This paper investigates the effect of youth sports participation on fifth grade math test scores using data from the Early Childhood Longitudinal Survey. Regression analyses found that sports participation had a positive and statistically significant effect on math scores. However, this effect was reduced when controlling for student demographics, family background, and school characteristics. The positive effect of sports was further reduced when controlling for potential mediating factors like physical fitness, TV watching, and participation in other activities. Certain demographic characteristics like region and participation in other activities also moderated the effect of sports on math scores. When students participated in all measured activities, sports participation had a negative effect on math scores.

1. The Effect of Sports Participation on Youth Academic Performance: A Study
of the Early Childhood Longitudinal Survey
Helen Carey, Maddie Organ, Rachael Quast, Thomas Yaeger (2:00 Section)
December 6, 2016
Abstract:
This paper investigates the effect of participation in athletics on youth academic
outcomes, specifically fifth grade math test scores, using data from the Early Childhood
Longitudinal Survey. Our results suggest that the effect of athletic participation on math test
scores is positive and statistically significant but that this effect is reduced when controls for
student demographics, family background, and school characteristics, are included. When
possible channels through which athletic participation may improve test scores are added
(including potentially-substitutable activities, such as television watching or other types of
activity participation, family time, and BMI, a measure of physical fitness of the student), the
effect of athletics on math scores was also reduced. Using interaction terms in subsequent
regressions determines that the effect of athletics on math scores also depends on demographic
characteristics, including living in the South or West, and on participation in other activities.
Notably, participation in music had a statistically significant reduction on the positive effect of
athletics on math scores (although gains due to athletics on test scores remained statistically
significant). However, when a student participated in all four other activities measured by the
ECLS, the effect of athletics on test scores became negative and statistically significant.

2. 1
I. Introduction
Participation in extracurricular activities can be argued to be either a burden or a benefit
for students’ academic achievement. One argument against athletic participation is that
extracurricular activities can detract from study or learning time, diminish family interaction, or
reduce sleep. Another argument, in favor of athletic participation, is that participation in
extracurricular activities can act as a source of valuable learning that cannot take place in a
classroom, can function as a creative or physical outlet for students, or can improve physical
fitness. In this paper we investigate the effect of participation in athletics on fifth grade students’
math test scores, after controlling for various student demographics, family backgrounds, and
school characteristics. We also analyze possible channels through which the effect of student
participation in athletics may affect other areas of student life; possible mechanisms include
improved physical fitness, reduced television watching, or limited participation in other
extracurricular activities. We then examine the effect of ways in which control variables might
affect the relationship between sports participation and math scores. Finally, we extend our
model to determine if similar relationships hold for different combinations of dependent and
independent variables.
II. Review of Relevant Academic Articles
There is an abundance of literature on the effect of activities, in particular athletics, on
academic outcomes. In “Secondary school extracurricular involvement and academic
achievement: a fixed effects approach,” Stephen Lipscomb utilizes the fixed-effects model to
measure the effect of club and sports participation on both middle and high school students in
order to determine if participation increases grades or human capital. The study uses data from
the National Education Longitudinal Study of 1988 (NELS.) The study finds that participating in
either clubs or athletics at either grade 8 or 12 will result, on average, in a 1.5 to 2 percent
improvement in test scores and a five percent improvement in Bachelor’s degree attainment
expectations, ceteris paribus. However, the effects of club and athletic participation is magnified
if a student is in a club or sport in both grade and 12. Similarly, if a student drops a sport or club
by 12th grade (having participated in 8th grade), there is a resulting decrease in test scores from
grade 8 to 12. The opposite is true if a student adds a sport or club after 8th grade.1
1
Lipscomb, Stephen. "Secondary school extracurricular involvement and academic achievement:
A fixed effects approach." Economics of Education Review 26, no. 4 (2007): 463-472.

