518638
2013
ASRXXX10.1177/0003122413518638American Sociological ReviewLeung
Dilettante or Renaissance
Person? How the Order of Job
Experiences Affects Hiring in
an External Labor Market1
American Sociological Review
2014, Vol. 79(1) 136­–158
© American Sociological
Association 2013
DOI: 10.1177/0003122413518638
http://asr.sagepub.com
Ming D. Leunga
Abstract
Social actors who move across categories are typically disadvantaged relative to their more
focused peers. Yet candidates who compile experiences across disparate areas can either
be appreciated as renaissance individuals or penalized as dilettantes. Extant literature has
focused on the comparison between single versus multiple category members and on skill
assessment, hindering its applicability. To discriminate between more versus less successful
category spanners, I suggest that the order of accumulated experiences matters, because it
serves as an indicator of commitment. I propose the concept of erraticism and predict that
employers will prefer candidates who demonstrate some erraticism, by moving incrementally
between similar jobs, over candidates who do not move and also over those with highly
erratic job histories. Furthermore, I suggest this relationship holds for more complex jobs,
less experienced freelancers, and is attenuated through working together. These issues are
particularly salient given the rise of external labor markets where careers are increasingly
marked by moves across traditional boundaries. I test and find support for these hypotheses
with data from an online crowd-sourced labor market for freelancing services, Elance.com. I
discuss how virtual mediated labor markets may alter hiring processes.
Keywords
categories, online labor market, commitment, freelancing
My starting point is the work that demonstrates how social actors who straddle multiple categories are often disadvantaged (Hsu,
Hannan, and Koçak 2009; Rao, Monin, and
Durand 2005; for a review, see Hannan 2010).
Categories are recognized social groupings of
like-concepts or objects (Hannan, Pólos, and
Carroll 2007; Zerubavel 1997), such as competitive rivals (Porac et al. 1995), movie
genres (Hsu 2006), high-technology product
markets (Kennedy 2008), investment styles
(Smith 2011), or styles of wine (Negro, Hannan, and Rao 2011). Actors who span classificatory distinctions, by combining disparate
characteristics or experiences, are difficult to
understand and audiences often ignore them
(Zuckerman 1999) or disapprove (Negro and
Leung 2013). The quintessential example in
labor markets is the typecasting of actors in
the feature film industry (Zuckerman et al.
2003). Because success at a particular task is
difficult to ascertain a priori, casting agents
prefer actors who specialize in a single movie
genre. Demonstrating such an achievement
acts as prima facie evidence of their potential
a
University of California-Berkeley
Corresponding Author:
Ming D. Leung, University of California-Berkeley,
Haas School of Business, 2220 Piedmont Avenue,
Berkeley, CA 94720
E-mail: [email protected]
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Leung
137
success in that categorical genre, but not necessarily in others.
However, labor market participants who
compile experiences across disparate areas
can be perceived as either multi-talented or
unsuccessful in any one. Because both types
of candidates have a breadth of experiences,
existing theories suggest audiences will discount them both. Do employers differentiate
between candidates with multiple disparate
categorical experiences? Extant literature
provides an incomplete answer to this question for two reasons. First, it focuses on the
comparison between single versus multiple
category members, that is, specialists versus
generalists (Hannan 2010; Hsu 2006). Instead
of examining which amalgamations of experiences are more attractive or advantaged,
generalists are all assumed to be similarly
disadvantaged. The line between more or less
successful boundary spanners could be drawn
more distinctly if investigators could disambiguate why some candidates, given a fixed
portfolio of multiple category experiences,
are more successful than others.2 Second,
most past work conceptualizes a candidate’s
identity as merely a function of what she did
most recently, thereby ignoring the ordered
nature of accumulated experiences. Instead, I
propose that this challenge can be accommodated, at least in part, by recognizing that
candidates’ identities are also drawn from the
historical path of their past experiences,
which may vary among generalists.
Demonstrations of progress, through hierarchically arranged work experiences, are
often seen as prototypical, or preferred, career
trajectories (Barley 1989). Terms used to describe work sequences, such as “up or out”
and “career ladder,” are imbued with such
beliefs. Temporal dynamics should therefore
play a role in understanding how job market
classifications and career trajectories interact.
As labor markets increasingly exhibit elements of boundarylessness, I ask: Does the
particular order of jobs a candidate compiles
continue to matter? If so, what distinguishes
“good” from “bad” moves? Some nascent
work relies on the paradigm of skill accumulation to understand career progress in labor
markets (Bidwell and Briscoe 2010;
O’Mahony and Bechky 2006), but sustained
investigation is yet forthcoming.
Beyond skill assessment, I point out that
employers also grapple with the uncertainty of
a candidate’s commitment, which affects how
much effort an actor will put into a task (Correll and Benard 2006; Turco 2010). This is
why the historical order of accumulated experiences matters. In contrast to extant categorization literature, I present evidence that suggests some movement across boundaries
makes a candidate more desirable, but too
much movement risks being labeled dilettantism. Employers, seeking cues as to how much
effort a candidate will exert, code incremental
moves as demonstrating commitment. These
candidates will be preferred over those who
do not (or cannot) move at all. On the other
hand, employers are wary of highly unstable
work histories (Holzer 1996), so candidates
who moved between extremely dissimilar experiences will be perceived as more erratic
and therefore less dedicated—in short, as dilettantes. This will weigh more heavily on an
employer’s decision when a job is more complex or when a candidate has an abbreviated
work history. I also provide evidence isolating
this effect from theories that suggest similar
patterns due to skill or learning.
My setting is a virtual external labor market for contract workers, www.elance.com.
Variously described as freelancers, contract
labor, or independent contractors (Barley and
Kunda 2004; Osnowitz 2010), this increasing
proportion of the labor market has chosen
self-employment, representing an extreme instantiation of a boundaryless career (Arthur
and Rousseau 1996). The inherent asymmetry
in presentation of work histories between
freelancers and employers online makes
Elance a particularly ideal setting. In contrast
to offline markets where freelancers have
more latitude in tailoring and framing their
work histories (O’Mahony and Bechky 2006),
freelancers here cannot influence how their
past jobs are listed to potential employers.
This reveals a pattern of decision making by
employers that will likely become more prevalent as labor markets become increasingly
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virtually mediated. Given the virtual nature of
the setting, employers are particularly sensitive to freelancers’ commitment, because
monitoring and social sanctions are harder to
rely on than in offline labor markets where
transactions are conducted in a dense embedded web of relations. Empirically, freelancers
represent an ideal population for examination
because of the velocity with which they accumulate disparate work experiences.
Categories and Hiring
A distinctive feature of contract work is the
need to continually seek employment and
market oneself. Sociologists of employment
are no stranger to the fact that the process of
getting hired is fraught with bias (Holzer
1996; Moss and Tilly 1996). Reliance on subjective cues, whether explicit or implicit, can
lead to utilization of stereotypes and thus biases in hiring, rewarding, or making promotion decisions. Examples include assuming
blacks are less compliant than members of
other immigrant groups (Neckerman and
Kirschenman 1991), viewing women as lessskilled musicians (Goldin and Rouse 2000),
and the association of certain gender and racial categories with biases in promotion and
bonus decisions (Castilla 2008; FernandezMateo 2009).
