Angels or Sharks? Decision Making in

Entrepreneurial Finance

Thomas J. Boulton

Farmer School of Business

Miami University

boultotj@miamioh.edu

Thomas D. Shohfi*

Lally School of Management

Rensselaer Polytechnic Institute

shohft@rpi.edu

Pengcheng Zhu

Katz Graduate School of Business

University of Pittsburgh

pez28@pitt.edu

First Draft: June, 30th, 2015

This Draft: July 8th, 2016

*Contact Author

For comments that improved this manuscript, we thank Christophe Bonnet (discussant), Jared Smith, Shawn Thomas,

and participants at the 2015 Annual Meeting of the Academy of Behavioral Finance & Economics and 2016 Eastern

Finance Association Annual Meeting. We also thank Shawn Cassidy, James Dennison, Kevin Fielden, Kevin Haffey,

Sergey Litvinenko, Thomas Muller, Glenn Myers, Connor Norton, and Allison Yoh for excellent research assistance.

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Angels or Sharks? Decision Making in

Entrepreneurial Finance

Abstract

We study angel investors’ investment decisions using hand-collected data from the reality

television show Shark Tank. Consistent with rational choice theory, we find that higher profit

margins, lower levels of prior investment, patents held, and entrepreneurial teams increase the

likelihood of investment. However, evidence that personal characteristics of entrepreneurs are

correlated with bidding and deal outcomes reveals various homophily preferences and influential

biases. For example, investors are less (more) likely to fund younger (older) entrepreneurs and

black entrepreneurs receive lower implied valuations for their ventures. Female, black, and

younger (highly-ranked university educated) entrepreneurs request lower (higher) pre-negotiation

venture valuations.

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I. Introduction

A recent study by the Center for Venture Research at the University of New Hampshire reports

total angel investments of $24.8 billion in 2013, which represents an increase of 8.3% over 2012.

During the same period, the total number of angel investors grew by 11.4% to nearly 300,000

investors. Additionally, angel investment lead to the creation of 290,020 new jobs in the U.S. in

2013, or 4.1 jobs per investment. Angel investment‒also known as informal venture capital‒is

distinct from institutional- or corporate-venture capital in that angels invest their own capital rather

than investing resources pooled from investors. By filling a funding gap between friends-and-

family money and venture capital, angel investment has a positive influence on both the

performance and survival of start-ups (Cassar, 2004). Due to the rapid growth of the angel

investment market, venture capital is often preceded by an angel investment (Freear and Wetzel,

1992; Carter et al, 2003) and, for many start-ups, is no longer the most critical source of private

equity financing (Brush et al., 2002; Sohl, 2003; Bygrave and Reynolds, 2004).

Despite the importance of angel investment, the academic research on this topic is limited.

Presumably, data availability is one of the factors limiting research in this area. The absence of a

central database or systematic source of angel investments results in research that often relies on

survey data to examine either angel investors or entrepreneurs (e.g., Becker-Blease and Sohl, 2007;

Wiltbank, Read, Dew, and Sarasvathy, 2009). We leverage the reality television show Shark Tank

to construct a database that includes 611 businesses fronted by 928 entrepreneurs seeking an angel

investment. The negotiations that take place on Shark Tank allow us to observe the characteristics

and behavior of both the angel investors and entrepreneurs. Our data, which is revealed by

participants on the show and supplemented from external public sources (e.g., LinkedIn),

encompasses the first seven seasons of Shark Tank (2009 – 2016). We believe that this approach

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is an improvement over anonymous survey data, as we expect that the entrepreneurs on this widely-

viewed television show are less likely to misrepresent details of their firm or personal

characteristics to angel investors.1 In addition, the structure of Shark Tank provides a sense of the

entire negotiation between entrepreneurs and investors from the initial approach to deal

consummation or failure, which allows us to connect investor and entrepreneur characteristics to

negotiation and deal outcomes.

Dal Cin et al. (1993) and Riding et al. (1997) provide a five-stage structure to frame an angel’s

investment decisions. The five stages are: (1) deal origination and first impressions; (2) review of

business plan; (3) screening and due diligence; (4) negotiation; and (5) consummation and deal

structuring. The interactions between entrepreneurs and angel investors that take place on Shark

Tank conform to this structure remarkably well. In Stage 1, entrepreneurs pitch their business to

the angel investors (“Sharks”) by demonstrating their product or service and commenting on its

unique aspects. Stage 2 follows and involves interaction between the entrepreneur and potential

investors. In a shortened, sound-bite-filled version of Stage 2, we observe discussions that typically

include details on past sales, cost and price per unit, age of the venture, and intended use of

proceeds. Typically, none of the angel investors have met the presenting entrepreneurs before the

sales pitch.2 Therefore, the angel can only conduct detailed due diligence after the show. Thus, in

Stage 3 angel investors screen based on limited information provided by the entrepreneur regarding

past performance, market potential, and the angel investor’s own experience in related firms and

industries. Consistent with Maxwell, Jeffrey, and Lévesque (2011), potential investors often

1 Levitt (2004) notes caveats regarding the use of television show data in evaluating decision making processes.

Despite some drawbacks, these data can present advantages over surveys. With respect to discrimination, Levitt (2004)

notes that “self-reported data are unlikely to accurately reflect attitudes if there is a perceived stigma attached to racist

views.” 2 Jason Cochran. 8 things you didn’t know about ‘Shark Tank’. Blog, April 3, 2013.

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withdraw at this point due to perceived flaws with the business model or entrepreneurs. If one or

more investors is interested, negotiations commence (Stage 4). We observe the bidding process,

competition among angel investors, offers and counteroffers, proposed deal structure, and implied

valuation. If the entrepreneur receives an attractive offer, a deal is consummated.

Importantly, the angel investors on Shark Tank put their personal capital at risk in each invested

venture.3 They receive compensation for appearing on the show, but their investments are often

many times larger than their appearance fees. As a result, we expect the investors to make

investment decisions in a manner consistent with rational choice theory, which should favor

ventures with financial performance, entrepreneur skills, and network relationships that point to

future success. Consistent with rational choice theory, Mason and Harrison (1996a) find that key

considerations for investors include the entrepreneurs’ attributes (e.g., expertise, enthusiasm,

honesty, and trustworthiness) and the market potential for the product or service being pitched.

Likewise, Greenberg (2013) finds that existing or pending patents can enhance the ability of start-

ups to attract venture or angel financing. Mason and Harrison (1996b) report that common deal

killers include flawed or incomplete marketing strategies and unrealistic financial projections,

while Carpentier and Suret (2015) find that market and execution risks are first order

considerations for angel investors. We collect financial data, including profit margin, past sales,

prior investment in the venture, patents held, and indicators of the quality of the entrepreneurship

team in order to study whether angel investors’ decisions are consistent with rational choice theory.

If rational choice theory explains angel investors’ behavior, we expect to observe greater interest

from the angel investors in ventures that have a measureable track record of success and/or

characteristics that point to future success.

3 Each episode of Shark Tank begins with the disclaimer “the following are actual negotiations between entrepreneurs

and investor ‘Sharks.’ The Sharks invest their own money at their discretion.”

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Our results indicate that angel investors are generally rational and choose investments in

ventures with higher profit margins and patents held or pending. Newer ventures and ventures with

lower levels of prior investment are also favored by angel investors. Namely, we find that older

ventures and ventures with larger prior investments are less likely to receive an angel investment.

Presumably, these results reflect both the reluctance of angels to negotiate with outside investors

and potential failure of entrepreneurs to recognize sunk costs.

Prior research finds that the quality of the entrepreneur/founding team is central for success

(Landström, 2007). Angel investors may prefer larger entrepreneurship teams with a more diverse

skill set. However, Collewaert (2012) notes that larger teams also introduce higher coordination

costs and contrasting incentives. Concentrated human capital can foster innovation but may also

introduce key human capital risk that is particularly detrimental to young, small, and high growth

firms (Israelsen and Yonker, 2015). We find that solo entrepreneurs are less likely to strike a deal

compared to teams of entrepreneurs.

Education, including the level (e.g., bachelors, masters, doctorate), prestige of the granting

institution, and type(s) of degrees obtained (e.g., business, engineering), may serve as a signal of

entrepreneur quality to potential investors. Prior research on the effects of business education on

entrepreneurship outcomes is mixed. Von Graevenitz, Harhoff, and Weber (2010) find positive

effects of entrepreneurship education while Lerner and Malmendier (2013) find that MBA

colleagues with venture experience share stories of failure that leads to less entrepreneurship in

peers. To the extent that business education increases risk aversion, we find some evidence that,

conditional on receiving an offer, entrepreneurs with a business education accept lower valuations

from investors.

