On Level Ground?
Gender, Trial Employment, and Initial Salaries in Organizations
Adina D. Sterling
Olin Business School Washington
University in St. Louis
sterling@wustl.edu
Roberto M. Fernandez
MIT Sloan School of Management
robertof@mit.edu
October 11, 2014
Do Not Cite or Circulate Without Permission
The authors thank Ezra Zuckerman, Emilio Castilla, Damon Phillips, Anne Marie Knott, participants at
the People and Organizations Conference at Wharton Business School and participants at the European Groups
and Organizations Conference for feedback on previous versions of this paper. All errors are the
responsibility of the authors.
ABSTRACT
Gender disparities in wages exist due to the shortfall in wages women sustain relative to men at
the point of organizational entry. This paper develops a trial approach to test for the presence
of demand-side contributions to initial wage inequality and proposes an organizational practice
through which it is lessened – internships. We predict that internships reduce the gender gap in
initial salaries because it provides organizations a first-hand look at candidates, differentially
benefiting women. Using data on several hundred professionals that attended an elite private
business school, we find that internships narrow that gap between men and women’s initial
salaries. For men, there is no difference in salary offers from employers where an internship
occurs versus one where an internship does not occur. However, women receive higher salaries
from employers where an internship takes place versus where an internship does not. Within
the limits of our research design we are able to pinpoint a mechanism through which salary
disadvantages for women in traditional hiring scenarios are generated and the remediation of it
in internships. We close by discussing implications of our findings for literature on inequality,
organizations, and labor markets.
2
Introduction
It is well-documented that within work organizations professional women do not earn
what men earn (England, 1992; Tomaskovic-Devey, 1993; Bielby, 2000). While scholars have
studied many reasons for wage inequality within organizations including supervisor evaluations
(Castilla, 2011), promotions (Elliott and Smith, 2004; Hultin and Szulkin, 2003) and the
demographic composition of organizations (Cohen and Huffman, 2007; Sorensen, 2007), most
prior studies investigate internal labor markets and their contributions to wage inequality
(Stolzenber, 1978; see Stainback, Tomaskovic-Devey, and Skaggs, 2010 for a review). Yet,
professional women earn less than men because they receive lower initial salaries at the point-ofhire (Petersen and Saporta, 2004; Morgan, 1998). Women are sorted into jobs or employers that
offer lower wages than men upon organizational entry (Petersen and Morgan, 1995; Reskin and
Roos, 1990; Fernandez and Mors, 2008). Once initial salaries are in place women’s salaries fall
behind men’s because initial salaries provide a baseline for future compensation (Barnett,
Baron and Stuart, 2000; Elman and O’Rand, 2004; DiPrete and Eirich, 2006; FernandezMateo, 2009).
One reason women might start out making less is that organizations evaluate men and
women differently during the hiring process. Yet, documenting the differential evaluation of
women and men during hiring is difficult. Organizations are less transparent about decisions
affecting salaries at the point-of-hire than they are after organizational entry (Petersen and
Saporta, 2004; Dencker, 2008). Additionally, explanations of men and women being
differentially evaluated are hard to disentangle from alternative explanations for wage
inequality regarding rationale assessments of candidates by employers. While models with
detailed aspects of individuals – i.e. grades and educational credentials – indicate women make
3
less than men, generally scholars implicitly assume that unexplained variance in these models is
due to quality differences in individuals unknown to researchers but observable to employers.
This paper develops a novel approach to test for the presence of demand-side
contributions to initial wage inequality and proposes an organizational practice through which
this inequality is lessened. Conceptualize a setting where employers screen candidates as they do
in a conventional hiring process but additionally, before employers make full-time salary offers,
they are able to observe candidates as they work in organizations over a period of time. If
implicit assumptions about unobserved quality hold there should be an improvement in wages
after a trial period for all candidates that are offered jobs, but preexisting ratios in wages between
groups of candidates should be unchanged (Becker, 1993; Jovanovic, 1979).
However, if
salaries increase for one group of candidates more than the other – e.g. for women more than
men – this would be evidence that a conventional hiring process discounts this group’s salaries.
In this paper we introduce this ‘trial test’ to evaluate the ways organizations assign
salaries to entry-level professionals. Trial employment, or a tryout period, is a period whereby
organizations observe individuals first-hand prior to making longer-term hiring and salary
decisions (Kalleberg, 2000). This personnel practice differs from the more commonly studied
practice of probationary employment (Lazear, 1999).
During probationary employment an
organization hires an employee for a period of time during which the organization may terminate
the employee at-will. Probationary employment is an “opt-out” policy after individuals have
been hired for a permanent job. In contrast, trial employment is an “opt-in” policy.
We argue that trial employment lessens the discount in women’s initial wages. Trial
employment provides a period of extended and heightened visibility to prospective employees.
Because longer-term hiring and salary decisions occur after organizations receive a first-hand
4
look at candidates, this may lead to a lessening of the differential evaluation of men and women,
reducing the gender gap in initial salaries.
To test this prediction we use a setting well-suited for our inquiry: internships.
Internships serve as a ‘twelve week job interview’ whereby candidates complete short-term
assignments in organizations. They are common in ‘learned’ professions including business,
law, and engineering. We collect data on several hundred new professionals that attend an elite,
private business school. This setting is well-suited for our inquiry. By electing to investigate
salary offers from new professionals at the same institution we are able to net out institutional
and employer differences in the pool of applicants to jobs. Because nearly all of the new
professionals attended the same graduate school and completed internships, supply-side factors
that might otherwise make isolating employer-side mechanisms difficult are largely mitigated.
Additionally, in our sample, new professionals apply and compete for the same jobs so structural
characteristics of jobs themselves are tempered as the probable cause of wage disparity
(Tomaskovic-Devey, 1993; Petersen and Morgan, 1995; Padavic and Reskin, 2002; Fernandez
and Mors, 2008).
We begin by modeling salary offers with a rich stock of detailed information about
candidates for jobs, such as grades and test scores. Once these variables are in the models, we
assess how full-time salary offers differ when individuals do and do not complete internships at
an employer. This allows us, within the limits of our research design, to investigate how
internships affect salaries, and to document a demand-side mechanism that contributes
women’s disadvantage in salary offers in traditional hiring.
5
Organizations, Inequality, and Hiring
Organizations influence inequality on the demand-side of the labor market by matching
individuals to organizations and jobs that vary in wages (Granovetter, 1981; Sorensen and
Sorenson, 2007). Research indicates that in professional occupations women are matched to
organizations and jobs where they receive lower returns on their educational credentials than
men (Hagan and Kay, 1995; Buchmann, Diprete, and McDaniel, 2008) and lower returns on their
academic performance while in pursuit of a professional degree (Dixon and Seron, 1995; Huang,
1996). This shortfall in wages serves as a source of inequality in earnings over the long-term.
Using detailed data from a single organization Gerhart (1990) finds that gender differences in
salary are largely the result of a one-time salary shortfall for women at the point of hire. In a
study of business professionals, Olson, Frieze, and Good (1987) find that men’s and women’s
salaries differ but once initial salary differences are accounted for disparities in wages are largely
non-existent. More recently, Weinberger (2011) finds similar results using data from the NSF
and the National Survey of College Graduates: a gap in earnings persists in the range of 30% to
45% depending on job-type, driven in large part by the initial salaries men and women receive
from organizations.
Sociological approaches to wage inequality suggest disparities exist because men are
allocated to jobs and organizations that pay more, and to higher pay structures within the same job
(Bielby and Baron, 1986; Petersen and Morgan, 1995; England et al., 1994; Fernandez and Mors,
2008).
