1
When Does a Peer Have an Effect?
Ryan Yeung
Assistant Professor of Public Administration
SUNY-Brockport
55 St. Paul Street
Rochester, NY 14604
ryeung@brockport.edu
Abstract
This study examines endogenous peer effects, which occur when a student’s behavior or
outcome is a function of the behavior or outcome of his or her peer group. Endogenous peer
effects have important implications for policy, especially as it relates to tracking. In this study, I
quantitatively review the literature on endogenous peer effects through the use of meta-analysis
and meta-regression analysis. Meta-analysis suggests the existence of a positive and significant
endogenous peer effect. The results of the meta-regression analysis suggest however, that
endogenous peer effects are much more contextualized. In particular, they are most likely with
smaller peer groups and when the peer effect is from grade point average. These findings are
consistent with a peer effect mechanism of peer norms.
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American children spend approximately 6.5 hours a day, 180 days a year, in school (Silva
2007). Much of this time is spent in the company of other children. This circumstance has not
been lost on the research community. Prompted by the publication of the influential Coleman
Report in 1966, a large body of research has examined the impact of school peers on student
outcomes. Harris (2010) defines a peer as, “…another student with whom the individual student
comes in contact in school-related activities.” Peer effects occur “…when the outcomes…of an
individual student are influenced by the behaviors, attitudes, or other characteristics of other
students with whom they interact during school activities.” Research on peer effects has
developed along two lines, one focusing on peer composition, or contextual effects, and one
focusing on peer behavior or outcomes, also known as endogenous effects. These endogenous
effects as they relate to education are the subject of this study.
Peer effects, if they exist, have important implications for education policy. In fiscally
strained times, governments are interested in maximizing the effect of every dollar spent. An
understanding of how students impact each other’s learning is essential to achieving this goal.
Endogenous peer effects in particular, are essential to studying issues related to tracking.
Tracking, or ability grouping, may be efficient if it allows teachers to better tailor the pace and
content of instruction to students’ needs. Proponents also argue that ability grouping makes
students more comfortable and engaged because they are surrounded by similar children.
Proponents also argue high-achievers flag when they are in classes with low-performers
(Westchester Institute for Human Services Research 2002). On the other hand, if low-achieving
children benefit from heterogeneous classes, and high-achievers are not harmed, ability grouping
would produce Pareto inferior outcomes. School voucher and other school choice programs have
been accused of cream-skimming i.e. luring the best students from regular public schools
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(Altonji, Huang, and Taber 2010), creating schools with better performing students and schools
with the remaining students who are of lesser performance. If “bad peers” gain more from
“good” peers than “good” peers are harmed by “bad” peers, such programs would create socially
inefficient outcomes (Gorman 2001).
This study extends the literature on endogenous effects in several ways. First, it presents
a meta-analysis (MA) of the empirical research on endogenous peer effects in education. Second,
it uses a meta-regression analysis (MRA) to correct for publication selection bias in metaanalysis and to uncover possible variables that may moderate endogenous peer effects. Metaanalysis results suggest strong support for positive endogenous peer effects. MRA results
however, suggest a highly contextualized effect from peers. Peer effects are strongest when the
peer group definition is the friendship network and when the peer effect is from GPA.
This article begins by briefly reviewing the literature on endogenous peer effects. This
section is followed by the presentation of an economic model of peer effects, with an emphasis
on the challenge of estimating peer effects. I present the methodology in section four. Section
five presents the variables used in the MRA. Section six presents the results and section seven
discusses the results and concludes.
Literature Review
The importance of peers to student outcomes has been identified as early as the seminal
Coleman Report in 1966. Coleman and his co-authors reported that “…a pupil’s achievement is
strongly related to the educational backgrounds and aspirations of the other students in the school.”
Goethals, Winston, and Zimmerman (1999) added that, “…peer characteristics were found to be
notably more important than teacher characteristics or non-social aspects of the school.” Another
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influential paper by Henderson, Mieszkowski, and Sauvageau (1978) found that average classroom
IQ was positively associated with gains in French and mathematics test scores for a sample of
Montreal students. This study did not identify endogenous peer effects per se as the IQ scores were
a different measure as the math and French test scores used to gauge student achievement.
Manski (1993) identified an important problem with the estimation of peer effects. Some
peer effects were endogenous and ordinary least squares estimates of endogenous peer effects
would suffer from bias. This endogeneity results from the fundamental simultaneous relationship
between a student’s behavior or outcome and the behaviors and outcomes of his or her peers.
Manski called this the problem of reflection. I will discuss this further in the next section. Manski’s
article continues to influence empirical research on endogenous peer effects today.
Hanushek, Kain, Markman, and Rivkin (2003) were cognizant of the reflection problem
and mitigated it by controlling for lagged peer achievement instead of contemporaneous peer
achievement. They used panel data from the State of Texas to remove student and school-bygrade fixed effects in addition to observable family and school characteristics that could
confound the estimate of peer effects from achievement. Their results suggested that a 0.1
standard deviation increase in lagged peer average achievement in the grade-cohort led to a
roughly 0.02 standard deviation increase in achievement.
Hoxby and Weingarth (2006) used school reassignment in Wake County, North Carolina to
identify the effect of peer achievement and peer exogenous characteristics on student outcomes.
One has to be concerned however, of the possibility that school reassignment is not random. As
Hoxby and Weingarth acknowledge, the goal of reassignment in Wake County was first to
equalize racial composition and then to equalize income composition. This fact suggests that the
children reassigned may be different in other ways as well that are not controlled for in the
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regression model. The results of their study suggested that an increase of mean initial achievement
by one point increased a student’s own achievement by approximately 0.25 points. They also
found that students in the extremes of the test score spectrum benefited from peers who had similar
levels of achievement.
Cooley’s (2010) study relied on the exogenous change in behavior caused by the
introduction of a student accountability policy by the State of North Carolina. Her results suggest
that a one standard deviation increase in mean classroom peer achievement was associated with a
0.22 standard deviation increase in a student’s own reading test score. This estimate is in line
with Kang (2007). In Kang’s study, a one standard deviation increase in mean peer math
achievement was associated with a 0.30 standard deviation increase in a student’s own math
achievement. Her source of variation came from a South Korean policy that required elementary
school graduates be randomly assigned to private or public middle schools in the relevant school
district.
Sojourner (2011) used data from Tennessee’s Project STAR experiment, which randomly
assigned students to classes of differing class size. This famous experiment has both its fair share
of supporters (Krueger 1999), and critics (Hanushek 2006; Hoxby 2000). Hanushek was
concerned that bias had been introduced into the experiment because of non-random transfers
between classes and non-random attrition out of schools. Hoxby’s concern regarded the
existence of Hawthorne Effects from teachers and students participating in the experiment. These
concerns notwithstanding, Sojourner estimated that an increase of average lagged mean
achievement of 10 percentile points in a classroom was associated with an increase in a student’s
first-grade achievement by approximately 2.5 percentile points.
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Several authors have examined natural experiments from developing and transition
economies. Duflo, Dupas, & Kremer (2008) used exogenous variation from a program in western
Kenya to reduce class sizes. Students were randomly assigned to sections from this program. The
results of this study suggested that a one standard deviation increase in average peer test score
increased a student’s own test score by 0.53 standard deviations. This result is consistent with
results from Wang (2010). Wang exploited the random assignment of students into classrooms in
a large secondary school in Malaysia to estimate peer effects on educational outcomes. Wang
found that a one standard deviation increase in the average baseline math score of classmates
resulted in a 0.50 standard deviation increase in a student’s own math score. In addition, Wang
found that high achieving peers lowered absence rates and the incidence of discipline violations.
Lai’s (2007) study of a natural experiment in Beijing’s middle schools is in contrast to most of
these studies. She found little evidence that variation in initial mean peer achievement caused by
within school lotteries for classroom assignment had any effect on student achievement.
Economic Model of Peer Effects
In this section, I present the economic theory that has been developed on peer effects.
