Job Quits and Human Resource Partitioning:
Evidence from the Korean TV Advertising Industry
Stanislav D. Dobrev
University of Wisconsin, Milwaukee
Lubar School of Business
Email: [email protected]
Tel. 801.712.8155
Tai-Young Kim
Sungkyunkwan University
SKK Graduate School of Business
Email: [email protected]
Tel: 822-740-1514; Fax: 822-740-1503
Draft: June 2015
_________
This research was supported by the Manegold Fund at the Lubar School of Business at
University of Wisconsin-Milwaukee and by the SKK Graduate School of Business at
Sungkyunkwan University. We appreciate helpful comments by Ozgecan Kocak, Giacomo
Negro, Olav Sorenson, Cameron Verhaal, and the participants at the organizations seminar at
Korea University.
Job Quits and Human Resource Partitioning:
Evidence from the Korean TV Advertising Industry
/abstract/
We know that the organizational makeup of an industry is relevant for the aggregate rates of
employee turnover in that industry but we don’t know whether and how organizational processes
at the industry level interact with individual-level predictors of job quits. Using resource
partitioning as a theoretical foundation, we claim that individual preferences and job properties
affect job quits differently in different organizations. Specifically, we argue that whether
someone is employed in a specialist or in a generalist organization will directly affect their quit
likelihood and also will modify the effects of human and social capital and of incentives on that
likelihood. Analyzing data from the Korean TV advertising industry between 1980 and 1997, we
model the job transitions of managers between agencies and demonstrate clear and discernible
patterns for the different career staging processes that unfold in specialist and generalist
organizations.
1
Introduction
Employee turnover is central to the functioning of any organization and is closely tied to
key strategic processes (e.g., capability development, resource allocation, conflict resolution) and
outcomes (e.g., business growth, competitive positioning, and overall performance). As a
complex phenomenon involving myriad related factors, the turnover process can be broken down
to its basic element—the instance of a job quit by an individual employee. Given the far-reaching
consequences of turnover, research interest in understanding job quits has been predictably
widespread and there exists a voluminous literature on the topic reflecting multiple perspectives
and disciplinary lenses. For labor economists, job quits are a function of the firm-specific human
capital accumulated with one’s tenure in the organization (Becker, 1962) and of the quality of the
job-person match at hiring (Jovanovic, 1979; 1984). Economic sociologists have stressed the
importance of social capital that arises from actors’ positions in the structure of social relations
for acquiring greater returns on the labor market. Sociologists also investigate how individuals’
socioeconomic and demographic makeup deters career progression and promotion chances due
to unequal opportunities and discrimination (Correll, Benard and Paik, 2007; Fuller, 2008;
Castilla, 2008; Stainback, Tomaskovic-Devey, and Skaggs, 2010). Through a different lens,
organization and industrial psychologists have documented how cognition and socialization
processes lead to greater organizational commitment and lower attrition (Lee and Mitchell, 1994;
Weller et al., 2009). The mechanisms put forth in this vein of research often revolve around job
embeddedness (Mitchell et al., 2001), job satisfaction (Hom and Kinicki, 2001; Lee et al., 2008),
and the nature of interpersonal relationships at the workplace (Holtom, Mitchell and Lee, 2008).
Although substantively diverse, most of these approaches provide theoretical accounts of
job quits that tend to focus primarily on individual-level explanations. As a result, supply-based
2
theories of turnover dominate—we know a fair amount about how psychological, demographic
and socioeconomic factors affect a person’s likelihood to quit a job, but much less about how
properties of the employing firm factor in.
Inroads in developing demand-side accounts of job quits have been carried largely by
researchers advocating a career approach to turnover (Sorensen 2000). Shifting attention from a
single to multiple jobs is motivated by the argument that individuals do not randomly move
between jobs and that there is purpose and logic to the sequence of jobs that string together to
comprise an individual career. Understanding this logic then can shed light on the specific
reasons associated with quitting any given job within that sequence. But the career approach to
turnover—by looking at an individual’s employment history over time and with multiple
employers—also draws more attention to the role of the employing organizations. Because with
this approach we observe variance in the employing firms for each individual, the individuallevel mechanisms tied to job quits can be compared across different organizations and the
properties of these organizations can be brought to bear on the quits likelihood.
Research on careers that specifically investigates the role of organizations in facilitating
or deterring job quits borrows heavily from research in organization theory (Baron, 1984;
Rosenfeld, 1992). For example, many studies look at the effect of organizational size on turnover
dynamics, linking large size to structural differentiation and internal labor markets (Cole, 1979;
Carroll and Mayer, 1986; DiPrete, 1993; Greve, 1994; Haveman and Cohen, 1994; FujiwaraGreve and Greve, 2000). Other career studies use theories of structural imprinting (Burton and
Beckman 2007), organizational change and inertia (Baron, Hannan and Burton 2001), structural
complexity and senescence (Dobrev and Barnett 2005; Dobrev 2012) and adapt their predictions
3
to the outcome of job cessation. Yet there is a clear disconnect between the plethora of
contemporary organization theories and the paucity of organization-related mechanisms that
have been adapted to careers research. We reckon that the main reason for this disconnect is that
while most modern organization theories emphasize an organization’s relationship with its
external environment, mostly comprised of other organizations, the careers research largely
favors explanations based on the internal structural or demographic characteristics of the
organization. On the other hand, research that investigates various employment outcomes as a
function of the external structure of opportunity does consider the effect of aggregate
organizational distributions in an industry but does not engage the individual-level mechanisms
in careers research (e.g., Haveman 1995; Sørensen and Sorenson 2007). So there is a gap:
research that integrates careers and organizations focuses either on internal organizational
dynamics (e.g., organizational age and size) and ties them to employees’ propensities to change
jobs, or on all organizations in an industry as engines of the external opportunity structure but
does not tie in the purported macro mechanisms (e.g. size diversity) to individual-level effects on
quit rates.
We further surmise that a key limitation to applying the rich stock of organization
theories to the turnover phenomenon emanates from research design limitations, namely, the
requirement that organization-level data complement data on individuals and their job
characteristics. Career studies that follow individuals through multiple jobs typically provide
data on their employing firms. Yet, while the sample selection of the individuals changing jobs
always follows some logical criterion based on occupation or geography, the employing
organizations are rarely subject to any selection criterion. And this is where the disconnect
between organization theories and turnover research occurs: Developing arguments about the
4
implications of organizations for turnover based on theories about the organization-environment
relation also requires looking at organizations that are related to each other and share the same
environment (e.g., same market or industry). In other words, further extending the crossover
between organization theory and the careers literature on job quits requires a research design that
tracks the job moves of the same individuals across time and space between organizations in the
same market. This would allow us to bring inter-organizational dynamics to bear on the interorganizational career moves of the individuals employed by organizations operating in the same
market.
Our goal in this study is to employ a unique research design that tracks the careers of all
managers in an entire industry over time. To explain the career moves of these managers we use
a macro-level theory that builds on an inter-organizational dynamic to make predictions about
individual-level career outcomes. Specifically, we apply arguments from resource partitioning
theory (Carroll 1985) to show that in a consolidated industry, the bifurcation of organizations
between two key forms—specialists and generalists—matters significantly for turnover and job
shifts within the industry. We demonstrate that the distinct human resource requirements of
specialist and generalist organizations are reflected in the quit rates of managers and their
differential responses to the incentives offered by specialist and generalist firms. Rather than
offer an alternative to the extant individual level arguments that predict job quits, we seek to
show that such micro accounts hold true but are modified in important ways by the
organizational landscape in the industry where the observed careers unfold.
5
Theory
The key insight of resource partitioning theory relates to the prediction that as a market
characterized by scale-based competition consolidates, resources become partitioned in two: a
resource abundant market center dominated by a few large generalist organizations whose broad
niches utilize a wide range of resources and are thus able to cater to a large segment of the
consumer audience, and peripheral segments where thinner resources do not allow for scale but
where specialist organizations thrive by focusing on a narrow band of resources and produce
outputs tailored to specialized customer tastes (Carroll et al., 2002).
If generalist and specialist firms pursue vastly different strategies, as is already known,
the resources they utilize should also vary systematically. Consider the case of human resources:
professional experience, educational background, duration of organization and labor market
tenure and other relevant forms of human and social capital may be valued differently by
specialists and generalists. Predicting why and how they do so is where we seek to make a
contribution to theory. Accordingly, the internal labor markets and systems of rewards and
promotion may also be organized differently by specialists and generalists. Attending to these
issues may reveal processes and features that further elucidate the original theory and
substantiate its logic. We view this approach as a productive next step in the research agenda of a
mature theory.
We pursue this research strategy in our empirical application here, the TV advertising
industry in South Korea. Our data contain information on the specialization of advertising
agencies which allows us to categorically distinguish between generalists and specialists. The
data also contain information on the career histories of the advertising managers in these
6
agencies along with demographic/experience information about the managers themselves.
Because we have data on all managers in the industry, we can aggregate individual managerlevel data to the firm and measure the agencies’ scope (i.e., niche width) and competitive
position in terms of the human resource profiles of their employment bases.
