Peer effects in college academic outcomes – Gender matters!

Economics of Education Review 31 (2012) 1102– 1115

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Peer effects in college academic outcomes – Gender matters!

Carlena Cochi Ficano ∗

Department of Economics, 229 Golisano Hall, Hartwick College, Oneonta, NY 13820, United States

a r t i c l e i n f o

Article history:Received 7 September 2010Received in revised form 3 July 2012Accepted 27 July 2012

a b s t r a c t

An extensive literature exploring a range of peer influences on both academic and non-academic outcomes continues to produce contradictory evidence regarding the existenceand magnitude of peer effects. Our results provide no evidence of peer effects in modelswhere peer academic ability is measured in the aggregate. However, models that control for

JEL classification:I21

Keywords:Peer effectsEducational economics

own-gender and other gender peer performance identify strong, positive, and statisticallysignificant male peer influence on male students. In contrast, females are unresponsive toeither male or female peer average academic rating. The results highlight the possibilitythat significant own gendered effects for males may be masked by insignificant effects inthe aggregate.

© 2012 Elsevier Ltd. All rights reserved.

1. Background and motivation

During the transition from high school to college,students encounter new academic challenge. They alsostruggle to establish their identity in an unfamiliar envi-ronment, leaving open the potential for peer influence onbehavior. An extensive empirical literature investigates thepresence of peer effects in higher education. Studies haveaddressed the impact of peer academic ability on ownacademic performance (e.g., Brunello, De Paola, & Scoppa,2010; Foster, 2006; Han & Li, 2009; Winston & Zimmerman,2003; Zimmerman, 2003); the effects of other peer charac-teristics such as family income, leadership ability or fitnesson own academic performance (e.g., Carrell, Fullerton, &West, 2008; Stinebrickner & Stinebrickner, 2005); and theeffects of peer characteristics on other behaviors such asthe decision to join a Greek organization or athletic teamor select and persist in a given academic major (e.g., DeGiorgi, Pellizzari, & Redaelli, 2010; Lyle, 2007; Ost, 2010;
Sacerdote, 2001). An understanding of the operation ofpeer influences on these outcomes can inform student
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admissions and tracking policy across a range of educa-tional institutions.

With respect to the impact of peer academic abilityon own academic performance, the literature recognizes anumber of distinct transmission mechanisms. In particular,Stinebrickner and Stinebrickner (2005) identify role mod-els with respect to time use, study habits, and class effortas a channel through which peer effects operate. Similarly,Eisenkopf (2010) and Foster and Frijters (2010) identifypeer influenced own motivation as a channel throughwhich peer effects operate. Carrell et al. (2008) allude tostudy group connections to explain stronger peer effects inmath and science than humanities courses. Goethals (2001)and Zimmerman (2003) draw heavily upon social compari-son theory (Festinger, 1954) to motivate their independentanalyses of peer effects on Williams College undergradu-ates.

A majority of the analyses focus on residential peergroupings, usually roommates or floor mates. Some excep-tions to this are De Paola and Scoppa (2010) and DeGiorgio, Pellizzari, and Redaelli (2010) who use class-mate peers; Carrell et al. (2008) and Lyle (2007) whose
military peer groups encompass both the academic andresidential simultaneously; Hansen, Owan, and Pan (2006)who exploit a natural experiment design in businessclass project grouping; and Eisenkopf (2010) and Goethals
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C.C. Ficano / Economics of Educ

2001) who use random treatment and control groupssignment.

The existing empirical evidence on peer effects is mixed.acerdote (2001), Winston and Zimmerman (2003), andtinebrickner and Stinebrickner (2005) find that roommatecademic ability has a significant and positive effect on ownPA. Brunello et al. (2010) find evidence of roommate peerffects in the hard sciences only while De Paola and Scoppa2010) identify positive classmate peer effects that corre-ate with the nature of peer interaction. Zimmerman (2003)nds no effect of roommate total SAT but does find a smallut positive and significant effect of roommate verbal SATcore. Carrell et al. (2008) also find significant effects of peererbal SAT, but unlike Sacerdote and Winston and Zimmer-an, they find this at the squadron level and not at the

oommate level. Foster (2006) finds weak evidence of peerffects while Lyle (2007) finds no significant effect of peercademic ability on own academic performance.

The size of peer effects appear to depend on wherene falls in a school’s academic distribution (Ost,010; Stinebrickner & Stinebrickner, 2005; Winston &immerman, 2003; Zimmerman, 2003) and the measuref peer academic performance used. In particular, Griffithnd Rask (2010) note that in their own and others’ anal-ses, high school achievement is a consistently strongereer measure than are standardized test scores. The liter-ture also finds that peer effects vary by gender (Foster,006; Goethals, 2001; Griffith & Rask, 2010; Han andi, 2009; Stinebrickner & Stinebrickner, 2005; Winston &immerman, 2003; Zimmerman, 2003), although studiesisagree on which gender is more strongly affected.

A potential source of the aforementioned inconsistencyn empirical evidence of peer effects may be variationcross schools in what constitutes the relevant peer groupFoster, 2006). For example, roommates may represent anffective peer group in some undergraduate settings, whileoor-mates or classmates may be more influential in otherettings. Further, students may be differentially affectedy peers who are more or less similar to themselves inace, ethnicity or ability (Bifulco, Fletcher, & Ross, 2009;letcher & Tienda, 2008; Goethals, 2001; Griffith, 2008). Forxample, studies using roommates frequently identify ownender peer effects – in some instances stronger for womenHan & Li, 2009; Stinebrickner & Stinebrickner, 2005) andn some instances stronger for men (Griffith & Rask, 2010),hat might have been diluted if own and other gender peersere considered in aggregate.

