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Firm Characteristics and Employee Entrepreneurs’ Choice

of Cofounders and Early Employees

Jing Chen

Copenhagen Business School

March 2013

Abstract

In the early stage of a startup, employees and founders are more integrated as one

workgroup. The boundary between their functional roles is not as clear as that in more

established firms. Given this special relationship between early employees and founders,

this paper revisits the view of functional diversity in explaining founding team formation,

taking in account the interplay between founding team members and early employees.

Using data from Statistics Denmark, I examine how a former coworker’s likelihood of

becoming a cofounder or an early employee is responsive to the skill match between him

and the entrepreneur as being complementary or substitutive. I find that a coworker is

more likely to become a cofounder if he has different skills from the entrepreneur, and to

become an early employee if they have the same skills. This result indicates the relevance

of common knowledge sharing between early employees and entrepreneurs, but not so

much among founding team members.

JEL Classification Codes: D21, D22, J62, L26

Keywords: Early employees, founding team, coworker, diversity.

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1. Introduction

This paper examines the distinction between founding partners and early employees, and

how this difference is perceived by entrepreneurs in their early recruitment decision.

With whom should an entrepreneur cofound a business? Who should an entrepreneur

hire as an early employee? What distinguishes a competent cofounder from a qualified

early employee? These are the questions that are central to the paper. Specifically, I

focus on the difference in selection criteria of co-founders and early employees, with

respect to skill complementarity between them and entrepreneurs.

There has been an extensive literature on founding team composition and its relationship

with business performance. At the core of these discussions is the question of whether

diversity is preferable to homophily for the structure of founding team, while diversity is

measured along different dimensions, such as demography, functional skills, industry

experience and prior employment affiliation (e.g., Pelled et al. 1999, Ruef et al. 2003,

Kor 2003, Beckman 2006, Beckman and Burton 2008, Eesley et al. 2013). The empirical

evidence on individual functional and industry experience tend to support a positive

relationship between founding team diversity and firm performance (Eisenhardt and

Schoonhoven 1990, Beckman et al., 2007, Fern et al. 2010), although the literature also

shows that work groups, more generally, tend to perform better when members have

similar and general, as opposed to different and specialized, knowledge (Rulke and

Galaskiewicz 2000, Liang 1994).

A potential issue with these studies of founding team structure is that founding team

members are often treated as independent of the rest of workforce in a startup. However,

in practice there is not always a clear line drawn between founders and those early

employees, in terms of their functional roles in the startup. Compared to a vast amount

of literature on entrepreneurial founding teams, few studies have explored this group of

early workforce who joined startups. Related issues, such as the sources of early

employees, their group composition, and its dynamic pattern, remain largely

underexplored in the entrepreneurship literature. However, because early employees

interact closely with founders, it raises a question of whether the observed structure of

founding team is influenced by skill composition of early employees. The rationale

underlying this hypothesis relies on the pros and cons of team diversity that have been

discussed in the literature. On the one hand, diversity is seen as a way to increase firm's

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competency with more accesses to resources, more complete functional structure, and a

faster process of problem solving (Eisenhardt and Schoonhoven 1990). On the other hand,

diversity could also cause inefficiency by creating higher barriers of idea communication

and knowledge distribution (See Rulke and Galaskiewicz 2000 for a review). How do

these pros and cons affect entrepreneurs' choice of founding team structure? By focusing

on founding team itself, previous studies suggest that entrepreneurs can only choose to

favor either competency or efficiency. In this paper, I argue that entrepreneurs could be

doing both by differentiating skill structures of founder and early employees. An

interesting question is whether entrepreneurs tend to place more values on founding

team diversity while improving task-specific communication through hiring employees

with the same skills as them, or they are more likely to select cofounders similar to them

while hiring employees with diversified skills to execute different functional roles in the

early stage of business operation.

To explore these two possibilities, I construct a dataset based on an employer-employee

matched database. The dataset links a cohort of entrepreneurs to all the coworkers from

their previous workplaces. I estimate the differing effects of having the same occupational

skills as the entrepreneur on the likelihood that the coworker becomes a cofounder as

opposed to becoming an early employee. By confining the analysis in the context of

coworker relationship, I attempt to identify all individuals who are at risk of joining the

entrepreneur’s new venture. The drawback of this design is that entrepreneurs may also

choose to hire people outside the coworker network. However, there are several reasons

why coworkers could be the most likely source for entrepreneurs to look for founding

partners and employees. First, entrepreneurs generally face network and geographic

constraints (Ruef et al. 2003). Aside from family members and friends, coworkers form

another group of social contacts with whom prospective entrepreneurs have more

opportunities to develop a strong interpersonal relationship. Second, coworker

relationship builds on common knowledge, language, and routines at work (Nahapiet and

Ghoshal 1998), which provides two advantages of involving coworkers in the process of

new business creation. On the one hand, because of shared working experience, coworker

relationship is more effective in stimulating business ideas and making decisions than

family ties, regular friendship or strangerhood (Eisenhardt and Schoonhoven 1990,

Beckman 2006). Indeed we have seen ample amount of evidence in cases where former

coworkers created employee spinoffs together (Klepper 2001, Agarwal et al. 2011). On

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the other hand, recruiting former coworkers in s startup also alleviates the problem with

information asymmetry (Akerlof 1970). Lastly, working with former coworkers could also

avoid issues such as nepotism that often plague family businesses (Bertrand and Schoar

2006). For these reasons, this study focuses on entrepreneurs’ prior coworkers as

potential candidates of cofounders and early employees, and examines how the type of

skill match between them and the entrepreneurs affects a specific recruiting outcome.

