Economics of Education Review 22 (2003) 307–315

www.elsevier.com/locate/econedurev

A new twist in the educational tracking debate

Ron Zimmer ∗

RAND Corporation, 1700 Main Street, Santa Monica, CA 90407-2138, USA

Received 5 December 2000; accepted 18 June 2002

Abstract

Recently, the practice of tracking has been receiving more attention by both educators and researchers and some

have questioned the policy merit. One of the strongest arguments against tracking is that it creates homogenous classes

according to ability and, therefore, reduces the positive spillover effect referred to as a peer effect. While peer effects

have been found to be an important input into the production of education no study has specifically examined whether

these effects are more or less prevalent in classes where tracking occurs. Utilizing individual student level data, this

current research examines whether the peer effect occurs in cases in which tracking is present. The results suggest that

the use of tracking diminishes the impact peers have on student achievement for low- and average-ability students

while the peer effect is unaffected by tracking for high-ability students.

2003 Elsevier Science Ltd. All rights reserved.

JEL classification: I21

Keywords: Educational economics

1. Introduction

Recently, the practice of tracking has been receiving

more attention by both educators and researchers, and

some have questioned the policy’s merit. In the education

arena, those who advocate tracking argue that all

students, regardless of ability, would learn more in a

tracked class relative to a nontracked class (Hallinan,

1994). In tracked classes the teacher can tailor the curriculum

to the ability level of the students, thus creating

the optimal level of educational gains for all students.

However, opponents argue against tracking for three primary

reasons. First, tracking leads to a different set of

resources being allocated to high-tracked versus low-

∗ Corresponding author. Tel.: +1 310 393 0411; fax: +1 310

451 7059.

E-mail address: rzimmer@rand.org (R. Zimmer).

0272-7757/03/$ - see front matter 2003 Elsevier Science Ltd. All rights reserved.

doi:10.1016/S0272-7757(02)00055-9

tracked classes (Oakes, 1990).1 Second, tracking breeds

social inequities as minority and low-income groups are

over-represented in low-track and under-represented in

high-track classes (Braddock & Dawkins, 1993; Gorman,

1987; Oakes, 1985, 1990). Third, tracking creates homogenous

classes according to ability, therefore reducing

the positive spillover effect, referred to as a peer effect

(Betts & Shkolnik, 2000). While further research is

needed to address the first two issues, this current

research examines the effect tracking has on the peer

effect. That is, does tracking diminish the positive spillover

effect from high-ability students to low-ability students?

In addition, the effect from tracking on student

achievement is examined while holding the peer level

constant.

The literature increasingly suggests there are differen-

1 However, Betts and Shkolnik (2000) found that neither

class size nor teacher characteristics vary much whether the

student is placed in a tracked or an untracked class.

308 R. Zimmer / Economics of Education Review 22 (2003) 307–315

tial effects from tracking for students of different abilities

(Argys, Rees & Brewer, 1996; Hoffer, 1992; Kerckhoff,

1986)2. However, this literature has failed to

determine whether this effect is from curriculum and

resource effects or a differential peer effect from the

organizational structure of grouping students. In the past,

researchers have found strong support for a peer effect

in the production of education (Hanushek, Kain, Markman,

& Rivkin, 2001; Henderson, Mieskowski, & Savageau,

1978; Summers & Wolfe, 1977; Zimmer &

Toma, 2000; Hoxby, 2000). However, no study has

examined whether these peer effects are more or less

prevalent in classes where tracking occurs and what

effect they are having on student achievement in tracked

classes3. Zimmer and Toma (2000) interacted the variance

of ability in a classroom with the mean ability

(peer-effect variable) of students and found that greater

variance within a classroom actually reduces the peer

effect. Therefore, it could be the case that peer effects

are less prevalent in schools that are not tracked as

opposed to schools that are. That is, students of different

ability levels who are mixed together in non-tracked

schools would not be able to interact effectively to create

the peer effect.

