primary school music education and test performance

Paul Larson Economics 101, Policy Analysis, Spring 2012 Professor Camille Landais Final Paper

Music education improves academic skills for financially disadvantaged

public elementary schools students

Introduction:

A particularly formidable and long-standing challenge to researchers is to quantify the

value of an education in music and the arts. For several decades, researchers have been trying

to find a provable theory that links education in music with overall academic achievement.

Using the results of modern brain imaging technology and statewide standardized testing, this

goal is becoming more achievable. Since the 1960s, experimental research has sought to

investigate the effects of music education in particular. The main idea has been that interaction

with music, either through targeted listening (e.g. the “Mozart effect”), or through formal music

lessons, can improve certain cognitive and psychosocial skills in children and adults. In this

paper, I will first review the relevant academic literature. Then, using a data set of statewide

standardized test results from the Minnesota Public Schools from 2008 to 2010, I will examine

the short-run effects of cancelling in 2009 of the mandatory 4th grade instrumental music

program in the Saint Paul Public School District, using a difference-in-difference analysis. My

results show that economically disadvantaged students (those who are eligible for subsidized

meals), gain the largest relative improvements in math and reading test scores when provided

with 4th grade instrumental music instruction. Students who had the music program on average

show statistically significant improvements on standardized math test scores, but show no

significant difference in reading test score improvements.

Lastly, I will discuss the limitations of my empirical approach, including details about the

quasi-experimental setting, the non-random assignment of schools to the treatment group, and

the short, three year sample period. By addressing these shortcomings I will make suggestions

for future research.

Background Information:

If elementary school music education is positively correlated with overall academic

achievement, and overall academic achievement is positively correlated with worker productivity

and lifetime earnings, then a policy requiring this type of music education could lead to long-

term economic improvements.

Since 2001, all American public schools that receive federal funding are subject to the

No Child Left Behind Act. This act requires that each state teach and assess the learning of a

basic skill set, which is focused on reading, writing, and mathematics. Student learning is

measured by performance on statewide standardized tests, and this directly affects the school’s

level of federal funding. When schools fail to achieve Adequate Yearly Progress, as designated

by the Act, the school or district may have its federal funding reduced or cut, potentially making

it more difficult for these troubled schools to improve. It is therefore very important to

understand the effects of different educational policies regarding curriculum, and in particular

how these policies affect the economically disadvantaged, in order to maximize the overall

return on investment for public education.

The recent economic recession has increased pressure on public schools to cut costs by

cutting programs, and since 2007 nearly 71% of schools have reduced instruction time in

history, arts, language, and/or music. The quasi-natural experiment setting for this study was

created by such a cut. Prior to the fall of 2009, all 46 schools in the Saint Paul Public School

District required instrumental music instruction as part of their 4th grade core curriculum. The

program was cut throughout the district, in all but eight schools, due to budgetary constraints. I

will use a difference-in-difference analysis to compare the test performance of students between

these two groups of schools.

Literature Review:

In recent decades many researchers have searched for a causational effect between

music education and academic performance. The beneficial effects have been thought of in two

general categories: passive enhancement (through targeted listening) and active transfer (from

formal music lessons). To briefly describe passive enhancement, two well-known studies

(Rauscher et al., 1993, 1995) document improvement in college student performance on spatial-

temporal tasks when listening to 10 minutes of music composed by Mozart. However,

subsequent research has not supported the idea that brief exposures to music can have a

significant positive effect on overall cognition (Stough, Kerkin, Bates, & Mangan, 1994;

Carstens, Huskins, & Hounshell, 1995; Rideout & Taylor, 1997; Nantais & Schellenberg, 1999;

Crncec, Wilson, & Prior, 2006).

Studies on the active transfer effects of formal music education, however, have had

more consistent findings. The idea behind active transfer effects is that training in one domain

(here, music), transfers over to a certain skill set found in other domains (here, math and

reading). One of the earliest and most-cited experimental studies of the effects of childhood

music education uses the Kodaly music training method as a treatment. Hurwitz, et al (1975)

find that this intensive music instruction (40 minute classes, five days a week, for seven months)

significantly improves reading ability in first graders. The study continued for a second year, in

which the students in the Kodaly-trained group again outperformed the control group on reading

tests. Although their sample size was small (10 boys and 10 girls in both the treated and

untreated groups, 40 students total), their experimental method was rigorous. The subjects

were matched by age, IQ, social class, and birth order, the experimental testing done was

extensive. They controlled for many difficult things, such as teaching style, by comparing

students of the same teacher, both with and without the Kodaly program. This study

demonstrated successful methods to control for many important unobserved variables when

trying to measure the effects of musical training.

