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SPECIAL ISSUE ARTICLE
Evolution of professionals' careers upon graduation in STEM
and occupational turnover over time: Patterns, diversity
characteristics, career success, and self-employment
Katja Dlouhy
1
| Ariane Froidevaux
2
1
Management Department, University of
Mannheim, Mannheim, Germany
2
Management Department, University of
Texas at Arlington, Arlington, Texas, USA
Correspondence
Katja Dlouhy, Management Department,
University of Mannheim, Mannheim, Germany.
Email: katja.dlouhy@bwl.uni-mannheim.de
Summary
While STEM occupational turnover constitutes a major concern for society given the
importance of innovation and technology in today's global economy, it also repre-
sents an opportunity to achieve career sustainability for individuals. There is ample
research on the reasons why students drop out from STEM education, but evidence
on STEM professionals' career patterns and on correlates of occupational turnover
after graduation is scarce. Drawing on the sustainable careers framework, the current
study examines how STEM graduates' careers evolve over time, revealing diverse
patterns of occupational turnover and the relationships of such career patterns with
work diversity characteristics in terms of sex and ethnic minority status, career suc-
cess, and self-employment. Using longitudinal data from 1512 STEM graduates over
10 years, results of an optimal matching analysis demonstrate six career patterns that
can be distinguished into three continuity (STEM, part-STEM, non-STEM) and three
change (hybrid, boomerang, dropout) sustainable career patterns. We find differences
in sex, but not in ethnic minority status, across career patterns. Further, professionals
who change from STEM occupations to non-STEM occupations show higher objec-
tive career success and are more often self-employed than those following a continu-
ous STEM career pattern. Theoretical and practical implications of these findings are
discussed.
KEYWORDS
career patterns, career success, minority, optimal matching analysis, self-employment, sex,
STEM leaky pipeline, turnover
1|INTRODUCTION
With the rising prevalence of innovation and technology in today's
global economy, the Science, Technology, Engineering, and Mathe-
matics (STEM) workforce plays a major role for the world's prosperity.
However, a significant number of these workers are leaving STEM
occupations—a phenomenon that has been referred to as the “STEM
leaky pipeline”(Metcalf, 2010). Notably, in the United States, 74% of
professionals with a STEM bachelor's degree are not employed in
STEM occupations (U.S. Census Bureau, 2014). Such occupational
turnover results in significant labor shortages of STEM professionals,
especially in the government and private industry sectors, both in the
Katja Dlouhy and Ariane Froidevaux contributed equally.
Received: 31 March 2021 Revised: 8 January 2022 Accepted: 22 February 2022
DOI: 10.1002/job.2615
This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium,
provided the original work is properly cited.
© 2022 The Authors. Journal of Organizational Behavior published by John Wiley & Sons Ltd.
J Organ Behav. 2022;1–18. wileyonlinelibrary.com/journal/job 1
United States (U.S. Bureau of Labor Statistics, 2015) and in the
European Union (Shapiro et al., 2015). Nonetheless, despite the wide
recognition of such a leaky pipeline challenge for societies and labor
markets, and its increasing relevance to the field of organizational
behavior, scholars' theoretical and empirical understanding of the evo-
lution of STEM graduates' careers over time, as well as the indicators
of why leaking occurs, and its implications for individuals, remains
limited.
Drawing on the sustainable careers framework (De Vos
et al., 2020; De Vos & Van der Heijden, 2015), we examine how
careers of professionals who completed studies in a STEM major evo-
lve over time starting with their first job upon graduation, as well as
the factors associated with different career patterns. The sustainable
careers framework is particularly relevant to address the challenge of
the STEM leaky pipeline, given that it conceptualizes individuals'
careers as a variety of possible sequences of positions that evolve or
remain stable over time, and that it emphasizes a long-term focus on
careers. Specifically, based on a systemic (i.e., multiple stakeholder)
perspective, the concept of sustainable careers refers to “a sequence
of career experiences reflected through a variety of patterns of conti-
nuity over time, thereby crossing several social spaces, characterized
by individual agency, herewith providing meaning to the individual”
(Van der Heijden & De Vos, 2015, p. 7). In the volatile and uncertain
labor market characterizing our global economy, individuals are likely
to engage in career transitions, thus aiming to maintain and/or
enhance their career sustainability (Castro et al., 2020). In other
words, according to this perspective, individuals' ongoing aim is to
design a sustainable career in which they can work longer because it
makes them happy, healthy, and successful (De Vos et al., 2020).
The purpose of the current study is threefold. First, we examine
how STEM graduates' careers evolve over time in terms of continuity
and change patterns as characterized by occupational turnover timing.
Second, we consider how STEM graduates' diversity characteristics—
namely, sex and ethnic minority status—may be associated with differ-
ent career patterns. Third, we examine how career patterns may be
related to objective career success in terms of hierarchical advance-
ment and self-employment. Given the dynamic nature of STEM gradu-
ates' careers, we apply sequence analysis (e.g., Biemann &
Datta, 2014) to derive patterns. We use a sample of 1512 profes-
sionals and a longitudinal design over 10 years starting from the first
job upon graduation in a STEM major in college.
Our study offers several contributions to the recently introduced
sustainable careers theoretical framework, and to the literature on
STEM professionals. Overall, we integrate the three key dimensions
of sustainable careers (De Vos et al., 2020)—time (career patterns and
occupational turnover timing), context (industry, organization changes,
hierarchical positions, and types of employment), and person (profes-
sionals' diversity characteristics)—to the STEM careers context. Spe-
cifically, in line with the notion that sustainable careers represent
sequences of career experiences, we address recent calls for studies
adopting a sustainable careers perspective using more solid longitudi-
nal designs (De Vos et al., 2020). By doing so, we clarify how sustain-
able careers concretely unfold over multiple organizations in the
STEM context, identifying the timeline of the STEM leaky pipeline
and of potential re-entry to STEM, as well as the occupational turn-
over destinations categorized according to Holland's (1987) typology
of Realistic, Investigative, Artistic, Social, Enterprising, and Conven-
tional (RIASEC) occupations. Further, we address limitations in exis-
ting STEM scholarship, as most prior studies have taken a static
approach to study STEM professionals' careers (e.g., Cardador &
Hill, 2018; Fouad et al., 2020; Tremblay et al., 2002). While there is
ample research on STEM pipeline leakage among STEM students (van
den Hurk et al., 2019), scholarship on professionals' career patterns
upon graduation in STEM is scarce. Limited evidence suggests the
existence of diverse career patterns followed by STEM graduates,
such as technical versus managerial career patterns (Bailyn &
Lynch, 1983; Joseph et al., 2012; Tremblay et al., 2002) and career
patterns in non-STEM occupations (Smith & White, 2019), but no sys-
tematic exploration exists. Further, the sustainable careers perspec-
tive on STEM careers is still nascent. Recently, in a qualitative study,
Castro et al. (2020) explored the transition of 28 academic researchers
in the STEM field into sustainable careers in data science, offering ini-
tial evidence for a sustainable careers perspective on STEM careers.
In the current study, we provide a more comprehensive and system-
atic investigation of career patterns of STEM graduates over a
10-year period, thus expanding current knowledge on how their
careers evolve over time.
Second, we contribute to expanding the nascent diversity per-
spective within the sustainable careers framework (see, for instance,
Baldridge & Kulkarni, 2017, on workers with a hearing loss disability),
thus addressing recent calls for studies examining career sustainability
of employees with a different set of sociodemographic characteristics
within a diversity perspective (Castro et al., 2020; De Vos
et al., 2020). Specifically, our study seeks to further understand
whether and how professionals' diversity characteristics (i.e., sex and
ethnic minority status) may be associated with sustainable career pat-
terns in the STEM context over time. By doing so, we also address
limitations in existing STEM scholarship, as few studies have exam-
ined the role of diversity characteristics in the career patterns of
STEM graduates. Notably, while previous literature has mostly
focused on how gender and racial minority status either affect individ-
uals' major of interest or study attrition (e.g., Hall et al., 2017; Saw
et al., 2018; van den Hurk et al., 2019) or the occurrence of their
occupational turnover (e.g., Cech & Blair-Loy, 2019; Jelks &
Crain, 2020; Makarem & Wang, 2020), our study provides clarification
as to which career patterns members of minorities in STEM may
follow.
Third, we contribute to the sustainable careers framework by
exploring whether and how different sustainable career patterns may
be related to career success in terms of career advancement
(i.e., reaching higher hierarchical positions) and to self-employment.
Further, by using a 10-year longitudinal design, we empirically investi-
gate the theoretical proposition that career success (i.e., reaching
higher hierarchical positions) may reflect sustainable careers in a long-
term perspective only, as career advancement may be accompanied
by greater strain in the short term (De Vos et al., 2020). Expanding
2DLOUHY AND FROIDEVAUX
burgeoning work incorporating objective career success as an indica-
tor of sustainable careers (Straub et al., 2020), and prior work focusing
mostly on organizational mobility outcomes (e.g., Li et al., 2021), our
study is the first to link sustainable occupational career patterns to
objective career success and self-employment, hence increasing our
knowledge of occupational mobility outcomes.
2|THEORETICAL BACKGROUND AND
HYPOTHESIS DEVELOPMENT
2.1 |STEM careers and the sustainable careers
framework
In line with prior work (Siekmann & Korbel, 2016; Xue &
Larson, 2015), we define STEM occupations as including not only
those in science, technology, engineering, and mathematics but also
those in architecture, information technology, and pharmacy. An
important limitation of prior literature, however, is that managerial,
sales, and teaching occupations that require STEM knowledge are
usually defined as either STEM or non-STEM occupations. In contrast,
we use the term “part-STEM occupations”to refer to those occupa-
tions that require a STEM degree but whose key tasks are not STEM-
specific (e.g., STEM-related sales or teaching).
Sustainable careers (De Vos et al., 2020; De Vos & Van der
Heijden, 2015) represent a useful framework for exploring how STEM
graduates' careers evolve over time, as well as the extent to which dif-
ferent career patterns may reflect occupational turnover timing and
be associated with professionals' diversity characteristics and career
outcomes. The first key tenet of this framework is that sustainable
careers are dynamic and evolve over time. Specifically, as explained
by De Vos et al. (2020, p. 2), sustainable careers represent:
a cyclical, self-regulatory process …in which (positive
and negative) experiences and events, and how these
are perceived and interpreted by the individual and the
different parties involved, provide opportunities for
“dynamic learning.”The latter, in turn, enables individ-
uals to adapt to and to influence their environment, as
their career evolves, by sharpening their understanding
of themselves, their personal and organizational con-
text, and the broader labor market. Ultimately, this
allows them to continuously refine perceptions regard-
ing their person–career fit over time.
As such, as STEM graduates progress in their careers, various
events and positive or negative experiences are likely to affect their
perceptions of fit with their careers, thus resulting in different career
patterns characterized by either continuity in STEM occupations
and/or transitions to different occupations or organizations. In sum,
professionals' increased understanding of themselves, their occupa-
tions, and their organizations over time is likely to affect both the
timing and the destinations of their turnover decisions.