The main finding in the data shows that having different occupational skills from the

entrepreneur increases the likelihood that a former coworker becomes a cofounder, while

having the same skills increases the likelihood of him becoming an early employee. This

result suggests that when selecting cofounders, entrepreneurs tend to place more value

on functional diversity than commonality of skills. In contrast, entrepreneurs emphasize

more homogeneity and probably communicative efficiency when recruiting early

employees.

Another interesting finding on the side is that the effect of skill matching on the

entrepreneur's recruiting outcome (for both cofounders and early employees) is

responsive to the size of the entrepreneurs’ previous workplace. Having the same skill as

then entrepreneur has a smaller positive effect on the likelihood that a coworker becomes

an early employee if the prior workplace is larger. But the positive effect of skill diversity

on the probability of a coworker becoming a cofounder is increasing with firm size. One

plausible explanation for these contrasting results is the distinct nature of jobs defined in

small and large firms, which may affect skill composition among entrepreneurs’

coworkers. Thus, workplace characteristics may influence employees' propensity to enter

entrepreneurship by creating advantage or constraints of finding cofounders and early

employees in the coworker network.

The remainder of the paper is organized as follows. In the next section, I describe in

details the data sources, the construction of dataset and the key variables, followed by

descriptive summaries. Section 3 presents the main results, and section 4 concludes.

2. The Data

2.1 Data Sources

To examine skill matching between entrepreneurs, founding partners and early

employees, I need a comprehensive dataset that meets three criteria. First, I need to

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identify all individuals in the startups and their positions, so as to distinguish a founder

from an early employee. Second, I need detailed information about each individual’s

occupational skills so that I can measure skill complementarity. Third, I need to identify

all potential co-founders and early employees who might have joined the startups, and

provide information about their occupational skills. In this empirical setting, the group of

individuals who were at risk of joining the startups is confined to entrepreneurs’

coworkers at the previous workplace. To construct such a dataset, I employ three

databases maintained by Statistics Denmark.

The Entrepreneur Database records all new businesses created in Denmark each year

from 1996 to 2006. A unique identifier is assigned to each business by Statistics Denmark.

Using this unique identifier, The Entrepreneur Database can be linked to The Firm

Database, which consists of employment information about all workers (full-time and

part-time) at all firms existing in Denmark from 1995 to 2008. Based on a variable that

specifically describes an individual’s position in the firm, it is possible to identify whether

a person was a founder or an early employee. The Firm Database can be further linked

to the Integrated Database for Labor Market Research (IDA), which is an employer-

employee matched panel from 1980 to 2008. Firms included in this database must have

at least one person working there as his primary occupation. The IDA database allows

me to track an individual’s previous employer prior to starting or joining a startup,

identify all his coworkers at the previous workplace, and collect information about their

occupational skills.

A somewhat controversial issue of conducting entrepreneurship research using data from

Denmark is whether the Danish context is too peculiar to provide more general insights

that are applicable to other settings. In fact, despite its small economy, Denmark has

some of Europe's highest levels of early-stage entrepreneurial activity. The nascent

entrepreneurship rate in 2011 is 3.1% in Denmark, which is lower than 8.3% in the US,

but comparable with 3.4% in Germany and 4.7% in UK.1 Between 2001 and 2002, the

time adjacent to the observation window of this analysis, the average new firm birth rate

                                                            1 GEM 2011 Global Report. Nascent entrepreneurs are defined by GEM as “individuals between the ages of 18 and 64 years, who have taken some action toward creating a new business in the past year. To qualify for this category, these individuals must also expect to own a share of the business they are starting and the business must not have paid any wages or salaries for more than three months.”

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is 9% in Denmark, compared to 7.7% in Germany, 10.4% in UK, and 11.5% in the US.2

The so-called Flexicurity system adopted in Danish labor market provides employers

with large flexibility regarding hiring and firing employees, while in the meantime offers

workers generous unemployment benefits and incentives to return to work. Like most

developed countries, Danish government has implemented a wide range of public

programs to promote entrepreneurship. Administrative burdens for entrepreneurship are

also kept at the minimum level. In general, Denmark has a favorable environment for

new business creation. The economy has its peculiar aspects, such as the high taxation

which may have a negative impact on entrepreneurship, but there are also many

commonalities between Denmark and other developed European countries with respect

to the overall conditions for entrepreneurship, including business environment,

government regulation and entrepreneurship culture.

2.2 Dataset Construction

2.2.1 Startup, Founder, and Early Employee

From the Entrepreneur Database, I collect 1,690 Danish startups created in 1999. Before

this year, accounting information was not complete for some industries. All these firms

can be matched to the IDA database, indicating that at least one person was working in

the firm as his primary occupation. Because it is an employer-employee matched

database, the IDA allows me to identify all individuals working (full-time or part-time)

at these firms in 1999, as well as collect their demographic, prior and current

employment information.