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Given pure rationality, entrepreneur characteristics such as race, gender, and age should not

impact angels’ investment decisions. However, a large body of research shows that investors’

decisions are also shaped by biases. This may also be true for angel investors, who could exhibit

preferences for or discriminate against entrepreneurs with certain characteristics. Such biases

could lead angel investors to make investment decisions that diverge from rational choice theory.

Shark Tank negotiations allow us to observe many of the personal characteristics thought to be

associated with biases. For each entrepreneur and potential investor, we collect personal

information that includes gender, race (both visual and algorithmic measures), age, and place of

residence. Our analysis considers the outcome of negotiations in light of the financial

characteristics of each venture and the personal characteristics of both the entrepreneurs and the

angel investors. If investor biases impact the outcome of negotiations, we expect to observe a

significant relation between these personal characteristics and both the probability that an

entrepreneur receives an investment and the size of the investment received (i.e., valuation).

We find evidence that biases may influence the outcome of some negotiations. For example,

our results suggest that older entrepreneurs (over age 50) are less likely to strike a deal with the

angel investors. The relation between race and angel investors’ decisions suggests that compared

to entrepreneurs of other races, Asian entrepreneurs are more likely to secure a deal, while black

and Latino entrepreneurs receive lower valuations for their businesses when a deal is struck.

Gender, however, is not significantly correlated with the likelihood of striking a deal or implied

valuations. This is consistent with Becker-Blease and Sohl (2007), who find that despite a lower

propensity to seek an angel investment, women are equally likely to actually receive an investment.

We also consider whether angel investors exhibit a preference for entrepreneurs with personal

characteristics that match their own. While we can observe Sharks saying things like “you remind

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me of myself” to entrepreneurs, identifying homophily in our sample is challenging due to the

limited population of Sharks.4 Of the investors who have appeared in more than ten episodes, only

one is black and none are Latino or Asian.5 Also, there are only two female investors and all of the

investors are married. For these reasons, we limit our examination of homophily to gender, race

(black only), geographic residence or origin, education level, and age.

Our results support the notion that homophily influences angel investors’ decisions.

Controlling for financial factors, we find that black investors are more likely to make an offer to a

black entrepreneur. We also find that angel investors are more likely to make an offer to a

(relatively) young entrepreneur, which suggests that angel investors value their position as mentors.

Homophily also seems to influence valuations, which tend to be lower when the angel and

entrepreneur are further apart in education level and share a U.S. state.

In Section II, we discuss rational choice theory and explore potential sources of bias for angel

investors. In Section III, we provide details on Shark Tank and discuss the codification of financial

and non-financial factors for the construction of our data set. In Section IV, we report the results

of univariate and multivariate analyses that examine the determinants of deal success and the

valuation received by the entrepreneurs. We conclude in Section V.

II. Rational Choice Theory and Behavioral Biases

1. Rational Choice Theory

Opp (1999) identifies three “core assumptions” of rational choice theory. First, the preference

proposition states that “individual preferences (or goals) are conditions of behaviors which are

instrumental in satisfying the respective preferences.” Second, the constraints proposition posits

4 For example, see Tereson Dupuy of FuzziBunz Interview at Shark Tank Blog. 5 See The Case of the Missing Latina/o Shark at Huffington Post.

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that “anything that increases or decreases the possibilities of an individual to be able to satisfy her

or his preferences by performing certain actions is a condition for performing these actions.” Third,

the utility maximization proposition states that “individuals choose those actions that satisfy their

preferences to the greatest extent, taking into account the constraints.” Thus, rational actors are

individuals with preferences who try to maximize utility subject to constraints.

Under rational choice theory, one might expect angel investors to exhibit preferences for

ventures with superior financial performance and entrepreneurs who possess the skills and network

relationships necessary to be successful. Consistent with this notion, Mason and Harrison (1996a)

find that market potential is a key consideration for angel investors and Greenberg (2013) finds

that existing or pending patents that protect intellectual capital help start-ups attract angel funding.

Angel investors are subject to many constraints when evaluating possible investments. Some of

the more obvious constraints include their investment opportunity set, investable capital, and

information. First, angel investors are presented with a limited number of potential investment

opportunities. This is an important constraint on Shark Tank as the businesses pitched to the angel

investors are pre-screened and typically limited to four per episode. Second, angel investors have

limited resources which may constrain their ability and investment amount. If an entrepreneur

seeks an investment that is above an interested investor’s available resources, the investor must

either collaborate with other investors or forego the opportunity to invest. Third, information

regarding the ventures that are pitched to angel investors is often limited. Many are pre-revenue

and even more are not profitable. Often, entrepreneurs pitch angel investors following proof of

concept. Again, this constraint is particularly binding on Shark Tank as angel investors negotiate

with entrepreneurs based on a limited amount of information. More detailed due diligence is

conducted after an initial investment agreement has been reached.

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2. Bias and Discrimination

Contrary to rational choice theory, a large body of research shows that decisions are also

shaped by biases. This may also be true for angel investors, who could exhibit preferences for or

against entrepreneurs with certain characteristics. Becker (1957) posits that taste-based

discrimination arises when economic agents prefer not to interact with certain groups of people.

He also notes that competition should reduce taste-based discrimination, as profits tend to be lower

for those who discriminate. Statistical discrimination posits that individuals make decisions based

on individuals’ attributes and those of their group. Such decision making may or may not be

discriminatory depending on whether the decisions are based on perceptions or prior personal

experience. Statistical discrimination may arise when angel investors, who are required to evaluate

entrepreneurs based on limited information, rely on perceptions or prior experience to aid in their

decision making. Examples of taste-based and statistical discrimination include discrimination

based on gender, ethnicity, or age.

Previous evidence on the impact of gender-based discrimination on external financing access

is mixed. Cavalluzzo et al. (2002) and Muravyev et al. (2009) both find that bank loan approval

rates are lower for firms managed by females. In contrast, Cavalluzzo and Cavalluzzo (1998),

Blanchflower et al. (2003), Storey (2004), and Cavalluzzo and Wolken (2005) find no statistical

differences between male-managed and female-managed firms. The relation between gender and

start-up firm financing tends to focus on the venture capital industry. Existing evidence suggests

that female entrepreneurs are less likely to receive debt financing (Buttner and Rosen, 1988; Fay

and Williams, 1993; Coleman, 2000) and venture capital funding (Seegull, 1998; Greene et al,

2001; Brush et al. 2002) than their male counterparts.

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The literature also provides several reasons why female entrepreneurs may experience

discrimination when seeking an angel investment. Compared to male entrepreneurs, women often

have more non-business-related responsibilities and lack the deep social networks that help to

support business growth (Boden and Nucci, 2000; Greene et al., 2001). Additionally, female-

owned businesses are less likely to succeed if they experience discrimination from customers

(Bates, 2002) or suppliers (Weiler and Bernasek, 2001). These, and related factors, could lead

angel investors to exhibit a preference for male entrepreneurs. Gender discrimination might not

manifest itself in lower investment rates. Instead, female entrepreneurs could respond to the weak

bargaining position created by discrimination by giving up a greater portion of their business or

accepting funding at a lower implied value than male entrepreneurs. Becker-Blease and Sohl (2007)

provide some evidence of gender-based homophily, as angel investors are slightly more willing to

invest in entrepreneurs with the same gender.

The study of race-based discrimination has a long history. Prior research finds that banks

discriminate against minority entrepreneurs in terms of access to credit and loan approval rates

(Bostic and Lampani, 1999; Blanchflower et al., 2003; Blanchard et al., 2008). Minority groups

are also less likely to obtain access to outside credit and, when they do, tend to pay a higher cost

of capital than otherwise comparable small businesses. On the other hand, certain minority groups

might experience a positive effect if, for example, angel investors perceive Asian American

entrepreneurs’ education and work ethic as superior to that of other groups (Asakaw and

Csikszentmihalyi, 2014; Hsin and Xie, 2014). Race-based homophily is difficult for us to uncover

due to the limited number of minority entrepreneurs and investors in our sample. Specifically,

there is only one non-white recurring investor on the program (Daymond John). 6 Further

6 Troy Carter appeared in one episode of The Shark Tank. See Troy Carter on Why he First Turned Down ‘Shark

Tank’, and the Conversation that Changed his Mind at Business Insider.