Economic sociologists have, until now, indirectly addressed these issues through their
study of how categories in markets partition
and therefore delineate boundaries between
candidates. Categories are socially recognized
groupings of like-objects that circumscribe
similar items and exclude dissimilar ones
(Hannan et al. 2007; Rosch 1973). This parallels our universal inclination to partition an
assortment of complex items or objects into
manageable and socially understood classificatory clusters (Douglas 1966; Fiske and Taylor
1991; Zerubavel 1997). In labor markets, categories ease comprehensibility for employers,
because jobs and people labeled in one category are understood to be similar to each other
and dissimilar to those in other categories. For
example, the labor market for sociologists
could be partitioned into quantitative versus
qualitative job positions. These labels evoke a
set of associated skills and training particularized to these categories.
In essence, sociologists who study market
behavior have expanded on previously identified race and gender categories to include
other socially consequential distinctions. Categories usefully partition an infinitely complex spectrum of objects, items, and people
precisely because they invoke assumptions
about what being a member of a category entails. But, by drawing attention to distinctions
between items, categories can lead to assumptions of exacerbated differences between market participants, whether they exist or not. In
this sense, categories are stereotypes (Allport
1954; Fiske and Taylor 1991) through which
judgments of appropriateness are reflexively
made based on category membership. Candidates who present categorically inappropriate
identities may be assumed to be less skilled or
of lower quality3 (Leung and Sharkey 2013),
or are harder to understand, evaluate, and
value (Hannan 2010; Hsu et al. 2009; Zuckerman 1999) and are therefore ignored.
Typecasting represents one example of
how categories affect hiring in labor markets
(Faulkner 1983). A priori skill assessment of
film actors is difficult. Because acting in a
dramatic film is generally seen as different
from acting in a comedic one, previous demonstrations of success in a dramatic role act as
prima facie evidence of both an actor’s skill
in that genre and her inability to act in a comedy. Actors who focus their experiences in a
single movie genre are thus more likely to be
subsequently hired for an identical role
(Zuckerman et al. 2003), but are quickly
screened out from consideration for different
roles. In labor markets, specialization—that
is, concentration in a single category—is theorized to signal greater skill than would be
found among generalists who compile multiple experiences (Ferguson and Hasan 2013).
Yet, this extant view suffers from two potential shortcomings. First, labor market participants with experiences across disparate
areas could be perceived as multi-talented
rather than unable to find success in any one.
Existing theory cannot disambiguate this
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difference because it focuses on comparing
actors with single versus multiple categorical
experiences—specialists versus generalists.
For example, film actors who move between
distinctly bounded categories of work could
be evincing broader skill (Zuckerman et al.
2003) and therefore cast as renaissance persons, rather than the dilettantes suggested by
the extant categorization literature. Second,
this past work conceptualizes candidates’
identities as merely a function of what they
did most recently or an aggregation of their
past experiences, thereby overlooking the ordered nature of accumulated experiences that
comprises the essence of a career (Barley
1989; Rosenfeld 1992; Spilerman 1977). This
concern is particularly germane to external
labor market participants whose careers are
increasingly likely to span the traditional
boundaries of employers, industries, or functions (Arthur and Rousseau 1996) and thereby
risk disadvantage in the labor market.
I propose that these challenges can be accommodated, at least in part, by recognizing
that labor market candidates’ identities are
also drawn from the historical path of their
past experiences, which may vary among generalists. Indeed, recent literature on careers
(Bidwell and Briscoe 2010; O’Mahony and
Bechky 2006) highlights the importance of
career progress in external labor markets as an
explicit skill accumulation strategy. Beyond
skill, I suggest that employers seek cues as to
how committed a potential candidate will
be—and this commitment is reflected in the
ordered choice of jobs an employee accumulates. Commitment refers to the amount of
effort that a candidate will be expected to put
forth, holding skill constant (Correll and Benard 2006; Turco 2010). I now turn to contract
employment and external labor markets.
Contract Employment
and the Boundaryless
Career
Variously described as freelancers, contract
labor, or independent contractors (Barley and
Kunda 2004; Osnowitz 2010), an increasing
proportion of the labor market has left permanent employment for self-employment, representing an extreme instantiation of a “boundaryless” career (Arthur and Rousseau 1996).
Falling under the umbrella term of “nonstandard employment,” some estimates suggest
that contingent work may comprise 29 percent of all jobs (Kalleberg 2000). Some examinations highlight the fact that these are
mostly “bad jobs” and represent poor employment opportunities in terms of pay, benefits, or advancement (Kalleberg 2011; Moss,
Salzman, and Tilly 2000), but nonstandard
workers encompass a surprisingly heterogeneous group (Kalleberg 2000).
This article focuses on the recent trend in
the skilled and technical labor force of embracing a contract-based arrangement, whereby
workers provide skilled labor on a project
basis and often command a pay premium over
their similarly tasked full-time counterparts
(Polivka, Cohany, and Hipple 2000). Writing,
editing, programming, and engineering fields,
for example, have well-institutionalized contractor and client bases (Osnowitz 2010).
Structural as well as individual changes have
contributed to this shift, including the speed
of technological advancements, corporate restructuring, and worker preferences (for reviews, see Carré et al. 2000; Ganz, Evans, and
Jalland 2000). By some reports, this skilled
portion of the temporary workforce has grown
dramatically in the past few decades, particularly in fields such as programming and multimedia design, and now makes up approximately a quarter of all nonstandard contingent
workers (Barley and Kunda 2004).
Freelancers often cite the freedom to
choose a variety of projects that interest them
as a reason to enter this type of employment
relationship. An informant in Kunda, Barley,
and Evans’s (2002:247) investigation opined,
“I thought, I’ve been doing this firmware stuff
and systems bit for quite a while. Maybe I
need to branch out and learn some of this IT
stuff, like client server and networks and
GUI’s and all this.” However, in return for this
flexibility, these workers have “careers [that]
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no longer follow a scripted progression of
stages” (Osnowitz 2010:9); Weick (1996:40)
calls this “improvised work experiences.” In
project-based employment, movement across
traditional employment boundaries is commonplace and a freelancer’s career is conceptualized as a patterned series or sequence of
jobs (Baker and Aldrich 1996; Jones 1996).
Eschewing traditional expectations of loyalty
to a single employer, freelancers generally
represent boundary-crossing moves between
employers and jobs as calculated trajectories
of skill and experience accumulation (Bidwell
and Briscoe 2010), disguising the fact that
they may not have appropriate experience
(O’Mahony and Bechky 2006).
The upshot is that freelancers now face the
market as arbiters of their own success, rather
than depending on a single employer. Selfdriven career progression contrasts with career
theorists’ conceptions of an “organizational
career” that structures and guides individuals
through a patterned series of jobs and positions
(Rosenbaum 1984; Spilerman 1977) and often
unfolds “in a single employment setting” (Arthur and Rousseau 1996:5). The concept of
career carries “overtones of some sort of progress or at least coherence to the jobs a person
holds over the work life” (Rosenfeld 1992:40;
see also Wilensky 1961). But careers in a freelancing environment lack the hierarchical nature of progress as previously understood
(Hirsch and Shanley 1996). Instead, a successful freelancing career means continually being
employable, which requires looking for “job
opportunities that go beyond the boundaries of
a single employment setting” (DeFillippe and
Arthur 1996:116) and beyond a single function, geography, industry, or even occupation
(Ganz et al. 2000). This move toward less
standard types of employment exposes our
lack of understanding as to what constitutes a
successful series of jobs shifts.