In this paper, we further explore the hypothesis thateer effects may differ by gender and that students may beifferentially affected by own and other gendered peers.imilar to Lyle (2007), we begin by modeling the effect of

student’s classmates’ academic quality on her own aca-emic performance in the first semester of college. Ourodels use data on two cohorts of students from a small,

elective liberal arts college. Consistent with the evidenceenerated by the Lyle analysis, our results using measuresf aggregate classmate academic quality provide no evi-
ence of peer effects.
However, acknowledging the well-documented genderap reversal in which males have underperformed femalesn college achievement and completion since at least the

view 31 (2012) 1102– 1115 1103

early 1990s (DiPrete & Buchmann, 2006), we extend thebase models to allow for gender differences in peer effects.Specifically, we separately consider the effect of male peeracademic quality on male and female grade outcomes andfemale peer academic quality on female and male gradeoutcomes, under the assumption that the effective peergroup may be gender determined. In contrast to the basemodel results, results from these models indicate that malepeer academic quality positively and significantly influ-ences male student performance but not female studentperformance, while female peer academic quality has nostatistically significant effect on either male or femalecourse GPA. In other words, males appear more suscep-tible to peer influence than females, and they appear to bedifferentially affected by male and female classmate aca-demic quality. If own and other-gender peer effects havethe potential to cancel one another or if positive peer effectsfor one gender can be diluted by insignificant peer effectsfor another gender, it becomes important to differentiateown and other-gendered peer effects where data allow.

The remainder of the paper is organized as follows. Sec-tion 2 presents the empirical techniques employed in theanalysis and addresses the means by which we address themultiple sources of endogeneity endemic to this type ofanalysis. Section 3 introduces our data source and definesthe variables used in the regressions. Section 4 presents theresults of the analysis and robustness checks and Section 5concludes.

2. Model

We follow a standard education production functionmodel where a student’s academic ability and prepared-ness (Ai) and the quality and context of the educationalprocess (Qi) are the primary production inputs that gen-erate the outcome of interest, student college academicperformance (Hanushek, 1979). Recognizing the poten-tial for one’s peers to influence the translation of studentability/preparedness into “produced output” (i.e., a grade)through both “peer mentor” channels (Stinebrickner &Stinebrickner, 2005) and by social comparison (Suls,Martin, & Wheeler, 2002), we extend the model to includepeer academic quality (Pi) as an additional production pro-cess input. We estimate regression models of own studentfirst semester course level GPA as a function of peer highschool academic performance and other controls:

GPAi = ˇ0 + ˇ1Ai + ˇ2Ai + ˇ2Qi + ˇ2Pi + ei (1)

2.1. Endogenous peer groupings

Interpretation of regression results from (1) as evidenceof peer effects introduces two distinct empirical concerns,the first of which is that the peer academic measure must beexogenous to the model. In other words, our specificationmust address endogeneity from selection of academicallysimilar individuals into the peer group and endogeneity
from the simultaneous determination of own and peer per-formance. Our use of high school academic performanceindicators as our peer ability measures removes the poten-tial for simultaneity as peer quality is determined prior to

ation Re

ter estimates on the course enrollment indicator variablesinteracted with the student schedule constraint variablesare robust to the inclusion of student level controls, andthe course enrollment indicators capture the majority of

1104 C.C. Ficano / Economics of Educ

college matriculation. Controls for selection endogeneityare less easily addressed.

In the literature, instances of random roommate orsquadron selection (Carrell et al., 2008; Han & Li, 2009;Lyle, 2007; Sacerdote, 2001; Stinebrickner & Stinebrickner,2005; Winston & Zimmerman, 2003), controlled exper-iments (Eisenkopf, 2010; Goethals, 2001; Hansen et al.,2006), instrumental variables techniques (De Paola &Scoppa, 2010), or course fixed effects (Ost, 2010) typicallyaddress selection. In this paper, we exploit the fact that firstsemester course selection, while not completely random, isgenerally made by the student prior to arriving on campuswith little peer input or influence and is thus exogenousconditional upon observed student characteristics. Further,selection into a given section of a course is made by thecollege and is random conditional upon student scheduleconstraints. The nature of the freshman course and coursesection selection process in our sample merits a brief expla-nation, and our claim of conditional exogeneity of peergroupings from that selection process merits justification.

The course selection process in our sample occursas follows. Guided by a course preference worksheetthat prompts student choice of a departmentally basedfreshman seminar and a distribution of other coursesacross academic divisions, students select and rank courseoptions, but not course sections, corresponding to their aca-demic interests during the summer prior to their collegematriculation. College faculty and staff register students fora subset of the selected course options based upon courseavailability, major requirements (if the student declares amajor or indicates a strong major interest), and incomingmathematics and writing placement scores.1 While fresh-men distribute themselves across academic departments,the majority of students takes a composition (607 out of889) or composition related (125 out of 889) writing courseand a freshman seminar within or outside of their majorarea of interest. When multiple sections of the same courseare offered, section selection is determined by the collegelargely based upon athletic participation (e.g., athletes areunable to take later afternoon sections due to practice con-flicts) and lab-course conflicts. Thus, athletes and sciencemajors are likely to congregate in similar sections, whilenon-science majors and non-athletes distribute more ran-domly.

With this course selection process, the potential forendogeneity at the course level remains but endogeneityat the section level of a given course is unlikely. Thus, weestimate course fixed effects models for the approximately69% of total courses for which we observe multiple sectionsof a course offered over the sample period. These coursesenroll over 90% of the freshman student academic course
load and capture some portion of the course schedule of873 of the 889 students in the full sample. In other words,we exploit both the implicit panel nature of the data and the
1 The data do not reveal students who, at the time of the census onemonth into the semester, have switched from their original course selec-tions. Endogenous switching between courses should be corrected by thecourse fixed effects estimation strategy. However, endogenous switchingbetween sections of a given course remains a potential problem in themodel.

view 31 (2012) 1102– 1115

semi-exogenous course selection process described abovein an attempt to identify peer effects within the followingframework:

GPAijk = ˇ0 + ˇ1Ai + ˇ2Qi + ˇ2Pijk + ∝j + �i(∝)j ∗ �i + �ijk

(2)

where i represents student, j represents course (e.g., Biol-ogy 101), k represents course section (e.g., Biology 101taught at 9:00 on Tuesdays and Thursdays), ∝j reflectsunobserved factors correlated with student performancein a course which vary across courses but remain constantfor students enrolled in a given course, and � i representsstudent schedule constraints likely to influence sectionchoice. Consistent with the course section assignment pro-cess described earlier, we define schedule constraints to beone of the following: athletic participation constraints, labrequirement constraints, both athletic and lab requirementconstraints, or no constraints (base case). Note that in thesemodels, classmates in a student’s section of a given courseconstitute the relevant peer group potentially acting uponthat student’s performance in the course. Variation in peerability across multiple sections of a given course identi-fies the peer effects in the presence of course fixed effectsestimation methods.2