The second key tenet of this framework is that dynamic pro-
cesses impacting career sustainability are likely to differ across types
of worker groups, as individuals' sociodemographic characteristics
may reflect a unique context in which their careers evolve (De Vos
et al., 2020). Indeed, the sustainable careers framework emphasizes
the role of context, such as society's gendered norms (Straub
et al., 2020), in shaping individuals' career experiences. Such cultural
norms may also result in inequalities at work, which have been
suggested to represent one of the major factors challenging career
sustainability in the 21st century (McDonald & Hite, 2018). Con-
cretely, such inequalities may imply more limited opportunities for
some workers, making them more vulnerable in their career develop-
ment (Urbanaviciute et al., 2019). Such vulnerability may imply a lack
of freedom in making career choices (e.g., fewer options) or having
access to a more limited set of career development resources. In sum,
as not all STEM professionals may benefit from the same career
opportunities, the proportion of some types of professionals may be
higher among certain career patterns compared with others.
The third key tenet of the sustainable careers framework is its
emphasis on workers' objective career success (De Vos et al., 2020;
Straub et al., 2020) as a core indicator of career sustainability. Specifi-
cally, the sustainable careers perspective emphasizes long-term career
success (i.e., career advancement), given that short-term intense suc-
cess may lead to burnout, hence putting the career's long-term suc-
cess at risk and resulting in career unsustainability (Van der Heijden &
De Vos, 2015). Furthermore, the sustainable careers framework rec-
ognizes self-employment as a particular category of work that may
contribute to career longevity, potentially explaining the rise of such a
work arrangement in the current economy (De Vos et al., 2020).
Accordingly, we argue that long-term objective career success and
self-employment represent important indicators of sustainable careers
for STEM professionals.
2.2 |Careers as a dynamic phenomenon
The sustainable careers framework highlights the dynamic nature of
careers (Van der Heijden & De Vos, 2015), emphasizing the existence
of different patterns of continuity in terms of a sequence of different
career experiences. Such experiences result from a cycle of events
and personal decisions, illustrating the interplay between agency and
context over time (De Vos et al., 2020), and determine professionals'
career changes between different occupations and organizations.
While prior work has offered supportive evidence for career sustain-
ability achievement within a single organization (e.g., Straub
et al., 2020), in the current study, we anticipate the existence of dif-
ferent occupational career patterns, revealing various turnover timings
and destinations over time across organizations. Specifically, the sus-
tainable careers framework highlights the occurrence of two main
career movements over time: career continuity and career change. To
achieve career sustainability in terms of mutually beneficial conse-
quences for both the person (i.e., being happy, healthy, and successful)
and their context (De Vos et al., 2020), individuals consistently engage
DLOUHY AND FROIDEVAUX 3
in either career change or continuity. Indeed, both movements reflect
accumulated work and career experiences, providing workers with
opportunities for dynamic learning, and allowing them, in turn, to
influence their environment and/or adapt to their environment
(De Vos et al., 2020).
A key challenge of dynamic careers (e.g., turnover in STEM
careers) lies in identifying individuals' turnover destinations. According
to Holland's (1987) RIASEC typology of occupational interests, indi-
viduals choose occupations that fit with their interests, occupations
being also classified using this typology. Table 1 defines and provides
examples of occupations for each of the six RIASEC occupational
interests identified by Holland (1987). We suggest that the RIASEC
typology allows to explore non-STEM occupations as turnover desti-
nations, as individuals may perceive their careers as being more sus-
tainable if their current job fits their occupational interests better. As
recent work indicates that investigative and realistic interests are
those that are the most closely related to a general interest in STEM
(Babarovi
c et al., 2019), it is possible that individuals pursuing more
sustainable careers in non-STEM fields may possess other occupa-
tional interests (i.e., artistic, conventional, enterprising, or social). Put
differently, turnover from STEM occupations may promote greater
career sustainability due to a greater fit with one's occupational
interests.
2.2.1 | Sustainable career patterns characterized by
continuity over time
A first set of career patterns may involve continuity and stability over
time, as STEM graduates might not be willing to engage in career tran-
sitions when they are satisfied with their careers. Such a career pat-
tern might be characterized by continuity in a STEM, part-STEM, or
non-STEM occupation. First, a continuity pattern may reflect the tra-
ditional expectation that STEM graduates would start a career in the
STEM workforce and work in STEM occupations for their entire
career. Prior evidence supports the existence of such a pattern, such
as Bailyn and Lynch's (1983) qualitative study among U.S. engineers
who graduated in the 1950s, which reported that about three quar-
ters were still staff engineers 20 years after graduation. More
recently, relying on a U.S. panel study, Joseph et al. (2012) found that
most individuals who graduated in IT-related majors followed a career
path within the IT field.
A second continuity pattern may be followed by individuals who
start working in part-STEM occupations (i.e., requiring a STEM degree
but whose key tasks are not STEM-specific, such as STEM-related
sales or consulting) after graduation and continue to do so. Prior work
has mostly focused on the distinction between individuals who work
in STEM occupations versus those who drop out of STEM
TABLE 1 Summary of Holland's (1987) RIASEC typology
Occupational interest Acronym Definition Examples of occupations
Realistic R Preference for working with things rather than people;
working with hands on, practical problems.
•Auto mechanic
•Computer technician
•Architect
•Medical technician
Investigative I Preference for working with ideas rather than people/
things; searching for facts, figuring out problems
mentally.
•Statistician
•Researcher
•Industrial engineer
•Business analyst
Artistic A Preference for working with ideas rather than people/
things; working with forms, designs, and patterns.
•Brand manager
•Visual manager
•Graphic designer
•Actor
Social S Preference for working with people rather than things;
working with and helping or teaching others.
•Coach
•Registered nurse
•Teacher
Enterprising E Preference for working with people and ideas
(projects) rather than things; starting and carrying
out projects, often business-related, by taking risks.
•Sales representative
•Banker
•Manager
•Politician
Conventional C Preference for working with papers and numbers;
working with data and details, following procedures.
•Accountant
•Financial analyst
•Travel agent
•Insurance specialist
Note: Definitions and examples have been taken from O'NET (2021).
4DLOUHY AND FROIDEVAUX
occupations, thus disregarding individuals who may follow a continu-
ous career pattern in part-STEM occupations. However, supporting
the existence of such a pattern, a recent longitudinal study among
U.K. STEM graduates showed that many STEM graduates start to
work and remain in “associate professional and technical”occupations
(Smith & White, 2019).
A third continuity pattern may illustrate the case of STEM gradu-
ates who left STEM occupations directly upon graduation to work in
non-STEM occupations (i.e., occupations that do not require a STEM
degree), thus immediately starting their careers in a non-STEM field
and pursuing them over time. In support of such a non-STEM pattern,
prior evidence showed that 54% of STEM graduates did not work
in STEM jobs (Smith & White, 2019). We argue that changes to
non-STEM occupations before even starting one's first job upon
graduation may enable a more sustainable career by increasing STEM
graduates' fit with their occupational interests. Providing preliminary
empirical support for this proposition, Kim and Beier (2020) showed
that greater major-occupational fit during college was positively
related to starting a STEM job upon graduation.
2.2.2 | Sustainable career patterns characterized by
change over time
A second set of career patterns is likely to involve change over time,
as STEM graduates might be willing to engage in career transitions
because they are unsatisfied with their current careers. Such transi-
tions make it possible to solve career tensions and achieve more sus-
tainable careers in a new organization or with a new job position
within the organization (Castro et al., 2020). Concretely, such changes
may differ with regard to both time and destination. Indeed, change
(i.e., turnover) may take place earlier or later in professionals' careers,
and it may only happen once to one destination or involve several
changes to different destinations. Such destinations may involve
changing to occupations relatively close to STEM (i.e., part-STEM
occupations with an enterprising or a social interest, such as STEM-
specific consulting, management, sales, and teaching) or to non-STEM
occupations associated with conventional or artistic interests.
Accordingly, first, a moderate change pattern may involve turn-
over from STEM to part-STEM occupations, with professionals
retaining their focus on STEM skills and knowledge while developing
others. Such a career pattern would be in line with the “hybrid”career
path proposed in earlier studies (i.e., combining technical and manage-
rial paths; Cardador & Hill, 2018). Indeed, prior work found that engi-
neers who left the standard engineering career path did so mostly to
shift away from specialized technical projects and towards more com-
plex responsibilities (i.e., part-STEM occupations; Bailyn &
Lynch, 1983) and that movement out of STEM jobs was mostly into
managerial roles (Smith & White, 2019).
A second change pattern may result in early or late turnover to
non-STEM occupations (i.e., dropping out from STEM careers alto-
gether). Prior work supporting the existence of such a pattern showed
that some STEM graduates first explored STEM jobs early in their
work histories before changing to non-STEM professional and mana-
gerial jobs (Joseph et al., 2012; Smith & White, 2019). Further, as Kim
and Beier (2020) showed that STEM graduates experienced increasing
job fit in terms of their occupational interests over time, it is possible
that a late turnover to non-STEM occupations may foster a more sus-
tainable career by increasing STEM graduates' fit with their occupa-
tional interests.
Finally, a third change pattern may involve early or late boomer-
ang career moves (Shipp et al., 2014), reflecting the case of those pro-
fessionals who quit STEM occupations to work in non-STEM
occupations but later return to STEM occupations. In line with prior
research reporting that, compared with workers who do not return,
boomerang workers may leave earlier in their tenure before coming
back in the future (Shipp et al., 2014), we expect a similar effect in the
STEM context. Furthermore, we also expect that compared with the
STEM graduates dropping out from STEM altogether, those following
a boomerang trajectory may possess less additional occupational
interests than the traditional STEM-related interests (i.e., realistic and
investigative), which may explain why they would ultimately come
back to a STEM career. While we could not find prior studies
supporting the existence of a boomerang pattern in STEM careers,
earlier work suggests that some STEM graduates enter the STEM field
several years after finishing their degree (Smith & White, 2019). Con-
sidering these various possible patterns, we thus propose the follow-
ing research question:
Research Question 1. What are the general patterns of
career continuity and change in professionals with a
STEM degree?
2.3 |Career patterns and professionals' diversity
characteristics
Inequalities represent one of the major factors challenging career sus-
tainability in the 21st century (McDonald & Hite, 2018). Specifically,
the sustainable careers framework emphasizes the role of individuals'
sociodemographic characteristics in shaping a unique context in which
their careers evolve (De Vos et al., 2020). In the context of STEM
careers, the two major sociodemographic characteristics of sex and
ethnic minority status may affect individuals' career experiences in
terms of facing additional obstacles to pursuing their desired career.
Such disadvantages or vulnerability (Urbanaviciute et al., 2019) may in
turn affect these professionals' turnover and pursuit of (non-)STEM
careers.