An Online Labor Market
Following a similar trend in tangible product
markets, labor market hiring has begun to
move online.4 Witness the recent proliferation of online crowd-sourced labor markets
that mediate employers and employees, such
as Monster or Career Builder; or sites that
specialize in temporary contract labor, such
as oDesk or Elance. Elance.com, the site
under study here, is the oldest firm in this
arena and acts as a virtual marketplace
where buyers of a broad range of business
services find and hire independent professionals on a contract basis to work remotely.
Freelancers (bidders) bid on projects that
employers post to the website (see Figure 1
for a sample job listing). Currently, more
than 100,000 jobs are posted each month,
and more than 2 million freelancers located
worldwide use the website. Since its founding in late 1999, cumulative transactions
worth more than $800 million have been
completed on the website, with an average
job value of more than $650.
As a necessity, given the volume of transactions, Elance.com job listings are organized
into job categories that represent conventionally recognized divisions of tasks. Examples
include Website Programming, Administrative Assistance, Translation Services, and
Logo Design (for the complete list of categories, see Part A in the online supplement
[http://asr.sagepub.com/supplemental]).
These categories distinctly partition jobs on
the website, but similarities between these
categories may vary (Kovacs and Hannan
2012; Ruef 2000). Tasks circumscribed in one
labor market category can be perceived as
more or less similar to those in another. This
similarity between jobs is precisely the
“stretchwork” freelancers exploit in attempting to demonstrate desirability to employers
(O’Mahony and Bechky 2006); for example,
moving from Menu Design to Logo Design
might appear more deliberate a move than
going from Website Programming to Business Plan Writing.
In offline markets, staffing agents often
mediate the relationship between contractors
and their employers by assisting freelancers
in tailoring their past experiences to best fit a
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Figure 1. Elance Job Listing
job (Barley and Kunda 2004). To the extent
that job categories lead to employers’ beliefs
regarding a particular candidate’s suitability
to a task, a candidate who can craft a résumé
that demonstrates relevant experience and
progress will be advantaged in securing subsequent work. Contractors and their brokers
often modify and frame freelancers’ past experiences to convey relevance (Osnowitz
2010), leaving truly valid and uncensored information a “casualty of trade” (Barley and
Kunda 2004:133).
In contrast, Elance, like most online markets, typically captures and displays all of a
seller’s previous transactions. A freelancer’s
history of past completed jobs from the website is visible, organized chronologically,5 and
identified by job category (see Figure 2 for an
example of a freelancer’s profile). Elance institutionalized the presentation of freelancers’
past work experiences by standardizing what
gets displayed. Freelancers’ lists of jobs pose
as their online careers and are immutable
when displayed to employers. This virtually
mediated labor market creates an asymmetry
of presentation between buyers and sellers.
Here, in contrast to offline applicants, freelancers cannot construct a career history that
might serve their interests. This asymmetry
can reveal a pattern of decision making by
employers who evaluate work histories mediated by the site.
Another particularly salient difference between freelancers online and their offline
counterparts is that much of the virtual labor
force is not dedicated to a single client. A recent Elance survey (Elance 2012) found that
25 percent of their freelancers hold a full-time
job while working online. Another 13 percent
work part-time, and 7 percent are actively
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Figure 2. Freelancer’s Profile
seeking a permanent position. The survey
also discovered that a majority of freelancers
(62 percent) work on two to six projects at
any given time. Precisely because virtual contract employment occurs remotely, freelancers can be engaged in multiple jobs simultaneously, making it more difficult for
employers to monitor effort and to ensure
their projects are given priority. Online exchange, a virtual and thin information environment, reduces assurances regarding a freelancer’s commitment. This contrasts with a
thick offline setting, where the contractor,
employer, and staffing agency may exist in a
tight-knit exchange network, and the localized nature of the transaction allows for inperson interviews and a greater likelihood
that references can be verified (Barley and
Kunda 2004; Osnowitz 2010), suggesting
risks to shirking or under-motivation are less
likely.
All this suggests that employers of freelancers will face uncertainty in at least two
forms: uncertainty regarding a contract worker’s skill and uncertainty regarding the
amount of effort contractors will put into a
job. Holding aside questions regarding expertise for the moment, I focus on how employers attempt to mitigate the risk of hiring
freelancers who may not put their full effort
into a job—freelancers who may be less than
fully committed. This is particularly salient
because we are studying transactions that
may have extensive leeway as to how the advertised services are accomplished. Contrast
this to well-established online product markets where there is less uncertainty of a commodity’s value and little interpretation of
what the product may be.
Elance implemented functions, such as escrow payments and computerized time-keeping,
to reduce the risk of shirking, but employers
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have little sense of how a job will be completed
and to what standards. Take car repair as an
example—a car may need many unseen parts
and fixes, but the car’s owner has very little
visibility into what is eventually done to his
automobile. Outcomes from different repair
shops may be observationally similar, but there
are many ways to accomplish the task, and
some will be better than others. Comments
from an Elance forum suggest users agree:
If a provider moves around between really
unrelated jobs like graphic design and accounting, then I think they would seem less
trustworthy because I would wonder if they
are really a professional in either area.
– icondesigner
In seeking more committed freelancers,
employers should prefer contractors who
move incrementally across different jobs over
those who move too much or do not (or cannot) move at all. First, progression in one’s
career should be favored because remaining
in merely one job category may be viewed as
evidence of stagnation (Bidwell and Briscoe
2010; Zuckerman et al. 2003). Freelancers
demonstrate advancement by accumulating
additional, broader, but cognitively proximate, experiences (O’Mahony and Bechky
2006). However, freelancers who move too
broadly, across highly disparate jobs, may
engender distrust by suggesting a lack of
commitment to their occupation—that they
are dilettantes. Instead, compiling a sequentially linked set of job experiences requires
nontrivial effort in choosing which jobs to bid
on and reflects freelancers’ dedication to their
online careers, because this may necessitate
refusing certain opportunities.
Figure 3 illustrates two different job paths:
circles are distinctly different jobs, and the
distances between them represent how similar
they may be to one another. The two potential
candidates illustrated in the figure have an
equal variety of past experiences; they have
the same total number and type of jobs in
their histories (jobs: A, B, C, D, E, and F).
Their categorical breadth, at this crosssectional point in time, is identical. However,
Candidate 1, on the left, chose a path between
more similar categories, as depicted by her
sequential moves between closer jobs. Contrast this to Candidate 2, on the right, who
stepped between the same number and type of
categories but moved between jobs of greater
distance; this lengthier path displays greater
movement. A third possibility is that a candidate does not move at all—remaining (or atrophying) as a specialist. This suggests three
prototypical histories: specialists, incremental
generalists, and erratic generalists. The theory
described earlier posits that specialists will
outcompete more erratic generalists, who
have worked in a variety of unrelated job
categories. Incremental freelancers, however,
who work consecutively in related categories
should outcompete both.