A placebo test of the ex post conditional randomnessof peer grouping at the course section level examines thecorrelation between section enrollment propensity andown ability, controlling for enrollment in any section ofthe course and student schedule constraint as indicatedabove. If (1) section enrollment propensities are uncor-related with student academic rating given controls forenrollment in the course and student schedule constraints,and if (2) the parameter estimates on the course enrollmentindicators are robust to the inclusion of student level con-trols, the estimation strategy is likely adequate to identifyexogenous peer effects. Fig. 1 presents the significance ofown academic ability in linear probability models of sec-tion enrollment into each of the different course sectionsoffered over the sample period. Here, the height of the barrepresents one minus the p-value on the own academic rat-ing variable. Note that the vast majority of the bar heights,217 out of 240, are below 0.90, indicating the statisticalinsignificance of peer academic rating in most of the sectionselection regressions. In all of these models, the parame-

2 For comparison, results without course fixed effects for all coursesoffered and parallel results for the relevant subsample of courses withmultiple sections are presented in the first and second columns ofAppendix Table A1, respectively. These results reveal negative own andother gender peer effects for all groups except men; own male peer effectsfor men are positive and marginally significant. The results are consis-tent with selection bias which is stronger for women than men. Studentsemester GPA models, in which peers are defined as all students sharingany class section with the reference student and in which the outcomeof interest is semester rather than course GPA, are presented in the thirdcolumn of Appendix Table A1. The results are similar to those presentedin Table 3 and provide an additional venue for comparison.

C.C. Ficano / Economics of Education Review 31 (2012) 1102– 1115 1105

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Fig. 1. Statistical significance of own academic rating in section enrollment propensity regressions (bar height = 1 − p). Note: Each bar represents thesignificance (1 − p) of the own academic rating variable in a linear probability model of section enrollment for a given course section. Additional modelcontrols include course (as opposed to section) indicator, student schedule constraint, and their interaction. Results are robust to the inclusion of the fulls ent proi

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et of model controls used in the paper. Of the 240 total course enrollmnstances.

he regressions’ explanatory power. Together, these resultsupport a claim of section peer grouping exogeneity nec-ssary for identification of peer effects.

.2. Relative grading norms

A second empirical concern introduced by the modelnvolves relative grading norms (i.e., grading on a curve).nterpretation of the model results as evidence of peerffects requires that own student GPA be measured inn absolute rather than relative (i.e., grading on a curve)erms. The grading policy at the institution from which ourata derive is nominally absolute, as indicated by formalescription of the grading policy as being based uponepth of mastery and originality of thought.3 However, ifaculty members do curve grades, then a student’s ownPA is negatively affected by strong peers and positivelyffected by weak peers by mathematical definition. Fur-
her, if grading norms vary departmentally, the peer-owntudent performance relationship will vary systematicallyy a student’s course distribution.4 To test for gradingorms, we examine grade distributions by department and
3 Source: Grading Policy, College Catalogue.4 Our use of controls for the departmental distribution of a student’s

ourses minimizes this concern.

pensities estimated, academic rating is significant at p < 0.10 in only 23

division. These distributions should not vary substantiallyin the presence of relative grading. Fig. 2 displays thegrade distributions in courses that enrolled any freshmenover the sample period by academic department wheredepartments are anonymously identified by the color ofthe bar. Table 1 provides the departmental and academicdivision (Social Science, Physical Science and Humanities)average mean, median and standard deviation of coursegrades over the same period. These data indicate that thePhysical Sciences assign slightly lower and the Humanitiesassign slightly higher grades relative to the Social Sciences,and that there is substantial random variation in gradedistributions within and between departments across thecollege. As such, we assume that no implicit or explicitrelative grading norms are in operation in the sample.

3. Data and variables

Our analysis dataset was collected from a small selec-tive private liberal arts college. Unless otherwise noted,all data were retrieved from a central college administra-tive database. The data follow the students who entered inthe Fall of 2006 and those who entered in the Fall of 2007
through the end of their first college semester, measuringsocio-demographics and high school achievement at thetime of admission, measuring course configurations andco-curricular affiliations approximately one month into

1106 C.C. Ficano / Economics of Education Review 31 (2012) 1102– 1115

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Fig. 2. Distribution of course grades by academic departments. Note: Ainterpretation of the references to color in this figure legend, the reader iSource: College administrative database.

the semester, and measuring realized course GPA aftersemester completion for each completed course.5 The datacontain a rich set of variables for not only each student inthe data, but also for each of the peers that they encounterin classroom and residential settings.

Following the structure of Eq. (2), our empirical GPAmodel includes academic rating and original math andwriting level to capture a student’s ability level. A student’sacademic rating is assigned on a scale of one to five by theAdmissions Office and is based on high school GPA, andSAT or ACT scores. A one point increase in academic rat-ing is roughly equivalent to a four point increase in highschool GPA and a 70 point increase in the combined ver-bal and quantitative SAT scores. A student’s writing level isdetermined by a placement test that students take in the
summer before their freshman year. Students scoring a “1”must take a writing tutorial before beginning college com-position; students scoring a “2” are immediately eligible
5 An additional empirical concern involves partial observability of theGPA data. We are able to observe course GPA only for those who retainthrough the first semester. We present results for the 97.5% of the stu-dents in the sample who complete their first college semester. Heckmanselection models on the full sample yield nearly identical results and areavailable upon request.

departments are anonymously identified by the color of the bar. (Ford to the web version of the article.)

for college composition; students scoring a “3” place outof composition and are immediately eligible for a writingacross the curriculum course; and students scoring a “4”satisfy the college writing requirement upon entry. A stu-dent’s math level is determined in a similar manner anddetermines the range of math courses a student is eligibleto take.

