Effects of College Peer Networks on Labor Market
Outcomes
Maria Zhu
October 31, 2016
Abstract
This paper uses a novel approach to detect the presence effect of college networks at the
classroom level and subsequently to measure the effects of networks on labor market outcomes.
Using statewide matched employer-employee data for community college students, I first
exploit exogenous variation of section enrollment within a course to analyze whether taking a
class together increases the probability two individuals work together in the future. I provide
evidence of significant classroom peer effects on future job finding and use this result to
look at the role of networks on labor market outcomes in terms of earnings and job tenure.
I find that workers who find jobs through networks have consistently lower turnover rates
over firm tenure. They also have higher initial wages, and this trend stays constant over
tenure. Notably, I find that while men and women do not differ in average tenure network
jobs, women receive a significantly higher initial earnings premium from these jobs than male
counterparts.
1
1
Introduction
Substantial research in economics and other social sciences show that social networks play an
important role in determining where people work. In fact, at least 50% of new jobs are found
through personal contacts, rather than formal search methods (Topa 2011). Furthermore, networks
operate within a variety of social contexts, such as families, residential neighborhoods, and ethnic
communities. One important area of research in this area seeks to understand how networks
operate at the college level. One challenge to detecting and measuring the effects of networks
is that students select non-randomly into many aspects of college life, such as majors, courses,
student organizations, and friend groups. Thus, it is often difficult to disentangle the role network
interactions from unobservable traits on outcomes.
This study provides a novel means of analyzing college networks through student interactions
at the classroom level. Data for the project come from transcript records for all community colleges
in Arkansas and are linked with employment information from Unemployment Insurance records.
I exploit variation in section enrollment within courses to identify network effects on job finding.
Specifically, each semester, schools offer multiple sections of the same course for many courses.
These sections take place in different classrooms (and possibly also at different times of the week
and/or with different instructors).
To detect network effects, I construct a dyadic dataset of matched pairs of students within
courses. I compare the propensity for an individual to begin working at a firm where a samesection peer is incumbent vs. a firm where a different-section peer is incumbent. I define a job
found through a network as a one in which a same-section peer already works when a student is
hired. The propensity for a student to start working a firm where a peer from a different-section
of the course works accounts for the fact that students have some baseline likelihood to work with
peers, absent of social networks. The baseline measurement encompasses the role of unobservables
that drive selection into courses on the propensity for a pair to work together. The key assumption
in this empirical strategy is that while students may select into non-randomly, they do not sort
into sections within a course along unobserved attributes that would also influence labor market
outcomes.1
Results indicate significant network effects at the classroom level. Enrolling in the same section
1
I run a series of robustness checks testing this assumption, which are not currently in the paper, but should be
soon.
2
as a peer increases the probability that a student will get a network job through the peer within
six years after the course by 12 percent. Taking into account the average number of students per
class and the average number of courses a student takes per semester, students have an increased
propensity of 3.2 percentage points to obtain a network job per semester or college within the first
six years after the course than they would absent classroom networks.
Additionally, an evaluation of results across pair demographics indicates significant heterogeneity in referral propensity across different types of pairs. A student is more likely to get a
job through a same-gender peer than an different-gender peer and females are more likely to use
networks than males overall. Furthermore, pairs in which both students are part-time are more
likely to engage in networking with peers than pairs in which both students are full-time. However,
both of these pairs have a higher prevalence of working together than mixed pairs in which the
full-time student leads and part-time student follows. Unsurprisingly, pairs in which the leader is
employed have higher propensities of forming networks compared to pairs in which the leader is
unemployed. Finally, I find no significant differences in referral patterns based on whether students
are first-time students.
After detecting the presence of classroom networks, I next analyze the effects of these networks
on job tenure and earnings. To do so, I redefine the unit of observation as individuals across
time. Analogous to the first section, I define a network job as one in which a student works at a
firm found through an incumbent same-section peer. Students working at a firm found through
an incumbent different-section peer serve as a baseline control group in measuring various labor
market outcomes. This group accounts for any effects on tenure or earnings that result from the
baseline propensity for individuals who take a section to end up working together. Intuitively,
the baseline variable measures the earnings or tenure effects that stem from working together as a
result of course selection.
Results indicate that students in jobs found through peer networks have an initial turnover rate
4.9% lower than those not using networks and a turnover rate of 2.3% lower for each additional
year at the firm. Furthermore, students gain an initial wage increase of 8% from obtaining a job
through a network, and this gap stays constant with tenure at firm. Finally, I analyze the gender
dynamics underlying peer networks. I find that men and women who get jobs through networks
do not differ in terms of firm tenure. However, women in network jobs earn 8.6% more than men
at time of hire, and this trend remains constant over time at the firm.