Moffitt (2001) presented a model where there were g = 1, … ,G groups and only two individuals
(i = 1, 2) per group. For each individual i in group g, let y be the outcome of interest, a test score
for example. Xig is an individual exogenous characteristic for individual i in group g, like free or
reduced price lunch status, and ig is a random error term. Assuming linearity gives the following
system of equations:
𝑦1𝑔 = 𝛽0 + 𝛽1 𝑋1𝑔 + 𝛽2 𝑋2𝑔 + 𝛽3 𝑦2𝑔 + 𝜀1𝑔 ; (1)
𝑦2𝑔 = 𝜃0 + 𝜃1 𝑋2𝑔 +𝜃2 𝑋1𝑔 + 𝜃3 𝑦1𝑔 + 𝜀2𝑔 . (2)
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X1g is an exogenous characteristic for student 1 in group g. To simplify, let us assume that
child 1 receives free lunch and child 2 does not. What this means is that the achievement for
student 2, y2g, is a function of the child’s free lunch status, X2, the achievement of student 2, y2,
the non-free lunch status of X1, and the achievement of student 1, y1g. Similarly, the achievement
of student 1 is a function of the free lunch status of X2, the achievement of student 2, y2, and the
individual’s own non-free lunch status.
Manski (1993) calls and the estimates of exogenous, or contextual, effects. In the
free lunch example the indicator variable for free lunch status, is an exogenous effect.  and
are the endogenous effects. They are endogenous because the variables appear on both the left
and right sides of equation. For student 1, his achievement is a function of student 2’s whose
achievement is a function of student 1. This “multiplier effect” creates an empirical challenge
called the reflection problem. The reflection problem makes it impossible to distinguish whether
student 1 impacts student 2’s achievement or vice-versa, without additional exclusion
restrictions.
According to Manski (1993, p. 532), endogenous effects occur when, “… the propensity of
an individual to behave in some way varies with the behavior of the group.” An example is a
measure of peer performance. Exogenous effects occur when, “… the propensity of an individual
to behave in some way varies with the exogenous characteristics of the group.” Examples of
exogenous characteristics are race, income, and nativity. Together, the literature has called both
exogenous and endogenous effects, peer effects (Cooley 2007).
A linearized model of peer effects at the group level would look something like equation
(3), which is adapted from Sacerdote (2011):
𝑌𝑖𝑔 = 𝛼 + 𝛽1 𝑌̅−𝑖𝑔 + 𝛾𝑋𝑖𝑔 + 𝛿𝑋̅−𝑖𝑔 + 𝜀𝑖𝑔 . (3)
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Y as before is an educational outcome, like a test score for student i, who is a member of group g. 𝑌̅
is the average level of outcome of student i’s peers in group g, not including student i. X is a vector
of student characteristics and 𝑋̅−𝑖𝑔 is a vector of the average levels of the same characteristics, not
including student i. Finally, 𝜀𝑖𝑔 is a random error term. As in equations 1 and 2, OLS estimates of
equation 3 are likely to be problematic because Y affects 𝑌̅, and vice-versa. This is the reflection
problem. I will address the reflection problem in further detail in the variables section.
Methodology
This study relies on two related but distinct methodologies: meta-analysis and metaregression analysis. The aim of both approaches is the same. The goal of both methods is to
systematically and quantitatively review and summarize the results of studies on the same or
similar subject. Each study in these frameworks is analogous to a single observation in a typical
study. As with other statistical methods, more observations that conform to a certain hypothesis
make it more likely that hypothesis did not occur by random chance.
Meta-Analysis
Meta-analysis is the older of the two methods. Wampold, Ahn, and Kim (2000) define
meta-analysis as, “a quantitative method to aggregate similar studies in order to test hypotheses.”
Rosenthal and DiMatteo (2001) add, “Meta-analysis allows researchers to arrive at conclusions
that are more accurate and more credible than can be presented in any one primary study or in a
non-quantitative, narrative review.” Meta-analysis has been used to resolve such contentious issues
as the benefits of psychotherapy (Smith and Glass 1983), treatment of acute myocardial infarction
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(Lau, Antman, Jimenez-Silva, Kupelnick, Mosteller, and Chalmers 1992), and school funding
(Hanushek 1989).
Meta-analysis is not without its critics, however. Rosenthal and DiMatteo (2001)
summarized four criticisms of meta-analysis. The first was bias in sampling the findings. Which
studies to include or exclude is an inherently subjective process. Journal and book editors have also
consistently demonstrated a bias towards the publication of significant findings which may
overstate the frequency of a certain phenomenon. The second concern has traditionally been called
garbage in and garbage out. A meta-analysis is comprised of many studies, with many different
methodologies and data sources. Amidst this variation is variation in study quality, which may
generate error in the analysis. A related concern is that the variation in studies in variables and
methodologies means they cannot be systematically compared with each other. The fourth and
final concern is an overemphasis on individual effects. Meta-analysis emphasizes individual
relationships between independent and dependent variables, and downplays the role of moderator
or interaction variables, i.e. the conditions that make it more or less likely for a certain outcome to
occur (Author, 2009).
Meta-Regression Analysis
Meta-regression analysis was created in reaction to many of these concerns. MRA can be
described as a multiple regression of multiple regressions (Author, 2009). With MRA, the features
of the studies themselves become part of the analysis. This can be demonstrated by equation (1),
the meta-regression model I estimate in this study:
𝑡𝑖𝑗 = 𝛼0 + 𝛽𝑋𝑖𝑗 + 𝜀𝑖𝑗 . (4)
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In equation (4), t, a peer effect estimate, in this study, a t-statistic, is a function of X, a vector of
study characteristics that can affect the estimate of an endogenous peer effect. In this study, they
are the sample size, the year a study is published, the type of endogenous effect, whether a study
used a time-series method, whether a study was set in a developing or transitional economy, the
solution to the reflection problem, the peer group, and whether a study was published in a peerreviewed journal. Why these variables were chosen is discussed in the variables section.
Through the use of multiple regression, MRA addresses many of the concerns with metaanalysis. Some, but not all, biases can be controlled through MRA. For example, a study with a
large sample size is more likely to produce significant estimates than a study with a small sample
size. MRA can control for the moderating effect of sample size on significance. It can also control
for differences between studies in study quality, methodology and data sources, creating a more
apples-to-apples comparison between study estimates. Finally, MRA makes moderator variables,
the focal points of the study. Potential moderator variables become the X variables in equation 4.
MRA identifies them and estimates their impact on endogenous peer effects
Process
Studies included in this meta-analysis had to meet a set of criteria. These criteria were
created primarily to narrow the scope of this article to ensure a set of studies that could be
compared validly. First, the study had to include an estimate of endogenous peer effects. As
discussed previously, an endogenous effect occurs when a behavior or outcome depends on the
presence of a behavior or outcome in a peer group. The most common form of endogenous effect
was a test score. Second, the peer group had to be a school peer group. While there is a long
literature on the role of neighborhood peer groups on school outcomes (e.g. Ainsworth (2002);
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Case and Katz (1991); Ellen and Turner (1997); Mayer and Jencks (1989); Vartanian and Gleason
(1999)), they are beyond the scope of this study. Third, studies included in the meta-analysis had to
study academic outcomes like student achievement or attainment.1 Fourth, studies included in the
1
Peers have also been associated with such outcomes as weight gain and obesity Carrell, Scott E., Mark Hoekstra,
and James E. West. 2010. "Is poor fitness contagious? Evidence from randomly assigned friends.", Christakis,
Nicholas A. and James H. Fowler. 2007. "The spread of obesity in a large social network over 32 years." New
England Journal of Medicine 357:370-379, Cohen-Cole, Ethan and Jason M. Fletcher. 2008. "Is obesity contagious?
Social networks vs. environmental factors in the obesity epidemic." Journal of Health Economics 27:1382-1387,
Fortin, Bernard and Myra Yazbeck. 2011. "Peer effects, fast food consumption and adolescent weight gain."
CIRANO, Halliday, Timothy J. and Sally Kwak. 2008. "Weight gain in adolescents and their peers." in IZA
Discussion Paper Series, July. Bonn, Germany: Institute for the Study of Labor, Trogdon, Justin G., James
Nonnemaker, and Joanne Pais. 2008. "Peer effects in adolescent overweight." Journal of Health Economics
27:1388-1399, Yakusheva, Olga, Kandice Kapinos, and Marianne Weiss. 2011. "Peer effects and the Freshman 15:
Evidence from a natural experiment." Economics & Human Biology 9:119-132., smoking Alexander, Cheryl,
Marina Piazza, Debra Mekos, and Thomas Valente. 2001. "Peers, schools, and adolescent cigarette smoking."