Keeping with our direction to develop a demand-side account of job quits that borrows
theoretical insight from organization theory, we build our arguments at the level of the
organization and then consider their implications for the quit rates of managers. Specifically, we
conjecture that there are three sets of features that differentiate specialist from generalist
organizations that affect their employees’ propensity to jump ship. First, we look at how the
firm’s specialization in terms of its labor force profile (i.e., its niche width) and the resulting
industry-level competition for similar human resources (i.e., competitive overlap) facilitate or
retard job quits. Second, we speculate that specialist and generalist firms differ in their incentive
and ability to develop, reward and retain human and social capital, and as a consequence,
experience differential attrition among employees that embody different forms of human and
social capital. Third, we reckon that since generalists and specialists are organized differently
both architecturally (e.g., formal hierarchy and incentives) and informally (e.g., social identities
and cultural norms and values), they will differ significantly in the reward systems and basis for
employee attraction that they each can offer. Moreover, managers’ reactions to milestone career
events like promotion and its timing will differ by organizational context and will be reflected in
their quit rates. In developing these arguments, we rely heavily both on established theory about
job quits at the “micro” level and on the “macro” mechanisms driving the bifurcation of a
consolidated market between specialist and generalist organizations per resource partitioning
theory.
7
Human resource partitioning by organizational niche width and niche overlap
Our first claim, which we formalize below, is that a manager’s chances of quitting a job
are impacted by some key characteristics of the managerial labor force in the employing
organization. Concretely, we posit that the variance in these characteristics affects the odds of
quitting. When a certain employee characteristic (e.g., individual age) is aggregated at the firm
level, it describes one dimension of the firm’s variance in human resource utilization—the firm
can employ labor that spans the entire distribution (e.g., young and old managers), or focus only
on recruiting from a narrow segment of it (e.g., only middle-age managers). A firm’s niche width
can be described with respect to its variance in resource utilization along multiple dimensions.
Generalists rely on a wide range of resources, as reflected in their broad organizational
niches. By contrast, specialists utilize a narrow resource band and accordingly their niche span is
narrow (Hannan and Freeman 1977; Freeman and Hannan 1983). In resource partitioning theory,
having a broad niche is viewed as advantageous when competing on scale because potential
complementarities between adjacent locations in the market center also provide economies of
scope (Carroll et al. 2002). Beyond scale and scope advantages, a wide niche provides a selection
buffer against resource fluctuations and temporary shortages thus increasing the firm’s adaptive
potential in shifting environments (Panzar and Willig 1981). While most measures of niche
typically use a product dimension that captures the firm’s output offerings to consumers, and
then use this measure to predict performance/survival, we construct a niche width measure based
on an input resource and use it to predict a firm’s ability to retain its human and social capital.
We argue that a broad niche is favorable to the management of human resources. Firms
that employ a diverse set of organizational members benefit from the greater variance of their
8
workforce which in turn facilitates the optimal matching of people with jobs (Jovanovic 1979,
1984). We look at two workforce characteristics, one demographic (employees’ age distribution),
and the other socioeconomic (employees’ reputations based on educational attainment). With
both, the argument in favor of spanning a wider range of the resource distribution rests on the
firm’s ability to increase variance and benefit from selection choices over the range of that
variance (March, 1981; Roberts, 2004). For example, a firm that recruits exclusively older
employees likely will not get skilled junior applicants and will have diminished opportunity for
internal mentoring or intergenerational knowledge transfer between old and young employee
cohorts. It will also probably be less likely to develop and benefit from internal labor markets
since firm tenure is by definition limited (an employee’s tenure at the firm and her age are
obviously related) if people are selected for membership in the firm during only a short span of
their life-course (e.g., older age). These espoused benefits of broad niche are amplified for a firm
pursuing an overall generalist strategy where a firm competes for the center of the market and on
scale. The wider variety of operational tasks and their greater frequency imply learning
economies which probably make training, mentoring, and inter-cohort knowledge transfer less
costly and more valuable.
A similar argument underpins the assertion that hiring employees with a varied
reputational background is beneficial. The design of a generalist firm is geared for scale
competition and this implies a high level of standardization in production methods (Blau and
Schoenherr, 1971). Consistent with operations on large scale, this design entails greater variance
in the quality of tasks and specialization benefits in utilizing human resources. The lower-quality
tasks that tend to be less interesting and more repetitive justify recruitment of lower-status
employees, who are presumably willing to assume more monotonous job roles. Status, of course,
9
is an imperfect predictor of actual ability and there are certainly hires whose high status conceals
subpar skill-set; the opposite is just as likely—out of the guise of low status may emerge an
exceedingly competent employee. Such variance in the initial job-person match, driven by
assumptions tying status to ability, can be absorbed and accommodated by a broad-niche firm
where managers have the potential to rotate underperforming high-status employees to less
desirable tasks while elevating high performing low-status members to more desirable tasks. A
manager in a narrow-niche firm (one that recruits only high-status or only low-status employees)
simply does not have such leeway. When an employee’s actual skills are revealed to exceed
initial expectations, a narrow-niche firm is challenged to provide intrinsic motivation and
professional development typical of more engaging and stimulating role tasks. And when the
opposite is true, the specialist firm cannot offer a lower set of skill requirements to which an
underachiever can be assigned. In either case, a job quit becomes more likely.
We surmise then that with both employee age and employee status, firms that span a
wider part of the resource distribution are likely to experience improved job-person match and
thus minimize turnover. If increasing niche width leads to a better job-person match, a manager’s
quits will decrease accordingly:
H1a: A manager’s chances of quitting are a decreasing function of his firm’s niche width
defined along an employee age dimension.
H1b: A manager’s chances of quitting are a decreasing function of his firm’s niche width
defined along an employee status dimension.
10
Although the empirical evidence from extant research offers strong support for the scale
and adaptive advantages of niche width, relying on a wide span of resources raises the cost of
competition. Researchers have pointed to the increased crowding pressures that firms become
subjected to when they expand their organizational niches. As Dobrev, Kim and Hannan (2001:
1303) observed, “when an organization broadens its niche, it cannot decrease overlaps and
generally increases them.” And such increases can completely wipe out the purported benefits of
broad niche. Empirical analyses have confirmed that the deleterious consequences of overlap
with market peers outweigh the benefits of broad niche width (Podolny et al. 1996; Dobrev et al.,
2001). Furthermore, these analyses show that overlap exerts a second-order negative effect on
firms’ survival chances when heightened competition prompts firms to embark on core process
changes in order to alleviate crowding pressures. As a result, firms become subject to inertial
forces and suffer elevated failure risks. Overlap crowding in human resource space has been
shown to depress new organizational foundings (Sørensen 2004) and organizational growth
(Sørensen 1999)
We argue that human resource niche overlap—that is, competition among firms for the
same employees—will increase individual turnover by increasing each overlapping firm’s costs
of retaining its employees. Because niche overlap is positively related to niche width, we
theorize about the crowding effect along the same two dimensions that we used to define niche
width—individual age and education status. Logically, when firms overlap on the age and status
dimensions of the human resource space (i.e., when they employ individuals within the same
ranges of age and status), their members have alternative employment options—other firms
where they can defect to. Just like a single employer in a bounded geographic location will not
experience labor competition (Glaeser 1994), turnover is unlikely to be high in a firm which is
11
the only one in the industry to employ only young professionals or only professionals with low
status.
It is well established in the literature (see Feldman and Ng 2007 for review) that turnover
is broadly driven by factors related to both features of the current job (i.e., push factors) and of
external opportunities (i.e., pull factors). From the standpoint of employees, competition
triggered by rising overlaps at the firm level strengthens the pull of external opportunity by
simply increasing the available choice-set of employment options. Holding conditions related to
the current employment constant, an employee with a certain asset profile will be more likely to
quit and take a job elsewhere, the greater the number of other firms that recruit employees with
that same asset profile. In this way, while the “push” to quit is reduced by niche width (as per
H1a and H1b), increasing competitive overlaps—that arise as a function of broad niche width—
raise the external “pull” effect on turnover:
H2a: A manager’s chances of quitting are an increasing function of his firm’s niche overlap
defined along an employee age dimension.
H2b: A manager’s chances of quitting are an increasing function of his firm’s niche overlap
defined along an employee status dimension.
Human resource partitioning by human and social capital
Most people structure their careers (i.e., consider and make job changes) so that they can
maximize the returns on the assets they possess that are of value to employers. From the set of
skills and expertise a person has developed by training and experience, to his personal network
of contacts allowing access to valuable information, every employee seeks to raise the appeal of
12
his employability profile by conjuring an offering based on his human and social capital. The
literatures in labor economics and economic sociology overwhelmingly demonstrate the value of
human and social capital to employers (for review, see Burt, 2005)1. We treat both human and
social capital as a desirable asset that employers seek to acquire and retain and thus consider
carefully in the management of their human resources. Below we consider four distinct forms of
human and social capital which are nested with respect to their generality as measures. First, the
quality of the educational endowment possessed by employees provides the most general proxy
for their ability and potentially valuable skills, along with expectations of participation in elite
networks comprised of other individuals in highly reputed positions (e.g., alumni networks,
social clubs, etc.). Second, employee age is also a general predictor of human and social capital
but one that varies with time. Both the development of useful networks and the accumulation of
experience unfold over time and are thus correlated with age. Third, the duration of industryspecific work experience also signals the attainment of skills and contacts, particularly relevant
in the focal industry. Finally, the managerial rank of an employee reveals most closely the
relationship between one’s experience and relevant career accomplishments.
Our starting premise for the expected relationship between these four forms of human
and social capital and organizational form is that generalist and specialist organizations differ
with respect to how they value and reward these assets. Of course, every employer would like to
maximize the human and social capital of its employees but we surmise that distinct market and
1
Although our goal is not to further elaborate the distinction between human and social capital,
we reiterate the uncontested conclusion that human capital pertains to what employees know
while social capital is about who they know. That is, human capital is about the knowledge and
experience accumulated over time and social capital resides in relationships and social networks
of which ego is a part.