This current research adds to the literature by examining

the impact tracking has on the peer effect by including

an interaction term. The results suggest that the use

of tracking diminishes the impact peers have on student

achievement for low and average-ability students while

the peer effect is unaffected by tracking for high-ability

students. A secondary result suggests that the institutional

process of tracking, when controlling for the

peer level, has no effect on high-ability students,

2 Kerchoff (1986) examined grouped versus non-grouped

students in British schools and found that high-ability students

learnt more than non-grouped students while low-grouped students

learnt less. Hoffer (1992) examined grouped versus nongrouped

students using the Longitudinal Study of America

(LSAY) and found that tracking had no effect for averageability

students, weak positive effects for high-ability students,

and strong negative effects for low-ability students. Argys et al.

(1996), using the National Longitudinal Study of 1988 (NELS),

suggest that tracking creates educational gains for high-ability

students, whereas low-ability students experience educational

losses. In contrast, Betts and Shkolnik (2000) suggest only weak

differential effects between ability groups, with tracking having

no effect on low-ability students but a small positive effect for

high-ability students and a small negative effect on averageability

students. In a more recent study, Figlio and Page (2002),

using the NELS data, support the finding from Betts and Shkolnik

as they control for the endogeneity of track placement and

finds no evidence that low-ability students are hurt by tracking.

3 White and Kane (1995) suggest that one of the main shortcomings

of the existing literature is that it does “not separate

the effects of course content and instruction from the effects of

homogenous grouping” (p. viii).

whereas, surprisingly, the institutional practice of tracking,

when controlling for the peer level, has a positive

effect on low-and average-ability students. Only if the

reduction in the peer effect is combined with the effect

from tracking does the positive effect from low-ability

students wash out. In other words, the loss of exposure

to more able students offsets the potential of tracking to

improve educational performance for the low-ability students.

2. Data

To examine the impact tracking has on the peer effect,

and ultimately, the impact peers and tracking have on

student achievement, a data set with characteristics of

the individual students is necessary, including whether

or not the student was in a tracked classroom. One such

data set is from the second study (SIMS) from International

Association for the Evaluation of Educational

Achievement (IEA)4. The IEA, through carefully constructed

surveys, collects family, school, classroom, and

peer characteristics of individual students for the purpose

of cross-country comparison, including the US5. This

data set includes characteristics of the students’ families

and teachers, the organizational structure of the schools,

including whether or not the classes are tracked, and preand

post-year mathematics test scores for individual students,

which allow for a value-added estimate6. In the

data, tracking is designated as a dichotomous variable (1

if there is tracking in the classroom and 0 if there is not)

and is interacted with a variable representing the ability

of peers (the mean test score of classmates). If the

resulting coefficient is negative for the interaction term,

then it can be concluded that tracking diminishes the peer

4 The most recent IEA data set was not used because it did

not have the pre-school-year test score to do a value-added

model.

5 In 1981, the IEA carefully selected a sample of schools

and administered a broad array of questionnaires answered by

students, teachers and administrators (Robitalle & Garden,

1989). The schools selected for the survey were designed to

reflect the socioeconomic characteristics of each country. The

students in the analysis were in the eighth grade.. The survey

asked questions in regard to the student’s family, schools, teachers,

and peers within the classrooms (Robitalle & Garden, 1989)

The IEA went to great lengths to ensure the quality of the data

through rigorous quality control steps including creating a manual

for inputting the data and auditing them once they were collected.

6 The mathematics tests, known as SIMS (Second International

Mathematics Study), were developed through the input

of mathematics experts and measurement specialists and the

tests were to reflect the curriculum of each country. The tests

went through a review process that included several pilot tests

before being implemented.

R. Zimmer / Economics of Education Review 22 (2003) 307–315 309

effect, whereas a positive coefficient suggests that the

peer effect is enhanced. Using the US public school portion

of the data, this study examines the impact of tracking

on the peer effect and student achievement for high-,

average-, and low-ability students using a standard education

production model. Together, the information provided

by the surveys and the test scores renders a robustness

to the results.

3. Model

To examine the effect from tracking, the peer effect,

and the interaction of tracking with the peer effect, I followed

a standard education production function model

employed by Betts and Shkolnik (2000). The model

examines the impact of various inputs, including family

and school resources, the student’s peers, and student’s

individual characteristics, including a proxy for ability7.