Journal of Adolescent Health 29:22-30, Ali, Mir M. and Debra S. Dwyer. 2010. "Social network effects in alcohol
consumption among adolescents." Addictive Behaviors 35:337-342, Argys, Laura M. and Daniel I. Rees. 2008.
"Searching for peer group effects: A test of the contagion hypothesis." Review of Economics and Statistics 90:442458, Bifulco, Robert, Jason M. Fletcher, and Stephen L. Ross. 2011. "The effect of classmate characteristics on postsecondary outcomes: Evidence from the Add Health." American Economic Journal: Economic Policy 3:25-53,
Clark, Andrew E. and Youenn Lohéac. 2007. "“It wasn’t me, it was them!” Social influence in risky behavior by
adolescents." Journal of Health Economics 26:763-784, Eisenberg, Daniel. 2004. "Peer effects for adolescent
substance use: Do they really exist?", School of Public Health. Berkeley, CA: UC-Berkeley, Fletcher, Jason M.
2010. "Social interactions and smoking: evidence using multiple student cohorts, instrumental variables, and school
fixed effects." Health Economics 19:466-484, Garnier, Helen E. and Judith A. Stein. 2002. "An 18-year model of
family and peer effects on adolescent drug use and delinquency." Journal of Youth and Adolescence 31:45-56,
Gaviria, Alejandro and Steven Raphael. 2001. "School-based peer effects and juvenile behavior." Review of
Economics and Statistics 83:257-268, Harris, Jeffrey E. and Beatriz González López-Valcárcel. 2008. "Asymmetric
peer effects in the analysis of cigarette smoking among young people in the United States, 1992–1999." Journal of
Health Economics 27:249-264, Kawaguchi, Daiji. 2004. "Peer effects on substance use among American teenagers."
Journal of Population Economics 17:351-367, Krauth, Brian V. 2005. "Peer effects and selection effects on smoking
among Canadian youth." Canadian Journal of Economics/Revue canadienne d'économique 38:735-757, Lundborg,
Petter. 2006. "Having the wrong friends? Peer effects in adolescent substance use." Journal of Health Economics
25:214-233, Nakajima, R. Y. O. 2007. "Measuring peer effects on youth smoking behaviour." Review of Economic
Studies 74:897-935, Powell, Lisa M., John A. Tauras, and Hana Ross. 2005. "The importance of peer effects,
cigarette prices and tobacco control policies for youth smoking behavior." Journal of Health Economics 24:950968., alcohol use Ali, Mir M. and Debra S. Dwyer. 2010. "Social network effects in alcohol consumption among
adolescents." Addictive Behaviors 35:337-342, Argys, Laura M. and Daniel I. Rees. 2008. "Searching for peer group
effects: A test of the contagion hypothesis." Review of Economics and Statistics 90:442-458, Bifulco, Robert, Jason
M. Fletcher, and Stephen L. Ross. 2011. "The effect of classmate characteristics on post-secondary outcomes:
Evidence from the Add Health." American Economic Journal: Economic Policy 3:25-53, Clark, Andrew E. and
Youenn Lohéac. 2007. "“It wasn’t me, it was them!” Social influence in risky behavior by adolescents." Journal of
Health Economics 26:763-784, Duncan, Greg J., Johanne Boisjoly, Michael Kremer, Dan M. Levy, and Jacque
Eccles. 2005. "Peer effects in drug use and sex among college students." Journal of Abnormal Child Psychology
33:375-385, Eisenberg, Daniel. 2004. "Peer effects for adolescent substance use: Do they really exist?", School of
Public Health. Berkeley, CA: UC-Berkeley, Garnier, Helen E. and Judith A. Stein. 2002. "An 18-year model of
family and peer effects on adolescent drug use and delinquency." Journal of Youth and Adolescence 31:45-56,
Gaviria, Alejandro and Steven Raphael. 2001. "School-based peer effects and juvenile behavior." Review of
Economics and Statistics 83:257-268, Jaccard, James, Hart Blanton, and Tonya Dodge. 2005. "Peer influences on
risk behavior: An analysis of the effects of a close friend." Developmental psychology 41:135-147, Kawaguchi,
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MA and MRA had to examine endogenous peer effects in K-12 education.2 Finally, the studies
included in this analysis had to rely on regression analysis.
Selection of the studies for the analyses in this article began with a search of the EconLit,
Wilson Social Sciences Full Text, Education Information Resources Center (ERIC) and CSA
Sociological Abstracts databases with search terms like “peer effects” and “peer influences.” An
Daiji. 2004. "Peer effects on substance use among American teenagers." Journal of Population Economics 17:351367, Kremer, Michael and Dan Levy. 2008. "Peer effects and alcohol use among college students." The Journal of
Economic Perspectives 22:189-3A, Lundborg, Petter. 2006. "Having the wrong friends? Peer effects in adolescent
substance use." Journal of Health Economics 25:214-233., illicit substance use Ali, Mir M. and Debra S. Dwyer.
2010. "Social network effects in alcohol consumption among adolescents." Addictive Behaviors 35:337-342, Argys,
Laura M. and Daniel I. Rees. 2008. "Searching for peer group effects: A test of the contagion hypothesis." Review of
Economics and Statistics 90:442-458, Bifulco, Robert, Jason M. Fletcher, and Stephen L. Ross. 2011. "The effect of
classmate characteristics on post-secondary outcomes: Evidence from the Add Health." American Economic
Journal: Economic Policy 3:25-53, Clark, Andrew E. and Youenn Lohéac. 2007. "“It wasn’t me, it was them!”
Social influence in risky behavior by adolescents." Journal of Health Economics 26:763-784, Duncan, Greg J.,
Johanne Boisjoly, Michael Kremer, Dan M. Levy, and Jacque Eccles. 2005. "Peer effects in drug use and sex among
college students." Journal of Abnormal Child Psychology 33:375-385, Eisenberg, Daniel. 2004. "Peer effects for
adolescent substance use: Do they really exist?", School of Public Health. Berkeley, CA: UC-Berkeley, Garnier,
Helen E. and Judith A. Stein. 2002. "An 18-year model of family and peer effects on adolescent drug use and
delinquency." Journal of Youth and Adolescence 31:45-56, Kawaguchi, Daiji. 2004. "Peer effects on substance use
among American teenagers." Journal of Population Economics 17:351-367., cheating Carrell, Scott E., Frederick V.
Malmstrom, and James E. West. 2008. "Peer effects in academic cheating." Journal of Human Resources 43:173207., labor market outcomes Marmaros, David and Bruce Sacerdote. 2002. "Peer and social networks in job search."
European Economic Review 46:870-879., sexual activity Duncan, Greg J., Johanne Boisjoly, Michael Kremer, Dan
M. Levy, and Jacque Eccles. 2005. "Peer effects in drug use and sex among college students." Journal of Abnormal
Child Psychology 33:375-385, Evans, William N., Wallace E. Oates, and Robert M. Schwab. 1992. "Measuring peer
group effects: A study of teenage behavior." Journal of Political Economy 100:966-991, Jaccard, James, Hart
Blanton, and Tonya Dodge. 2005. "Peer influences on risk behavior: An analysis of the effects of a close friend."
Developmental psychology 41:135-147, Richards, Seth O. 2010. "Peer effects in sexual initiation: Separating social
norms and partner supply." dissertation Thesis, Economics, University of Pennsylvania, Philadelphia., delinquency
Bayer, Patrick, Randi Hjalmarsson, and David Pozen. 2007. "Building criminal capital behind bars: Peer effects in
juvenile corrections." in NBER Working Paper Series. Cambridge, MA: National Bureau of Economic Research,
Garnier, Helen E. and Judith A. Stein. 2002. "An 18-year model of family and peer effects on adolescent drug use
and delinquency." Journal of Youth and Adolescence 31:45-56, Henry, David B., Patrick H. Tolan, and Deborah
Gorman-Smith. 2001. "Longitudinal family and peer group effects on violence and nonviolent delinquency."
Journal of Clinical Child & Adolescent Psychology 30:172-186., and even church-going Gaviria, Alejandro and
Steven Raphael. 2001. "School-based peer effects and juvenile behavior." Review of Economics and Statistics
83:257-268..