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investigation is warranted, however, as prior research suggests that African American homophily

is persistent in business school social networks (Mollica et al., 2003)

Age discrimination is widely accepted to be prevalent and applies to both younger and older

populations. Garstka et al. (2005) find that both younger and older workers believe that they are

discriminated against relative to other age groups. Levitt (2004) finds systematic, taste-based

discrimination against older contestants on the reality television show Weakest Link. Presumably,

age-based discrimination is driven by a belief that younger or older workers are less adaptable

and/or productive than other age groups. However, Welford (1988) fails to find evidence that older

workers are less capable of learning new skills and Guest and Shacklock (2005) note both benefits

and costs of an age-diverse workforce. While research on older entrepreneurs is limited, Singh and

DeNoble (2003) and Weber and Schaper (2004) suggest that financial and social capital favor

older entrepreneurs when starting a business. Age also has different implications for ability,

education, motivation, and experience. Angel investors who value their role as mentors may be

reluctant to invest in ventures with older entrepreneurs. When angel investors place a high value

on mentorship, age-based homophily may bias towards older investors funding ventures with

younger entrepreneurs

III. Background of Shark Tank and Summary Statistics

Shark Tank is a reality television show in which angel investors, called “Sharks” decide

whether or not and how much to invest of their own money in a company pitched by

entrepreneur(s). At the beginning of each pitch, entrepreneurs start with their desired investment

amount in exchange for a certain equity percentage of their company. The entrepreneurs then

present their business plans, products or services, and current status to a panel of five angel

investors. After the presentation, investors ask questions to identify weaknesses and faults in an

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entrepreneurs’ concept, product, and/or business model. Investors ask questions about the

company’s past performance and pricing strategy, which ultimately help to shape their own

valuations of the company. Negotiations culminate with the decision and negotiation stage where

each investor expresses their intention to invest or not by indicating that they are “in” or “out.”

Interested investors bid independently or in teams in an attempt to partner with the entrepreneur.7

If all the investors opt out, the entrepreneur leaves empty-handed.

Despite our comprehensive data collection efforts, there are still some caveats in the data we

are able to collect. First, both entrepreneurs and angel investors on this show are highly selected,

and the selection process is not known to the public. They may or may not be representative of the

universe of entrepreneurs and angel investors. For example, entrepreneurs on the show tend to

have excellent presentation skills and novel ideas, while the Shark investors are top-tier in their

own business fields.

Second, what we observe on the show is an edited version of interaction between the

entrepreneurs and investors. The negotiations typically last from 15 minutes to more than 2 hours.8

While this editing is understandable because Shark Tank is a prime time television program, it

may result in partial information for some of the negotiations. However, we expect that key

moments that are crucial to the outcome are included in the negotiations that we observe. Because

the presentations and negotiations are genuine and broadcast sequentially, we believe that the data

collected provides a good foundation for the analysis of angel investors’ investment decisions.

7 Joint bids occur frequently. The average number of angel investors participating in a deal on the show is 1.3919. 8 James Cochran notes at 8 Things You Didn’t Know about ‘Shark Tank’ that “some negotiations can go on for an hour

or much longer, but the key moments are edited into palatable acts for television. Everything you see is true, none of

it is re-taped, and the elements that are crucial to the outcome are included.”

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Third, it is possible that as many as two-thirds of the televised deals never come to fruition.9

Data from follow-ups to the original negotiations confirm that no less than one-third of the deals

are ultimately invested.10 Because investors receive no information prior to filming, they perform

due diligence after taping. A deal can be broken due to legal, tax, or financial red flags discovered

by the angel investors. Entrepreneurs can also back out of a deal. We observe the investors’

decision based on the information revealed by entrepreneurs on the show and analyze the factors

related to their decisions on the show. Thus, decisions made outside of the televised negotiations

are not part of our primary analyses.11

Fourth, in the first four seasons, entrepreneurs were required to give up either 5% of their

company or a 2% royalty on operating profits just for appearing on the show, regardless of the

outcome of negotiations. This requirement was removed after Mark Cuban, the wealthiest and

arguably the most well-known Shark Tank investor, threatened to leave the show.12 The quality of

participating ventures may be higher after this requirement was removed as talented entrepreneurs

will be unlikely to sacrifice equity or cash flow for an appearance on the show with no certainty

of reaching a deal.13 Nonetheless, we account for this change by including season dummy variables

in all multivariate analysis.

Fifth, a significant challenge in the data collection process is that some product and

entrepreneur attributes are not observable or cannot be collected systematically across seasons.

9 Karen Aho. Do two-thirds of Shark Tank deals fall apart? Bloomberg, July 15, 2014. 10 We collect follow-up data from episodes of both Shark Tank and the spin-off series Beyond the Tank. We note that

follow-ups that are covered during these episodes report primarily successful ventures. For this reason, the percentage

of verified deals is likely to be understated. 11 We discuss robustness checks using subsequent episode follow-up data for funded deals later in this manuscript. 12 Will Yakowicz. Mark Cuban made Shark Tank change its contracts. Inc.com, Oct 2, 2013. According to Mr.

Yakowicz, “Cuban said that the clause was removed retroactively, meaning every contestant who’s appeared on the

show since Season One will be relieved of the commitment.” 13 Supporting this claim, mean venture sales (in 2015 USD) in season 1 (4) increased from $214,728 ($385,765) to

over $494,166 in season 7.

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Some variables, such as product market potential and the quality and honesty of the entrepreneur,

cannot be directly observed or measured. Other potentially material variables, such as the venture’s

recent sales growth rate, forecasted future sales, and entrepreneurs’ prior work experience, are not

available for the vast majority of the negotiations.

[Insert Figure 1 here]

As of May 2016, Shark Tank has aired 7 seasons and 151 episodes. We collect investment

amount, equity percentage, bidding process, and company past performance by watching each

episode. The codification of an example pitch, question/answer session, and negotiation is

presented in Figure 1. In each episode, there are approximately four companies/sales pitches. In

each sales pitch, one or more entrepreneurs present in front of a panel of five angel investors. We

report summary statistics in Table 1. Our data includes 611 venture companies and 928

entrepreneurs. In each season, around 50% of the companies strike a deal with an investor. This

rate is likely much higher than is the case in the real world, as companies that appear on the show

are pre-screened by the production team. Presumably, the higher investment rate is also due to the

angel investors’ captured benefits from the publicity boost the business receives when the show

airs.14 Nealy 13% of ventures turn down bids from investors due to insufficient valuation or other

restrictive deal conditions. The mean investment requested by the entrepreneurs across seasons

varies from $185,000 to $325,000. The average amount invested by the angel investors is lower

than requested in seasons 1, 3, and 4, while in seasons 2, 5, 6, and 7 investors tend to invest more

than the entrepreneurs’ initial request. This pattern is consistent with an increase in entrepreneur

and venture quality in more recent seasons of the show.

[Insert Table 1 here]

14 See, for example, What’s a “Shark Tank” Appearance Worth? A Top Spot in the App Store at WSJ All Things D.

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The show also regularly presents follow-up reports (“Shark Tank Updates”) about businesses

that had previously appeared on the show.15 In addition, a spin-off show, titled Beyond the Tank,

focuses on how the Sharks mentor the entrepreneurs, access the businesses, and provide advice

and expertise. In Table 1 we report that 36.13% of deals are verified as ultimately invested

representing 31.76% of total investment value. We believe that this is a reasonable percentage after

investors conduct their due diligence on the entrepreneurs and their business plans. The investors

get paid about $30,000 for each episode.16 Extrapolating this across the investors’ appearances on

the show we find that, except for Robert Herjavec ($3.9M verified unreported investment) and

Kevin O’Leary ($1.46M verified), all of the regular investors’ verifiable total investment value

exceeds their payment to be on the show.17 This again supports our argument that this show

provides credible data for studying angel investors’ investment decisions.

Through the first seven seasons, seven angel investors appear regularly on the show (Table 2,

Panel A). These seven angel investors possess demographic features that are representative of the

angel investor universe. According to Ramadani (2012), most angel investors are male, between

40 and 65 years old, and hold an undergraduate university degree. On Shark Tank, five of the seven

main investors are male, all are between 40 and 65 years old, and all have only a Bachelors degree

except Daymond John (high school degree) and Kevin O’Leary (MBA). Net worth of the regular

investors ranges from $50 million (Lori Greiner) to $2.7 billion (Mark Cuban).18

[Insert Table 2 here]

15 Not all realized deals are featured in these follow-up stories. The vast majority of the follow-ups feature successful

investments, but some failures and deals involving entrepreneurs who did not strike deals are also featured. 16 Jason Cochran Blog. How much do the Sharks make per episode? The Sony hack revealed it. 17 This is a conservative estimate since the $30,000 compensation per episode is specific to Mark Cuban and it is

unlikely that the other sharks earn as high an amount. 18 Detailed information regarding the net worth and holdings of these investors can be found at Celebrity Net Worth.

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In Table 2, Panel B we report that Kevin O’Leary makes offers most frequently (35.56%) but

also has the lowest acceptance rate (22.77%). In contrast, Robert Herjavec bids on fewer ventures

(26.75%) but invests the largest dollar amount ($290,031) in his accepted bids (41.83%). Mark

Cuban stands out as a generous and popular investor, as he claims the highest total investment

amount, the second highest maximum individual investment amount, and by far the highest

percentage of bids accepted (90.65%).

