Founding Team Dynamics

Ronald Anderson Temple University

Masud Karim

Temple University

Ezgi Ottolenghi* Texas Tech University

September 1, 2020

Abstract

We examine the effect of multiple versus single founders on firm performance. Our analysis indicates that multiple founder firms exhibit valuation premiums over single founder or professionally managed firms. The results further indicate that greater heterogeneity between founders (age, education, gender, ethnicity, and experience) appears to detract from firm performance. When segregating founder characteristics along social versus professional dimensions, we find that occupational heterogeneity appears to be more harmful than social. Overall, founder teams appear to provide greater value to outside investors and this effect tends to be most pronounced when the founders mirror one another along professional backgrounds. * Anderson (ron.anderson@temple.edu) and Karim (masud.karim@temple.edu) are with Fox School of Business, Temple University and Ottolenghi (ezgi.ottolenghi@ttu.edu) is with Rawls College of Business, Texas Tech University. We benefited from comments from seminar participants at Temple University and Texas Tech University. We also thank Harvard Business School’s Leadership Initiative for sharing “Great American Business Leader Database”.

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I. Introduction

Founders play a key role in firm formation and development. Some founders maintain long-lasting

ties with their company, whereas others opt for early exits. The growing literature on founders suggests

that founders provide benefits to the firm through strong monitoring, innovative ideas, and assertive

risk taking (Anderson and Reeb, 2003; Villalonga and Amit, 2006; Fahlenbrach, 2009; Adams et al.

2009; Duran, Kammerlander, Essen, and Zellweger, 2016; Fitzgerald, 2018). However, many of the

popular and academic presses make little distinction between firms with single and multiple founders

despite the widespread presence of firms with two or more founders. For example, Bill Gates often

receives the bulk of the credit for Microsoft’s success, yet cofounder Paul Allen also played an

instrumental role in establishing and launching the company. Although conventional wisdom indicates

strong benefits from founder presence, little research examines the effect of single versus multiple

founders on firm performance.

Multiple founders have the potential to even bring greater benefits to the firm than a solo founder

considering the impacts of idea sharing, more experiences, and varying skillsets. Cofounders can also

support one another in difficult and stressful periods such as financing rounds, initial public offerings,

and transitioning from entrepreneurial to professional management. Human resource theory (Jung,

Vissa, and Pich, 2017; Knight, Greer, and Jong, 2020) and information processing theory (Bergh,

Ketchen, Orlandi, Heugens, and Boyd, 2019) suggest that teams bring greater variation in skillsets and

thus more resources to solving complex business problems and reaching quality decisions relative to

a single individual. The governance literature suggests that the checks-and-balances arising from

cofounders’ interactions likely leads to better decision-making, mitigates managerial entrenchment,

and limits expropriation of firm resources. Hellmann and Wasserman (2016) indicate that negotiations

between founding teams that require longer timeframes and lead to unequal divisions of equity stakes

attract more financing from outside investors, suggesting that cofounder interactions enhance decision

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quality and improve project quality. Cooper and Bruno (1977) argue that multiple founder firms are

more likely to survive than single founder firms because of the psychological support provided by

fellow entrepreneurs on the management team. As noted in a cofounder interviewed by Cooper and

Bruno (1977), “When you wake up in the middle of the night and begin to wonder if you were crazy

to have started a company, it helps to know that others are in it with you.”

Cofounders can also impose costs on the firm and harm performance. By involving two or

more agents, firms – almost by definition – introduce additional conflict into the decision making

process. Conflict and different viewpoints can lengthen the time to achieve consensus, create more

dissonance in the management suite, and introduce more risk aversion in project selection relative

to single decision maker (Ancona, Okhuysen, and Perlow, 2001; Mathieu, Maynard, Rapp, and

Gilson, 2008; Mathieu, Hollenbeck, van Knippenberg, and Ilgen, 2017). Differences among

cofounders can have dramatic effects on the firm and its founders. Many of today’s big-name

companies are or were founded by pairs (Brin and Page at Alphabet, Jobs and Wozniak at Apple,

and Gates and Allen at Microsoft), but Wasserman (2012) indicates that roughly 65% of high-

potential startups fail due to conflicts among cofounders. Although founding pairs bring more skills

to the table than a single person, they also bring the potential for more conflict over strategy,

leadership, and visibility.

Our central research focuses on the effect of founder team structure on firm performance. We

address two specific research questions. First, we ask whether multiple founder firms perform better

than single founder firms or firms with professional managers. Second, if multiple founder firms

perform better (or worse) than single, we investigate if differences in founder traits account for

performance differentials. Specifically, we examine whether differences in cofounder characteristics –

age, gender, ethnicity, education, experiences – influence their relationship to firm performance.

Our analysis indicates that multiple-founder-led firms perform better than solo-founder-led

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firms or firms with professional managers. For our empirical analysis, we start with a sample of

Russell 3000 firms as of December 31, 2001, excluding public utilities and financial firms because

they are regulated industries. We utilize matched sample methodology to reduce the number of

firms for which we hand collect data. Our initial matched sample consists of 5,338 firm-year

observations, comprising 2,669 founder-CEO-led firm-year observations and 2,669 professional-

CEO-led firm-year observations. We lose 662 firm year observations, as we require detailed data on

each cofounder. Our final sample consists of 1,091 unique industrial firms with 4,676 firm-year

observations, spanning from 2001 through 2015. The full sample comprises firms managed by solo

founders, multiple founders, and professional managers. We use the natural log of Tobin’s Q as our

measure of outside investors’ perception of firm value. Tobin’s Q is defined as the ratio of market

value of total assets to the book value of assets (Masulis 2009; Fahlenbrach 2009; Adams et al. 2009).

Our analysis suggests that multiple founder firms exhibit valuation premiums of 12.1% over single

founder firms.

The relationship between firm performance and number of founders could be affected by

endogeneity due to reverse causality and/or an omitted variable bias. Because founders arguably

maintain an information advantage over professional managers, future performance may influence

their decision to remain in the firm or to exit their equity ownership stake. Unobservable firm

characteristics also influence both performance and founder presence. For example, diverging

opinions on the future of the technology in the industry may compel one founder to exit the firm

while another remains. To account for the endogenous nature of the relationship between founder

presence and firm performance, we instrument for founder presence with the fraction of original

founders who died before the beginning of the sample period relative to the number of original

founders at firm inception. Using IV-2SLS, our analysis continues to indicate a valuation premium

for multi-founder firms relative to single founder or professionally managed firms.

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To further investigate the multi-founder firm valuation premium, we examine whether

differences in founder traits and experience influence this premium. Founder heterogeneity may

detract from or improve firm performance. On the one hand, greater team heterogeneity arguably

improves performance because diversity in education, experiences, social status and networks may

enhance inter-team understanding, improve communication with customers and suppliers, and

promote the development of new projects, services, and strategies. Similarly, cofounders’ diverse

professional experience (e.g. years of work, functional areas, and industry exposure) potentially

bring different perspectives to problem solving and increase competitiveness (Williams and

O’Reilly, 1998; Dijk, Engen, and Knippenberg, 2012; Guillaume, Dawson, Otaye-Ebede, Woods,

and West, 2017; Bernile, Bhagwat, Yonker, 2018). Greater social diversity (age, gender, and

ethnicity) arguably brings different perspectives, ideas and problem-solving attributes useful in

product design and business strategies. Top management team (TMT) studies, for example, show

that age heterogeneity exhibits a positive relation to performance (Murray 1989; Herring, 2009; Dijk,

Engen, and Knippenberg, 2012; Gompers, Mukharlyamov, and Xuan, 2016). Age heterogeneity in

cofounder teams is particularly critical for new ventures. Older cofounders may lend greater stability

and experiential wisdom while younger cofounders may bring greater energy and less risk aversion

to decision making (Steffens, Terjesen and Davidson, 2012).

On the other hand, founder heterogeneity may impede firm efficacy and performance. Team

members with diverse educational, social, and professional backgrounds likely hold different views

on technology advancement, competitive tactics, and strategy, leading to conflict and prolonged

indecision (Eisenhardt and Schoonhoven, 1990; Greenberg & Mollick, 2018; Chen, Elfenbein,

Posen, Wang, 2020). Dissimilar cofounders potentially spend more time coordinating and building

trust (De Jong, Dirks, and Gillespie, 2016) than founders with similar traits, and may be less willing

to understand their’ partners idiosyncrasies and strengths (Eisenhardt and Schoonhoven, 1990;

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Costa, Fulmer, and Anderson, 2018). Demographic and ethnic dissimilarity among team members

also appear to increase conflict and hinder cooperation (Jehn 1997). Team survival research further

indicates that school friendships across gender and race are less likely to remain intact than ties

among same-gender or same-race friendships (Hallinan and Williams,1989). Similarly, age

heterogeneity decreases the probability of founding a company together (Jehn, 1997) and increases

the probability that a tie would dissolve over time (Burt, 2000).

We examine the impact of founder heterogeneity on performance using 157 multi-founder firms

(825 firm-year observations) spanning from 2001 through 2015. First, we use a broad heterogeneity

index to calculate diversity among founder characteristics related to age, gender, ethnicity, education

and experience. The heterogeneity index value ranges from a minimum of seven to a maximum of

20, with a higher index value corresponding to higher heterogeneity among founders. Our results

show that overall cofounder heterogeneity is negatively associated with firm performance.

Specifically, our analysis suggests that Tobin’s Q decreases by 7.48% (=3.4%*2.2) as cofounder

heterogeneity increases by one standard deviation (2.2 unit). We further divide the sample of firms

into three categories based on the heterogeneity index scores: high, medium and low. Results of this

analysis shows that Tobin’s Q is especially lower for firms with high heterogeneity score, suggesting

investors view too much founder heterogeneity as hurting their interest.

We further examine founder heterogeneity and decompose the heterogeneity index into two

dimensions: social and occupational (Anderson et al., 2011). Social heterogeneity consists of

heterogeneity measures for founders’ age, gender and ethnicity. Occupational heterogeneity includes

measures for variation in founder education and work experience. Based on our analysis,

occupational heterogeneity appears to be more harmful than social heterogeneity. When

occupational heterogeneity increases by 1 unit, Tobin’s Q decreases by 4.4%. Social heterogeneity

does not have a statistically significant relationship to firm performance in the OLS regressions.

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Our analysis indicates that founder teams appear to provide greater value to outside investors, and

this effect tends to be most pronounced when the founders are more similar to each other along

professional backgrounds.