To distinguish founders from early employees, I use a variable available in the Firm

Database, which describes a person’s job position in the firm. A person is defined as a

founder if he meets one of the following four criteria: (1) his job position is classified as

self-employed or employer; (2) his position is top manager in a business with fewer than

four employees; (3) he works in a business with fewer than four employees, where no one

falls into the job position categories listed in (1) and (2); and (4) the person does not

meet the first three criteria, but he is the one who registered the business and is

                                                            2 GEM 2005 Global Report. New firm birth rate is defined by GEM as the number of expected births per 100 of existing firms.

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currently working at the business.3 The rest of individuals at a startup who do not meet

any of the four criteria are treated as early employees.

Based on this definition, I identify total 2,120 (co)founders and 3,645 early employees

who were working for the original 1,690 startups in 1999. These businesses are in general

very small. The average number of people working in the business is 3.4. Thirty-eight

percent of these businesses (642) did not hire any employee in the founding year.

2.2.2 Identifying coworkers at the previous workplace

The IDA database provides comprehensive employment records for each individual

included in the data, which can be traced back to 1980. To simplify the analysis, I focus

on their most recent employer in 1998 or 1997 (if not employed in 1998). Another reason

is that entrepreneurs are likely to make better assessment of skills of the most recent

coworkers. A brief exploration of the data shows that 6.6 percent of the businesses in the

data were co-founded by people with joint recent work experience at the same plant, and

twenty-three percent hired an early employee with whom at least one cofounder shared

the same workplace in recent years.

To identify the pool of potential founding partners and early employees at entrepreneurs’

previous workplace, I take advantage of the employer-employee matched feature of the

database and collect all the individuals who worked at the same plant as the

entrepreneurs in 1998 or 1997. There are 227 cases where none of the founders in the

business had an employment record in the past two years prior to founding. After

removing those cases, I identify 1,657 firms and 1,772 plants, where 1,954 (out of 2,120)

(co)founders in the sample worked in 1998 or 1997. Table 1 shows that thirty-five

percent of these parent firms are related to the wholesales and retails industry. Thirty

percent is evenly distributed between manufacturing and construction. Public services

and knowledge intensive business services account for another twenty percent of

observations. By comparison, startups spawned by these firms are most likely to

concentrate in wholesale and retail, construction and knowledge intensive business

services, but less likely in manufacturing or public and personal services.

                                                            3 The first three criteria were first suggested by Sørensen (2007).

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There are 177,793 individuals working at these plants around the same time as the

entrepreneurs. Among them, 1,064 left the plants and joined various startups in 1999 as

early employees. The majority of these people (982) joined startups created by their

coworkers, and the rest became early employees at startups founded by people who were

not from the same plant.

[Insert Table 1 about here]

2.2.3 Occupational skills

To compare the occupational skill of entrepreneurs with that of their coworkers, I

restructure the dataset so that, for each individual, there is a match between him and a

startup founded by his coworker in 1999, which he could possibly join as a cofounder or

an early employee. If more than one employee startup was spawned from his workplace,

I create a match between the focal individual and each of these businesses. Each

observation on the match includes occupational information for the focal individual and

for all the founders in the startup who were his coworkers at the same plant. I measure

occupational skills using the four-digit occupation code, which is based on the Danish

version of the international standard classification of occupations.4

The dataset is structured at the individual level. Depending on how many startups were

spawned in 1999 from a plant, there could be several observations on each focal

individual who worked at this plant. If the focal individual himself cofounded a startup

with another coworker, this could result in a duplicated observation on the match. Table

2 illustrates the structure of the dataset with an artificial example. In this example,

there are two startups created in 1999 by former employees at workplace W1. The first

startup, N001, was solely founded by employee P2. The second startup, N002, was

cofounded by employees P3 and P5. For each individual who was working at workplace

W1 around the same time as the three entrepreneurs (in 1998 or 1997), there are two

observations on the match between him and the two startups, respectively. However, if

the person is the only founder of a startup who worked at workplace W1, the

                                                            4 The reason for using the four-digit rather than a more detailed six-digit code is that the nuance between close categories at the six-digit level rests more on the same occupation associated with different industries. For instance, occupation code 3111 refers to technicians in science. At the six-digit level, this occupation category is further classified as technician working in geology, chemical processes, electrical systems and equipment, machinery, etc.

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observation that links him to his own startup is removed from the sample (row 3).

Moreover, if the startup has two founders who worked at the same plant before, there is

a duplicated observation on the matching between them (rows 6 and 8). This duplicate

is not removed from the data, because the empirical analysis focuses on employment

outcome of each focal individual and his employment outcome, controlling for his

demographic and employment characteristics.

[Insert Table 2 about here]

I compare a focal individual’s occupational skill with all (remaining) founders in the

startup who were his coworkers at the previous workplace. A person’s occupational skill

is considered being complementary to the founding team if none of the (remaining)

founders shares with him the same occupation code. Otherwise, the person is considered

having the same skill as one or more cofounders.

2.2.4 Occupational choice

I create three categories for individuals’ employment options in 1999. These options

include (1) starting a business with coworkers; (2) joining a coworker’s startup as an

early employee; and (3) all the others, such as staying at the same workplace, starting a

business without a coworker, joining startups that were not founded by coworkers, etc.