2.3.1 | Sex
Women remain underrepresented in the STEM workforce in the
United States (i.e., 27%; U.S. Census Bureau, 2021) and in Europe
(i.e., 41%; Eurostat, 2021). Prior empirical evidence reported the
existence of gendered career patterns among industry engineers in
DLOUHY AND FROIDEVAUX 5
the United States (Cardador, 2017) and found that women in STEM
occupations were significantly more likely to leave this occupational
field, especially early in their careers, than women in other profes-
sions (Glass et al., 2013). Moreover, women who graduated from
STEM majors were less likely to have jobs related to their major
than men 10 years after graduation (Xu, 2013). This may be
explained by findings suggesting that female STEM students
(Leaper & Starr, 2019) and female STEM workers (Makarem &
Wang, 2020) may be more likely to face gender bias and hostile
male-dominated work environments. Taken together, prior evidence
seems to suggest that female workers may have a harder time than
male workers entering and persisting in STEM occupations after
graduation. Thus, as females might be more likely to anticipate, or
actually experience, an unsustainable career in STEM occupations
than males, they might also be more likely to transition to occupa-
tions out of STEM in which they expect to experience more sustain-
able careers. Hence, they may be less likely to follow a traditional,
lifelong STEM career pattern (i.e., characterized by continuously
working in STEM occupations after graduation in a STEM major
without further transitions). We thus hypothesize that:
Hypothesis 1. Female professionals will be more likely
to follow career patterns characterized by (a) never
working in STEM occupations and (b) dropping out of
STEM occupations than the traditional STEM career
pattern.
2.3.2 | Ethnic minority status
Ethnic minorities are underrepresented in the STEM workforce. In the
United States, African and Hispanic Americans, who characterize 11%
and 16% of the workforce, represent only 9% and 7% of all STEM
workers, respectively (Pew Research Center, 2018). In Europe, it is
individuals from an immigration background who represent ethnic
minorities. For instance, prior work showed that despite being dual
citizens and holding Swiss degrees, children of first-generation immi-
grant parents in Switzerland needed to send 30% more applications
than children of native Swiss parents to be called back for an appren-
ticeship interview, which may be explained by the non-Swiss-
sounding names on their resumes (Zschirnt & Fibbi, 2019/2020).
Applied to the STEM context, in Germany, individuals with a migration
background (26% of the workforce; Destatis, 2020a) represent only
20% of all STEM workers (Anger et al., 2020). Prior work has
suggested potential reasons explaining such underrepresentation of
ethnic minorities in STEM, namely stereotypes (Eaton et al., 2020) and
workplace discrimination (e.g., Hall et al., 2017; Saw et al., 2018), as
well as cultural isolation, a low sense of belonging, and self-doubt
(Byars-Winston, 2014). Therefore, not entering STEM occupations or
leaving STEM occupations might be a career choice aiming to pre-
serve or enhance career sustainability for professionals from an ethnic
minority background. We thus hypothesize that:
Hypothesis 2. Ethnic minority professionals will be
more likely to follow career patterns characterized by
(a) never working in STEM occupations and (b) dropping
out of STEM occupations than the traditional STEM
career pattern.
2.4 |Career patterns, career success, and self-
employment
According to the sustainable careers framework (De Vos
et al., 2020), career sustainability over time depends on individuals'
perceptions of being successful at work, in terms of an adequate
and dynamic fit between their values and career goals (person-
career fit). Such a fit results from a cyclical process in which posi-
tive and negative experiences at work are interpreted by individ-
uals and provide them with opportunities for dynamic learning.
Specifically, workers improve their understanding of themselves,
and of their organizational and labor market contexts, allowing
them to refine their person-career fit over time to achieve more
sustainable careers. In the STEM context, negative experiences of
a misfit are likely to take place between individuals' (prospective)
career in STEM and their career expectations in terms of career
advancement and/or innovation-seeking opportunities. As STEM
graduates achieve a greater understanding of themselves
(i.e., having a strong need for career advancement or to contribute
to innovation) through a dynamic learning process, they may start
to look for better opportunities and engage in deeper exploration
of their labor market context to ultimately achieve greater career
sustainability.
2.4.1 | Career success
Career advancement, or reaching higher hierarchical positions, is a
specific form of career success (Seibert et al., 2001), which, in its long-
term form, has been argued to represent a core indicator of career
sustainability (De Vos et al., 2020; Straub et al., 2020). As STEM grad-
uates perceiving a misfit between their careers and advancement
expectations explore the labor market, they may realize that the
STEM field rapidly evolves due to the fast-paced development of new
technologies. Indeed, STEM graduates' skills and knowledge become
more rapidly outdated compared with those of non-STEM graduates
(Deming & Noray, 2018), which may lead to lower employability, and
thus lower career sustainability (De Vos et al., 2020). Accordingly, a
continuous career pattern in part-STEM occupations (e.g., consulting,
management, sales), or dropping out of STEM occupations, might
offer better career advancement opportunities. This suggestion is
supported by prior research highlighting an exit from STEM occupa-
tions after the first decade of STEM graduates' careers for non-STEM
occupations (Deming & Noray, 2018) or for managerial occupations
that promise better long-term career success in terms of hierarchical
6DLOUHY AND FROIDEVAUX
position (Bailyn & Lynch, 1983). Accordingly, career patterns charac-
terized by either part-STEM occupations or dropout from STEM occu-
pations may allow STEM graduates who experience a person-career
misfit in terms of career advancement expectations to solve such a
career tension through transitioning to more rewarding occupations
(Castro et al., 2020), hence allowing for more sustainable careers.
Thus, we hypothesize:
Hypothesis 3. Professionals following career patterns
characterized by (a) part-STEM occupations and
(b) dropping out from STEM occupations will have
higher objective career success than those following the
traditional STEM career pattern.
2.4.2 | Self-employment
As self-employment has been identified as an alternative work
arrangement that may allow workers to attain more sustainable
careers (De Vos et al., 2020), self-employment may represent a
path towards greater career sustainability for professionals follow-
ing career patterns characterized by working in non-STEM occupa-
tions (either continuously or after dropping out), compared with
those following a traditional STEM career pattern. Indeed, STEM
graduates who dropped out of STEM occupations or who never
worked in STEM occupations may be more likely to interpret the
self-employment work arrangement as a positive experience leading
to greater person-career fit because it may better meet their
expectations for innovation-seeking opportunities. While a non-
STEM career context may provide less opportunities for innovation
than a STEM context that allows being involved in actively devel-
oping or engineering the latest technological innovation,
transitioning to self-employment may be motivated by the prospect
of greater innovation opportunities (hence greater career sustain-
ability) for STEM graduates who never worked in STEM occupa-
tions or who dropped out from STEM occupations—compared with
those who follow a traditional STEM career pattern. Indeed, prior
work has shown that individuals experiencing an educational mis-
match (i.e., who were trained as scientists and engineers but whose
jobs did not utilize the skills acquired during education) were
more likely to transition into entrepreneurship (Stenard &
Sauermann, 2016). Further, working in an occupation unrelated to
one's highest degree was found to be associated with higher job
satisfaction among self-employed compared with employed
STEM graduates (Bender & Roche, 2013). Thus, we hypothesize
that:
Hypothesis 4. Professionals following career patterns
characterized by (a) never working in STEM occupations
and (b) dropping out from STEM occupations will have
higher levels of self-employment than those following
the traditional STEM career pattern.
3|METHOD
3.1 |The Xing database
We derived career data of professionals who graduated in STEM from
Xing, the leading social network for German-speaking business profes-
sionals (similar to, e.g., the LinkedIn platform) with more than 18 mil-
lion profiles mainly in Germany, Austria, and Switzerland (Xing, 2020).
Public profiles from this and similar platforms have been used in
career research (e.g., Flöthmann & Hoberg, 2017), as they provide a
reliable and representative data source. Users of these platforms keep
their profiles up to date for representation purposes in their profes-
sional environment and per their agreement with the social network's
terms to provide correct and up-to-date information. Specifically, pro-
files consist of a full curriculum vitae, including information on users'
occupation (e.g., senior software engineer) with each position's spe-
cific dates and employer name, educational background (e.g., a mas-
ter's degree in computer science with the university's name), skills
(e.g., big data analytics), languages (e.g., basic language skills in Italian),
and personal interests.
3.2 |Sample and procedure
From this database, we searched for individuals who had completed a
STEM degree (Destatis, 2020b) from one of the 24 technical universi-
ties in Germany (19 universities), Austria (3 universities), and
Switzerland (2 universities). We first randomly selected 200 individuals
per university who indicated having earned their degree there, thus
obtaining a total of 4800 individuals. We then extracted their online
information using the Python software's (Van Rossum & Drake, 1995)
libraries requests and BeautifulSoup. Of those randomly selected, we
excluded 2407 individuals who had not graduated in STEM but in
other majors (e.g., business administration, psychology). We also
excluded 62 individuals whose information regarding their occupa-
tional position was missing for more than 1 year across the examined
career years, as well as 25 individuals whose study major and univer-
sity degree information was missing or unclear.
Individuals' occupational career sequences began in the month
they started their job upon graduation. To obtain sequences of com-
parable length (Dlouhy & Biemann, 2015), we set career length to
120 months (i.e., 10 years); that is, we examined the first 10 years in
individuals' careers upon graduation with a 1-month interval,
irrespective of their actual career tenure.
1
Accordingly, we excluded
794 individuals whose career since graduation had a duration of less
than 10 years. The final sample for this study is thus 1512. Individuals
in our sample had an average career duration of 20.7 years
(SD =6.84) and had been members of the Xing social network for an
average of 10.1 years (SD =3.79). Women made up 16.7%
2
of our
sample. Regarding country of residence, 79.3% lived in Germany,
11.2% in Switzerland, 8.3% in Austria, and 1.2% in another country.
Consistent with the mainly German context (Destatis, 2020b), 3.8% of
DLOUHY AND FROIDEVAUX 7
our sample held a Bachelor's degree as their highest degree, 66.7% a
Master's degree, and 29.5% a doctoral degree. Finally, 4.6% addition-
ally held an MBA degree.
3.3 |Measures
3.3.1 | Career characteristics
Occupation
The Xing database provides information on users' occupations using
the prompt “job title”with required open answers (e.g., “senior soft-
ware engineer”) and the prompt “job details”with optional open
answers (e.g., “design software using collected data”or “systems anal-
ysis and bug reports”). Start and end dates (by month) are provided.
For this study, two native German-speaking research assistants inde-
pendently coded a total of 8678 different job titles. Coding was 1for
STEM occupations (45.1% of occupations coded; i.e., science, technol-
ogy, engineering, and mathematics occupations), 2for part-STEM
occupations that usually require a STEM degree but whose key tasks
are not STEM-specific (33.8% of occupations coded; i.e., STEM-
specific consulting, management, sales, and teaching occupations),
3for non-STEM occupations that do not require a STEM degree,
which were further subcoded according to Holland's (1987) RIASEC
typology (16.5% of occupations coded; see Table 1), and 4for holding
a non-paid primary activity (4.6% of occupations coded; i.e., parental
leave or care, career break, volunteering activities, and full-time stud-
ies). The number of occupations coded by both raters that is required
to calculate Cohen's κwas determined to be 157 with the kappaSize
package for R (Rotondi & Donner, 2012). Overall agreement between
coders was supported by a Cohen's κof .76, indicating substantial
agreement above the threshold of .70 (Fleiss & Cohen, 1973).