Experimental evidence suggests one reason this may occur is that organizations
differentially evaluate men and women. Organizational managers may rely on stereotypes when
making hiring and salary decisions (Tosi and Einbender, 1985; Heilman, 1995; Reskin and Roos,
1990; Reskin and McBrier, 2000; Heilman et al., 2004). In the case of professionals, women do
not fit the normative expectation of those that pursue professional careers (Ridgeway, 1997). As
6
a result, employers perceptions of women’s value may be lower than men’s, leading to a female
wage discount (Heilman and Hayes, 2005).
Despite laboratory evidence that employers engage in differential evaluation, documenting
its occurrence at the organization-labor market interface is difficult. The main reason is that it is
hard to unravel the outcomes of differential evaluation from efficiency claims. In neoclassic
analysis rational employers hire to maximize efficient output and may discount women’s wages
after a detailed assessment of that expected output (Becker, 1993). Specifically, employers may
screen candidates on hard-to-observe factors and use these assessments in ways unknown to
scholars (Heckman, and Siegelman, 1993). For this reason, an implicit assumption in most large
scale studies is that differences in wages that exist between men and women is attributable to the
unobserved heterogeneity of candidates (see Neumark, 2012 for a review). That is, wage
inequality is due to differences in human capital, training, or motivation that are unobservable to
researchers (Becker, 1975; Barron et al., 1985).
Compounding the difficultly, approaches suitable for examining other phases of hiring are
less tenable at the salary-setting stage. For example, audit studies find that gender affects the
likelihood that individuals are interviewed or receive call-backs for jobs. At the salary-setting
stage, however, audit studies are unpredictable: candidates must make it past several stages of
the hiring process to secure salary offers, thus requiring candidates to interact face-to-face in
interviews, complete tests, and make it successfully past other screens (Neumark, 1996; Bertrand
and Mullainathan, 2004; Pager, 2007). Given variation on the applicant and employer side it is
7
hard to examine processes affecting salary-setting because it is the final stage of the hiring
process. 1
We believe an alternative approach lends itself to significant gains in documenting initial
wage inequality and understanding its sources. As prior literature on labor markets suggest,
employers and researchers vary in the type of information each has available (Altonji and Pierret,
2001). 2 On the research side, rather than collect more detailed information on candidates, we
can find hiring scenarios when some candidates are hired twice: once during a trial period and
then again as a full-time employee. If there are different adjustments in salaries across men and
women over the pre- and post-trial periods, this informs our understanding of differential
evaluation in the pre-period – i.e. what would have occurred in the absence of the trial.
A Trial Approach to Differential Evaluation
Here we present a framework for our trial approach to assessing whether or not
organizations differentially evaluate candidates on the basis of gender. Consistent with prior
literature on differential treatment, we begin with a model that relates individual-level
characteristics to a value assessment by employer – i.e. salaries. Suppose that candidates have
two characteristics related to compensation, X1 and X2. Let’s assume the first characteristic,
X1, is easily observed (i.e. GPA), while the second characteristic, X2, may or may not be
1
No audit studies to date have directly examined the effects of gender on starting salaries. Neumark, Bank, and Van
Nort (1996) investigate the influence of gender on hiring decisions at restaurants that vary in quality. In a recent
Australian study, Booth and Leigh (2010) study the effect of gender on call-backs. Riach and Rich (1987 and 2006)
investigate the effects of gender on requests for interviews and follow-up phone calls.
2
Labor market studies, such as Altonji and Pierret (2001) note that this is a particularly difficult problem for
researchers. In that study the authors used a fact unobservable to employers (AFQT scores) but observable to
scholars to investigate discrimination.
8
observable to organizations but is definitely unobservable to researchers. The output of a
candidate within an organization is given by
V(X) where X=(X1, X2)
Candidates belong to one of two groups G, equal to 1 for women and 0 for men. Salary, S, is a
function of a candidate’s characteristics and gender:
S(V(X),G)
Let’s assume there is no difference in the actual performance of men and women once they are
hired or that VM*=VW*. We can define a term gamma as follows:
S(V*, 0) – S(V*,1) = γ
Differential evaluation is detectable when gamma is not equal to 0. Existing approaches
to testing may find that initial salary differences exist for men and women, but cannot “rule in”
differential evaluation because of what is unobserved on the part of researchers.
That is,
differential treatment in salary-setting, or gamma, is only detectable by researchers if they are
able to observe
γ + E(XW) –E(XM)
In other words, identifying differential evaluation relies on getting the expected values of
both X1 and X2 correct. In our trial approach we avoid the problem of unobservability by
examining salaries of the groups during the pre and post-period. That is, by observing the ratio of
men’s and women’s salaries, differential treatment in salary-setting in the pre-period can be
taken as differences in these ratios
T2W/T2M – T1W/T1M
9
In sum, our trial approach framework permits us to model the outputs – i.e. salaries. As
long as the differential evaluation is not persistent, as in the case of taste-based discrimination
(Becker, 1957) or status-based characteristics (Correll and Bernard, 2006), we are able to
identify the level of differential treatment under a typical pre-period scenario within a given
context as long as 1) the pre-period hiring scenario conforms to the traditional hiring process and
2) there is a theoretical rationale underlying how differential evaluation in the interim (between
the pre-post period) is attenuated. We suggest internships meet these requirements.
Internships, Inequality and the Tryout Period
Internships refer to a period of time organizations employ individuals to complete tasks
prior to making long-term hiring decisions. It is a practice consistent with the externalization of
the labor market, whereby organizations engage in more market-like transactions with
individuals before fully internalizing some as employees (Baron, 2000; Davis-Blake and Uzzi,
1993). In the United States internships are an important way that employers and entry-level
employees are matched (Baron and Kreps, 1999).
Surveys of organizations indicate that
recruiting full-time employees is the primary aim of employer internship programs. The National
Association of Colleges and Employers (NACE) survey in 2009 indicated that nearly 68% of
interns within organizations were offered fulltime positions, and that 84% of these offers were
accepted. 3
Evidence suggests that because organizations use internships as an interim step to hiring
full-time employees, organizations screen and select interns in largely the same way as they do in
3
Overall, more than one-third of the employers’ full-time, entry-level college hires came from their internship
programs.
10
traditional hiring processes. Interns must make it past several stages of the hiring process,
including initial and later-stage interviews. Additionally, the qualifications for interns are similar
to qualifications for full-time employees, including grades, references, and extra-curricular
activities (ERC report). 4 That is, the selection for internships is similar to selection for full-time
employees.
Having discussed how internship selection is similar to the traditional hiring process we
now lay out a theoretical rationale for why internships may lessen wage inequality post the trial
period. Literature on organizations and labor markets suggests organizations select individuals
and assign salaries based on two types of candidate characteristics: hard and soft skills.
Organizations weigh hard skills, or those based on technical ability to complete task when
assigning salaries. They also weigh soft skills or those involving “personality, attitude, and
behavior rather than to formal or technical knowledge” (Moss and Tilly, 2001: 44). Hard skills
involve what tasks get completed, while soft skills involve how work gets done. For example,
writing computer software involves hard skills like coding, but also soft skills involving
behaviors – e.g. fact-finding, interactions with leadership, political savvy – that determine the
fate of the commercial computer software (i.e. whether or not it is sold in the market).