In more recent years, advancements in medical imaging technology have allowed

neuroscientists to investigate the effects of music education on brain development and activity.

Overall, these studies find that learning to play a musical instrument at an early age causes

physical differences in brain development (Schlaug, et al., 1995). However, this difference only

becomes measurable if the musicians start music lessons before age 7, suggesting that in order

to obtain the physical advantages in brain structure, musical instruction must begin during a

period of intense brain plasticity. If elementary school children obtain relatively greater returns

on the investment of musical education than their older counterparts, educational policy should

allocate a greater share of the resources for musical instruction to the elementary school

curriculum. Other neurological studies have shown that musical training affects the growth of

neural architecture in both children and adolescents, in the regions used in processing

mathematics (Bahr & Christensen, 2000; Shaw, 2000; Schmithorst and Holland, 2004;

Helmrich, 2010). Using neuroimaging, Pascual-Leone (2005) finds that plasticity changes occur

in the human brain when learning to play piano, and Giedd, et al. (1999) find that during early

adolescence the brain experiences a synaptogenesis (a surge in the formation of new

synapses), which peaks between the ages of 10 and 12. These findings suggest that, because

of the brain’s neural plasticity, early adolescence is a critical phase for physical brain

development in one’s educational career.

Piro and Ortiz (2009) study the effects of piano lessons on 2nd grade students, and find

a surprising result. Although the students already had received 2 years of private piano

instruction at the beginning of the study, their initial literacy test scores were not statistically

different from those of students with no prior instruction. However, by the end of the study the

following year (also the end of the students’ third year of private piano instruction), students with

piano instruction significantly outscored their counterparts. This further suggests that rapid

neural development occurs between the ages of 8 and 10, which is also the age group of the

students in my data set.

Using a sample of over 6,000 adolescents from Maryland, Helmrich (2010) finds a

significant improvement in high school algebra achievement on standardized tests for students

who had enrolled in formal instrumental or choral instruction during middle school. The positive

correlation with higher algebra scores was strongest among students who had studied formal

instrumental instruction, and weaker (but still significant) for students who had choral instruction.

The achievement gap was largest among African American students, where the average scores

were failing the state’s standardized test in 5th grade, but, among the African American students

who had received choral or instrumental instruction in middle school, the means became

passing by 9th grade. Among the African American students who did not receive musical

instruction, the means were still failing in 9th grade.

When studying ability levels in very young subjects, it is difficult to separate correlation

from causation. Anvari et al. (2002) examine musical skills, phonological processing, and early

reading ability in preschool children, and find that musical skills are significantly correlated with

both phonological ability and reading development. However, because it is extremely rare that

preschool students have already received formal music instruction, I interpret this correlation to

mean that musical ability in preschool-age children is more likely an early indicator of natural

intellectual ability, rather than a result of formal music education or training. Unfortunately,

without more information about these children’s home environments regarding music, or the

musical background of the parents, there is strong potential for omitted variables biasing these

results.

Despite the problems with identification, evaluation, and estimation, music education has

become increasingly justified on the basis that it may enhance ability in areas like math and

language. Ho, et al. (2003) and Rickard, et al. (2010) both report that it enhances verbal

memory. Schellenberg (2001, 2004) finds that it enhances general intelligence. Wilson, et al.

(2006), Orsmond & Miller (1999), and Rauscher & Zupan (2000), all report that it improves

spatial learning. O’Connell (2005) finds it improves schools attendance and motivation. In

preschool and primary school students, regular music classes and instrumental training have

been associated with improved pre-reading and writing skills, higher mathematics scores, and

improved memory and differential brain development (Cheek & Smith, 1999, Fujioka et al.,

2006). And many other researchers have found that music education improves overall

academic achievement (Anvari et al., 2002, Hodges & O’Connell, 2005, Southgate & Roscigno,

2009; and others; see Schellenberg, 2001; Bolduc, 2008; and Hallam, 2010 for extensive

reviews). The College Board reported in 2004 that students who have had coursework or

experience in music performance outscored their peers on the SAT by 43 points on the math

section, and by 57 points on the verbal section (out of a possible 800 points each). A common

shortcoming among these studies, however, is being able to convincingly control for the omitted

and unobservable variables.