3
This paper provides two main literature contributions. First, I provide a new way to detect
networks in higher education by adapting a framework originally used to exploit geographic variation in place-based networks.2 I abstract from geographic variation to look at networks across
classes at a school. One benefit to this classroom-level approach is that I provide an alternative
to cohort-level analysis, such as that used by Zimmerman (2015). Network analysis of cohorts is
well-suited in countries where students apply, are accepted, and take courses within a well-defined
degree program. However, this kind of analysis is not well suited for educational systems such
as the US, in which students have much more flexibility in terms of course selection and timing
of courses, even within majors. Furthermore, flexibility in time to degree completion the option
of switching majors makes it even trickier to have well-defined cohorts. Another benefit of my
approach is that I am able to see interactions between individuals, which previous studies have not
been able to do. I take advantage of this in the analysis to provide information on who is engaging
in networks and with whom, based on observable characteristics.
Secondly, this study expands the empirical literature on the effects of social networks in higher
education. To my knowledge, this is the first study to look at the labor market effects of college
peer networks. Furthermore, the paper provides an in-depth analysis of network interaction by
pair characteristics to see which types of students are providing and receiving network benefits.
Additionally, while previous studies have focused on the role of networks in top management
positions, this study looks at networks over an entire labor market.3
In the remainder of the paper, Section II discusses and prior literature on social networks.
Section III describes the data and the institutional context of the study, and Section IV presents
the empirical strategy. Results are shown in Section V, and Section VI concludes.
2
Previous Literature
This paper contributes to the literature in economics looking at the mechanisms through which
college affects labor market trajectories. Two ways researchers have traditionally conceptualized
the role of education on labor market outcomes via a broad human capital (Becker 1964) or signaling framework (Spence 1973, Weiss 1995). More recently, researchers have begun looking at
2
See Bayer, Ross & Topa (2008).
For future work in this paper, I plan to look more into understanding the mechanisms driving gender differences
in network usage and outcomes.
3
4
the role of network formation on labor market outcomes. Kramarz & Thesmar (2013) analyze the
effects of social networks on boardroom composition at French firms by looking at the relationship
between a CEO’s educational history and that of his or her board of directors within elite educational institutions. Zimmerman (2015) measures the effect of elite college cohort networks in Chile
on hiring patterns in top management positions. This paper builds on these studies by expanding
the scope of peer networks across all positions in a labor market, as well as looking at the effects
of networks on individual welfare.
Methodologically, this paper is most closely related to Bayer, Ross & Topa (2008), which looks
at the effects of residential networks on hiring networks. The paper exploits variation in location
within neighborhood residence groups to look at the effects of proximity on a pair of individuals
working together. This study uses the same matched pair framework but abstracts from geographic
variation and instead looks at variation across classrooms for identification. More broadly, this
paper also relates to a large literature looking at the effects of networks in a variety of contexts.
Kramarz & Skans (2014) also look at the role networks in the transition out of college. However,
they focus on the role of parental ties rather than peer ties in helping students find jobs. Beaman
(2010) and Dustmann et al. (2015) both use ethnic group affiliation as a proxy for network
formation to look at the effects of social ties. Cingano & Rosolia (2012) and Hensvik & Skans
(2013) measure networks via networks of former coworkers to analyze mechanisms underlying the
labor market effects of networks.
3
Data and Institutional Context
The data for this project come from the Arkansas Research Center. Files contain student transcript
information for all public institutions in Arkansas from the academic calendar years4 2004-2011 and
student enrollment information from 1997-2011. The student files contain information on each class
(defining classes at the section level) a student takes, as well as the grade earned in the class and
an identifier for the instructor. I observe the student composition of each class by linking together
class identification numbers. Additionally, the enrollment files contain background information on
students, such as gender, employment status, whether they are part time or full time students,
and the semester they enter college. I link these student files to labor market information using
4
An academic calendar year starts from the fall of the previous calendar year to the summer of the listed year.
5
Arkansas Unemployment Insurance (UI) records, for which I have information from the fiscal years
2001-2011. The UI records provide annual records on a worker’s firm identifier code5 , firm industry
6
, and total earnings. These records include all employees in the state of Arkansas who are not
self-employed or employed by the federal government (e.g. military, USPS, and federal agency
jobs).
In this paper, I focus on students enrolled in public two-year colleges (22 total). I look at
community college students for multiple reasons; first, classroom peer interactions likely play
a larger role in relative to overall social network channels in two-year colleges than four-year
colleges. Four year colleges generally have a greater focus on campus life and interaction at a
broader level beyond the classroom, via residential campus structures and greater focus on student
clubs and organizations. These competing social interaction channels make classroom interactions
relatively less prominent at these institutions. Secondly, community college students generally
conduct job searches in a more localized labor market as compared to four-year college students,
making interpersonal connections with peers an especially salient for job outcomes both from the
perspective of providing and receiving network benefits. Finally, two-year college students have
more vocationally-geared educational goals on average than four-year students and thus enter the
labor market sooner, providing a more tractable labor market sample for the range of years in the
data.
For the empirical analysis, I restrict the sample to students taking courses in the fall semester of
the 2006 academic year.7 I choose this cohort because they allow me to track previous employment
history, as well as enough years to look at labor market outcomes in the time frame of the data.