The value-added model is displayed as follows8:

At,j f(Ft,j,St,j,Pt,j,At-1). (1)

By using the model represented above, the valueadded

impacts of various inputs are measured as student

achievement for the period t–1 to t9. In this formulation,

the effects of all prior inputs are captured in achievement

from the previous period, or At-1

10. Formally, output is

measured by individual student j’s educational achievement

(At) in the current period as a function of inputs

that include student j’s family characteristics (Ft) over

period At-1 to At, school inputs (St) for student j over

7 Conceptually, the ideal model (as displayed below)

includes family inputs (F), school inputs (S), environmental

inputs of peers (P), and the innate ability of the student (I) in

period t:At,j = f(Ft,j,St,j,Pt,j,It,j).However, it is difficult to gain an

accurate proxy for the innate ability of the students (Hanushek,

1979) and the lack of an ability variable is generally assumed to

bias upward the effects of family background on achievement.

Therefore, researchers often use a value-added model to mitigate

the negative effect of not including such a variable. By

estimating the value-added model, the biases are minimized

because only the growth effect of innate ability is omitted.

8 Argys et al. suggests that “if there are unobservable student

or school characteristics that affect both achievement and track

placement, than any association between achievement and

tracking may simply be due to these characteristics” (1996,

p.624). To control for the selection, Argys et al. model the process

through which students are assigned to a particular track

and then include these selectivity corrections in the main

achievement model. Unfortunately, I was unable to find a suitable

instrument in the data set for this present analysis.

9 Betts and Shkolnik (2000) argue that it is critical to control

for the student’s initial achievement if accurate estimates are to

be obtained.

10 In this case, achievement in all other periods, such as t–1,

is a function of the cumulative inputs in t–1.

period At-1 to At, peer influence (Pt) of student j over

period At-1 to At, and student j’s achievement in the previous

period (At-1)11.

To apply this model I measure achievement, both in

the current and previous period, as the number of questions

answered correctly by student j on a mathematics

test with a possible range of 0 to 40. The achievement

level (At) is the students’ end-of-the-year test score and

is labeled POSTTEST, while the beginning-of-the-year

test (At-1) is labeled PRETEST12. Other inputs include a

vector of family inputs (F) and a vector of school inputs

(S), including the characteristics of the student’s teacher

and school13. (A complete list of variables with their

summary statistics is included in Table A1 in the

appendix.)

Two variables of primary importance in this current

research are the tracking and peer variables14. The tracking

variable is simply a dummy variable represented by

a 1 when tracking is used in the classroom and a 0 when

it is not. The peer variable is defined as the mean test

score of students in a classroom and is represented by

the mean of the scores at the beginning of the school year

of all students in the observed student’s classroom15. As

consistent with the literature, I also square the mean

score to capture nonlinear as well as linear effects on

fellow students16. This peer variable is also interacted

with the tracking variable. If tracking is utilized within

the classroom, then tracking is equal to 1 and the coefficient

of the interaction term represents the impact tracking

has on the peer effect. Because the peer level is also

included, the impact of tracking on the peer effect is

measured while holding the peer level constant. A negative

and significant coefficient suggests that tracking

diminishes the peer effect, while a positive and significant

coefficient suggests that tracking enhances the peer

11 For further information on the benefits of using the valueadded

model, see Boardman and Murnane (1979).

12 It should be noted that students that did not appear in both

the pre- and post-test scores were deleted from our sample.

13 Argys et al. (1996) suggest that it is critical to control

teacher and school characteristics if low-ability tracks and highability

tracks are systematically assigned different resources.

14 It should be noted that the model suffers from endogenity

problems. Part of the purpose of tracking is to put students in

more homogenous classes. Therefore, the peers of a student are

partially a function of whether a student is in a tracked class.

No suitable instrument could be found to perform an IV model

to correct for this problem.

15 This is one of the definitions used by Zimmer and Toma

(2000) for peers.

16 Henderson et. al. (1978) along with Zimmer and Toma

(2000) squared the mean effect to capture a peer effect that is

increasing at a decreasing rate (i.e., the squared term is expected

to be negative).

310 R. Zimmer / Economics of Education Review 22 (2003) 307–315

effect. Finally, an insignificant coefficient suggests that

tracking has no effect on the peer effect.

To examine the effect from tracking, the peer variable,

and the interaction of tracking and the peer variable, four

samples of data from US public schools were utilized.

The first sample uses the full data set of students as the

observations. Therefore, the first model examines the

impact on achievement from tracking, peers, and the

interaction of tracking with the peer effect, along with

the student’s other school, family, and the past inputs.