High school quality variables capture student prepared-ness for college. Specifically, we include dummy variablesindicating whether a student’s high school offered fewerthan 3 AP courses or 3 or more6 and whether the studentattended a public high school (private or parochial repre-sent the base case). We include demographic variables toaccount for a student’s gender, race, and home commu-nity’s degree of urbanization (metropolitan area representsthe base case) that might further influence preparedness.Finally, we include measures of a student’s financial needand level of financial aid receipt likely to both influencepreparedness prior to enrollment and student level access
to academic and co-curricular inputs after matriculation.
To capture the quantity and quality of the academicand co-curricular inputs in the production function more

6 Data used was drawn from the College Board’s Enrollment PlanningService (EPS®).


Table 1Grade distribution statistics for freshmen courses by academic department and division.

Academic department (anonymouslyidentified by number)

Departmental/divisionalaverage mean grade

Departmental/divisionalaverage median grade

Departmental/divisional averagegrade standard deviation

1 2.433 2.540 1.2222 3.115 3.317 0.9053 3.188 3.400 0.8134 2.504 2.595 1.0495 2.848 3.000 0.7136 2.498 2.655 1.1237 3.049 3.327 1.0718 2.466 2.570 1.0709 3.196 3.370 0.79710 2.963 3.135 0.85511 2.912 3.240 1.00312 2.582 2.715 0.98813 2.805 3.100 1.04014 2.638 2.793 0.97315 3.216 3.400 0.69216 2.654 2.738 1.06317 3.105 3.289 0.81518 3.180 3.250 0.86919 3.013 3.188 0.85020 2.979 3.185 0.97321 2.808 2.940 0.91822 2.919 3.063 0.86923 2.691 2.858 0.92524 2.913 3.000 0.93725 2.578 2.700 1.09026 2.937 3.018 0.82527 2.715 2.873 1.06528 3.329 3.527 0.775

Social science 2.812 2.955 0.927Physical science 2.678 2.821 1.037Humanities 3.011 3.199 0.884

Source: College administrative database.N deviatioa

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ote: Numbers in the table represent grade mean, median, and standard

ll course sections enrolling freshmen students.

irectly, we include whether or not a student participatedn the college’s honors program, a student’s academic creditoad, and the average class size encountered by the student.

To capture the influence of one’s peers, we create aeries of classmate peer average academic achievementariables. We begin with aggregate measures that reflecthe mean academic rating of one’s classmates in the courseection. Then, recognizing the potential for gender differ-nces in the norms of mentoring and social comparison, weistinguish between the course section mean academic rat-

ngs of males and females. Finally, we allow students to beifferentially affected by own-gender academic rating andther gender academic rating by interacting the gender-ifferentiated academic rating variables with a student’swn gender.

Table 2 provides an overview of the background char-cteristics, college experience variables, and own and peercademic quality for students in the sample, both overallnd by gender, and highlights important gender differ-nces. Female students are more likely to have attendedublic high schools and come from small towns. They aren average financially needier than males, but they have

smaller proportion of that need met through aid thano males. Nonetheless, relative to male students, femaletudents exhibit stronger academic aptitude as indicatedy statistically significant higher mean academic rating,

n at the course section level averaged by department and division across

honors program participation and college GPA. Theygenerally carry more credits and are less likely to major inbusiness or be “undeclared” than males. They are also lesslikely to be schedule constrained by athletics but morelikely to be schedule constrained by lab requirementsthan are their male counterparts. On average, they are incourses with lower proportions of male students, but theirmale classmates are more highly rated academically thanthe male classmates of the average male student.

Fig. 3A–C provides a more complete picture of relevantgender differences in own and peer academic rating.As is evident from the first histograms, the distributionof class female academic ratings lies to the right of thecorresponding distribution for males. Further, as shownin Fig. 2B, women encounter a slightly stronger but morevariable set of male peers than do men (p-value for differ-ence in subsample mean = 0.000) while men and womenencounter similar distributions of females in classes.Thus, women are both academically stronger and are, onaverage, in class with academically stronger male peersthan are men. This gender difference in exposure to maleand female peers may in part explain differences in peer
sensitivity. For this reason, we run models that incorporatea variety of distributional peer academic rating measures(i.e., mean, minimum, maximum, median, inter-quartilerange and standard deviation) but present only those


Table 2Estimation sample descriptive statistics.

Full sample Male sample Female sample

Outcome variable

Own GPA*** 2.746 2.511 2.919(0.875) (0.941) (0.779)

Pre-matriculation characteristicsHigh school offers fewer than 3 AP courses 0.143 0.137 0.147

(0.350) (0.345) (0.355)

Attended a public high school*** 0.832 0.792 0.861(0.374) (0.406) (0.347)

Home residence (omitted category = urban)Micropolitan area 0.094 0.081 0.104

(0.292) (0.273) (0.305)

Small town*** 0.064 0.035 0.086(0.245) (0.184) (0.280)

Rural area 0.078 0.094 0.066(0.268) (0.293) (0.248)

Gender (1 if male) 0.425 1.000 0.000(0.495) (0.000) (0.000)

Race (1 if white) 0.654 0.652 0.655(0.476) (0.477) (0.476)

Academic and co-curricular activities

Business major*** 0.090 0.162 0.038(0.287) (0.369) (0.191)

Social science major (not business)* 0.174 0.146 0.195(0.379) (0.353) (0.397)

Science major (not nursing) 0.145 0.146 0.145(0.353) (0.353) (0.353)

Nursing major*** 0.078 0.016 0.124(0.268) (0.126) (0.329)

Undecided major*** 0.385 0.458 0.331(0.487) (0.499) (0.471)

Total academic credits*** 13.801 13.534 13.998(1.560) (1.458) (1.603)

Athletic participation constraint alone*** 0.159 0.240 0.100(0.366) (0.428) (0.300)

Lab course constraint alone*** 0.247 0.159 0.313(0.431) (0.364) (0.464)

Multiple schedule constraints 0.055 0.043 0.064(0.228) (0.204) (0.245)

Average class size 26.246 25.919 26.487(9.335) (8.683) (9.790)

Membership in college honor’s program*** 0.110 0.059 0.147(0.313) (0.237) (0.355)

Financial considerations

Student need*** 21.657 18.834 23.744(15.123) (15.620) (14.409)

Student aid*** 15.710 14.311 16.744(7.326) (7.155) (7.287)

Student college ability level

Own academic rating*** 2.716 2.423 2.932(1.336) (1.257) (1.352)

Own writing level 2.024 1.995 2.046(0.514) (0.493) (0.529)

Own math level 3.451 3.501 3.414(1.416) (1.401) (1.427)

Peer effects

Proportion of class that is male*** 0.434 0.526 0.367(0.134) (0.109) (0.108)

Average class AR*** 2.818 2.768 2.855(0.320) (0.315) (0.319)

Average class male AR*** 2.654 2.582 2.707(0.430) (0.389) (0.451)

Average class female AR 2.988 2.970 3.001(0.341) (0.327) (0.351)

Median class male AR*** 2.519 2.402 2.606(0.534) (0.503) (0.541)

Median class female AR 2.985 2.989 2.983(0.449) (0.409) (0.477)

Sample size 873 371 502

Asterisks indicate significant mean differences by gender.** p < 0.05.