Career start
Career start was the year individuals started their first job upon
graduation.
Industry
The prompt “industry”that was required for each job position pro-
vided a choice from various fixed answers, such as “IT and telecom-
munication”or “financial and legal services.”Additionally, in line with
prior work (Adams & Kirchmaier, 2016), we classified an industry as a
STEM industry (1=yes, 0=no) when more than 50% of people in
these industries usually held a STEM degree, based mainly on data
from the German Economic Institute (Anger et al., 2020). We relied
on the German industry given that most (79.3%) of our sample was
from that country. For example, the IT and telecommunication indus-
try was classified as STEM, while the financial and legal services
industry was not. Table 2 lists all the industries present in our sample,
indicating whether an industry would be considered a STEM industry
and what percentage of job positions in our sample was in this indus-
try (58.3% of job positions in our sample were in STEM industries,
while 41.7% were not).
Occupation changes
We derived the number of occupation changes between STEM, part-
STEM, non-STEM, and non-paid occupations in the first 10 years of
individuals' careers.
Organization changes
The Xing database provides information on users' organizations with
required open answers (e.g., “SAP SE”). We were thus able to derive
the number of organization changes in the first 10 years of individuals'
careers.
3.3.2 | Professionals' diversity characteristics
Sex
While the Xing database does not provide direct information on a per-
son's sex, the person's name and an optional photo are available. Sex
was obtained using the application programming interface
NamSor (2020), which infers sex based on a person's name with an
accuracy of 98.6% (Santamaría & Mihaljevi
c, 2018). NamSor recog-
nizes the country of origin of names in any alphabet or language, thus
allowing it to infer sex. For instance, “Artur Schmidt”would be classi-
fied as male. A likelihood value for correctness of classification is
given. We checked individuals' profile photos for cases with low
TABLE 2 Industry classifications of occupations
Industry
STEM
industry
% job positions in
study
1. Academia and education No 5.3%
2. Architecture and
construction
Yes 6.8%
3. Art, culture, and sports No 1.2%
4. Consulting No 5.5%
5. Consumer goods and
trade
No 13.5%
6. Energy and environment Yes 5.1%
7. Engineering Yes 16.2%
8. Financial and legal
services
No 2.6%
9. IT and
telecommunication
Yes 15.2%
10. Marketing, PR, and
design
No 1.2%
11. Other No 8.8%
12. Pharmaceutical and
medical
Yes 5.0%
13. Public and non-profit No 2.8%
14. Social work, care, and
health
No 1.2%
15. Tourism and
gastronomy
No 1.2%
16. Vehicle construction Yes 10.0%
8DLOUHY AND FROIDEVAUX
values (i.e., lower than 15.0%), but no adjustment was necessary, as
the photos confirmed NamSor's attribution. Sex was coded as 0for
female and 1for male.
Ethnic minority status
In contrast to the United States, which differentiates Americans'
immigration country of origin in terms of race and ethnicity, because
we relied on a European dataset, a measure of race or ethnicity as
defined in the U.S. context was not meaningful as an indicator of
minority status. In Germany, Switzerland, and Austria, ethnic minority
status instead relates to immigration status from another country of
origin and/or a different native language than the official one in the
focal country (Volodina et al., 2020). Similarly as with sex, we deter-
mined foreign country of origin using the NamSor (2020) software,
based on a person's name as NamSor has processed more than 6 bil-
lion names. For instance, “Artur Schmidt's”country of origin would be
inferred as Germany (coded as 0), whereas “Artur Aktas's”would be
Turkey. The latter would thus result in a label of ethnic minority status
(coded as 1). We randomly checked NamSor's determination of for-
eign country of origin, but no adjustment was necessary. Further, the
Xing database provides information about non-native speaker status
through an optional indication of language skills (possible answers:
native, fluent, good, basic, none). For Germany and Austria, ethnic
minority status was coded as 1for non-native German speakers, with
German being the official language; because Switzerland has four offi-
cial languages, individuals in this country were considered to have an
ethnic minority status when their native language was not German,
French, Italian, or Romansh.
3.3.3 | Career success and self-employment
Career success
In line with prior literature (Seibert et al., 2001), we considered indi-
viduals' hierarchical level (i.e., career advancement) to reflect their
objective career success. Specifically, we measured participants' hier-
archical level in employed labor 10 years after starting their career
(i.e., in month 120 of their career), based on answers to the prompts
“job title”and “job details”in Xing. Hierarchical level was coded as
1for non-managerial positions, 2for managerial positions, 3for direc-
tor or vice president positions, and 4for chief functional officer, presi-
dent, or partner positions. For instance, “senior software engineer”
would be coded as 1. Overall agreement between the coders was
supported by a Cohen's κof .95, representing an extremely high level
of agreement (Fleiss & Cohen, 1973).
Self-employment
We considered individuals' self-employment status 10 years after
starting their career (i.e., in month 120 of their career). The prompt
“type of employment”in Xing required a choice from various fixed
answers for each job position, such as “full-time employed,”“intern,”
or “self-employed.”Self-employment was coded as 1if individuals
were self-employed and as 0otherwise.
3.4 |Analytical strategy
We used optimal matching analysis (e.g., Biemann & Datta, 2014) to
calculate the dissimilarities in individuals' occupational careers and to
derive career patterns. Given that we had monthly information on
individuals' occupations, we created occupational career sequences
by appending each monthly occupation status to the preceding one.
When individuals' monthly occupation information was missing, a
missing value category Xwas appended (Biemann & Datta, 2014). As
an example, the occupational career sequence of an individual who
worked in a STEM occupation for 6 months (coded as 1) and then
worked in a non-STEM occupation (3) for another 6 months would be
coded as 1-1-1-1-1-1-3-3-3-3-3-3. With optimal matching analysis,
the dissimilarity between this sequence and another sequence (e.g., 1-
1-1-1-1-1-1-1-1-1-1-1 for an individual who worked in a STEM occu-
pation for 12 months) can be computed by counting the number of
operations that are necessary to transform one sequence into
another.
Possible operations for this purpose are deleting an element,
inserting an element, and substituting an element for another element.
These operations have specific costs and mirror the extent to which
sequences have to be altered. Higher costs arise whenever a higher
number of operations is necessary to align two sequences, such that
higher costs indicate greater dissimilarity between the two sequences.
Specifically, insertion and deletion usually have costs of 1, while sub-
stitutions need to be based on a theoretical rationale and involve cus-
tomized costs (Biemann & Datta, 2014). In the current study, we
customized substitution costs by setting the substitution costs of the
STEM versus part-STEM occupations (and vice versa) to 0.5. Indeed,
while a distinction between these occupational positions is substan-
tive, these occupations are still similar to each other, and our over-
arching aim is to examine turnover out of STEM (or part-STEM)
occupations. Substitution costs between all other occupational posi-
tions (e.g., 1vs. 3,or1vs. the missing category X) were set to 2, the
standard cost setting (Dlouhy & Biemann, 2015).
Applying this reasoning to our two example sequences (1-
1-1-1-1-1-3-3-3-3-3-3 and 1-1-1-1-1-1-1-1-1-1-1-1) that differ in
their seventh to twelfth elements, several operations are required to
align these two sequences (i.e., replacing the 3s in the first sequence
with 1s). This results in total costs, or a dissimilarity, of 12: based on
either six substitution operations with a cost of 2 each or 6 deletion
and 6 insertion operations with a cost of 1 each. Applied to another
set of two sequences, such as 1-1-1-1-1-1-2-2-2-2-2-2 and 1-
1-1-1-1-1-1-1-1-1-1-1, the dissimilarity would be 3, given that we set
substitution costs of part-STEM (coded as 2) with STEM occupations
(coded as 1) to 0.5. In sum, there are various ways to align sequences,
and the optimal matching algorithm identifies the one that is the least
costly (i.e., optimal).
After conducting an optimal matching analysis for all occupational
career sequences in our sample, we obtained a symmetric
1512 1512 dissimilarity matrix. Following prior recommendations
(Biemann & Datta, 2014; Dlouhy & Biemann, 2015), we then clus-
tered sequences based on dissimilarity using the Ward clustering
DLOUHY AND FROIDEVAUX 9
algorithm. The TraMineR package for R (Gabadinho et al., 2011) was
used for the analyses. Finally, we tested our hypotheses using vari-
ance analyses with contrasts between clusters.
4|RESULTS
4.1 |Career patterns
Based on recommended measures of the quality of a cluster solu-
tion (i.e., Point Biserial Correlation, Hubert's Gamma, Hubert's C,
Average Silhouette Width; Studer, 2013), the solutions with six,
seven, eight, and nine clusters were equally preferable. In solutions
with more than six clusters, some clusters were very small (<5% of
sample); hence, we retained six clusters. Each cluster contains indi-
viduals with similar occupational career patterns, thereby forming
six different career patterns. Cluster tempograms are displayed in
Figure 1. The x-axes represent months, while the y-axes represent
the percentage of individuals who worked in a specific occupation
in a given month. Clusters were derived solely from occupations
coded as STEM,part-STEM,non-STEM, and non-paid. Descriptive
career characteristics of individuals (career start, industries and
occupations worked in) by career pattern are displayed in Table 3.
Mean values for our variables of interest (occupation and
organization changes, professionals' diversity characteristics, career
success, and self-employment) by career pattern are displayed in
Table 4.
4.1.1 | Pattern 1: STEM career
This career pattern is followed by individuals who started working in a
STEM occupation after graduation and mostly continued to do so
over time (n=531; see Figure 1). If they changed occupations, they
mostly did so from STEM occupations to part-STEM occupations after
the first 6 years of their career (see Figure 1). Individuals following this
pattern mostly worked in STEM industries (see Table 3) and had on
average 0.45 occupation changes and 1.94 organization changes in
the first 10 years of their career (see Table 4).