Because founder heterogeneity and firm performance likely influence one another, we use a

two-stage instrumental variable approach to address endogeneity concerns. We instrument for

founder heterogeneity using the heterogeneity of the county population where the firm headquarters

are located (Anderson et al. 2011). We offer two reasons for using county heterogeneity as our

instrument for founder heterogeneity. First, many founders come from the firm’s local geographic

area. Therefore, greater diversity in the local county would provide a greater, more diverse pool of

individuals that could potentially become founders. Second, founders who come from more diverse

geographical regions may be more comfortable with varying backgrounds and choose to start a new

company with more diverse cofounder teams than founders from small, rural, less diverse towns.

We argue that the instrument is exogenous because firm performance is not the primary cause of

county diversity. Our results get stronger with county heterogeneity as our instrument to address

endogeneity concerns. These findings suggest that founder heterogeneity arising from being

educated in different schools, areas, and/or gaining experience in dissimilar workplaces and fields

is harmful to firm performance. This result could be due to the lack of a common ground among

founders and/or not being able to communicate efficiently. Overall, our analysis indicates that

founder teams appear to provide greater value to outside investors, and this effect tends to be most

pronounced when the founders mirror one another along professional dimensions.

Our study makes at least two important contributions to the literature. First, we extend the

founder literature by documenting that firms with the study provides evidence consistent with

earlier literature that firms with multiple founders perform better than solo founder firms or

professionally managed firms (Anderson and Reeb, 2003; Villalonga and Amit, 2006; Fahlenbrach,

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2009; Adams et al. 2009; Fitzgerald, 2018). Prior literature typically aggregates single- and multi-

founder firms into one group, defining them collectively as founder-run firms. This is the first study,

to the best of our knowledge, that distinguishes between the two types in a public firm setting. In a

related study, Shrivastava and Tamvada, (2011) use private firm data to show the impact of a

founding team versus a solo founder on firm performance. In our study, using novel public firm data,

we attempt to establish a causal relationship using an instrumental variable approach.

Second, this study is closely related to the literature examining the effect of founding team

heterogeneity (e.g. Gompers et al., 2016; Ruef et al. 2003) and board of director heterogeneity

(Anderson et al. 2011; Adams et al. 2018; Bernile, Bhagwat, Yonker, 2018) on firm performance.

Using board of directors’ data, Anderson et al. (2011) find that heterogeneous boards are better in

complex firms, whereas homogenous boards may be better for less complex firms. Gompers et al.

(2016) investigate venture capital partnerships and document that venture capitalists that choose

similar partners hurt investor returns. Adams et al. (2018) examine boards of directors’ skillsets and

provide evidence that firm performance decreases with more diverse board skills, which is attributed

to the lack of common ground in more heterogeneous boards. Unlike Adams et al. (2018), which

studies heterogeneity of boards of directors, our analysis focuses on heterogeneity among founder

teams. Similar to Adams et al. (2018), our study finds that greater heterogeneity on founder teams

appears to detract from firm performance.

II. Data Description and Definition of Variables

A. Sample

For our empirical analysis, we start with the Russell 3000 firms as of December 31, 2001. We

exclude public utilities (SIC codes 4812, 4813, 4911 through 4991) and financial firms (SIC codes

6020 through 6799) because these are regulated industries. Data on equity ownership structure,

inside owners’ cash flow and voting rights, and the family’s role in management comes from annual

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corporate proxy statements. We gather firm specific control and primary variables from CompuStat.

CEO information comes from ExecuComp. To control for survivorship basis, we allow firms to

exit and re-enter the sample. Data on founder CEO status, number of founders, founders’ position

in firm, founders’ death, founders’ education, experience, and demographical background (e.g. age,

gender) come primarily from corporate proxy statements. Because corporate proxy statements do

not provide complete information about a firm’s founding history, number of original and current

founders, founders’ death, founders’ educational, work experience, and social backgrounds, we

manually collect these data from other sources as well. This hand-collected data comes from

FundingUniverse, Reference for Business, company websites, Wikipedia, Hoovers, Bloomberg,

Who is Who in Finance and Industry, Capital IQ, CrunchBase, equilar.com, wikiinvest.com,

Legacy.com, Intellius.com, Orbituary.com, relationshipscience.com, ancestry.com.

Our paper uses a matched sample methodology to reduce the number of firms for which we

hand collect data. We form our matched sample using coarsened exact matching (CEM) (Iacus,

King, and Porro, 2009) and match firms where one of their founders is CEO with firms where the

CEO is not a founder based on exact Fama-French (1997) industry code, and then on total assets

and firm age. Our initial matched sample consists of 5,338 firm-year observations, comprising 2,669

founder CEO led firm-year observations and 2,669 professional CEO led firm year observations.

We lose 662 firm year observations, as we require detailed data on each cofounder. Our final sample

consists of 1,091 unique industrial firms (non-financial and non-utility) with 4,676 firm-year

observations, spanning from 2001 through 2015. The full sample is a matched sample of firms

comprised of solo founders, multiple founders, and professionally managed firms. For our founder

heterogeneity analysis, we use a subsample of firms with 825 firm year observations; this is discussed

in detail in later sections. Data used for the county heterogeneity instrument used in heterogeneity

analysis comes from US Census Bureau. Detailed data definitions are provided in Table 1.

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B. Dependent Variables

We use natural log of Tobin’s Q as our measure of outside investors’ perception of firm value.

Tobin’s Q is defined as the ratio of market value of total assets to the book value of assets (Masulis,

2009; Fahlenbrach, 2009, Adams et al. 2009). The market value of total assets is the sum of the book

value of assets and the market value of common stock less the book value of common stock. We

measure the market value of common equity at the end of each calendar year.

C. Key Variables of Interest

Our initial tests center on whether multi-founder firms (multi founder) perform better than single

founder firms (solo founder) or the firms that no longer have any founder involved (professional

manager). To be considered a founder firm, one of the original cofounders must be still involved with

the firm as a CEO, Chairman (or holding a position as a director), employee of the firm, consultant,

or beneficial holder of equity shares. We identify cofounders in the original founding team primarily

from google searches, company websites, funding universe, reference of business, Wikipedia, and

proxy statements. We use proxy statements to identify the current number of founders that stay with

the firm in each year. If proxy statements include any of the original founding team as CEO and/ or

other positions (e.g. beneficial owner, member of board, top management position), we consider them

a founder who currently stays with firm. If there is no reference made to the original cofounder in the

proxy statement, it is possible that either that cofounder died before 2001 (start of our sample period)

or they left the firm (voluntary or forced out).

We define firms as multi founder firms if two or more cofounders are involved in each firm year for

our sample of firms. A firm is defined as a solo founder firm if only one cofounder is involved for that

year. This founder could be the sole founder of a firm or the only cofounder remaining after other

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cofounders have exited. Professionally managed firms are those where any cofounders exited prior to

that year.

Table 2 shows the distribution of our sample for these three non-overlapping categories. There are

1,197 multi founder firm year observations which represent 25.6% of the full sample. Solo founder firms

are the largest category with 2,425 firm year observations representing 51.9% of the sample. There are

1,054 professionally managed firm year observations which constitutes 22.5% of the sample.

D. Control Variables

We follow earlier literature and introduce several variables into our analysis to control for time,

industry, firm characteristics, and cofounder characteristics. Firm size is one of our control variables

and is measured as the natural logarithm of total assets. Firm age is measured as the natural

logarithm of the years since inception and controls for firm maturity. Firm risk is defined as the

standard deviation of stock returns of 36 months prior to each firm year observations. We also

control for leverage which is defined as long term debt divided by total assets. We use R&D scaled

by sales and capex scaled by sales to control for firm growth opportunities and investment strategies.

As the stock ownership of the management team might be related to performance, we also control

for combined stock ownership of the cofounder team. Additional tests control for the following

CEO and firm characteristics (Fahlenbrach, 2009): being incorporated in Delaware, S&P 500 binary

variable, CEO tenure, CEO ownership and CEO age. In the tests examining cofounder

heterogeneity, we control for the percentage of cofounders who graduated from a top school

following Gompers et al. (2016) definition of top academic institution. In addition, our analysis

controls for the percentage of family members in top management team to disentangle family

influence from non-family cofounder team’s influence.

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III. Descriptive Statistics and Univariate Results

The full sample consists of 4,676 firm-year observations. Table 3 shows descriptive statistics for

our sample of firms. Panel A reports the mean, median, and maximum values along with standard

deviations for the primary variables in our analysis. Panel B presents the results of the difference in

mean tests of our key variables for multi-founder, solo-founder, and professionally managed firms.

Table 1 provides definitions of the key variables used for our tests.

The average firm in our sample has market capital of $1.6 billion. The average amount for our size

proxies, total assets and total sales, are both at $1 billion. Minimum and maximum values for total

assets range from $14.7 million to $11.6 billion, showing that our sample varies in size and is

representative of different types of firms. Firm age, defined as years since inception, varies from

minimum of 1 year to 70 years while averaging at 21 years for the overall sample. Total cofounder

equity ownership averages at 13.8%. Average debt to total asset ratio, our proxy for leverage, is 17%.

Our measure of firm risk is 18% on average for the sample. R&D over sales and capex over sales are

56.3% and 12.3%, respectively for the average firm in our sample. Our proxy for firm value, Tobin’s

Q is 2.13 for average firm in our sample.

Table 3, panel B represents our univariate results as we compare differences among multi-founder,

solo-founder, and professionally managed firms. Multi-founder firms show unique characteristics for

most of our key variables. Multi-founder firms on average have larger market capitalization ($1.8bln)

than both solo founder firms ($1.5 bln) and professionally managed firms ($1.5 bln). Multi-founder

firms are smaller than solo-founder firms in terms of both total assets ($947 mln) and total sales ($822

mln), respectively. In line with our expectation, multi founder firms are younger than both solo

founder and professionally managed firms because solo founders or cofounders are more likely to exit

the firm as it matures. The average age of a multi-founder firm is 18 years, whereas the average age of

solo-founder and professionally managed firms are 21 and 24, respectively. As anticipated, total

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cofounder team equity ownership is higher for multi-founder firms (18.4%) than solo-founder firms

(16.2%) and professionally managed firms (3.1%). Leverage is lower for multi-founder firms compared

to both solo-founder and professionally managed firms. Multi-founder firms demonstrate higher risk

than both solo-founder and professionally managed firms. Multi-founder firms exhibit higher growth

opportunities as measured by both R&D/Sales and Capex/Sales ratios. We use Tobin’s Q as our firm

value measure and find that it is significantly higher for multi founder firms compared to the other

two categories. Average ROA for multi-founder firms is slightly lower than solo-founder firms and is

not statistically different from professionally managed firms.