Research suggests that when hiring new professionals organizations may view soft skills
as at least as important as hard skills (Bills, 2003; Rivera, 2011). Since professionals enter jobs
with a greater span of control than others, they require skills such as leadership, team-building,
and strategic thinking to gain buy-in for their ideas and move projects forward within
4
For example, the ERC reported that in a survey of 122 organizations, college major, professionalism,
communication skills, work ethic, career goals, and coursework are heavily relied upon by employers when
selecting interns. These largely coincided with the selection criteria organizations reported when hiring recent
college grads (http://www.yourerc.com/_CE/pagecontent/Documents/survey/research-studies/13-ERC-NOCHEIntern-Pay-Rates-and-Practices-Survey.pdf).
11
organizations (Briscoe and Kellogg, 2011; Kalev, 2009; Pfeffer, 1997; Podolny and Baron,
1997). This may be particularly problematic for female professionals, since soft skills are more
apt to be linked to stereotypes of behavior (Ridgeway, 1999). An evaluation of soft skills by
organizations is inherently subjective, as hiring managers extrapolate behavior based on limited
data gained during the hiring process (Moss and Tilly, 2001; Cappelli, 1999). Because
professional occupations are associated with masculine characteristics, organizations may doubt
that female professionals have the soft skills needed to perform (Castilla, 2008; Reskin and
McBrier, 2000).
Men and women may both benefit from internships, as it provides the chance to develop
skills within a specific organization (Becker, 1993). However, we expect that internships benefit
women more than men. Internships provide the chance for individuals to have a period of
extended visibility with decision-makers prior to the salary-setting stage (Beenen and Rousseau,
2010). This visibility may allow women to disconfirm stereotypes of behavior and an
opportunity for organizations to update their evaluations (Leahey, 2007; Kalev, 2009; Briscoe
and Kellogg, 2011). In traditional hiring contexts, women are segregated to lower-levels of
organizations, removing the opportunity for them to interact with decision-makers (England,
1992; Pierce, 1995; Petersen and Morgan, 1995; Cohen and Huffman, 2007). Internships offer a
chance for individuals to interact with managers and peers in the organization in highly visible
ways –i.e. assignments, presentations, networking events, and on project teams. In this way,
prospective employees’ skills become better known.
It is possible, of course, that differential evaluations of men and women may persist
during internships.
However, compared to a traditional hiring scenario, internships offer
organizations a much better chance to counter differential evaluations based on more detailed
12
information (Altonji and Blank, 1999; Rissing and Castilla, 2014). Further, because of the
visibility an internship affords, managers may be held more accountable to pay individuals
according to their demonstrated level of skill within organizations (Lerner and Tetlock, 1999;
Padavic and Reskin, 2002). Because internships make hiring and salary assessments that are
typically external to organizations more transparent, this may reduce differential evaluation.
Our rationale leads to a set of testable hypotheses. Overall, we expect that women are
offered lower initial full-time starting salaries than men. We expect however, that the full-time
salary offers from organizations where individuals did internships versus where they did not are
greater for women than for men.
DATA AND SETTING
We investigate the salaries of entry-level business professionals from an elite private
institution that graduated with their Masters of Business Administration (MBA) degree
approximately one year after completing internships. There are several aspects this setting that
help inform our understanding of inequality in entry-level professional careers. The MBA degree
is a popular degree for women in U.S. business schools. In 1970 women made up only 4% of
those earning MBA (Masters of Business Administration) degrees, a number that increased tenfold by 2006, to 43% (Bertrand, Goldin, and Katz, 2010). Moreover, business management
represents an important segment of professionals. More women enter business than any other
profession, including law, medicine, architecture, engineering, and natural science combined
(Jacobs, 1992). 5
5
Bureau of Labor Statistics, Household Data 2013, Table 11, “Employed persons by detailed occupation, race, and
Hispanic or Latino ethnicity”, http://www.bls.gov/cps/cpsaat11.pdf.
13
Internships are an important route through which individuals are matched to
organizations in the early stages of their careers. Approximately three-quarters of organizations
hiring MBA graduates indicate they consider job candidates that completed internships for fulltime jobs before opening up their recruiting to those without internship experience at the firm. 6
Our interviews with managers indicate that internships in this setting are used as a tryout in the
organization (Baron and Kreps, 1999). Managers indicate there are benefits to hiring candidates
through internships versus other methods of hiring.
In a statement representative of the
sentiments of managers, one HR manager put it this way,
I see the internship as a long term interview within the department. The interns are at an
advantage against the candidate who might look equally good on paper, because they have had
that experience with the company. They’ve gotten some internal experience and they’re not as
much, they’re not a risk, as much of a risk, as somebody you only interview and assume that
they’ll do well based on the interview. And that sounds negative calling them a risk, but you
know, you haven’t had experience with them.
Sample
Our sample is comprised of business professionals that completed internships in 2008 and
2009 and graduated from the same institution. In the spring of their graduating year the MBA
Career Development Office (CDO) sent an email to all of the individuals in a cohort inviting
them to participate in a survey. There were 394 individuals in the class of 2009 and 389
individuals in the class of 2010. Among these 783 individuals, 708 responded to the survey, a
response rate of 90%. We checked for response bias to determine if respondents differ from the
larger MBA population at the institution regarding gender and GMAT scores. There is no
difference.
6
The Graduate Management Admission Council® surveyed 690 employers of MBAs, and three-quarters of the
organizations reported using interns to fill positions prior to opening up an employee search to the broader labor
market. http://sacramento.bizjournals.com/sacramento/stories/2003/03/17/daily11.html
14
In our setting new professionals apply and compete for the same jobs. This is important
because jobs can yield different wages and act as a source of inequality (Tomaskovic-Devey,
1993; Petersen and Morgan, 1995; Padavic and Reskin, 2002; Fernandez and Mors, 2008).
Nearly all (97%) of those eligible to complete an internship prior to the second year of their
MBA program do so. Given that almost all individuals opt to do internships this reduces
concerns about self-selection. 7
We assess the ways in which men and women select internships. For one of the cohort
years – i.e. 2010 – we were able to gather in-depth information about the internship selection
process using data from a survey conducted by the CDO. We found that in this setting men and
women select internship offers from employers for largely the same reasons (see Figure 1
below). This is important because if women in our setting select internship offers in different
ways than men then an improvement in women’s full-time salaries from an employer where an
internship occurred might reflect a “better match” from the internship search. Overall, no
evidence suggests this is the case. We find that the job function, industry, and job content are the
primary factors for internship selection for both men and women. Where there are differences
across groups they largely point toward women facing more constraint in their internship
selection than men. For example, 4% of men but 8% of women indicated that location was a
primary reason for selecting an offer from an internship employer.
[INSERT FIGURE 1 HERE]
7
This internship completion rate is similarly in line with the internship completion rates of MBA students in the top
50 MBA programs (i.e. based on Business Week rankings).
15
Variables
The dependent variable is the maximum initial salary offer that business professionals
received. The maximum initial salary offer received is used as the dependent variable rather than
the total compensation, because the initial salary provides the baseline for future salary growth
(Gerhart, 1990; Petersen and Saporta, 2004). 8 On the survey respondents had space to list up to
four offers. This was enough space for nearly all respondents to list their full slate of offers (i.e.
less than five reported having more than four offers). The median number of offers respondents
received is 1, the average number is 1.69.
The average difference in the maximum and
minimum salary offers is $9,627 for the entire sample, and $20,707 for those with two or more
offers. We focus on the maximum salary offer received because it indicates the best offer
available from the job search.