Table 1 provides an overview of students enrolled in a two-year college in Arkansas during this
term. The first column group shows statistics for the entire sample of students, and the next two
columns break down the analysis by gender. Approximately 65% of students are female, slightly
less than half enroll part-time as opposed to full-time, and 81% of students are employed at some
point in the year the course occurs. The average number of years students have been enrolled in
college is 1.8 and 37% are classified as first-time students. Female students are slightly more likely
to attend-part time and have been out of high school for longer.8
5
The firm identifier code is constructed from Employer Identification Codes, and firms are identified at the
establishment level. However, the actual firm cannot be extracted using the data.
6
Using North American Industry Classification System codes.
7
Eventually I will add on more years and semesters.
8
All means for men and women are significantly different at a p-value of 001.
6
Table 1: Student Body Composition
Female (%)
Part-time (%)
First-time Student (%)
Employed (%)
Years enrolled
Years since HS
Number of Courses
N
All
Mean
SE
65.05
46.42
36.86
81.41
1.77 (3.07)
8.80 (8.99)
2.85 (1.51)
36,795
Male
Mean
SE
0.00
44.62
40.13
80.55
1.40 (2.73)
7.43 (8.32)
2.81 (1.60)
12,861
Female
Mean
SE
100.00
47.38
35.10
81.88
1.98 (3.21)
9.54 (9.24)
2.87 (1.46)
23,934
Fall semester, 2006 academic year
On average, students take 2.85 courses per semester, as shown in table1. Many courses in the
sample offer provide multiple sections for student enrollment. For the semester in this analysis,
the schools together offer 1,494 courses consisting of a total of 6,692 sections. Figure 1 shows the
distribution of sections per course in the sample. The median number of sections per course is
three, and 6% of courses contain only one section. I drop these courses from the main analysis
since there is no section level variation within them.
I include all students who are not currently in high school in the analysis. I also exclude
independent study courses, defined as any section that only contains one student. Figure 2 shows
the distribution of students per section for all students included in the final analysis. The sample
contains 427,176 student/section observations, consisting of 36,795 unique students. The mean
number of students per section is 15.8 and the median is 16.
From the sample in table 1, I construct observations of matched pairs of individuals in a course
to set up the data for estimating the presence of networks. I drop pairs in which both students
attended the same high school or worked at the same firm prior to the course to address the
potential concern that previous social ties may bias my estimates by influencing both enrollment
behavior and later labor market decisions. The final sample I use consists of 19,333,652 observations
of matched pairs, and table 3 characterizes the demographics of the matched pairs.
Table 2 provides comparisons of the probability for an individual to have worked at a firm
with an incumbent different-section vs. same-section peer in a course. Numbers are represented
as percentage points and measure the propensity for an individual to have begun working at a
firm with an incumbent peer within x ∈ {1, 2, ..., 6} after the course. Column 1 looks at pairs
in different sections of a course, while column 2 looks at pairs in the same section of a course.
7
0
.1
Density
.2
.3
.4
Figure 1: Distribution of Sections Per Course
0
5
10
15
Number of Sections per Course
>20
0
.05
Density
.1
.15
.2
Figure 2: Distribution of Students Per Section
2−5
6−10
11−15 16−20 21−25 26−30 31−35
Number of Students per Section
8
36−40
>40
Column 3 shows the results of a two-sample proportion test for equality of the estimates in the
first two columns.
For all of the first six years after the course, table 2 shows a higher propensity for same section
pairs vs. different section pairs both to have worked together at some point. Furthermore, the
two-sample proportion test indicates these numbers significantly different for all years. While
table 2 suggests the existence of labor market networks in the classroom, the numbers in the
table do not disentangle the effects of specific courses on network effects. As a result, these figures
disproportionately weight pairs in courses with higher enrollment levels, which will potentially bias
results since there is selection into the courses individuals choose to take. To address this issue,
the empirical strategy presented later in the paper includes a course fixed effect to ascertain the
effects measured arise solely from comparisons within courses.
Table 2: Percentage Who Have Worked at Firm where Matched Pair is Incumbent
+1 Yr.
(1)
Different Section
0.16
(2)
Same Section
0.21
(3)
Prop. Test (p-value)
(0.00)∗∗∗
+2 Yr.
0.28
0.38
(0.00)∗∗∗
+3 Yr.
0.39
0.53
(0.00)∗∗∗
+4 Yr.
0.48
0.65
(0.00)∗∗∗
+5 Yr.
0.55
0.75
(0.00)∗∗∗
+6 Yr.
0.62
0.82
(0.00)∗∗∗
*** p<0.01, ** p<0.05, * p<0.1
Col. 1-2 represented as percentages. Excludes pairs from same high school and/or prior coworkers.
Additionally, I drop pairs who appear both as same course, different section pairs and same section
pairs in the sample for different courses, and I weight remaining observations by the inverse of the
number of times the pair appears in the data.
Table 3 provides an overview of the breakdown of pair demographics by category (gender,
attendance status, employment status and first-time student status) for all pairs in the sample.
The second and third columns show the percentage of pairs from the full sample who work at the
same firm at some point within six years after the course took place. Column two shows statistics
for pairs enrolled in different sections of the course, and column three shows pairs who enrolled
in the same section. The table shows that the baseline propensity for a pair to have worked
together at some point is less than 1%, with this number being .49% for same course but different
section pairs and .67% for same section pairs. Table 3 shows all demographic pair groups within a
9
characteristic constitutes at least 10% except for pairs consisting of an employed student matched
with an unemployed student, which consists of 2.62% of matches within employment status.