The average equity percentage reported in Panel B of Table 2 is above 15% for all of the

regularly appearing investors. This indicates that these angel investors require a material equity

position in order to invest. Not surprisingly, investors that offer (venture) debt, Kevin O’Leary and

Lori Greiner particularly, have lower average equity percentages. Kevin O’Leary is also most

likely to include royalties (31.19%) in bids and partner with other investors on a deal (65.22%)

further demonstrating his risk aversion. Barbara Corcoran introduces contingencies into her offers

most frequently (12.09%). These contingencies can include successful meetings with distributors,

patent validation, and other special situations.

Angel investors’ investment decisions are also likely to be influenced by entrepreneur

attributes. In Table 3, we report that 616 of the 928 entrepreneurs are male. We classify

entrepreneurs into five race categories: white, black, East Asian (Chinese, Japanese, Vietnamese,

etc.), South Asian (Indian, Pakistani, etc.), and Latino. Based on these classifications,

entrepreneurs in our sample are predominantly white (85%). Because manual racial classifications

may introduce subjectivity bias, we also use the Bayesian Improved Surname Geocoding (BISG)

proxy method. BISG (Elliot et al., 2009) estimates race by combining U.S. Census Bureau data

with an individual’s surname and venture location (zip code). While the algorithm is limited by

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inabilities to identify uncommon surnames and individuals within certain geocodes, it identifies

67.6% of entrepreneurs in our sample of which 74.5% are white.

When not stated specifically during the show, we estimate entrepreneur age using information

provided on LinkedIn. Specifically, we add 22 (18) to the year the episode airs minus college (high

school) graduation year to approximate their age. In Table 3, we report that most entrepreneurs

(77.5%) are between 25 to 50 years old. This is intuitive, as people in this age range are often

equipped with the necessary capital, motivation, work experience, and professional network

connections to start a new company.

[Insert Table 3 here]

Table 3 also reports that the vast majority of the entrepreneurs are from the United States,

including 41.5% from the western U.S. 19 Approximately one-third (33.8%) of entrepreneurs

indicate that they are married during airing of the show. Finally, 63.7% of entrepreneurs hold a

Bachelors degree or greater, 9.7% have a business degree (BSBA or MBA), and 19.6% hold a

degree from a top 50 university according to U.S. News and World Report’s 2015 university

rankings. 20 We explore the relation between angel investor decisions and entrepreneur

characteristics in subsequent sections.

We report venture-level summary statistics in Table 4, Panel A. Every presenting entrepreneur

requests an investment amount (average of $277,867 in CPI-adjusted 2015 dollars) in exchange

for an equity stake (16.8%). The requested valuation is calculated as the investment amount

divided by the equity stake. Nearly all firms (592 out of 611) provide information about prior 12

month sales. The average firm has $0.267 of sales per dollar of requested valuation. This price-to-

19 We determine geographic region by classifying states according to the Census Regions and Divisions of the United

States at the U.S. Census Bureau. 20 2015 National Universities Rankings at U.S. News and World Report.

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sales ratio of approximately 4 is lower than Sievers, Mokwa, and Keienburg (2013) who report

multiples as high as 13. However, this lower valuation reflects the mix of high growth and

established businesses presented on the show. Prior investments are reported for 217 of the 611

ventures. For entrepreneurs that do not report this variable, we assume that previous investment is

not meaningful and assign a value of zero. We perform a similar transformation for firms that do

not report an existing patent, patent pending, or absence of a patent. We record gross margin and/or

net profit margin if reported, defaulting to net profit margin. Often, margins are not explicitly

stated. For ventures that specify the sale price and cost per unit sold, we deduce gross margins

from this information. When information to calculate margins is completely missing, we fill in the

missing values with the 2-digit industry average margin from the sample.

[Insert Table 4 here]

The average age of each venture is 2.74 years. Many ventures are start-ups in the purest sense,

with 71 (327) founded in the same year as (within two years of) the Shark Tank episode on which

they appear. The oldest firm is 39 years old (Cornucpia - Season 1, Episode 9). We also report the

number of entrepreneurs that participate in the company’s presentation and the geographical

location of the company (which may or may not be the same as the place of residence of the

entrepreneurs). A single entrepreneur appears in 327 of the presentations, with a maximum number

of presenting entrepreneurs of 6 (Smartwheel - Season 4, Episode 16).

Without due diligence before taping, investors’ expertise in certain industries plays an

important role in the investment decision. In Table 4, Panel B, we display the top 19 industries (by

2-digit SIC code) for our sample. We estimate a 4-digit SIC code for each venture according to

industry descriptions available at the U.S. Department of Labor and other industrial classification

19

websites.21 The largest number of firms is from the food products and nondurable goods industry,

but there is substantial industrial variety. The probability of striking a deal with an angel investor

ranges differs across industries from 33.3% (apparel and accessory stores) to 77.8% (fabricated

metal products). Average deal valuation by industry also exhibits large variation from $356,259

for apparel and accessory stores to $19.6 million for industrial and commercial machinery. For

these reasons, we incorporate industry fixed effects for robustness in our multivariate analyses.

[Insert Table 5 here]

Summary statistics for the negotiation process are reported in Table 5. Our detailed data

collection process allows us to capture specific details of the negotiation between the investors and

angel investors. Angels make bids for 63.67% of all ventures pitched, but only 50.74% of

entrepreneurs accept a deal. When a bid is made, the average number of bids (unique bidders) is

2.48 (1.94). Entrepreneurs extend an average of 0.56 counteroffers during the negotiation process,

but only 13.23% of deals are finalized via a counteroffer. The average number of investors per

deal (1.39) indicates that multiple investor deals are common. The average entrepreneur receives

less than 70% of their requested valuation and deals with contingencies, debt, or royalties are

uncommon (8% or lower). These summary statistics suggest that there is a great deal of variation

in the intensity, nature, and outcome of the bidding process.

IV. Analyses

1. Deals and Venture Valuation

As detailed above, we collect financial and personal factors for each venture pitched on Shark

Tank. According to rational choice theory, financial and entrepreneur quality factors should be the

21 We use descriptions available at both U.S. Department of Labor’s Standard Industrial Classification (SIC) System

Search and siccode.com.

20

primary determinants of angel investors’ decisions. Financial factors include profit margin, past

sales, prior investment in the venture, and patents held. Company sales can have mixed

implications for future growth and investment. A young start-up may have greater market potential,

but little to no sales. A relatively mature company with an established reputation and demonstrable

sales may be less attractive to angels if investors believe it will be hard to shape the business

according to their vision. In addition, a mature company seeking a big investment could be a sign

of a bad financial position or an opportunity for restructuring and revitalization. Intellectual

property protection provided by patents may increase the probability of a bid by investors and

valuation offered. Finally, bidder competition is an important consideration as it reflects the

bargaining power an entrepreneur has relative to the remaining investors and may influence the

resulting post-deal valuation of the firm.

In addition to pure financial considerations, entrepreneur characteristics may also influence the

decision process. We explore entrepreneur’s gender, race, age, marital status, and other non-

financial variables in a univariate setting in Table 6. While no significant differences are observed

for gender and marital status, there is weak evidence that Asian entrepreneurs are more likely to

reach a deal. More significantly, black entrepreneurs are 16.85% less likely to strike a deal.

[Insert Table 6 here]

Teams of entrepreneurs are 12.44% more likely to strike a deal than solo entrepreneurs,

providing initial evidence that investors are hesitant to invest in ventures with a higher potential

for key man risk. The probability of striking a deal is higher (15%) for ventures in which the

average entrepreneur age is under 25 and lower (11.2%) for entrepreneurs over 50. This provides

initial evidence of potential discrimination favoring (against) younger (older) entrepreneurs.

Alternatively, a rational explanation suggests that the mentorship provided by investors may be of

21

greater (lesser) value to younger (older) entrepreneurial teams. There is weak evidence that teams

with members from the southern U.S. are more likely to strike a deal. Finally, teams that include

entrepreneurs with formal business degrees (Bachelors or Masters degrees in business

administration) have a 9.8% higher probability of reaching a deal. The results are similar for

entrepreneurial teams with members who have attended a top 25 (as defined by 2015 U.S. News

and World Reports rankings) higher education institution.

Conditional on reaching a deal, the cross-sectional differences in the percentage of received

valuation relative to sought valuation presented in the second model are predominantly

insignificant. The only significant (non-parametric) difference occurs for venture teams with

members from top 25 universities (6.7% greater valuation).

We measure the entrepreneur’s requested valuation as the ratio of the initial investment request

to the percentage of equity offered before negotiation begins. We examine the determinants of

requested valuation in Table 7. Not surprisingly, ventures with sales and prior investment are

positively correlated with requested valuations. Interestingly, ventures with lower profit margins

also request higher valuations. This result may reflect the entrepreneurs’ expectation of cost

savings from future margin expansion.