*** p < 0.01.* p < .10.

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distributional measures that significantly impact uponown student course GPA. Controls for the proportion ofpeers who are male are included in all regressions.

4. Regression results

Table 3 presents coefficients from the course fixedeffects models that estimate student grade point average(GPA) in a given section of a course as a func-tion of covariates measuring pre-matriculation studentsocio-demographics, student academic and co-curricularactivities during the freshman fall semester, own studentacademic ability, and an increasingly specific set of coursesection level peer academic controls. A brief discussion ofthe non-peer covariate results follows the presentation ofthe peer results below.

4.1. The role of gender in peer effects

Panel A of Table 3 presents the coefficients on the peervariables. Consistent with the results of Lyle (2007), thebase GPA models using overall and gender differentiatedpeer academic rating provide no evidence of aggregate peereffects as shown by the lack of statistical significance on the“Class AR,” “Class Male AR,” and “Class Female AR” param-eter estimates in columns 1 and 2. However, interestingpatterns arise when we separately control for the perfor-mance of one’s male and female peers and interact thesegendered peer variables with a student’s own gender.

Columns 3–5 present the results from an empirical spec-ification that includes measures of male peer academicrating and female peer academic rating and their inter-action with a student’s own gender. Each column usesa different distributional measure of peer academic rat-ing, namely peer mean (column 3), peer median (column4), and peer mean standardized against the college-widemean (column 5). Column 6 presents the gendered andinteracted mean academic rating results separately for theroughly 90% of the course sections that passed the placebotest described earlier and serves as a robustness check.In columns 3–6, note the consistently significant positivecoefficient on the interaction of “Male” and “Class MaleAR” and the individually and jointly insignificant coefficientestimates on the remaining peer variables. This evidencethat males are both responsive to peer influence and thatthey are responsive to male peers parallels the positiveroommate peer effects identified for males by Griffith andRask (2010). It also highlights the possibility that significantpeer influence may be obscured when peers are defined inthe aggregate.

Tables 4–7 convert the results from panel A ofTable 3 into own and cross-gendered effects for ease
of interpretation. Table 4 indicates that a one pointincrease in the mean academic rating of one’s male peerscorrelates with a 0.103 point increase in predicted courseGPA for males (standard error = 0.059) but has no impact
Fig. 3. (A) Distribution of class average gendered academic rating, by gen-der. (B) Distribution of class male average academic rating, by own gender.(C) Distribution of class female average academic rating, by own gender.


Table 3GPA regression results.

1 2 3 4 5 6

(A) Peer academic performance variablesProportion of class that is male 0.279 0.290 0.286 0.271 0.286 0.265

(0.176) (0.240) (0.252) (0.266) (0.252) (0.256)Average class AR −0.001

(0.052)Average class male AR 0.018 −0.032 −0.031 −0.024 −0.030

(0.037) (0.038) (0.030) (0.029) (0.048)Average class female AR −0.012 −0.031 −0.045 −0.020 −0.017

(0.039) (0.061) (0.044) (0.038) (0.062)Male * average class male AR 0.135** 0.109** 0.101** 0.150**

(0.059) (0.045) (0.044) (0.063)Female * average class female AR 0.045 0.044 0.028 0.050

(0.070) (0.059) (0.044) (0.072)Joint significance of peer (p-value) 0.992 0.872 0.145 0.082 0.145 0.155

(B) Pre-matriculationHigh school offers <3 AP courses −0.203*** −0.204*** −0.203*** −0.203*** −0.203*** −0.192***

(0.054) (0.054) (0.054) (0.054) (0.054) (0.054)Attended a public high school 0.302*** 0.303*** 0.299*** 0.308*** 0.299*** 0.261***

(0.053) (0.053) (0.053) (0.053) (0.053) (0.056)Micropolitan area 0.196*** 0.197*** 0.191*** 0.187*** 0.191*** 0.169***

(0.040) (0.040) (0.039) (0.040) (0.039) (0.040)Small town 0.028 0.028 0.027 0.027 0.027 0.013

(0.062) (0.062) (0.062) (0.061) (0.062) (0.071)Rural area 0.131* 0.131* 0.125* 0.127* 0.125* 0.101

(0.102) (0.068) (0.068) (0.068) (0.068) (0.075)Gender (1 if male) −0.216*** −0.217*** −0.433*** −0.359*** −0.212*** −0.424**

(0.054) (0.048) (0.157) (0.159) (0.046) (0.182)Race (1 if white) 0.114*** 0.113*** 0.112*** 0.111*** 0.112*** 0.111**

(0.041) (0.041) (0.041) (0.041) (0.046) (0.043)

(C) Academic and co-curricular activitiesUndecided major 0.081 0.081 0.083 0.080 0.083 0.098

(0.062) (0.062) (0.063) (0.062) (0.063) (0.068)Business major −0.057 −0.056 −0.051 −0.058 −0.051 −0.067

(0.085) (0.084) (0.084) (0.082) (0.084) (0.092)Science major (not nursing) 0.109 0.108 0.108 0.112 0.109 0.104

(0.091) (0.091) (0.091) (0.091) (0.091) (0.099)Social science major (not business) 0.026 0.026 0.023 0.017 0.023 0.014

(0.077) (0.077) (0.077) (0.076) (0.077) (0.087)Nursing major 0.369 0.377 0.359 0.344 0.359 0.421

(0.250) (0.352) (0.252) (0.257) (0.252) (0.255)Total academic credits 0.048*** 0.047*** 0.047*** 0.049*** 0.047*** 0.039***