In addition to using all students in the sample, three

additional samples are used for analysis: one that is

restricted to high-ability students, one that is restricted to

low-ability students, and one that is restricted to averageability

students17. Therefore, the second estimated model

uses all the same inputs, but restricts the data to include

only high-ability students. A student is classified as highability

if he or she is placed in the upper 20th percentile

of all students across the nation on the math test at the

beginning of the year. Again, the peer-effects literature

assumes that a student placed in a class with a higher

mean peer group will experience a positive achievement

effect. A third model uses the same inputs but restricts

the data to include only low-ability students. A student

is classified as low-ability if he or she placed in the lower

20th percentile of all students across the nation on the

same mathematics test. A fourth model uses the same

inputs but restricts the sample to average-ability students.

A student is classified as average-ability if he or she

scored between the upper 20th percentile and the lower

20th percentile on the same mathematics test.

As in Betts and Shkolink (2000) and Zimmer and

Toma (2000), each model includes variables that represent

family and school inputs. The variables that represent

the family socioeconomic characteristics (F) of a

student is the occupation level of the father and the

mother and the education level of the father and the

mother18. The variables that represent school inputs (S)

include the size of the classroom, the general experience

of the teacher, the math experience of the teacher, the

teacher’s pedagogy training, the number of classes of

mathematics training for the teacher, the teacher’s age,

and the teacher’s gender. Finally, the student’s inputs

include the student prior achievement and student’s gender.

The following section will present the results of the

17 The restricting of data in this way is consistent with the

work of Kerckhoff (1986); Hoffer (1992), and Argys et al.

(1996).

18 The variables for the education level of the father and

mother are limited education measures defined as a binary variable

that is equal to one when the father’s or mother’s education

is at least secondary, respectfully. Because of the limitations of

these variables, readers should interpret the results with caution.

model focusing primarily on the interaction term, the

peer effect, and tracking.

4. Results

Using a model specification similar to that of Betts

and Shkolnik (2000), I estimate the effect from tracking,

peers, and the interaction of tracking and peers from four

segmented data sets. Table 1 displays the results. Column

one of the table lists the variables, while columns

two and three display the estimated coefficient and tscores

of each of the variables from the full set of data.

Columns four and five display the estimated coefficient

and t-scores of the model that restricts the data to only

high-ability students. Columns six and seven display the

estimated coefficient and t-scores of the model that

restricts the data to only low-ability students. Finally,

columns eight and nine display the estimated coefficient

and t-scores of the model that restricts the data to only

average-ability students.

In all the models, the tracking variable is a dichotomous

dummy variable, 1 if tracking is present in the

classroom, 0 if it is not. The peer variable is the average

of beginning-of-year test scores of all students in individual

j’s classroom. The interaction term is the interaction

of the tracking variable and the peer variable. Included

in each of the models, in addition to the peer variable,

tracking variable, and interaction term, are family and

school inputs, along with the student’s gender and prior

achievement (as proxy for past inputs). The dependent

variable is the achievement score at the end of the

school year.

For this study, the primary focus is on the coefficient

for the interaction term. Also of great interest are the

coefficients of the tracking and peer variable and the total

effect from tracking, including the mechanism of tracking,

the interaction, and peer effects. First, let us focus

on the interaction term. For the full sample, the averageability

students, and the low-ability students, the coefficients

are negative and significant. However, when the

data are restricted to only high-ability students, the coefficient

is insignificant, suggesting that tracking neither

enhances nor diminishes the peer effect for high-ability

students. Therefore, the results suggest that tracking

diminishes the positive peer effect for low- and averageability

students while neither enhancing nor diminishing

it for high-ability students19.

19 The sensitivity of these results is tested by alternatively

defining high- average- and low-ability students. In the first

alternative, high-ability is defined as students placed in the top

25th percentile, average-ability is defined as students placed in

the middle 50th percentile, and low-ability is defined as students

placed in the lower 25th percentile. In the second alternative,

high-ability is defined as students placed in the top 15th percentile,

average-ability is defined as students placed in the middle

R. Zimmer / Economics of Education Review 22 (2003) 307–315 311

Focusing on the secondary points, let us examine the

results of the peer variable and tracking. To interpret the

magnitude (and significance) of the coefficient for the

peer effect and tracking requires taking into account the

coefficient and joint significance of all variables that

include multiplicatives of the tracking and peer variables.