Hypothesis 1: There is an inverted, downward
sloping, U-shaped relationship between
erraticism and winning a subsequent bid.
Yet, employers may favor incremental
movement because normative expectations
lead them to appreciate an orderly career
progression. Employers may hold strong, but
perhaps outdated, notions of career progression from more structured internal labor
markets, which lead to a preference for systematic career progression (Barley 1989). To
better isolate my contention that employers
seek to infer commitment by a freelancer,
and are not simply displaying a preference
for incremental trajectories, I further
hypothesize that the curvilinear relationship
between movement and the likelihood
of being hired will vary by job complexity
and as a function of a freelancer’s online
history.
Issues of commitment should loom larger
for buyers of more complex jobs. Complexity
increases the risk of shirking by exposing the
employer to more opportunities where a freelancer can cut corners. Job complexity also
increases the difficulty an employer will have
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Figure 3. Sequences of Category Moves
in understanding what needs to be accomplished and the different ways it can be done,
so monitoring will be more difficult. Finally,
more complex jobs may require more time
and effort because they may be larger or take
longer to complete—in short, they require
more commitment. Conversely, simpler jobs
may be more commoditized and therefore
will have less leeway for misinterpretation of
the scope of work.
Hypothesis 2: The curvilinear relationship between erraticism and likelihood of being
hired is attenuated or eliminated for less
complex, simpler jobs.
Another way for freelancers to demonstrate their commitment is by developing an
extensive history on the website. Commitment
implies that freelancers take their projects
seriously; having an extended record of successfully completed jobs means one has been
continuously engaged and working with other
employers remotely. Issues of dedication
should also be more salient in the beginning
of a freelancer’s online career. Early on, with
a limited history to point to, commitment may
loom larger to an employer evaluating a freelancer. If freelancers are at risk of leaving the
website, and therefore not fully invested in
working online, their virtual job histories will
likely be curtailed.
Hypothesis 3: The curvilinear relationship between erraticism and likelihood of being
hired is attenuated or eliminated for more
experienced freelancers.
Data and Methods
I examine all transactions conducted on
Elance.com from 2000 to 2004, which they
provided me. This included complete job histories of each freelancer who ever bid for a
job on the website, a record of every job
posted on the website, the category a job appeared in, all bidders for a job and the prices,
and the eventual winner. Elance also records
a feedback score (on a one- to five-star scale)
that freelancers receive upon completion of a
job. Between 2000 and 2004, there were
964,034 bids from 16,569 different freelancers for 119,648 jobs posted. I use data from
2000 to 2003 to operationalize my independent variables and to test my hypotheses on
bids for jobs in 2004.
Dependent Variables
In a freelancing environment, being awarded
a job is the primary measure of success.
Therefore, my dependent variable is the likelihood a freelancer’s bid is chosen. Unsuccessful (losing) bids were coded as 0 and
successful (winning) bids were coded 1.6 I
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isolated my analyses to all jobs posted in
2004; this equaled 381,516 bids from 2,779
freelancers for 32,686 jobs with 36,949 chosen winning bids. The likelihood of a bid
being chosen was 9.7 percent (36,949 winners / 381,516 bids). Note that some jobs do
not close with a bidder being chosen and
some jobs have multiple winners.
Independent Variables
My main independent variable of interest is
how much job hopping is in a freelancer’s
past history, what I term erraticism. Erraticism is a function of the perceived sociocognitive distance between any two job categories, which is merely an inverse of similarity
(Tversky 1977). As two job categories cooccur more often, their similarity should increase due to familiarity of that pairing by
external observers (Kovacs and Hannan 2012;
Rao et al. 2005). I operationalized similarity
as a function of the co-occurrence of two job
categories in any freelancer’s past. Increased
likelihood that a freelancer is hired to perform
tasks across two categories should parallel
website users’ beliefs as to the similarity of
the two job categories. Similarity is commonly understood as being asymmetric, that
is, category i can be more similar to j than j is
to i. This is why more prominent objects are
judged as less similar to less prominent ones;
for example, North Korea is more similar to
China than vice versa (Tversky 1977). I therefore denote similarity from job category i to
job category j as the following:
Similarityi , j =
|i
∩ j|
i
where i and j represent instances or occurrences of jobs in categories i and j, respectively. Therefore, the similarity from categories i
to j is equal to the number of times both categories i and j occur in all sellers’ past histories,
summed, and divided by the total number of
occurrences of category i jobs on the website.
I used all transactions from the first full
year of the website’s operation in 2000
through 2003 to calculate the measure of
similarity. This three-year timeframe prior to
my window of analysis ensures market participants had developed an understanding of
these similarities. I calculated this measure
for categories with more than 100 total jobs
as any less would be very sparse and difficult
to justify as affecting an audience’s beliefs.
Categories with fewer than 100 jobs were set
at a minimum similarity of 0 from all other
categories.7
The distance between categories is an inverse relationship of how similar they are
(Kovacs and Hannan 2012). Here, I measure
distance simply as the complement to 1 of
similarity:
Distancei , j = 1 − Similarityi , j
where the distance, Di,j , from category i to
category j is the complement to 1 of the similarity from category i to j, where similarity
from i to j is restricted, 0 < Similarityij < 1.
This relationship ensures a negative and
monotonically decreasing relationship for
distance between the two categories as their
similarity increases. Distance ranges from 0
to 1; it equals 0 only when category i always
occurs with category j and equals 1 when they
never do. Results of the calculated distances
are graphically displayed in Figure 4.
I used NetDraw (Borgatti 2002) and the
MDS (multi-dimensional scaling) algorithm
set to node repulsion to depict distance between job categories. The node repulsion option optimized distances between nodes to
minimize overcrowding, thereby allowing
visibility of the labels by de-emphasizing the
close clustering of highly similar job categories. Because the two-dimensional space can
represent only one distance, I display the average of the two category pair of asymmetric
distances (i.e., Dij + Dji / 2). The closer two
categories are, the more similar; the farther
apart, the less so.
Two points are worth noting from Figure
4.8 First, we can see clusters of domains the
job categories are nested within. This depicts
an understandable grouping of categories. For
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American Sociological Review 79(1)
Figure 4. Distance Map of Elance Job Categories
example, focusing on the dense cluster left of
center, we see jobs in the Web and Programming domain, such as Simple Website, Web
Programming, Flash Animation, and Blogs.
The second point to note is the differences in
space within each domain. For example, jobs
in the Web and Programming domain are very
tightly associated with each other, whereas
jobs in the Legal arena have greater distances
between them. This may reflect an underlying
belief by buyers of services as to different
similarities among the job categories within a
particular domain.
I calculate the measure of how erratic freelancers’ histories have been as the average
distance between job categories they have
consecutively worked; this is updated each
time they take on a new job. Specifically,
Erraticismk ,t =
Total Distancek ,t
N −1k ,t
N −1
Total Distancek ,t =
∑Distance
n =1
in jn+1
where bidder k’s measure of erraticism at
time t is the average distance of moves she
made between jobs, calculated by dividing
the total distance she moved by the total
number of jobs, less one, she completed. The
total consecutive distance of the path between
jobs moved for bidder k is calculated by summing, from 1 to N – 1 (N being the total number of chronologically ordered jobs completed by bidder k at time t), the distance between
job n’s category compared to job n + 1’s category. If category in equals jn+1, the distance is
zero. This measure is updated each time a
freelancer wins a consecutive job.