Table 3: Composition of Course Pairs
Full Sample
Gender
Female/Female
Male/Male
Male/Female
Female Leader/Male Follower
Male Leader/Female Follower
Attendance Status
Part-time/Part-time
Full-time/Full-time
Full-time/Part-time
Full-time Leader/Part-time Follower
Part-time Leader/Full-time Follower
Employment Status
Employed/Employed
Unemployed/Unemployed
Employed/Unemployed
Unemployed Leader/Employed Follower
Employed Leader/Unemployed Follower
First time Student
First time/First time
Non-FT/Non-FT
First time/Non-FT
First time Leader/Non-FT Follower
Non-FT Leader/First time Follower
N
All Course Pairs
(1)
100.00
45.28
12.16
42.57
0.12
Same Firm within 6 Years
Same Course, Diff. Section Same Section
(2)
(3)
.49
.67
0.32
0.08
0.23
0.13
0.11
0.14
10.50
49.66
39.84
0.10
0.28
0.25
0.10
0.15
0.15
0.40
0.29
0.11
0.17
71.77
2.62
25.61
0.48
0.01
0.13
0.04
0.10
0.62
0.02
0.19
0.06
0.13
29.82
27.82
42.36
0.17
0.20
0.25
0.11
0.14
17,985,276
0.16
0.39
0.29
0.13
0.16
1,348,376
19,333,652
0.44
0.13
0.27
All numbers expressed as percentages.
Columns (2) and (3) indicate percentage of pairs who work at the same firm at some point within four years after the course.
Next, table 4 provides a summary of labor market outcomes for students from table 1 in the
years after the course. The first column provides an overview for the entire sample, and the second
and third columns separate the analysis by gender. For the selected sample, the data contain
outcomes for up to six years after the course. On average, students are employed for 4.63 of those
years. I define a turnover as occurring if, conditional on being employed, a student does not work
at the firm the next period. The probability of turnover for employed students in a given year is
10
48% on average. Finally, students on average earn $12,814 annually each year after the course.9
In looking at differences across genders, women are generally employed for a longer duration of
the first six years after the course and have a lower turnover rate than men. However, on average
they earn slightly over $2,000 less than men annually, conditional on having a job at some point
in the year.10
To examine labor market trends over time, figures 3 and 4 further break down the summary
statistics by number of years after the course for employment and annual earnings, respectively.
Figure 3 shows that employment in Arkansas falls over time in the first six years after the course.11
Women have a higher employment rate than men for all six years after the course, and this gap
widens over time. On the other hand, figure 4 shows annual earnings rising over time, with men
consistently earning more than women.
Table 4: Post-Course Student Employment
All
Years Employed (6 max.)
4.63
(1.86)
Turnover Probability
.48
Annual Earnings ($)
12,814
(10595.75)
N
36,605
Male
4.48
(1.95)
.50
14,290
(12153.99)
12,758
Female
4.71
(1.80)
.47
12,033
(9580.39)
23,847
Standard deviations in parentheses.
4
Empirical Strategy
4.1
Detecting Classroom Networks
The empirical model measures the propensity for a student to begin working at a firm where
someone in her section already works. Self-selection into courses presents a key challenge to
measuring causality in this context: students choose the courses they register for each semester,
and unobserved characteristics correlated with course selection may also drive market decisions.
To address this concern, I restrict comparisons to pairs of students within a course, which provides
9
Earnings have been inflation-adjusted to 2010 dollars. Individuals appear in the earnings data for a year if they
are employed in a covered sector at any time in the year.
10
All means for men and women are significantly different at a p-value of 001.
11
The attrition in employment could be due to either greater unemployment or employment outside of Arkansas.
11
.65
.7
Percent Employed
.75
.8
.85
Figure 3: Post-Course Employment
1
2
3
4
Years since Course
Male
5
6
5
6
Female
10000
Average Annual Earnings ($)
12000 14000 16000 18000
20000
Figure 4: Post-Course Earnings
1
2
3
4
Years since Course
Male
12
Female
unbiased estimates if students do not select into sections within courses along unobservable traits
that affect the outcome of interest.12 Under this assumption, variation in section enrollment across
courses identifies network effects at the classroom level. Figure 5 provides a visual interpretation
of the organization of courses and sections within courses.
Figure 5: Course Structure
Northwest
ArkansasCC
Fall2005
Courses(ρ)
Introduc8on
toBiology
Sec8on
1
Sec8on
2
World
History
Sec8on
3
Sec8on
1
Graphic
Design
Sec8on
1
Sec8on
2
Sec8ons(Sourceof
Iden8fica8on)
I run the following equation to compare the propensity for an individual to begin working at
the same firm where a same-section peer as oppose a different-section peer already works within y
years after the course:
Fijy = ρcy + γy Pij + κ1y Indij + κ2y (Pij × Indij ) + ijy
(1)
In equation 1, the matched pair (i, j) denotes two individuals in the same course. The outcome
variable, Fijy is an indicator variable for whether i has started working at a firm where j works
within y years after the course. For each matched pair, the first listed individual of the pair is
the potential candidate to “receive” a job through a network contact and the second individual is
the potential incumbent.13 The course fixed effect, ρcy restricts comparisons to pairs of students
in the same course and represents the baseline propensity for any pair that i starts working at j’s
12
I plan to include multiple robustness checks testing the plausibility of this assumption.