More formally, our model is

At,j B0 B1(Pt,j) B2(P2

t,j) B3(P∗

t,jTt,j) (2)

B4(Tt,j) ....

where At,j is the post-year level of student achievement

for student j in period t and is a function of the student

j’s peers (Pt,j or the mean pre-year test score of classroom

for student j), student j’s peer squared (P2

t,j or the

square of the mean pre-year test score of classroom for

student j), the interaction term (the mean pre-year test

score of the classroom, Pt,j, for student j times the dichotomous

tracking variable, Tt,j, for student j), the tracking

variable (Tt,j or the dichotomous 1/0 dummy variable for

tracking within the classroom for student j), and all other

explanatory variables. The estimated peer effect is the

partial derivative of At,j with respect to the peer variable

(Pt,j) and is ∂A/∂P = B1 + 2B2(Pt,j) + B3(Tt,j), which is

the estimated effect from student j’s peers. The estimated

tracking effect is the partial derivative of At,j with respect

to tracking (Tt,j) and is ∂A/∂T = B3(Pt,j) + B4, which is

the estimated effect from tracking. To test the significance

of the peer effect and tracking, the resulting beta

coefficients from the partial derivatives must be jointly

tested. Therefore, an F-test was conducted on the joint

significance of the peer variable and tracking.

Focusing first on the peer effect, the F-test indicates

significance at the 0.001 level for each of the models,

except the model that restricts the data to high-ability

students. In other words, the higher the mean test scores

of a student’s classmates, the better the average- and

low-ability students will perform20. It is surprising that

this effect does not show up for the high-ability students

as well. However, Argys et al. (1996) suggest that in

many cases, placement in high-tracks is not strictly based

upon ability, but on other factors including parental

influence on the decision process. In other words, highability

students may not always be placed exclusively

with high-ability students and therefore, have less of a

chance of gaining a positive peer effect.

70th percentile, and low-ability is defined as students placed in

the lower 15th percentile. In both cases, the interaction term

remained insignificant for high-ability students and negative

and significant for average- and low-ability students. This suggests

this robustness of the results. These results are available

upon request from the author.

20 These results are also true for the alternative definitions of

high-, average-, and low-ability as specified in footnote 21.

Again, results are available upon request from the author.

As noted above, to interpret the results of the peer

effect, the coefficient of all variables that include the

peer variable must be considered, including the coefficient

of the interaction term when tracking is present

(the interaction term does not have to be considered

when tracking is not present because the variable takes

on a value of zero). For instance, in the full sample, the

peer effect yields an estimated coefficient of 0.22 when

tracking is present and 0.38 when it is not21. The coefficient

implies that, on average, increasing the class mean

on the beginning-of-year scores by one point increases

a representative student’s end-of-year math test score by

approximately 0.22 points when tracking is present and

0.38 when it is not.

As with the peer coefficient, the F-test is employed to

test the significance of the tracking coefficient. In these

models, tracking is significant for the full sample and the

average- and low-ability students, and insignificant for

the models in which the data set was restricted to highability

students22. The results from the full sample of

data and the data restricted to average-ability students

suggest a negative effect from tracking, net of the peer

effect. However, unlike previous research (which measured

the total effect from tracking), the tracking variable,

on the margin, has a positive coefficient for low- and

average-ability students when controlling for the peer

level.

The total tracking effects, including the peer effect and

the effect from the mechanism of tracking (e.g., curriculum,

curriculum pace, etc.) are illustrated in Figs. 1–3.

Each figure compares student test scores in a tracked versus

a non-tracked class at different peer levels. Fig. 1

suggests that high-ability students are better off in a

tracked class at higher peer levels and better in a nontracked

class at lower levels. This fact is due to the offsetting

effects from tracking and peer variables. In

assessing the overall tracking effect, the estimates suggest

that the negative effect from the mechanism of

tracking (–4.05) is countered by the positive effect of

peers (0.38 and 0.2). The figure indicates that at the

approximate mean peer level of 18, as measured by the

pre-test score, the positive peer effect dominates the

21 For the full sample, the total coefficient of the peer effect

is derived by adding the coefficient of the peer variable (0.69)

plus two times the coefficient of the squared peer variable (-

0.01) times the mean value of the peer variable (15.81) plus the

coefficient of the interaction term (-0.15). The coefficient of the

interaction term is added only when tracking is present.