It is not surprising that in the sample, 99 percent of observations fall into the third

category, and the majority of them did not join or start any startup in 1999. There are

1,954 individuals who cofounded a business with previous coworkers, and 982 individuals

who joined startups created by their coworkers as early employees.

Table 3 reports descriptive statistics by individuals’ occupational choice. There is no

significant demographic difference across the three groups of people. But compared to

those who became early employees, individuals who became founders on average earn

more but have shorter tenure at the previous workplace. They are also more likely to be

employers, have entrepreneurial experience, and come from larger firms.

[Insert Table 3 about here]

3. Empirical Analysis

3.1. Skill Match and the Choice of Cofounders versus Early Employees

(1) The Baseline Specification

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The likelihood that an entrepreneur recruits a former coworker as a cofounder or an

early employee depends on an array of factors, including the coworker’s skill quality, his

relationship with the entrepreneur, his preference for working for startups, as well as the

entrepreneur’s preference for working with former coworkers. The entrepreneur's

recruiting outcome can be expressed as the following baseline equation:

                                                                                                   (1) 

is the outcome variable, indicating whether or not a former co-worker i join

entrepreneur j as a co-founder or an early employee. is an indicator of skill similarity

between coworker i and entrepreneur j. It equals one if the coworker has the same skills

as the entrepreneur, and zero if otherwise. is the size of their previous workplace w.

This variable expects to capture two things. One is how closely entrepreneur j and

coworker i worked together at workplace w, and the other is coworker i’s unobserved

preference for working for startups, given the possible difference between small and large

firm employees with respect to entrepreneurial propensity (Elfenbein et al. 2010). The

covariates include co-worker i’s demographic characteristics such as age, gender, race,

marital status, number of children under 18, education, self-employment experience, as

well as his employment information at workplace w, such as tenure, position, and hourly

wage.

(2) The Interaction between Firm Size and Skill Similarity

Skill similarity between entrepreneur j and a coworker i affects the likelihood of coworker

i becoming a cofounder or an early employee in two ways. On the one hand, if skills

required for a cofounder significantly differ from those required for an early employee, we

should expect to see the contrasting effects of having the same skills as the entrepreneur

on the likelihood that a coworker becomes a cofounder, compared to becoming an early

employee. This effect comes through the level effect of skill similarity, , estimated in

equation (1).

On the other hand, skill similarity may not affect these recruiting outcomes in the same

way across all workplaces. The differences could be driven by three possible mechanisms.

First, small and large firm employees may differ in their preference and skills for working

for small entrepreneurial ventures (Elfenbein et al 2010). Small firm employees are

perceived as having more entrepreneurial-oriented personalities and having more

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diversified skill sets, which may increase their propensity to join entrepreneurial ventures

(Lazear 2005, Sørensen 2007, Gompers et al 2005). This difference between small and

large firm employees may strengthen or attenuate the effect of skill similarity on the

likelihood of recruiting coworkers in a startup as cofounders or early employees.

The second mechanism focuses on the information effect of firm size. As the size of

workplace increases, the relatedness of the entrepreneur to a focal coworker declines.

This means that entrepreneurs obtain better information about former coworkers' skills if

their previous workplace is smaller. Thus, because of information asymmetry, firm size

may diminish the effect of skill similarity on the likelihood of former coworkers being

recruited in a startup.

The third mechanism considers skill composition among coworkers, and its variation

with firm size. Because job categories in large firms are more narrowly defined than

small firms (Elfenbein et al. 2010), coworkers in large firms, who have close interactions

with each other at work, are more likely to share similar expertise in a specific field. In

contrast, small firm employees tend to engage in more broadly-defined job activities,

partly because the number of positions in each job category is more limited at firms with

smaller size. Thus, the network built by small firm employees is likely to consist of

coworkers with more diversified skills from each other, while large firm employees are

more likely to have coworkers with similar skills. For entrepreneur j, the fraction of his

former coworkers with the same skills as him is expected to increase with the size of his

previous workplace. If skill similarity has a positive impact on the likelihood of coworker

i being the best choice for a position in entrepreneur i's startup, this effect would become

smaller in a larger workplace because there would be more candidates with similar skills

to the entrepreneur. In contrast, if skill diversity has a positive effect on the likelihood

that coworker i joins the startup, increasing the size of workplace would further enlarge

this effect, as there are fewer coworkers in entrepreneur j’s network who have diversified

skills from the entrepreneur.

To take these effects into account, I estimate the following full equation with the

interaction between skill similarity and the size of entrepreneurs' previous workplace

, measured by the number of employees in the firm before the departure of

entrepreneurs,

                           ∗                                                (2) 

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All other controls remain the same as in the baseline equation (1).

(3) Results

I am particularly interested in comparing skills required for cofounders and early

employees. I first estimate equations (1) and (2) using the regular logit model based on a

subsample of individuals who join a coworker’s startup as either a cofounder or an early

employee. There is an advantage of focusing on the restricted sample. Because

individuals included in the restricted sample become either a cofounder or an early

employee, I could partly control for the relatedness of the coworker to the entrepreneur,

and therefore minimize the relatedness effect of firm size. In the full sample, I estimate

equation (2) using the multinomial probit model. For all individuals in the sample, they

have three career choices in 1999: (1) join a coworker's new business as a cofounder, (2)

join a coworker's new business as an early employee, and (3) do not join any coworker's

business. The results are reported in table 4.