[Insert Table 7 here]

Consistent with the findings of Poczter and Shapsis (2016), Model 3 reports that teams with

female entrepreneurs request significantly lower valuation.22 Models 4 and 5 show similar effects

for teams with black entrepreneurs and teams with an average age of less than 25. When controlling

for industry fixed effects in Model 11, we observe that negative impact of age on requested

22 Unreported results are nearly identical using a variable for teams comprised of all female members rather than one

or more female members.

22

valuation is economically larger than the effect of race and gender combined.23 Model 8-11 include

indicator variables when an entrepreneur team includes a member with a degree from a top 25

university. The coefficient is highly significantly and suggests that entrepreneurs with more

prestigious educational backgrounds place higher valuations on their businesses.

Next, we examine the factors that influence angels’ investment decisions in logit regressions

where the dependent variable indicates that an investor and entrepreneur reach a mutual agreement

on a deal. In addition to the variables noted previously, we also include season fixed effects. This

helps control for the varying mix of investors from season to season and features that are specific

to certain seasons, such as the 5% equity or 2% royalty clause in the participation agreement during

the first four seasons. We begin by reporting a model that includes only financial factors. The

results in the first model of Table 8, Panel A indicate that higher margins are an important

determinant of making a deal with investors. Additional financial variables are introduced in

subsequent models. Prior investment and venture age are negatively correlated with the likelihood

that a deal is reached, which suggests that investors are hesitant to enter deals with older firms

with large sunk costs and/or previous outside investment. Consistent with the notion that

intellectual property protection is valuable for entrepreneurial ventures, the presence of patents

significantly increases the likelihood that an offer is accepted.

[Insert Table 8 here]

Subsequent models in Table 8 consider the impact of non-financial characteristics on the

likelihood that a deal is struck. Model 3 suggests that teams with female members are slightly more

likely to strike a deal but the results are not significant. When we control for entrepreneur race,

23 Unreported results using BISG race variables are qualitatively similar.

23

only teams with East Asian members face a significantly higher probability of striking a deal.24

The control for age in Model 5 indicates that investors are reluctant to invest in older (average age

above 50) entrepreneurial teams. Whether or not this result is due to age discrimination is unclear.

We return to the topic of age in homophily analyses in the following section. Confirming the

univariate results, individual entrepreneurs are less likely to strike a deal, confirming investors’

concerns with respect to key man risk. Geographical residence of the entrepreneurs in Model 7

does not enter significantly, but investors show insignificant preferences for teams from southern

U.S. Model 8 examines the impact of entrepreneur education. The results suggest that teams with

members who have degrees from top 25 universities or business degrees have a higher probability

of striking a deal but coefficients on these variables are not significant. When we incorporate all

of the financial and non-financial variables in Model 9, the presence of patents and higher (lower)

margins (prior investment) are associated with higher deal probability, while age and teams are

also significant. Model 11 incorporates 2-digit SIC industry fixed effects with similar results.

Conditional on reaching a deal between investors and the venture, we turn our attention to the

ratio of valuation the venture receives in the deal relative to that which was initially requested. We

report the result of Tobit models in Table 8, Panel B. In addition to the variables used in Panel A,

we include number of investors remaining in the negotiation when a deal is reached to control for

the entrepreneur’s relative bargaining power. We also control for deals that include debt or

royalties and deals reached on a counteroffer. To address selection bias, all models also include

the inverse mills ratio from Panel A, Model 11.25 The number of bidders remaining is highly

significant in each model, which emphasizes the important role of bargaining power in valuation.

24 Combining the East Asian and South Asian classification (as is the case in the BISG race definitions) produces

similar results. 25 Results are similar using the inverse mills ratio from logit Model 9.

24

In Model 2 we include additional financial variables. Patent protection is positively correlated with

valuation, which provides further support for the benefits of intellectual property protection.

Next, we turn to the impact of non-financial variables on valuation. We do not find significant

valuation differences based on entrepreneur gender (Model 3). However, Model 4 indicates that

entrepreneurial teams with blacks or Latinos receive lower valuations. Age, number of team

members, and place of residence are all uncorrelated with valuation in Models 5-7. Top 25

education also enters insignificantly, but venture teams with a business education receive lower

valuations. Given the expertise and mentorship that the investors bring to a venture, this result may

reflect reduced value investors place on entrepreneurs’ business education. Alternatively,

entrepreneurs with formal business education may overvalue their businesses when opening the

negotiation.

2. Homophily

Investors may prefer working with entrepreneurs who they identify with more closely.

Anecdotally, we have observed Shark Tank investors saying things like “you remind me of myself”

to entrepreneurs.26 In this section, we consider the impact of homophily, which is the tendency to

associate with others who are similar, on angel investment outcomes. Identifying homophily in

our empirical setting is challenging for a couple of reasons. First, the population of investors is

limited. Of the investors who have appeared in more than ten episodes, only one is black (Daymond

John) and none are Latino or Asian.27 Additionally, there are only two female investors (Barbara

Corcoran and Lori Greiner) and all of the investors are married. For these reasons, we limit the

variables we use to examine homophily to gender, race, geographic residence, education level, and

26 For example, see Tereson Dupuy of FuzziBunz Interview at Shark Tank Blog. 27 See The Case of the Missing Latina/o Shark at Huffington Post.

25

age. For the first three variables, we calculate proportion of entrepreneurs in the venture that match

the investor’s characteristic. For educational level and age, we calculate a numeric difference

between the value of the investor and entrepreneurial team average.28

Univariate statistics are presented in Table 9, Panel A. Of the 2,801 investor-venture pairs,

8.95% have some level of female homophily. Race (black) homophily is present for 1.45% of the

observations (Daymond John and Troy Carter as investors).29 We consider several measures of

shared geography. Investor region of origin (current residence) exhibits homophily in 11%

(16.57%) of pairs. We also create an indicator variable set to one when the investor state of origin

or residence matches the state of residence of any of the entrepreneurial team members (26.95%

of observations). The investor is on average 16.31 years older than the entrepreneurial team and

holds a higher education level.

[Insert Table 9 here]

Table 9, Panel B examines cross-sectional differences when homophily is present. There are

no significant differences in the likelihood that an offer is extended or offer valuation when an

investor is of the same gender as the entrepreneurial team. Race homophily does not appear to be

present in our multivariate results, but results may be affected by the small sample size (for

example, 9 observations in the black BISG results). Interestingly, investors are 3.66% more likely

to make an offer when the entrepreneurial team has a lower average education level. Non-

parametric tests in Model 2 also report higher valuations for lower entrepreneurial education levels

relative to the investor. No univariate differences in homophily among place of residence can be

seen. Reiterating the unconditional results in Table 6, there is evidence to suggest that younger

28 For education level we encode high school or unknown as 0, associates as 1, bachelors as 2, masters as 3, and

doctorates as 4. 29 This percentage decreases to 1.08% when using BISG race variables.

26

entrepreneurs (relative to investor age) have a higher probability of receiving investment and are

offered higher valuations.

[Insert Table 10 here]

To examine homophily further, we report the results of logit and Tobit models in Table 10. To

address endogeneity, each model controls for a variety of fixed-effect combinations including

season, industry, and investor. Logit Models 1-4 confirm that investors favor ventures with higher

levels of sales (sales/requested valuation), higher profit margins, younger ventures, and intellectual

property protection. While there is no evidence of gender-based homophily, the results suggest

that black investors prefer making offers to teams of black entrepreneurs. Controlling for investor

fixed effects, investors show homophily towards entrepreneurs from a shared U.S. state of

residence or origin. Age also enters negatively in each logit specification. In contrast to Table 8,

this result considers relative rather than absolute age. Thus, it is less likely that discrimination is

driving the result. Alternatively, angel investors are able to offer more mentorship to younger

entrepreneurs, which influences their decision to invest.

In Models 5-8 we examine the maximum valuation offered during the negotiation process by

each bidding investor relative to that requested by the venture. Not surprisingly, greater

entrepreneurial bargaining power is associated with higher maximum valuations. We find weak

evidence that higher levels of prior investment are associated with greater valuation, which is

consistent with existing investors’ unwillingness to accept lesser valuations and dilution.

Entrepreneurial teams receive lower valuations in this analysis. If larger entrepreneurial teams

introduce higher coordination costs or incentive conflicts, investors may lower their offers

accordingly. Interestingly, investors offer lower valuations to entrepreneurs with relatively higher

27

education levels. If investor utility from mentoring is lower when entrepreneurs have greater

intellectual capital than the investor, lower valuations may result.30

In aggregate, unconditional and homophily results indicate that investors act rationally in their

decision making and focus on sales, profit margins, newer ventures, and intellectual property

protection. However, Table 10 provides evidence that these angel investors are more willing to

invest in ventures where mentorship is valuable to younger entrepreneurs, among black

entrepreneur-investor pairs, and with shared state geography.