2
While beyond the scope of this study, there have been several excellent studies of peer effects in higher education
including Gigi, Foster. 2006. "It's not your peers, and it's not your friends: Some progress toward understanding the
educational peer effect mechanism." Journal of Public Economics 90:1455-1475, Lyle, David S. 2007. "Estimating
and interpreting peer and role model effects from randomly assigned social groups at West Point." Review of
Economics and Statistics 89:289-299, Sacerdote, Bruce. 2001. "Peer effects with random assignment: Results for
Dartmouth roommates." The Quarterly Journal of Economics 116:681-704, Stinebrickner, Ralph and Todd R.
Stinebrickner. 2006. "What can be learned about peer effects using college roommates? Evidence from new survey
data and students from disadvantaged backgrounds." Journal of Public Economics 90:1435-1454, Zimmerman,
David J. 2003. "Peer effects in academic outcomes: Evidence from a natural experiment." Review of Economics and
Statistics 85:9-23., with very mixed results.
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initial study of the articles from this search suggested several moderator variables that were
noted and coded. The second stage involved a “snowball” search using citations from the initial
search to identify additional studies for inclusion. This snowball search continued for another
iteration before failing to produce additional new studies. As a final step, I searched for terms
like “peer effects” and “peer influences” in Google and Google Scholar to uncover additional
studies, particularly studies which have not been published yet.
41 studies with 69 estimates were selected for inclusion altogether, 16 of which were
published in peer-reviewed journals. Studies were coded twice and compared to ensure correct
coding of data. When a study included more than one estimate, the “best” estimate as argued by
the author was coded. When estimates differed in terms of one of the moderating variables in
Table 1, multiple estimates were coded as one of the goals of this study was to uncover how
differences in methodology affected the estimate of school peer effects. The studies selected for
inclusion in the MA and MRA are presented in Table 1.
Variables
Review of the studies in Table 1 suggested several potential moderator variables which the
MRA could control for. I discuss these variables in this section and explain how each variable may
affect the estimation of endogenous peer effects. Descriptive statistics on each of these variables
are presented in Table II.
Dependent Variable
As seen in equation (4), t-statistics from individual regression estimates comprised the
values of the dependent variable. T-statistics make intuitive sense as a measure of effect. As
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discussed by Author (2009), t-statistics are a unitless measure and allow different studies based on
different units to be compared easily. The average t-statistic in this study is 3.890, which suggests a
positive and significant peer effect. There is considerable range in this variable however, with tstatistics ranging from -1.645 to 53.289.
Independent Variables
Six independent variables were used in the MRA to control for the effect of differences in
study design on the estimate of school peer effects. These variables were the type of peer effect, the
solution to the reflection problem, the choice of peer group, the year a study was published, the
number of observations a particular estimate used, and dummy variables for the use of time-series
methods, if the study was set in a developing or transition economy, and publication in a peerreviewed journal.
Peer effect. While this study is interested in studying endogenous peer effects, there were
many forms of endogenous peer effects. The most common type of peer effect, as seen by Table II
was a math test score. Studies interested in endogenous peer effects from math test scores were
interested in how a student’s own math test score varied with the math test score of his or her peer
group. Other peer effect variables were reading test scores, combined (multiple subjects) test
scores, test scores for non-math and reading subjects, grade point average and educational
attainment.
Reflection problem solution. While the reflection problem seems intractable on its face,
scholars have proposed several methods of address it. I describe these methods in this section and
they are summarized in Table I. As will be seen, these methods rely on different assumptions and
may lead to very different results.
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Many studies on peer effects do not directly address the reflection problem and instead
estimate a “reduced-form” model. There are several important weaknesses to a reduced-form
model. With a reduced-form model, it becomes virtually impossible to understanding the
underlying behavior involved in the effect that is estimated. In addition, a structural model would
provide precise estimates of treatment effects as the results are not confounded by other
coefficients. However, the reflection problem may not be exceptionally relevant as it comes to
policy. On average, reduced-form model appear to be better able to make correct predictions than
structural-form models, as their estimation is a function of the relationships in the data (Timmins
and Schlenker 2009).
Another method to address this simultaneity issue is to use instrumental variables (IV)
regression. The strength of the IV design is dependent on the validity of the instrument. A valid
instrument must satisfy two conditions. First, the endogenous effect must be correlated with the
instrument. This condition can be tested with a partial F-test, but is a necessary condition as IV
regression with weak instruments can result in estimates more biased than under ordinary least
squares (Murray 2006). Second, the instrument cannot be correlated with the error term. This is a
more difficult task accomplish as overidentification tests can only test for the exogeneity of at
least one of the instruments if models are overidentified (Wooldridge 2009).
A popular approach is to use lagged peer behavior, e.g. test scores as a proxy for peer
achievement. The strength of this method depends on the strength of the relationship between
current achievement and peer achievement. As discussed by Hanushek, et al. (2003), lagged
achievement is a good proxy for current achievement if there are no year-to-year shocks in
current behavior. If the difference between current and lagged measures of peer achievement is
random, the estimates of peer effects are attenuated. In addition, lagged average achievement is
15
likely to be endogenous due to serial correlation with unobserved teacher, school and individual
factors. This lagged measure is the most common method of addressing the reflection problem in
this study.
Peer group. The choice of peer group also may play a role in the estimation of
endogenous peer effects. The majority of estimates in the sample were interested in classroom
peer effects followed by school peer effects, grade peer effects and finally interactions among
friends. Halliday and Kwak (2007) explained the importance of peer group:
In comparing these effects across varying definitions of ‘peer group’, we see that these definitions are
important to identifying correct effects for policy. If the most important peer influences occur at the level
of individual friendship ties, a finding of positive school-grade cohort effects does not capture the true
parameter of interest. In fact, the true effect will depend on the sorting of students into friend groups and
cannot be directly inferred from the schoolgrade correlations. Therefore, school-wide policies based on
estimates of school-grade cohort effects will not necessarily be effective.
The classroom is the majority peer group in the study followed by children in the same schoolgrade.
Year. I generated a continuous variable based on the year of publication date or in the
case of non-published manuscripts, the year of the most recent draft. I control for year for two
reasons. First, peer effects may differ according to the year, based on temporal factors like class
size or student demographics. Second, certain topics tend to be more popular during certain
periods than others. Controlling for year as a result, may be additional control against bias.
Sample size. The formula for the t-statistic incorporates the number of observations used
to calculate an estimate as the number of observations affects the standard error of a coefficient. I
control for the number of observations to ensure that studies with a large sample size are not
driving my results.
Time-series method. Regression coefficients based on a time-series method were coded
1, with other regressions coded 0. According to Author (2009, p. 15), time series methods, “have
the advantage of reducing endogeneity but also reduce variation that may be interesting and
16
important.” Consequently, time-series analyses and cross-sectional analyses can have drastically
different results. Estimates generated from value-added models or fixed effects models3 were
considered time-series methods for this study. 63.8 percent of the estimates in this study used a
time series method.
Developing or transition economy. Studies set in developing or transition economies
were coded 1, and 0 otherwise. I controlled for this variable to see if the relationship between
peers and student outcomes differed between developing or transition countries and
industrialized countries. Over a quarter of estimates came from a developing or transition
economy.
Published in a peer-reviewed journal. Finally, I controlled for whether an estimate came
from a peer-reviewed journal. Generally, peer-reviewed studies are considered to be of superior
quality than studies that have not been published in peer-reviewed studies (Author, 2009).
However, the possibility of publication bias means these studies may be biased towards a
significant finding (Rosenthal 1978), which is why I control for publication in a peer-reviewed
journal. A third of the estimates for this study came from peer-reviewed publications.
Results
This section presents the results from the meta-analysis and meta-regression analysis
performed in this study. I begin by presenting the results from the meta-analysis. To reiterate, the
meta-analysis does not control for differences between studies that may affect the estimate of
endogenous peer effects. I present these results in Table III.
3
McCaffrey, Daniel F., J.R. Lockwood, Daniel M. Koretz, and Laura S. Hamilton. 2003. "Evaluating value-added
models for teacher accountability." RAND, Santa Monica, CA. state that “value-added methods attempt to
determine the effects of incremental inputs into education, controlling for achievement at a point in the past.” Fixed
effects models use intra-group variation in students, schools, etc., over time to estimate peer effects.