IV. Multivariate Analysis

A. Multiple-founders and Firm Performance: OLS regressions

While the results of the univariate tests suggest that multi-founder firms differ from solo-

founder and professionally managed firms in many respects and have higher Tobin’s Q, the analysis

does not control for other variables that might affect firm performance. Next, we move to a multi-

variate setting to examine the effects of firm characteristics and cofounder involvement with firm

performance. We start with using ordinary least squares (OLS) to test whether or not multi founder

firms perform better than solo founder or professionally managed firms. To test this, we estimate

the following regression model:

Firm performance = α +β1 multi founder + β2 solo founder + β3-9 Control variables + β10 industry dummies

+ β11 year dummies + ε (1)

Our measure of firm performance is the natural logarithm of Tobin’s Q (Log Q) as defined in

Adams et al. (2009). Multi founder is a binary variable which takes value of one when number of

founders involved with the firm is two or more, zero otherwise. Our first set of control variables

include natural log of firm age, natural log of total assets, firm risk, leverage, growth opportunities,

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cofounder team equity ownership. We use Fama-French' 48 industry classification codes as industry

dummies. Year dummies include one for each year in our sample. We do not use firm-fixed effects

in our specification because the main explanatory variable (multi founder dummy) varies little over

time for a given firm. We also use a second set of control variables following Fahlenbrach (2009)

that incorporates CEO characteristics and reduces our sample size. This second set of controls

includes Delaware dummy, S&P 500 dummy, natural log of CEO tenure, CEO equity ownership,

and CEO age. Initial tests show that our main results do not change when these controls are used

(Table 4). Therefore, we continue our analysis with the first set of control variables to maintain a

larger number of observations.

Table 4 reports the relationship between multi-founder firms and firm performance. Column 1

reports the regression results of Log Q on multi founder firms and control variables. Our regression

results show that multiple cofounder firms are positively associated with firm performance. This

finding is consistent with the hypothesis that having several founders involved with a firm is

beneficial because each cofounder can bring varying types and levels of expertise to decision making.

The analysis suggests that being a multi founder firm is associated with a 12.3% increase in Tobin’s

Q1.

Table 4, column 3 reports the regression results of Log Q with different set of control variables

based on Fahlenbrach (2009). These control variables are primarily related to CEO characteristics

and decrease the sample size from 4,676 to 2,121 firm year observations. Similar to column 1,

column 3 reports that multi-founder firms are associated with higher Tobin’s Q. This additional

analysis with new controls shows even stronger effects on firm performance. Going forward, we

continue to use our first set of controls from column 1 and column 2 to retain larger number of

1 We calculate this impact on Log Q as (exponent (0.116)-1) = 0.123.

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observations. This first set of controls also appears more conservative.

These OLS estimates of the effects of multiple founders on firm performance has no direct

comparable in the literature because this is the first study, to the best of our knowledge, that uses

public firm data when investigating cofounder dynamics. Shrivastava and Tamvada (2011) uses

Kauffman Firm Survey startup data, which does not include public firms or our performance measure

(Log Q). Instead, the study uses sales growth, revenue, and employment growth as measures of firm

performance and shows that number of founders is positively correlated to firm performance.

Column 2 investigates the relationship between the number of cofounders and firm performance.

As we anticipate, the number of current cofounders is positively associated with our performance

measure, which is consistent with Shrivastava and Tamvada (2011).

B. Multiple-founders and Firm Performance: IV regressions

The analysis thus far suggests that multi founder firms has better firm performance than solo

founder or professionally managed firms. Multiple founders still involved with firm could be beneficial

to firm value as diverse backgrounds and experiences may bring different decision making skills

together. Cofounders may improve firm performance when these attributes complement each other.

On the other hand, multiple founders can also be costly to a firm if founders do not communicate

well and argue about strategic decisions.

Earlier OLS analysis suffers from potential endogeneity concerns. Our hypothesis suggests that

multi founder firms perform better than solo founder and professionally managed firms and having

multiple founders involved with a firm results in this better performance. However earlier analysis is

not sufficient to establish this causal relationship. Next, we use instrumental variable, two-stage least

squares (IV-2SLS) approach to test our hypothesis. We propose two instruments. Our first proposed

instrument is the fraction of founders (or single founder) who died before 2001 (prior to our sample

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period). This instrument is similar to the dead founder instrument that is used in Adams et al. (2009),

where a binary variable of whether a founder dies before the sample period is used as an instrument.

In our analysis we use a fraction of the founders that die prior to our sample period because our data

includes firms with multiple founders and the fraction captures this variation better than a binary

variable. We hand collect data on founder deaths that happened prior to 2001 for all firms in our

sample (multi founder, solo founder, and professionally managed firms). Our instrument fulfills both

criteria of a good instrument – (i) relevance and (ii) exclusion. The argument behind this instrument

is that founders have to be alive to stay with the firm. It will be less likely that a firm will be classified

as multi founder if a larger fraction of the original founders dies before the sample period. As expected,

the fraction of dead founders before the sample period is negatively correlated with the multi founder

binary variable (-0.12, 1 % statistical significance). We also argue that the instrument is exogenous

because firm performance in our sample period (2001-2015) is not the primary cause of the founder

death prior to 2001. On a related note, firm age can be correlated with the founder death fraction.

While we cannot fully eliminate this concern, we control for the log of firm age in all our tests.

Our second proposed instrument is the number of original founders similar to Adams et al (2009).

We argue that this instrument also meets the criteria for a valid instrument. The instrument is relevant

as the number of original founders (founders at the inception of the firm) increase the likelihood of a

firm being classified as a multi-founder firm in our sample period. The instrument satisfies the

exclusion criteria, as the number of original cofounders at the inception, does not directly impact firm

performance in later years. The average firm age in our sample is 16 years at the beginning of our

sample period. This suggests that there seems to be enough time before founders can exit the sample

and create variation in our multi founder firm variable. Even though our first instrument may be

stronger, we investigate predictions of both tests.

In the first stage regressions, we use founder death fraction and number of original founders as

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instruments and model the probability of a firm being classified as multi-founder. In the second stage,

we regress firm performance on the predicted value of multi founder and the controls that are used

in the first stage regression. Our IV-2SLS regressions are as follows:

Stage 1: Prob (Multi founder) = α0+ α1 (instrument) + α 2-8 (Control variables) + μ (2)

Stage 2: Firm Performance= β0+ β1 (predicted value of multi founder) + β2-8 Control variables +ε (3)

We implement an endogeneity test to evaluate whether our IV procedure provides a better

estimate than OLS. Our endogeneity test results in a χ2 statistics of 11.21 and 20.2 for our first and

second instruments, respectively. Therefore, we reject the null hypothesis that the multi founder binary

variable is exogenous, and we proceed with our proposed IV methodology.

Table 5 reports the IV regression results. We find that multiple-founder firms result in better firm

performance. Columns 1 and 3 present first-stage results of our IV regressions using founder death

fraction and number of original founders as our instruments. As we anticipate, death fraction before

2001 (instrument 1) is negatively related to the likelihood of having a multi founder firm, while number

of original founders (instrument 2) is positively related. Coefficients for both instruments are

statistically significant at 1% level, which is an indication of the strength of our instruments in

predicting multi founder status of a firm. Columns 2 and 4 report the main results of the second-stage

regressions using our first and second instruments, respectively. These second-stage regression results

show that multi founder firms are associated with higher Log Q. This analysis suggests evidence of a

causal effect on firm value. When we compare Table 5’s second-stage results to Table 4’s OLS results,

multi founder coefficients using both instruments are a lot larger in magnitude. Our analysis from

Table 5, column 2 suggests that investors are willing to pay 1.5 times2 more for a multi-founder firm,

which is economically and statistically significant. Our second instrument suggests a more modest, but

still economically and statistically significant effect: Based on column 4, multi-founder firms increase

2 We calculate this impact as exponent (0.914) – 1 = 1.49.

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firm value by 42%3. Such valuation premium is much higher using instrumental variable approach

compared to that of endogenous model. Of the related studies, Fahlenbrach (2009) shows that founder

firms enjoy 25.9 % valuation premium compared to professionally managed firms. Our findings

suggest that much of such founder CEO valuation may be attributed to multiple founder firms.

V. Cofounder Heterogeneity

Earlier analysis shows that multi founder firms perform better than solo founder and

professionally managed firms as evidenced by higher Tobin’s Q. Next, we investigate cofounder

dynamics further as we explore a potential channel for our findings. Multiple founders could be

beneficial to a firm if cofounders communicate well and complement each other with their varying

backgrounds (education, professional and social). On the other hand, multiple founders involved in a

firm could be harmful if cofounders do not communicate well and argue about strategic decisions due

to their different backgrounds and personal traits. Having similar skills (supplement) and backgrounds

could be beneficial if it helps cofounders communicate and work well together (McPherson et al.,

2001; Cohen et al., 2008; Gompers and Xuan, 2010). However, if cofounders are very similar it could

also lead to groupthink, which may result in bad decisions (Asch, 1951; Ishii and Xuan, 2014; Gompers

et al., 2016). We do not have a prior expectation as to which affect will dominate in our sample. The

next section investigates cofounder heterogeneity as we intend to answer these questions using a hand

collected unique dataset.

A. Measuring Cofounder Heterogeneity

This section studies cofounder heterogeneity as we investigate complementary and substitute skills

and backgrounds cofounders may bring to firm management as they advise firms they co-found. To

better understand cofounders and answer our questions, we hand collect extensive data on cofounder

3 We calculate this impact as exponent (0.351) – 1 = 0.42.

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backgrounds. Using our unique data, we categorize each cofounder across seven dimensions: 1) age,

2) gender, 3) ethnicity, 4) professional experience, 5) education and 6) working at the same place and

7) attending the same school. Then we aggregate these individual cofounder categories to develop a

composite cofounder heterogeneity index. Because we do not have a prior expectation as to which

one of these categories has higher impact on our heterogeneity index, we use equal weights when we

aggregate as we create the heterogeneity index. Based on these seven different categories, our

heterogeneity index ranges from a minimum value of 7 to a maximum value of 20 points. Lower values

indicate less cofounder heterogeneity while higher values represent higher cofounder heterogeneity.

In the following section, we define the motivation behind each of the seven cofounder characteristics.

1. Age Heterogeneity

We start by measuring variation in cofounders age following Anderson et al (2011). Older founders

might have more experience and contribute with better due diligence skills, while younger founders

might be more enthusiastic, have higher energy, and offer newer ideas. Therefore, greater

heterogeneity in cofounders’ ages can increase the heterogeneity in decision making among the

cofounders. We use the coefficient of variation of cofounders age as the measure for age heterogeneity

among cofounders. We then rank our sample of firm year observations into 3 based on the age

heterogeneity variable. Rank equals to 1 if the age heterogeneity falls in the lowest tercile (less

heterogeneity), rank equals to 2 for middle tercile, and rank equals to 3 if the age heterogeneity falls in

the highest tercile (more heterogeneity) of our sample.