Our first explanatory variable, gender, is noted as female. Our second explanatory
variable is the source of the salary offer. In the survey individuals were asked about the
primary source of a job offer for each of the offers received. They were given a set of sixteen
options from which to choose. We collapsed this list of sixteen options into four main
categories: internships, formal job sources, informal job sources, and other. The internship
category refers to offers received from an employer after an internship is complete. The formal
job search category includes offers from formal job search methods, including on-campus
recruiting by employers, online applications, and newspaper applications. The informal job
search category includes offers that originated from information or leads from social contacts
such as family, friends, faculty and alumni. Our fourth and omitted category, other, is coded as
8
Additionally, total compensation is a less direct indicator of the expected value an employer perceives of an
employee. Total compensation is more related to an individual’s specific needs (e.g. their location relative to the
employer, education, and partner status).
16
such when the respondent chose this option on the survey (i.e. the method of job finding was
outside of the sixteen listed categories).
We observe the source of offers for individuals in the study. We might be concerned that
if source rates differ across groups this reflects different criteria for assessing candidates. For
example, if full-time offers from an internship employer are lower for women, this might
indicate that employers are using more stringent criteria for selection. 9 The proportion of offers
from each source for all maximum offers received is shown in Table 1. We find that in our
setting the proportion of offers from internships for men and women is 30% for both groups (i.e.
30.4% and 29.7% for men and women, respectively). There is also very little difference in the
proportion of offers coming from other sources. To assess this more fully we conduct an
analysis of variance to examine if there are differences in the sources of offers for each gender.
We find there is no statistically significant difference in the source through which men and
women receive offers (F-stat < 0.3).
[INSERT TABLE 1 HERE]
Control Variables
To isolate the hypothesized relationships several other factors that might affect salaries
are controlled. Notably, by studying the job-finding outcomes of professionals from the same
institution we are able to implicitly account for factors that affect salaries but differ across
educational institutions such as the institution’s reputation and the availability of career
9
Because we are comparing the relative effect of internships on salary offers across men and women, one concern
might be that women who have full-time offers from their internship employers are comparably better than men who
have full-time offers from their internship employer. In addition to the steps we took above, we ran a logit model
where we regressed a dichotomous offer variable onto the control variables and gender to predict the likelihood of
getting a full-time job offer from an employer, conditional on having an internship. Gender did not have an effect
on getting a full-time offer from an employer after an internship is complete.
17
services. Additionally, we control for several other factors that may affect salary offers using
detailed information collected from the survey.
We control for the relative quality of applicants using their GPA and GMAT scores.
Since all respondents attended the same MBA program these measures usefully provide an
indication of professionals’ relative quality versus their peers (Chatman, 1991; Bills, 1990;
Rivera, 2011). We account for respondents’ pre-MBA salary using a previous salary control
variable. Additionally, the years of prior work experience is included in the models. On the
survey respondents were asked to report their years of prior work experience before pursuing
an MBA. We include experience intervals for the number of years of prior experience (1-3
years, 3-5 years, more than 5 years). The one-year-or-less interval is omitted from the models.
Some respondents took part in a sponsorship program while getting their MBA.
Because these individuals have experiences that might be valued by employers we include a
sponsorship dummy variable in the models. Further, prior work suggests that salary differences
might be due to supply-side factors. For instance, there is some indication that women in
business are less likely to pursue more quantitative fields such as finance, which pay
professionals more (Correll, 2001; Barbulescu and Bidwell, 2013). To account for this we
include functional dummy variables for finance, consulting, and general management (other is
the omitted category). Finally, we include demographic variables for age (measured in years),
and race — i.e. Caucasian, African-American, Hispanic-American, Asian-American (other is
the omitted category).
Analysis
Our baseline analysis is OLS regression. Because respondents might appear more than
once – i.e. have a maximum offer from two or more sources – non-independence is a problem,
18
so we cluster standard errors by respondents. We begin by regressing maximum salary offer
on the female dichotomous variable, and then gradually add the controls. Consistent with our
theorizing, this allows us to assess if there is a direct effect of being female on salary offers,
and to ascertain if there is possible discounting of women in our sample even after accounting
for factors that might explain salary. Next, to test the relative importance of the source of
offers for men and women we add the job source variables into our models. We interact the
internship source variable with the female dichotomous variable to evaluate the relative impact
of internships on salary offers.
After conducting these baseline analyses we run a selection model (Heckman, 1976), to
account for the fact that some respondents sought out job offers but did not receive them. In the
first stage we regress whether or not a respondent received a job offer using the model
covariates and a selection variable. The selection variable predicts the likelihood of the first
stage dependent variable – i.e. getting an offer – but not salary. Our selection variable is
whether or not a respondent originated outside of the U.S. This affects the likelihood of getting
a job offer because firms are legally required to cover the visa expenses for non-domestic
workers. 10 We examined differences in job offers for all the respondents for whom this data
was available in our study (N=434 domestic, 209 international). These differences are shown in
Table 2. Non-domestic individuals were significantly less likely to have job offers (t=4.78, p <
0.01), but there was no significant difference in salary offers conditional on an offer being
received (t=-.21, not significant).
[INSERT TABLE 2 HERE]
10
http://www.uscis.gov/
19
RESULTS
Out of 708 respondents to the survey, 566 individuals indicated they sought
employment after graduation, and 497 received offers. Out of these, 147 respondents did not
report salaries or they had other missing data. Our final sample for the main analysis is 350
respondents. Table 3 provides the means, standard deviations, and correlations for the variables
for the main sample. The average maximum offer was $110,929 and respondents made $74,995
prior to entering the MBA program. Respondents had a 711 total GMAT score, on average, and
a 4.61 GPA (on a five-point scale). Approximately 40% of the respondents had more than 5
years of work experience prior to entering the MBA program, and averaged 27.7 years of age.
In the sample 46%, 2%, 4%, and 19% of the respondents are white, black, Hispanic, and AsianAmerican, respectively.
[INSERT TABLE 3 HERE]
Table 4 provides descriptive statistics on the maximum salary offer for men and women in
the main sample. Men and women have an average maximum salary offer of $113,147 and
$106,023, respectively. The $7,124 difference is statistically significant (p < 0.05).
[INSERT TABLE 4 HERE]
Descriptive statistics may not fully depict the relationship between gender and salary
offers, particularly if gender masks underlying differences between the groups. To understand
the effects of gender net of other factors that might affect salaries we conduct multivariate
regression analysis. In Model 1 we regress the maximum salary offer on the female dummy
variable using OLS regression with clustered standard errors. Consistent with our baseline
20
prediction we find a negative and statistically significant effect of being female on salary (p <
0.01). To see if the gender effect is due to quality differences between men and women, we
include the GPA and GMAT variables in Model 2.
GPA has a positive and statistically
significant effect on salary. The GMAT score is not significant. 11 Notably, with inclusion of
these variables the negative and statistically significant effect of being female on salaries
remains.
[INSERT TABLE 5 HERE]
In Model 3 we add variables for prior salary and years of experience. None of the
intervals for years of experience significantly impact salaries, though previous salary has a
positive and statistically significant effect (p < 0.01). Model 4 adds additional variables for
supply-side factors that may affect salaries, including the field in which a respondent is
employed and whether or not the individual is sponsored while getting an MBA. Model 4
indicates individuals that chose to enter consulting fields had higher salaries than those who did
not, while the other fields – i.e. finance and general management – did not have a significant
effect on salaries. Model 5 includes the full model with all the covariates. In this model the
negative and statistically significant effect of being female on salaries remains (p < 0.01). The
marginal effect of being female on salaries is $6,390, or women receive a maximum salary that
is 5.8% lower, net of all other factors affecting salaries, than men.
Next, we investigate the relative influence of different job offer sources in Table 6. In
Models 6-8 we add each job source into the models. The only source that affects salaries is
informal job sources, which has a negative and statistically significant effect on salaries. The
11
One reason for a lack of an effect for GMAT score may be that the distribution is highly skewed to the right: 71%
of the respondents had a GMAT score of 700 or higher, or scored in the 89th percentile or above. Given that almost
all respondents scored well on the GMAT, these scores may have been less influential on salary offers.