V. Conclusion

This paper studies angel investors’ investment decisions using hand-collected data from the

reality show Shark Tank. On average, 50% of the companies on this show strike a deal with an

investor. The investment amount requested by the entrepreneurs varies from $10,000 to

$5,000,000, and the equity percentage that entrepreneurs are willing to sell varies from 2.5% to

100% of the company. Among invested companies, angel investors tend to believe that

entrepreneurs overestimate the value of their companies, as investors ultimately offer nearly $0.70

of each dollar requested by the entrepreneur.

The factors influencing angel investors’ decisions can be divided into two categories: 1)

financial factors: profit margin, past sales relative to requested valuation, prior investment amount,

and patents held/pending and 2) entrepreneur characteristics: gender, race, age, place of residence,

and education. We find that angel investors are fairly rational and focus more on the financial

factors than entrepreneur characteristics. Even though there are only seven main investors on the

show, which is far from a perfectly competitive capital market, we find no evidence to support

30 As an additional robustness check, we replicate the analyses of Table 10 using only bids on subsequently verified

deals. The results are qualitatively similar.

28

discrimination against female entrepreneurs. However, we do find some evidence of bias against

against blacks and Latinos with respect to offer valuation. Angel investors also appear to prefer

working with young entrepreneurs. Homophily analyses indicate that this is driven by increased

benefits of mentorship with less experienced entrepreneurs. High profit margin, patents

held/pending, and teams of entrepreneurs appear to have a positive effect on entrepreneurs looking

for an investment.

The data collected from the Shark Tank program provide a unique laboratory to study the

impact of both financial and non-financial characteristics of investors and entrepreneurs as well as

the negotiation process between these two parties. While the predominant rationality in the

decision making of these investors is encouraging, potential discrimination against entrepreneurs

with respect to age, race, and education should be continuously examined to avoid socially induced

suboptimal capital allocation.

29

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33

Figure 1: Data Construction Process

Figure 1 provides an example (Titin: Season 6, Episode 7) of how a shark-company negotiation is codified.

Entrepreneur Name: Patrick Whaley

Gender: Male, Age: 26

State: GA, Marital Status: Married

Education Level: BS, Georgia Institute of Technology

Company Founded: 2005, Number of Entrepreneurs: 1

Age, State, Education, Company Founding Date Obtained from

https://www.linkedin.com/in/whaleypatrick

00:01:55 I’m here seeking $500,000 for a 5% equity stake in my company.

Requested Investment: $500,000, Requested Percentage: 5%

Requested Valuation: $10,000,000

00:02:00 Titin is a patented, form-fitting, weighted compression gear…

Patent: Yes, Industry Code: 3949 (Sport and Athletic Goods)

00:03:54 Whaley: So, last month, we almost broke $1

million in revenue.

00:05:33 Whaley: $1.4 million in purchase orders that I

can’t fulfill…

Sales: $2,400,000

00:04:36 Whaley: We burned through the entire

investment within six months.

00:04:39 Robert: How much was the investment?

00:04:41 Whaley: It was about $1 million.

Prior Investment: $1,000,000

00:04:56 Whaley: We’re about 30% net profit.

Profit Margin: 0.30

00:06:58 Mark: I’m out.

Mark ID-Venture ID: Out (Action 1)

00:07:36 Robert: I’m out.

Robert ID-Venture ID: Out (Action 2)

00:07:50 Lori: But it’s not hitting me as something that I

want to invest in, and so, for that reason, I’m

out.

Lori ID-Venture ID: Out (Action 3)

00:08:11 Kevin: I’ll give you $500,000 for 15%.

Kevin ID-Venture ID: In (Action 4)

Investment Amount: $500,000

Equity Percentage: 15%

Valuation Offered: $3,333,333

00:09:19 Daymond: So I’m… I’m willing to give you

the $500K for 20%.

Daymond ID-Venture ID: In (Action 5)

Investment Amount: $500,000

Equity Percentage: 20%

Valuation Offered: $2,500,000

00:13:36 Whaley: I’m fine with a $5 million valuation

if we can get there, we can make this thing a

stratospheric success.

00:13:53 Robert: He made you a counter.

00:13:55 Kevin: Mmm—10%? It’s not enough equity.

It’s too small.

Kevin ID-Venture ID: In (Action 6)

Counteroffer: Yes

Investment Amount: $500,000

Equity Percentage: 10%

Valuation Offered: $5,000,000

00:14:27 Whaley: Daymond… I’d love to take your

offer.

Action 5 accepted, action 4 and 6 rejected.

34

Table 1: Episode Summary Statistics

Table 1 presents summary statistics in the reality show Shark Tank by season. Verifiable deals are those that have appeared in “Shark Tank Updates” or on the

spin-off program Beyond the Tank. All dollar results are presented in 2015 dollars.

1 2 3 4 5 6 7 Total

First Episode Date (D/M/Y) 8/9/2009 3/20/2011 1/20/2012 9/14/2012 9/20/2013 9/26/2014 9/25/2015 8/9/2009

Last Episode Date (D/M/Y) 2/5/2010 5/13/2011 5/18/2012 5/17/2013 5/16/2014 5/15/2015 5/20/2016 5/20/2016

Episodes (N) 14 9 15 26 29 29 29 151

Investors (N) 5 7 6 6 8 7 9 14

Ventures (N) 64 36 60 103 116 116 116 611

Entrepreneurs (N) 85 48 83 161 181 185 185 928

Deals (N) 27 18 27 50 60 63 65 310

Deals as a % of Ventures 42.188% 50.000% 45.000% 48.544% 51.724% 54.310% 56.034% 50.736%

Verifiable Deals (N) 13 7 16 24 22 25 5 112

Verifiable Deals as a % of Deals 48.148% 38.889% 59.259% 48.000% 36.667% 39.683% 7.692% 36.129%

Venture Refuses all Bids (N) 6 7 8 14 13 19 12 79

Refusals as a % of all Ventures (N) 9.375% 19.444% 13.333% 13.592% 11.207% 16.379% 10.345% 12.930%

Bids per Negotiation (Mean) 2.7576 2.4400 2.3429 2.2344 2.1096 2.4756 2.9740 2.4756

Counteroffers per Negotiation (Mean) 0.6061 0.6400 0.6286 0.3438 0.4110 0.5732 0.8052 0.5630

# of Investors Left out of Deal (Mean) 0.5556 0.7222 0.8889 0.7000 0.7667 0.9206 0.9692 0.8194

# of Investors Left out of Verifiable Deal (Mean) 1.0000 0.5714 0.6875 0.7083 0.9545 0.8800 1.6000 0.8571

# of Investors per Deal (Mean) 1.6667 1.6111 1.3333 1.4000 1.3500 1.2540 1.4000 1.3903

# of Investors per Verifiable Deal (Mean) 1.6923 1.4286 1.3125 1.2917 1.2727 1.3200 1.2000 1.3482

Requested Investment (Mean) $260,470 $184,782 $251,868 $256,569 $263,154 $325,127 $316,164 $277,867

Requested Investment (Median) $165,718 $103,261 $105,369 $129,041 $152,614 $150,178 $200,000 $152,614

Deal Investment (Mean) $215,024 $240,036 $170,152 $191,374 $271,653 $367,340 $320,708 $272,828

Deal Investment (Median) $165,718 $108,695 $105,369 $154,850 $165,332 $150,178 $200,000 $154,850

Verifiable Deal Investment (Mean) $218,407 $118,012 $143,566 $171,367 $373,674 $308,766 $170,000 $239,868

Verifiable Deal Investment (Median) $165,718 $86,956 $105,369 $154,850 $165,332 $200,237 $100,000 $153,732

Shark Tank Season

35

Table 2: Investor Summary Statistics

Table 2 provide summary statistics for investor characteristics (Panel A) and investor actions (Panel B) within each episode of Shark Tank. Net

worth is obtained from public sources including Fortune. Dollar values are reported in 2015 dollars.