4.1.2 | Pattern 2: Part-STEM career
This career pattern is followed by individuals who started working in a
part-STEM occupation after graduation and mostly continued to do
so over time (n=338). About 15% also changed from part-STEM
occupations to STEM occupations, mostly after the first 5 years of
their career. Individuals following this pattern mostly worked in STEM
FIGURE 1 Occupational career patterns. Note:N=1512 [Colour figure can be viewed at wileyonlinelibrary.com]
10 DLOUHY AND FROIDEVAUX
TABLE 3 Characteristics of occupational career patterns
1. STEM
career pattern
2. Part-STEM
career pattern
3. Hybrid
career pattern
4. Boomerang
career pattern
5. Dropout
career pattern
6. Non-STEM
career pattern
Career start: Year 1999.2 (6.9)
4
1999.3 (6.6)
4
1998.7 (6.7)
4
2001.3 (6.3)
1,2,3,5,6
1998.7 (7.2)
4
1999.2 (7.1)
4
Career start: STEM
industry
a
0.68 (0.47)
4,6
0.70 (0.46)
4,6
0.69 (0.46)
4,6
0.53 (0.50)
1,2,3,5,6
0.65 (0.48)
4,6
0.39 (0.48)
1,2,3,4,5
Entire career: STEM
industry
a
0.71 (0.48)
4,6
0.69 (0.49)
4,6
0.71 (0.50)
4,6
0.60 (0.48)
1,2,3,6
0.55 (0.49)
6
0.39 (0.50)
1,2,3,4,5
Entire career: Main
industries
Engineering:
19%
IT & tele.: 18%
Vehicle con.:
11%
Cons. goods:
11%
Architecture:
8%
Engineering: 19%
IT & tele.: 14%
Vehicle con.: 12%
Other: 9%
Cons. goods: 8%
IT & tele.: 18%
Engineering:
17%
Vehicle con.:
13%
Cons. goods:
10%
Architecture:
8%
Engineering: 14%
Cons. goods: 12%
IT & tele.: 12%
Vehicle con.: 9%
Other: 9%
Cons. goods:
16%
IT & tele.: 16%
Consulting: 10%
Engineering: 9%
Other: 9%
Cons. goods: 15%
Other: 12%
Consulting: 9%
Fin. & legal: 8%
IT & tele.: 8%
Entire career: Non-
STEM occupations
All < 1% All < 1% All < 1% Investigative: 8%
Enterprising: 7%
Realistic: 6%
Conventional: 4%
Social: 3%
Artistic: 2%
Enterprising:
16%
Investigative:
8%
Social: 6%
Conventional:
5%
Realistic: 3%
Artistic: 2%
Enterprising: 32%
Conventional:
17%
Artistic: 11%
Realistic: 11%
Investigative: 8%
Social: 5%
Note: Numbers in parentheses indicate the standard deviation; superscripted numbers indicate significant (Tukey adjusted pvalues) mean differences with
other clusters; entire career are the first 10 years after graduation.
Abbreviations: cons. goods, consumer goods; fin. & legal, finance and legal; tele., telecommunication; vehicle con., vehicle construction.
a
0=no, 1 =yes
TABLE 4 Mean differences between occupational career patterns
1. STEM career
pattern
2. Part-STEM
career pattern
3. Hybrid career
pattern
4. Boomerang
career pattern
5. Dropout
career pattern
6. Non-STEM
career pattern
Fvalue
ANOVA
Continuity versus change
Occupation
changes
0.45 (0.56)
2,3,4,5,6
0.67 (0.62)
1,3,4,5
1.02 (0.15)
1,2,4,5,6
1.40 (0.49)
1,2,3,6
1.29 (0.47)
1,2,3,6
0.61 (0.62)
1,3,4,5
128.40***
Organization
changes
1.94 (1.36)
3,4,5
1.94 (1.47)
3,4,5
2.29 (1.31)
1,2,6
3.11 (1.72)
1,2,6
2.44 (1.57)
1,2,6
1.86 (1.54)
3,4,5
20.29***
Diversity characteristics
Sex
a
0.15 (0.35)
6
0.16 (0.37)
6
0.13 (0.34)
6
0.19 (0.39) 0.15 (0.36)
6
0.28 (0.45)
1,2,3,5
4.12***
Non-native
speaker
b
0.07 (0.26) 0.07 (0.25) 0.08 (0.27) 0.07 (0.25) 0.06 (0.24) 0.06 (0.23) 0.19
Foreign
country of
origin
b
0.20 (0.40) 0.18 (0.39) 0.13 (0.34) 0.13 (0.34) 0.15 (0.35) 0.17 (0.38) 1.71
Career outcomes after 10 years
Hierarchical
position
1.56 (0.82)
2,5
1.82 (0.93)
1
1.62 (0.82) 1.64 (0.91) 1.81 (0.96)
1
1.64 (0.97) 4.21***
Self-employed
b
0.07 (0.25)
5,6
0.06 (0.25)
5,6
0.07 (0.26)
5,6
0.07 (0.26)
5,6
0.21 (0.41)
1,2,3,4
0.17 (0.38)
1,2,3,4
9.33***
Note: Numbers in parentheses indicate the standard deviation; superscripted numbers indicate significant (Tukey adjusted p-values) mean differences with
other clusters.
a
0=male, 1 =female.
b
0=no, 1 =yes.
*p< .05. **p< .01. ***p< .001.
DLOUHY AND FROIDEVAUX 11
industries and had on average 0.67 occupation changes and 1.94
organization changes.
4.1.3 | Pattern 3: Hybrid career
This career pattern is followed by individuals who started working in a
STEM occupation after graduation and then changed to part-STEM
occupations, mostly around 2 to 5 years after starting their career
(n=171). Individuals following this pattern mostly worked in STEM
industries and had on average 1.02 occupation changes and 2.29
organization changes.
4.1.4 | Pattern 4: Boomerang career
This career pattern is followed by individuals who either (1) started in
a non-STEM occupation after graduating from STEM studies and then
changed back to STEM or part-STEM occupations or (2) started in
STEM or part-STEM occupations, changed to non-STEM or non-paid
occupations, and then changed back to STEM or part-STEM occupa-
tions (n=166). In general, about 8 years after starting their career,
they worked either in a STEM or a part-STEM occupation. During the
time spent in non-STEM occupations, as seen in Table 2, individuals
most often worked in occupations characterized as investigative (8%),
enterprising (7%), or realistic (6%). Individuals following this pattern
had on average 1.40 occupation changes and 3.11 organization
changes.
4.1.5 | Pattern 5: Dropout career
This career pattern is followed by individuals who started working in
STEM or part-STEM occupations and then changed to non-STEM
occupations 4 to 8 years after starting their career (n=144). In con-
trast to those in Pattern 4, they did not come back to STEM. During
the time they spent working in non-STEM occupations, they most
often worked in occupations characterized as enterprising (16%),
investigative (8%), or social (6%). Individuals following this pattern had
on average 1.29 occupation changes and 2.44 organization changes.
4.1.6 | Pattern 6: Non-STEM career
This career pattern is followed by individuals who started working in a
non-STEM occupation after graduation and mostly continued to do so
over time (i.e., never worked in STEM; n=162). Most non-STEM
industries included consumer goods and trade, consulting, and finan-
cial and legal services. During their career half of the individuals fol-
lowing this pattern worked in occupations characterized as
enterprising (32%) or conventional (17%). Individuals following this
pattern had on average 0.61 occupation changes and 1.86 organiza-
tion changes.
4.2 |Continuity versus change in career patterns
We were able to show that professionals with a STEM degree exhibit
distinct career patterns, varying in the content of their occupations,
the pattern of their occupations over time, and the industries in which
they started or spent their careers. Tukey post-hoc analyses with
adjusted p-values revealed further important differences between
individuals following these career patterns. As can be seen in Table 4,
individuals following the STEM, part-STEM, and non-STEM career
patterns had significantly fewer occupational and organizational
changes than individuals following the hybrid, boomerang, and drop-
out career patterns. The career patterns can thus be distinguished into
continuity (STEM, part-STEM, and non-STEM; 68.2% of our sample)
and change (hybrid, boomerang, and dropout; 31.8%) patterns based
on these characteristics, thus answering our Research Question 1.
3
4.3 |Professionals' diversity characteristics
In terms of sex, we found significant differences between career pat-
terns (F=4.12, p< .001). The share of females was significantly
higher among those individuals who followed the non-STEM career
pattern (28%) than the STEM career pattern, 15%, t(1506) =4.17,
p< .001, supporting Hypothesis 1a. However, as the share of females
was the same among those individuals who followed the dropout
career pattern (15%) and the STEM career pattern, 15%, t(1506)
=0.22, n.s., Hypothesis 1b was not supported.
4
The Fvalues from the
analyses of variance for ethnic minority status, in terms of non-native
speaker (F=0.19, n.s.) and foreign country of origin (F=1.71, n.s.),
were not significant, and variable means across career patterns did
not vary significantly. Thus, Hypotheses 2a and 2b were not
supported.
4.4 |Career success and self-employment
Hypothesis 3a predicted that individuals following a career pattern
characterized by part-STEM occupations would have higher objective
career success than those following the traditional STEM career pat-
tern. As aforementioned, two career patterns were indeed character-
ized by part-STEM occupations: the part-STEM and the hybrid career
pattern. Our findings demonstrate that 10 years after university grad-
uation, individuals following the part-STEM career pattern did indeed
have a significantly higher hierarchical position, M=1.82; t(1506)
=4.17, p< .001, than those following the STEM career pattern
(M=1.56), while the same was not true of those following the hybrid
career pattern, M=1.62; t(1506) =0.73, n.s. Thus, there was only
partial support for Hypothesis 3a. Further, individuals following the
dropout career pattern reached on average a higher hierarchical posi-
tion, M=1.81, t(1506) =2.72, p< .05, than those following the
STEM career pattern (M=1.56), thus supporting Hypothesis 3b.
Finally, in terms of self-employment, 17% of individuals who followed
the non-STEM career pattern and 21% of individuals who followed
12 DLOUHY AND FROIDEVAUX
the dropout career pattern were self-employed 10 years after their
career start, representing a significantly higher share than among indi-
viduals following the STEM career pattern, 7%; t(1506) =4.09,
p< .001; t(1506) =5.22, p< .001, thus supporting Hypotheses 4a
and 4b.
55
5|DISCUSSION
This study breaks new ground by investigating the STEM leaky pipe-
line as a salient societal challenge and looking into different career
patterns among individuals who graduated in STEM over a 10-year
time span. We found six career patterns, which could be distinguished
into three continuity (STEM, part-STEM, and non-STEM) and three
change (hybrid, boomerang, dropout) career patterns. We further
found that female professionals more frequently followed a non-
STEM career pattern than a STEM career pattern. However, we did
not find any differences between career patterns for professionals
from an ethnic minority (i.e., immigration) background. Moreover, we
demonstrated that professionals following a part-STEM or dropout
career pattern were more successful in their careers in terms of career
advancement, than were those following a traditional STEM career
pattern. Finally, we found that professionals following a dropout or
non-STEM career pattern were more often self-employed than were
those following a STEM career pattern (as well as all other career
patterns).
5.1 |Theoretical implications
Our findings offer several important theoretical implications for the
sustainable careers framework (De Vos et al., 2020; De Vos & Van der
Heijden, 2015). First, we clarify how sequences of career experiences
unfold in the STEM context. Specifically, our study has the potential
to define a new conversation regarding the extent to which variability
and movement within and out of organizations and occupations
unfold for STEM graduates as they strive to achieve career sustain-
ability. Indeed, while limited evidence suggests the existence of career
patterns over time for STEM graduates, and the existing evidence
relies mostly on conceptual rather than empirical work (e.g., Tremblay
et al., 2002), we proposed that STEM graduates' career patterns may
involve either continuity or change over time. Specifically, in a first
systematic exploration of career patterns, the current study demon-
strates the existence of three continuity (STEM, part-STEM, non-
STEM; 68.2% of the sample) and three change (hybrid, boomerang,
dropout; 31.8%) career patterns. Thus, continuity patterns clearly
dominated among STEM graduates.