13
organizational strategies and finite resources combine to sort out employees with different
endowments into different types of organizations. Again, the key distinction we posit is one
between generalist and specialist organizations and we surmise that as individuals’ human and
social capital increases, they will become less likely to quit jobs in specialist firms than in
generalist ones. We base this claim on two conjectures. First, human and social capital is partly
accumulated as one’s career unfolds and working in a generalist firm is more conducive to
accruing it than being employed by a specialist. Generalist firms tend to be larger and more
differentiated and such firms are more likely to offer professional development programs and
formal training (Bidwell and Briscoe, 2010; Lynch and Black 1998; Hu 2003). This implies that
specialist firms will do more to prevent the loss of human and social capital which they cannot
easily replace by internal development. Second, the work environment and nature of work
offered by specialist firms is more appealing to employees with high levels of human and social
capital. Specialists’ system of rewards, aligned with their small size and less rigid environment,
is typically based on soft intrinsic rewards that are inimical to the formal designs of large
generalist firms (Wagner 2001; Kalleberg and Van Buren 1996; Zipp 1991).
Before we elaborate the specific mechanisms that underpin the lower quit rates of human
and social capital-rich employees in specialist firms (an implied interaction effect), we need to
establish the two sets of relevant baseline predictions: about the effect of working in a specialist
firm on quits, and about the main effect of human and social capital on quits. Consistent with our
prediction in hypotheses H1a and H1b, we expect that, ceteris paribus, quits will be lower
(higher) in generalist (specialist) firms. Unlike specialists, generalists benefit from large scale
which lowers production costs and allows wages to rise (Brown and Medoff 1989; Lester 1967).
Moreover, generalists are more likely to have well developed career ladders with internal
14
promotion opportunities and opportunities for professional development (Kalleberg et al 1996).
Both higher wages and promotion have been shown to significantly decrease quits (Hammida
2004).
As far as the baseline effect of human and social capital on quits, we think it varies by the
specific form of capital so we briefly summarize the mechanisms driving the relationships
between the four forms of capital we consider here and job quits. In doing so we find it useful
again to distinguish between push and pull factors in job transitions. We view individual age and
position rank as factors that depress the “push” to quit. Research in organizational behavior has
shown that job commitment is not uniform across one’s career-path (Porter, Steers, and Mowday,
1974). ‘Job-shopping’ and experimentation typically occur early in one’s career but job
commitment rises with age as risk-taking becomes harder to justify as a career’s end approaches
(Fuller, 2008; Topel and Ward, 1992). So, an employee’s odds of quitting decrease with her age.
Similarly, while blocked or delayed career progression is often cited as a “push” to change jobs,
achieving high rank serves as a strong deterrent to seeking alternative employment thereby
reducing quits.
On the other hand, forms of human and social capital that increase the attractiveness of an
employee’s profile exert a “pull” out of the current job an into another by increasing his overall
employability and external job opportunities. An employee’s educational status, for example, at
once signals competence and valuable connections, both of which likely trigger increased
interest by employers (Lai et al. 1998). Thus, holding performance with the current employer
constant, an employee with a degree from a highly reputed educational institution is likely to
receive more external job offers than a colleague with a lower-rank educational attainment. By
15
similar logic, as an employee accumulates work experience in the industry, the knowledge and
skillset that comes along with that experience opens new employment opportunities and raises
the odds of quitting the current job (Fulgate, Kinicki and Ashforth 2004).
To reiterate, individual age and managerial rank deter quits by lowering risk-taking
propensity and increasing job satisfaction, respectively. By contrast, educational status and
industry experience elevate quits by increasing the overall appeal and employability of a
manager to prospective employers. With the baseline effects specified, our next step is to
articulate how the organizational context affects these likelihoods in specialist vs generalist
firms. Our theoretical suppositions imply interaction effects between four distinct forms of
human and social capital and employment in a specialist firm. We build on our baseline
prediction for the organizational effect, namely that quit rates will be higher in specialist firms,
but posit that increasing human and social capital will work in the opposite direction, lowering
quits in specialists.
As we surmised earlier, the inverse relationship between quit rates and the accrual of
human and social capital in specialists stems from the fundamental differences in how work is
organized in specialists and generalists. Generalist firms compete on scale and so tend to be
larger and much more differentiated both horizontally (in terms of product, technology, customer
etc.) and vertically (in terms of hierarchy). The presence of formal structure allows for internal
career progressions, including professional development and the acquisition of valuable skills
and knowledge (Baron et al. 1987; Kalleberg et al. 1996; Bidwell and Briscoe 2010). By
contrast, specialist firms are typically small, less differentiated and offering much less formal
training and less stringently defined roles (Marsden et al. 1996). This difference in design
16
suggests two reasons why the outflow of human and social capital would be greater in generalist
firms: first, unlike specialists, generalist firms have the capacity to replace the loss of human and
social capital through internal development. Specifically with respect to age, research shows that
older employees are less interested in acquiring new skills and much more prone to capitalizing
on the ones they already have (Oosterbeek 1998; Bidwell and Briscoe 2010). Second,
formalization also poses constraints on the autonomy with which employees are permitted to go
about their jobs. Formally defined routines and processes limit the ability to exercise own
judgment based on accumulated knowledge and expertise (Dobrev 2012).
We also think that the more flexible, less programmatic nature of work in specialist firms
appeals to proven, successful managers who have reached the peak of the managerial hierarchy
and at that stage are likely to develop job preferences that seek predominantly independence and
a greater sense of personal contribution (Smola and Sutton 2002; Tolbert and Moen 1998). Or,
highly placed managers may seek a higher pay-off which specialist firms may be willing to
accommodate given the greater value-add they extract from such managers (Pogson et al. 2003).
Again, this value-add resides in the human and social capital which seems important to both
generalists and specialists but crucial for specialists who may not have the bandwidth to develop
it from within. So while we expect increasing individual age and position rank to lower quits in
any organization, we think this effect will be stronger in specialist firms:
H3a: The negative effect of age on a manager’s chances of quitting is stronger in specialist
than in generalist organizations.
H3b: The negative effect of rank on a manager’s chances of quitting is stronger in specialist
than in generalist organizations.
17
With respect to educational status and industry experience, we propose that while they
will increase quits in all firms, this effect will be subdued in specialists. If work in specialist
firms is less programmatic, allowing for more initiative, and offering incentives that emphasize
intrinsic rewards, it would be a better fit with employees with prestigious educational
backgrounds. Attending an elite institution creates expectations to be employed in a work setting
conducive to greater creativity and decision-making ingenuity. So a specialist firm will carry
greater appeal to high-status graduates than a generalist one. Furthermore, high-status employees
may contribute to the reputation of a specialist firm more so than to a generalist one. A specialist
strategy often relies on word-of-mouth, selective advertising (consistent with production on
small scale) which makes social capital embedded in high-tier alumni networks valuable.
We also propose that industry experience is more valuable to specialists because of their
strategic emphasis on non-serial, customized production or service. If a specialist firm tailors its
offerings to the customer, it would likely require the service of employees with broad
experiences who have been through both vicarious and trial-by-fire learning and have
experimented with a variety of tactics and methods of response to a broad array of customer
needs. Simply put, we reckon that specialist firms have a taste for employees with generalist (i.e.,
broad and varied) experience, and assume that the generality (and diversity) of experience
increases with industry tenure. Thus we predict that with both status and industry experience, the
odds of quitting will rise less in specialist than in generalist firms.
H3c: The positive effect of status on a manager’s chances of quitting is weaker in specialist
than in generalist organizations.
18
H3d: The positive effect of industry experience on a manager’s chances of quitting is
weaker in specialist than in generalist organizations.
Human resource partitioning by incentives
We began by arguing that quit rates decrease as the span of resources utilized by a firm
increases, or in other words, as the firm becomes more of a generalist. We then argued that
although quit rates on average may be higher in specialist firms, the quit rates of employees with
certain human and social capital are likely lower in specialists. We based this claim on the fact
that—concurrent with the accrual of human and social capital—such employees develop a
preference for softer, intrinsic job rewards which specialist firms are better suited to offer
because of the less constrained nature of work in these organizations. In the last set of arguments
we develop, we seek to complete our theory by addressing the question of how then generalist
firms manage to protect the human and social capital they develop, and ultimately, to exhibit
lower overall attrition rates than specialists.
We think that while some employees are attracted by the soft intrinsic reward system
offered by specialist firms, this tendency is compensated by the formal rewards system offered
by generalist firms where extrinsic rewards in the form of promotion and career development are
stronger. We alluded earlier that the difference in incentive systems between generalist and
specialist firms parallels the distinction between the two in terms of size and structure. While
large scale and formalization clearly have implications for the type and amount of compensation
and promotion chances that a firm can offer, we think it is important to test these suppositions
19
directly and develop some of the concrete mechanisms by which the generalist retention
advantage comes about.
To this end, we develop two related arguments about the relationship between the number
and timing of promotions an employee experiences in a focal organization, on the one hand, and
the odds of quitting a job in a specialist firm, on the other. Again, before we articulate the role of
the organizational context, we first settle on the baseline effects of promotion and promotion
timing on quits. The stipulation that quits decrease with promotions is well established in prior
research (Petersen and Spilerman 1990; Dobrev 2012) and accords with our earlier prediction
that high position rank decreases quits in both specialists and generalists. But while high rank
itself conveys the accumulation of human and social capital, the relationship between actual
number of promotions and the odds of quitting convey the employee’s response to the presence
(or absence) of promotion opportunities in the firm. Specifically, we expect that as the number of
promotions an employee has been granted in the organization increases, the odds of quitting the
firm will decrease. Not only do promotions increase organizational embeddedness but they
validate and reaffirm an employee’s position with the firm and the opportunity for future career
advancement. For the same reasons that promotions generally decrease quits, the waiting times
between them have the opposite effect—the longer the time elapsed since the last promotion, the
more likely an employee becomes to quit the firm.