Other researchers have found some conflicting evidence for the above findings,

suggesting that the positive effects of a music education are quite variable, difficult to measure,

and not yet fully understood. Johnson and Memmott (2007) compare low- and high-quality

music education programs, and find, unsurprisingly, that students in schools with the highest

quality music programs perform better on language and math tests. However, students in

schools with “deficient” music programs still outperform those in schools with no music program

at all. Upitis et al. (2001) find that only elementary students with private music instruction

outside of school show improvements in language and mathematical abilities, suggesting that

the group class format for musical instruction is not as beneficial to brain development in

children. Rickard et al. (2010) find that the improvements in math and language abilities are

only associated with programs in economically-disadvantaged schools, and not in schools which

are “arts-poor” in the same neighborhood.

Empirical Strategy:

For my analysis, I use statewide standardized math and reading test data collected from

the Saint Paul Public School District over the years 2008 to 2010. This large data set includes

51 variables with 1,052 observations at the classroom level, 918 for the untreated group and

136 for the treated group. Prior to the 2009-2010 academic year, all 4th grade students in the

46 elementary schools of the Saint Paul Public School District received instrumental music

instruction. Of these schools, 38 cut this program due to budgetary constraints, but 8 of the

schools chose to continue requiring the program in the curriculum (Lorenzen, personal

communication). The treatment group, therefore, will be the students in the 8 schools that

chose not to cancel this program during the 2009-2010 academic year. I will examine student

performance on the Minnesota statewide standardized math and reading tests by comparing

test scores of students who lost instrumental music as part of their core curriculum (the

“untreated” group) with the scores of students in the 8 schools that kept the program (the

“treated” group).

My regression equation format is:

averagescore (reading or math) = (fixed effects parameters) + B(post_treatment) + error

This is a simple, fixed effects, difference-in-difference regression, where “B” is the

coefficient of interest, measuring the effect on “average test score” of being in the treatment

group after the treatment has occurred.

I focus my statistical analysis on the performance of students who were in 3rd grade in

2008. As 4th graders in 2009, they become separated into treated and untreated groups, and

are then tested at the end of the 2009, and again at the end of 2010.

In order to verify the findings of previous studies, I examine the treatment effect on some

of the subgroups within my data, such as students who are eligible for subsidized meals,

students receiving special education services, students eligible for Limited English Proficiency

(LEP) services, as well students of African American heritage.

There are several factors that complicate my analysis. First, the 4th grade instrumental

music program was only cancelled as a part of the uniform required curriculum during the

school day. Most schools in the Saint Paul Public School District continued offering similar,

optional classes twice a week after school, but data was not available for this program.

As with most studies into the effects of music education, unobserved variables may bias

my results. The data set does not include information on extracurricular music instruction,

parental musical experience, or the amount of musical exposure children have in their homes,

which could create one or more omitted variable biases. In addition to the selection bias among

which schools chose to keep the program, there is potentially another selection bias, as parents

may have non-randomly transferred children into schools that did not cut the 4th grade

instrumental music education program.

Lastly, I am examining a short time period with panel data, and it may be that the most

important treatment effects on academic performance only become statistically significant

further in the future. Researchers have shown that physical differences occur in brain

development among this age group when they are given instrumental music instruction.

Hopefully by observing longer-term correlations with these physical differences, we may be able

to gain a better understanding of how the brain’s physiology affects academic achievement.

Results

My most interesting result came by including only students who are eligible for free or

reduced-price meals in my data set. This group shows a 4.75 point improvement in math test

scores, and this result is significant at the 95% level. Among this “low economic status” group,

there is also a 2.93 point increase in relative reading test scores associated with the financially-

disadvantaged students in the treatment group. This correlation is significant at the 90% level.

When the students eligible for free or reduced price meals are removed from the data set, there

is no statistically significant association between the treatment group and average math or

reading scores (See Appendix B: Economic Status).

My general analysis shows that continuing the 4th grade instrumental music education

program is associated with an overall 3.58-point improvement in math scores, which is

significant at the 90% level. However, I find no significant correlation between continuing the

music program and reading scores (See Appendix A: General Findings).

My other subgroup analyses were inconclusive. Among the students receiving special

education services, and when these students are removed from the sample, there is no

significant correlation between treatment group and test scores. The students eligible for

Limited English Proficiency (LEP) services the results are similarly inconclusive, with low

coefficients and high standard errors on the “post_treatment” variable. However, when the

students eligible for LEP services were removed from the sample, the coefficient on the

post_treatment variable returns to nearly the exact same level as when the regression was run

on all students (3.56, also significant at the 90% level). For the African American student

subgroup, the findings were inconclusive (See Appendix C: Special Education, LEP, and African

American).