Note that this means matched pairs are not symmetrical, so the pairs (i, j) and (j, i) may have different
outcomes.
13
13
firm at some point within y years after the course. The variable of interest, Pij , is an indicator
variable capturing whether i and j enroll in the same section of the course. Finally, the parameter
of interest, γy , captures the effect of peer social ties on labor market outcomes. Specifically, the
coefficient estimate answers the question, “How much does enrolling in the same section increase
the probability that an individual will begin working at the same firm as a peer within y years
after the course, compared to enrolling in different sections of the course?”
One additional challenge to measurement is that different pairs of students take different numbers of courses together. I drop pairs who appear in multiple courses together and are same-section
peers for some courses different-section peers for other courses since coefficient interpretation of
these pairs is ambiguous. Remaining pairs either only appear in one course together, or who appear
in multiple courses and are in all the same sections or in all different sections.14 There are two
issues with running matched pair regressions without addressing pairs who take multiple courses
together. First, network effects may be heterogeneous over the number of courses taken together.
Secondly, the estimation strategy would overweight peers who take more sections together since
pairs appear in the data once for each course taken together. To address the first issue, I could
add in an index variable, Indij , that accounts for the number of courses a pair takes together.15 I
then weigh observations by the inverse of the number of courses the pair takes together.
4.1.1
Heterogeneity
Additionally, I adjust equation 1 to analyze whether pairs with certain attributes have a higher
propensity to form networks than others. To do so, I include a vector of covariates that describe the
demographics of the pair as well as a term interacting the vector with the same-section indicator
variable, Pij 16 :
0
Fijy = ρcy + βy0 Xij + (γ0y + γ1y
Xij )Pij + ijy
(2)
In this setup, Xij represents a vector of matched pair attributes. The coefficient of interest, γ1y ,
measures the relative network interaction for different pair characteristics in terms of propensity
for i begin working at a firm where j is incumbent, within y years of the course. As in equation
1, the outcome variable, Fijy indicates whether a network occurs for the pair within y years of
14
In the data, 86% of these pairs take one course together, 12% take two, and 2% take three or more.
For a pair taking N courses together, Indij = n − 1.
16
For ease of notation, I drop the index variables and interactions in the specification, although I include them in
the actual regression. The demographics I use for analysis are gender, attendance status, employment status, and
first-time student status.
15
14
the course. The panel nature of the data reveals timing of employment at individual firms, which
allows me to observe which individual in a pair provides vs. receives a job network. This provides
an advantageous insight in studying heterogeneous network effects by addressing inherently asymmetric dimension of networks, which earlier papers using cross-sectional dyadic analyses were not
able to do.17
4.2
Labor Market Outcomes of Networks
Having detected and measured the magnitude of classroom network usage, the next portion of
the analysis looks at the effects of these networks on job turnover and annual earnings. While
data structure used in the detection of networks consisted of matched pairs within a course, the
analysis of labor market outcomes focuses on individuals across time. Thus, in the first step of
estimation, I reshape the data from cross-sectional dyads individual panel observations. I create
an indicator variable, Fit , that turns on if a student works in a jobs where either a same-section
or different-section peer already worked there when the student was hired. For student i with N
total course peers, this variable is defined as:
Fit = 1PNk=1 Fij
kt
>0
(3)
where Fijt measures whether i works in year t at a job where j was incumbent at time of hire, as
defined in equation 1. Next, I create an indicator variable Pit that turns on only if a student works
in a jobs where a same-section peer already worked there when the student was hired:
Pit = 1PNk=1 Fij
kt
×Pijk >0
(4)
where Pij measures whether i and j enroll in the same section. Thus, Pit ⊆ Fit . Next, I use Fit and
Pit in the main empirical specification to analyze at the effects of classroom networks on outcomes:
Yit = φi + ηt + α1 Fit + α2 (Fit × tenit ) + β1 Pit + β2 (Pit × tenit ) + δ 0 Xit + it
(5)
The dependent variable, Yit , represents either job turnover or log of annual earnings, depending
on the outcome of interest. Job turnover equals one if an individual leaves the firm at time t + 1
17
e.g. Bayer et al. 2008 and Zimmerman 2015
15
and zero otherwise. Individual fixed effects, φi , control for time-constant unobservable differences
between individuals such as ability and motivation that may influence both earnings and propensity
for network involvement. I also add year fixed effects, ηt , to control for time-varying trends. The
vector Xit captures a set of time-varying individual control variables, including attendance status
(part-time, full-time, or not enrolled in college), highest degree attained, job switching, and tenure.