Second, our study represents one of the first empirical confirma-
tions that career sustainability should only be evaluated using a long-
term perspective. Within the STEM context, we find, notably, change
career patterns for 31.8% of our sample, which further highlights the
importance of considering how STEM careers evolve over time. It is
possible that past studies reported, for example, individuals following
the boomerang career pattern as dropouts, while our analyses over
10 years revealed that a significant proportion of professionals ret-
urned to STEM occupations. Our paper further contributes to the
identification of the timeline of such a re-entry into STEM, as we
found that occupational turnover from STEM to non-STEM occupa-
tions happened earlier in the boomerang career pattern than in the
dropout career pattern. This is in line with literature reporting that
boomerang workers may leave earlier in their career before coming
back, compared with those who leave for good (Shipp et al., 2014).
In addition, we empirically demonstrate that a long-term perspec-
tive is needed to evaluate career success as an indicator of sustainable
careers (De Vos et al., 2020), by nuancing the conditions and timing
under which part-STEM occupations may be associated with greater
career success. We find that individuals starting their careers upon
graduation in a part-STEM occupation were more successful
(i.e., experienced more career advancement) than those following the
STEM career pattern in the long term. However, this was not the case
for individuals following the hybrid career pattern who had trans-
itioned from a STEM to a part-STEM occupation a few years after
starting their career.
6
The part-STEM career pattern seems more sus-
tainable in terms of long-term success than the hybrid career pattern,
which may be explained by the high worth of early career graduates
with up-to-date knowledge on the job market, compared with profes-
sionals with a longer tenure (Deming & Noray, 2018).
Third, our paper focusing on the STEM leaky pipeline phenome-
non demonstrates the complexity of the person-context interactions
required for careers to be sustainable. Specifically, while in this paper
we adopted an individual (micro) perspective according to which
STEM graduates continuously strive to achieve a sustainable career,
at the societal and economical (macro) levels, such efforts may result
in losses. Indeed, individual career patterns implying leaving STEM
occupations for those in which a STEM degree is not required (i.e., the
dropout and non-STEM career patterns, representing 20.2% of our
sample)
7
may result in a loss of investment from society in individuals'
education and subsequently in a possible labor shortage for the econ-
omy. By highlighting such possible tensions in person-context interac-
tions, this study thus questions the sustainable careers framework's
proposition that “sustainable careers are characterized by mutually
beneficial consequences for the person and for their surrounding con-
text”(De Vos et al., 2020, p. 24). Rather, our findings suggest that, in
some cases, greater career sustainability for the person may occur at
the detriment of the context. Thus, a particularly interesting question
arises for the sustainable careers scholarship: Are career patterns sus-
tainable when their sequence of career episodes is no longer charac-
terized by mutually beneficial consequences for both the person and
the context?
Fourth, our findings clarify how sustainable careers concretely
unfold over multiple occupations in the STEM context, by identifying
professionals' turnover destinations in non-STEM occupations in
terms of occupational interests (Holland, 1987). Expanding recent
STEM literature on career interests (e.g., Babarovi
c et al., 2019; Kim &
Beier, 2020), our analysis revealed that when working in non-STEM
occupations, future boomerang professionals mainly chose
DLOUHY AND FROIDEVAUX 13
investigative, enterprising, and realistic occupations—hence remaining
close to STEM-related career interests (i.e., investigative and realistic;
Babarovi
c et al., 2019). In contrast, those following a non-STEM
career pattern mostly worked in enterprising and conventional occu-
pations, thus placing them further away from typical STEM career
interests. Altogether, these results indicate that individuals seem to
perceive their careers as more sustainable if their current job fits their
occupational interests better (i.e., STEM graduates with interests
mostly for realistic and investigative occupations experience a greater
fit more likely to result in boomerang patterns if leaving STEM fields)
and suggest that dropping out from STEM fields may lead to greater
career sustainability through a better fit with one's occupational
interests.
Fifth, we integrate the sustainable career framework with the
workforce diversity perspective (Roberson, 2019) in the context of
STEM careers, hence contributing to a better understanding of
whether and how professionals' diversity characteristics may be asso-
ciated with sustainable career patterns over time. Specifically, our
study examined two underlying stable sociodemographic characteris-
tics (i.e., sex and ethnic minority status) that may be associated with
greater vulnerability for some STEM graduates. Expanding prior litera-
ture on female workers in STEM (Makarem & Wang, 2020), we
observed more female professionals in the non-STEM career pattern
but not in the dropout career pattern. This suggests that for female
STEM graduates, the pipeline is likely to leak very early, even before
getting a first job upon graduation. By doing so, we expand prior work
mostly adopting a static approach and showing that nearly one third
of women intended to leave STEM jobs within their first year upon
graduation (Hewlett et al., 2014). Further expanding prior findings
showing an absence of significant differences among racial groups of
STEM graduates in their occupational career choices (Xu, 2013), but
in contrast with other prior studies (e.g., Hall et al., 2017; Saw
et al., 2018), we found no variation across career patterns based on
professionals' ethnic minority status. This may be explained by our
specific study context, as the German government has actively sought
since 2005 to attract foreign STEM professionals to Germany
(Maaß & Icks, 2012). This may have provided ethnic minorities in
STEM with an official recognition of the necessity of their contribu-
tion to the German economy.
Sixth, this study contributes to broadening scholars' current
understanding of how sustainable career patterns may relate to long-
term career success (Straub et al., 2020) in terms of reaching higher
hierarchical positions. Indeed, our findings seem to indicate that
career advancement may be related to transitioning out of a STEM
occupation (i.e., dropout and part-STEM career patterns), thus shed-
ding light on the possibility that the STEM leaky pipeline may repre-
sent a way for some STEM graduates to transition into more
sustainable careers (Castro et al., 2020). It is important to note, how-
ever, that our investigation of career success was limited by our
dataset, allowing us to focus on hierarchical positions only, hence
overlooking other objective and subjective indicators of sustainable
careers (De Vos et al., 2020). Finally, our results suggest that self-
employment may also represent a better opportunity to achieve
career sustainability for professionals following the dropout and non-
STEM career patterns, compared with those in STEM occupations
who may benefit from higher salaries and greater opportunities for
innovation that can enable them to more easily achieve career sus-
tainability in their employed positions.
5.2 |Practical implications
Findings of our study have practical implications for organizations'
efforts to enhance all their employees' career longevity in a sustain-
able careers perspective (McDonald & Hite, 2018) through their
human resource management practices. In particular, our results can
inform managers and organizations about the best time, and on
which employees' socio-demographic characteristics to focus such
retention efforts. As we found that occupational turnover in the
dropout career pattern was taking place 4 to 8 years after individ-
uals' career start, that professionals following the part-STEM or
dropout career patterns (mostly in the consumer goods and consult-
ing industries) were the most successful in terms of career advance-
ment, and that female (vs. male) STEM graduates were less often in
higher hierarchical positions, we recommend that retention interven-
tions focus on early-career and female employees. Concretely, orga-
nizations in the STEM industry should adopt a more sustainable HR
management perspective by rethinking their employees' career man-
agement and access to higher hierarchical positions to avoid reten-
tion issues. Specifically, organizations should provide their
employees with regular access to formal education, allowing them
to update their skills and knowledge on a regular basis and to
advance accordingly in the succession planning to reach higher hier-
archical positions. Such efforts are in line with the sustainable
careers framework, which highlights career renewability through
“re-education”to allow employees to be more engaged and resilient
as their careers evolve (Newman, 2011).
Another important finding of our study is that female STEM grad-
uates most frequently followed the non-STEM career pattern, hence
never starting a first job in STEM in the first place. This concretely
implies that universities should develop programs supporting their
female STEM students to increase their odds of actually starting a
STEM career. As female graduates are more likely to face gender bias
and hostile work environments upon their career start in a STEM
occupation (Leaper & Starr, 2019), such programs should provide
access to successful female role models (Roemer et al., 2020). Con-
cretely, universities' career development services should develop part-
nerships with organizations in the STEM industry to allow their
female students to be paired with successful STEM female profes-
sionals during an internship and/or mentorship program. Having
observed their mentor as role models in terms of how STEM occupa-
tions may concretely represent a sustainable career choice, female
students may ultimately make a more informed decision of whether
or not to enter a STEM career upon graduation based on more realis-
tic expectations on how a STEM career may be sustainable for them-
selves as well.
14 DLOUHY AND FROIDEVAUX
5.3 |Limitations and avenues for future research
Although the reliance on career data from a large public professional
networking platform represents a strength of the current study, it also
has several limitations. First, because the Xing data were archival in
nature and thus not collected with our research questions in mind, the
availability of indicators was limited. Notably, no information was pro-
vided on respondents' age, occupational interests (e.g., using the Self-
Directed Search assessment tool; Holland, 1994), on whether the
reported career changes reflected voluntary or involuntary turnover,
or on other subjective (e.g., career or job satisfaction, health) and
objective (e.g., salary) measures highlighted by the sustainable careers
framework. Thus, we acknowledge the existence of several other fac-
tors that are relevant to STEM graduates' career patterns and warrant
future research. Furthermore, as is also the case in many other STEM
studies, an implicit assumption of our study of professionals' diversity
characteristics is that these characteristics are biologically based
(Metcalf, 2010). We used limited categories that are mutually exclu-
sive; accordingly, future studies should explore the role of gender
(e.g., non-binary) instead of sex and of racial/ethnicity (e.g., Latino/a/
x) identities in STEM to further address calls for more research on
“the [career] sustainability of employees belonging to minority
groups”(De Vos et al., 2020, p. 10).
Second, our sample from the social network Xing might not be
representative of the entire STEM workforce. Like LinkedIn, Xing
and other similar social network platforms tend to be used more
by people in knowledge-intensive sectors and less by individuals
with lower education and income (Blank & Lutz, 2017;
Hargittai, 2020). While we focused on individuals with a STEM
university degree, some professionals (e.g., self-employed profes-
sionals or those who work in enterprising occupations) might be
more likely than others to use a professional social network like
Xing. Of note, the percentage of individuals in our sample that
were self-employed after 10 years (9%) corresponds to the propor-
tion of individuals that are self-employed in Germany (Destatis,
2020c). Still, our dataset allows us to avoid limitations of other
types of longitudinal data (e.g., from national panel studies), such
as a limited share of STEM workers and panel attrition, especially
for individuals who work long or odd hours (Uhrig, 2008), and are
self-employed or have an enterprising occupation (Ezzedeen &
Zikic, 2017). Therefore, we recommend that future studies rely on
data from different sources to contribute to a complete picture of
STEM careers and occupational turnover.
