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Career Paths in Online Labor Markets: Same same but different?

Career Paths in Online Labor Markets: Same same but different?

Mareike Seifried, Ludwig Maximilian University of Munich,
Roman Jurowetzki, Aalborg University,
Tobias Kretschmer, Ludwig Maximilian University of Munich
 
Career research has studied how workers progress through their working life within and across organizations structured by internal labor markets (Ng, Eby, Sorensen, & Feldman, 2005; O’Mahony & Bechky, 2006). Over time, employees gather experience and acquire additional skills and their role in the organization changes. Internal labor markets match firm needs and employee skills by allocating labor, determining wages, and defining tasks to be completed (Doeringer & Piore, 1971). An “organization man’s” career involved (Whyte, 1956) climbing a stable and clearly defined path to the top. Climbing the career ladder involves completing jobs with increasing responsibility (Hall, 1976). This creates a shared understanding of how one job follows the next and what is needed to progress (Lawrence, 1990).
However, in the “gig economy” a growing number of workers “are hired for gigs under flexible arrangements as independent contractors or consultants, working only to complete a particular task or for defined time and with no more connection with their employer (…)” (Friedman, 2014: 171). One major facilitator of flexible, one-time jobs is the emergence of online labor markets (OLMs) “where labor is exchanged for money, the product of that labor is delivered over a wire and the allocation of labor and money is determined by a collection of buyers and sellers operating within a price system” (Horton, 2010: 516). OLMs facilitate hiring globally and broker a variety of knowledge tasks (Agrawal et al., 2016; Malone et al., 2010), ranging from translations and transcriptions to web and software development. OLMs thus enable diverse workers of varying skill levels to enter the gig workforce and pursue an online career.
We document the careers of online gig workers and develop a taxonomy of career paths in OLMs. Put differently, we are interested in understanding the differential types of careers that might emerge. This addresses recent calls for research to update and refine our theories and understanding of careers (Rahman et al., 2016). To do this, we adopt a quantitative-inductive approach (Bamberger & Ang, 2016). We use an extensive and novel dataset on individuals’ work histories on Upwork.com, the world’s largest online freelancing website. Our dataset comprises over 74,000 freelancers and more than 2.6 million jobs posted on the platform with detailed information on freelancers, employers, work histories, and job characteristics. We use a novel and state-of-the-art clustering algorithm (UMAP+HDBScan) to group career patterns and to develop our data-driven taxonomy. We identify three career dimensions: coherence, direction, and time. Then, we first cluster across the task categories freelancers have worked in to identify dominant career paths and their coherence. We then link the generated clusters with skill data (skills required to perform tasks) to examine the skill coherence of freelancers in these clusters. We describe the clusters in terms of freelancers’ education, origin, and financial outcomes. Second, we group work histories based on measures related to platform and geographical activity to identify differential usage patterns. This helps us understand the coherence and time dimension of existing careers and to examine how online careers might differ from traditional career models.
Some freelancers have a fairly traditional career. Careers are “same, same” in the sense that we observe specialist and more generalist career paths. In general, task and skill portfolios are coherent and random careers are not a ubiquitous pattern. Like in traditional settings, some workers seem to remain within occupational and geographical boundaries, suggesting an overall logic and purpose. However, careers are “different” because novel occupations such as transcriptionists and ecommerce developers emerge, enabled by the digitization of labor markets. Careers also become fragmented and narrow due to the skill-based and more short-term oriented nature of work. We therefore develop a taxonomy of four different career types: a category specialist offering a narrow set of skills (Micro career), a category specialist offering a broad set of skills (Field expert), a category generalist who carries a specific skill across task categories (Firefighter), and a category generalist with a broad skillset (Opportunist).
We contribute to a number of research streams: First, we add to the growing literature on online labor markets and their specificities. While most prior research is at the job or employee level, we study worker careers, an important but understudied unit of analysis. Second, we extend the emerging literature on careers in external labor markets to the online context. We are among the first to do so quantitatively and on a large scale. Our taxonomy can be a starting point for analyzing online careers. Finally, given Upwork.com’s focus on highly skilled workers and relatively complex tasks, we also touch on work on high-skilled workers in external labor markets, especially high-tech contract workers (O’Mahony & Bechky, 2006) and technical professionals (Kunda, Barley, Evans, 2002).