Proficiency
was affected by the difference in variability on the
Percent Participation on the Meal Program.
Conversely, there are some school districts with high
percent participation in the program that acquired
fairly good percent proficiency rate in the English 1.
Thought-provoking results were found in the
correlation between the Free and Reduced-Price Meal
program and the SAT and ACT Percent of
Proficiency. On the first three years of the
observation period, very strong correlations have
manifested in SAT Result. However, in the
succeeding school years the correlation coefficient
plunged. Except for one school year, the correlation
from school year 2009-10 to school year 2013-14
was low. On the other hand, the ACT result
demonstrated a very strong negative correlation in the
three-year observation period. Table 5, shows the
complete correlation coefficients derived from the
evaluation of the data.
Subject
School Year
% Participation in
Free and Reduced
Meal Program
Subjects
SAT M+CR
ACT
Composite
2006-07
2007-08
2008-09
2009-10
2010-11
2011-12
2012-13
2013-14
2006-07
2007-08
2008-09
2009-10
2010-11
2011-12
2012-13
2013-14
-0.818
-0.793
-0.768
-0.353
-0.350
-0.772
-0.351
-0.306
-0.715
-0.794
-0.729
Table 5: Correlation in SAT and ACT Percent of
Proficient and the Percent of Free and Reduced-Price
Meal
Very weak positive correlations are found in
the dropout and graduation rate versus the percent
participation for Free and Reduced-Price Meal while
low negative correlations for graduation rate. There
are two things that really stand out in this data. First,
it is predominantly observed that all the school
districts have a single digit dropout rate. In the 8
years observation period, the highest dropout rate
recorded was in school year 2006-07 at 8.7 percent
and the lowest was 0 percent in school year 2013-14.
Second, it was observed that the graduation rate in
some school districts with high participation rate in
the Free and Reduced Lunch program have
remarkable graduation rates and comparatively low
dropout rates. Summing it up, the highest graduation
rate recorded was in school year 2013-14 was 95
percent and the lowest graduation rate was recorded
in school year 2007-08 at 15.4 percent. Table 6 and
Figures 7a and 7b show the correlation coefficients
and the scatterplots of the Dropout and Graduation
Rates.
% Participants in
Free and ReducedPrice Meal
0.223
0.169
0.083
0.341
0.315
0.267
0.445
0.200
-0.397
-0.560
-0.564
-0.476
-0.389
-0.362
-0.400
-0.386
School Year
2006-07
2007-08
2008-09
2009-10
2010-11
2011-12
2012-13
2013-14
2006-07
2007-08
2008-09
2009-10
2010-11
2011-12
2012-13
2013-14
Dropout
Rates
Graduation
Rates
Table 6: Correlation in Dropout and Graduation Rate
over the Percent Participation in Free and ReducedPrice Meal
Graduation Rate vs % Free and
Reduced
100
80
60
40
20
0
0.0
20.0
40.0
60.0
80.0
100.0
Figure 7a: Scatterplot on Graduation Rate vs Percent
Participation on Free and Reduced-Price lunch for
School Year 2010-11
Dropout Rate vs % Free and
Reduced
10
8
English
1 EOC
49
50
49
54
52
48
-
School Year
2006-2007
2007-2008
2008-2009
2009-2010
2010-2011
2011-2012
2012-2013
2013-2014
6
4
2
Biology
EOC
47
47
45
52
50
46
32
38
Math
EOC
43
52
50
52
47
49
40
41
0
20.0
40.0
60.0
80.0
100.0
Figure 7b: Scatterplot on Dropout Rate vs Percent
Participation in Free and Reduced-Price lunch for
School Year 2010-11
Tables 7a-7b and Figures 8a-8c illustrate the
number of counties reached the percent proficiency
level on End-of-Course Subjects base on the percent
proficiency level attained by the State of North
Carolina. From school year 2006-07 to school year
2011-12, there was an average of 50 counties
achieved the percent proficiency in English 1, an
average of 48 counties for Biology and an average of
49 counties for Mathematics. However, there was a
tremendous decrease on the number of counties that
achieve the state percent proficiency for school year
2012-13 and school year 2013-14. These were the
years when the common core curriculum was
implemented with more rigorous testing standards.
Figures 9a and 9b show this decrease from the year
immediately preceding the curriculum change and the
year immediately after the curriculum change.