17
Meta-Analysis
Table III reports the number of estimates that meet certain criteria. 47 (68.116 percent) of
the estimates were positive and significant at the .10 level, which corresponds to a t-statistic of
1.65. A slightly smaller number of estimates, 44, were positive and significant at the more
conservative .05 level (t-statistic of 1.96). At the .01 level of significance, the number of
estimates reaching significance falls considerably to 31 or 44.9 percent. Only one estimate was
negative and significant in the entire dataset at the .10 level. None of the estimates were negative
and reached significance at the .05 level. Less than a third of the estimates failed to reach
significance at any conventional level.
These results suggest a positive effect and significant effect from peer behavior. They are
consistent with the findings of the seminal Coleman Report. Children who are surrounded by
children who perform well on exams, or expect to attend school more years, are more likely to
perform well on exams or attend school more years. Likewise, surrounding children with lowerperforming students can have negative effects on a student’s own outcomes. The results from
this meta-analysis however, are subject to the criticisms of meta-analysis, which meta-regression
analysis aims to resolve.
Meta-Regression Analysis Results
Table IV presents the results from the meta-regression analysis of endogenous peer effect
studies. After controlling for the moderator variables in the MRA, the constant term is positive,
suggesting that controlling for study design, peers have positive effects. However, the constant
term is not significant and may have occurred by chance. Several of the moderator variables are
18
significant in this analysis. This new set of results suggests that peers can have positive and
significant effects on student outcomes, but this phenomenon is highly contextualized.
The peer effect that appears to have the greatest impact on student outcomes appears to
be grade point average. Student GPA scores are more impacted by peer GPA than are student
test scores by peer test scores or student educational attainment by peer educational attainment.
Study design also appears to play an important role in the estimation of endogenous peer effects.
For example, scholars are more likely to estimate significant and positive peer effects when they
resolve the reflection problem through instrumental variables or a lagged peer variable. The
meta-analysis on the other hand did not control for differences in study design, assuming all
study designs were equal.
The estimates in Table IV also suggest that peer effects are strongest at more micro peer
group levels. Peer effects are most likely to be positive and significant when the peer group is
measured at the friends’ level. None of the other variables are significant, which is somewhat
surprising. For example, publication in a peer-reviewed journal while negatively associated with
t-statistics is not significant and hence not different from zero. This suggests publication bias is
not a major issue in the dataset. 35.1 percent of the variation in t-statistics is explained by the
variables in the regression model.
Discussion and Conclusion
This study used meta-analysis and meta-regression analysis to examine endogenous peer
effects in K-12 education. Endogenous peer effects occur when a student’s behavior or outcome
is a function of the behavior or outcome of the peer group the student belongs to. These
19
endogenous peer effects have important implications for policy, especially for tracking or ability
grouping within schools. Do children perform better surrounded by “better” peers?
Using meta-analytic techniques, this study finds that most studies of endogenous peer
effects find positive and significant effects from peers. A child who is in a class with highperforming peers is more likely on average to also have high performance. In addition, almost no
studies find negative and significant effects from a higher performing peer groups. These results
are encouraging but lack context. In what environments and conditions are endogenous peer
effects most likely to occur?
To answer this question, I performed a meta-regression analysis. An MRA can be thought
of as a multiple regression of multiple regressions. MRA uses differences in the studies
themselves to control and identify potential moderator variables that may affect the estimation of
endogenous peer effects. The results of the MRA suggest that methodology can explain the
likelihood of finding a significant result. The traditional MA obscures differences in study
methodology.
More importantly, the results of the MRA suggest that endogenous peer effects are most
likely with smaller peer groups and when the peer effect is grade point average. How can we
explain this result? One possibility involves peer norms. Children in schools are members of
multiple peer groups. They are members of friendship networks, classroom groups, grade
cohorts, and the school as a whole. Each of these peer groups may have its own unique set of
norms. A group with a social norm that values academic engagement means that members of this
group are also more likely to value academic engagement, which is reflected in their practices
and behaviors (Wilkinson, Hattie, Parr, Townsend, Thrupp, Lauder, and Robinson 2000). Norms
are strongest when the groups are small, because small groups are better able to recognize and
20
reward favorable behaviors while punishing individuals who do not subscribe to a specific norm
(Bishop and Bishop 2007).
If the results of the MRA are correct, school officials would be best served by targeting
policies at friendship networks. School-wide policies may have little effect on student outcomes.
Ability grouping may be effective precisely because it does narrow the peer group, creating peer
groups with social norms of high and low achievement. Case studies of highly tracked schools
may be necessary to confirm this speculation.
21
References
Ainsworth, James W. 2002. "Why does it take a village? The mediation of neighborhood effects
on educational achievement." Social Forces 81:117-152.
Alexander, Cheryl, Marina Piazza, Debra Mekos, and Thomas Valente. 2001. "Peers, schools,
and adolescent cigarette smoking." Journal of Adolescent Health 29:22-30.
Ali, Mir M. and Debra S. Dwyer. 2010. "Social network effects in alcohol consumption among
adolescents." Addictive Behaviors 35:337-342.
Altonji, Joseph G., Ching-I Huang, and Christopher R. Taber. 2010. "Estimating the cream
skimming effect of school choice." in NBER Working Paper Series. Cambridge, MA:
National Bureau of Economic Research.
Argys, Laura M. and Daniel I. Rees. 2008. "Searching for peer group effects: A test of the
contagion hypothesis." Review of Economics and Statistics 90:442-458.
Asadullah, Mohammad Niaz and Nazmul Chaudhury. 2008. "Social interactions and student
achievement in a developing country: An instrumental variables approach." in Policy
Research Working Papers, Human Development Unit. Washington, DC: The World
Bank.
Atkinson, Adele, Simon Burgess, Paul Gregg, Carol Propper, and Steven Proud. 2008. "The
impact of classroom peer groups on pupil GCSE results." in CMPO Working Paper
Series. Bristol, UK: Centre for Market and Public Organisation.
Bayer, Patrick, Randi Hjalmarsson, and David Pozen. 2007. "Building criminal capital behind
bars: Peer effects in juvenile corrections." in NBER Working Paper Series. Cambridge,
MA: National Bureau of Economic Research.
Betts, Julian R. and Andrew Zau. 2004. "Peer groups and academic achievement: Panel evidence
from administrative data." Sacramento, CA: Public Policy Institute of California.
Bifulco, Robert, Jason M. Fletcher, and Stephen L. Ross. 2011. "The effect of classmate
characteristics on post-secondary outcomes: Evidence from the Add Health." American
Economic Journal: Economic Policy 3:25-53.
Bishop, John H. and Michael M. Bishop. 2007. "An economic theory of academic engagement
norms: The struggle for popularity and normative hegemony in secondary schools." in
CAHRS Working Paper Serie. Ithaca, NY: Center for Advanced Human Resource
Studies.
Boozer, Michael A. and Stephen E. Cacciola. 2001. "Inside the 'black box' of Project STAR:
Estimation of peer effects using experimental data." edited by C. D. Papers. New Haven,
CT: Economic Growth Center.
Boucher, Vincent, Yann Bramoullé, Habiba Djebbari, and Bernard Fortin. 2011. "Do peers affect
student achievement?: Evidence from Canada using group size variation."
Bradley, Steve and Jim Taylor. 2008. "Do peers matter? Estimation of peer effects from pupil
mobility between schools." Department of Economics. Lancaster, UK: Lancaster
University.
Burke, Mary A. and Tim R. Sass. 2011. "Classroom peer effects and student achievement." in
Public Policy Discussion Paper. Boston: Federal Reserve Bank of Boston.
Carrell, Scott E., Mark Hoekstra, and James E. West. 2010. "Is poor fitness contagious?
Evidence from randomly assigned friends."
Carrell, Scott E., Frederick V. Malmstrom, and James E. West. 2008. "Peer effects in academic
cheating." Journal of Human Resources 43:173-207.
22
Case, Anne C. and Lawrence F. Katz. 1991. "The company you keep: The effects of family and
neighborhood on disadvantaged youths." in NBER Working Paper Series. Cambridge,
MA: National Bureau of Economic Research.
Christakis, Nicholas A. and James H. Fowler. 2007. "The spread of obesity in a large social
network over 32 years." New England Journal of Medicine 357:370-379.
Clark, Andrew E. and Youenn Lohéac. 2007. "“It wasn’t me, it was them!” Social influence in
risky behavior by adolescents." Journal of Health Economics 26:763-784.