21
negative and statistically significant effect of informal sources on salary is also seen in Model 9,
which includes all of the source variables. While somewhat surprising, this is consistent with
other research that suggests individuals may turn to their networks only after exhausting other
sources to locate jobs.
In Model 10 we interact the female dummy variable with the internship source.
The
female-internship interaction is positive and statistically significant (p < 0.05). Because the
female dummy dichotomous variable is included in the model, the coefficient on the interaction
variable indicates that the marginal effect of a woman getting an offer from her internship
employer versus the offer coming from another source. The marginal effect of salaries on
women getting an offer from an internship employer versus any other source is $9,857.
[INSERT TABLE 6 HERE]
As mentioned previously, we additionally run Heckman selection models to account for
the fact that salary offers cannot be observed for all those looking for jobs. We run a first stage
model where we regress having a job offer on the covariates and the international selection
variable. Table 7 shows the results. In Model 11 we show the full model with control variables.
The female dichotomous variable remains negative and statistically significant (p < 0.01). In
Model 12 we run the two-stage Heckman with the source variables. Similar to the baseline
models, only the informal job source has a significant effect on maximum salary. In Model 13
we show the interaction effect.
Consistent with our expectations, the interaction effect is
positive and marginally significant (p= 0.07).
22
[INSERT TABLE 7 HERE]
The baseline analysis and Heckman selection models provide support for our predictions.
Women receive lower offers from employers than their male counterparts. However, this
discount seems to lessen when offers are generated from employers where an internship has
occurred. There is a positive and statistically significant interaction effect between gender and
internship source. This is consistent with the idea that internships provide a period of extended
visibility within organizations, leading to a lessening of a differential evaluation for women after
an internship is complete.
Salaries before and after internships
Thus far our analysis has provided evidence that women receive higher full-time salary
offers from organizations where an internship has occurred versus organizations where an
internship has not occurred. While our setting and the nature of our data allow us to eliminate
many explanations for our effects, ideally we could observe what would have occurred in the
absence of internships for the same individuals. To do this we collect data on the salaries of
professionals in our study at following stages 1) prior to entering the MBA program 2) during
the internship and 3) full-time employment. The first two stages serve as a ‘pre-treatment stage’,
before organizations are able to observe behavior during a trial period. The third stage allows us
to examine the effect of salaries on men and women post-trial.
We start by investigating salaries across these stages for all those reporting salary
information (i.e. the sample is no longer restricted to those whom have a full set of controls
available), or for 181 women and 418 men. Women and men made $68,685 and $75,560
23
annually prior to getting an MBA respectively, a difference that is statistically significant (p <
0.01). There are 152 women and 297 men in our data that had internships and reported their
salaries (on a monthly basis). These annualize to $76,272 and $83,676 for women and men,
respectively, which again is a statistically significant difference (p < 0.01). Turning to the posttreatment stage the maximum salary for men and women is $112,075 and $106,258,
respectively. These are slightly different from the salaries reported in Table 4 because this is the
average maximum salary for all 317 men and 147 women that reported salary information. The
difference in the maximum offers for this sample is statistically significant (p < 0.01). Notably,
though, there is a narrowing of the difference in salaries. In the two pre-treatment stages,
women made approximately 91% of the salary that men made. In the post-treatment stage this
ratio improved to 95%.
Now we turn to how the offers differed depending on the job source. A total of 136 men
and 60 women received offers from their internship employer and reported their salaries.
Among these 196 individuals, men received an initial salary offer of $108,196 and women
received a salary offer of $108,600. That is, women did slightly better than men (the few
hundred dollar difference is not statistically significant). Overall, we see a leveling effect on
salaries generated from internships (see Figure 3 below).
[INSERT FIGURE 3 HERE]
Further, we investigate the effects of trial employment based on pre and post-treatment
outcomes for a subset of individuals that appear across the stages.
This allows us to see the
comparative effect of internships by gender for the very same set of individuals, or what would
24
have occurred absent the trial. Out of the136 men and 60 women with offers from intern
employers, 130 men and 55 women reported salaries prior to the MBA. The distribution of these
individuals’ prior MBA salaries and the distribution of their salaries after internships are shown
in Figure 4. An absolute improvement in salaries across the pre and post-treatment conditions
are likely attributable to the MBA; by assessing the relative conditions we net out these effects.
Figure 4 indicates that prior to the internship and MBA men’s salaries are higher than women’s
across almost the entire distribution. For example, the 50th percentile and 80th percentile salary
for men prior to the MBA is $74,500 and $100,000, respectively. These same percentiles are
$70,000 and $80,000 for women (ratios of 0.94 and 0.80, respectively). The offers received
from employer where at internship occurred at the same percentile for the exact same set of
individuals is $110,000 and $125,000 for men, and $108,000 and $125,000 for women (0.98 and
1.0, respectively). Interestingly, there are no salary differences or women’s salaries are higher
than men’s from internship employers except in the 37th -50th and 55th -69th percentile range.
[INSERT FIGURE 4 HERE]
Alternative explanations
We have argued that internships provide individuals a period of extended visibility in
organizations prior to hiring and salary-setting decisions, and that this has a differential impact
of the salaries of women versus men. Evidence in the cross-section and across stages strongly
supports this claim. Nonetheless, we explore two alternative explanations for our findings:
omitted heterogeneity in organizations and negotiations by candidates.
It is possible that organizations using internships are more equitable than organizations
that do not. That is, perhaps organizations know that internships provide some advantages to
25
women. In our interviews with 20 line and HR managers not a single interviewee stated any
specific recruiting aims associated with their organizations’ internship programs (diversity or
otherwise).
Nonetheless, if having an internship programs is linked to some aspect of
organizations related to equity, then the effect of internships on the improvement of women’s
salaries may be spurious. It would not be that internships counteract differential evaluation and
lead to salary improvement: if these organizations have more equitable pay policies this would
naturally boost women’s salaries.
To consider this possibility we code a dummy variable, equal to 1 if an organization has
an internship program, else 0. Information about organizations’ internship programs were coded
based on data we had from the CDO, company websites, and related websites such as vault.com.
We include this dummy variable to proxy for unobserved heterogeneity across organizations
that may lead to a spurious increase in women’s wages. 12 Including this variable in our models
did not change the results. This suggests, inclusive of accounting for the ways organizations
differ, the relative importance of internships as a source of salary offers for women remain.
Additionally, we consider an alternative explanation that women are better at negotiating
salary increases at places where they have completed internships versus places where they have
not. It is possible that men are good negotiators across job sources, but women are only good at
negotiating offers within organizations where internships occurred. To assess this, we examine
data that we collected as a part of the survey. For every offer respondents accepted we asked,
“Did you negotiate or try to negotiate about ANYTHING with your employer?” We coded each
of these 549 responses across all offers received to determine which respondents successfully
negotiated an increase in base salary. Overall, 10% of women and 16% of men negotiated
12
We know the identity of the organizations only for those offers that respondents accepted.
26
successfully with an employer on some facet of their offer (salary, relocation, tuition, vacation
time).
Figure 5 summarizes the negotiation outcomes of men and women. Of those that
negotiated a base salary increase, they were much more likely to do so outside of places where
internships occurred, rather than with an internship employer. Successful negotiations in base
salary increases occurred between individuals and firms outside of the internship employer 71%
of the time for men, and 85% of the time for women. More specifically, there were only two
women (out of 140) that indicated they successfully negotiated a salary increase with their
internship employer. In sum, women in this study did not earn higher salaries from their
internship employer due to salary negotiations.