To measure the outcome of interest, I look at the effects of obtaining a job via a network on
outcomes. Since I do not observe networks directly in the data, I proxy for network jobs as ones
at firms where a same-section peer was working when a student started. The variable Pit , as
defined in equation 4, is an indicator variable that equals one for network jobs and zero otherwise.
However, there is also some probability that a student starts working at the same firm as a peer
even without networks. Not accounting for this would lead to biased results if unobserved traits
leading students to select into the same courses also affect labor market outcomes. I include Fit ,
defined in equation 4, to account for any labor market effects driven by selection.
I also interact Pit and Fit with individual i’s tenure at the firm to capture how turnover and
earnings dynamics change over time. The first coefficient of interest, β1 , captures the effect of
network jobs on earnings or turnover in the first year of hire. The second coefficient of interest,
β2 measures how this effect changes with tenure in the firm. Specifically, the coefficient estimates
answer the questions, “How much does acquiring a job through a classroom network increase
annual earnings or turnover probability at time of hire? How do these effects change over time
tenure at the firm?”
4.2.1
Alternative Specification
One estimation concern is that student selection on courses may create distored comparisons
between students who take different sets of courses. This would happen if unobservables driving
the set of courses students take affect the rate of Pit relative to Fit and labor market outcomes
differentially. To address this potential issue, I include an alternative specification that includes a
fixed effect for the bundle of courses a student takes, Γc :
Yit = φi + Γct + α1 Fit + α2 (Fit × tenit ) + β1 Pit + β2 (Pit × tenit ) + δ 0 Xit + it
(6)
The inclusion of a course bundle fixed effect restricts comparisons to students who take the same
group of courses, which controls for any course composition effects driving results. I interact this
16
Table 5: Effects of Same-Section Enrollment on Working at Firm with Incumbent Peer
Year +1
Year +2
Year +3
Year +4
Year +5
Year +6
Course FE
Individual FE
Instructor FE
N
(1)
Coefficient
SE
0.0199*** (0.00606)
0.0389*** (0.00817)
0.0534*** (0.00940)
0.0661*** (0.00982)
0.0810*** (0.0106)
0.0820*** (0.0110)
Y
N
N
19,295,150
(2)
Coefficient
SE
0.0170*** (0.00583)
0.0344*** (0.00782)
0.0485*** (0.00904)
0.0581*** (0.00920)
0.0712*** (0.00975)
0.0719*** (0.01000)
Y
Y
N
19,295,126
(3)
Coefficient
SE
0.0168*** (0.00588)
0.0341*** (0.00788)
0.0480*** (0.00913)
0.0573*** (0.00926)
0.0707*** (0.00980)
0.0715*** (0.0101)
Y
Y
Y
19,295,126
*** p<0.01, ** p<0.05, * p<0.1; Standard errors clustered at course level.
Coefficient estimates multiplied by 100 to represent percentage point changes.
variable with time indicator variables since to avoid collinearity with individual fixed effects. The
intuition behind the inclusion of course bundle fixed effects is similar to the inclusion of a course
fixed effect, ρc , used in equation 1.
5
Results
5.1
Detecting Classroom Networks
Table 5 shows the results of the estimation of γy in equation 1 for up to six years after the course.
Column (1) shows results for the baseline specification from the equation. In column (2), I add
individual fixed effects to address student sorting into classes. For example, I do not see the time of
day the section was held, but it may be that some who choose to enroll in a night time section also
have a propensity to work at the firms that have night shift jobs. The inclusion of individual fixed
effects accounts for the fact that these individuals who sort on unobservables may have a higher
propensity of working with someone from their section. Finally, in column (3), I add instructor
fixed effects to address the concern that perhaps certain instructors have connections to certain
companies or channel students toward specific firms.
Coefficient estimates in table 5 measure the effect of same-section enrollment on propensity to
begin working at a firm with an incumbent peer within x ∈ {1, 2, ..., 6} years of the course. Results
for all specifications in all years are positive and statistically significant. All results are displayed as
17
percentages, and the magnitude of the estimates are small, as expected. To analyze the economic
significance of the results, I look at the scale of the effect. Table 2 reports that the baseline
propensity for an individual to work at a firm where a different-section peer is incumbent is .62
percentage points within six years after the course. From table 5, the most conservative estimate
for the increase in propensity to work with a peer within six years is 0.72 percentage points. Thus,
enrolling in the same section increases the probability that a student will get a job through a
peer by 12%. Additionally, students take approximately 2.85 courses per semester, and classes on
average contain 15.8 students. Thus, students enroll in the same section with approximately 45
peers each semester. Thus, a network effect of .072 for one pair observation translates into a 3.2
percentage point increase in the probability that a student will begin working at the same firm as
at least one same-section peer from a given semester within six years of the course.18
5.1.1
Heterogeneity
Next, I look at the heterogeneity of network effects across pair demographics. Table 3 provides
a summary of the composition of matched pairs across gender, attendance, employment, and
first time student status. The figures show that overall, there is a higher incidence of same section
pairs working at the same firm sometime within four years of the course vs. different section course
except when both individuals are first time students. The numbers also suggest differential effects
of same-section enrollment across pair types within each demographic category, although these
figures do not control for course of enrollment. Table 6 reports estimation results from equation 2,
which include course fixed effects to look at the heterogeneous effects of pair demogrpahics. As in
the main results, I use three different specifications, with column (1) as the baseline, column (2)
adding individual fixed effects, and column (3) adding instructor fixed effects. I define a “leader”
as the student who arrives at the firm first and a follower as the student who arrives at the firm
second.