(0.014) (0.014) (0.014) (0.014) (0.014) (0.014)Average class size 0.000 0.000 0.000 0.000 0.000 0.000

(0.002) (0.003) (0.003) (0.003) (0.003) (0.003)Membership in college honor’s program 0.141** 0.139** 0.146** 0.148** 0.146** 0.110*

(0.057) (0.058) (0.058) (0.059) (0.058) (0.061)

(D) Student college ability levelOwn academic rating 0.261*** 0.262*** 0.264*** 0.258*** 0.264*** 0.280***

(0.022) (0.020) (0.020) (0.021) (0.020) (0.018)Own math level 0.033** 0.032** 0.031** 0.032** 0.031** 0.028*

(0.015) (0.015) (0.015) (0.015) (0.015) (0.017)Own writing level 0.150*** 0.150*** 0.151*** 0.151*** 0.151*** 0.148***

(0.031) (0.031) (0.030) (0.030) (0.030) (0.035)

E. FinancialFinancial need −0.002 −0.002 −0.001 −0.002 −0.001 −0.001

(0.002) (0.002) (0.002) (0.002) (0.001) (0.002)Financial aid −0.001 0.001 −0.001 0.001 0.001 0.002

(0.004) (0.004) (0.004) (0.004) (0.004) (0.004)

Sample size – students 873 873 873 873 873 871Sample size – student course sections 3439 3439 3439 3439 3439 3100

Standard errors presented in parentheses. All models include additional controls for departmental distribution of courses and are estimated with course Xstudent schedule constraint fixed effects on a sample of courses where multiple sections are offered.

* p < 0.10.** p < 0.05.

*** p < 0.01.


Table 4Gendered peer effects on own student GPA, peer variable = mean class academic rating.

Level effect Incremental own gender effect Total effect

(bacademic rating) (bacademic rating×gender) On malestudent GPA

On femalestudent GPA

Average class mean male academic rating −0.032 0.135** 0.103* −0.032−0.038 −0.059 −0.059 −0.038

Average class mean female academic rating −0.031 0.045 −0.031 0.014−0.061 −0.07 −0.061 −0.045

Note: Standard error of the total effect in all models presented in Tables 4–7 calculated as follows: se (total effect) =√

var(bacademic rating) +var(bacademic rating×gender) + 2cov(bacademic rating,bacademic rating×gender). All models include the following additional covariates: gender, race, public high schoolattendance, indicator of whether the high school offered less than three advanced placement courses, student’s community of origin (suburban, smalltown, and rural), indicator of student participation in the college honors program, student writing level, student academic rating, student financial needand aid receipt, student major (business, science, social science, nursing, and humanities), and distribution of the student’s courses across departments.Models are estimated with course X student schedule constraint fixed effects.

** p < 0.05.* p < 0.10.

*** p < 0.01.

Table 5Gendered peer effects on own student GPA, peer variable = median class academic rating.




Average class median male academic rating −0.031 0.109** 0.078 −0.031−0.03 −0.045 −0.052 −0.03

Average class median female academic rating −0.045 0.044 −0.045 −0.001−0.044 −0.059 −0.044 −0.037


var(bacademic rating) +var(bacademic rating×gender) + 2cov(bacademic rating,bacademic rating×gender). All models include the following additional covariates: gender, race, public high schoolattendance, indicator of whether the high school offered less than three advanced placement courses, student’s community of origin (suburban, smalltown, and rural), indicator of student participation in the college honors program, student writing level, student academic rating, student financial needand aid receipt, student major (business, science, social science, nursing, and humanities), and distribution of the student’s courses across departments.Models are estimated with course X student schedule constraint fixed effects.*p < 0.10

*

ooom

TG

NvataM

*

** p < 0.05.** p < 0.01.

n GPA for females. The average academic rating of
ne’s female peers has no statistically significant effectn GPA for either gender. Similar results obtain usingedian and standardized mean peer measures as shown
able 6endered peer effects on own student GPA, peer variable = standardized mean cla

Level effect Incre

(bacademic rating) (bacad

Average class std mean male academic rating −0.024 0.10−0.029 −0.04

Average class std mean female academic rating −0.02 0.02−0.038 −0.04

ote: Standard error of the total effect in all models presented in Tablear(bacademic rating×gender) + 2cov(bacademic rating,bacademic rating×gender). All models incluttendance, indicator of whether the high school offered less than three advancown, and rural), indicator of student participation in the college honors programnd aid receipt, student major (business, science, social science, nursing, and huodels are estimated with course X student schedule constraint fixed effects.* p < 0.10.

** p < 0.05.** p < 0.01.

in Tables 5 and 6, respectively. Specifically, a one point
increase in the median academic rating of one’s malepeers associates with a 0.078 point increase in predictedcourse GPA for males (standard error = .052) but not for
ss academic rating.

mental own gender effect Total effect

emic rating×gender) On malestudent GPA


1** 0.077* −0.0244 −0.043 −0.029

8 −0.02 0.0084 −0.038 −0.028

s 4–7 calculated as follows: se (total effect) =√

var(bacademic rating) +de the following additional covariates: gender, race, public high schooled placement courses, student’s community of origin (suburban, small, student writing level, student academic rating, student financial need

manities), and distribution of the student’s courses across departments.


Table 7Gendered peer effects on own student GPA, peer variable = mean class academic rating on subsample of exogenously selected courses.




Average class std mean male academic rating −0.03 0.150** 0.120* −0.03−0.048 −0.063 −0.063 −0.048

Average class std mean female academic rating −0.017 0.05 −0.017 0.033−0.062 −0.072 −0.062 −0.043


var(bacademic rating) +var(bacademic rating×gender) + 2cov(bacademic rating,bacademic rating×gender). All models include the following additional covariates: gender, race, public high schoolattendance, indicator of whether the high school offered less than three advanced placement courses, student’s community of origin (suburban, smalltown, and rural), indicator of student participation in the college honors program, student writing level, student academic rating, student financial needand aid receipt, student major (business, science, social science, nursing, and humanities), and distribution of the student’s courses across departments.Models are estimated with course X student schedule constraint fixed effects.