22 The alternative cases have similar results except for highability

students (defined as the top 15th percentile) and averageability

students (defined as the middle 50th percentile). In the

high-ability case, tracking has a negative and significant effect.

In the average-ability case, tracking becomes insignificant when

defined more narrowly. Once again, results are available upon

request from the author.

312 R. Zimmer / Economics of Education Review 22 (2003) 307–315

Table 1

Public school results

Variables Full sample High-ability students only Low-ability students only Average-ability students only

Est. coefficient t-scores Est. coefficient t-scores Est. t-scores Est. coefficient t-scores

coefficient

Tracking 1.98 2.61a –4.05 –1.30 3.56 2.77a 2.36 2.08a

Peer variable 0.69 6.21a 0.38 0.97 0.83 2.83a 0.73 3.85a

Peer variable squared –0.01 –3.14a –0.01 –1.01 –0.02 –1.47 0.01 –1.91b

Interaction –0.15 –2.96a 0.20 1.15 –0.30 –2.96a 0.17 –2.22a

Father’s occupation 0.28 1.51 0.26 0.75 0.93 2.34a 0.12 0.50

Mother’s occupation 0.14 0.74 –0.28 –0.79 0.16 0.42 0.25 0.96

Father’s education 0.37 1.10 –0.65 –0.82 0.91 1.66b 0.28 0.58

Mother’s education 0.46 1.22 1.86 1.92b –0.08 –0.14 0.42 0.77

Pre-Test 0.85 59.66a 0.67 14.66a 0.41 3.87a 0.97 27.96a

Teacher’sexperience 0.04 1.80b –0.04 –1.00 –0.01 –0.06 0.07 2.56a

Teacher’s math –0.01 –0.56 –0.03 –0.95 0.01 0.18 –0.01 –0.49

experience

Teacher’s gender 0.50 2.72 0.28 0.79 0.85 2.19a 0.54 2.11a

Teacher’s age 0.01 1.02 0.05 1.90a 0.02 0.62 0.002 0.08

Teacher’s education –0.002 –0.82 –0.004 –0.95 –0.01 0.28 –0.001 –0.38

Teacher’s math 0.01 0.56 0.03 0.95 0.01 0.03 0.002 0.10

education

Class size –0.03 –2.33a –0.05 –2.06a –0.03 –0.10 –0.03 –1.60

Student’s gender 0.51 2.83a 0.44 1.35 0.07 0.18 0.65 2.62a

Intercept –2.80 –2.67a 7.78 2.02a –2.03 –2.46b –4.94 –2.99a

F-Value for peer effect 27.96a 0.79 8.51a 11.89a

F-value for tracking 6.96a 1.67 7.80a 4.38a

Sample size 3706 763 703 2238

R-square 0.6536 0.2966 0.1711 0.3602

a Indicates significance at the 0.05 level.

b Indicates significance at the 0.10 level.

R. Zimmer / Economics of Education Review 22 (2003) 307–315 313

Fig. 1. High-ability sample.

Fig. 2. Low-ability sample.

Fig. 3. Average-ablility sample.

negative mechanism of the tracking effect and the overall

effect is positive.

Fig. 2 suggests that low-ability students are better off

in a tracked class at lower peer levels and better off in

a non-tracked class at higher peer levels. In assessing

the overall tracking effect, the estimates suggest that the

positive effect of tracking (3.56) is countered by the

negative effect of a reduced peer effect from tracking,

as highlighted by the coefficient of the interaction term

(–0.3). The figure indicates that at an approximate mean

peer level of 12, the reduction of the peer effect dominates

the positive mechanism of tracking and the overall

tracking effect is negative.

Finally, Fig. 3 suggests that average-ability students

are better off in a tracked class at lower peer levels and

better off in a non-tracked class at higher peer levels. In

assessing the overall tracking effect, like the low-ability

sample, the estimates suggest that the positive effect of

tracking (2.36) is countered by the negative effect of a

reduced peer effect from tracking, as highlighted by the

coefficient of the interaction term (–0.17). The figure

indicates that at the approximate mean peer level of 14,

the reduction of the peer effect dominates the positive

mechanism of tracking and the overall tracking effect

is negative.