Clark, Christopher, Benjamin Scafidi, and John R. Swinton. 2009. "Do peers influence
achievement in high school economics? Evidence from Georgia's economic end of course
test." Department of Economics. Milledgeville, GA: Georgia College and State
University.
Cohen-Cole, Ethan and Jason M. Fletcher. 2008. "Is obesity contagious? Social networks vs.
environmental factors in the obesity epidemic." Journal of Health Economics 27:13821387.
Coleman, James S., E. Q. Campbell, C. J. Hobson, F. McPartland, A. M. Mood, F. D. Weinfeld,
and R. L. York. 1966. Equality of educational opportunity. Washington, DC: U.S.
Government Printing Office.
Cook, Thomas D., Yingying Deng, and Emily Morgano. 2007. "Friendship Influences During
Early Adolescence: The Special Role of Friends' Grade Point Average." Journal of
Research on Adolescence 17:325-356.
Cooley, Jane. 2007. "Alternative mechanisms of peer achievement spillovers: Implications for
identification and policy." Department of Economics. 2007: University of WisconsinMadison.
—. 2010. "Desegregation and the achievement gap: Do diverse peers help." Department of
Economics. Madison, WI: University of Wisconsin-Madison.
Ding, Weili and Steven F. Lehrer. 200. "Do peers affect student achievement in China's
secondary schools?" Review of Economics and Statistics 89:300-312.
Duflo, Esther, Pascaline Dupas, and Michael Kremer. 2008. "Peer effects and the impact of
tracking: Evidence from randomized evaluation in Kenya."
Dumay, Xavier and Vincent Dupriez. 2008. "Does the school composition effect matter?
Evidence from Belgian data." British Journal of Educational Studies 56:440-477.
Duncan, Greg J., Johanne Boisjoly, Michael Kremer, Dan M. Levy, and Jacque Eccles. 2005.
"Peer effects in drug use and sex among college students." Journal of Abnormal Child
Psychology 33:375-385.
Eisenberg, Daniel. 2004. "Peer effects for adolescent substance use: Do they really exist?",
School of Public Health. Berkeley, CA: UC-Berkeley.
Ellen, Ingrid Gould and Margery Austin Turner. 1997. "Does neighborhood matter? Assessing
recent evidence." Housing Policy Debate 8:833-866.
Evans, William N., Wallace E. Oates, and Robert M. Schwab. 1992. "Measuring peer group
effects: A study of teenage behavior." Journal of Political Economy 100:966-991.
Fletcher, Jason M. 2010. "Social interactions and smoking: evidence using multiple student
cohorts, instrumental variables, and school fixed effects." Health Economics 19:466-484.
Fortin, Bernard and Myra Yazbeck. 2011. "Peer effects, fast food consumption and adolescent
weight gain." CIRANO.
23
Fortner, C. Kevin. 2010. "Within classroom peer effects and tracking: Assessing the impact of
classroom peer variation and ability tracking with disaggregated high school."
dissertation Thesis, Public Management and Policy, Georgia State University, Atlanta.
Garnier, Helen E. and Judith A. Stein. 2002. "An 18-year model of family and peer effects on
adolescent drug use and delinquency." Journal of Youth and Adolescence 31:45-56.
Gaviria, Alejandro and Steven Raphael. 2001. "School-based peer effects and juvenile behavior."
Review of Economics and Statistics 83:257-268.
Gibbons, Stephen and Shqiponja Telhaj. 2005. "Peer effects and pupil attainment: Evidence from
secondary school transition." London: Centre for Economic Performance.
—. 2008. "Peers and achievement in England's secondary schools." in SERC Discussion Papers.
London: Spatial Economics Research Centre.
Gigi, Foster. 2006. "It's not your peers, and it's not your friends: Some progress toward
understanding the educational peer effect mechanism." Journal of Public Economics
90:1455-1475.
Goethals, Goerge, Gordon Winston, and David Zimmerman. 1999. "Students educating students:
The emerging role of peer effects in higher education." Pp. 25-45 in Forum Futures
1999: Forum Publishing.
Gorman, Linda. 2001. "Peer effects in the classroom." in NBER Digest. Cambridge, MA:
National Bureau of Economic Research.
Graham, Bryan S. 2008. "Identifying social interactions through conditional variance
restrictions." Econometrica 76:643-660.
Halliday, Timothy J. and Sally Kwak. 2007. "Bad apples, goody two shoes." Department of
Economics. Manoa, HI: University of Hawai’i at Manoa.
—. 2008. "Weight gain in adolescents and their peers." in IZA Discussion Paper Series, July.
Bonn, Germany: Institute for the Study of Labor.
Hanushek, Eric A. 1989. "The impact of differential expenditures of school performance."
Educational Researcher 18:45-62.
—. 2006. "School resources." Pp. 856-908 in Handbook of the economics of education, Volume
2, edited by E. A. Hanushek and F. Welch: North Holland.
Hanushek, Eric A., John F. Kain, Jacob M. Markman, and Steven G. Rivkin. 2003. "Does peer
ability affect student achievement?" Journal of Applied Econometrics 18:527-544.
Harris, Douglas N. 2010. "How do school peers influence student educational outcomes? Theory
and evidence from economics and other social sciences." Teachers College Record
112:1163-1197.
Harris, Jeffrey E. and Beatriz González López-Valcárcel. 2008. "Asymmetric peer effects in the
analysis of cigarette smoking among young people in the United States, 1992–1999."
Journal of Health Economics 27:249-264.
Henderson, Vernon, Peter Mieszkowski, and Yvon Sauvageau. 1978. "Peer group effects and
educational production functions." Journal of Public Economics 10:97-106.
Henry, David B., Patrick H. Tolan, and Deborah Gorman-Smith. 2001. "Longitudinal family and
peer group effects on violence and nonviolent delinquency." Journal of Clinical Child &
Adolescent Psychology 30:172-186.
Hoxby, Caroline M. 2000. "The effects of class size on student achievement: New evidence from
population variation." The Quarterly Journal of Economics 115:1239-1285.
Hoxby, Caroline M. and Gretchen Weingarth. 2006. "Taking race out of the equation: School
reassignment and the structure of peer effects."
24
Jaccard, James, Hart Blanton, and Tonya Dodge. 2005. "Peer influences on risk behavior: An
analysis of the effects of a close friend." Developmental psychology 41:135-147.
Jackson, C. Kirabo. 2009. "Peer quality or input quality?: Clean evidence from Trinidad and
Tobago."
Kang, Changhui. 2007. "Classroom peer effects and academic achievement: Quasirandomization evidence from South Korea." Journal of Urban Economics 61:458-495.
Kawaguchi, Daiji. 2004. "Peer effects on substance use among American teenagers." Journal of
Population Economics 17:351-367.
Kiss, David. 2011. "The impact of peer ability and heterogeneity on student achievement:
Evidence from a natural experiment." Nuremberg, Germany: University of ErlangenNuremberg.
Kramarz, Francis, Stephen Machin, and Amine Ouazad. 2010. "Using compulsory mobility to
identify heterogeneous school quality and peer effects." London: Centre for Economic
Performance.
Krauth, Brian V. 2005. "Peer effects and selection effects on smoking among Canadian youth."
Canadian Journal of Economics/Revue canadienne d'économique 38:735-757.
Kremer, Michael and Dan Levy. 2008. "Peer effects and alcohol use among college students."
The Journal of Economic Perspectives 22:189-3A.
Krueger, Alan B. 1999. "Experimental estimates of education production functions." The
Quarterly Journal of Economics 114:497-532.
Lai, Fang. 2007. "How do classroom peers affect student outcomes?: Evidence from a natural
experiment in Beijing's middle schools." New York: New York University.
Lau, Joseph, Elliot M. Antman, Jeanette Jimenez-Silva, Bruce Kupelnick, Frederick Mosteller,
and Thomas C. Chalmers. 1992. "Cumulative meta-analysis of therapeutic trials for
myocardial infarction." New England Journal of Medicine 327:248-254.
Lavy, Victor, Olmo Silva, and Felix Weinhardt. 2011. "The good, the bad and the average:
Evidence on ability peer effects in schools." Jerusalem: Hebrew University of Jerusalem.
Lefgren, Lars. 2004. "Educational peer effects and the Chicago public schools." Journal of
Urban Economics 56:169-191.