Our last set of hypotheses make the claim that both of these promotion-related effects are
modified by the organizational context. Specifically, the design of specialist firms is not geared
to support an internal hierarchy with multiple promotion opportunities thereby reducing the
20
promotion advantage and exacerbating the “delayed promotion” disadvantage associated with
quit rates.
A steeper hierarchy increases promotion opportunities as the presence of a hierarchy
allows for the operation of vacancy chains (Sørensen 1977; White 1970; Stewman and Konda
1983) and internal labor markets (Doeringer and Piore 1971; Baron and Bielby 1984; Pfeffer and
Cohen 1984; Baron, Davis-Blake and Bielby 1986). Individual careers can unfold entirely within
the bounds of a single organization which provides a strong incentive for employees to remain
with their firm over a long span of their labor market tenures. Generalist firms are likely to have
developed internal structures designed to offer promotion opportunities and career development
that a specialist firm cannot afford to avail to its employees. In light of the limited promotion
opportunities in specialist firms then, we expect their employees to be sensitive to the “dead-endcareer” threat, given these firms’ unelaborate hierarchies. To repeat, the retention advantage
associated with promotions is twofold: each promotion constitutes a reward that validates an
employee’s contemporaneous contribution to the company. But, promotions also signal the
future likelihood of career advancement that an individual can expect throughout his tenure with
the firm. It is this purported benefit of future promotion that seems untenable in specialist firms.
Given a specialist firm’s likely flat hierarchy, the limits to how far one’s career can unfold within
the firm are approached with each subsequent promotion. Hence, we expect cumulative
promotions in the firm to have a stronger negative effect on quits in generalist firms where
opportunities for long-term internal career advancement are not easily exhausted:
H4a: The negative effect of number of promotions on a manager’s chances of quitting is
weaker in specialist than in generalist organizations.
21
Turning to the timing of promotion effect, we think that as time elapsed since last
promotion increases, managers in specialist firms will be more likely to quit (than their peers in
generalist firms) not only because promotion opportunities are more scarce and more tightly
contested but also because advancement tracks are less rigidly structured. If internal labor
markets are unlikely to exist in specialists, then the sequence and duration of promotions and the
gaps between them cannot be well defined either. While this flexibility allows for rewarding
employee contribution more fairly and adequately by way of dismissing mandatory waiting times
between promotions (Carroll and Mayer, 1986), it also discourages those who do not move ahead
as fast as they anticipate. While an employee’s turnover propensity due to an extended lag time
between promotions in a generalist firm may be tempered by knowledge of the ‘normally’
expected duration between promotions, his peer in a specialist firm is likely to interpret the
‘recency’ of promotions not in proscribed systemic terms but subjectively. When this subjective
interpretation of how the firm views ego’s relative contribution conflicts with ego’s own
anticipated “right time” for promotion, turnover is likely to increase beyond the baseline
“delayed promotion” effect that exists in both specialist and generalist firms.
H4b: The positive effect of time since last promotion on a manager’s chances of quitting is
stronger in specialist than in generalist organizations.
With our theory now complete, we next proceed to establish the contextual validity of our
predictions in the setting of the Korean advertising industry, and—having justified our choice of
data and method—to test them empirically.
22
Organizational Dynamics in the Korean advertising industry
The Korean advertising industry grew along with the growth of Korean companies. For
example, the total advertisement expenditure in Korea increased from about 200 million dollars
in 1980 to 4,500 million dollars in 1997. Korea saw the first television commercials in 1956,
after the Korean War (1950-1953). Three major TV stations, KBS (Korea Broadcasting System),
MBC (Munhwa Broadcasting System), and SBS (Seoul Broadcasting System) were founded in
1947, 1961, and 1990 respectively.
An advertising agency is an intermediary firm between client firms and consumers, which
creates new promotional ideas, conducts advertising campaigns, commissions research and
surveys, and provides other such services that help a client in entering and succeeding in a
chosen market. The first two advertising agencies, AdKorea and Impact were founded in 1958
and 1962 respectively. Several large advertising agencies including Jeil Communication (a
subsidiary of Samsung Group), MBC Adcom, and Oricom were founded in the 1970s. Although
several advertising agencies were active before the 1980s, we chose 1980 as the starting year of
this study due to three fundamental changes in the industry. First, it was in 1980 when General
Doo-Hwan Chun and his collaborators took over power by taking an excessive military action2 in
defiance of a constitutional order. Through National Defense Emergency Policy Committee, the
new authoritarian regime implemented various policies including Political Activity Constraints,
Mass Media Consolidation Act, and University Graduation Quota System to suppress resistance
of politicians, mass media and universities again the regime. Second, the Korea Broadcasting
2
Nearly 200 citizens who participated in democratization rallies in the southern city of Kwangju were
killed.
23
Advertising Corp (KOBACO) was established in 1980 as a government agency that played an
intermediary role between advertising agencies and television stations (e.g., KBS-TV, MBC-TV,
SBS-TV). It was the only media representative selling advertisement slots on behalf of Korea’s
national TV networks. That is, the establishment of KOBACO fundamentally shifted the way the
advertising agencies did their businesses due to its monopoly right on selling advertisement slots.
And third, it was in the 1980s when large corporations started to diversify their businesses into
the advertisement industry and established their own in-house agencies such as Korad, DaeHong Planning, KeumKang Planning, and SamHee Planning in. This led to severe competition
due to a heavily concentrated customer base and a few large clients. We chose 1997 as the
ending year of our observations because Korean economy including the advertising industry was
heavily hit by the financial crisis at the end of 1997. The Korean government had to ask for
assistance from the International Monetary Fund (IMF). As a response, IMF required the
government to implement tight macroeconomic policy as well as comprehensive structural
adjustment in the corporate and financial sections.
The advertising industry in Korea has consisted of the advertising agencies that manage
such comprehensive activities ranging from client-management to buying slots from KOBACO
through promotion-planning, and specialty firms that participate in only a part of the advertisingproduction process. For example, a specialty firm called Gwang-go-bang has been specialized in
production-processes only. In addition, due to the KOBACO’s exclusive right on selling
advertisement slots since 1980, clients have tended to evaluate an advertising agency in terms of
not only its creativity and promotion capability but also its corporate relationships with
KOBACO and its ability to secure the slots. This has led to a dual industry structure in which
clients have a choice between large, well-known generalist agencies such as Jeil Comunication
24
and small, boutique agencies that specialize by industry. With government deregulation of the
industry in 1989 the number of small specialist agencies has proliferated.
Methods
Data sources and structure
We compiled career history data of all managerial employees in the advertising industry
in Korea during 1980-1997. While the advertising market includes various media including TV,
radio, print and specialty, we limited our data to the market for TV advertising for two reasons.
First, while TV commercials are shown through three major TV stations such as KBS (Korea
Broadcasting System), MBC (Munhwa Broadcasting System), and SBS (Seoul Broadcasting
System)3, the total number of daily and regional newspapers was as high as 286 in 1997.
Obtaining information on small advertising agencies that tailor to regional newspapers and
customers in local regions is intractable and very costly. Second, the top three advertising
agencies in our data (Jeil communication, LG Ads, and DaeHong) account for more than half of
the total revenues from TV commercials (53.9%, 61.6%, and 61.8% of total revenues
respectively in 1993). It is clear that the TV advertising market is clearly partitioned between
large generalist and small specialist firms, a necessary condition for our analysis of human
resource partitioning.
To construct our data, we coded the annual Advertising Directory compiled by the Korean
3
These three broadcasting systems occupied 92.6% of TV commercial ads market in 1997, while 8 other
regional broadcasting systems including PSB, TBC, KBC, TJB and CBS covered the rest 8% of the
market.
25
Advertisers’ Association from 1984 to 1997. This directory contains comprehensive information
on the careers of individuals employed in the industry including their rank and position, age,
education level and rank, as well as information on the advertising agencies like number of
employees, organizational age, and media type. The directory lists only employees whose ranks
are at least at the basic manager level (Gwajang) or above. In reality, since most employees carry
a managerial title, the data only exclude short-term employees in intern-like positions who are
given the title of assistant managers (Daeri). We reconstructed managers’ careers paths by
compiling the annual directories from different years through rigorous matching processes based
on first and last name, birth year, rank, and educational background.
Variables
Our dependent variable is turnover which is coded as a dummy that takes the value of one
when an individual moves from one firm to another within the advertising industry in Korea.
Technically, the directories do not identify a job transition or turnover so we coded it by
comparing each individual’s position between two consecutive years (as listed in the directories)
and identifying the cases in which a job change has occurred involving a transition between
agencies. The total number of turnover events is 1,437 during the study period.
Our key independent variables relate to measures of generalism and specialism and we
employ two such measures. First, in line with most previous research, we mark up specialist
firms based on a categorical distinction. The criteria we used for this categorical distinction were:
diversity of customer portfolios, large scale, and market dominance. In Korea, generalist
agencies command a diverse customer base with a broad range of service portfolios in various
industries, with high economy of scales in terms of assets, number of employees, and market
26
share. For example, Jeil Communcation had numbers of employees ranging from about 500 to
900 during the 1990s, compared to small boutique-type specialist agencies employing fewer than
50 people. In addition, it manages a variety of activities including market research, consulting,
marketing planning, and client-management as a representative for TV commercial slots from
KOBACO. In addition to Jeil Communication, other generalist firms include LG Ads, Oricom,
DaeHong, KeumKang, Korad, Dongbang, SamHee, MBC AdCom, HanIn, HanCom, JeilBosel,
HanKook, JeSon, and Seoul Ads. The total market share of these generalists ranged from almost
100% in the early 1980s to about 80% in the 1990s.4 The former outside director of the largest
advertising agency, Jeil Communication said, “there have been clear differences between these
firms and boutique-like agencies in terms of business scales and networks in this industry. As a
consequence, most small agencies occupy a very small market.”
