We study how startup teams are assembled in terms of team member human capital characteristics. To this end, we derive a statistically motivated benchmark for new venture team heterogeneity in terms of observed team member characteristics to generate stylized facts about team member diversity at startup and how it evolves as the new venture matures. We use the population of Danish startups that were established in 1998 and track them until 2001. Main findings are that teams are relatively more homogeneous at startup compared to our benchmark, indicating that difficulties associated with workforce heterogeneity (like affective conflict or coordination cost) as well as “homophily” (people’s inclination to bound with others with similar characteristics) may overweigh the benefits of heterogeneity. While workforce heterogeneity does increase over time, the increase is smaller compared to our benchmark but substantially larger than if team additions and replacements had the same characteristics as the initial team members.
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Existing studies consider very high growth firms (Ensley et al. 1998; Kamm et al. 1990), firms founded by US MBA graduates (Ruef 2000), high-technology firms (Clarysse and Moray 2004; Eisenhardt and Bird Schoonhoven 1990; Higgins and Gulati 2003; Roberts 1991; Roure and Maidique 1986; Shrader and Siegel 2007), firms that eventually went public (Zimmerman 2008), venture capital-backed firms (MacMillan et al. 1985; Zacharakis and Meyer 1998) and university spin-offs (Forbes et al. 2006; Vanaelst et al. 2006; Santoni et al. 2012). Ruef et al. (2003) provide a critical discussion of existing empirical sociological studies of team assembly.
Our methodology is best compared to Ellison and Glaeser’s (1997) “dartboard approach” of geographic industry concentration. Their thought experiment is to write down the identity of each firm in their data on a dart and to throw these darts on a map of the USA, thereby generating a random distribution of firms across the USA which they compare to the observed distribution of firms.
Amason and Sapienza (1997), Bunderson and Sutcliffe (2002), Hambrick et al. 1996, Hambrick and Mason (1984) as well as Kilduff et al. (2000) underscore the importance of cognitive conflict to avoid group think. Cognitive conflict also induces people to reconsider their ideas, generates a variety of perspectives (Miller et al. 1998; Simons et al. 1999) and leads to more creativity (Ensley et al. 2002; Smith et al. 1994).
The underlying assumption here is that, e.g., an individual with a technical education is likely to fulfill technological-oriented tasks and that individuals with a management education fulfill managerial tasks. While it indeed seems unlikely that individuals with a management education fulfill technological-oriented task, the reverse is, however, questionable. At the same time, however, individuals with the same education are more likely to be similar in how they think and work, irrespective of the actual task they perform.
In competitive markets, wages correspond to marginal productivity of worker. Productivity can be interpreted as ability because it reflects individuals’ talent to create valuable economic output.
Chandler et al. (2005) study the determinants of startup team membership changes over time using a large sample of USA and Swedish firms, finding some evidence for initial team heterogeneity being positively linked to the probability of changes in the assembly of startup teams.
We cannot rule out that some of our startups are set up for tax reasons only, even though all but twelve firms have positive sales. In Denmark, it is not only possible to deduct losses from self-employment from income from wage work, it is also possible to put profits from self-employment aside and have it taxed after retirement. While this might affect the number of startups and the number of team foundations, it is not clear if and in what direction that would influence team heterogeneity.
Firms that started in 1998 and that were also shut down within that year are not included in the data set.
Examples of categories of this variable are “degree after a vocational training at a bank,” “bachelor degree in management” or “Ph.D. in engineering.” The categories are a mix of years of education and the field of education. We do not separate these components in the analyses because we think that someone with, e.g., a vocational degree in a technical field has a different education than an engineer with a master’s degree.
In addition, even if our benchmark did not take differences in net team additions into account, our figures would still be meaningful since these additions could both increase or decrease team heterogeneity.
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See Table 7.
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Kaiser, U., Müller, B. Skill heterogeneity in startups and its development over time. Small Bus Econ 45, 787–804 (2015). https://doi.org/10.1007/s11187-015-9667-8