2. Gender Heterogeneity

Gender heterogeneity potentially brings different perspectives, risk attitudes, and decision making

styles as cofounders continue to be involved with the firm (Cox et al.1991; Hillman et al. 2002; Adams

and Ferreira 2009; Gul, Srinidhi and Ng 2011; Adams and Funk 2012, Huang and Kisgen, 2013). We

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use the percentage of female cofounders for each year as a proxy for the cofounder gender

heterogeneity. We also rank this variable into three categories with lowest gender heterogeneity getting

a rank of 1, middle category of 2 and highest category of 3.

3. Ethnic Heterogeneity

We also argue that ethnic heterogeneity may bring varying cultural and social backgrounds to

decision making and hence produce new ideas and perspectives for management to consider as they

make strategic decisions. We follow Gompers et al (2016) to determine different ethnic minorities and

divide our sample into five distinct categories: East Asian, Indian, Jewish, Middle Eastern, and all

others. We give 1 point for each different ethnic minority categories and aggregate the points to get a

score for each cofounder team per year. We then rank these scores into 3 categories to be consistent

with the other heterogeneity input variables (from rank 1, 2 and 3). We assign a final rank of 1 for the

lower ethnic heterogeneity group and a rank of 3 for firms with more ethnically heterogeneous

cofounder team, while the middle group gets a rank of 2.

4. Professional Heterogeneity

A cofounder’s prior work experience is one of the important traits they can contribute when they

start a new company. If a cofounder who has marketing expertise partners with another cofounder

who has financial expertise, this might be beneficial to the overall management of the company since

they may complement each other. However, these cofounders might also think very differently than

one another and not communicate efficiently. Therefore, heterogeneity in cofounder team’s work

experience is one of the categories we investigate as we try to measure overall heterogeneity among

cofounder teams.

We go through backgrounds of each cofounder and classify their work experiences into six broad

categories: 1) science, 2) marketing, sales and business, 3) technology, 4) operations, 5) legal, and 6)

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finance and real estate. We measure professional heterogeneity as the sum of unique areas of work

experience the cofounder team has. For instance, if a cofounder has work experience in finance and

another cofounder has work experience in operations, then this cofounder team gets 2 points. Because

we want to measure the variation in experience levels, we do not assign more points if more than one

cofounder has experience in finance. The firm will get 1 point assigned for having a cofounder in

finance (event though there may be more than one cofounder that share a similar experience). Then

we aggregate total points assigned for each cofounder team per year to calculate the final professional

heterogeneity score. Then we rank these from 1 to 3, similar to the earlier measures to get a final

comparable ranking.

In addition, cofounders who have worked at the same company previously might share a strong

connection and may be less heterogeneous (more similar). Therefore, we incorporate this information

into our heterogeneity index as our sixth measure in this section.

5. Educational Heterogeneity

Education can shape how a person thinks and analyses information. Heterogeneity in cofounder

team’s education can arise from whether a cofounder has an advanced degree and which area that

degree is in or a combination of both. To address this conjecture, we measure education heterogeneity

using two different categories: education level and degree type. When measuring education level, we

classify each cofounder into following categories using binary variables: no-bachelor’s degree, only a

bachelor’s degree, master’s degree and advanced degree (PhD, MD and/or JD). Then we calculate

Herfindahl index based on percentage cofounders that hold no bachelor’s degree, only a bachelor’s

degree, master’s degree, and advanced degree following a similar measure used in Anderson et al.

(2011). Since Herfindahl Index is an inverse measure for heterogeneity, we inversely rank each

company’s cofounder team into three groups based on their education level.

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To measure degree type, we examine each cofounder’s background and identify five areas each

cofounder might have majored in (starting with the bachelor’s degree). These five areas are: a) business

and/or accounting, b) MBA or CPA, c) technology and engineering, d) law, and e) science. Then we

calculate the Herfindahl index based on percentage cofounders that have degrees in each of these five

categories. Since the Herfindahl Index is an inverse measure for heterogeneity, we inversely rank each

company’s cofounder team into three groups based on their education degree type variation.

Next, we aggregate scores from the first ranking for education level (1 through 3) and education

type (1 through 3) and then rank once more into three groups and calculate a final education

heterogeneity ranking for each firm year in our sample (rank 1, 2 or 3).

6. Working at the same company previously

Cofounders who have worked at the same company prior to co-founding a firm together might

share more similarities (less heterogeneity) than cofounders who have not. Cofounders who have more

commonalities (working at the same company) might approach decision making and problem solving

in similar ways. To incorporate this conjecture into our heterogeneity index we calculate percentage

of cofounders who worked at the same institution previously. Then we rank this measure into three

categories. Since, working at the same firm is a similarity measure (opposite of heterogeneity), we

inverse rank this variable into 3 scores. We classify cofounder teams that have higher percentage of

cofounders who have worked at the same place prior to co-founding a firm has lower heterogeneity

rank (rank=1). While cofounder teams that have lower percentage of cofounders who have worked

at the same place prior to co-founding a firm gets assigned a higher heterogeneity rank (rank=3).

7. Attending same school

We use a similar measure to capture cofounders who have graduated from the same school.

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Attending the same school might indicate cofounders share more commonalities in terms of decision

making and communication. We use the percentage of cofounders who attended the same school and

inverse rank this percentage into three groups to capture heterogeneity of cofounders. Because we are

interested in capturing heterogeneity, we inverse rank this measure as well and assign rank of 1 to

higher percentage of cofounders attending same school and assign rank of 3 to the lowest percentage.

The middle category gets a rank of 2, similarly to the other measures.

Additional Control Variables for Heterogeneity Analysis

Our heterogeneity tests include several additional control variables than in our earlier tests. We

calculate percentage of cofounders who have previously attended any of the top schools defined in

Gompers et al. (2016) and include this variable in all our heterogeneity regressions to control for

ability. In our sample there are cofounders who come from the same family (siblings, parent and child,

husband and wife…etc.). We keep track of these cofounders and calculate percentage of cofounders

that belong to the same family. We use this variable to control for the close connections that family

cofounders might have compared to other non-family cofounders in all our heterogeneity regressions.

B. Cofounder Heterogeneity Descriptive Statistics and Univariate Results

This section intends to capture differences (heterogeneity) among cofounder teams. Therefore,

our heterogeneity sample comprise only multi founder firms. Our sample size is reduced to 825 firm-

year observations because heterogeneity index requires extensive information to be collected for each

cofounder. Table 6 presents descriptive statistics for the heterogeneity sample. Panel A reports the

number of cofounder distribution in our sample. We find that having two cofounders is the most

common occurrence for our multi founder sample, representing 76.3% of the total observations. Five

is the maximum number of cofounders for a given firm-year observation, comprising only 0.9% of

the sample. Panel B shows the summary statistics for key variables in this sample. Average market

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capitalization and total assets are $1.9 billion and $1 billion, respectively, which is similar to the full

sample used in our earlier analysis. Heterogeneity index ranges from a minimum of 7 and maximum

of 20 and has a mean (median) of 13.3 (13). When examining individual components of the

heterogeneity index, we find that average cofounder in our sample is 55 years old. Females comprise

5.9% of our sample. The number of total ethnic minorities defined in our sample ranges from 0 to a

maximum of 2. On average, 34.7% of our sample of cofounders previously worked at the same firm

or institution. In our sample, on average 20.8% of the cofounders attended the same school prior to

co-founding a firm. On average, 24.1% of the cofounders attended at least one of the top schools as

defined by Gompers et al. (2016). While 25.6% of these cofounders on average belongs to the same

family.

Table 6, panel C provides differences for the mean tests for our key variables among firms with

high low and medium cofounder heterogeneity index scores. We consider a firm as a high

heterogeneity firm if its cofounder team has total heterogeneity index of 15 points and above (15-20).

Heterogeneity index score for medium cofounder heterogeneity team ranges from 13 through 14

points and the low cofounder heterogeneity index ranges from 7 through 12 points. This separation

produces the most balanced number of observations in each category. Defining high, medium and

low heterogeneity firms using different criteria produces similar results in our main analysis. Panel C

shows that there are unique characteristics each of the three levels of cofounder heterogeneity firms

portray. Firms with high cofounder heterogeneity are younger and have lower total cofounder equity

ownership than firms with low cofounder heterogeneity. On the other hand, firms in the high

heterogeneity group have higher leverage and firm risk than the firms in the low heterogeneity

category. When we compare firms with high cofounder heterogeneity to firms that have middle level

heterogeneity, we find that the high group has lower market capital, total assets, total sales, Tobin’s Q

and total cofounder equity ownership than firms with middle cofounder heterogeneity. Among the

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three categories, low heterogeneity firms are the oldest, followed by the high heterogeneity and middle

heterogeneity groups. Other variables shown in panel C are mostly inputs that determine our

heterogeneity index. Therefore, as expected, the high heterogeneity group has the higher cofounder

age variation, larger percentage of females and ethnic minorities, and higher number of professional

areas than the other two categories. For the inverse measures, also as expected, high heterogeneity

firms have lower education level HHI, education type HHI, percentage of sharing same workplace

and attending same school. Higher and medium heterogeneity firms have similar levels of family

member percentages which is higher than the low heterogeneity category. Middle heterogeneity firms

have the highest cofounders that attended at least one of the top schools.

C. Cofounder Heterogeneity and Firm Performance

In this section, we further investigate the relationship between cofounder heterogeneity and firm

performance in a multivariate setting. We do not have a prior expectation regarding the relationship

with firm performance and cofounder heterogeneity. High cofounder heterogeneity can be beneficial

to firms if the cofounders can complement each other’s skills and backgrounds, and can work well

together. However, high cofounder heterogeneity may be harmful to firms if cofounders do not

communicate and work efficiently due to lack of common ground (Adams et al. 2018). Table 7,

column 1 shows the results of regressing our performance measure (Log Q) on our broad

heterogeneity index. The heterogeneity index has a negative relationship with Log Q. The

heterogeneity index coefficient of 3.46% is statistically significant at the 1% level. This finding can be

interpreted as a 1 unit decrease in heterogeneity index resulting in a 3.4% increase in Tobin’s Q on

average. Columns 2 through 4 show regressing Log Q on each of the three heterogeneity categories

separately. High cofounder heterogeneity index has a strong and negative relationship to firm

26

performance. Multiple founder firms that fall in the high heterogeneity category have 15%4 lower

Tobin’s Q relative to the other multiple-founder firms. Taken together with the results presented in

column 1, these regressions show that having radically different cofounder teams may harm firm

performance. When the cofounder team varies in skillsets and backgrounds, they might have a hard

time finding a common ground and working efficiently (Adams et al. 2018). Column 4 shows that

firms with medium cofounder heterogeneity perform well, which supports the argument that there

might be a middle ground of heterogeneity that is beneficial for firms. Multiple-founder firms with

medium level of heterogeneity outperform other multi founder firms by 14.9%5. Among the control

variables, firm age, firm size, firm risk, r&d/sales, percentage of top school, and percentage of family

members have positive relationship with firm performance.