Column (1) presents two interesting baseline results. First, the positive coefficient

estimate of skill similarity suggests that a coworker is more likely to become an early

employee if he has the same occupational skill as the entrepreneur. Alternatively

speaking, skill dissimilarity increases the likelihood that a coworker becomes a cofounder.

The two opposing roles played by skill similarity in the selection of cofounders and early

employees suggest that even though the line between founders and early employees may

not be particularly clear sometimes, there is in fact distinction between them with

respect to the match of their skills to the entrepreneur's expertise. The results imply that

functional diversity is more emphasized in the selection of founding partners, while

homogeneity in skills is more preferred while entrepreneurs choose early employees. This

is probably because entrepreneurs could more easily communicate with employees about

how to perform a specific task if they share more common skills or technical knowledge.

[Insert Table 4 about here]

Column (1) also shows a strong and negative effect of previous workplace size on the

likelihood that coworker i becomes an early employee, relative to becoming a cofounder.

A similar relationship is evidenced in Table 3. At first glance, this result implies that

coworkers at small firms are more likely to become early employees while those at large

firms are more likely to become cofounders. This implication is at odds with the so called

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“the small firm effect” consistently documented in the literature, which describes a

negative relationship between firm size and the likelihood of employees becoming

entrepreneurs. However, it is worth noting that the analysis so far is based on a

restricted sample of either early employees or cofounders. Thus, the correct way to

interpret the negative coefficient of firm size is to focus on the effect of firm size on an

individual's likelihood of becoming a cofounder, compared to becoming an early employee.

The result suggests that although small firm employees on average are more likely to

become entrepreneurs, large firm employees are even less likely to join startups as early

employees than starting their own business. Simple tabulation statistics presented in

table 5 confirm this intuition. Dividing observations into five categories based on firm

size, the table shows a substantially higher percentage of small firm employees who

become early employees, compared to the fraction of large firm employees who make the

same occupational choice. This descriptive result is consistent with the notion that small

firm employees have higher preference for working in more entrepreneurial environment,

either as founders or early employees. Moreover, the result that coworkers at larger firms

are less likely to become early employees may also be driven by the fact that there are

fewer employee startups spawned by large firms, which otherwise could provide

alternative career opportunities for fellow coworkers.

[Insert Table 5 about here]

Column (2) reports the results after considering the interaction between firm size and

skill similarity. The estimated main effects of the two variables are quantitatively

comparable with those reported in column (1). The negative coefficient of the interaction

term suggests that the positive effect of skill similarity on a coworker’s likelihood of

becoming an early employee relative to being a cofounder is decreasing with firm size.

Since the information effect of firm size is rather trivial in a sample consisting of only

coworkers who become cofounders or early employees, the negative interaction effect

should be mainly attributed to the other two mechanisms discussed above. On the one

hand, large firm employees are less likely to join a coworker’s entrepreneurial firm as an

early employee. Thus, firm size mitigates the positive role of skill similarity in the

selection of early employees. On the other hand, as firm size increases, the entrepreneur

is likely to have more coworkers with whom he shares similar skills. This decreases each

individual coworker’s likelihood of being selected for the position. Both mechanisms

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suggest a reduction in the effect of skill similarity on the likelihood of a coworker

becoming an early employee. However, given the small magnitude of the interaction

effect, the net effect of skill similarity remains positive.

Additional results for individual demographic characteristics further show that a

coworker's likelihood of becoming an early employee rather than a cofounder is higher if

the coworker is female or older. The likelihood is also higher if the coworker has longer

tenure at the workplace or has lower hourly earnings. Moreover, individuals who are

employers at the workplace are less likely to join a former employee’s startup as an early

employee.

Columns (3) and (4) report the multinomial probit estimates of equation (2). Becoming

a cofounder is treated as the base outcome is. Consistent with the previous two columns,

skill diversity appears to be strongly and positively correlated with the likelihood that

coworker i becomes a cofounder. He is more likely to become an early employee or not

join up entrepreneur j's startup if they share the same occupation. The results for firm

size are also intuitive. Compared to being a cofounder, large firm employees are more

likely to not join a coworker's startup, and even less likely to become an early employee

in those startups. As firm size increases, there are more coworkers who have the same

skills as entrepreneur j. This reduces the positive effect of skill similarity on the

likelihood that coworker i becomes an early employee, but increases its positive effect on

the likelihood that coworker i does not join entrepreneur j's startup.

To summarize, the results reported in table 4 highlight two factors that influence how

entrepreneurs choose cofounders and early employees. One factor is skill match between

entrepreneurs and a potential candidate. Skill diversity is found to be more valuable for

a cofounder, while skill similarity is more preferable for an early employee. The other

factor is the size of prior employer. As firm size is related to the type of human capital

accumulated by employees, it may affect skill composition among coworkers. Small firm

employees are more likely to be surrounded by coworkers with diversified skills, while

large firm employees tend to have coworkers with similar skills. This distinction between

small and large firms provides an alternative way to examine the role of skill match in

selecting cofounders and early employees. If it is true that entrepreneurs choose

cofounders with diversified skills and hire coworkers with the same skills as them, we

would expect to see that entrepreneurs who worked for small firms are likely to have

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more former coworkers in the founding team because of their diversified skills, while

those who worked for large firms are likely to hire more coworkers as early employees

due to skill similarity. I explore this relationship between previous workplace size and

the representation of coworkers in the founding and early employee teams in the

following subsection.