Substantively, this means freelancers who
have worked in only one type of job will
have an erraticism score of 0, indicating they
have not moved at all. A freelancer who
moved between highly dissimilar jobs will
garner an erraticism score greater than average, and freelancers with incremental moves
will get a score somewhere in between. Figure 5 shows the distribution of the observed
erraticism of all bidders in 2004. The graph
on the left plots bidders with at least one job.
Notice the large number of specialists (~9
percent of all bidders). However, this graph
mixes bidders with varying amounts of experience, complicating interpretation. The
graph on the right presents bidders with an
average number of completed jobs (n = 34)
and is easier to interpret. After so many
jobs, it is difficult to remain a specialist and
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Figure 5. Percent of Bidders by Erraticism in 2004
we see bidders distributed across erraticism
levels.
Control Variables
I control for a freelancer’s underlying skill
using several variables. First, I include the
average feedback score on previous jobs that
freelancers had at the time of bidding, updated each time they get feedback. As with other
online markets, for each completed transaction, the buyer is encouraged to provide feedback in the form of a rating from one to five
stars. In 2004, approximately 61 percent of
completed jobs by freelancers received feedback.9 Second, I include the number of jobs
freelancers had completed in the focal bidding category as a direct measure of their experience in the job they are bidding for, updated each time they win a new job. Finally, I
include the number of previous jobs (incremented by 1, then logged) a freelancer has
completed, because this should be correlated
with skill, also continuously updated each
time a new job is completed. All three variables should be positively correlated with
winning a subsequent bid.
The more movement in a bidder’s past history, the greater number of job categories she
would have worked across. Spanning across
job categories likely leaves a freelancer less
skilled in any one. I control for this by
including a measure of the number of distinct
job categories bidders have worked in, updated each time they work in a new category.
To distinguish my findings more clearly from
existing research, I also include a measure of
distance to the most recently completed job
category. Finally, I include the amount of the
bid and the number of times a bidder has previously worked with the buyer (incremented
by 1, then logged). Summary statistics of the
variables are presented in Table 1 and correlations in Table 2.
Modeling Methodology
My risk set of interest is all jobs posted and
bid on in 2004. Because I consider moves
between jobs, this necessitated eliminating
bidders who had completed fewer than two
jobs prior to their bid, this left 322,434 bids. I
also removed jobs where no winner was chosen (or all bidders were winners). This left
268,047 bids for 20,174 jobs from 2,461
qualified bidders. Note that bids are not independent: freelancers enter multiple bids and
each listed job has several bids; conversely,
multiple jobs can be posted by individual
buyers. I therefore tested the effects of job
movement on winning a subsequent bid by
utilizing a fixed-effects logistic regression,
grouped by each job posting, to estimate, between all bidders for a job, the effect of their
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Table 1. Summary Statistics
Variable
Obs.
Winning Bid
Bid Amount
Average Feedback
Number of Previous Jobs (logged)
Number of Jobs in Focal Category
Number of Different Category Jobs
Number of Jobs with Buyer (logged)
Distance to Last Job
Erraticism
268,047
268,047
268,047
268,047
268,047
268,047
268,047
268,047
268,047
Mean
SD
.12
601.21
4.26
3.38
19.43
5.43
.05
.59
.41
Min.
Max.
0
0
1
0
0
0
0
0
0
1
987,743
5
6.96
528
47
4.69
1
.99
.32
3009.95
.54
1.59
48.02
4.90
.28
.39
.20
Table 2. Correlations
Variable
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(9)
Winning Bid
Bid Amount
Average Feedback
Number of Previous Jobs
(logged)
Number of Jobs in Focal
Category
Number of Different
Category Jobs
Number of Jobs with Buyer
(logged)
Distance to Last Job
Erraticism
(1)
(2)
1
–.009
.022
.042
1
.014
.019
1
.364
1
.046
–.013
.185
.464
1
.016
.008
.171
.530
.037
1
.395
–.004
.005
.058
.035
.006
1
–.010
–.029
.020
.017
–.036
.121
–.100
.489
–.279
.008
.223
.466
–.030
–.031
movement on their likelihood of being chosen. Results of a Hausman test on the base
model (Hausman 1978) led me to reject the
null (χ2 = 1689.33, d.f. = 6, p < .001) and
choose a fixed-effects over a random-effects
model. Coefficients can be interpreted in the
same way as in a standard logistic regression
where the general form is as follows:
κ = log
π
1–π = X'β
where κ represents the linear transformation
of the log odds (the ratio of the probability, π,
of the dependent variable occurring, winning
a bid, divided by the probability of the bid
losing). This is estimated with X' as a vector
of independent and control covariates that are
updated each time a freelancer completes a
(3)
(4)
(5)
(6)
(7)
(8)
1
.099
job, and β as the parameter estimates of those
variables.
Results
Results of logistic regressions estimating the
likelihood of winning a bid are reported in
Table 3. Model 1 estimates effects of the control variables. They generally behave as expected. The estimate of the effect of the
amount of a bid is negative and significant as
expected. In terms of observable skill, the
better average feedback score a bidder had,
the greater significant likelihood they would
win the bid. The greater number of previous
jobs a bidder had completed increased the
likelihood of winning, as did the greater number of jobs in the focal bidding category.
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Table 3. Logged Odds of Winning a Bid (Fixed-Effects Logistic Regressions Grouped by Job)
Model 1
Bid Amount
Average Feedback
Number of Previous Jobs (logged)
Number of Jobs in Focal Category
Number of Different Category Jobs
Number of Jobs with Buyer (logged)
Distance to Last Job
Erraticism
Erraticism2
Observations
Number of Groups
Min.
Mean
Max.
Log-likelihood
Chi2
–4.59e–05***
(1.04e–05)
.114***
Model 2
–4.26e–05***
(1.04e–05)
.095***
Model 3
–4.08e–05***
(1.03e–05)
.094***
(.015)
.057***
(.007)
.003***
(.015)
.096***
(.007)
.002***
(.015)
.069***
(.008)
.002***
(.000)
–.022***
(.002)
2.848***
(.000)
–.012***
(.002)
2.833***
(.000)
–.003
(.002)
2.845***
(.061)
–.167***
(.021)
(.061)
–.140***
(.021)
–.752***
(.061)
–.121***
(.021)
.807***
(.049)
268,047
20,174
2
13.3
83
–50826.97
4326.54
268,047
20,174
2
13.3
83
–50712.25
4555.99
(.142)
–2.235***
(.190)
268,047
20,174
2
13.3
83
–50641.67
4697.15
Note: Standard errors in parentheses.
*p < .05; **p < .01; ***p < .001 (two-tailed tests).
Working across an increasing number of different job categories reduced a freelancer’s
chances of being picked. The more distant the
last job completed, the less likely they would
win the current bid. Finally, previously working with a buyer increased a freelancer’s
chances of garnering subsequent work from
them.