The analysis thus far suggests that firms with multiple founders perform better than single founder

or professionally managed firms. Homogenous cofounder teams appear to be driving this better

performance as evidenced by the negative association of higher founder heterogeneity to firm

performance. However, this analysis suffers from potential endogeneity concerns and fails to establish

a causal relationship. Next, we use instrumental variable, IV-2SLS approach to test our hypothesis of

cofounder heterogeneity (homogeneity) is harmful (beneficial) to firm performance. Our proposed

instrument is the heterogeneity of the county population where firm headquarters are located

(Anderson et al. 2011). We posit two reasons for using county heterogeneity as our instrument for

founder heterogeneity. The first reason behind this instrument is that many founders come from the

firm’s local geographic area. Therefore, greater diversity in the local county would provide a more

diverse pool of individuals that could potentially become cofounders. The second reason using this

instrument is founders who come from more diverse geographical regions may be more comfortable

4 We calculate this impact as exponent (-0.162) – 1 = 0.15. 5 We calculate this impact as exponent (0.139) – 1 = 0.149.

27

with varying backgrounds and choose to start a new company with more diverse cofounder teams

than if they were from a small rural less diverse town. We argue that the instrument is exogenous

because firm performance is not the primary cause of county diversity. On a related note, diverse

geographical locations may be more likely to be in close proximity to larger cities. Being located closer

to a large city might provide better economic opportunities for firms, which can affect firm

performance. While we cannot fully eliminate this concern, we do not think that county diversity is

the primary reason for better firm performance.

Data regarding the location of each headquarters come from Compustat. County population and

unique county codes come from US Census Bureau. We measure county heterogeneity based on

information on the age, race, gender and employment of county population. Table 1 discusses the

process of creating the county heterogeneity index in detail.

In the first stage regressions, we use county heterogeneity as an instrument and model cofounder

team heterogeneity. In the second stage, we regress firm performance on the predicted value of

cofounder team heterogeneity and the controls that are used in the first stage regression. Our IV-2SLS

regressions are as follows:

Stage 1: Cofounder team heterogeneity = α0+ α1 (county heterogeneity) + α 2-10 (Control variables) + μ (4)

Stage 2: Firm Performance = β0+ β1 (predicted value of cofounder heterogeneity) + β2-10 Control variables +ε (5)

We implement an endogeneity test to evaluate whether our IV procedure provides better estimate

than OLS. Our endogeneity test results in a χ2 statistics of 22.5 for our instrument. Therefore, we

reject the null hypothesis that cofounder heterogeneity is exogenous, and we proceed with our

proposed IV methodology.

Table 8 reports the IV regression results. We find that higher founder team heterogeneity results

in worse firm performance. Column 2 presents first-stage results of our IV regressions using county

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heterogeneity as our instrument. As we anticipate, county heterogeneity is positively related to having

more diverse cofounder team. Coefficient for the instrument is statistically significant at 1% level,

which is an indication of the strength of our instruments in predicting higher cofounder team

heterogeneity. Column 3 reports the main results of the second-stage regressions using our instrument.

These second-stage regression results show that higher cofounder team heterogeneity results in lower

firm performance. This analysis suggests evidence of causal effect on firm value. When we compare

Table 8 second stage results to Column 1 OLS results, heterogeneity index coefficient is a lot larger in

magnitude. Overall, our study suggests that diverse founder teams are not as beneficial as more

homogenous founder teams for firm performance. This could be due to lack of common ground (Adams

et al. 2018).

D. Occupational and Social Cofounder Heterogeneity

Previous analysis illustrates the relevance of our heterogeneity index to firm performance and

suggests that cofounder teams that are too different can harm firm performance. In this section we

investigate the heterogeneity index even further as we try to understand its components. We divide

our composite heterogeneity index into two areas, social and occupational cofounder heterogeneity

following Anderson et al. (2011), which examines board of director heterogeneity. Social heterogeneity

consists of heterogeneity measures for founder age, gender and ethnicity. These are characteristics

that founders grow up with and reflects certain traits and culture each founder brings to management.

Social heterogeneity characteristics are imbedded in each cofounders identity (age, gender and

ethnicity). A person’s gender and ethnicity can form certain beliefs and way of thinking which could

be different than another person with different gender and ethnicity. Age, while changing over time,

also can shape the way an individual views problems and his/her maturity level. Therefore, we suggest

heterogeneity related to these social characteristics are relevant to cofounder team’s decision making

skills and potentially to firm performance. Occupational heterogeneity includes measures for

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education (education type, education level and attending same school) and work experience

(functional area and working at the same workplace). Occupational characteristics evolve with

environment and are less entrenched than the social heterogeneity characteristics. As each cofounder

attends school and starts to work, his/her decision-making skills and personality can also be shaped

by their environment and experience. Therefore, we suggest both measures can be relevant to firm

performance in our heterogeneity analysis while capturing different attributes.

Table 9 shows the result of these tests. Based on OLS regression in Table 9, column 1,

occupational heterogeneity appears to be the primary channel as to why broad cofounder

heterogeneity has a negative relationship to firm performance. Social heterogeneity does not have any

statistically significant relationship to firm performance in the OLS regressions. Occupational

cofounder heterogeneity on the other hand has a higher impact than our overall heterogeneity index.

Occupational cofounder heterogeneity coefficient of 4.4% can be interpreted as with a 1 unit increase

in the occupational heterogeneity, Tobin’s Q is expected to decrease by 4.4% (compared to 3.4% in

shown in Table 8, column 1 for the overall cofounder heterogeneity index). However, these OLS

regressions suffer from endogeneity concerns, as mentioned in the previous section. Therefore, we

use the county heterogeneity as an instrument in this analysis as well. Table 9, columns 2 and 4 report

the first stages of the IV-2SLS regressions. Table 9, columns 3 and 5 report second stages of IV-2SLS

regressions and provides us the main result of this analysis. Once corrected for endogeneity issues

using instrumental variable approach, these results suggest that occupational heterogeneity remains

negatively related to firm performance and its coefficient estimate grows in magnitude. Social

heterogeneity on the other hand becomes significant and negative compared to non-significant results

in the OLS regressions. Occupational heterogeneity remains strong and negative in both OLS and IV-

2SLS methods while social heterogeneity becomes significant at 5% level once we use IV-2SLS

method. When separating founder characteristics along social versus professional dimensions, we find

30

that occupational heterogeneity appears to be more harmful than social heterogeneity. Our analysis

indicates that founder teams provide greater value to outside investors, and this effect tends to be

most pronounced when the founders are more similar to each other along professional backgrounds.

VI. Conclusion

Academic literature suggest that founder run firms perform better than professionally run firms

(Anderson and Reeb, 2003; Villalonga and Amit, 2006; Fahlenbrach, 2009; Adams et al. 2009;

Fitzgerald, 2018), but the popular and academic presses make little distinction between firms with

single and multiple founders despite the widespread presence of firms with two or more founders.

Venture capitalists also appear to prefer investing in start-ups that have multiple founders who might

have complementary skillsets. Motivated by this common occurrence, we examine two related

questions using a hand collected data set covering public firms.

First, we ask whether multiple founder run firms perform financially better than solo founder run

and/or professionally run firms. We use instrumental variable approach and find that multi founder

firms enjoy a valuation premium than a solo founder or professionally run firms. Second, given that

multiple founder run firms perform better, we investigate what the primary channel is for this better

performance. Multiple founders may bring different backgrounds, skillsets, and experiences to

management and add multi dimensionality to decision making unlike a solo founder, hence add value.

Using a comprehensive hand collected data, we explore heterogeneity along different dimensions (age,

gender, ethnicity, education, professional experience) and calculate an aggregate heterogeneity index

for each cofounder team. The results indicate high levels of founder heterogeneity (age, education,

gender, ethnicity, and experience) negatively impact firm performance. When segregating founder

characteristics along social versus professional dimensions, we find that occupational heterogeneity

appears to be more harmful than social. Our analysis suggests that founder teams provide greater value

31

to outside investors and this effect tends to be most pronounced when the founders are more similar

along professional dimensions.

First, our new cofounder data set on public firms shows that multi founders add value to firm

performance. Second, our analysis suggests that the heterogeneity among cofounders is an important

channel to consider as we investigate multi founder firm performance. Results indicate that too much

heterogeneity in cofounder teams is harmful to firm performance, which could be due to lack of

common ground, inefficient communication and/or disagreeing on strategic business decisions. While

having cofounders to start a new firm appears to be common, this is the first paper, to the best of our

knowledge, which examines cofounder dynamics with new public firm data.

32

REFERENCES

Adams, R., Almeida, H., & Ferreira, D. (2009). Understanding the relationship between founder–CEOs and firm performance. Journal of Empirical Finance, 16(1), 136-150.

Adams, R. B., & Ferreira, D. (2009). Women in the boardroom and their impact on governance and performance. Journal of Financial Economics, 94(2), 291-309.

Adams, R. B., & Funk, P. (2012). Beyond the glass ceiling: Does gender matter?. Management Science, 58(2), 219-235.

Adams, R. B., Akyol, A. C., & Verwijmeren, P. (2018). Director skill sets. Journal of Financial Economics, 130(3), 641-662.

Ancona, D. G., Okhuysen, G. A., & Perlow, L. A. (2001). Taking time to integrate temporal research. Academy of Management Review, 26(4), 512-529.

Anderson, R. C., & Reeb, D. M. (2003). Founding‐family ownership and firm performance: evidence from the S&P 500. The Journal of finance, 58(3), 1301-1328.

Anderson, R. C., Reeb, D. M., Upadhyay, A., & Zhao, W. (2011). The economics of director heterogeneity. Financial Management, 40(1), 5-38.

Asch, S. E., & Guetzkow, H. (1951). Effects of group pressure upon the modification and distortion of judgments. Documents of Gestalt Psychology, 222-236.

Bergh, D. D., Ketchen, D.J., Orlandi, I., Heugens, P.P.M.A.R., & Boyd, B.K. (2019). Information asymmetry in management research: Past accomplishments and future opportunities. Journal of Management, 45(1), 122-158.

Bernile, G., Bhagwat, V., & Yonker, S. (2018). Board diversity, firm risk, and corporate policies. Journal of Financial Economics, 127(3), 588-612.

Burt, R. S. (2000). Decay functions. Social Networks, 22(1), 1-28. Chen, J.S., Elfenbein, D.W., Posen, H.E., & Wang, M.Z. (2020). Organizational design,

overconfidence, and learning entrepreneurial teams. Academy of Management Review (conditionally accepted).

Cohen, L., Frazzini, A., & Malloy, C. (2008). The small world of investing: Board connections and mutual fund returns. Journal of Political Economy, 116(5), 951-979.