[INSERT FIGURE 5 HERE]
A within firm analysis
In a final set of regression analyses we examine how internships influence salary offers
that come from the same organization. We suggested above that internships could operate by
improving the wages for women relative for men, but this could occur because women earn
more working for the same organizations as men, or because they work for a different set of
organizations. To assess this, we run additional analyses with employer fixed effects and show
these results in Table 8. Here, the dependent variable is the salary received for the accepted job
offer, not max offer (employer information is only available for the accepted offer). The sample
size is smaller than in the previous models because only one offer was accepted per person, and
not every respondent specified the name of their employer. Model 14 shows that inclusion of
27
firm-fixed effects leads the gender main effect to disappear. That is, there is some element of
sorting of individuals across firms that leads to a gender effect in the baseline models. Once
individuals are compared within the same firm men and women do not receive significantly
different offers.
Yet, we can also see why men’s and women’s salaries do not differ
significantly. In Model 15 we include the interaction between the female dichotomous variable
and internships and find the positive interaction effect remains. This indicates that within
organizations individuals are hired through different methods, and the reason women do not
receive significantly different salaries from men is there is a leveling effect through internships.
To see this graphically, in Figure 6 we show the three the largest employers in our context that
had varied methods of hiring. In these three firms we observe the salaries that men and women
received from different sources. In support of our predictions, in each of these firms women
with prior internship experience in the organization received higher offers than women that were
hired outside of internships.
[INSERT FIGURE 6 HERE]
To gain further insight into the leveling effect we interviewed HR managers and line
managers about internships. As stated previously, none of the managers interviewed for this
study indicated that they use internships with the intention of assessing the quality of women
specifically. Managers did convey, however, that internships are used to assess individuals on
characteristics that tend to be linked to masculine characteristics. For example, when asked what
her company looks for during internships, one HR executive replied:
28
“I don’t think we look for anything different than any other company. We need strategic thinkers. It doesn’t matter
what your role is. . .HR or manufacturing. We have a strategic way of doing business. . We are financially driven,
but we tend to be more strategic thinkers. For me first and foremost I look for people that can be strategic. I look
for people that can run with the best of them so to speak. You cannot be intimidated by authority. You can’t be. . .
you have to run with the best of them is the way I say it, and not everybody can do that.”
The general sentiment expressed by this HR executive, that individuals in this setting are
expected to “run with the best of them”, underscores the specific nature of these professional
jobs, and that these jobs include a set of expected behaviors around competition, authority, and
leadership. The internship yields the chance for all individuals to demonstrate these
characteristics. 13
Where is inequality being generated?
Our analyses suggest that a narrowing of salaries after internships. For women and men
that complete internships and receive offers from their employers, there is no statistically
significant difference in the salaries that they receive. But, women’s initial salary offers are still
only 95% of what men are offered. To understand why, we further investigate the salary offers
that professionals receive when they do not receive offers from their internship employer. Men
that did not receive an offer from their internship employer (either because they did not have an
internship or did not receive an offer) had an average maximum offer of $109,899. This
amounted to a $1,703 increase in salary versus the full-time offer from an internship employer.
Meanwhile, women that did not receive an offer from their internship employer (either because
they did not have an internship or did not receive an offer) had an average maximum offer of
$102,165, or made significantly less than men that did not have offers from internship
employers. Among those who did not do internships are sponsored students that may have
13
In addition to interviews with managers, we conducted interviews with MBA interns. They stated they had ample
opportunity to interact with employees during internships, including at networking events, alumni meetings, and
their end-of-summer presentations.
29
received full-time offers from their employers. Excluding those with sponsors, the average
maximum salaries are even lower for women, $99,438, while they are $109,913 for nonsponsored men.
We assess the salary offers of those individuals that did complete internships but did not
receive offers. For those individuals that did complete internships and did not receive offers
from their internship employer, men received maximum offers of $109,904 while women
received maximum offers of $96,730. When men and women receive offers from organizations
where they have had internships, the gap in salaries is virtually non-existent. However, it seems
that while failed internships do not have any penalties for men they carry penalties for women.
Discussion and Conclusion
While there have been a great number of studies documenting that women start out
earning less than men within organizations, the demand-side contributions to initial inequality in
wages are not yet clear. At the organization-labor market interface it is hard to disentangle
explanations of differential evaluation and treatment of men and women from more rational
explanations for wage disparity. In this paper we contribute to literature on inequality,
organizations and labor markets by presenting a new framework to addresses this issue. In our
trial approach we can avoid the problem that organizations may always know more about
candidates than researchers, by investigating the outcomes of individuals when organizations
‘hire twice’. The contributions of this approach are not limited to a specific mechanism per se
but can be extended to address issues that persist in a diverse set of literatures on hiring such as
statistical discrimination (Altonji and Pierret, 2001) and human capital (Tomaskovic-Skaggs,
2002; Fernandez-Mateo, 2009).
30
Further, we contribute to literature on inequality, organizations, and labor markets by
investigating a practice through which inequality is lessened. Using detailed data on several
hundred professionals we document that internships have a leveling effect. Further, we were
able, within the limits of our research design to suggest a demand-side mechanism leading to
this reduction. Internships are useful because they provide professionals extended visibility as
they complete tasks within organizations. These internships showcase, in particular, soft-skills
that may lead to women’s salaries being lower in traditional hiring processes.
This is an important finding not only because of its theoretical contributions, but also
because of the prevalence with which internships are used to hire professionals. In our setting
internships are nearly as common as other methods of job finding, such as formal hiring, and
they are more than twice as common as more well-studied methods such as referrals. The
referrals literature has largely outlined disadvantages to the referrals practice for women and
minorities (Fernandez and Fernandez-Mateo 2006; Reskin and Padavic, 1994; Korenman and
Turner, 1996; Moore, 1990; Reskin, McBrier, and Kmec, 1999; Rubineau and Fernandez 2013),
because networks that provide information about prospective candidates may not be equally
available to all groups. Here, in contrast we find a lessening of salary disadvantages for women
due to internships. Our findings are consistent research on social contact (e.g. Allport, 1950) and
research that has found workplaces that promote contact in teams may provide benefits to
disadvantaged groups because it allows them to come into contact with powerful others (Kalev,
2009). In the case of internships, individuals from traditionally disadvantages groups benefit
because this provides a chance for individuals to visibly demonstrate skills and receive resources
(Burt, 1992; Baron and Pfeffer, 1994; Podolny and Baron, 1997; Sparrowe and Liden, 2005;
Castilla, 2011; Briscoe and Kellogg, 2011).
31
While this study makes a substantial contribution, future work should proceed on several
fronts. Our chosen context limits the generalizability of our findings. However, similar methods
of matching individuals to employers exist in other professions, including engineering and law. It
would be useful to investigate what, if any, effect internships have on wage inequality in these
professions. In addition, it would be useful to understand more directly how organizations behave
when hiring through internships. Using available data we were able to rule out unobserved
heterogeneity in organizations as an explanation for our leveling effect. This does not preclude,
however, that within organizations managers use hiring practices for different objectives. Future
work should examine the ways in which practices may be used by managers.
In closing, much is known about new employment relationships, and how the
externalization of the employment relationship affects individuals and organizations (Bidwell,
Briscoe, Fernandez-Mateo, and Sterling, 2013). Trial employment represents a fairly new way
that firms temporarily externalize employment. As scholars of organizations, labor markets, and
inequality it is necessary to understand the outcomes of this externalization on individuals and
organizations and its unintended consequences (Baron, 2000). Here, we find that unintended
consequences need not always lead to inequality, suggesting we must continue to investigate the
complex set of interactions between individuals, organizations, and markets.