Our second measure of specialism/generalism is continuous and computes a firm’s niche
width in terms of the age and status distributions of its employment base. In Korea, individual
age and education background (especially university status) are arguably the most important
criteria when firms recruit new employees. Rooted in Confucian culture, age confers social status
and a shared evaluation of where an individual belongs in society. Reputation based on elite
university credentials elicits a shared understanding of where an individual belongs in the labor
market. An agency’s niche width is measured as the range between the minimum and maximum
of its employees’ age and as the range of their minimum and maximum university ranking score.
An advertising agency’s niche overlap in the labor market is the degree to which other agencies
4
The decrease in concentration corresponds to the phase in the resource partitioning process during which
the consolidation of the market center by large generalists frees up space in the near periphery where
specialists find fertile ground.
27
also occupy the agency’s niche range. So, niche overlap density is the number of other agencies
whose niches fall within at least some portion of the range (Dobrev et al. 2001, 2002). We
measure niche overlap density for each firm in terms of its niche width defined both along the
individual age dimension and the status dimension.
An employee’s age is measured in years and controls for lifecourse effects on the
likelihood of turnover. Job tenure is the time (in years) elapsed since an employee joined the
firm and industry tenure measures the duration of time (in years) since an employee first began
working in the advertising industry. We coded an individual’s rank as consisting of seven levels
based on the conventional position rank system in Korea: 7) CEO, chairman, or president, 6) vice
president 5) executive director (Jeon-Mu), 4) managing director (Sangmu), 3) director (Isa), 2)
general manager (Bujang), 1) deputy general manager (Chajang) and manager (Gwajang). In
roughly one percent of the cases, instead of being listed as managers, employees are called
directors. We recoded such cases conservatively to an equivalent of the seven ranks after we took
a close look at the entire career path of each individual in terms of age, previous job titles,
employing firms, and educational background. We measured status as the reputation of an
employee’s educational institution. It is widely accepted in Korea that which university an
individual graduates from is arguably the most important factor that affects recruitment,
promotion and compensation. To construct this measure we used the average scores of a
university’s students matriculated in the department of business administration. We used scores
from the national college entrance examinations (equivalent to SAT in the U.S. educational
system). We chose the department of business administration because most of the universities
have this department and because many of our managers hold business degrees. For specialized
universities (e.g., engineering or medicine) that lacked a business department we computed and
28
used instead the average scores of students in the university’s most highly ranked department. By
our measure, an individual’s (educational) status increases with the value of the variable. We
computed cumulative number of promotions as the count of prior promotions in the focal agency
and time passed since the last promotion as the duration (in years) since last promotion in the
current firm.
We also computed demographic variables at the firm level (organizational size as
number of employees, organizational age, and number of employees in the same rank), and at
the level of the industry (firm foundings, firm failures, and total yearly number of job
transitions5). We also included in our models three macro-economic controls: yearly gross
national product (GNP), industry resources computed as the percentage of advertising
expenditure by client firms from the Gross National Product (GNP), and top 100 budgets—a
measure which totals the advertising expenditures of the 100 largest companies in Korea.
Method
We model the turnover rate of advertising managers as a function of their tenure duration
in the focal agency employing them. That is, each manager is at risk of leaving his employer at
the end of the year and transitioning to a job in another agency, as long as he was employed by
the agency at the start of the year. We treat exits to jobs outside of the industry as noninformatively censored on the right, and are not included in the event of interest. Our reasoning
is that we do not know what causes exit outside of the industry and the employment staging
processes at play in such cases complicate interpretation of our causal variables. We use event
5
This variable counting the number of all job transitions in a year controls for yearly variations
in job mobility in the labor market.
29
history analysis to model the job change rate in a piecewise exponential model, breaking the
tenure duration at 3 and 7 years (based on the distribution of events and model fit), thus
producing three tenure pieces. The turnover rate is then constrained to vary between the periods
but is constant within them. All independent variables in the model are lagged by one year.
Results
Descriptive statistics and coefficients are presented in Table 1, and inferential statistics
appear in Tables 2 through 5. The baseline model 2.1 in Table 2 sets the stage for testing the
hypotheses and offers some predictable effects that accord with earlier findings in the career
mobility literature: First, at the individual level, in addition to the negative tenure dependence in
quits, the baseline human and social capital effects are as anticipated: individual age and position
rank depress the quit rate, while status and industry tenure elevate it. Also as expected, the
promotion and time since promotion effects are negative and positive, respectively. Second, the
organization level effects confirm earlier findings that quits decline inversely with organizational
size. Consistent with earlier findings that heterogeneity increases turnover (Lawrence 1997), we
report an interesting homophily effect showing that a manager’s chances of leaving the
organization decline as the number of peers in his position increases. Third, we report important
market/industry level effects that distinguish organization-related from macro-economic
dynamics. At the organizational population level, the number of newly founded agencies
increases a manager’s quit rate while the number of agency closures decreases it. These effects
control for variations in demand and are consistent with earlier findings (Haveman and Cohen
1994; Haveman 1995). As for industry dynamics, we find that as the resources flowing to the
industry as a share of the total economy increase, a person’s likelihood to change jobs decreases.
30
However, as resources in the industry become more concentrated (i.e., increasing the share of the
largest 100 agencies), a manager’s quit rate increases. Beyond the advertising industry, periods
of national economic growth denoted by rising GNP generally encourage opportunism and lessen
concerns about job insecurity hence encouraging job transitions (DiPrete1993; Doeringer 1990;
Hachen 1992).
It is against the backdrop of this elaborate multi-level baseline model that we test the
predictions in our hypotheses. In model 2.2, we add the niche width and overlap measures along
the employee age dimension and their effects are negative and positive on the rate, respectively;
both are significant as is the improvement in model fit compared with the baseline model 2.1.
Agencies that employ managers of different ages experience less turnover but the extent of
overlap with other agencies in terms of the age range of their recruitment bases increases
turnover. A manager working in a broad niche firm is less likely to quit than his counterpart from
a narrow-niche firm; at the same time, that manager is more likely to quit, the greater the number
of other firms whose niches overlap his firm’s niche in human resource space. Hypotheses H1a
and H2a receive strong support.
In model 2.3, we test the same effects but along the employee status dimension and
obtain analogous results: the greater the variance in the educational prestige of its employees, the
less likely a manager is to quit that firm, holding constant his own educational endowment
(though this effect is marginally significant at the .10 level). Recruiting overlap along the status
dimension between agencies significantly increases turnover. The more firms there are that
employ managers with the same prestigious educational background as employed by the focal
firm, the more likely a manager is to quit that firm regardless of whether the overlap is in his
31
own educational prestige segment. That is, the quits likelihood is not an individual but an
organizational-level effect: crowding in the educational status dimension means that firms face
increased competition in recruiting and retaining employees with specific educational
backgrounds which in turn affects the firm’s overall ability to manage human resources.
Increased competition for one segment of target employees diverts resources needed to recruit
and retain employees from other segments—the quintessential weakness of the generalist design
as per the principle of allocation (Hannan and Freeman 1977). Overall, the results from model
2.3 offer significant improvement of model fit relative to the baseline model 2.1 and convey
moderate support for hypothesis H1b and strong support for hypothesis H2b.
Our next set of hypotheses relate to the advantage of specialist firms in attracting
employees with high-level human and social capital. These hypotheses imply interaction effects
between our measures of human and social capital and specialism so we begin the tests by
including a categorical measure of specialism in model 3.1 in Table 3. This baseline effect is
positive and significant and provides an important check on the validity and consistency of our
measures—it accords with the negative effect of niche width on quits which suggests that
turnover increases as the niche narrows. Beyond logical consistency in the data, the baseline
specialism effect is important because it does show a recruitment and retention advantage for
generalists which we argued is modified by employees’ human and social capital endowments
and by the firm’s incentive system. Our supposition that specialist firms—lacking the benefits
associated with broad niche and scale—will have higher attrition rates is confirmed.
Models 3.2 and 3.3 test the amplified negative effect of human and social capital on quits
in specialist firms. As expected, we find that the interactions of individual age and position rank
32
with specialist firm both have negative and significant effects. Interestingly, the main effects of
these two variables become statistically non-significant suggesting that a manager’s quits only
decrease as a function of age and rank when he is employed in a specialist firm. Both models
significantly improve fit and offer strong support for hypotheses H3a and H3b.
In models 3.4 and 3.5 we include the interactions between working in a specialist firm
and human and social capital that we surmised drive quits higher, namely, educational status and
industry tenure. We expected that the main effects of these measures will be positive but that the
interactions will be negative, suggesting that the increased turnover as a function of status and
industry tenure will be tempered in specialist firms. We report that the main effects do remain
positive and significant (as discussed in the baseline model) and the interaction effects are
positive but not statistically significant and model fit does not improve in either model so we find
no support for hypotheses H3c and H3d.