Leiter, Jeffrey. 1983. "Classroom composition and achievement gains." Sociology of Education
56:126-132.
Lin, Xu. 2010. "Identifying peer effects in student academic achievement by spatial
autoregressive models with group unobservables." Journal of Labor Economics 28:825860.
Link, Charles R. and James G. Mulligan. 1991. "Classmates' effects on black student
achievement in public school classrooms." Economics of Education Review 10:297-310.
Lundborg, Petter. 2006. "Having the wrong friends? Peer effects in adolescent substance use."
Journal of Health Economics 25:214-233.
Lyle, David S. 2007. "Estimating and interpreting peer and role model effects from randomly
assigned social groups at West Point." Review of Economics and Statistics 89:289-299.
Manski, Charles F. 1993. "Identification of endogenous social effects: The reflection problem."
Review of Economic Studies 60:531-542.
Marmaros, David and Bruce Sacerdote. 2002. "Peer and social networks in job search."
European Economic Review 46:870-879.
Mayer, Susan E. and Christopher Jencks. 1989. "Growing up in poor neighborhoods: How much
does it matter?" Science 243:1441-1445.
25
McCaffrey, Daniel F., J.R. Lockwood, Daniel M. Koretz, and Laura S. Hamilton. 2003.
"Evaluating value-added models for teacher accountability." RAND, Santa Monica, CA.
Moffitt, Robert A. 2001. "Policy interventions, low-level equilibria, and social interactions." Pp.
45-82 in Social dynamics, edited by S. N. Durlauf and H. P. Young. Washington, DC:
Brookings Institution Press.
Mora, Toni and Philip Oreopoulos. 2011. "Peer effects on high school aspirations: Evidence
from a sample of close and not-so-close friends." Economics of Education Review
30:575-581.
Murray, Michael P. 2006. "Avoiding invalid instruments and coping with weak instruments."
Journal of Economic Perspectives 20:111-132.
Nakajima, R. Y. O. 2007. "Measuring peer effects on youth smoking behaviour." Review of
Economic Studies 74:897-935.
Patacchini, Eleonoa, Edoardo Rainone, and Yves Zenous. 2011. "Dynamic aspects of teenage
friendships and educational attainment."
Powell, Lisa M., John A. Tauras, and Hana Ross. 2005. "The importance of peer effects,
cigarette prices and tobacco control policies for youth smoking behavior." Journal of
Health Economics 24:950-968.
Richards, Seth O. 2010. "Peer effects in sexual initiation: Separating social norms and partner
supply." dissertation Thesis, Economics, University of Pennsylvania, Philadelphia.
Rosenthal, Robert. 1978. "How often are our numbers wrong?" American Psychologist 33:10051008.
Rosenthal, Robert and M. R. DiMatteo. 2001. "META-ANALYSIS: Recent developments in
quantitative methods for literature reviews." Annual Review of Psychology 52:59-82.
Ryabov, Igor. 2011. "Adolescent academic outcomes in school context: Network effects
reexamined." Journal of Adolescence 34:915-927.
Sacerdote, Bruce. 2001. "Peer effects with random assignment: Results for Dartmouth
roommates." The Quarterly Journal of Economics 116:681-704.
—. 2011. "Peer effects in education: How might they work, how big are they and how much do
we know thus far?" in Handbook of the economics of education, volume 3, edited by E.
A. Hanushek, S. J. Machin, and L. Woessmann. San Diego, CA: North Holland.
Silva, Elena. 2007. "On the clock: Rethinking the ways schools use time." Education Sector,
Washington, DC.
Smith, Mary Lee and Gene V. Glass. 1983. "Meta-analysis of psychotherapy outcome studies."
American Psychologist 32:752-760.
Sojourner, Aaron. 2011. "Identification of peer effects with missing peer data: Evidence from
Project STAR." in IZA Discussion Paper Series. Bonn, Germany: Institute for the Study
of Labor.
Stinebrickner, Ralph and Todd R. Stinebrickner. 2006. "What can be learned about peer effects
using college roommates? Evidence from new survey data and students from
disadvantaged backgrounds." Journal of Public Economics 90:1435-1454.
Sund, Krister. 2009. "Estimating peer effects in Swedish high school using school, teacher, and
student fixed effects." Economics of Education Review 28:329-336.
Thomas, Wayne and Don J. Webber. 2001. "'Because my friends are': the impact of peer groups
on the intention to stay on at sixteen." Research in Post-Compulsory Education 6:339354.
26
Timmins, Christopher and Wolfram Schlenker. 2009. "Reduced-form versus structual modeling
in environmental and resource economics." Annual Review of Resource Economics
1:351-380.
Trogdon, Justin G., James Nonnemaker, and Joanne Pais. 2008. "Peer effects in adolescent
overweight." Journal of Health Economics 27:1388-1399.
Vartanian, Thomas P. and Philip M. Gleason. 1999. "Do neighborhood conditions affect high
school dropout and college graduation rates?" Journal of Socio-Economics 28:21-41.
Vigdor, Jacob and Thomas Nechyba. 2008. "Causal inference when assignment may have been
random: Peer effects in North Carolina elementary schools." Durham, NC: Duke
University.
Wampold, Bruce, Hyunnie Ahn, and Dongmin Kim. 2000. "Meta-analysis in the social
sciences." Asia Pacific Education Review 1:67-74.
Wang, Liang Choon. 2010. "Peer effects in the classroom: Evidence from a natural experiment in
Malaysia." Department of Economics. San Diego: University of California, San Diego.
Westchester Institute for Human Services Research. 2002. "Ability grouping." Westchester
Institute for Human Services Research, White Plains, NY.
Wilkinson, Ian A. G., John A. Hattie, Judy M. Parr, Michael A. R. Townsend, Martin Thrupp,
Hugh Lauder, and Tony Robinson. 2000. "Influence of peer effects on learning outcomes:
A review of the literature." Ministry of Education, Wellington, NZ.
Wooldridge, Jeffrey M. 2009. Introductory Econometrics: A Modern Approach. Mason, OH:
South-Western.
Yakusheva, Olga, Kandice Kapinos, and Marianne Weiss. 2011. "Peer effects and the Freshman
15: Evidence from a natural experiment." Economics & Human Biology 9:119-132.
Zabel, Jeffrey E. 2008. "The impact of peer effects on student outcomes in New York City public
schools." Education Finance and Policy 3:197-249.
Zhang, Hongliang. 2009. "Peer effects and student achievement: Evidence from elementary to
middle school transition." Department of Economics. Hong Kong: Chinese University of
Hong Kong.
Zimmer, Ron. 2003. "A new twist in the educational tracking debate." Economics of Education
Review 22:307-315.
Zimmerman, David J. 2003. "Peer effects in academic outcomes: Evidence from a natural
experiment." Review of Economics and Statistics 85:9-23.
27
Study
Asadullah & Chaudhury
Atkinson et al.
Atkinson et al.
Betts & Zau
Betts & Zau
Betts & Zau
Betts & Zau
Boozer & Cacciola
Boucher et al.
Boucher et al.
Boucher et al.
Boucher et al.
Bradley & Taylor
Burke & Sass
Burke & Sass
Burke & Sass
Burke & Sass
Clark et al.
Cook et al.
Cook et al.
Cooley
Ding & Lehrer
Duflo et al.
Duflo et al.
Duflo et al.
Duflo et al.
Duflo et al.
Duflo et al.
Dumay & Dupriez
Fortner
Fortner
Gibbons & Telhaj
Gibbons & Telhaj
Graham
Graham
Hanushek et al.
Hoxby & Weingarth
Jackson
Year
2008
2008
2008
2004
2004
2004
2004
2001
2011
2011
2011
2011
2008
2011
2011
2011
2011
2009
2007
2007
2010
2007
2008
2008
2008
2008
2008
2008
2008
2010
2010
2005
2008
2008
2008
2003
2006
2009
Table I
Studies Used in Meta-Analysis
Developing or
Time-Series Transitional
Endogenous Effect
Method?