3.2 Firm Size and Early Team Composition

(1) Empirical Specifications

The baseline equation can be expressed as,

(3)

The dependent variable, , is a fraction. The numerator is the number of either

cofounders or early employees at startup s, who are entrepreneur j’s coworkers at firm w.

The denominator is the total number of entrepreneur j’s coworkers who are working at

startup s. is the size of firm w. The covariates include indicators for entrepreneur

j’s demographic characteristics and employer status at firm w.

An alternative way to construct the dependent variable is to replace the denominator

with the size of founding team or the total number of early employees at startup s,

respectively. However, using this dependent variable relies on the underlying assumption

that entrepreneurs always prefer coworker network to other sources when searching for

cofounders or early employees. This could be due to network constraints or information

asymmetry. However, there are certainly cases in which entrepreneurs prefer to work

with non-coworkers in startups. In those cases, we would expect to see fewer coworkers

in the founding team or among the early employees, which could be mistakenly

interpreted as the evidence of disadvantage faced by large or small firm employees. This

issue can be avoided by focusing on startups that recruit coworkers, which is the purpose

of constructing the current dependent variable.

Another issue that could cause bias in the estimate of again concerns the small firm

effect. Entrepreneurs who worked at small firms may have more coworkers who are

willing to join startups as cofounders or early employees. While examining the

relationship between previous firm size and the fraction of former coworkers working in

startups as cofounders or early employees, it is important to tease out this part of the

effect that might be attributed to differentials in entrepreneurial preference. The

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remaining effect then can be explained by difference in skill composition among

coworkers between large and small firms. Because entrepreneurial preference is

unobserved, I make an initial attempt by constructing a variable that measures

employees' transition rate from their previous workplace w to any startup founded in

1999. Employees can work as either cofounders or early employees in these startups,

which are not necessarily formed by their coworkers at firm w. The idea of using this

variable is to imperfectly capture the general preference for entrepreneurship possessed

by entrepreneur j's former coworkers at firm w.

Moreover, it is reasonable to argue that entrepreneurs are more inclined to work with

former coworkers if their startups are founded in the same industry as prior employers.

Thus, industry similarity is another factor that needs to be considered in the model. The

full regression model is given by

(4)

where is a dummy variable, which equals one if startup s and prior employer w are

in the same industry, and zero if otherwise.5 is the number of workers working in firm

w who left to work for startups in 1999, divided by the size of firm w, .

(2) Results

The analysis is carried out in the base sample consisting of 2,120 entrepreneurs and 1,690

startups. Columns (1) and (2) in table 6 focus on the percentage of founding team

members at startup s who are entrepreneur j’s coworkers. The baseline results reported

in column (1) show that entrepreneurs’ demographic characteristics do not have any

significant impact on the composition of founding team. However, being the employer of

prior workplace is associated with lower density of coworkers in the entrepreneur's

founding team. This result is not surprising, as employers are probably less likely to

team up with employees to found a business. The positive estimated coefficient on

industry similarity is expected since entrepreneurs would value more industry experience

of former coworkers if the startup is in the same industry as entrepreneurs’ prior

employer.

[Insert Table 6 about here]

                                                            5 Industry comparison is based on the six-digit level of Danish Industrial Classification.

17

 

The variable of particular interest is the (log of) size of prior employer w. The OLS

estimate of the coefficient shows that entrepreneurs who worked at small firms are more

likely to recruit coworkers in their startups as cofounders relative to early employees.

This negative relationship is consistent with the argument in the paper that the degree

of skill diversity is higher among coworkers at smaller workplace, which provides

potential employee entrepreneurs with an advantage to seek cofounders among coworkers.

However, this result could also be explained by the negative relationship between firm

size and employees’ entrepreneurial preference in general. To account for difference in

employees’ entrepreneurial propensity across firms, column (2) includes employees'

transition rate to startups. As expected, this variable appears to have a strong and

positive effect on the outcome of interest, indicating that entrepreneurs are more likely

to include former coworkers in the founding team if employees' preference for working for

startups is generally higher at their prior workplace. After including this variable, the

size effect, however, becomes insignificant, although it remains negative. The loss of

significance is mostly due to the high negative correlation between the two variables (-

0.62). As the size variable is now expected to only capture difference in skill composition

among coworkers, the result in column (2) provides weak support for the hypothesis that

skill diversity, which is more common among coworkers at small firms, increases the

likelihood of employee entrepreneurs drawing founding team members from the network

of former coworkers. But this outcome of interest is, by comparison, more influenced by

the extent of entrepreneurial preference exhibited by employees at entrepreneurs’ prior

workplace.