Our last set of hypotheses claimed that the impact of the formal incentive system varies
by organizational type. Specifically, we argued that the negative effect of cumulative number of
promotions and the positive effect of waiting time since last promotion on job cessation are both
modified to increase attrition in specialist organizations. We predicted that the interactions
between cumulative number of promotions and between promotion waiting time and a specialist
firm will be positive. We test these two predictions in models 3.6 and 3.7, respectively, and the
results agree with our theorizing. Both interaction coefficients are significant, in the predicted
direction, and offer improved model fit. Hypotheses 4a and 4b are supported, indicating that a
manager’s decreasing likelihood to quit as a function of prior promotions is tempered in
33
specialist firms, while his odds of leaving as a function of promotion wait time are amplified
when employed in a specialist firm.
Overall, our results offer strong support for all hypotheses except H3c and H3d in which
the direction of effects is as predicted but the interaction coefficients are not statistically
significant. This prompted us to speculate that the effects of status and industry tenure on
turnover in specialist firms may actually be nonmonotonic so we tested for it. Model 4.1 in
Table 4 includes interactions of specialist firm with the linear and quadratic terms of education
status. The effects are positive and negative, respectively; both are highly significant and
improvement in model fit (relative to both models 3.1 and 3.3) justifies the nonmonotonic
specification. Similarly, the nonmonotonic specification for the industry tenure effect on quits in
specialist firms is tested and confirmed in model 4.2. The interactions between specialist firm
and the linear and squared terms of employee’s industry tenure are positive and negative
respectively; both are highly significant and improve model fit relative to models 3.1 and 3.4.
These results indicate that for all employees, the odds of quitting increase as a function of
education status but for employees working in specialist firms, the odds of quitting are tempered
not just for high-status employees (as we predicted in H3c) but for those at the low end of the
status distribution as well. Similarly, while all employees become more likely to change jobs as
their industry experience increases, those working in specialist firms are less likely to quit not
only when their industry experience is substantial (as we hypothesized in H3d) but also when it
is minimal.
Finally, as a means of checking for the robustness of our results we include all the
specialist firm interactions (hypotheses H3a, H3b, H3c, H3d, H4a and H4b) in one saturated
34
model (model 5.1 in Table 5). By and large, the results remain unchanged, except that the
interaction between specialist firm and employee age loses significance, most likely because of
the high correlation of age with rank (0.65) and with industry experience (0.51). The log
likelihood test confirms a significant improvement of fit relative to the baseline model 2.1 and to
the simple model (without interactions) 3.1. Tellingly, the main effect of working in a specialist
firm loses significance in this saturated model suggesting that the human resource advantage that
specialists enjoy may be fully explained by these firms’ stronger appeal to employees with
higher status, more experience, and of higher rank. On the other hand, the advantage that accrues
to generalist firms is that they are better able than specialists to motivate their employees through
a formal system of promotions.
Discussion and Conclusion
Our results provide an account of turnover within an industry over time which shows how
individuals’ different employment profiles combine with organizational form differences to
predict quit rates. We demonstrated that when a consolidated market is partitioned between
generalist and specialist organizations, employees’ job preferences and characteristics lead them
to match with the incentive and work system in either form thus eventually leading to the
partitioning of human resources in the industry. We developed three mechanisms that drive this
partitioning.
First, we showed that demographic and cultural variance within an organization makes
employees less likely to quit while increased overlap with many other firms that employ
35
individuals within that variance increases turnover. The novelty of these predictions is that they
explicitly theorize about organizational, rather than individual mechanisms, but still apply them
to an individual outcome. Established accounts of turnover emphasize the negative relationship
between diversity and retention (Tsui, Egan and O’Reilly 1992)—the greater the heterogeneity
of the group within which an employee works, the more likely she is to quit. The purported
driver of this effect is at the employee level—she is less likely to establish rapport,
communication and consensus with coworkers in a diverse group, hence her chances of exit
increase. By contrast, our arguments pertain to an organization’s increased ability to benefit from
the variance of its workforce characteristics. Such benefits include complementarities between
segments in the variance (e.g., intergenerational knowledge transfer) or better matching of
persons and jobs (e.g., assigning overachieving low-status employees to more desirable tasks).
Greater variance in employee characteristics then gives the organization an advantage in
managing retention favorably.
Second, we argued and confirmed that generalist organizations, precisely because of their
broad niches and reliance on a greater band of human resources, have a baseline retention
advantage over specialist firms but that this advantage is countered as employees’ human and
social capital increases. Employees’ embeddedness in the firm generally increases with their
rank and age but this effect is substantially stronger in specialist firms, even after controlling for
arguably the strongest predictor of embeddedness, time spent with the organization (i.e.,
organizational tenure). Similarly, an employee’s overall appeal to potential employers and the
according job opportunities tend to rise with her educational endowment and industry experience
but these forces that drive up the potential to be hired away from the current employer are
36
subdued in specialist firms. The preference for employees with high levels of human and social
capital endowments is, we argued, the key mechanism by which specialist firms are able to offset
the retention advantage of generalists.
Lastly, we conjectured that although all employees are motivated to increase their
commitment to the firm by multiple and frequent promotions, this motivation is less pronounced
in specialist firms because of these firms’ unelaborate structural designs characterized by flat
hierarchy, and the corresponding lack of internal promotion ladders. Although this lack of formal
constraint is what drives the appeal of working in specialist firms by allowing for greater
autonomy and creative initiative, it also works against the ability to benefit from offering formal
incentives and internal labor markets. In short, then, generalist firms benefit from scale and
scope and enjoy a greater retention advantage which is strengthened by the formal system of
hard incentives (number and frequency of promotions) that their structures allow them to offer.
By contrast, specialist firms benefit from appealing to a specific segment of the labor force—
those with higher human and social capital who are motivated by softer, intrinsic rewards. And
this duality of employment models between generalist and specialist firms is what, we argue,
leads to the partitioning of human resources—a process by which employees sort themselves into
working for different types of organizations which in turn perpetuates both the functional and
categorical distinctions between specialist and generalist firms, in accord with resource
partitioning theory.
We began by proposing that our understanding of job quits and turnover would be
enhanced if we integrated theory and evidence from contemporary organization theory into the
careers literature, which—of all approaches to studying job quits—seems best suited to account
37
for organizational factors because of its emphasis on sequences of jobs and the pertinent firm-tofirm job transitions. In fact, we have learned much about how internal organizational processes
affect quits. Most influential has been demographic research tying variance in firm size to its
employees’ chances of leaving (Cole, 1979; Carroll and Mayer, 1986; DiPrete, 1993; Greve,
1994; Dobrev and Barnett 2005; Dobrev 2012). Large, structurally differentiated organizations
are seen as offering internal labor markets (Doeringer and Piore 1971; Kalleberg et al. 1996),
training and advancement opportunities (Hu 2003; Bidwell and Briscoe 2010), and more job
security (Kalleberg and Van Buren 1996; Villemez and Bridges 1988). By contrast, small
organizations emphasize autonomy, creativity, a sense of purpose and self-realization, and
altogether softer, more intrinsic rewards (MacDermid et al. 2001, Zipp 1991). Employees then,
sort themselves into large vs. small organizations based on personal preferences and abilities.
At first blush, our theory of human resource partitioning may seem as a mere elaboration
of extant arguments about the relationship between organizational size and quits. We think such
an interpretation would be missing the mark for two reasons. First, existing theories treat
organizational size as a given and are not concerned with the external processes leading to shifts
in the size distribution of the relevant market—one can distinguish between small and large firms
across market, industry or geographic boundaries without any need for investigating the
interdependence between large and small organizations. Such a broad context makes it difficult
to source insight from contemporary organization theories that emphasize the interdependence
between organizations and their environment, comprised of other like organizations. Second,
although size is frequently a reasonable proxy for specialism, resource partitioning theory
attributes features to specialist organizations that allow theoretical import and thus clearer
38
mechanisms for the observed job transitions. For example, resource partitioning theory attributes
strong and focused identities to specialists and portrays them as commanding higher appeal to
the narrow segment of the audience that they target (Carroll and Swaminathan, 2000; Negro,
Hannan and Rao, 2010). In the case of human resource recruitment, we show that specialists
appeal to individuals with certain human and social capital profiles related to age, rank, status
and experience. This cannot be attributed to differences in size alone. Actually, whether size
alone can account for the observed differences in quit rates is an empirical matter and our results
show strong and pronounced effects associated with our measures of specialism even in the
presence of strong and significant effects associated with organizational size.
The advantage of our approach is that by looking at the job transitions of all employees
within a meaningfully defined set of interrelated organizations in a market, we bring the interorganizational dynamic to bear on the likelihood of the job transitions. This approach follows in
the footsteps of earlier studies in the macro organizational demographic tradition. Greve’s (1994)
seminal work on the effect of industry size distributions on job transitions was the first empirical
test of Hannan’s (1988) conjecture that shifts in employment opportunities in an industry can be
understood as a function of its organizational distributions. Greve showed that workers’ job
transitions between firms in an industry are positively correlated with organizational size
diversity in the industry. Subsequent research in this direction (Haveman 1995; Haveman and
Cohen 1994, Sørensen 2000; 2004, Fujiwara-Greve and Greve, 2000; Sørensen and Sorenson
2007) contributed valuable insight into the relationship between macro-level processes and
aggregate employment outcomes. What we add to this vibrant stream of research is the
integration of micro and macro-level processes. To the literature emphasizing individual effects
39
on job quits we add a layer of organizational effects emanating from interorganizational
dynamics that modify (but also validate) the arguments that support these individual effects. To
the literature on macro demographic determinants of labor market outcomes, we add the
integration with the individual-level effects. These analyses are primarily concerned with the
external structure of opportunity and its effect on quits but have not explicitly theorized about
how rising or declining job opportunities brought about by organizational processes trigger
responses by individuals based on their career histories and labor market experiences.