Model 2 in Table 3 includes the measure of
erraticism and estimates its main effect on a
bidder’s likelihood of winning the bid. The
estimate is negative and significant (β =
–.752, p < .001). Concretely, a one standard
deviation increase from the average erraticism score (.41 to .61) decreases a freelancer’s chances of winning a bid by approximately 10.2 percent, from a discount of –26.6
percent to –36.8 percent, holding all other
variables constant. Model 3 includes the
quadratic term of erraticism to test Hypothesis 1. The main effect of erraticism is now
positive and the quadratic term is negative,
both are significant. This supports Hypothesis
1 by demonstrating that some movement between similar jobs is actually beneficial to a
freelancer. Excessive movement, however, is
detrimental. Figure 6 allows us to visualize
the effect of erraticism. Here, we see a slight
increase in the likelihood of winning a bid as
a bidder demonstrates movement between
more similar jobs. The inflection point comes
at an erraticism score of approximately .18;
after that, any movement further away becomes increasingly damaging to a freelancer’s ability to garner additional work. For
example, a freelancer who moves between the
job categories Litigation and Contracts, or
between Label and Package Design and
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American Sociological Review 79(1)
Figure 6. Multiplier of Erraticism on Likelihood of Winning a Bid
Logos, would increase her ability to get hired
by 7.6 percent over not moving at all.
To test Hypothesis 2, I operationalized job
complexity by counting the number of words
that appeared in a job listing’s description
(recall Figure 1). To the extent a job is more
complex, or may require more components or
explanation, the length of the text used to
describe the job should correlate with the
job’s intricacy. This variable ranged from 4 to
784 words (mean = 149.9, SD = 116.2). To
avoid a difficult to interpret triple interaction
of job complexity and the quadratic erraticism measure, I partitioned my sample at the
median and ran two separate fixed-effects logistic regressions grouped by job ( job descriptions greater versus less than the median
of 114 words). Similarly, to test Hypothesis 3,
I split the sample into two groups consisting
of more versus less experienced freelancers,
partitioned by the number of previous jobs
they had completed on the website, split at the
median of 34 jobs. Results of both tests are
reported in Table 4.
Model 1 in Table 4 estimates the effects of
erraticism and the quadratic term on the likelihood of winning a bid for a set of less complex
jobs. Model 2 runs an identical regression on
bids for more complex jobs. In support of
Hypothesis 2, results suggest that the inverted
U-shaped relationship between erraticism and
winning a bid holds for more complex jobs
(Model 2), whereas the coefficient on the
quadratic term is not significant for less complex jobs (χ2(1) = 3.29, p < .07).
Model 3 estimates the hypothesized relationship on freelancers who were less established in the job market. Model 4 estimates
the same regression on freelancers with a
more extensive job history. As Hypothesis 3
predicts, the coefficient of the quadratic term
is negative and significant (β = –2.159, p <
.001) in Model 3 (with the first-order effect of
erraticism being positive and significant) and
not significant in Model 4. This suggests that
buyers are attending to the incremental moves
of less experienced, but not more experienced, freelancers.
Additional
Considerations and
Robustness Checks
At least two theories pertaining to labor markets predict similar results. First, cognitive research on learning (Ellis 1965; Estes 1970)
suggests that individuals’ historical experience
paths will affect their future abilities. Specifically, individuals’ ability to incorporate new
experiences is dependent on their past experiences. Ability to recognize and assimilate new
information is thus a function of a freelancer’s
prior related knowledge.10 Given this, greater
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Table 4. Logged Odds of Winning a Job (Fixed-Effects Logistic Regression, Grouped by Job)
Split by Job
Complexity
Model 1.
Less Complex
Bid Amount
Average Feedback
Number of Previous Jobs
(logged) Number of Jobs in Focal
Category Number of Different Category
Jobs Number of Jobs with Buyer
(logged) Distance to Last Job
Erraticism
Erraticism2
Observations
Number of Groups
Min.
Mean
Max.
Log-likelihood
Chi2
–1.82e–04***
(2.35e–05)
.137***
(.023)
.039**
(.012)
.003***
(.000)
–.002
(.003)
3.595***
(.104)
–.040
(.031)
–.603**
(.206)
–.496
(.273)
128,327
9,612
2
13.4
82
–23536.19
2859.24
Split by Number of
Previous Wins
Model 2.
More Complex
Model 3.
Fewer Wins
Mode. 4.
More Wins
–4.10e–06
(5.44e–06)
.089***
(.021)
.011
(.011)
.003***
(.000)
.005
(.003)
2.287***
(.077)
–.008
(.029)
.400*
(.197)
–1.107***
–1.88e–06
(2.57e–06)
.049**
(.018)
.138***
–1.16e–04***
(1.71e–05)
.350***
(.036)
–.052*
(.265)
132,196
10,503
2
12.6
74
–26313.03
1869.58
(.017)
.034***
(.002)
.050***
(.021)
.003***
(.000)
–.015***
(.005)
2.154***
(.091)
.063*
(.003)
3.468***
(.115)
–.125***
(.032)
.549**
(.192)
–2.159***
(.034)
.033
(.355)
–.709
(.409)
86,279
9,933
2
8.7
47
–18799.69
2598.02
(.271)
78,103
10,366
2
7.5
49
–19378.93
2012.02
Note: Standard errors in parentheses.
*p < .05; **p < .01; ***p < .001 (two-tailed tests).
distance between consecutive jobs might reduce a freelancer’s ability to learn additional
skills and apply them to subsequent projects.
Ability may degrade over time if jobs are taken
erratically. Two reasons make this unlikely
here. First, many jobs on Elance are very small
and are completed in days, thereby reducing
the likelihood any cumulative learning is occurring. Second, categorization in this market
is controlled by the website, so job distinctions
are not consciously partitioned into sequential
learning experiences.
The second set of theories, on signaling
(Spence 1973) and information asymmetries
in markets in general (Akerlof 1970), suggest
that workers are forced to move between jobs
because they have no choice. Workers who
possess sufficiently poor abilities cannot help
but move from failure to failure (Gibbons and
Katz 1991), or better employees are shielded
from excessive turnover (Greenwald 1986),
leaving those of lower ability to move between employment. Note that my theory
makes no claim as to an applicant’s underlying ability, but rather theorizes on the perceptions of commitment an employer may hold.
Moreover, in an external labor market for
freelancers, the dissolution of a job should not
act as a signal because short-term contracts
are the nature of these transactions.
Empirically, I will attempt to disambiguate
these theories from mine. To the extent that
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American Sociological Review 79(1)
Figure 7. Multiplier of Erraticism and Number of Times Worked with Buyer on Likelihood
of Winning a Bid
employers are merely demonstrating a preference against job histories with too much
movement, the negative effect of erraticism
should diminish among employers who have
previously worked with the freelancer. The
sharing of common goals and cooperation
necessitated by working together should
eliminate any lingering negative feeling an
employer may hold due to a freelancer’s excessive movement (Allport 1954; Pettigrew
1998). Support for this would suggest that the
discount to erratic behavior is predominantly
driven by preferences an employer holds for a
freelancer, and not the worker’s underlying
competence, which presumably would be revealed upon working together.