Several estimates are robust across specifications. Women are more likely to receive jobs
through networks with other women than with men. Furthermore men are less likely to obtain
jobs through networks than women in general. Networks are more likely to form between paris
of part-time students than pairs of full-time students. However, networks consisting of a full-time
18
Since approximately 14% of pairs take multiple courses together, the number of unique peers will be slightly
lower than 45. However, this number is also offset by the fact that students who take multiple sections together
have a greater network effect than students who only take one course together, and estimates in table 5 show the
effect of taking one course together.
18
Table 6: Covariate Analysis of Effects of Peer Interactions (Cumulative Estimate, 6 Years After
Course)
(1)
Coefficient
SE
0.0958** (0.0433)
(2)
Coefficient
SE
0.0553
(0.0440)
(3)
Coefficient
SE
0.0560
(0.0441)
-0.0161
(0.0259)
0.0472** (0.0195)
omitted
-0.0317
(0.0231)
0.0314
(0.0262)
0.0430** (0.0198)
omitted
-0.0336
(0.0238)
0.0328
(0.0262)
0.0453** (0.0198)
omitted
-0.0294
(0.0238)
Attendance Status
Full-time/Full-time
Part-time/Part-time
FT Leader/PT Follower
PT Leader/FT Follower
omitted
0.0813*** (0.0234)
-0.0612*** (0.0201)
0.0532*** (0.0201)
omitted
0.0611** (0.0240)
-0.0326
(0.0204)
0.00412
(0.0204)
omitted
0.0566** (0.0240)
-0.0369* (0.0205)
0.00371
(0.0205)
Employment Status
Employed/Employed
Unemployed/Unemployed
Emp. Leader/UE Follower
UE. Leader/Emp. Follower
omitted
-0.0873*** (0.0164)
-0.0534
(0.0362)
-0.180*** (0.0362)
omitted
-0.0664*** (0.0162)
-0.0247
(0.0368)
-0.113*** (0.0368)
omitted
-0.0656*** (0.0163)
-0.0222
(0.0369)
-0.111*** (0.0369)
-0.0174
(0.0233)
0.0329
(0.0208)
-0.00772 (0.0242)
omitted
Y
N
N
12,880,366
0.00879
(0.0233)
0.0324
(0.0209)
0.0247
(0.0249)
omitted
Y
Y
N
12,880,334
0.00887
(0.0233)
0.0322
(0.0210)
0.0263
(0.0250)
omitted
Y
Y
Y
12,880,334
Same Class
Gender
Male/Male
Female/Female
M Leader/F Follower
F Leader/M Follower
First Time Student Status
First Time/First Time
Non-FT/Non-FT
FT Leader/Non-FT Follower
Non-FT Leader/FT Follower
Course FE
Individual FE
Instructor FE
N
*** p<0.01, ** p<0.05, * p<0.1
Coefficient estimates have been multiplied by 100 to represent percentage point changes.
leader and part-time follower are less likely than both. Pairs of employed students are more likely
to form networks than pairs of unemployed students, and unsurprisingly, unemployed students are
significantly less likely to provide networks employed students. Finally, I do not observe much
heterogeneity in network effects based on first time student status.
5.2
Labor Market Outcomes
Table 7 shows the results of networks on turnover rates. Columns (1) and (3) display results from
the main specification in equation5, and columns (2) and (4) add in course bundle fixed effects,
as described in equation 6. Additionally, I include interactions of the variables of interest and
19
Table 7: Effects of Networks on Turnover
(1)
-0.0544***
(0.00691)
(2)
-0.0487***
(0.00710)
(3)
-0.0616***
(0.0128)
(4)
-0.0569***
(0.0130)
-0.0234***
(0.00577)
-0.0234***
(0.00589)
-0.0189*
(0.0105)
-0.0158
(0.0106)
FemaleXNet (Initial)
0.0105
(0.0152)
0.0119
(0.0155)
FemaleXNetXTenure
-0.00683
(0.0125)
NO
YES
235,062
-0.0110
(0.0127)
YES
YES
234,279
Network (Initial)
NetworkXTenure
Course Bundle FE
Gender Interaction FE
N
NO
NO
235,062
YES
NO
234,279
*** p<0.01, ** p<0.05, * p<0.1
Standard Errors in parentheses.
corresponding baseline variables with gender in columns (3) and (4).19 All estimates are consistent
across specifications. They show that networks jobs lead to an initial decrease in turnover rate, and
that network jobs continue to have a lower turnover rate that control group jobs over tenure at the
firm. Networks lead to a decrease in turnover by approximately 4.9% the year after being hired.
Compared to the control group, network jobs decrease the probability of leaving a firm by 2.3%
each subsequent year a student is employed there, compared to jobs not found through classroom
networks. On average, men and women using networks do not differ in turnover propensity across
firm tenure.