Most research on the structure of opportunity assumes demand factors are exogenous to
organizations and are a function of market/industry level forces like changes in technology. By
this account, once demand is structured in response to broader technological and market forces,
employees respond by sorting themselves into organizations depending on the match between
employer’s offerings (based on the technology and skill required) and employee’s valuable assets
and abilities. What we argue is that the sorting of employees into different organizations and the
ensuing job mobility by which this sorting unfolds is also structured endogenously through the
inter-organizational dynamics within a market.
Beyond a contribution to the literature on turnover, we think our approach helps to push
further the theoretical mechanisms in the organizational theory of resource partitioning whose
predicted outcome—the partitioning of resources in a consolidated market between specialist and
generalist organizations—we employed to develop our theory.
Clearly, there are two types of resources—inputs and outputs. When predictions about
resource selection and utilization by organizations are made at a macro level, such predictions
40
necessarily treat resources of all kind in an aggregate fashion, which in turn results in higherlevel predictions where the theorized mechanisms behind the predictions are hard to measure and
test directly and invariably rely on assumptions. While this approach has been remarkably
productive for advancing resource partitioning theory (Carroll et al. 2005), the maturity of this
theory now allows for breaking down its basic conceptual elements and refining its key
mechanisms and assumptions.
In resource partitioning theory, generalist and specialist firms are considered distinct
organizational forms (Carroll and Swaminathan 2000) with specialists seeking to avoid
competition on scale by selecting niche positions that maximize appeal to a particular audience
segment in contrast to generalists who seek to attract audience members across a broad range of
the market where scope economies and complementarities lead to increased scale (Carroll et al.,
2002). We know a great deal about the strategies and organizational features of specialists and
generalists, but precious little about whether and how they differ in terms of their employee
profiles. Our study confirmed that there is clear generalist-specialist segmentation along the
human resource dimension.
Although, as its name conspicuously suggests, resource partitioning theory is about the
partitioning of resources, most studies do not directly measure resources or differentiate among
their type (for a notable exception, see Boone, Carroll and Van Wittelloostuijn, 2002).6 Instead,
6
In their study of Dutch newspapers, these researchers directly measure several characteristics of
the consumer audience, including age, political preference, education and religious affiliation.
While this allows to evaluate some of the key assumptions in the theory (heterogeneous audience
tastes and a unimodal resource distribution), it only pertains to characteristics of the audience on
the demand side. We are not aware of any studies that do the same for input resources.
41
resources are measured by organizational size in terms of production scale. Since the partitioning
of resources is inferred from the distinction between firms that sell a lot of units and those that
sell a lot less, it is only relevant for describing the bimodal distribution of outputs even though
the theory predicts the partitioning of all relevant resources, including inputs. Yet, input
resources, to our knowledge, have not been analytically and empirically investigated before in
the context of resource partitioning theory. Taking up this task in this study we were able to
contribute to the original theory and validate its causal logic. We view this approach as a
productive next step in the research agenda of a mature theory.
To investigate whether processes theorized to operate at the level of market demand
(output resources) also operate at the level of input resources, we investigated the Korean TV
advertising industry which by the time of our data window was clearly partitioned on the demand
side between large-scale broad service providers and small, specialized agencies. Our research
showed that these generalists and specialists were also distinct in how they value, recruit and
retain their employees. Importantly, we theorized and confirmed the specific mechanisms that
underpin this human resource partitioning between generalist and specialist firms.
We were able to develop these ‘micro’ mechanisms by shifting the outcome of interest
from organizational failure and position change to labor force turnover. Our analysis usefully
complements received theory about the effects of organizational identity, competition and inertia
on firm failure and change. Working to further develop and substantiate with more detail and
precision the general logic and predictions of established macro organization theories, as we did
here, is an effort that makes these theories stronger and that we hope will be followed in the
future.
42
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48
Table 1: Descriptive statistics and correlation coefficients for variables
Variable
1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
11.
12.
13.
14.
15.
16.
17.
18.
19.
20.
21.
22.
23.
Job tenure in agency
Ln(GNP)
Age
Rank
Status (education)
Status2
Industry tenure
Industry tenure2
N promotions
Promotion time clock
Ln Firm size
Firm age
N same rank in firm
N firm foundings
N firm failures
N all job transitions
Industry resources *103
Top 100 ads budgets * 10-5
Firm NW (age)
Firm NO (age)
Firm NW (status)
Firm NO (status)
Specialist agency
Variable
15. N firm failures
16. N all job transitions
17. Industry resources *103
18. Top 100 ads budgets * 10-5
19. Firm NW (age)
20 Firm NO (age)
21. Firm NW (status)
22. Firm NO (status)
23. Specialist agency
Min.
1.00
12.82
26.00
1.00
1.25
0.16
1.00
1.00
0.00
0.00
1.10
1.00
0.00
0.00
0.00
0.00
0.70
0.99
1.00
2.00
1.00
8.00
0.00
Max.
Mean
S.D.
1
2
3
4
5
6
7
8
9
10
18.00
3.60
15.24 14.63
86.00 39.80
7.00
2.37
3.32
2.63
1.10
0.72
18.00
4.64
324.00 34.79
4.00
0.39
17.00
0.93
6.83
4.89
32.00 12.98
175.00 31.37
40.00 14.46
26.00 10.78
58.00
3.08
1.45
1.17
16.84
8.81
56.00 25.12
227.00 144.53
208.00 175.47
228.00 151.17
1.00
0.34
2.94
0.58
6.75
1.84
0.55
0.25
3.64
52.58
0.58
1.97
1.29
7.00
35.31
12.22
9.78
4.99
0.20
4.62
10.96
64.24
63.57
65.77
0.48
0.16
0.41
0.24
0.05
0.05
0.74
0.66
0.42
0.41
0.14
0.27
0.06
-0.04
0.11
0.14
0.14
0.14
0.05
0.12
0.03
0.12
-0.13
0.05
-0.01
-0.06
-0.07
0.23
0.22
0.15
0.19
0.10
0.31
0.25
-0.31
0.77
0.14
0.95
0.91
0.01
0.83
0.03
0.84
0.11
0.65
0.02
0.03
0.51
0.48
0.21
0.25
-0.21
0.02
-0.24
-0.02
0.04
0.00
0.05
0.04
-0.11
-0.06
-0.22
-0.07
0.18
0.03
0.04
0.36
0.34
0.07
0.09
-0.53
-0.20
-0.40
0.03
-0.02
-0.15
-0.02
-0.02
-0.44
-0.25
-0.56
-0.25
0.45
0.99
0.10
0.09
0.09
0.06
0.16
0.05
0.08
-0.01
-0.03
0.07
-0.05
-0.04
0.07
0.00
0.06
0.00
-0.14
0.10
0.09
0.09
0.06
0.15
0.05
0.07
-0.01
-0.04
0.06
-0.05
-0.05
0.07
-0.01
0.06
-0.01
-0.14
0.95
0.48
0.55
-0.01
0.15
-0.06
-0.06
0.17
0.18
0.21
0.20
-0.04
0.14
-0.08
0.14
0.01
0.44
0.52
0.03
0.14
-0.08
-0.08
0.18
0.12
0.22