3. 2
Another study, “Sports participation and academic performance: Evidence from the
National Longitudinal Study of Adolescent Health,” examined the effect of sports participation
and general academic performance and found that OLS overstates the positive effects of athletic
participation and GPA.2
In fact, when this study added individual fixed effects as a means to
control for the influence of unmeasured or unobservable student characteristics (motivation,
future-orientedness, or self-discipline), they found that the coefficients on athletic participation
predicted by fixed effects were 58-78% smaller than the OLS counterparts. Although time series
data as is necessary to perform fixed effects (data is required from before and after institution
treatment, in this case, activity participation), which is not included in our dataset , the results of
this and the Lipscomb study are nevertheless relevant to our research question. In addition, these
authors used height as an instrumental variable for participation in school athletics and found
2SLS results to be fairly consistent with their results from the fixed effects model and the
overstatement of academic effects by OLS models.
Finally, a group of economists and medical doctors analyze the relationships between
academic achievement in math and English tests and physical fitness in public, urban middle
school children in "Is There a Relationship between Physical Fitness and Academic
Achievement?"3
Using bivariate and multivariate regression analysis, they measured the effect of
fitness test scores on math and English standardized testing scores (as measured by scores from
MCAS (Massachusetts Comprehensive Assessment System), controlling for race, BMI, gender,
and family socioeconomic status. The study found that ethnicity, socioeconomic status, and
fitness tests were all statistically significant predictors of math and English scores: Higher fitness
scores, on average, were associated with higher MCAS scores, while students’ weight was
inversely associated with math scores alone. Further research is needed to determine causality,
but the effects of physical fitness could be a method by which athletic participation improves
math test scores, as measured by BMI in our dataset.
2
Rees, Daniel I., and Joseph J. Sabia. "Sports participation and academic performance: Evidence
from the National Longitudinal Study of Adolescent Health." Economics of Education Review
29.5 (2010): 751-759.
3
Chomitz, Virginia R., PhD, Meghan M. Slining, MS, MPH, Robert J. McGowan, EdD, Suzanne
E. Mitchell, MD, MS, Glen F. Dawson, MA, and Karen A. Hacker, MD, MPH. "Is There a
Relationship Between Physical Fitness and Academic Achievement? Positive Results From
Public School Children in the Northeastern United States." Journal of School Health 79, no. 1
(January 2009): 30-37. Accessed November 28, 2016.

4. 3
III. Description of Data
The data we use in our study comes from the Early Childhood Longitudinal Survey,
which contains student, family, and school characteristics from the kindergarten class of 1998-
99, re-surveyed in the first, third, and fifth grades. Our main objective is to examine the effect of
athletic participation on fifth grade math test scores. A summary of test scores and demographics
for students who do and do not participate in sports is given below in Table 1. The base cases of
private school, white, male, and Northwest respectively were not included. For each variable
tested (math score, female, black, Hispanic, South, Midwest, West, public school, total
activities), the difference of means is statistically significant. The finding that the differences
between students who do and do not participate in athletics, based on these control factors, is
statistically significant prompted our analysis of interactions of these demographic factors with
athletic participation, which is detailed below and summarized in Table 3. Test scores are
standardized in the data with mean 100 and standard deviation 10; any predicted point increases
or decreases based on student characteristics or participation are also on this scale.
IV. Description of Econometric Model
We used Ordinary Least Squares to estimate the following equation:
log⁡( 𝑚𝑎𝑡ℎ⁡𝑠𝑐𝑜𝑟𝑒𝑠)⁡=⁡ 𝛽0 +⁡ 𝛽1 𝑎𝑡ℎ𝑙𝑒𝑡𝑖𝑐𝑠 +⁡ 𝛽2 𝑋 +⁡ 𝛽3 𝑌 + 𝐵4 𝑍 + 𝑢⁡
Here, X is a vector of controls for student demographics (gender, race), Y is a vector of controls
for family background (mother’s education, family type, household size, mother’s marital status
at birth, mother’s work status, and family income), and Z is a vector of controls for school
characteristics (school type, region, percent of minority students, and a number of school
problems, including crowding, turnover, gangs, crime, drugs, attack, and weapons).
V. Discussion of Econometric Results
Table 2 reports the regression estimates between athletic participation and math test
scores. As one moves from column 1 to column 5, more controls are sequentially added to the
original model. The first column reports the simple regression of athletic participation on math
test scores. The second column adds student demographics into the regression, in order to control
for different characteristics of a student, which may be correlated with math test scores. Adding
these controls causes both the R-squared to increase and the standard error on athletic
participation to decrease: thus, we see an increase in the model's accuracy, but also its predictive
power. This trend continues through column 4 as we add family and school characteristics as