Third, while we propose that the career patterns of profes-
sionals who graduated in STEM identified in our study can be found
in other countries as well, the prevalence of STEM graduates who
follow these patterns might differ across countries. In our study,
there were no country differences between Germany, Austria, and
Switzerland. However, cultural or institutional differences that either
favor dropout or that tie individuals to STEM occupations might
nevertheless exist. Hence, a fruitful avenue for future research may
be to examine and compare STEM graduates' career patterns in
more distant countries.
6|CONCLUSION
Overall, the leaky pipeline of STEM careers represents a major chal-
lenge at the societal and economical levels, given the important role
played by STEM occupations for the prosperity of a world character-
ized by rapidly evolving innovation and technology. Moreover, it also
represents an opportunity to achieve career sustainability for STEM
professionals. As such, it is becoming increasingly necessary to under-
stand how STEM graduates' careers evolve over time, as well as corre-
lates of occupational turnover after graduation. The current study
offers a sustainable careers perspective that explicitly considers the
existence of continuity and change career patterns. Our results sug-
gest three continuity and three change career patterns, as well as
associations with sex (but not with ethnic minority background),
career success, and self-employment. These findings suggest that
organizations should aim to enhance their employees' career longevity
in a sustainable careers perspective through their human resource
management practices, focusing their retention efforts on early-career
and female employees. Moreover, universities should develop pro-
grams supporting their female STEM students so that they may ulti-
mately decide whether or not to pursue a STEM career under the best
conditions.
ACKNOWLEDGMENT
Open Access funding enabled and organized by Projekt DEAL.
DATA AVAILABILITY STATEMENT
Data available on request from the authors.
ORCID
Katja Dlouhy https://orcid.org/0000-0002-0836-4541
Ariane Froidevaux https://orcid.org/0000-0002-7100-6747
ENDNOTES
1
In a sensitivity analysis, we conducted optimal matching analyses for
sequences with lengths of 15 years (N=810) and 20 years (N=407).
These sensitivity analyses yielded essentially the same patterns as the
ones we later report in the main text. Interested readers may contact
the authors for more details on these findings.
2
This percentage is representative for STEM graduates in German-
speaking countries (German Federal Employment Agency, 2021).
3
In a sensitivity analysis, we repeated these tests in cluster solutions with
seven, eight, and nine clusters. In all solutions, Research Question 1 was
answered similarly. Interested readers may contact the authors for more
details on these findings.
4
In a post hoc analysis, we further explored whether female versus male
professionals may also be less likely to have reached a higher hierarchical
status 10 years after starting their careers. We found that women
indeed had a significantly lower hierarchical level after 10 years
(M=1.43) than men, M=1.71, t(1506) =4.34, p< .001.
5
There were no significant differences between career patterns with
regard to highest degree (F=1.43, n.s.), MBA (F=0.43, n.s.), or country
(Germany vs. other, F=1.71, n.s.).
6
In a sensitivity analysis, we explored whether individuals in the hybrid
pattern might simply need some time after their transition to part-STEM
occupations to secure higher positions. We found that the results did
DLOUHY AND FROIDEVAUX 15
not change for careers with lengths of 15 years (N=810) and 20 years
(N=407). Interested readers may contact the authors for more details
on these findings.
7
While prior studies reported that up to 75% of STEM graduates might
not work in STEM occupations (e.g., U.S. Census Bureau, 2014), this dif-
ference may be explained by the fact that only STEM versus non-STEM
occupations were considered, hence overlooking part-STEM occupa-
tions (with the recent exception of Smith & White, 2019). While Smith
and White (2019) found that 17–22% of U.K. STEM graduates worked
in associate professional and technical occupations, we found that 22.1–
33.7% worked in part-STEM occupations.
REFERENCES
Adams, R. B., & Kirchmaier, T. (2016). Women on boards in finance and
STEM industries. American Economic Review,106(5), 277–281. https://
doi.org/10.1257/aer.p20161034
Anger, C., Kohlisch, E., Koppel, O., Plünnecke, A., & Schüler, R. M. (2020).
MINT-Frühjahrsreport 2020 [STEM report spring 2020]. https://www.
iwkoeln.de/fileadmin/user_upload/Studien/Gutachten/PDF/2020/
MINT-Fruehjahrsreport_2020.pdf
Babarovi
c, T., Devi
c, I., & Buruši
c, J. (2019). Fitting the STEM interests of
middle school children into the RIASEC structural space. International
Journal for Educational and Vocational Guidance,19(1), 111–128.
https://doi.org/10.1007/s10775-018-9371-8
Bailyn, L., & Lynch, J. T. (1983). Engineering as a life-long career: Its mean-
ing, its satisfactions, its difficulties. Journal of Occupational Behaviour,
4(4), 263–283. https://www.jstor.org/stable/3000315
Baldridge, D. C., & Kulkarni, M. (2017). The shaping of sustainable careers
post hearing loss: Toward greater understanding of adult onset disabil-
ity, disability identity, and career transitions. Human Relations,70(10),
1217–1236. https://doi.org/10.1177/0018726716687388
Bender, K. A., & Roche, K. (2013). Educational mismatch and self-employ-
ment. Economics of Education Review,34,85–95. https://doi.org/10.
1016/j.econedurev.2013.01.010
Biemann, T., & Datta, D. K. (2014). Analyzing sequence data: Optimal
matching in management research. Organizational Research Methods,
17(1), 51–76. https://doi.org/10.1177/1094428113499408
Blank, G., & Lutz, C. (2017). Representativeness of social media in great
britain: Investigating Facebook, Linkedin, Twitter, Pinterest, Google+,
and Instagram. American Behavioral Scientist,61(7), 741–756. https://
doi.org/10.1177/0002764217717559
Byars-Winston, A. (2014). Toward a framework for multicultural
STEM-focused career interventions. The Career Development Quar-
terly,62(4), 340–357. https://doi.org/10.1002/j.2161-0045.2014.
00087.x
Cardador, M. T. (2017). Promoted up but also out? The unintended conse-
quences of increasing women's representation in managerial roles in
engineering. Organization Science,28(4), 597–617. https://doi.org/10.
1287/orsc.2017.1132
Cardador, M. T., & Hill, P. L. (2018). Career paths in engineering firms:
Gendered patterns and implications. Journal of Career Assessment,
26(1), 95–110. https://doi.org/10.1177/1069072716679987
Castro, M. R., Van der Heijden, B., & Henderson, E. L. (2020). Catalysts in
career transitions: Academic researchers transitioning into sustainable
careers in data science. Journal of Vocational Behavior,122, 103479.
https://doi.org/10.1016/j.jvb.2020.103479
Cech, E. A., & Blair-Loy, M. (2019). The changing career trajectories of
new parents in STEM. Proceedings of the National Academy of Sci-
ences,116(10), 4182–4187. https://doi.org/10.1073/pnas.
1810862116
De Vos, A., & Van der Heijden, B. (2015). Handbook of research on sustain-
able careers. Edward Elgar Publishing Limited. https://books.google.
ch/books?id=YA7AoAEACAAJ
De Vos, A., Van der Heijden, B. I., & Akkermans, J. (2020). Sustainable
careers: Towards a conceptual model. Journal of Vocational Behavior,
117, 103196. https://doi.org/10.1016/j.jvb.2018.06.011
Deming, D. J., & Noray, K. L. (2018). STEM careers and technological
change [Working papers from the National Bureau of Economic
Research]. https://scholar.harvard.edu/files/ddeming/files/
demingnoray_stem_sept2018.pdf
Destatis (2020a). Migration und Integration [Migration and integration].
https://www.destatis.de/DE/Themen/Gesellschaft-Umwelt/
Bevoelkerung/Migration-Integration/_inhalt.html
Destatis. (2020b). Studierende in MINT-Fächern [Students in STEM sub-
jects]. https://www.destatis.de/DE/Themen/Gesellschaft-Umwelt/
Bildung-Forschung-Kultur/Hochschulen/Tabellen/studierende-mint-
faechern.html
Destatis. (2020c). Pressemitteilung Nr. N 013 [Press release no. 103].
https://www.destatis.de/DE/Presse/Pressemitteilungen/2020/03/
PD20_N013_132.html
Dlouhy, K., & Biemann, T. (2015). Optimal matching analysis in career
research: A review and some best-practice recommendations. Journal
of Vocational Behavior,90, 163–173. https://doi.org/10.1016/j.jvb.
2015.04.005
Eaton, A. A., Saunders, J. F., Jacobson, R. K., & West, K. (2020). How gen-
der and race stereotypes impact the advancement of scholars in
STEM: Professors' biased evaluations of physics and biology post-
doctoral candidates. Sex Roles,82(3), 127–141. https://doi.org/10.
1007/s11199-019-01052-w
Eurostat. (2021). Women in science and engineering. https://ec.europa.
eu/eurostat/web/products-eurostat-news/-/edn-20210210-1
Ezzedeen, S. R., & Zikic, J. (2017). Finding balance amid boundarylessness:
An interpretive study of entrepreneurial work–life balance and bound-
ary management. Journal of Family Issues,38(11), 1546–1576. https://
doi.org/10.1177/0192513X15600731
Fleiss, J. L., & Cohen, J. (1973). The equivalence of weighted kappa and
the intraclass correlation coefficient as measures of reliability. Educa-
tional and Psychological Measurement,33(3), 613–619. https://doi.org/
10.1177/001316447303300309
Flöthmann, C., & Hoberg, K. (2017). Career patterns of supply chain execu-
tives: An optimal matching analysis. Journal of Business Logistics,38(1),
35–54. https://doi.org/10.1111/jbl.12150
Fouad, N. A., Kozlowski, M. B., Singh, R., Linneman, N. G., Schams, S. S., &
Weber, K. N. (2020). Exploring the odds: Gender differences in
departing the engineering profession. Journal of Career Assessment,
28(3), 446–461. https://doi.org/10.1177/1069072719876892
Gabadinho, A., Ritschard, G., Mueller, N. S., & Studer, M. (2011). Analyzing
and visualizing state sequences in R with TraMineR. Journal of Statisti-
cal Software,40(4), 1–37. https://doi.org/10.18637/jss.v040.i04
German Federal Employment Agency. (2021). Berufe auf einen Blick:
MINT [Occupations at a glance: STEM]. https://statistik.
arbeitsagentur.de/DE/Navigation/Statistiken/Interaktive-Angebote/
Berufe-auf-einen-Blick-MINT/Berufe-auf-einen-Blick-MINT-Nav.html
Glass, J. L., Sassler, S., Levitte, Y., & Michelmore, K. M. (2013). What's so
special about STEM? A comparison of women's retention in STEM and
professional occupations. Social Forces,92(2), 723–756. https://doi.
org/10.1093/sf/sot092
Hall, A. R., Nishina, A., & Lewis, J. A. (2017). Discrimination, friendship
diversity, and STEM-related outcomes for incoming ethnic minority
college students. Journal of Vocational Behavior,103,76–87. https://
doi.org/10.1016/j.jvb.2017.08.010
Hargittai, E. (2020). Potential biases in big data: Omitted voices on social
media. Social Science Computer Review,38(1), 10–24. https://doi.org/
10.1177/0894439318788322
Hewlett, S. A., Sherbin, L., Dieudonne, F., Fargnoli, C., & Fredman, C.