* p < 0.10.
** p < 0.05.
*** p < 0.01.

females. Having male peers one standard deviation abovethe male sample mean associates with a 0.077 point courseGPA increase for males (standard error = .043) but not forfemales. Note that these results derive from course fixedeffects models on a sample of multiple section courses inthe presence of controls for distribution of student coursesacross departments and proportion of classmates thatare male, minimizing the chance for spurious correlationbased upon course selection. Table 7 presents results fromthe subsample of courses into which selection bias wasleast evident based upon the placebo test. These results,which indicate a total male peer effect of 0.120 (p = 0.063),are larger than but consistent with those from the full sam-ple analysis and provide further evidence that course levelselection is not biasing the identification of peer effects.It bears mention that endogenous section level selectionremains a possibility in the model, and that results shouldbe treated as suggestive rather than definitive.

4.2. Non-peer determinants of own student GPA

The coefficients on the non-peer covariates from thecourse GPA models, presented in panels B through D ofTable 3 are stable across the various model specifica-tions and consistent with results generated elsewhere.In all models, characteristics of the high school fromwhich a student graduated impact student college per-formance. Students from high schools offering fewer thanthree advanced placement (AP) courses score approxi-mately 0.20 points lower in courses during their firstcollege semester than do those from high schools offer-ing three or more AP courses while students from publicschools or schools located in suburban areas score approxi-mately 0.30 and 0.19 points higher, respectively, than thosefrom private/parochial schools or schools located in urbancenters. These coefficient estimates point to the role thathigh school quality likely plays in college success (Betts &
Morrell, 1998).
Not surprisingly, own academic ability, measuredthrough an index of high school GPA and SAT scorescorrelates positively and significantly with first semester

GPA in all models. Specifically, a one point increase inacademic rating, which roughly equates to a 4 pointincrease in high school GPA (on a 100 point scale) anda 70 point increase in SAT score, associates with a 0.26point increase in predicted course GPA. Students withstronger assessed writing skills exhibit an additional 0.15point course GPA gain, even controlling for own academicability. Women outperform men academically, consistentwith numerous peer effects papers that identify genderdifferences in higher education performance (e.g., Betts& Morrell, 1998; Stinebrickner & Stinebrickner, 2005;Winston & Zimmerman, 2003; Zimmerman, 2003), andnon-minorities outperform minorities. Ceteris paribus,GPA does not vary systematically by major, but doesvary somewhat by course distribution (results availableupon request) and course load. Each additional credithour correlates with an approximate 0.05 point courseGPA gain.

5. Conclusion

An extensive literature exploring a range of peer influ-ences on both academic and non-academic outcomescontinues to produce contradictory evidence regardingthe existence and sign of peer effects. The diversity ofresults may be linked to inconsistency across settings inwhat constitutes the effective peer group or inconsistencyacross settings in the influence that peer groups exerton student outcomes. This analysis is distinctive in thatwe further the investigation of own gendered peer effectsimplicit in the residential peer literature by distinguish-ing between the academic ratings of male and femalepeers and allowing students to be differentially affectedby own-gender and other-gender peers in academicsettings.

Our results provide no evidence of peer effects whenpeer academic ability is measured in the aggregate. How-
ever, regressions that control for own-gender and othergender average peer performance consistently identifypositive and statistically significant male peer influenceon males but no significant male or female peer effect on


Table A1OLS Results without fixed effects controls.

1 2 3

Peer academic performance variablesProportion of class that is male −0.034 −0.024 0.178

(0.096) (0.096) (0.304)Average class male AR −0.054 −0.054 0.003

(0.029)* (0.030)* (0.089)Average class female AR −0.070 −0.064 −0.204

(0.044) (0.044) (0.132)Male * average class male AR 0.090 0.088 0.231

(0.051)* (0.051)* (0.145)*

Female * average class female AR 0.022 0.017 0.122(0.056) (0.056) (0.167)

Pre-matriculationHigh school offers fewer than 3 AP courses −0.193 −0.184 −0.227

(0.065)*** (0.066)*** (0.068)***

Attended a public high school 0.293 0.292 0.266(0.079)*** (0.079)*** (0.062)***

Metropolitan area 0.193 0.201 0.157(0.060)*** (0.060)*** (0.079)**

Small town 0.051 0.056 0.053(0.090) (0.091) (0.094)

Rural area 0.133 0.127 0.065(0.094) (0.095) (0.086)

Gender (1 if male) −0.378 −0.388 −0.424(0.187)** (0.187)** (0.479)

Race (1 if white) 0.116 0.113 0.089(0.049)** (0.049)*** (0.047)*

Academic and co-curricular activitiesUndecided major 0.038 0.047 0.030

(0.085) (0.086) (0.083)Business major −0.098 −0.087 −0.110

(0.118) (0.118) (0.121)Science major (not nursing) 0.058 0.067 0.134

(0.122) (0.123) (0.118)Social science major (not business) −0.008 0.001 −0.037

(0.093) (0.093) (0.093)Nursing major 0.684 0.703 0.240

(0.336)* (0.360)* (0.666)Total academic credits 0.050 0.048 0.048

(0.018)*** (0.018)*** (0.018)***

Average class size −0.003 −0.003 −0.002(0.004) (0.004) (0.004)

Membership in college honor’s program 0.182 0.182 0.206(0.065)*** (0.065)*** (0.089)**

Student college ability levelOwn academic rating 0.266 0.264 0.249

(0.025)*** (0.025)*** (0.025)***

Own math level 0.020 0.023 0.022(0.020) (0.020) (0.018)

Own writing level 0.132 0.138 0.147(0.045)*** (0.045)*** (0.047)***

FinancialFinancial need −0.001 −0.002 −0.002

(0.002) (0.002) (0.002)Financial aid −0.002 −0.002 0.003

(0.005) (0.005) (0.005)

Sample size – students 873 873 867Sample size – student course sections 3492 3439 N/A

Standard errors presented in parentheses. All models include additional controls for departmental distribution of courses.*** p < 0.01.** p < 0.05.* p < 0.10 † .


Table A2Test of the exogeneity of peer grouping (dependent variable = own academic rating).