Discussion

Schools which kept the program also have higher average test scores, both before and

after treatment, and this shows that the selection of schools into the treatment group was non-

random. The schools in the treatment group have significantly higher average student test

scores, both before and after treatment. However, by using difference-in-difference analysis I

find that the schools which kept the program, on average, also show a relative improvement in

students’ average math scores. Students eligible for subsidized meals show the most robust

results among the treatment group, which is consistent with the findings from Rickard et al.

(2010). This finding supports the idea that economically disadvantaged students will enjoy the

largest relative gains from an early music education. If these same economically disadvantaged

students are also, as a group, less likely to graduate high school, attend college, or become

skilled productive workers, then the long-term effects of early music education on this group

should be carefully considered when designing policy.

Music education in public elementary schools, especially among the poor, is fertile

ground for economic policy research at this time. In the aftermath of the financial meltdown and

amidst a slow-to-recover recession, the federal budget continues to face many program cuts.

Public education will continue to take its share of these, so more opportunities for quasi-natural

experiments like this are likely to appear. My hope is that future researchers will continue to

search for a provable theory linking education in music and the arts with overall academic

achievement and other measures of success. In particular, there is a dearth of academic

literature examining the long-term effects of having a music education during childhood, and I

believe that variables such as high school dropout rates, college graduation rates, average

yearly income, divorce and even incarceration rates, may show interesting correlations with

being musically-trained and educated during childhood.

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APPENDIX A: General Findings

All St. Paul School District Math Scores

(1)

VARIABLES averagescore

post_treat 3.58*

[1.807]

yd1 0.33

[0.712]

yd3 0.64

[0.670]

Constant 546.64***

[0.600]

Observations 154

R-squared 0.08

Number of schoolnumber 55

Standard errors in brackets *** p<0.01, ** p<0.05, * p<0.1

Summary Statistics - All Saint Paul School District Math Scores

-> treat = 0, post = 0 Variable

Obs Mean Std. Dev. Min Max

averagescore

263 449.2373 80.36244 316.2 567



averagescore

520 450.2027 80.62629 318.1 567.3



averagescore

48 457.2021 80.68595 343.5 566.9



averagescore

88 456.0966 80.16654 340 567.9

APPENDIX A: General Findings (continued)

All Saint Paul School District Reading Scores

(1)


post_treat 0.76

[1.271]

Constant 550.05***

[0.434]

Observations 153

R-squared 0.19



APPENDIX A: General Findings (continued)

All Saint Paul School District Reading Scores Summary Statistics



averagescore

263 449.2373 80.36244 316.2 567



averagescore

520 450.2027 80.62629 318.1 567.3



averagescore

48 457.2021 80.68595 343.5 566.9



averagescore

88 456.0966 80.16654 340 567.9

Appendix B: Economic Status

Saint Paul School District Math Scores Economic Status "Low" (eligible for free/reduced price meals)

(2) VARIABLES averagescore

post_treat 4.75**

[1.997]

Constant 544.60***

[0.663]

Observations 151 R-squared 0.08 Number of schoolnumber 55


St Paul School District Math Scores Summary Statistics Economic Status "Low" (eligible for free/reduced price meals)

-> treat = 0, post = 0

Variable

Obs Mean Std. Dev. Min Max averagescore

256 446.9746 80.85825 313.1 557.6



averagescore

514 447.7031 80.38121 318.7 559.5



averagescore

48 451.6083 80.46089 342.7 556.6



averagescore

84 447.3631 81.0165 340 559.8

Appendix B: Economic Status (continued)

Saint Paul School District Reading Scores Economic Status "Low" (eligible for free/reduced price meals)

(2)


post_treat 2.93*

[1.502]

Constant 547.30***

[0.498]

Observations 151

R-squared 0.22



Appendix B: Economic Status (continued) All Saint Paul School District Reading Scores Summary Statistics

Economic Status "Low" (eligible for free/reduced price meals)



averagescore

256 446.9746 80.85825 313.1 557.6



averagescore

514 447.7031 80.38121 318.7 559.5



averagescore

48 451.6083 80.46089 342.7 556.6



averagescore

84 447.3631 81.0165 340 559.8

Appendix B: Economic Status (continued) (Additional summary statistics are located in APPENDIX Z: Additional Summary Statistics) Saint Paul School District Math Scores Economic Status "Regular" (not eligible for reduced price meals)