(2014). Athena factor 2.0: Accelerating female talent in science, engi-
neering & technology: Executive summary. https://coqual.org/wp-
content/uploads/2020/09/23_athenafactor2point0_keyfindings-1.pdf
16 DLOUHY AND FROIDEVAUX
Holland, J. L. (1987). Current status of Holland's theory of careers: Another
perspective. Career Development Quarterly,36(1), 24–30. https://doi.
org/10.1002/j.2161-0045.1987.tb00478.x
Holland, J. L. (1994). The self-directed search. Psychological Assessment
Resources.
Jelks, S. M., & Crain, A. M. (2020). Sticking with STEM: Understanding
STEM career persistence among STEM bachelor's degree holders. The
Journal of Higher Education,91(5), 805–831. https://doi.org/10.1080/
00221546.2019.1700477
Joseph, D., Boh, W. F., Ang, S., & Slaughter, S. A. (2012). The career paths
less (or more) traveled: A sequence analysis of IT career histories,
mobility patterns, and career success. MIS Quarterly, 427–452.
https://doi.org/10.2307/41703462
Kim, M. H., & Beier, M. E. (2020). The college-to-career transition in STEM:
An eleven-year longitudinal study of perceived and objective voca-
tional interest fit. Journal of Vocational Behavior,123, 103506. https://
doi.org/10.1016/j.jvb.2020.103506
Leaper, C., & Starr, C. R. (2019). Helping and hindering undergraduate
women's STEM motivation: Experiences with STEM encouragement,
STEM-related gender bias, and sexual harassment. Psychology of
Women Quarterly,43(2), 165–183. https://doi.org/10.1177/
0361684318806302
Li, C. S., Goering, D. D., Montanye, M. R., & Su, R. (2021). Understanding
the career and job outcomes of contemporary career attitudes within
the context of career environments: An integrative meta-analysis.
Journal of Organizational Behavior,43, 286–309. https://doi.org/10.
1002/job.2510
Maaß, F., & Icks, A. (2012). Analyse des deutschen Zuwanderungssystems
im Hinblick auf den Fachkräftebedarf im Mittelstand [Analysis of the
German migration system regarding the workforce demand in
medium-sized companies]. IfM-Materialien No. 217. https://www.
econstor.eu/handle/10419/62129
Makarem, Y., & Wang, J. (2020). Career experiences of women in science,
technology, engineering, and mathematics fields: A systematic litera-
ture review. Human Resource Development Quarterly,31(1), 91–111.
https://doi.org/10.1002/hrdq.21380
McDonald, K. S., & Hite, L. M. (2018). Conceptualizing and creating sus-
tainable careers. Human Resource Development Review,17(4),
349–372. https://doi.org/10.1177/1534484318796318
Metcalf, H. (2010). Stuck in the pipeline: A critical review of STEM work-
force literature. InterActions: UCLA Journal of Education and Information
Studies,6(2). https://escholarship.org/uc/item/6zf09176, https://doi.
org/10.5070/D462000681
NamSor. (2020). Onomastics extension for R. https://github.com/
wing328/namsor-r-client/
Newman, K. L. (2011). Sustainable careers: Lifecycle engagement in work.
Organizational Dynamics,40, 136–143. https://doi.org/10.1016/j.
orgdyn.2011.01.008
O'NET. (2021). Interests. https://www.onetonline.org/find/descriptor/
browse/Interests/
Pew Research Center. (2018). Diversity in the STEM workforce varies
widely across jobs. https://www.pewresearch.org/social-trends/
2018/01/09/diversity-in-the-stem-workforce-varies-widely-across-
jobs/
Roberson, Q. M. (2019). Diversity in the workplace: A review, synthesis,
and future research agenda. Annual Review of Organizational Psychol-
ogy and Organizational Behavior,6(1), 69–88. https://doi.org/10.1146/
annurev-orgpsych-012218-015243
Roemer, C., Rundle-Thiele, S., Pang, B., David, P., Kim, J., Durl, J.,
Dietrich, T., & Carins, J. (2020). Rewiring the STEM pipeline-a CBE
framework to female retention. Journal of Social Marketing,10(4),
427–446. https://doi.org/10.1108/JSOCM-10-2019-0152
Rotondi, M. A., & Donner, A. (2012). A confidence interval approach to
sample size estimation for interobserver agreement studies with
multiple raters and outcomes. Journal of Clinical Epidemiology,65(7),
778–784. https://doi.org/10.1016/j.jclinepi.2011.10.019
Santamaría, L., & Mihaljevi
c, H. (2018). Comparison and benchmark of
name-to-gender inference services. PeerJ Computer Science,4,
156–171. https://doi.org/10.7717/peerj-cs.156
Saw, G., Chang, C.-N., & Chan, H.-Y. (2018). Cross-sectional and longitudi-
nal disparities in STEM career aspirations at the intersection of gender,
race/ethnicity, and socioeconomic status. Educational Researcher,
47(8), 525–531. https://doi.org/10.3102/0013189X18787818
Seibert, S. E., Kraimer, M. L., & Liden, R. C. (2001). A social capital theory
of career success. Academy of Management Journal,44(2), 219–237.
https://doi.org/10.2307/3069452
Shapiro, H., Ostergaard, S. F., & Hougaard, K. F. (2015). Does the EU need
more STEM graduates?: Final report. http://hdl.voced.edu.au/10707/
399589
Shipp, A. J., Furst-Holloway, S., Harris, T. B., & Rosen, B. (2014). Gone
today but here tomorrow: Extending the unfolding model of turnover
to consider boomerang employees. Personnel Psychology,67(2),
421–462. https://doi.org/10.1111/peps.12039
Siekmann, G., & Korbel, P. (2016). Defining STEM skills: Review and syn-
thesis of the literature. National Centre for Vocational Education
Research. https://eric.ed.gov/?id=ED570655
Smith, E., & White, P. (2019). Where do all the STEM graduates go? Higher
education, the labour market and career trajectories in the UK. Journal
of Science Education and Technology,28(1), 26–40. https://doi.org/10.
1007/s10956-018-9741-5
Stenard, B. S., & Sauermann, H. (2016). Educational mismatch, work out-
comes, and entry into entrepreneurship. Organization Science,27(4),
801–824. https://doi.org/10.1287/orsc.2016.1071
Straub, C., Vinkenburg, C. J., & van Kleef, M. (2020). Career customization:
Putting an organizational practice to facilitate sustainable careers to
the test. Journal of Vocational Behavior,117, 103320. https://doi.org/
10.1016/j.jvb.2019.103320
Studer, M. (2013). WeightedCluster Library Manual: A practical guide to
creating typologies of trajectories in the social sciences with R. LIVES
Working Papers, 24. https://doi.org/10.12682/lives.2296-1658.
2013.24
Tremblay, M., Wils, T., & Proulx, C. (2002). Determinants of career path
preferences among Canadian engineers. Journal of Engineering and
Technology Management,19(1), 1–23. https://doi.org/10.1016/S0923-
4748(01)00043-1
U.S. Bureau of Labor Statistics. (2015). STEM crisis or STEM surplus? Yes
and yes. https://www.bls.gov/opub/mlr/2015/article/stem-crisis-or-
stem-surplus-yes-and-yes.htm
U.S. Census Bureau. (2014). Census Bureau reports majority of STEM col-
lege graduates do not work in STEM occupations https://www.
census.gov/newsroom/press-releases/2014/cb14-130.html
U.S. Census Bureau. (2021). Women are nearly half of U.S. workforce but
only 27% of STEM workers. https://www.census.gov/library/stories/
2021/01/women-making-gains-in-stem-occupations-but-still-
underrepresented.html
Uhrig, S. N. (2008). The nature and causes of attrition in the British House-
hold Panel Survey. ISER Working Paper Series. https://finchley.essex.
ac.uk/research/publications/working-papers/iser/2008-05.pdf
Urbanaviciute, I., Bühlmann, F., & Rossier, J. (2019). Sustainable careers,
vulnerability, and well-being: Towards an integrative approach. In J.
Maree (Ed.), Handbook of innovative career counselling (pp. 53–70).
Springer. https://doi.org/10.1007/978-3-030-22799-9_4
van den Hurk, A., Meelissen, M., & van Langen, A. (2019). Interventions in
education to prevent STEM pipeline leakage. International Journal of
Science Education,41(2), 150–164. https://doi.org/10.1080/
09500693.2018.1540897
van der Heijden, B., & De Vos, A. (2015). Sustainable careers: Introductory
chapter. In A. De Vos & B. van der Heijden (Eds.), Handbook of research
DLOUHY AND FROIDEVAUX 17
on sustainable careers (pp. 1–19). Edward Elgar Publishing. https://doi.
org/10.4337/9781782547037.00006
van Rossum, G., & Drake Jr, F. L. (1995). Python reference manual.
https://docs.python.org/3/reference/
Volodina, A., Weinert, S., & Mursin, K. (2020). Development of academic
vocabulary across primary school age: Differential growth and influen-
tial factors for German monolinguals and language minority learners.
Developmental Psychology,56(5), 922–936. https://doi.org/10.1037/
dev0000910
Xing. (2020). Facts & figures. https://www.new-work.se/en/about-new-
work-se/facts-and-figures
Xu, Y. J. (2013). Career outcomes of STEM and non-STEM college gradu-
ates: Persistence in majored-field and influential factors in career
choices. Research in Higher Education,54(3), 349–382. https://doi.org/
10.1007/s11162-012-9275-2
Xue, Y., & Larson, R. C. (2015). STEM crisis or STEM surplus: Yes and yes.
Monthly Labor Review,138(1), 1–13. https://doi.org/10.21916/mlr.
2015.14
Zschirnt, E., & Fibbi, R. (2019/2020). Do Swiss citizens of immigrant origin
face hiring discrimination in the labour market? NCCR Working Papers,
20, 65726. http://hdl.handle.net/1814/65726
AUTHOR BIOGRAPHIES
Katja Dlouhy is Assistant Professor of Human Resource Manage-
ment at the University of Mannheim. She received her PhD in
Management from the University of Mannheim in 2017. Her
research focuses on career development, career mobility, and
methods in management research. Her work appears in Journal of
Vocational Behavior and Human Resource Management.
Ariane Froidevaux is Assistant Professor of Management at the
University of Texas at Arlington. She received her PhD in Psychol-
ogy from the University of Lausanne in 2016. Her research inter-
ests include career transitions, retirement, and identity. Her work
appears notably in Journal of Applied Psychology and Journal of
Vocational Behavior.
How to cite this article: Dlouhy, K., & Froidevaux, A. (2022).
Evolution of professionals' careers upon graduation in STEM
and occupational turnover over time: Patterns, diversity
characteristics, career success, and self-employment. Journal of
Organizational Behavior,1–18. https://doi.org/10.1002/job.
2615
18 DLOUHY AND FROIDEVAUX