32
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Table 1. Source of Full-Time Offers for Men and Women
Men
Women
Internship*
30.4%
29.7%
Formal
35.0%
36.9%
Informal
12.3%
16.2%
Other
22.3%
17.2%
*Source of maximum offers for all respondents reporting at least one full-time job offer (N= 350)
40
Table 2. Offers and Salaries for Domestic and Non-Domestic Professionals
Domestic
International
*p < 0.01
Offer Received
0.64*
0.44
StD
0.48
0.50
41
Average
Max Salary
$110,715
$111,301
StD
$17,769
$35,551
Table 3. Descriptive Statistics
Mean
(1) Maximum Offer
(2) Female
(3) GPA
(4) GMAT
(5) Previous Salary
(6) Exp1 (>1 yrs, <3 ys)
(7) Exp2 (> 3 yrs, <5 yrs)
(8) Exp3 (>5 yrs)
(9) Sponsored
(10) Finance
(11) Consulting
(12) General Management
(13) Age
(14) White
(15) Black
(16) Hispanic
(17) Asian
N = 350, *p < 0.05
$110,929
0.31
4.61
711
StD
$24,582
0.22
41.96
$27,345
0.40
0.49
0.17
0.16
0.20
0.39
0.06
27.68
0.46
0.02
0.04
0.19
1.00
0.46 -0.13*
$74,995
0.42
(1)
0.37
0.49
0.36
0.24
2.46
0.50
0.15
0.20
0.39
1.00
0.13*
-0.06
-0.05
0.11*
0.05
0.22*
-0.05
0.10
0.07
0.40 -0.11*
0.49
(2)
0.22*
0.35*
0.02
0.05
-0.10
0.00
1.00
0.05
(5)
1.00
-0.08 -0.20*
(6)
1.00
0.07 -0.14* -0.38*
(7)
1.00
-0.03
-0.01
0.32* -0.37* -0.70*
0.01
0.17*
0.19*
-0.03
0.00
0.21*
0.14*
0.08
-0.01
0.05
-0.07
0.04
0.00
-0.08 -0.18* -0.20*
-0.03
-0.06
0.07 -0.13*
-0.02
0.10
(4)
-0.10
-0.07
-0.02
1.00
-0.08
-0.04
0.06
(3)
-0.08
-0.04
-0.05
-0.01
0.01
-0.07 -0.20*
-0.03 -0.12*
0.03
0.06
0.02
0.02
-0.04
0.04
-0.05
0.05
0.03
-0.05
-0.04
0.05
0.02
(8)
1.00
0.00
0.08 -0.13* -0.21*
-0.06
-0.02
-0.08
42
0.05
0.07
1.00
-0.02 -0.11* -0.41*
-0.09
0.15*
-0.08
-0.05
(11)
1.00
0.67*
0.04
(10)
0.05
0.03 -0.20*
0.33* -0.45* -0.30*
0.05
(9)
0.10
0.01
1.00
-0.04
-0.08
0.00
0.14* -0.24*
0.12*
0.00
-0.04
-0.01
-0.07
0.06
0.11*
0.00
0.11*
1.00
0.03
0.04
0.03
(12)
-0.08
0.02
0.04
0.00
(13)
1.00
-0.04
(14)
1.00
0.00 -0.14*
-0.04 -0.14*
-0.04 -0.38*
(15)
1.00
-0.03
-0.07
(16)
1.00
-0.10
(17)
1.00
Table 4. Maximum Salary Offer
Men
Women
*p < 0.05
N
241
109
Average Max Offer
$113,147*
$106,023
StDev
$25,914
$20,617
Table 5. OLS Regressions of Maximum Salary Offers
Female
GPA
(1)
-7272.3 **
(2515.0)
GMAT
(2)
-6992.7 **
(2608.4)
-5851.9 *
-0.61
-23.4
(34.2)
(33.4)
Exp1 (>1 yrs, <3 ys)
(5194.3)
0.15 **
(0.05)
13132.8
(19254.5)
Exp2 (> 3 yrs, <5 yrs)
12050.8
(19301.1)
Exp3 (>5 yrs)
14684.6
(19553.0)
Sponsored
-6270.3 **
(2333.9)
10914.7* *
(5405.3)
(4)
(2505.6)
12583.1* *
-1.74
Previous Salary
(3)
Finance
7374.8
(4864.4)
(29.9)
0.16 **
(0.05)
12183.7
(17046.4)
10632.2
(17033.0)
12804.9
(17253.0)
4263.1
(2369.7)
-2813.8
(4219.4)
Consulting
10375.0 ***
(2236.1)
General Management
-2001.6
(3442.9)
Age
White
N
-2443
7217.6
(4886.7)
-25.2
(32.3)
0.18 **
(0.1)
12872.5
(18325.9)
12906.1
(18321.7)
17851.6
(19043.2)
4868.3 *
(2397.0)
-2788.9
(4173.2)
10619.3 ***
(2279.2)
-2326.3
(3480.1)
-1050.9
(761.4)
(2875.1)
-5763.4
(5763.1)
Hispanic
Constant
-6390.3 **
-1750.8
Black
Asian
(5)
-1058
(5090.8)
113186.5 ***
(1585.1)
371
56367.1
(36081.6)
371
38464.9
(38118.6)
371
67481.6 *
(33550.4)
371
Clustered standard errors, ***p< 0.001, **p< 0.01, *p < 0.05, two-tailed tests
44
-3062.7
(3611.2)
95427.7 *
(46496.8)
371
Table 6. The Effect of Job Source on Salary
Female
GPA
GMAT
Previous Salary
Exp1 (>1 yrs, <3 ys)
Exp2 (> 3 yrs, <5 yrs)
Exp3 (>5 yrs)
Sponsored
Finance
Consulting
General Management
Age
White
Black
Hispanic
Asian
Formal
Informal
(6)
-6460.0 **
(2436.9)
7369.4
(4888.8)
-26.3
(32.1)
0.18 ***
(0.05)
12983.9
(17968.7)
12761.9
(17982.2)
17821.7
Constant
N
(2333.2)
6323.6
(4766.6)
-25.4
(32.2)
0.18 ***
(0.05)
12567.9
(18672.5)
11708.5
(18674.8)
16901.3
(8)
-6370.4 **
(2427.5)
6643.1
(4855.1)
-28.1
(31.5)
0.19 ***
(0.05)
11781.2
(18284.1)
12264.3
(18301.8)
16934.5
(9)
-5954.0 **
(2285.3)
6224.1
(4748.5)
-25.6
(29.3)
0.18 ***
(0.05)
12407.8
(18715.6)
11655.7
(18780.3)
16791.4
(10)
-8873.1 **
(2909.4)
6575.9
(4673.9)
-22.4
(28.8)
0.18 ***
(0.05)
12370.3
(19338.6)
11655.2
(19409.6)
17101.6
(18705.5)
(19289.0)
(19101.5)
(19558.4)
(20202.7)
-2661.4
-2449.8
-3157.4
-2521.6
-2660.2
4431.4
(2353.1)
(4162.7)
10320.9 ***
(2301.9)
-2470.2
4141.2
(2320.3)
(4152.3)
9807.3 ***
(2176.0)
-1869.0
5320.3*
(2418.1)
(4302.5)
10719.6 ***
(2238.6)
-2263.6
4281.7
(2309.8)
(4374.5)
9877.3 ***
(2216.2)
-1835.6
3832.4
(2411.0)
(4346.9)
10187.0 ***
(2214.1)
-1374.7
(3471.6)
(3587.0)
(3496.0)
(3556.8)
(3581.8)
-1684.5
-812.1
-1709.3
-821.5
-958.8
-1058.4
(758.6)
(2892.7)
-6022.9
(5733.6)
-1344.8
(5031.7)
-3335.1
(3574.1)
1947.6
(1816.6)
Internship
Internship x Female
(7)
-5968.3 *
95288.5 *