School
Year
2006-2007
2007-2008
2008-2009
2009-2010
2010-2011
2011-2012
2012-2013
2013-2014
English 1
EOC
72.1%
73.1%
73.8%
82.4%
80.6%
82.9%
-
Biology
EOC
65.3%
68.0%
71.2%
81.2%
79.9%
83.0%
45.6%
53.9%
Math
EOC
66.7%
69.0%
67.7%
77.8%
76.6%
78.7%
36.3%
60.0%
Table 7a: Percent of States Proficiency in EOC
Subjects in North Carolina
Table 7b: Number of Counties at or above state
average of proficiency in EOC Subjects
% Proficient in English 1 EOC
40%
% From State Average
0.0
20%
0%
0
20
40
60
80
100
-20%
-40%
-60%
County (Alphabetical order 1-100)
Figure 8a: Scatterplot of the Counties for the Percent
Proficiency level in EOC English 1
% Proficiency in Biology EOC
40%
20%
0%
0
20
40
60
80
100
-20%
-40%
-60%
Figure 8b: Scatterplot of the Counties for the Percent
Proficiency Level in EOC Biology (SY 2006-07)
Last Year of Algebra 1, 1st
Year of Math 1 vs % Free and
Reduced
% Proficient in Algebra 1 EOC
40%
20%
100.0
0%
-20%
0
20
40
60
80
100
50.0
-40%
0.0
-60%
0.0
Figure 8c: Scatterplot of the Counties for the Percent
Proficiency in EOC Math (SY 2006-07)
Note: The points above the zero (0) line represent the
counties in which percent proficiency level is at or
above the state average percent proficiency in EOC
Subjects
Statewide Correlation Year
Before and Year After
Standard Change Biology
100
80
60
40
20
0
0.0
20.0
40.0
60.0
80.0
100.0
Figure 9a: Scatterplot of the years before and after
testing standards 2011-12 and 2012-13 for biology
20.0
40.0
60.0
80.0
100.0
Figure 9 b: The years before and after testing and
curriculum standards changed from Algebra 1 (201112) to Math 1 (2012-13).
IV. CONCLUSION
The data that were evaluated in this research
indicates that the level of school poverty and the
number of participants in the Free and ReducedPrice Meals in the school indicates a strong
correlation. The degree of the relationships varies
from the multiple standardized assessments,
graduation and dropout rates to other factors affecting
students’ academic achievement. However, in years
when a new curriculum or testing standard is
implemented, proficiency drops for everyone. It is
concluded that the percentage of the drop is relatively
small for counties with fewer free and reduced lunch
percentages and larger for counties with higher free
and reduced lunch percentages. This leads the
authors to believe that when changes are
implemented to testing or curriculum, it has a larger,
more negative impact on poorer counties than more
affluent counties.
In spite of the increase in the number of
participation for the Free and Reduce-Price Meals in
the State of North Carolina, the program was not
strong enough to significantly influence the
educational outcomes of the high school students.
Furthermore, it is not conclusive that a larger amount
of participation in the program in the future may
yield to academic benefits. The result of this analyses
point out the importance of the program especially on
the nutrition and well-being of the students but
clearly illustrates that additional strategies and
interventions shall be done to improve the academic
achievements of the high school students across the
state.
V. ACKNOWLEDGEMENTS:
The authors would like to acknowledge Mr.
Mark Woody and Mr. Eric Pierce, school Principals
of Mitchell High School and Western Vance high
School for the approval and support to attend in the
Research Experience for Teachers Program 2015
funded by the National Science Foundation. We
would like to thank the Department of Computer
Science of Appalachian State University, Dr.
Rahman Tashakkori, and Dr. Mitch Parry for hosting
this event. A special thanks is given to Dr. Mary Beth
Searcy for her assistance in data analysis during this
study.
VI. REFERENCES:
1.
2.
3.
4.
5.
6.
7.
8.
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ull
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NCDPI Free and Reduced Lunch Data
http://www.ncpublicschools.org/fbs/resourc
es/data/
2015 Federal Poverty Level (FPL)
Guidelines by Family Size All Programs
(Excluding SNAP)
http://www.dhs.ri.gov/Portals/0/Uploads/Do
cuments/Public/General%20DHS/FPL.pdf
Poverty Levels in North Carolina
http://www.census.gov/did/www/saipe/data/
statecounty/data/2012.html
Standardized Assessments Results
North Carolina Department of Public
Instruction
http://www.ncpublicschools.org/accountabili
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Carolina
NCDPI
http://apps.schools.nc.gov/pls/apex/f?p=1:19
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Brain & Behavioral Development of
Toddlers
and
Preschool
Children:
Identifying and Overcoming Methodological
Barriers
http://www.ncbi.nlm.nih.gov/pmc/articles/P
MC2776771/
9. Percent of Students Enrolled in the Free and
Reduced-Price Lunch
Kids Count Data Center
http://datacenter.kidscount.org/data/tables/2
239-percent-of-students-enrolled-in-freeand-reducedlunch?loc=35#detailed/2/any/false/1021,909
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Factors Affecting Student participation
http://frac.org/pdf/national_school_lunch_re
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11. Pearson
product-moment
correlation
coefficient
Wikipedia the Free Encyclopedia
https://en.wikipedia.org/wiki/Pearson_produ
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