Therefore, in each of these cases, whether a student

is better off in a tracked versus a non-tracked class

depends upon the level of peers and suggests contradictory

choices. For instance, low-ability students perform

better in tracking classes only when they have lowability

peers, which is more likely when classes are

tracked, and perform better in a non-tracked class when

they have high-ability peers, which is more likely when

classes are not tracked.

Other patterns from the analysis should also be noted.

Father’s occupation and education is significant (and

positive) only for low-ability students, while mother’s

education is significant (and positive) only for highability

students and mother’s occupation is insignificant

in all cases. School inputs of the teacher’s general

experience, math experience, gender, age, general education,

math education, and also the class size have

mixed results. The teacher’s experience is positive and

significant for the full sample as well as for averageability

students, whereas the teacher’s experience in

teaching mathematics, as well as the teacher’s training

in mathematics, is insignificant in all cases. The teacher’s

gender is positive and significant for low- and averageability

students and the teacher’s age is positive and significant

for high-ability students only. For the class-size

variable, the coefficient is negative and significant in two

cases (high-ability students and the full sample). The student’s

gender is positive and significant for low-ability

students only. Finally, the control variable for previous

educational attainment (pre-test) is positive and significant

in each of the cases.

5. Conclusions

In the introduction to the paper, I noted that there has

been a brewing debate over the effects of tracking on

the educational achievement of students. Opponents

argue against tracking for a number of reasons including

a reduced peer effect for low-ability students. In this

study, I found that tracking does indeed reduce the posi314

R. Zimmer / Economics of Education Review 22 (2003) 307–315

tive peer effect for low- and average-ability students.

However, as a secondary point, I also found that the

mechanism of tracking, at the margin, when holding the

peer level constant, has a positive effect on these same

students. In total, however, tracking, including both the

mechanical and peer effect of tracking, can have a positive

effect on low- and average-ability students, but only

when these students have lower-level peers, which is

more likely to occur with tracking. Therefore, it is hard

to advocate tracking when the results suggest that it

reduces the peer effect for low- and average-ability students

and in total has an insignificant effect on highability

students. It also has a positive effect on low- and

average ability students only when these students are surrounded

by lower-level peers, which tracking is more

likely to create.

Acknowledgements

I thank Dominic Brewer, Richard Buddin, Dan Goldhaber,

Eric Eide, Eugenia Toma, and two anonymous

reviewers for their helpful comments.

Appendix

Table A1

Descriptive statement

Variable Description Mean SD

Peer variable Mean beginning-of-the- 15.81 4.70

year test score (out of

possible 40) of students

in a classroom

Tracking Binary variable=1 if the 0.59 0.49

student is in a tracked

class; 0 if not

Father’s Binary variable=1 if 0.39 0.49

occupation father’s occupation is

professional or skilled; 0

if unskilled

Mother’s Binary variable=1 if 0.62 0.49

occupation mother’s occupation is

professional or skilled; 0

if unskilled

Father’s Binary variable=1 if 0.88 0.32

education father’s level of education

is at least secondary; 0

otherwise

Mother’s Binary variable=1 if 0.91 0.28

education mother’s level of

education is at least

secondary; 0 otherwise

Student math score at

beginning of the year.

Pretest 15.81 7.93

Student’s Binary variable=1 if

gender student’s gender is male;

0 if female

Teacher’s Binary variable=1 if 0.49 0.49

gender teacher’s gender is male;

0 if female

Teacher’s Number of years of 14.30 8.11

experience teaching experience.

Teacher’s Number of years of 9.78 8.92

math experience teaching

experience mathematics

Teacher’s age Age of teacher 38.94 11.08

Teacher’s Teacher’s number of 28.68 40.75

education semester units of general

pedagogy in

postsecondary education

Class size Total number of students 26.64 6.99

enrolled in the class

Teacher’s Teacher’s number of 10.59 6.49

math training semester units of math

training

Post-test Student math score at end 19.79 9.18

of year.

R. Zimmer / Economics of Education Review 22 (2003) 307–315 315

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