5. 4
controls. The resulting change in R-squared from columns 1 – 4 was an increase by 0.2017
ending at 0.2468, and, notably, coefficient on athletic participation fell from 4.5339 to 1.3933 but
remained statistically significant at the 5% level throughout (the standard error on athletic
participation also fell, from 0.245 in column 1 to 0.234 in column 4): that is, in our fourth model,
participating in athletics on average increases math test scores by 1.3933 standardized points,
ceteris paribus. In column 5 we add variables we consider possible channels through which
athletic participation may affect math scores. However, holding these constant in this regression
may limit our ability to capture the effect of athletics on math scores. The additional channels
account for several variables, which maybe be correlated with athletic participation, for example:
less time for other leisure activities such as television watching or, more notably, participation in
other activities, time spent with family, measured through number of dinners per week the family
ate together, or their physical fitness, as measured through BMI. Although the addition of these
controls continues the trends above (participation in sports is predicted to increase math scores
by 1.182 points, ceteris paribus, a reduction in magnitude from previous models but still
statistically significant), it may not create the most accurate model. Restricting the mechanisms
by which athletic participation can affect math scores would limit the observable effects of
athletics on math scores; thus, we remove the channel variables from the control section in all
further investigation. While our estimates were constructed using heteroskedasticity-robust
standard errors, using the White Test on the regression in Column 4 of Table 2 indicated
evidence of heteroskedasticity (p = 0.00). Therefore, Column 6 of Table 2 gives the results from
Feasible GLS for the same set of controls as in Column 4. While these FGLS estimates are not
unbiased, they are consistent as well as asymptotically more efficient than OLS would be. We
will use this model, adjusting for the form of heteroskedasticity in Column 4, as our optimal
model for subsequent analyses (and acknowledge the simplification involved in assuming the
form of the heteroskedasticity is the same across all subsequent models). While the RESET test
identified functional form misspecification in this model (p = 0.00); we seek to alleviate some of
this misspecification by introducing interaction terms (described in Tables 3 and 4).
Table 3 displays the effects of interacting participation in athletics with various dummy
variables to determine how the effect of athletic participation on math test scores may change.
Rows 1-7 of the table correspond to models adding interaction terms between athletic
participation and the dummy variables for school type, race, gender, and region. The base cases