Panel A

Panel B

Investor Name Episodes Gender Race Age (2015) State (Origin) State (Residence) Education Married Net Worth (2015)

Kevin O'Leary 151 Male White 55 QC MA MBA Yes $400,000,000

Robert Herjavec 145 Male White 47 N/A (Croatia) ON BA Yes $200,000,000

Mark Cuban 131 Male White 51 PA TX BS Yes $2,700,000,000

Daymond John 110 Male Black 40 NY NY Yes $250,000,000

Lori Greiner 97 Female White 40 IL IL BA Yes $50,000,000

Barbara Corcoran 93 Female White 60 NJ NY BA Yes $80,000,000

Kevin Harrington 18 Male White 52 OH FL Yes $450,000,000

Chris Sacca 4 Male White 34 NY CA JD Yes $1,070,000,000

Jeff Foxworthy 2 Male White 52 GA GA Yes $100,000,000

Nick Woodman 2 Male White 34 CA CA BA Yes $1,610,000,000

Ashton Kutcher 2 Male White 31 IA CA Yes $140,000,000

John Paul DeJoria 1 Male White 64 TX CA Yes $3,300,000,000

Steve Tisch 1 Male White 60 NJ CA BA No $1,300,000,000

Troy Carter 1 Male Black 36 PA CA Yes $30,000,000

Ventures

Investor Name Bid Deals Mean Max

Robert Herjavec 572 153 26.75% 7.19% 1.31% 9.80% 64 41.83% 51.56% $290,031 $5,005,935 17.69%

Kevin O'Leary 568 202 35.56% 5.94% 9.90% 31.19% 46 22.77% 65.22% $172,831 $1,032,331 15.60%

Daymond John 439 130 29.61% 8.46% 0.00% 6.92% 66 50.77% 42.42% $148,864 $552,392 26.74%

Lori Greiner 372 112 30.11% 8.04% 8.04% 11.61% 76 67.86% 43.42% $191,975 $1,000,000 19.16%

Barbara Corcoran 367 91 24.80% 12.09% 0.00% 6.59% 57 62.64% 42.11% $112,802 $386,674 26.17%

Mark Cuban 351 107 30.48% 5.61% 1.87% 7.48% 97 90.65% 52.58% $251,871 $2,034,856 20.96%

Kevin Harrington 80 16 20.00% 0.00% 0.00% 18.75% 12 75.00% 58.33% $106,914 $276,196 34.76%

Chris Sacca 16 6 37.50% 0.00% 0.00% 0.00% 4 66.67% 75.00% $180,000 $300,000 5.42%

Jeff Foxworthy 8 2 25.00% 0.00% 0.00% 50.00% 2 100.00% 50.00% $40,761 $54,348 41.67%

Nick Woodman 8 2 25.00% 50.00% 0.00% 50.00% 2 100.00% 100.00% $87,604 $100,119 11.25%

Ashton Kutcher 8 2 25.00% 0.00% 0.00% 0.00% 2 100.00% 100.00% $100,000 $100,000 13.00%

Partnership

Rate

Bid

Rate

Mean

Equity Pct

Royalty

Frequency

InvestmentVenture

Decisions

Deal Accept

Rate

Contingency

Rate

Debt

Frequency

36

Table 3: Entrepreneur Summary Statistics

Table 3 provides summary statistics of entrepreneur characteristics from the reality show Shark Tank. If not stated

explicitly on the show, characteristics such as geographical residence, age, and education status are manually obtained

from public sources including LinkedIn. Race is approximated both manually (East Asian includes individuals who

are Chinese, Japanese, Vietnamese, etc. while South Asian includes Indian, Pakistani, etc.) and using the U.S Census

Bureau’s Bayesian improved surname geocoding method (BISG) with the entrepreneur’s surname and venture zip

code.

N N

Male 616 Female 312

White 793 Black 74

East Asian 29 South Asian 18

Latino 14

White 467 Black 60

Asian 40 Latino 60

Indeterminable 301

Under 25 78 25 to 35 403

36 to 50 316 51 to 65 51

Over 65 8 Unknown 72

Northeast 187 Midwest 103

South 234 West 385

Outside USA 24 Unknown USA 3

California 240 New York 89

Texas 60 Florida 58

Illinois 42 Utah 35

Pennsylvania 32 Colorado 30

Oregon 25 Other/Outside/Unknown 317

Married 314 Married Females 146

Unmarried 16 Unknown 598

HS or Unknown 320 Associates 17

Bachelors 390 Masters 184

Doctorate 17 Female Postgraduates 67

Business Degree 90 Top 10 School 64

Top 25 School 128 Top 50 School 182

Race

Gender

Education Characteristics

Education Level

Marital Status

Geography (Region)

Age

Race (BISG)

Geography (State)

37

Table 4: Venture Summary Statistics

Table 4 provides summary statistics of venture level data. Panel A shows mean, median, and standard deviation for

data collected directly from each episode of Shark Tank. We adjust Prior Investment and Patent to be zero (zero fill)

if these variables are not specified during the episode. If Margin is not specified, we use the 2-digit SIC industry

average of other ventures presented in Shark Tank episodes. Panel B includes industrial representation of all ventures

including the number of firms, deals struck with firms, and mean deal valuation within each 2-digit SIC. Dollar values

are reported in 2015 dollars.

Panel A

Panel B

N Mean Median Stdev

Requested Investment 611 $277,867 $152,614 $457,520

Requested Equity % 611 16.80% 15.00% 9.58%

Requested Valuation 611 $2,490,064 $1,017,428 $4,236,117

ln(Requested Valuation) 611 13.9353 13.8328 1.2045

Sales 592 $541,868 $145,841 $1,107,353

Sales/Requested Valuation 592 0.2672 0.1337 0.4227

Prior Investment 217 $687,420 $163,043 $1,506,697

Prior Investment (Zero Fill) 611 $244,141 $0 $955,119

Margin 434 50.58% 57.04% 31.45%

Margin (Industry Mean Fill) 606 48.67% 54.82% 32.81%

Patent 147 0.9252 1.0000 0.2640

Patent (Zero Fill) 611 0.2226 0.0000 0.4163

Company Age 611 2.7381 2.0000 3.5487

Number of Entrepreneurs 611 1.5188 1.0000 0.6279

Company Based in Northeast 611 0.1893 0.0000 0.3800

Midwest 611 0.1137 0.0000 0.3119

South 611 0.2594 0.0000 0.4303

West 611 0.4182 0.0000 0.4846

Two-digit SIC Industry Description Ventures Deals Deal %Mean Deal

Valuation

20 Food and Kindred Products 72 34 47.22% $852,022

51 Wholesale Trade-Nondurable Goods 62 31 50.00% $1,055,337

39 Miscellaneous Manufacturing Industries 50 28 56.00% $923,715

50 Wholesale Trade-Durable Goods 49 23 46.94% $2,070,480

28 Chemicals and Allied Products 39 20 51.28% $1,572,569

23 Apparel and other Finished Products Made from Fabrics and Similar Materials 37 16 43.24% $1,219,154

73 Business Services 29 14 48.28% $3,566,490

72 Personal Services 26 9 34.62% $318,537

79 Amusement and Recreation Services 25 12 48.00% $3,751,842

36 Electronic and other Electrical Equipment and Components, except Computer Equipment 24 17 70.83% $1,629,179

30 Rubber and Miscellaneous Plastics Products 19 11 57.89% $594,637

56 Apparel and Accessory Stores 15 5 33.33% $356,259

27 Printing, Publishing, and Allied Industries 13 8 61.54% $1,902,085

37 Transportation Equipment 11 8 72.73% $1,684,488

26 Paper and Allied Products 9 6 66.67% $882,697

34 Fabricated Metal Products, except Machinery and Transportation Equipment 9 7 77.78% $1,006,715

35 Industrial And Commercial Machinery And Computer Equipment 9 4 44.44% $19,601,738

31 Leather and Leather Products 9 5 55.56% $3,187,213

82 Educational Services 9 6 66.67% $701,388

38

Table 5: Negotiation Process Summary Statistics

Table 5 displays summary statistics for the bidding process for each venture pitched during episodes of Shark Tank.

A bid is recorded if investor(s) makes a specific offer. We record the investment amount, equity percentage, and any

contingencies/other characteristics (debt, royalties) associated with the bid. If the parameters of the deal are specified

by the entrepreneur after the initial requested investment, we record a counteroffer flag. Dollar values are reported in

2015 dollars.

N Mean Median Stdev

Received Bid 611 0.6367 1.0000 0.4814

Maximum Valuation Offered 380 $2,454,653 $731,008 $7,488,877

Number of Bidders 389 1.9434 2.0000 1.0035

Number of Bids 389 2.4756 2.0000 1.7913

Number of Counteroffers 389 0.5630 0.0000 0.7694

Struck Deal 611 0.5074 1.0000 0.5004

Investment 310 $272,828 $154,850 $423,716

Valuation 304 $1,643,686 $618,011 $4,467,553

% of Sought Valuation 304 0.6928 0.6061 0.4180

Maximum Valuation Offered 309 $2,050,453 $724,637 $5,582,986

Number of Bidders 310 2.0677 2.0000 1.0233

Number of Bids 310 2.7097 2.0000 1.8555

Number of Counteroffers 310 0.5581 0.0000 0.7726

Number of Investors 310 1.3903 1.0000 0.7008

Counteroffer Accepted 310 0.1323 0.0000 0.3393

Winning Bid Position 310 5.4290 5.0000 2.1602

Bidders Remaining at Winning Bid 310 2.4871 2.0000 1.3456

Deal with Contingency 310 0.0677 0.0000 0.2517

Deal with Debt 310 0.0258 0.0000 0.1588

Deal with Royalties 310 0.0742 0.0000 0.2625

Negotiations with Bids

Struck Deals

39

Table 6: Univariate Analysis of Investment and Valuation

Table 6 displays cross-sectional differences and test statistics across a variety of entrepreneurial non-financial statistics

including gender, race, marital status, and the size of the entrepreneurial team. The second page of Table 6 includes

statistics for age, geographical residence, and education. Race is approximated both manually (East Asian includes

individuals who are Chinese, Japanese, Vietnamese, etc. while South Asian includes Indian, Pakistani, etc.) and using

the U.S Census Bureau’s Bayesian improved surname geocoding method (BISG) with the entrepreneur’s surname and

venture zip code.. Data is recorded directly from information provided by presenting entrepreneurs on Shark tank. If

a specific data field is not provided, we use LinkedIn and other online sources to obtain these data. ***, **, and *

indicate statistical significance at the 1%, 5%, and 10% levels respectively.