(2)


post_treat 1.11

[2.180]

Constant 555.49***

[1.665]

Observations 67

R-squared 0.28



Appendix B: Economic Status (continued)

Saint Paul School District Reading Scores Economic Status "Regular" (not eligible for reduced price meals)

(2)


post_treat -2.21

[1.811]

Constant 560.16***

[1.449]

Observations 68

R-squared 0.32



Appendix C: Special Education, Limited English Proficiency (LEP) and African American Saint Paul School District Math Scores

Special Education Students

(2)


post_treat 6.96

[5.827]

Constant 536.12***

[1.393]

Observations 53

R-squared 0.10


Standard errors in brackets

*** p<0.01, ** p<0.05, * p<0.1

Saint Paul School District Reading Scores

Special Education Students

(2)


post_treat -3.29

[4.398]

Constant 540.58***

[1.296]

Observations 58

R-squared 0.17



*** p<0.01, ** p<0.05, * p<0.1

Appendix C: Special Education, Limited English Proficiency (LEP) and African American (continued)

Saint Paul School District Math Scores

Not Including Special Education Students

(2)


post_treat 2.21

[1.721]

Constant 549.48***

[0.591]

Observations 149

R-squared 0.08




Not Including Special EducationStudents

(2)


post_treat 0.27

[1.217]

Constant 552.46***

[0.418]

Observations 149

R-squared 0.23




Saint Paul School District Math Scores Limited English Proficiency (LEP) Students

(2)


post_treat 3.47

[4.599]

Constant 543.99***

[1.044]

Observations 91

R-squared 0.20




Limited English Proficiency (LEP) Students

(2)


post_treat 2.31

[2.120]

Constant 545.66***

[0.608]

Observations 100

R-squared 0.39




Saint Paul School District Reading Scores Non Limited English Proficiency (LEP) Students

(2)


post_treat -0.43

[1.616]

Constant 552.54***

[0.557]

Observations 150

R-squared 0.08



*** p<0.01, ** p<0.05, * p<0.1

Saint Paul School District Math Scores Without Limited English Proficiency (LEP) Students

(2)


post_treat 3.56*

[1.846]

Constant 548.27***

[0.621]

Observations 150

R-squared 0.07




Saint Paul School District Math Scores

African American Students

(2)


post_treat 3.09

[3.891]

Constant 542.90***

[0.814]

Observations 104

R-squared 0.06





(2)


post_treat -3.80

[3.452]

Constant 547.06***

[0.728]

Observations 103

R-squared 0.16



APPENDIX Z: Additional Summary Statistics Saint Paul School District Math Scores Summary Statistics

Economic Status "Regular" (not eligible for reduced price meals)



averagescore

106 461.3858 78.29727 354.9 570.2



averagescore

195 457.3805 81.26991 345.8 570.5



averagescore

42 463.6405 80.61837 357.8 570.7



averagescore

77 462.4234 80.63973 355.5 571.2 Saint Paul School District Reading Scores Summary Statistics

Economic Status "Regular" (not eligible for reduced price meals)



averagescore

106 461.3858 78.29727 354.9 570.2



averagescore

195 457.3805 81.26991 345.8 570.5



averagescore

42 463.6405 80.61837 357.8 570.7



averagescore

77 462.4234 80.63973 355.5 571.2

APPENDIX Z: Additional Summary Statistics All Saint Paul School District Math Scores Summary Statistics

Limited English Proficiency (LEP) Students



averagescore

193 440.6943 79.06928 332.6 557



averagescore

359 441.7808 80.81343 316.2 563.2



averagescore

33 437.5818 76.62015 340.2 552.8



averagescore

60 436.06 77.40521 341.9 554.3 Saint Paul School District Math Scores Summary Statistics

Non Limited English Proficiency (LEP) Students



averagescore

255 450.3706 80.76034 315.5 567.5



averagescore

510 452.0112 80.19104 318.7 568.7



averagescore

46 464.3239 79.56314 353.5 567.7



averagescore

88 458.2284 80.34004 335.6 568.9

Saint Paul School District Reading Scores Summary Statistics




averagescore

204 442.8809 79.89926 332.2 560.6



averagescore

396 442.5184 79.30829 318.7 559.5



averagescore

19 448.5526 84.67098 345.8 559.2



averagescore

52 440.2135 79.94242 332.8 563.3

primary school music education and test performance

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