Next, table 8 shows the results of networks on annual earnings. Once again, I include estimates
for both the main specification as well as a specification including course bundle fixed effects, as
shown in equation 6. As in the results showing turnover effects, the last two columns include
gender interactions on the variables of interest. All estimates are consistent across specifications.
Results for the overall effects of networks without gender interactions show that network jobs make
7.8% more than non-network jobs in earnings in the period of hire. Furthermore, this earnings
boost remains constant over tenure at the firm. Columns (3) and (4) show that women actually
drive almost all of this initial earnings boost. In fact, women earn an 8.6% higher initial premium
from network jobs than men, and this trend stays constant over tenure at firm.
19
Specifically, the variables I interact with gender are: Fit , (Fit × tenit ), Pit , and (Pit × tenit ).
20
Table 8: Effects of Networks on Earnings
(1)
0.0936***
(0.0148)
(2)
0.0771***
(0.0152)
(3)
0.0212
(0.0274)
(4)
0.0169
(0.0278)
-0.00204
(0.0101)
-0.00361
(0.0102)
-0.00322
(0.0184)
-0.0117
(0.0186)
FemaleXNet (Initial)
0.103***
(0.0326)
0.0857***
(0.0330)
FemaleXNetXTenure
0.00157
(0.0220)
NO
YES
232,330
0.0114
(0.0223)
YES
YES
231,314
Network (Initial)
NetworkXTenure
Course Bundle FE
Gender Interaction FE
N
NO
NO
232,330
YES
NO
231,314
*** p<0.01, ** p<0.05, * p<0.1
Standard Errors in parentheses.
6
Discussion
This paper analyzes the magnitude of college peer networks using a novel identification strategy
that measures networks within classrooms. I use community college transcript data linked with
employment files for an entire state to look at the effects of enrolling in a class with a peer on the
probability a student will work at a firm where the peer is incumbent in the future. I control for
selection into classes by analyzing variation for matched pairs of individuals within sections of a
course. Results indicate classroom networks increase the probability of working at a firm with an
incumbent peer by 12%.
Furthermore, I find that classroom networks decrease probability of job turnover both in the
initial period of hire and over tenure at firm. Network jobs also provide individuals with an 8%
increase in wages at time of hire compared to peers not using networks, and this trend remains
constant over tenure at firm. The gender breakdown of results shows that women who use network
jobs earn a 9% higher wage premiums than their male counterparts who get network jobs, and this
difference persists with tenure at the firm. Men and women in networks do no differ in average
turnover probabilities.
The results of this study point to multiple channels for future work. This paper finds that
network usage in matched pairs is heterogenous across pair characteristics. Thus, from a policy
21
perspective, one direction for future research is to look further at the effects of altering classroom
composition on network formation and labor market outcomes for different types of students.
Additionally, I would also like to look further into the mechanism driving the gender differences in
earnings effects from networks. Finally, the framework used in this study can be adapted in future
research to look at the effects of college peer interaction on alternative outcomes beyond the labor
market such as health, marriage, charitable giving, and future educational choices.
22
References
[1] Bayer, Patrick, Stephen Ross, and Giorgio Topa. “Place of Work and Place of Residence:
Informal Hiring Networks and Labor Market Outcomes.” Journal of Political Economy, 2008,
116:1150-1196.
[2] Beaman, Lori. “Social Networks and the Dynamicso f Labor Market Outcomes: Evidence from
Refugees Resettled in the U.S.” Review of Economic Studies, 2012, 79(1):128-161.
[3] Becker, Gary. Human Capital: A Theoretical and Empirical Analysis with a Special Reference
to Education. University of Chicago Press, 2009.
[4] Cingano, Federico and Alfonso Rosolia. “People I Know: Job Search and Social Networks.”
Journal of Labor Economics, 2011, 30(2):291-332.
[5] Dustmann, Christian, Albrecht Glitz, Uta Scönberg, and Herbert Brücker. “Referral-based Job
Search Networks.” Review of Economic Studies, 2013, 11:780-807.
[6] Hensvik, Lena and Oskar Skans. “Social Networks, Employee Selection, and Labor Market
Outcomes.” Journal of Labor Economics, 2016, 83:524-546.
[7] Kramarz, Francis and Oskar Skans. “When Strong Ties are Strong: Networks and Youth Labor
Market Entry.” Review of Economic Studies, 2014, 81:1164-1200.
[8] Kramarz, Francis and David Thesmar. “Social Networks in the Boardroom.” Journal of the
European Economic Association, 2013, 11:780-807.
[9] Spence, Michael. “Job Market Signaling.” The Quarterly Journal of Economics, 1973,
87(3):355-74.
[10] Topa, Giorgio. “Labor Markets and Referrals.” Hand book of Social Economics, 2011, 1:11931221.
[11] Weiss, Andrew. “Human Capital vs. Signalling Explanations of Wages.” Journal of Economic
Perspectives, 1995, 9(4):133-54.
[12] Zimmerman, Seth. “Making Top Managers: The Role of Elite Universities and Elite Peers.”
Working Paper, 2015.
23