0.21
-0.06
0.14
-0.08
0.14
0.03
0.57
0.20
0.16
0.03
-0.02
0.11
0.19
0.14
0.12
0.15
0.21
0.16
0.21
-0.16
0.13
0.15
0.07
-0.09
0.16
0.17
0.19
0.18
0.09
0.19
0.10
0.19
-0.11
19
20
21
22
14
-0.51
0.02
-0.53
-0.61
-0.03
-0.16
-0.03
-0.16
0.00
15
16
17
18
0.13
0.84
0.88
0.03
0.56
0.04
0.60
0.08
0.12
0.11
0.19
0.23
0.24
0.23
-0.31
0.98
0.02
0.75
0.03
0.76
0.10
0.02
0.68
0.04
0.69
0.09
11
12
13
0.59
0.71 0.55
-0.09 -0.20 -0.15
0.10 0.30 0.23
0.34 0.25 0.25
0.11 0.32 0.26
0.12 0.33 0.27
0.61 0.18 0.34
0.38 0.37 0.38
0.71 0.33 0.40
0.37 0.38 0.38
-0.82 -0.44 -0.55
0.37
0.67 0.37
0.29 0.96 0.43
-0.58 -0.15 -0.61 -0.14
49
Table 2. Firm niche width and overlap effects on the job change rate of managers in the Korean advertising industry
Model 2.1
Model 2.2
Model 2.3
Job tenure (0-3)
Job tenure (3-7)
Job tenure (7+)
Ln(GNP)
Age
Rank
Status (education)
Industry tenure
N promotions
Promotion time clock
Ln Firm size
Firm age
N same rank in firm
N firm foundings
N firm failures
N all job transitions
Industry resources *103
Top 100 ads budgets * 10-5
Firm NW (age)
Firm NO (age)
Firm NW (status)
Firm NO (status)
-6.22**
-6.22**
-6.48**
0.40*
-0.012*
-0.27**
0.29**
0.08**
-0.34**
0.06**
-0.13**
0.01
-0.01**
0.005
-0.02**
0.01**
-1.48*
0.06*
(2.46)
(2.46)
(2.46)
(0.21)
(0.006)
(0.03)
(0.05)
(0.01)
(0.07)
(0.02)
(0.04)
(0.01)
(0.001)
(0.004)
(0.01)
(0.004)
(0.86)
(0.04)
Final Log Likelihood
-4861.05
N spells=17,810; N events=1,437;
Figures in parentheses are standard errors; ** p < .05, * p < .10
†
p< .10 (one-tailed test)
2.49
2.52
2.26
-0.20
-0.013**
-0.25**
0.28**
0.08**
-0.36**
0.06**
-0.14**
0.01
-0.01**
0.007*
-0.02**
0.01**
-2.28**
0.13**
-0.01**
0.004**
(3.53)
(3.54)
(3.54)
(0.27)
(0.007)
(0.03)
(0.05)
(0.01)
(0.07)
(0.02)
(0.04)
(0.01)
(0.001)
(0.004)
(0.01)
(0.004)
(0.90)
(0.04)
(0.004)
(0.001)
6.64
6.67
4.40
-0.35
-0.014**
-0.25**
0.28**
0.08**
-0.36**
0.06**
-0.16**
0.01
-0.01**
0.008*
-0.02**
0.01**
-2.46**
0.15**
†
-0.0013
0.005**
-4855.25
(3.82)
(3.82)
(3.83)
(0.29)
(0.007)
(0.03)
(0.05)
(0.01)
(0.07)
(0.02)
(0.05)
(0.01)
(0.001)
(0.004)
(0.01)
(0.004)
(0.92)
(0.04)
(0.0009)
(0.002)
-4853.70
50
Table 3 Human and social capital effects (by type of firm) on the job change rate of managers in the Korean advertising industry
Model 3.1
Model 3.2
Model 3.3
Model 3.4
Model 3.5
Model 3.6
Job tenure (0-3)
Job tenure (3-7)
Job tenure (7+)
Ln(GNP)
Age
Rank
Status (education)
Industry tenure
N promotions
Promotion time clock
Ln Firm size
Firm age
N same rank in firm
N firm foundings
N firm failures
N all job transitions
Industry resources * 103
Top 100 ads budgets * 10-5
Specialist
Specialist × Age
Specialist × Rank
Specialist × Status
Specialist × Ind Tenure
Specialist × N promotions
Specialist × Promotion clock
-6.20**
-6.19**
-6.45**
0.37*
-0.012*
-0.27**
0.29**
0.08**
0.34**
0.06**
-0.07
0.004
-0.01**
0.005
-0.02**
0.01**
-1.48*
0.06
0.21**
(2.46)
(2.46)
(2.46)
(0.21)
(0.007)
(0.03)
(0.05)
(0.01)
(0.07)
(0.02)
(0.05)
(0.005)
(0.001)
(0.004)
(0.01)
(0.004)
(0.86)
(0.04)
(0.10)
-6.57**
-6.57**
-6.86**
0.37*
0.002
-0.25**
0.29**
0.08**
-0.36**
0.06**
-0.08
0.005
-0.01**
0.005
-0.02**
0.01**
-1.52*
0.06*
1.19**
-0.03**
(2.46)
(2.46)
(2.47)
(0.21)
(0.01)
(0.03)
(0.05)
(0.01)
(0.07)
(0.02)
(0.05)
(0.005)
(0.001)
(0.004)
(0.01)
(0.004)
(0.86)
(0.04)
(0.37)
(0.01)
-5.88**
-5.88**
-6.17**
0.36*
-0.02**
-0.05
0.28**
0.09**
-0.41**
0.06**
-0.11**
0.01
-0.01**
0.005
-0.02**
0.01**
-1.53*
0.06*
0.68**
(2.46)
(2.46)
(2.46)
(0.21)
(0.01)
(0.04)
(0.05)
(0.01)
(0.07)
(0.02)
(0.05)
(0.01)
(0.001)
(0.004)
(0.01)
(0.004)
(0.86)
(0.04)
(0.13)
-0.26**
(0.05)
Final Log Likelihood
-4858.67
-4854.97
-4843.45
N spells=17,810; N events=1,437;
Figures in parentheses are standard errors; ** p < .05, * p < .10
-6.28**
-6.27**
-6.53**
0.37*
0.012*
-0.27**
0.32**
0.08**
-0.35**
0.06**
0.07
0.004
-0.01**
0.005
-0.02**
0.01**
-1.48*
0.06
0.36
(2.46)
(2.46)
(2.47)
(0.21)
(0.007)
(0.03)
(0.08)
(0.01)
(0.07)
(0.02)
(0.05)
(0.005)
(0.001)
(0.004)
(0.01)
(0.004)
(0.86)
(0.04)
(0.30)
-0.06
(0.11)
-4858.53
-6.19***
-6.18***
-6.46***
0.37*
-0.013**
-0.26**
0.29**
0.08**
-0.35**
0.06**
-0.07
0.004
-0.01**
0.05
-0.02**
0.01**
-1.48
0.06
0.25**
(2.46)
(2.46)
(2.46)
(0.21)
(0.007)
(0.03)
(0.05)
(0.01)
(0.07)
(0.02)
(0.05)
(0.005)
(0.001)
(0.004)
(0.01)
(0.004)
(0.86)
(0.04)
(0.12)
-0.009
(-0.015)
-4858.51
-6.34**
-6.32**
-6.53**
0.38*
-0.012*
-0.27**
0.28**
0.08**
-0.47**
0.06**
-0.07
0.004
-0.01**
0.005
-0.02**
0.01**
-1.48*
0.06
0.10
(2.46)
(2.46)
(2.46)
(0.21)
(0.007)
(0.03)
(0.05)
(0.01)
(0.08)
(0.02)
(0.05)
(0.005)
(0.001)
(0.004)
(-0.01)
(0.004)
(0.86)
(0.04)
(0.10)
0.32**
(0.10)
Model 3.7
-6.21**
-6.20**
-6.41**
0.37*
-0.012*
-0.27**
0.28**
0.08**
-0.35**
0.04**
-0.07
0.005
-0.01**
0.005
-0.02**
0.01**
-1.46*
0.06*
0.15*
(2.46)
(2.46)
(2.46)
(0.21)
(0.007)
(0.03)
(0.05)
(0.01)
(0.07)
(0.02)
(0.05)
(0.01)
(0.001)
(0.004)
(0.01)
(0.004)
(0.86)
(0.04)
(0.10)
0.06**
(0.02)
-4853.97
-4856.04
51
Table 4 Nonmonotonic effects of status and industry tenure on the job change rate of managers in the Korean advertising industry
Model 4.1
Job tenure (0-3)
Job tenure (3-7)
Job tenure (7+)
Ln(GNP)
Age
Rank
Status (education)
Tenure in industry
N promotions
Promotion time clock
Ln Firm size
Firm age
N same rank in firm
N firm foundings
N firm failures
N all job transitions
Industry resources * 103
Top 100 ads budgets * 10-5
Specialist
Specialist × Status
Specialist × Status 2
Specialist × Ind Tenure
Specialist × Ind Tenure 2
-6.28**
-6.27**
-6.53**
0.37*
-0.012*
-0.27**
0.31**
0.08**
-0.35**
0.06**
-0.06
0.004
-0.01**
0.005
-0.02**
0.01**
-1.49*
0.06
-0.87
(2.46)
(2.46)
(2.47)
(0.21)
(0.007)
(0.03)
(0.08)
(0.01)
(0.07)
(0.02)
(0.05)
(0.005)
(0.001)
(0.004)
(0.01)
(0.004)
(0.86)
(0.04)
(0.62)
1.14**
-2.64**
(0.53)
(1.16)
Model 4.2
-5.97**
-6.01**
-6.24**
0.35*
-0.013**
-0.26**
0.29**
0.09**
-0.36**
0.06**
-0.06
0.004
-0.01**
0.005
-0.02**
0.01**
-1.47*
0.061*
-0.02
(2.46)
(2.46)
(2.47)
(0.21)
(0.007)
(0.03)
(0.05)
(0.01)
(0.07)
(0.02)
(0.05)
(0.005)
(0.001)
(0.004)
(0.01)
(0.004)
(0.86)
(0.039)
(0.14)
0.12**
-0.01**
(0.04)
(0.003)
Final Log Likelihood
-4855.86
-4852.21
N spells=17,810; N events=1,437;
Figures in parentheses are standard errors; ** p < .05, * p < .10
52
Table 5 Full specification of covariate effects on the job change rate of managers in the Korean advertising
industry
Model 5.1
Job tenure (0-3)
Job tenure (3-7)
Job tenure (7+)
Ln(GNP)
Age
Rank
Status (education)
Industry tenure
N promotions
Promotion time clock
Ln Firm size
Firm age
N same rank in firm
N firm foundings
N firm failures
N all job transitions
Industry resources *103
Top 100 ads budgets * 10-5
-5.68**
-5.70**
-5.89**
0.35*
-0.02**
-0.004
0.27**
0.09**
-0.58**
0.04*
-0.13**
0.009*
-0.01**
0.005
-0.02**
0.01**
-1.54
0.07
(2.47)
(2.47)
(2.48)
(0.21)
(0.01)
(0.05)
(0.08)
(0.02)
(0.09)
(0.021)
(0.05)
(0.005)
(0.001)
(0.004)
(0.01)
(0.005)
(0.86)
(0.04)
Specialist
Specialist × Age
Specialist × Status
Specialist × Status 2
Specialist × Ind Tenure
Specialist × Ind Tenure 2
Specialist × Rank
Specialist × N promotions
Specialist × Promotion clock
-0.90
0.003
1.15**
-2.54**
0.10**
-0.01**
-0.32**
0.36**
0.064*
(0.77)
(0.013)
(0.54)
(1.16)
(0.04)
(0.003)
(0.06)
(0.14)
(0.035)
Final Log Likelihood
-4823.21
N spells=17,810; N events=1,437;
Figures in parentheses are standard errors; ** p < .05, * p < .10
53