6. 5
of private school, white, male, and Northwest, respectively, were not included. Except for the
interaction term between athletic participation and the dummy variable for black, all interaction
terms render the effect of athletic participation on math test scores positive and statistically
significant at the 10% level. The only interaction terms that are statistically significant are those
corresponding to the South dummy variable and the West dummy variable. Since the coefficient
on the interaction between athletic participation and the South dummy variable is negative and
statistically significant, though lower in magnitude than the coefficient on raw athletic
participation, participation in athletics can be associated with an increase in math test scores on
average, but the gains to athletic participation are reduced by living in the South. Specifically, if
a student is from the South, athletic participation increases math scores by 0.724 points, while a
non-southerner has gains from athletic participation on math scores of 1.6692 points. On the
other hand, a positive and statistically significant coefficient on the interaction between athletic
participation and the West dummy variable indicates that the academic gains to athletics are
increased by living in the West (gains for westerners and non-westerners are 2.0577 and 1.1638
points, respectively). The interaction terms for other dummy variables are not statistically
significant, so we conclude that type of school, race, gender, and living in a region outside of the
South or West have no significant effect on the relationship between athletics and math scores.
Table 4 gives the results from interacting participation in athletics with participation in
other activities to determine whether the returns to math test scores from athletic participation
differ based on a student’s participation in other activities. The first four rows of Table 4
correspond to models in which athletic participation is interacted with the dummy variables for
participation in each of the other four activities. With some variation, typically the coefficient on
the athletics variable is positive and statistically significant, but that on the interaction term is
negative but lower in magnitude, and only statistically significant for music participation. Thus,
participation in athletics is associated with an increase in test scores, on average, but the gains to
athletic participation are reduced by participation in music. Specifically, the increase in math
scores due to athletic participation for a student who does participate in music is 0.576 points,
but for a student who does not participate in music, this increase is 1.5631 points, ceteris paribus.
Since the interaction terms for participation in other forms of activities are statistically
insignificant, we can conclude that those types of activities do not have a significant effect on the
relationship between athletic participation and math scores. The final row of Table 4 reports the

7. 6
coefficients of a regression involving the interaction of athletic participation and the number of
non-athletic activities in which a student participated (range zero to four) to determine whether
the effect of athletic participation on math test scores depends on the number of other, non-
athletic activities in which a student participates. Here, all three coefficients were statistically
significant. Since the coefficient on participation in athletics is positive and that on the
interaction term is negative, we can conclude that there are significant positive returns to
participation in athletics, but that these gains diminish as a student participates in additional, non-
athletic activities. However, it is only when the student participates in four (that is, all) non-
athletic activities that the effect of athletic participation on math scores becomes negative (the
effect of athletics on math test scores, given participation in four non-athletic activities, is to
reduce test scores by 0.2103 points, ceteris paribus). Given the pattern of reduction of the effects
of athletic participation on math tests; that is, that the gains to participation in athletics decrease
and even become negative in one case, we can conclude that the effect of athletic participation
on math scores, to some degree, may be merely an effect of participation in activity in general.
We then expanded on the equation used in column 6 of Table 2 by substituting in
different independent and dependent variables; the results are summarized in Tables 5 and 6,
respectively. Table 5 reports the regression estimates of the relationship between other
extracurricular activity participation and math test scores. We calculated the estimates from
Table 5 using the regression from Table 2, column 6, which includes student demographics,
family characteristics and school characteristics as controls and used GLS to address the issue of
heteroscedasticity, but substituting a different activity for the dependent variable of interest. Only
the coefficients on athletic participation, music and club participation were statistically
significant at the 5% level. The results show that of all extracurricular activities tested, music
participation results in the highest increase in math test scores, 2.003 points, followed by
athletics and clubs, 1.3577 and 0.8642 points respectively, ceteris paribus. These GLS estimates
are made under the (limiting) assumption that substituting a different activity into the model
retains the form of heteroscedasticity in the original model, with athletics.
We next extend our exploration by estimating the relationship between athletic
participation and other test scores. The estimates in Table 6 are from a modification of the
regression used in Table 2, column 6 by replacing only the dependent variable with the test score
listed in each row. Only the resultant estimates of the effect of athletic participation are statically