I test this contention (results reported in
Part B of the online supplement) and find that
increasing contact with a particular employer
weakens the negative effect of erraticism on
winning a bid from that employer. The coefficient of the interaction term of erraticism
and prior number of times worked is positive
and significant ( β = 3.3, p < .001), suggesting
that the negative effect of erraticism on being
hired is attenuated as an employer gains experience with that freelancer. Figure 7 plots the
multiplier effect on the likelihood of being
hired as a surface, with erraticism and the
number of previous jobs with a buyer on the
two horizontal axes. For a freelancer with the
maximum level of erraticism, merely working with a buyer more than once (exp[.3] =
1.35 times) eliminates any discernible discount arising from her erraticism.
If erratic past jobs contributes to poorer
learning, we would expect more erratic freelancers to perform worst. To account for this,
I tested whether the feedback a freelancer receives for her on-the-job performance is affected by how erratic her past experiences
have been. Feedback on a job measures how
well sellers did, in short, a proxy for their
skills. Feedback scores on Elance.com are
continuous (because they are averaged from
several indicators) and range from one to five
stars. If excessive movement leads to poorer
learning, we should expect to see a negative
effect of erraticism.
I tested two versions of the dependent variable. First, I modeled the effect of the covariates on the feedback score a freelancer received as a continuous variable ranging from
one to five. Second, because of the skewed
nature of feedback, I also tested it as a categorical variable equal to one if the seller received a five-star score (the best possible)
versus anything else. Results, unreported for
brevity, suggest no indication that a seller’s
level of erraticism affected the eventual
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quality of the delivered service, as measured
by feedback received.
As additional robustness checks, I leveraged the fact that all job categories are further
categorized into high level divisions, such as
Web and Programming or Design and Multimedia. If my contention that movement negatively affects a freelancer’s ability to garner
additional work is valid, we should see similar results when there is movement between
these higher level domains. I created a variable that averages the number of moves between domains that a freelancer made in past
jobs and estimated its effect on the likelihood
of winning the next job. Results, not reported
for brevity, support my contentions.
The more erratic a candidate’s job history,
the less coherent her portfolio of past jobs may
be (Zuckerman et al. 2003). Not surprisingly,
the measure of coherence is highly correlated
with my measure of erraticism (–.50, p < .001);
I was therefore hesitant to over-identify the
models above. However, as an additional
check, I included this as a control variable in
models unreported for brevity. Coherence is
positively correlated with winning. More important, effects of erraticism remained significant as predicted (for variable operationalization of coherence and a more stringent matching
test, see Part C of the online supplement).
To address potential concerns of time-invariant heterogeneity among freelancers, such
as innate skill, I also modeled a fixed-effects
logistic regression on the within-bidder effect
of changes in individual erraticism on the
likelihood of winning a bid. This should also
alleviate concerns when the sample is split
between more versus less experienced freelancers in Table 4. Unreported results are
consistent with expectations.
Conclusions and
Discussion
By advancing the notion that candidates are
judged by the paths through which they accumulated their experiences, my findings,
that freelancers who move between more related job categories on Elance.com—compared to those who never moved or moved
between distant jobs in their past (recall Figure 6)—were most likely to win additional
assignments, contrast with previous literature
that suggests disadvantages accrue to any
boundary spanning (Hannan 2010; Hsu et al.
2009; Rao et al. 2005). Because buyers attempt to decipher not only skill but also
which freelancers are most committed, a concern particularly acute in virtual environments, contract workers with more incremental moves were preferred. I also distinguished
this mechanism from a general preference for
progress by demonstrating how this held for
more complex jobs and for less experienced
freelancers. This finding held net of alternative extant sociological explanations, such as
penalties to being a generalist (Hsu et al.
2009) or incoherence (Zuckerman 1999;
Zuckerman et al. 2003). These findings were
also difficult to reconcile with explanations
based on learning or actual ability.
This article informs the recent work by organizational sociologists who have moved toward deciphering the conditions under which
categorical transgressions may or may not be
disadvantageous (Dobrev, Ozdemir, and Teo
2006; Kovacs and Hannan 2012; Negro et al.
2011; Pontikes 2012) by identifying the relevance of the relationship among categories.
For example, Dobrev and colleagues (2006)
suggest that a “violation by comparison” effect
disadvantages firms from emergent populations when they attempt to move into established identity spaces. My theory may qualify
this finding by suggesting that moves into established spaces may be best undertaken once
the nascent population establishes a more cognitively proximate identity space. Future work
could study how the order in which organizations attempt to diversify their lines of business
affects audience reception of their efforts.
This study also represents an early attempt
at better understanding what career progression entails in a boundaryless external labor
market for highly skilled freelancers. The
traditional organizational career is waning
(Arthur and Rousseau 1996; Carré et al.
2000), and skilled labor market participants
are increasingly embarking on self-employment (Barley and Kunda 2004; Osnowitz
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American Sociological Review 79(1)
2010). Theoretical investigation has lagged
behind this novel and seemingly burgeoning
work arrangement (Polivka et al. 2000). I remedy this by examining what types of job
moves influence freelancers’ outcomes in a
virtual labor market for services. Furthermore,
I highlighted an asymmetry between buyers
and sellers of freelancing services that may
exist in other mediated virtual environments.
Controls for skill and additional tests demonstrating attenuation of the effect with prior
contact and no correlation between erraticism
on subsequent feedback scores distinguish
my findings from theories that implicate actual skill, but unequivocal proof that skill is
not playing a dominant role is elusive. However, if we are to believe that a freelancer’s
skill is the only factor to hiring, then my findings are only suspect if there are unobservable better indicators of skill, unaccounted for
in my regressions. For example, the current
version of Elance allows freelancers to display actual samples of their work. This may
serve as a better measure of skill, but for
transactions completed in 2004, this functionality was unavailable. All website observables are accounted for in my analyses. In
short, decisions to hire likely did not include
additional information.11 Certainly, a more
direct way of disambiguating actual skill
from commitment would be through a controlled experiment. The pair of articles by
Castilla (2008) and Castilla and Benard
(2010), where they pinpoint the cause of the
performance-reward paradox in a controlled
setting, exemplifies this multi-pronged approach. Such a path forward could also unambiguously distinguish competing explanations
raised earlier, such as perceptions of skill or
normative expectations an employer holds of
highly erratic workers.
Another potential limitation to this article
is that Elance.com merely represents one
channel by which freelancers and employers
find each other. Participants on this website
may differ from those in the contract work
domain more generally. These contractors
may exist on the fringe of the freelancing
market or be new to the domain, because,
presumably, they are unable to secure consistent work offline. I point out two considerations. First, virtual platforms offer a low barrier to entering the contract labor force.
Consequently, workers in this study may actually represent a broader and more diverse
freelancing population than previously studied. For example, 60 percent of freelancers on
Elance.com are female, 31 percent of clients
reside outside the United States, and freelancers from 156 countries are represented on the
site. Among freelancers on Elance, 36 percent
say they freelance full-time, and 29 percent
hold full-time jobs while freelancing (Elance
.com 2012). Second, and related, the shift toward more virtual platforms for labor matching is certainly becoming more prevalent,
suggesting that questions regarding virtual
market mediation will continue to be valuable. In fact, given the statistics quoted earlier,
online labor markets may represent a very
different market of their own—one that may
not merely parallel the offline world. For example, some freelancers only work through
this online virtual market (Elance.com 2012).