Cooper, A. C., & Bruno, A. V. (1977). Success among high-technology firms. Business Horizons, 20(2), 16-22.

Costa, A. C., Fulmer, C.A., & Anderson, N.R. (2018). Journal of Organizational Behavior, 39(2), 169-184. Cox, T. H., Lobel, S. A., & McLeod, P. L. (1991). Effects of ethnic group cultural differences on

cooperative and competitive behavior on a group task. Academy of Management Journal, 34(4), 827-847.

De Jong, B. A., Dirks, K. T., & Gillespie, N. (2016). Trust and team performance: A meta-analysis of main effects, moderators, and covariates. Journal of Applied Psychology, 101(8), 1134–1150.

Dijk, H.V., Engen, M.L.V., & Knippenberg, D.V. (2012). Defying conventional wisdom: A meta-analytical examination of the differences between demographic and job-related diversity relationships with performance. Organizational Behavior and Human Decision Processes, 119(1), 38-53.

Duran, P., Kammerlander, N., Essen, M. V., & Zellweger, T. (2016). Doing more with less: Innovation input and output in family firms. Academy of Management Journal, 59(4), 1224-1264.

Eisenhardt, K. M., & Schoonhoven, C. B. (1990). Organizational growth: Linking founding team, strategy, environment, and growth among US semiconductor ventures, 1978-1988. Administrative Science Quarterly, 504-529.

33

Fahlenbrach, R. (2009). Founder-CEOs, investment decisions, and stock market performance. Journal of Financial and Quantitative Analysis, 44(2), 439-466.

Fama, Eugene F., and Kenneth R. French. (1997). Industry costs of equity. Journal of Financial Economics 43.2: 153-193.

Fitzgerald, T., J. (2018). Till death do us part: The relative merits of founder CEOs. Working paper. Texas A&M University.

Gompers, P. A., & Xuan, Y. (2010). Bridge building in venture capital-backed acquisitions. Unpublished working paper. Harvard Business School.

Gompers, P. A., Mukharlyamov, V., & Xuan, Y. (2016). The cost of friendship. Journal of Financial Economics, 119(3), 626-644.

Greenberg, J. & Mollick, E. R. (2018). Sole survivors: Solo ventures versus founding teams. Working paper. New York University and University of Pennsylvania.

Guillaume, Y. R.F., Dawson, J. F., Otaye-Ebede, L., Woods, S.A., & West, M.A. (2017). Harnessing demographic differences in organizations: What moderates the workplace diversity? Journal of Organizational Behavior, 38(2), 276-303.

Gul, F. A., Srinidhi, B., & Ng, A. C. (2011). Does board gender diversity improve the informativeness of stock prices?. Journal of Accounting and Economics, 51(3), 314-338.

Hallinan, M. T., & Williams, R. A. (1989). Interracial friendship choices in secondary schools. American Sociological Review, 67-78.

Hellmann, T., & Wasserman, N. (2016). The first deal: The division of founder equity in new ventures. Management Science, 63(8), 2647-2666.

Herring, C. (2009). Does diversity pay? Race, gender, and the business case for diversity. American Sociological Review, 74(2), 208-224.

Hillman, A. J., Cannella Jr, A. A., & Harris, I. C. (2002). Women and racial minorities in the boardroom: how do directors differ?. Journal of Management, 28(6), 747-763.

Huang, J., & Kisgen, D. J. (2013). Gender and corporate finance: Are male executives overconfident relative to female executives?. Journal of Financial Economics, 108(3), 822-839.

Iacus, S. M., King, G., & Porro, G. (2009). CEM: software for coarsened exact matching. Ishii, J., & Xuan, Y. (2014). Acquirer-target social ties and merger outcomes. Journal of Financial

Economics, 112(3), 344-363. Jehn, K. A. (1997). A qualitative analysis of conflict types and dimensions in organizational

groups. Administrative Science Quarterly, 530-557. Jung, H., Vissa, B., & Pich, M. (2017). How do entrepreneurial founding teams allocate task positions?

Academy of Management Journal, 60(1), 264-294. Knight, A. P., Greer, L. L., & Jong, B. D. (2020). Start-up teams: A multidimensional

conceptualization, integrative review of past research, and future research agenda. Academy of Management Annals, 14(1), 231-266.

Masulis, R. W., Wang, C., & Xie, F. (2009). Agency problems at dual‐class companies. The Journal of Finance, 64(4), 1697-1727.

Mathieu, J. E., Hollenbeck, J. R., van Knippenberg, D., & Ilgen, D. R. (2017). A century of work teams in the Journal of Applied Psychology. Journal of Applied Psychology, 102(3), 452–467.

Mathieu, J., Maynard, M. T., Rapp, T., & Gilson, L, (2008). Team effectiveness 1997-2007: A review of recent advancements and a glimpse into the future. Journal of Management, 34 (3), 410-476.

McPherson, M., Smith-Lovin, L., & Cook, J. M. (2001). Birds of a feather: Homophily in social networks. Annual Review of Sociology, 27(1), 415-444.

Murray, A. I. (1989). Top management group heterogeneity and firm performance. Strategic Management Journal, 10(S1), 125-141.

34

Reagans, R., & Zuckerman, E. W. (2001). Networks, diversity, and productivity: The social capital of corporate R&D teams. Organization Science, 12(4), 502-517.

Ruef, M., Aldrich, H. E., & Carter, N. M. (2003). The structure of founding teams: Homophily, strong ties, and isolation among US entrepreneurs. American Sociological Review, 68(2), 195-222.

Shrivastava, M., & Tamvada, J. P. (2011). Entrepreneurial teams, optimal team size, and founder exits. Working Paper, University of Jena.

Steffens, P., Terjesen, S., & Davidsson, P. (2012). Birds of a feather get lost together: new venture team composition and performance. Small Business Economics, 39(3), 727-743.

Villalonga, B., & Amit, R. (2006). How do family ownership, control and management affect firm value?. Journal of Financial Economics, 80(2), 385-417.

Wasserman, N. (2012). The founder's dilemmas: Anticipating and avoiding the pitfalls that can sink a startup. Princeton University Press.

Williams, K. Y., & Charles, A. O’Reilly. (1998). Demography and diversity in organizations: A review of 40 years of research. Research in Organizational Behavior, 20(20), 77-140.

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Table 1: Data Definitions Key Variables for Multi founder Analysis Capex/Sales: Capital expenditures (item# 128, set to 0 if missing) divided by sales/turnover net

(item# 12). Source: Compustat. CEO age: CEO age during each year. Source: Execucomp. CEO ownership: Number of shares owned by the CEO divided by the total outstanding shares.

Sources: Execucomp and Compustat. CEO tenure: Number of years since CEO received CEO title at the current firm. Source:

Execucomp. Debt/Asset Ratio (leverage): Total Long Term Debt (item# 9) divided by total assets (item# 6).

Source: Compustat. Delaware dummy: Binary variable equals to one if the state of incorporation is Delaware and zero

otherwise. Death Fraction before 2001: Number of original cofounders that died prior to 2001 divided by the

total number of original cofounders. Firm Age: Number of years since firm’s foundation. Firm Risk: Standard deviation of monthly stock returns during past three years. Founder Team Ownership: Total cash-flow right ownership by founding cofounders and their

family members. Market Capital: Market value of company stock: shares outstanding multiplied by stock price (item#

25 × item# 199). Source: Compustat. Multi founder firm: Binary variable equals to one if there are two or more cofounders involved with

firm during that year and zero otherwise. Number of Original Founders: Number of original cofounders who founded each firm. Professionally managed firm: Binary variable equals to one if all the original cofounders (or a solo

founder) already exited the firm prior to that year and zero otherwise. R&D/Sales: Research and development (item# 46, set to 0 if missing) divided by sales/turnover net

(item# 12). Source: Compustat. ROA: Operating Income before Depreciation (item# 13)/total assets (item# 6). Source: Compustat. S&P 500 dummy: Binary variable equals to one if firm belongs to S&P 500 and zero otherwise. Solo founder firm: Binary variable equals to one if there is one founder (or cofounder) involved with

firm during that year and zero otherwise. Tobin’s Q: Market value of assets over book value of assets: (item# 6 − item# 60 + item# 25 ×

item# 199) / item# 6. Source: Compustat. Total Assets: Book value of total assets (item# 6). Source: Compustat. Key Variables for Heterogeneity Analysis Cofounder Age Covariance: The coefficient of variation (CV=standard deviation/mean) of

cofounders age across the cofounder team. We then rank our sample into 3 based on the age variable. Higher ranking means more heterogeneity.

Percentage of Female Cofounder: Number of female cofounders with the firm divided by the total number of cofounders with the firm, measured for each year. We rank this variable into three categories with lowest gender heterogeneity getting a rank of 1, middle category of 2 and highest category of 3.

Minority Type: Each cofounder gets classified into five distinct ethnic categories: East Asian, Indian, Jewish, Middle Eastern and all others. Measured as the aggregate points for each unique type

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of ethnic category the cofounder team has for each year. If there are more than one Jewish cofounders, the Jewish category gets 1 point, not 2. We rank these scores into 1, 2 and 3.

Professional Functional Area: Each cofounder’s work experience gets classified into six board categories: 1) science, 2) marketing, sales and business, 3) technology, 4) operations, 5) legal, and 6) finance and real estate. Measured as the sum of unique areas of work experience the cofounder team has. If a cofounder has work experience in finance and another cofounder has work experience in operations, then this cofounder team gets 2 points. No additional points get assigned if more than one cofounder has experience in finance. We aggregate total points assigned for each cofounder team to calculate the professional heterogeneity score and rank these scores as 1, 2 and 3.

Education Level HHI: Each cofounder’s highest education level gets classified into the following categories: no-bachelor degree, bachelor only degree, master degree and advanced degree (PhD, MD and/or JD). HHI gets calculated based on the percentage of cofounders that hold no bachelor degree, bachelor only, master and advanced degree. HHI is an inverse measure for heterogeneity. We then inversely rank each company’s cofounder team into three groups based on their education level.

Education Type HHI: Each cofounder’s highest education major (type) gets classified into following categories: a) business and or accounting, b) MBA or CPA, c) technology and engineering, d) law and e) science. HHI gets calculated based on the percentage of cofounders that have degrees in each of these five categories. Since HHI is an inverse measure for heterogeneity, we inversely rank each company’s cofounder team into three groups based on their education degree type. Next we aggregate scores from the first ranking for education level HHI (1 through 3) and education type HHI (1 through 3) and then rank once more into three groups and calculate a final education heterogeneity score for each firm year in our sample (score 1, 2 or 3).

Percentage of Same Work: Percentage of cofounders who worked at the same company previously. Since, working at the same company is a similarity measure (opposite of heterogeneity), we inverse rank this variable into 3 scores.