(46457.7)
371
-1192.9
(760.6)
(2769.6)
-6341.1
(6135.7)
-191.3
(4834.9)
-4099.3
(3596.4)
-13184.6 ***
(3156.9)
106486.0 *
(47396.6)
371
Clustered standard errors, ***p < 0.001, **p < 0.01, *p < 0.05
45
-1071.2
(754.4)
(2888.9)
-5398.2
(5932.1)
-640.2
(5148.4)
-3057.0
(3605.9)
3366.0
(2328.1)
100272.3 *
(45014.5)
371
-1193.7
(757.4)
(2757.0)
-6244.8
(6248.1)
-87.0
(4790.9)
-4045.6
-1216.9
(768.1)
(2752.4)
-5999.2
(6269.5)
-22.4
(4935.6)
-4121.9
(3427.1)
(3435.0)
-13150.2 *
-12799.8 *
(4750.1)
(5107.3)
-359.1
(4115.8)
(5279.0)
432.8
107105.6 *
(45203.5)
371
-246.0
(4109.1)
(5231.4)
-2423.2
9856.9*
(4083.4)
104364.2 *
(45199.0)
371
Table 7. Heckman Selection Model of Salary Offers
Female
GPA
GMAT
Previous Salary
Exp1 (>1 yrs, <3 ys)
Exp2 (> 3 yrs, <5 yrs)
Exp3 (>5 yrs)
Sponsored
Finance
Consulting
General Management
Age
White
Black
Hispanic
Asian
Formal
(11)
-8857.3
(2845.45)
-1306.9
(756.81)
0.17
(0.05)
19804.7
(14187.62)
18778.0
(14097.29)
22967.8
(14649.18)
9341.7
(3692.41)
-2689.9
(3336.22)
11938.9
(2795.68)
-476.8
(5234.35)
13230.9*
(6172.13)
-34.4
(33.47)
-1129.8
(2849.71)
-3356.3
(7721.00)
8.5
(6200.52)
-1838.0
(3494.16)
Informal
Internship
Internship x Female
Constant
N
70710.0
(38573.61)
427
**p < 0.01, *p < 0.05, † p < 0.1
(12)
-5915.3
(2641.49)
-1189.0
(692.29)
**
0.18
(0.05)
12307.5
(13425.23)
11570.2
(13325.82)
16718.5
(13816.59)
*
4204.4
(3454.74)
-2522.4
(3423.26)
***
9875.1
(2791.34)
-1836.2
(5141.37)
6111.0
(5818.65)
-25.5
(30.76)
-823.2
(2729.66)
-6246.2
(7784.68)
-89.6
(6111.57)
-4045.6
(3433.56)
-359.1
(3232.02)
-13147.7
(4067.92)
434.1
(3325.01)
**
(13)
-8828.4 **
(3087.71)
-1211.1
(689.31)
**
0.18 **
(0.05)
12249.6
(13365.22)
11552.4
(13266.79)
17014.0
(13756.87)
3739.2
(3444.46)
-2661.3
(3409.33)
*** 10184.5
(2784.59)
-1375.1
(5125.57)
6440.2
(5789.94)
-22.2
(30.67)
-960.9
(2718.95)
-6000.6
(7752.22)
-25.5
(6085.28)
-4121.9
(3418.99)
-246.0
(3218.66)
** -12796.7 **
(4055.03)
-2423.4
(3672.42)
*
9862.9 †
(5486.10)
107547.7 ** 104894.0 **
(36165.45) (36024.20)
427
427
46
Table 8. Regression Models of Accepted Salary Offers
Female
GPA
GMAT
Previous Salary
Exp1 (>1 yrs, <3 ys)
Exp2 (> 3 yrs, <5 yrs)
Exp3 (>5 yrs)
Sponsored
Finance
Consulting
General Management
Age
White
Black
Hispanic
Asian
Formal
Informal
Internship
Internship x Female
Constant
Firm Fixed Effects
N
(14)
627.6
(1795.3)
3101.6
(3604.6)
11.0
(20.8)
0.098 **
(0.03)
13938.1
(10446.0)
10431.6
(10328.2)
11993.4
(10646.4)
-3361.4
(2656.8)
-9785.7 *
(4325.7)
-6018.0
(6554.5)
-8533.9 *
(3815.1)
-573.0
(459.1)
-1504.8
(1869.6)
-5938.9
(6347.7)
2952.8
(4223.0)
-3872.0
(2151.0)
(15)
-3303.3
(2423.5)
4497.5
(3620.9)
7.9
(20.9)
0.10
(0.03)
14924.9
(10402.2)
11794.1
(10285.6)
13345.9
(10631.4)
-2199.4
(2751.4)
-10286.4
(4376.0)
-5230.3
(6611.2)
-8875.6
(3870.8)
-713.4
(475.3)
-1639.2
(1859.1)
-6000.5
(6384.8)
3417.0
(4215.6)
-3935.7
(2143.6)
1577.6
(3351.9)
120.8
(4918.7)
1407.4
(3551.4)
8025.2
(3493.4)
91188.4 ***
88046
(24797.2)
(24977.3)
***p < 0.001, **p < 0.01, *p < 0.05
Yes
318
***
*
*
*
***
Yes
318
47
Figure 1. Reasons Individuals Select an Internship Offer
30.0
25.0
Percent
20.0
Women
15.0
Men
10.0
5.0
0.0
Growth Potential
Industry
Job Content
Job Function
Location
Note: Respondents are from the 2010 cohort and completed internships in 2009. N=285
48
Figure 2. Job Source by Gender
40.0
35.0
Proportion
30.0
25.0
Men
Women
20.0
15.0
10.0
5.0
0.0
Internship
Formal
Job Source
49
Informal
Figure 3. Comparison of Salary Offers Across Stages
Pre-Treatment 1
(Prior to MBA)
Women/Men:
Pre-Treatment 2
(Internship)
Women/Men:
181 women, 418 men
152 women, 297 men
$68,685/$75,560
= 90.9%
$76,272/$83,676
= 91.1%
Intern Employer:
(Received Offer)
Women/Men:
$108,600/$108,196
= 100.3%
N=60 women, 136 men
Non-Intern Employer
Women/Men:
(Max Offer)
$102,165/$109,899
= 93.0%
N=87 women, 181 men
50
Post-Treatment
(Max Offer)
Women/Men:
$106,258/$112,075
= 95%
147 women, 317 men
Figure 4. Percentile Distribution of Salaries
$160,000
Men's Salaries
$140,000
$120,000
Equity Line
$100,000
Prior Salaries
$80,000
Full-Time Salaries After
Internship
$60,000
$40,000
$70,000
$90,000
Women's Salaries
51
$110,000
$130,000
Figure 5. Comparison of Negotiations by Source
350
300
Frequency
250
200
Men
Women
150
100
50
0
Total
Negotiated, Yes
52
Negotiated, No
Internship
Negotiated, Internship
Figure 6. Salary Offers across Different Methods of Hiring
125
Men, Intern
Starting Salary (1000s)
120
Men, NI
115
Women,
Intern
Women, NI
110
105
100
Firm A
Firm B
Firm C
Note: Salary levels are shown for three firms for 65 offers, NI = Non-Internship
53