In columns (3) and (4), I replace the dependent variable with the percentage of early

employees at startup s who are entrepreneur j’s former coworkers. Columns (3) and (4)

show that entrepreneurs are more likely to hire former coworkers as early employees, if

they are employers of the previous workplace. One plausible explanation is that

employers are more likely to share similar work value and knowledge with their own

employees than outsiders. Moreover, similar to columns (1) and (2) industry similarity

between startups and prior employers also predicts a higher likelihood that entrepreneurs

would prefer to hire coworkers as early employees in startups, indicating that symmetric

information about coworkers’ industry-specific skills plays a significant role in spinoffs’

recruitment of early employees and cofounders.

18

 

More important, without controlling for coworkers’ entrepreneurial preference, column (3)

shows that there is no significant difference between large and small firm entrepreneurs

with respect to their propensity to hire coworkers as early employees. This insignificant

relationship could be a joint result of two offsetting effects of firm size: the positive effect

attributed to skill similarity among coworkers at large firms, and the negative effect due

to lower entrepreneurial preference that is often associated with large firm employees. To

take into account the negative effect of firm size, I include in column (4) the variable

that measures employees' transition rate to startups at entrepreneurs' prior workplace.

As in column (2), this variable also predicts higher density of coworkers among early

employees in the startup.

After including this variable, the estimated coefficient on firm size remains positive and

but becomes significant at one percent level. This result suggests that entrepreneurs who

came from large firms are more likely to recruit their former coworkers as early

employees relative to cofounders in new startups. The reason behind this relationship is

that skill similarity is more commonly perceived among coworkers at large firms.

The results presented in table 6 provide additional evidence that entrepreneurs place

more value on skill diversity when selecting founding partners, while emphasize shared

understanding of knowledge and skills when choosing early employees.

4. Conclusions

This paper is motivated by an emerging discussion (among both researchers and

practitioners) on the important role played by early employees in startup formation and

performance (Roach and Sauermann 2013). What makes early employees distinctive

from regular workers is their closer interaction with founders and deeper involvement in

the early stage of business operation. From this perspective, early employees play an

equally important role as founders in building and scaling startups. They are also similar

to founders with respect to the preference for working for small entrepreneurial firms,

but probably fundamentally different from founders in other aspects. Given the special

relationship between early employees and founders, the primary goal of this paper is to

rethink some of the insights about founding team formation that are drawn from

19

 

previous studies, which rarely take into account the interplay between founders and

early employees.

One of these insights that have been repeatedly presented in the literature is the

relevance of functional diversity on founding team performance. Despite of the perceived

barriers of communication among people with different backgrounds, previous studies

consistently show a positive relationship between founding team diversity and firm

performance. This paper investigates this paradox by comparing skills of cofounders and

early employees, and their match with the entrepreneurs. The interesting finding is that

individuals who have different skills from the entrepreneur are more likely to become

cofounders, and those with the same skills tend to become early employees. This result

suggests that efficiency and competency could be simultaneously achieved in startups

with different skill structures of founding team and early employees. While diversity

appears to be crucial for entrepreneurial decision making, entrepreneurs are inclined to

hire early employees who share the same knowledge as them to ensure the flow of

knowledge transmission and communication.

This paper is also related to an emerging literature, which studies the relationship

between workplace characteristics and employees’ propensity to become entrepreneurs.

The most prominent finding in this literature is the so-called the small firm effect, which

shows a robust negative effect of firm size on the likelihood of employees becoming

entrepreneurs (e.g. Wagner 2004, Dobrev and Barnett 2005, Gompers et al., 2005, Parker

2006, Sørensen 2007, Sørensen and Phillips 2011, Elfenbein et al. 2010, Chen 2012).

Several mechanisms have been proposed to interpret this relationship. The most well-

known ones include self-selection, suggesting that individuals with innate entrepreneurial

attributes (e.g. preference for autonomy) are more likely to work for small firms

(Sørensen 2007). Meanwhile, it is also argued that the nature of small-firm jobs which

are more broadly defined provides employees with peculiar opportunities to engage in a

variety of activities and develop diversified skills that are suitable for entrepreneurship

(Dobrev and Barnett 2005, Gompers et al., 2005, Parker 2009, Werner and Moog 2009,

Elfenbein et al. 2010). Moreover, differences in industry entry barriers facing small

versus large firm employees are also found to account for part of the small firm effect

(Chen 2012).

20

 

Given the difference in skills required for cofounders and early employees, this paper

further investigates whether the nature of large or small organization affects the

structure of its employees’ local network, particularly the skill composition, which

subsequently defines the pool of potential cofounders and early employees faced by

prospective employee entrepreneurs. The result shows that prospective entrepreneurs

who worked for large firms tend to have more coworkers who are suitable for early

employees. But meanwhile, they are lack of cofounder candidates in the network

developed at the workplace. This finding provides an alternative perspective to

understand why small firm employees are more likely to transition into entrepreneurship

than large firm employees.