Percentage of Same School: Percentage of cofounders who graduated from the same school. To capture heterogeneity, we inverse rank this measure into 1, 2 and 3. We assign score of 1 to higher percentage of cofounders attending same school and assign score of 3 to the lowest percentage.

Heterogeneity Index: Each cofounder team gets categorized across seven dimensions defined above: 1) age, 2) gender, 3) ethnicity, 4) professional experience, 5) education and 6) working at the same place and 7) attending the same school. Then these individual cofounder categories gets aggregated to develop a composite cofounder heterogeneity index. Index ranges from a minimum value of 7 to a maximum value of 20 points. Lower value indicates less cofounder heterogeneity while higher values represent higher cofounder heterogeneity.

High Heterogeneity Index Firms: A high heterogeneity firm is defined as such if its cofounder team has total heterogeneity index of 15 points and above (15-20).

Medium Heterogeneity Index Firms: A medium heterogeneity firm is defined as such if its cofounder heterogeneity index ranges from 13 through 14 points.

Low Heterogeneity Index Firms: A low heterogeneity firm is defined as such if its cofounder heterogeneity index ranges from 7 through 12 points.

Percentage of Top School: Percentage of cofounders who have previously attended any of the top schools defined in Gompers et al. (2016).

Percentage of Family Members: Percentage of cofounders that belong to the same family (siblings, parent and child, husband and wife…etc.).

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Key Variables for County Heterogeneity Instrument Age Heterogeneity of County Population: US Census Bureau annual estimate data provides

information on age distribution of county population. We use 18 age brackets: ages 0-4, 5 to 9, 10-14 and remaining age brackets for 5 year intervals up to 85 years old. The last age bracket is 85 and over. We then calculate percentages of population that corresponds to each age bracket per county and measure Herfindahl Index (HHI) using these percentages. HHI is an inverse measure of age heterogeneity for county population.

Gender Heterogeneity of County Population: Percentage of female population of the county from US Census Bureau annual estimate data is used for gender heterogeneity.

Race Heterogeneity of County Population: US Census Bureau categorizes the population into six ethnic groups: 1) White, 2) Black, 3) American Indian/Alaska Native, 4) Asian, 5) Native Hawaiian/Other Pacific Islander, and 6) two or more races. We then calculate HHI based on the percentages of each ethnic group in county population. Since HHI is an inverse measure, lower HHI corresponds to higher county heterogeneity.

Employment Heterogeneity of County Population: US Census Bureau County Business Patters (CBP) report provides county employment for 20 two digit NAICS industry codes. We calculate percentage of employment for each of these industries among total county employment. Then we calculate employment HHI using these percentages.

County Heterogeneity Index: Following Anderson et al. 2011, we compile the above mentioned four county heterogeneity measures into a composite County Heterogeneity Index. First, each county heterogeneity measure gets ranked into quartiles. Lower (higher) quartiles correspond to a lower (higher) county heterogeneity measure. We then aggregate ranking for each county heterogeneity measure and divide the total raking by 16 so that composite County Heterogeneity ranges from 0.25 to 1.

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Table 2: Categories of Firm year observations This table presents number and percentage of multi founder firms, solo founder firms and professionally managed firms for the sample period from 2001 through 2015. Multi founder firm is defined as such if there are two or more cofounders involved with firm during that year. Solo founder firms is defined as such if there is one founder (or cofounder) involved with firm during that year. Professionally managed firm is defined as such if all the original cofounders (or a solo founder) already exited the firm prior to that year. Categories of firms Number of

observations Percentage

Multi founder firms 1,197 25.6%

Solo founder firms 2,425 51.9%

Professionally managed firms 1,054 22.5%

Total 4,676 100%

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Table 3: Summary Statistics Panel A presents mean, median, standard deviation, minimum and maximum values for our variables. Panel B presents mean values and t-values for difference of mean tests for multi founder firms, solo founder firms, and professionally managed firms. Data definitions are supplied in Table 1. *, **, and *** indicate significance at the 1%, 5%, and 10% levels, respectively. Panel A: Summary Statistics of Full Sample

Variables N Mean Median Standard Deviation Min Max

Market Cap ($mln) 4676 1594 515.90 3325 9.964 22135 Total Assets ($mln) 4676 1087 453.10 1818 14.790 11620 Total Sales ($mln) 4676 1045 373.70 1955 0.089 12261 Firm Age 4676 21.11 19.00 10.72 1 70 Founder team ownership (%) 4676 13.8 6.4 0.177 0 98.9 Debt / Asset Ratio 4676 0.170 0.053 0.234 0 1.148 Firm Risk 4676 0.180 0.154 0.0942 0.041 0.470 R&D/Sales 4676 0.563 0.024 2.505 0 21.89 Capex/Sales 4676 0.123 0.038 0.311 0.001 2.256 Tobin's Q 4676 2.136 1.683 1.433 0.626 8.718 ROA 4676 0.0494 0.095 0.206 -1.026 0.398

Panel B: Difference of Mean Tests (Multiple founder vs solo founder and professionally run firm)

Variables

Multiple Founder Firms

Single Founder Firms

Multi vs Single Founder mean diff. t-statistics

Profession. Firms

Multi vs Professional mean diff. t-statistics

Mean Mean Mean Market Cap ($ mln) 1816 1536 2.305** 1476 2.381** Total Assets ($ mln) 947.9 1170 -3.359*** 1053 -1.477 Total Sales ($ mln) 822.0 1130 -4.497*** 1100 -3.618*** Firm Age 18.06 21.37 -9.118*** 23.97 -13.900*** Founder team ownership (%) 18.4 16.2 3.323*** 3.11 24.262*** Debt / Asset Ratio 0.147 0.182 -4.234*** 0.167 1.993** Firm Risk 0.194 0.180 4.081*** 0.165 7.283*** R&D/Sales 0.765 0.575 1.947* 0.306 4.740*** Capex/Sales 0.157 0.130 2.258** 0.0702 6.919*** Tobin's Q 2.434 2.004 8.459*** 2.102 5.228*** ROA 0.0403 0.0540 -1.904* 0.0493 -0.987 N 1197 2425 1054

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Table 4: OLS Regressions of Firm Value This table presents OLS regressions of natural log of Tobin’s Q (Log Q) on founder or cofounder team characteristics for the full sample of firms from 2001 through 2015. P-values are shown in parenthesis. Table 1 provides data definitions. *, **, and *** indicate significance at the 1%, 5%, and 10% levels, respectively. (1) (2) (3) Log Q Log Q Log Q Multi founder dummy 0.116*** 0.238*** (0.000) (0.000) Solo founder dummy -0.0244 0.0126 (0.225) (0.639) Nr. of cofounders 0.0582*** (0.000) Log of firm age -0.0230 -0.0180 0.0628** (0.166) (0.281) (0.018) Log of Total Assets -0.00900 -0.0117* -0.0633*** (0.197) (0.092) (0.000) Firm Risk -0.0519 -0.0504 (0.629) (0.640) Leverage -0.0896** 0.0912** (0.018) (0.017) R&D/Sales 0.0207*** 0.0201*** (0.000) (0.000) Capex/Sales -0.129*** -0.130*** (0.000) (0.000) Founder team ownership -0.142*** -0.187*** (0.003) (0.000) Delaware dummy 0.107*** (0.000) S&P 500 dummy 0.383*** (0.000) Log of CEO tenure 0.0423*** (0.001) CEO ownership -0.136 (0.243) CEO age -0.008*** (0.000) Constant 0.640*** 0.597*** 1.367*** (0.000) (0.000) (0.000) Observations 4676 4676 2121 Industry Fixed Effects Yes Yes Yes Year Fixed Effects Yes Yes Yes Adjusted R2 (%) 18.66 18.32 24.00

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Table 5: Instrumental Variable Regressions This table presents first and second stages of instrumental variable regressions. Column 1 shows the first stage of Column 2 and Column 3 shows the first stage of Column 4. Column 1 shows the first stage of Probit model of whether the firm is a multi founder firm with our first instrument (founder death fraction before 2001). Column 2 shows the second stage regression of Log Q on the predicted values of multi founder dummy from the first stage in Column 1. Column 3 shows the first stage of Probit model of whether the firm is a multi founder firm with our second instrument (number of original cofounders at the inception). Column 4 shows the second stage regression of Log Q on the predicted values of multi founder dummy from the first stage in Column 3. Details on the construction of each instrument is provided in the text. P-values are shown in parenthesis. Table 1 provides data definitions. *, **, and *** indicate significance at the 1%, 5%, and 10% levels, respectively.

(1) (2) (3) (4) First Stage Second Stage First Stage Second Stage Multi founder dummy 0.914*** 0.351*** (0.001) (0.000) Death fraction before 2001 -1.769*** (0.000) Nr. of original founders 0.424*** (0.000) Log of firm age -0.356*** 0.0659* -0.439*** -0.00506 (0.000) (0.087) (0.000) (0.765) Log of Total Assets 0.0236 -0.0342*** -0.0360* -0.0312*** (0.200) (0.000) (0.065) (0.000) Firm Risk 0.261 -0.177 0.0391 -0.133 (0.293) (0.127) (0.881) (0.180) Leverage -0.441*** -0.0420 -0.274*** -0.121*** (0.000) (0.458) (0.006) (0.001) R&D/Sales 0.0150 0.0380*** 0.0115 0.0407*** (0.132) (0.000) (0.272) (0.000) Capex/Sales 0.143* -0.190*** 0.263*** -0.161*** (0.071) (0.000) (0.002) (0.000) Founder Team Ownership 1.339*** -0.641*** 1.791*** -0.400*** (0.000) (0.000) (0.000) (0.000) Constant 0.0526 0.511*** -0.343* 0.809*** (0.775) (0.002) (0.078) (0.000) Observations 4669 4669 4673 4673 Pseudo R2 (%) 6.43 16.81

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Table 6: Cofounder Heterogeneity Sample Panel A presents the distribution of number of cofounders and percentages for the multi founder only sample of firms from 2001 through 2015. Panel B presents mean, median, standard deviation, minimum and maximum values for our key variables and heterogeneity index specific variables. Heterogeneity Index is a composite index based on age, gender, ethnicity, education and professional experience of each cofounder team as defined in the text. Panel C presents mean values and t-values for difference of mean tests for high-heterogeneity firms, medium-heterogeneity firms, and low-heterogeneity firms. Data definitions are supplied in Table 1. *, **, and *** indicate significance at the 1%, 5%, and 10% levels, respectively. Panel A Number of cofounders in Multi founder Sample