Economy
Math Test Score
Yes
Yes
Reading Test Score
Yes
No
Math Test Score
Yes
No
Reading Test Score
Yes
No
Math Test Score
Yes
No
Reading Test Score
Yes
No
Math Test Score
Yes
No
Combined Test Score
No
No
Reading Test Score
No
No
Other Test Score
No
No
Math Test Score
No
No
Other Test Score
No
No
Combined Test Score
Yes
No
Math Test Score
Yes
No
Reading Test Score
Yes
No
Math Test Score
Yes
No
Reading Test Score
Yes
No
Other Test Score
No
No
Math Test Score
No
No
GPA
No
No
Reading Test Score
Yes
No
Combined Test Score
Yes
Yes
Combined Test Score
No
Yes
Combined Test Score
No
Yes
Math Test Score
No
Yes
Reading Test Score
No
Yes
Math Test Score
No
Yes
Reading Test Score
No
Yes
Reading Test Score
Yes
No
Reading Test Score
Yes
No
Math Test Score
Yes
No
Attainment
Yes
No
Combined Test Score
Yes
No
Math Test Score
No
No
Reading Test Score
No
No
Math Test Score
Yes
No
Combined Test Score
Yes
No
Combined Test Score
Yes
Yes
Reflection Solution
Instrumental variables
Lagged peer variable
Lagged peer variable
Lagged peer variable
Lagged peer variable
Lagged peer variable
Lagged peer variable
Instrumental variables
Instrumental variables
Instrumental variables
Instrumental variables
Instrumental variables
Lagged peer variable
Reduced-form
Reduced-form
Reduced-form
Reduced-form
Instrumental variables
Reduced-form
Reduced-form
Instrumental variables
Lagged peer variable
Lagged peer variable
Instrumental variables
Lagged peer variable
Lagged peer variable
Instrumental variables
Instrumental variables
Lagged peer variable
Lagged peer variable
Lagged peer variable
Lagged peer variable
Lagged peer variable
Instrumental variables
Instrumental variables
Lagged peer variable
Lagged peer variable
Instrumental variables
Peer Group Refereed?
School
No
Classroom
No
Classroom
No
Classroom
No
Classroom
No
Grade
No
Grade
No
Classroom
No
School
No
School
No
School
No
School
No
School
No
Classroom
No
Classroom
No
Grade
No
Grade
No
School
No
Friends
Yes
Friends
Yes
Classroom
No
School
Yes
Classroom
No
Classroom
No
Classroom
No
Classroom
No
Classroom
No
Classroom
No
School
Yes
Classroom
No
Classroom
No
School
No
School
No
Classroom
Yes
Classroom
Yes
Grade
Yes
Grade
No
School
No
28
Study
Kang
Kiss
Kramarz et al.
Lai
Lavy et al.
Lefgren
Lefgren
Leiter
Leiter
Lin
Link & Mulligan
Link & Mulligan
Mora & Oreopoulos
Mora & Oreopoulos
Pattacchini et al.
Ryabov
Sojourner
Sund
Thomas & Webber
Vigdor & Nechyba
Vigdor & Nechyba
Vigdor & Nechyba
Vigdor & Nechyba
Vigdor & Nechyba
Vigdor & Nechyba
Wang
Zabel
Zabel
Zhang
Zhang
Zimmer
Year
2007
2011
2010
2010
2011
2004
2004
1983
1983
2010
1991
1991
2011
2011
2011
2011
2011
2009
2001
2007
2007
2007
2007
2008
2008
2010
2008
2008
2009
2009
2003
Table I, Continued
Studies Used in Meta-Analysis
Developing or
Time-Series Transitional
Endogenous Effect
Method?
Economy
Math Test Score
No
No
Math Test Score
Yes
No
Combined Test Score
Yes
Yes
Combined Test Score
Yes
Yes
Combined Test Score
Yes
Yes
Reading Test Score
Yes
No
Math Test Score
Yes
No
Reading Test Score
No
No
Math Test Score
No
No
GPA
No
No
Reading Test Score
Yes
No
Math Test Score
Yes
No
Attainment
No
No
Attainment
No
No
Attainment
No
No
GPA
No
No
Combined Test Score
Yes
No
GPA
Yes
No
Attainment
No
No
Math Test Score
Yes
Yes
Math Test Score
Yes
Yes
Reading Test Score
Yes
Yes
Reading Test Score
Yes
Yes
Math Test Score
Yes
No
Reading Test Score
Yes
No
Math Test Score
Yes
Yes
Reading Test Score
Yes
No
Math Test Score
Yes
No
Math Test Score
Yes
Yes
Math Test Score
Yes
Yes
Math Test Score
Yes
No
Reflection Solution
Instrumental variables
Lagged peer variable
Instrumental variables
Reduced-form
Lagged peer variable
Instrumental variables
Instrumental variables
Reduced-form
Reduced-form
Instrumental variables
Lagged peer variable
Lagged peer variable
Reduced-form
Reduced-form
Instrumental variables
Reduced-form
Lagged peer variable
Lagged peer variable
Reduced-form
Lagged peer variable
Lagged peer variable
Lagged peer variable
Lagged peer variable
Lagged peer variable
Lagged peer variable
Lagged peer variable
Instrumental variables
Instrumental variables
Instrumental variables
Instrumental variables
Lagged peer variable
Peer Group Refereed?
Classroom
Yes
Grade
No
Grade
No
Classroom
No
Grade
No
Classroom
Yes
Classroom
Yes
Classroom
Yes
Classroom
Yes
Friends
Yes
Classroom
Yes
Classroom
Yes
Classroom
Yes
Friends
Yes
Friends
No
School
Yes
Classroom
No
Classroom
Yes
School
Yes
Grade
No
Classroom
No
Grade
No
Classroom
No
Classroom
No
Classroom
No
Classroom
No
Classroom
Yes
Classroom
Yes
Grade
No
Grade
No
Classroom
Yes
29
Table II
Descriptive Statistics
Dependent Variable
T-statistic
Independent Variables
Year
Endogenous Effect
Math test score
Reading test score
Combined test score
Other test score
GPA
Attainment
Sample size
Time-Series Method? (1= Yes, 0 =No)
Developing or Transitional Economy (1= Yes, 0 =No)
Reflection Solution
Instrumental variable
Lagged peer variable
Reduced-form
Peer Group
School
Grade
Classroom
Friends
Published in peer-reviewed journal? (1= Yes, 0 =No)
Note: There are 69 observations
Mean
Standard
Deviation
3.890
6.726
-1.645
53.289
2006.826
5.644
1983.000
2011.000
0.377
0.275
0.174
0.043
0.058
0.072
196355
0.638
0.275
0.488
0.45
0.382
0.205
0.235
0.261
405848.400
0.484
0.45
0.348
0.464
0.188
0.48
0.502
0.394
0.000
0.000
0.000
1.000
1.000
1.000
0.203
0.188
0.536
0.072
0.333
0.405
0.394
0.502
0.261
0.475
0.000
0.000
0.000
0.000
0.000
1.000
1.000
1.000
1.000
1.000
Minimum Maximum
0.000
1.000
0.000
1.000
0.000
1.000
0.000
1.000
0.000
1.000
0.000
1.000
294.000 2200213.000
0.000
1.000
0.000
1.000
30
Table III
Frequency of Significant Estimates
Estimate
Frequency
Percentage
Positive and significant at .10
47
68.116
Positive and significant at .05
44
63.768
Positive and significant at .01
31
44.928
Negative and significant at .10
1
1.449
Negative and significant at .05
0
Not significant
21
Note: There are 69 total observations.
30.435
31
Table IV
Meta-Regression Results
Year
Peer Effect (Math test score is omitted category)
Reading test score
Combined test score
Other test score
GPA
Attainment
Sample size
Time-Series Method? (1= Yes, 0 =No)
Developing or Transitional Economy (1= Yes, 0 =No)
Reflection Solution (Reduced-form is omitted category)
Instrumental variable
Lagged peer variable
Peer Group (School is omitted category)
Grade
Classroom
Friends
Published in peer-reviewed journal? (1= Yes, 0 =No)
Constant
R-squared
Notes: a) There are 69 observations.
b) *p<.10; **p<.05; ***p<.01.
c) Standard errors in parentheses.
-0.172
(0.166)
0.677
(1.885)
-0.740
(2.317)
-1.128
(4.700)
12.153
(4.105)
0.380
(3.636)
0.000
(0.000)
1.190
(2.207)
-0.347
(2.003)
5.799
(2.460)
5.489
(2.428)
-0.147
(2.800)
1.818
(2.316)
9.368
(3.865)
-1.166
(2.192)
341.526
(332.651)
0.351
***
**
**
**