A worker’s past progression informs perceptions others hold. Instead of categories
acting as positional markers in an audience’s
cognitive space, one could, for example, examine the status differences a candidate
moves between. For example, jazz compositions are more likely to be covered by later
performers if they were written by black musicians and then performed by white early
adopters (Kahl, Kim, and Phillips 2010), suggesting that historical provenance and status
ordering matter. When opera companies order
their unconventional versus conventional performances, it influences which elements are
more salient, thereby appealing to particular
audiences (Kim and Jensen 2011). Future
work could identify other spatial measures
through which external audiences regard
movement. Indeed, a potential concern readers may have is my category distance measure being a function of observed jobs within a
freelancer’s background—thereby making a
prediction of winning future work look endogenously determined. Yet this should not
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be of concern because the measure varies
among freelancers with identical jobs that are
simply ordered differently. However, better
measures of distance, such as text overlap
measures of job descriptions, could certainly
improve our understanding of perceived
distance.
This article also engages and complements
work at the intersection of labor markets and
networks that demonstrates how occupying a
brokerage position linking disparate groups
accrues advantages such as having better ideas
(Burt 2004), more creative output (Fleming,
Mingo, and Chen 2007), or faster promotion
(Brass 1984). Recent work in this vein is uncovering the origins to these robust positions.
In particular, employees with more erratic internal career sequences increase their brokerage opportunities (Kleinbaum 2012) by developing a broader range of contacts through
their movement between disparate groups.
Future work could explore the benefits that
accrue to freelancers with erratic job histories.
Although they are at a disadvantage with unfamiliar employers, could freelancers with increasingly erratic histories offer more creative
solutions, have better job prospects in volatile
markets, or advance faster?
An implication of this trend toward virtual
mediated labor markets is the fact that social
power, in the form of freelancers exploiting
information asymmetries with employers, is
being assumed by the website itself. Studies
of lower-wage temporary employment point
to the role of staffing agencies as power brokers (Henson 1996; Rogers 2000). Yet,
higher-wage contract workers find less overt
exercises of authority (Barley and Kunda
2004), in part because staffing agencies are
not gatekeepers and freelancers can find work
through multiple channels. However, the
standardization of career histories imposes
limitations on freelancers online, versus contractors in offline markets who have some
latitude to present themselves differently. The
advantages accrued by mediators such as
Elance allow them to provide buyers standard
information, making comparisons easier and
perhaps eliminating some deception at the
expense of commodifying career histories. If
sites like Elance become the dominant market
mediators for freelance employment, then external labor market workers may find their
mobility more constrained than recent studies
of contract employment would suggest.
All this presents new opportunities for
theoretical investigation, which I believe represents a considerably fecund area for future
exploration. If a significant segment of any
external labor market migrates online, using
the model represented by Elance, are freelancers’ options at agency minimized? Will
some populations of freelancers face significant disadvantages? How will this alter the
choices freelancers make in applying for
jobs? Because Elance retains the ability to
manage the market by controlling information flow, it has the potential to hone matching between employers and employees (Gale
and Shapley 1962; Roth 2008). To the extent
that potential inefficiencies persist in labor
markets, can large mediators who reside at
the nexus between buyers and sellers wield
their informational advantages to improve
market outcomes?
Acknowledgments
This article benefitted from comments by Toby Stuart,
Heather Haveman, Jim Lincoln, Huggy Rao, Mike Hannan,
Jesper Sørensen, Ezra Zuckerman, Damon Phillips, Jerker
Denrell, Sameer Strivastava, and participants at the 2009
Organizational Ecology Conference. Seminar participants
at the University of Chicago, University of Los Angeles,
University of Southern California, National University of
Singapore, Hong Kong University of Science and Technology, and Columbia University also helped develop this
paper. I thank Ved Sinha, Fabio Rosati, and James Lee of
Elance for allowing me to use their data, and Stanford
University and the University of California-Berkeley for
financial support. All errors remain my responsibility.
Notes
1. While the phrase “Renaissance Man” is well known,
the ASA Style Guide dissuades the use of gendered
terms. My data include both men and women and
therefore the term “person” rather than “man” in the
title is appropriate.
2. The literature on “robust identities” also suggests
strategies for a multi-vocal identity. Success here,
however, hinges on an actor’s ability to partition
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156
3.
4.
5.
6.
7.
8.
9.
10.
American Sociological Review 79(1)
the audience. This eliminates the risk of presenting
a confusing identity, because actors manage their
identities for each individual partner. In contrast,
I present a theory that examines situations where
audience partitioning is not possible.
This is not to say no differences exist between items
that are labeled as belonging to different categories
from single category members, but these functional
differences exist in tandem with perceptual ones
(Hsu et al. 2009) and evaluation may often exaggerate assumptions of actual differences (Negro and
Leung 2013).
Although it is difficult to ascertain precise numbers
of freelancers who seek employment online, or
even more generally the proportion of labor market
transactions that occur online, popular press reports
suggest this is prevalent and growing quickly. Nevertheless, this is a valuable scope condition of my
findings to note, and I thank an anonymous review
for pointing this out. I speculate further on this in
the Discussion section.
This functionality pertains to the window of observation in the analyses presented here. Elance has
since changed buyers’ ability to view a freelancer’s
past work experiences by allowing buyers to sort by
particular domains of work.
Winners were coded as 1 whether they eventually
completed the job or not—they only needed to
be picked by the employer. To the extent that this
website may be considered a lower status employment channel, this method of measurement should
alleviate concerns that “winners” on this website
are actually the freelancers who were “available”
(which suggests lower skilled or poorly connected)
to perform the task.
Robustness checks, which included measuring similarity between all categories, even those with fewer
than 100 total jobs, did not result in any differences.
This is not surprising because any category with so
few jobs is not likely to have a meaningful impact.
Figure 4 is used merely for descriptive purposes to
demonstrate the face validity of the distance/similarity measure. It also represents a visual depiction
of my mechanism to parallel my conceptual example used earlier.
Although only 61 percent of jobs get feedback, I
include average feedback on past jobs. Among bids,
98 percent were associated with a freelancer with
a nonmissing average feedback score, because all
freelancers had completed at least two jobs and most
(over 50 percent) had completed more than 34.
This is certainly in line with the spirit of O’Mahony
and Bechky’s (2006) and Bidwell and Briscoe’s
(2010) articles, but theirs stop short of demonstrating
actual skill attainment and success and instead focus
on how the market structure funnels workers accordingly (Bidwell and Briscoe 2010) or the strategies
freelancers take to move from one domain to another
(O’Mahony and Bechky 2006).
11. Of course, communication between freelancers and
employers could still occur and sample work may
have been shared.
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Ming D. Leung is an Assistant Professor in the Haas
School of Business at the University of California,
Berkeley. He is an economic sociologist whose research
interests revolve around categorization processes and
contemporary markets. One of his current streams of inquiry is applying a categorization focused theoretical
lens to understand how employers and employees are
being affected by the merging of online and offline labor
markets. In another stream of work he is examining the
processes that induce people to participate in the collective production of online goods, such as movie reviews
and the crowd-funding of consumer debt.
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