Peer variablea Male sample (n = 371) Female sample (n = 502)

OLS model Course FE model OLS model Course FE model

Average class male academic rating 0.163** −0.326*** 0.245*** 0.388***

−0.067 −0.082 −0.052 −0.07

Average class female academic rating 0.269*** 0.088 0.213*** −0.267***

−0.067 −0.1 −0.07 −0.08

Average class academic rating 0.103 −0.917*** 0.182** −0.387***

−0.086 −0.159 −0.073 −0.115

Note: “OLS model” refers to a regression of a student’s own academic rating on only a constant and either the gender specific academic ratings for malesand females or the aggregate average class academic rating. “Course FE model” refers to a course fixed effects model that includes either the gender specificacademic ratings for males and females or the aggregate average class academic rating, controlling for course by schedule constraint fixed effects. The samefixed effects specification is used in the regression models presented in Table 4.

a Average Class Male Academic Rating” and “Average Cass Female Academic Rating” are included together in a single regression model that does notinclude “Average Class Academic Rating.” “Average Class Academic Rating” is included in a separate regression model where it acts as an alternative to themale and female average academic rating variables.*p < 0.100

** p < 0.05.
*** p < 0.01.
females. Specifically, for each point increase on a five pointscale in the average academic rating of one’s male class-mates, the predicted male student course GPA increasesby approximately 0.10 points. In contrast, females areunresponsive to either male or female peer average aca-demic rating. Our results highlight the importance ofdifferentiating between own and other-gendered peereffects where data will allow and demonstrate that signif-icant own or other gendered effects that differ by gendermay be masked by insignificant effects in the aggregate.

These results also have important implications foradmissions policy and for the assignment of freshmenstudents to classes based upon gender and prior achieve-ment. The implications for the former are obvious. Grantingadmission to marginally qualified students, in particularmales, has the potential to generate negative academicperformance spillovers that draw down classmate aca-demic performance. As discussed extensively in Winstonand Zimmerman (2003), this justifies the college selec-tivity to which most institutions of higher educationaspire.

In regards to the latter, while roommate match hasreceived attention for some time as an important driverof academic and social success at college, classmate matchand class assignment have largely ignored peer implica-tions. Going forward, college orientation staff and first yearacademic advisors might do well to consider the optimalmix of students in regular and honor classes with an eyetowards male on male peer influence, in particular.

Appendix A.

See Tables A1 and A2.

References

Betts, J. R., & Morrell, D. (1998). The determinants of undergraduate gradepoint average. The Journal of Human Resources, 34(2), 268–293.

Bifulco, R., Fletcher, J., & Ross, S. (2009). The effect of classmate char-acteristics on individual outcomes: Evidence from the add health.University of Connecticut, Department of Economics. Working paper2009-15.

Brunello, G., De Paola, M., & Scoppa, V. (2010). Peer effects in highereducation: Does the field of study matter? Economic Inquiry, 48(3),621–634.

Carrell, S. E., Fullerton, R. L., & West J. E. (2008). Does your cohort mat-ter? Measuring peer effects in college achievement. NBER Working paper14032.

De Giorgi, G., Pellizzari, M., & Redaelli, S. (2010). Identification of socialinteractions through partially overlapping peer groups. American Eco-nomic Journal: Applied Economics, 2(2), 241–275.

De Paola, M., & Scoppa, V. (2010). Per group effects on the aca-demic performance of Italian students. Applied Economics, 42,2203–2215.

DiPrete, T. A., & Buchmann, C. (2006). Gender-specific trends in the valueof education and the emerging gender gap in college completion.Demography, 43(1), 1–24.

Eisenkopf, Gerald. (2010). Peer effects, motivation, and learning. Eco-nomics of Education Review, 29(3), 364–374.

Festinger, L. (1954). A theory of social comparison processes. Human Rela-tions, 7, 117–140.

Fletcher, J. M., & Tienda, M. (2008). High school peer networks and col-lege success: Lessons from Texas. Discussion Paper Series. University ofKentucky Center for Poverty Research.

Foster, Gigi. (2006). “It’s not your peers, and it’s not your friends” Someprogress towards understanding the educational peer effect mecha-nism. Journal of Public Economics, 90, 1455–1475.

Foster, G., & Frijters, P. (2010). Students’ beliefs about peer effects. Eco-nomics Letters, 108, 260–263.

Goethals, G. R. (2001). Peer effects, gender, and intellectual performanceamong students at a highly selective college: A social comparison of abil-ities analysis. Williams Project on the Economics of Higher EducationDiscussion Paper 61.

Griffith, A. L. (2008). Determinants of grades, persistence and major choicefor low-income and minority students. Unpublished manuscript.

Griffith, A. L., & Rask, K. N. (2010). Peer effects in higher education: A look atheterogenous impacts. Unpublished manuscript.

Han, Li, & Li, T. (2009). The gender difference of peer influence in highereducation. Economics of Education Review, 28(1), 129–134.

Hanushek, Eric A. (1979). Conceptual and empirical issues in the esti-mation of education production functions. The Journal of HumanResources, 14(3), 351–388.

Hansen, Z., Owan, H., & Pan J. (2006) The impact of group diversity on perfor-
mance and knowledge spillover – An experiment in a college classroom.NBER Working paper 12251.
Lyle, D. S. (2007). Estimating and interpreting peer and role model effectsfrom randomly assigned social groups at west point. The Review ofEconomics and Statistics, 89(2), 289–299.

ation Re

O

S

S

C.C. Ficano / Economics of Educ

st, B. (2010). The role of peers and grades in determining major persis-tence in the sciences. Economics of Education Review, 29(6), 923–934.

acerdote, B. (2001). Peer effects with random assignment: Results for
Dartmouth roommates. The Quarterly Journal of Economics, 116(2),681–703.
tinebrickner, T. R., & Stinebrickner, R. (2005) What can be learned aboutpeer effects using college roommates? Evidence from new survey data andstudents from disadvantaged backgrounds. CIBC Working Paper 2005-4.

view 31 (2012) 1102– 1115 1115

Suls, J., Martin, R., & Wheeler, L. (2002). Social comparison: Why, withwhom and with what effect? Current Directions in Psychological Sci-ence, 11(5), 159–163.

Winston, G. C, & Zimmerman, D. J. (2003). Peer effects in higher education.NBER Working paper 9501.

Zimmerman, D. J. (2003). Peer effects in academic outcomes: Evidencefrom a natural experiment. The Review of Economics and Statistics,85(1), 9–23.

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