8. 7
significant for math, reading and total test scores at the 5% level. The estimates show that, of the
individual tests, athletic participation increases math scores the most, by 1.3577 points, ceteris
paribus, followed by reading scores increase of 0.6923 pointes, ceteris paribus. The total score
was also estimated to increase by 2.2054 points with sports participation, ceteris paribus, which
is notably greater than the sum of math and reading scores. This discrepancy may point to
imprecision when estimating the scores. Even still, our estimates still indicate a positive
correlation between athletic participation and more general academic (excluding science), which
suggests that the merits of extra-curricular activities (potential source of out-of-classroom
learning, improved physical well-being, or new creative outlets) outweigh the possible costs
(reduced time for school work, sleep, or family interaction).
It is important to note that in some of cases these effects, while statistically significant,
are so small in magnitude as to not be practically significant. Moreover, the goal or purpose of
activity participation is not always tied to academic performance; indeed, many parents or
students value activity participation for benefits in spheres outside of classroom or academic
improvement. Our conclusions are valuable, however, in determining that participation in
athletics typically does not harm a student's academic performance, as measured by math test
scores. Our research into other measures of academic performance (science, reading, and total
test scores), support this conclusion.
VI. References
Chomitz, Virginia R., PhD, Meghan M. Slining, MS, MPH, Robert J. McGowan, EdD, Suzanne
E. Mitchell, MD, MS, Glen F. Dawson, MA, and Karen A. Hacker, MD, MPH. "Is There
a Relationship Between Physical Fitness and Academic Achievement? Positive Results
From Public School Children in the Northeastern United States." Journal of School
Health 79, no. 1 (January 2009): 30-37. Accessed November 28, 2016.
Lipscomb, Stephen. "Secondary School Extracurricular Involvement and Academic
Achievement: A Fixed Effects Approach." Economics of Education Review 26, no. 4
(August 2007): 463-72.
Rees, Daniel I., and Joseph J. Sabia. "Sports participation and academic performance: Evidence
from the National Longitudinal Study of Adolescent Health." Economics of Education
Review 29.5 (2010): 751-759.

9. 8
VII. Appendix: Figures and Tables
Notes: The following conventions hold for reporting statistical significance:
* indicates statistical significance at the 10% level
** indicates statistical significance at the 5% level
*** indicates statistical significance at the 1% level
Table 1. Mean Demographic Comparisons for Students by Athletic Participation
Does Not Participate in
Athletics (n = 2,638)
Does Participate in
Athletics (n = 5,467)
Average Math
Test Score
96.94
(10.91)
101.48**
(9.17)
Female 0.5781
(0.0096)
0.4507**
(0.0067)
Black 0.1315
(0.0066)
0.0706**
(0.0035)
Hispanic 0.2657
(0.0086)
0.1397**
(0.0047)
South 0.3719
(0.0094)
0.2799**
(0.0061)
Midwest 0.2074
(0.0079)
0.3079**
(0.0062)
West 0.2517
(0.0085)
0.2060**
(0.0055)
Public School 0.8916
(0.0061)
0.7481**
(0.0059)
Total Activities
0.6850
(0.0166)
1.9537**
(0.0127)
Notes: This table gives mean comparisons for students who do and do not participate in athletics.
Standard errors are given below, in parentheses. ** in a given row indicates the difference of
means is statistically significant at the 5% level. “Total Activities” is the count of activities a
student participates in (range 0-5).

10. 9
Table 2. Regression Estimates of the Relationship between Athletic Participation and Math
Test Scores with Various Controls
Math Test Scores
[1] [2] [3] [4] [5] [6]
Effects of Athletic
Participation
4.5339**
(.246)
3.10685**
(.246)
1.3527**
(.234)
1.3933**
(.235)
1.1822**
(.234)
1.3577**
(.229)
Student
Demographics
X X X X X
Family
Characteristics
X X X X
School Characteristics X X X
Possible Channels X
R-squared 0.0451 0.1347 0.2400 0.2468 0.2577 0.2237
Notes: Columns 1 through 5 in this table give the heteroskedasticity-robust OLS estimate for
athletic participation on math test scores under various levels of controls (indicated in the shaded
region). Heteroskedastic-robust standard errors are given below, in parenthesesAfter the
regression in column 4 was determined to be optimal, column 6 gives Feasible GLS results for a
regression with the same controls. ** indicate statistical significance at the 5% level.