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Table 1 Comparing Parent Firms and Startups

Parent Firm Startups

Number of employees 140.3 3.4

Industry

Agriculture 1.33 0.06

Manufacturing 15.21 6.63

Utility 0.36

Construction 15.45 16.64

Wholesale and Retail 35.00 45.49

Trans, Post, and telecommunication 5.97 5.10

Low-Tech Intensive Business Activities 4.95 6.51

Public and Personal Services 11.41 4.60

Knowledge Intensive Business Services 10.32 14.98

Obs. 1,657 1,629

Table 2 Illustration of Data Structure

Individual ID Workplace Startup ID Founder 1 ID Founder 2 ID

1 P1 W1 N001 P2 .

2 P1 W1 N002 P3 P5

3 P2 W1 N001 P2

4 P2 W1 N002 P3 P5

5 P3 W1 N001 P2 .

6 P3 W1 N002 P5 .

7 P5 W1 N001 P2

8 P5 W1 N002 P3

Table 3 Summary Statistics by Employment Choice

Early Employees Founders Others

Age 34.75 34.96 37.91

College 0.06 0.08 0.13

Male 0.65 0.72 0.58

Married 0.42 0.48 0.49

Danish 0.96 0.95 0.96

Number of children under 18 0.60 0.85 0.64

Hourly wage at previous workplace 134.70 155.78 168.10

Tenure at previous workplace 4.28 3.75 5.46

Employer at previous workplace 0.02 0.10 0.003

Self-employed previously 0.03 0.09 0.008

Number of employees at previous workplace 98.79 465.79 4,859.17

Obs. 982 1,954 176,811

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Table 4 What Determines Entrepreneurs’ Selection of Cofounders vs. Early Employees

Logit Multinomial Probit Dept Var = 1

if becoming an early employee Not Join Early

Employees (1) (2) (3) (4)

Prior Employment Characteristics Same Skill 1.425*** 1.997*** 1.443*** 1.000***

(9.01) (6.54) (11.25) (5.99) Firm Size -0.178*** -0.155*** 0.535*** -0.160***

(-6.19) (-5.17) (56.28) (-8.96) Same Skill*Firm Size -0.193** -0.160*** -0.078*

(-2.20) (-5.84) (-1.82) Individual Characteristics Age 0.010* 0.010* 0.010*** 0.005*

(1.90) (1.91) (5.56) (1.71) College 0.152 0.142 0.032 0.105

(0.77) (0.71) (0.56) (1.06) Male -0.484*** -0.490*** -0.295*** -0.259***

(-4.44) (-4.49) (-8.00) (-4.50) Married -0.006 -0.001 -0.101** -0.034

(-0.05) (-0.01) (-2.49) (-0.53) Dane 0.191 0.184 -0.096 0.285**

(0.75) (0.73) (-1.13) (2.01) No. Of Children -0.291*** -0.289*** -0.105*** -0.159***

(-5.34) (-5.30) (-6.13) (-5.50) Hourly Wage -0.006*** -0.006*** -0.001*** -0.002***

(-7.14) (-7.17) (-5.16) (-4.79) Tenure 0.043*** 0.043*** 0.017*** 0.032***

(3.76) (3.72) (4.16) (5.14) Employer -3.034*** -3.092*** -0.505*** -1.609***

(-8.14) (-8.20) (-4.17) (-7.84) Prev. Self-Employed -0.019 -0.007 -0.101 -0.052

(-0.06) (-0.02) (-0.90) (-0.29) Controls for parent firm industry

Y Y Y Y

Ave. Log Likelihood -0.540 -0.539 -0.050 N. 2,645 2,645 197,663

z-scores are in parentheses. Significance levels: ***0.01, **0.05, *0.1.

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Table 5 Percentages of Early Employees and Cofounders by Firm Size

Firm size Early Employee Cofounder N

25th percentile 35.07 64.93 2,740

50th percentile 5.1 94.9 98

75th percentile 16.13 83.87 93

above 75th percentile 4 96 25

Table 6

How Prior Employer Size Affects the Likelihood of Recruiting Coworkers as Cofounders vs. Early Employees

OLS Regressions Dept Var:

Fraction of Cofounders Dept Var:

Fraction of Early Employees (1) (2) (3) (4)

Prior Employer Characteristics Log Size of Firm -0.018*** -0.004 0.002 0.020***

(-5.39) (-1.06) (0.69) (5.23) Rate of Transition

0.195***

0.244***

(4.28)

(5.73)

Entrepreneur Characteristics Age 0.001 0.001 0.002 0.001

(1.17) (0.53) (1.64) (0.81) College 0.043 0.042 -0.039 -0.039

(1.20) (1.16) (-1.16) (-1.22) Male -0.022 -0.022 0.024 0.023

(-1.11) (-1.16) (1.26) (1.25) Married -0.022 -0.020 -0.004 0.000

(-1.31) (-1.18) (-0.19) (-0.02) Dane 0.041 0.041 0.022 0.021

(1.20) (1.19) (0.61) (0.62) Log of Hourly Wage -0.001 0.016*** -0.045*** -0.024***

(-0.21) (2.78) (-6.39) (-3.27) Tenure 0.000 0.000 0.016*** 0.015***

(0.06) (-0.07) (6.16) (6.11) Employer -0.045** -0.046** 0.086*** 0.084***

(-2.10) (-2.20) (3.28) (3.23) Same Industry 0.056** 0.038 0.186*** 0.163***

(2.10) (1.41) (8.37) (7.30) Controls for Startup Industry Y Y Y Y R2 0.063 0.089 0.197 0.226 N 1928 1928 1928 1928 t statistics are in parentheses. Standard errors are clustered on startups. Significance levels: ***0.01, **0.05, *0.1.