Number of observations Percentage

Two cofounders 630 76.3% Three cofounders 131 15.9% Four cofounders 57 6.9% Five cofounders 7 0.9% Total 825 100% Panel B Variables N Mean Median Std. Dev. Min Max Market Cap ($ mln) 825 1945 621.4 3786 13.58 22135 Total Assets ($ mln) 825 1014 425.7 1697 14.79 11620 Total Sales ($ mln) 825 888.2 340.1 1650 0.0890 12261 Firm Age 825 18.70 18 8.655 3 49 Founder team owner. 825 0.188 0.127 0.173 0 0.820 Debt / Asset Ratio 825 0.160 0.0289 0.236 0 1.148 Firm Risk 825 0.190 0.159 0.101 0.0411 0.470 R&D/Sales 825 0.604 0.0610 2.510 0 21.89 Capex/Sales 825 0.143 0.0438 0.348 0.000721 2.256 Tobin's Q 825 2.371 1.871 1.605 0.626 8.718 ROA 825 0.0583 0.101 0.201 -1.026 0.398 Cofounder Age Avg. 825 54.81 55 8.201 35.50 81 Cofounder Age Covar. 825 0.0868 0.0575 0.0806 0 0.437 Perc. Female Cofounder 825 0.0599 0 0.155 0 0.500 Minority Type 825 0.365 0 0.516 0 2 Profess. Functional Area 825 1.975 2 0.789 0 4 Education Level HHI 825 0.705 0.556 0.259 0.333 1 Education Type HHI 825 0.629 0.556 0.410 0 2 Perc. Same Work 825 0.347 0 0.471 0 1 Perc. Same School 825 0.208 0 0.398 0 1 Perc. Top School 825 0.241 0 0.354 0 1 Perc. Family Members 825 0.256 0 0.428 0 1

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Panel C

Variables

Heterogeneity High Cofounder Firms

Heterogeneity Low Cofounder Firms

High vs Low Hetero. Mean diff. t-statistics

Heterogeneity Middle Cofounder Firms

High vs Middle Hetero. Mean diff. t-statistics

Mean Mean Mean Market Cap ($ mln) 1588 1815 -0.763 2382 -2.382** Total Assets ($ mln) 935.1 891.7 0.319 1195 -1.694* Total Sales ($ mln) 769.4 836.7 -0.536 1042 -1.805* Firm Age 18.77 20.34 -1.996** 17.15 2.347** Founder team owner. 0.149 0.182 -2.588*** 0.228 -5.121*** Debt / Asset Ratio 0.193 0.119 3.587*** 0.168 1.256 Firm Risk 0.197 0.175 2.599*** 0.197 -0.015 R&D/Sales 0.466 0.582 -0.618 0.747 -1.392 Capex/Sales 0.136 0.147 -0.373 0.145 -0.335 Tobin's Q 2.196 2.394 -1.556 2.506 -2.176** ROA 0.0482 0.0640 -0.887 0.0623 -0.890 Cofounder Age Avg. 55.71 55.13 0.807 53.70 3.141*** Cofounder Age Covar. 0.122 0.0489 13.174*** 0.0893 4.576*** Perc. Female Cofounder 0.0843 0.0112 6.125*** 0.0822 0.138 Minority Type 0.530 0.202 7.432*** 0.364 3.637*** Profess. Functional Area 2.352 1.629 11.736*** 1.949 5.94*** Education Level HHI 0.595 0.802 -10.021*** 0.715 -5.717*** Education Type HHI 0.422 0.808 -11.699*** 0.652 -7.826*** Perc. Same Work 0.0833 0.584 -14.569*** 0.369 -8.641*** Perc. Same School 0.0562 0.454 -12.085*** 0.121 -2.800*** Perc. Top School 0.2105 0.2244 -0.471 0.282 -2.474** Perc. Family Members 0.282 0.174 3.024*** 0.308 -0.710 N 264 267 294

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Table 7: Log Q and Heterogeneity Index OLS Regressions This table presents OLS regressions of natural log of Tobin’s Q (Log Q) on cofounder team characteristics for the multi founder sample of firms from 2001 through 2015. Heterogeneity Index is a composite index based on age, gender, ethnicity, education and professional experience of each cofounder team as defined in the text. High heterogeneity index is a binary variable which equals to one for multi founder firms that have high heterogeneity index values (15-20) and zero otherwise. Medium heterogeneity index is a binary variable which equals to one for multi founder firms that have medium heterogeneity index values (13-14) and zero otherwise. Low heterogeneity index is a binary variable which equals to one for multi founder firms that have low heterogeneity index values (7-12) and zero otherwise. P-values are shown in parenthesis. Table 1 provides data definitions. *, **, and *** indicate significance at the 1%, 5%, and 10% levels, respectively.

(1) Log Q

(2) Log Q

(3) Log Q

(4) Log Q

Heterogeneity Index -0.0346*** (0.000) Hi Heterogeneity Index -0.162*** (0.001) Low Heterogeneity Index 0.0158 (0.728) Med Heterogeneity Index 0.139*** (0.003) Log of firm age 0.165*** 0.186*** 0.188*** 0.205*** (0.001) (0.000) (0.000) (0.000) Log of Total Assets 0.0778*** 0.0745*** 0.0787*** 0.0717*** (0.000) (0.000) (0.000) (0.001) Firm Risk 0.966*** 0.885*** 0.862*** 0.764*** (0.001) (0.002) (0.003) (0.009) Leverage -0.0610 -0.0390 -0.0959 -0.0683 (0.549) (0.704) (0.349) (0.503) R&D/Sales 0.0273** 0.0245* 0.0314** 0.0276** (0.033) (0.057) (0.015) (0.031) Capex/Sales -0.166* -0.145 -0.179* -0.157* (0.077) (0.124) (0.057) (0.095) Founder Team Ownership -0.151 -0.229 -0.0945 -0.204 (0.261) (0.101) (0.483) (0.142) Percentage of Top School 0.149** 0.122** 0.133** 0.113* (0.014) (0.043) (0.028) (0.062) Percentage of Family Cofounders 0.171*** 0.154*** 0.151*** 0.122** (0.001) (0.004) (0.006) (0.024) Constant 0.358 -0.0796 -0.0937 -0.116 (0.328) (0.817) (0.788) (0.736) Observations 825 825 825 825 Industry Fixed Effects Yes Yes Yes Yes Year Fixed Effects Yes Yes Yes Yes Adjusted R2 23.56 23.49 22.32 23.21

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Table 8: Firm Performance and Heterogeneity Index IV Regressions This table presents OLS and IV-2SLS regressions of natural log of Tobin’s Q (Log Q) on cofounder team characteristics for the multi founder sample of firms from 2001 through 2015. Heterogeneity Index is a composite index based on age, gender, ethnicity, education and experience of each cofounder team as defined in the text. Column 1 shows the result of OLS regressions. Column 2 shows the first stage of Column 3 IV-2SLS regression. Details on the construction the instrument (county heterogeneity) is provided in the text. P-values are shown in parenthesis. Table 1 provides data definitions. *, **, and *** indicate significance at the 1%, 5%, and 10% levels, respectively.

(1) OLS

(2) First Stage

(3) Second Stage

County heterogeneity 3.667*** (0.000) Heterogeneity Index -0.0346*** -0.279*** (0.000) (0.000) Log of firm age 0.165*** -0.841*** -0.00261 (0.001) (0.000) (0.974) Log of Total Assets 0.0778*** 0.0666 0.0940*** (0.000) (0.396) (0.001) Firm Risk 0.966*** 3.542*** 1.721*** (0.001) (0.001) (0.000) Leverage -0.0610 0.964*** 0.163 (0.549) (0.009) (0.270) R&D/Sales 0.0273** -0.116** -0.00638 (0.033) (0.013) (0.733) Capex/Sales -0.166* 0.302 -0.101 (0.077) (0.374) (0.410) Founder Team Ownership -0.151 -1.358*** -0.586*** (0.261) (0.006) (0.005) Percentage of Top School 0.149** 0.322 0.223*** (0.014) (0.143) (0.007) Percentage of Family Cofounders 0.171*** 0.703*** 0.348*** (0.001) (0.000) (0.000) Constant 0.358 10.02*** 3.345*** (0.328) (0.000) (0.001) Observations 825 818 818 Industry Fixed Effects Yes Yes Yes Year Fixed Effects Yes Yes Yes Adjusted R2 23.56 24.36

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Table 9: Firm Performance, Occupational and Social Heterogeneity This table presents OLS and IV-2SLS regressions examining the firm performance implication of co-founder team’s occupational and social heterogeneity. Occupational heterogeneity comprises education and experience heterogeneity. Social heterogeneity consists of age, gender and ethnic heterogeneity. The dependent variable is natural log of Tobin’s Q. Column 1 shows the result of OLS regressions. Column 2 and Column 4 show the first stages of Column 3 and Column 5 IV-SLS regressions, respectively. Details on the construction the instrument (county heterogeneity) is provided in the text. P-values are shown in parenthesis. Table 1 provides data definitions. *, **, and *** indicate significance at the 1%, 5%, and 10% levels, respectively. (1) (2) (3) (4) (5)

OLS First Stage

Second Stage

First Stage

Second Stage

County heterogeneity 2.847*** 0.820** (0.000) (0.024) Occupational Heterogeneity -0.0439*** -0.359*** (0.000) (0.000) Social Heterogeneity -0.0116 -1.247** (0.553) (0.030) Log of firm age 0.164*** -0.625*** 0.00755 -0.216** -0.0379 (0.001) (0.000) (0.927) (0.023) (0.816) Log of Total Assets 0.0750*** -0.0354 0.0627** 0.102** 0.203*** (0.000) (0.584) (0.032) (0.011) (0.006) Firm Risk 0.934*** 1.564* 1.295*** 1.977*** 3.199** (0.001) (0.072) (0.001) (0.000) (0.014) Leverage -0.0541 0.895*** 0.216 0.0688 -0.0199 (0.595) (0.003) (0.174) (0.715) (0.936) R&D/Sales 0.0262** -0.114*** -0.0152 -0.00139 0.0241 (0.040) (0.003) (0.457) (0.953) (0.432) Capex/Sales -0.163* 0.276 -0.0862 0.0257 -0.154 (0.081) (0.324) (0.498) (0.882) (0.497) Founder Team Ownership -0.158 -1.138*** -0.616*** -0.220 -0.482 (0.242) (0.005) (0.005) (0.378) (0.186) Perc. of Top School 0.143** 0.0533 0.152* 0.269** 0.468** (0.018) (0.769) (0.060) (0.017) (0.033) Perc. of Family Cofounders 0.173*** 0.548*** 0.348*** 0.155 0.345** (0.001) (0.001) (0.000) (0.113) (0.027) Constant 0.344 6.542*** 2.901*** 3.476*** 4.886** (0.347) (0.000) (0.001) (0.000) (0.047) Observations 825 818 818 818 818 Industry Fixed Effects Yes Yes Yes Yes Yes Year Fixed Effects Yes Yes Yes Yes Yes Adjusted R2 or Pseudo R2 23.64 20.94 23.61