11. 10
Table 3: Interactions between Athletic Participation and Relevant Dummy Variables
Note: This table modifies the equation for effect of athletic participation on math test scores by
adding interaction terms. The first seven rows correspond to models in which a variable already
included as a control is interacted with participation in athletics. Regression estimates are given
both for athletic participation as well as the interaction term. The final row gives the regression
estimate for the model without any interaction terms as a comparison. The base cases of private
school, white, male, and Northwest respectively were not included. Heteroskedastic-robust
standard errors are included in parentheses, and ** and * indicate statistical significance at the
5% and 10% levels respectively.
Coefficient on Athletic
Participation
Coefficient on
Interaction
Public School
0.5327
(0.513)
1.0105
(0.564)
Black
1.3350**
(0.241)
0.2602
(0.739)
Hispanic
1.3882**
(0.251)
-0.1756
(0.652)
Female
1.3933**
(0.235)
-0.3558
(0.440)
South
1.6692**
(0.280)
-0.9452**
(0.458)
Midwest
1.1416**
(0.263)
-0.2369
(0.493)
West
1.1638**
(0.252)
0.8939*
(0.538)
No Interactions
1.3577**
(0.229)
–

12. 11
Table 4. Interaction between Athletic Participation and Participation in Other Activities
Other Activity
Coefficient on
Participation in
Athletics
Coefficient on
Participation in
Other Activity
Coefficient on
Interaction Term
Dance 1.3352***
(0.247)
0.1755
(0.524)
0.1279
(0.605)
Music 1.5631***
(0.273)
2.7497***
(0.415)
-0.9871**
(0.463)
Art 1.4602***
(0.244)
1.0556*
(0.586)
-0.9409
(0.680)
Club 1.4431***
(0.270)
1.4431***
(0.436)
-0.4747
(0.486)
Number of
Non-Athletic
Activities
1.6385***
(0.311)
1.2074***
(0.212)
-0.4622*
(0.237)
Note: This table modifies the equation used in Column 6 of Table 2 to include a binary variable
for “other activity” and an interaction term between participating in athletics and the other
activity of interest; four regressions were run, each corresponding to a different type of “other”
activity (dance, music, art, and club). The fifth regression included an interaction with the
number of non-athletic activities in which a student participated; this variable had a range of 0
(participates in no non-athletic activities) to 4 (participates in all non-athletic activities). In this
way we measure whether the effect of athletic participation depends on participation in other
activities. Heteroskedastic-robust standard errors are given below, in parentheses. ***, **, and *
indicate statistical significance at the 1%, 5%, and 10% levels, respectively. However, it can be
noted that the coefficient for the interaction of athletics and non-athletic participation is
statistically significant at 5.1%, narrowly missing the 5% cutoff.

13. 12
Table 5. Regression Estimates: Extracurricular Activity Participation and Math Test Scores
Coefficient
Estimates
Athletic
Participation
1.3577**
(0.229)
Dance
Participation
0.2924
(0.278)
Music
Participation
2.003**
(0.203)
Art
Participation
0.3468
(0.298)
Club
Participation
0.8462**
(0.202)
Notes: This table modifies the equation used for column 6 in Table 2; each row corresponds to
an alternate independent variable of interest consisting of a dummy variable equal to 1 if the
student participated in that activity and 0 otherwise. ** and * indicate statistical significant at the
5% and 10% levels, respectively.
Table 6. Regression Estimates: Athletic Participation and Multiple Test Scores
Notes: This table modifies the equation used for column 6 in Table 2; each row corresponds to a
in the dependent variable. ** and * indicate statistically significant at the 5% and 10% levels,
respectively.
Coefficient on
Athletic
Participation
Math
Scores
1.3577**
(0.229)
Science
Scores
0.1554
(.224)
Reading
Scores
0.6924**
(.2253678)
Total
Scores
2.2055**
(0.602)