(1) Deal (2) % of Sought Valuation

N Mean Median N Mean Median

Female Member 237 0.5285 1.0000 127 0.6719 0.6061

No Female Member 346 0.4932 0.0000 177 0.7078 0.6000

Difference 0.0353 1.0000 -0.0359 0.0061

All Female Members 153 0.5359 1.0000 81 0.6853 0.6579

Male Members 458 0.4978 0.0000 223 0.6955 0.6000

Difference 0.0381 1.0000 -0.1885 0.0579

East Asian Member 23 0.6522 1.0000 15 0.6260 0.6000

No Asian Member 588 0.5017 1.0000 289 0.6962 0.6061

Difference 0.1505 0.0000 -0.0702 -0.0061

South Asian Member 15 0.6667 1.0000 10 0.6841 0.6125

No South Asian Member 596 0.5034 1.0000 294 0.6931 0.6061

Difference 0.1633 0.0000 -0.0090 0.0064

Black Member 60 0.4500 0.0000 26 0.5710 0.5227

No Black Member 551 0.5136 1.0000 278 0.7042 0.6155

Difference -0.0636 -1.0000 -0.1332 -0.0928

Latino Member 13 0.6923 1.0000 9 0.6056 0.6061

No Latino Member 598 0.5033 1.0000 295 0.6954 0.6061

Difference 0.1890 0.0000 -0.0898 0.0000

Asian Member 32 0.6563 0.0000 21 0.7061 0.6667

No Asian Member 579 0.4991 1.0000 283 0.6918 0.6000

Difference 0.1571 * -1.0000 * 0.0143 0.0667

Black Member 51 0.3529 0.0000 18 0.7073 0.6000

No Black Member 560 0.5214 1.0000 286 0.6919 0.6155

Difference -0.1685 ** -1.0000 ** 0.0155 -0.0155

Latino Member 45 0.5556 1.0000 25 0.7780 0.6667

No Latino Member 566 0.5035 1.0000 279 0.6851 0.6061

Difference 0.0520 0.0000 0.0928 0.0606

White Member 339 0.5044 1.0000 166 0.6702 0.6250

No White Member 272 0.5110 1.0000 138 0.7199 0.6000

Difference -0.0066 0.0000 -0.0497 0.0250

Married 230 0.5478 1.0000 114 0.6689 0.6061

Unmarried or Unspecified 381 0.4829 0.0000 180 0.7092 0.6061

Difference 0.0649 1.0000 -0.0404 0.0000

Married Female Member 128 0.5625 1.0000 70 0.6800 0.6458

No Married Female Member 483 0.4928 0.0000 234 0.6966 0.6000

Difference 0.0697 1.0000 -0.0166 0.0458

Race (BISG)

Race

Gender

Marital Status

40

Table 6: Univariate Analysis of Investment and Valuation (Continued)

Individual Entrepreneur 327 0.4495 0.0000 146 0.6960 0.6250

Team of Entrepreneurs 284 0.5739 1.0000 158 0.6898 0.6000

Difference -0.1244 *** -1.0000 *** 0.0062 0.0250

Average Age Under 25 37 0.6486 1.0000 24 0.6936 0.6155

Average Age 25 or Over 574 0.4983 0.0000 280 0.6927 0.6061

Difference 0.1504 * 1.0000 * 0.0009 0.0095

Average Age Under 50 540 0.5204 1.0000 276 0.6973 0.6061

Avarage Age 50 or Over 71 0.4085 0.0000 28 0.6485 0.6125

Difference 0.1119 * 1.0000 * 0.0488 -0.0064

Member from Northeast 127 0.5197 1.0000 63 0.7425 0.6250

No Member from Northeast 484 0.5041 1.0000 241 0.6798 0.6061

Difference 0.0156 0.0000 0.0627 0.0189

Member from Midwest 74 0.4730 0.0000 35 0.6559 0.6000

No Member from Midwest 537 0.5121 1.0000 269 0.6976 0.6061

Difference -0.0391 -1.0000 -0.0417 -0.0061

Member from South 168 0.5655 1.0000 95 0.6395 0.6000

No Member from South 443 0.4853 0.0000 209 0.7170 0.6250

Difference 0.0801 * 1.0000 * -0.0775 -0.0250

Member from West 266 0.5038 1.0000 130 0.7049 0.6250

No Member from West 345 0.5101 1.0000 174 0.6837 0.6000

Difference -0.0064 0.0000 0.0212 0.0250

Member with Business Education 81 0.5926 1.0000 48 0.6455 0.6030

No Member with Business Education 530 0.4943 0.0000 256 0.7016 0.6155

Difference 0.0983 * 1.0000 -0.0561 -0.0125

Member with Top 25 School 107 0.5888 1.0000 61 0.7222 0.6667

No Member with Top 25 School 504 0.4901 0.0000 232 0.6854 0.6000

Difference 0.0987 * 1.0000 * 0.0368 0.0667 *

Average Education Greater than Bachelors 325 0.5108 1.0000 152 0.6696 0.6000

Average Education Less than or Equal to Bachelors 286 0.5035 1.0000 138 0.7192 0.6522

Difference 0.0073 0.0000 -0.0496 -0.0522

Member is Female Postgraduate 61 0.5246 1.0000 31 0.6802 0.5714

No Member is Female Postgraduate 550 0.5055 1.0000 273 0.6942 0.6250

Difference 0.0191 0.0000 -0.0140 -0.0536

Member is Female from Top 25 School 32 0.5625 1.0000 18 0.7031 0.7083

No Member is Female from Top 25 School 579 0.5043 1.0000 286 0.6921 0.6030

Difference 0.0582 0.0000 0.0109 0.1053

Member is Female with Business Education 16 0.6875 1.0000 11 0.7135 0.7500

No Member is Female with Business Education 595 0.5025 1.0000 293 0.6920 0.6000

Difference 0.1850 0.0000 0.0215 0.1500

Team

Geography

Age

Education

41

Table 7: Multivariate Analysis of Requested Valuation

Table 7 includes multivariate OLS analyses of financial and non-financial factors on the pre-negotiation venture valuation requested by entrepreneurs. T-statistics

calculated with standard errors clustered by season and industry (2-digit SIC code) are presented in parentheses. ***, **, and * indicate statistical significance at

the 1%, 5%, and 10% levels respectively.

(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11)

ln(Sales+1) 0.0719*** 0.0698*** 0.0716*** 0.0718*** 0.0690*** 0.0716*** 0.0731*** 0.0719*** 0.0667*** 0.0710*** 0.0747***

(4.2636) (3.6970) (4.4511) (4.1962) (4.8119) (4.2479) (4.6491) (4.3279) (4.0986) (4.7086) (3.2277)

Margin -0.7795*** -0.6864*** -0.7340*** -0.7823*** -0.7835*** -0.7807*** -0.7657*** -0.7631*** -0.6363** -0.6629*** -0.6084**

(-3.5682) (-2.7732) (-3.3649) (-3.6794) (-3.4497) (-3.5407) (-3.5809) (-3.2840) (-2.3294) (-2.7610) (-2.4643)

Venture Age 0.0256 0.0276

(1.1336) (1.1429)

ln(Prior Investment+1) 0.0335*** 0.0307*** 0.0290*** 0.0266***

(3.1630) (4.4589) (4.0566) (5.5128)

Patent 0.1603* 0.1020 0.1195 0.1223

(1.7246) (0.7105) (0.9027) (1.0023)

Female Member -0.4825*** -0.4705*** -0.4397*** -0.2894***

(-8.6259) (-4.4732) (-4.9731) (-2.8712)

East Asian Member 0.2262 0.2122

(1.4259) (0.6134)

Black Member -0.2845*** -0.2281 -0.2650** -0.3167**

(-4.7293) (-1.4075) (-2.2548) (-2.5622)

Latino Member 0.1645 0.2961

(0.6265) (0.6008)

South Asian Member 0.3223 0.1624

(1.3976) (0.3424)

Average Age Under 25 -0.7315*** -0.6333** -0.6474*** -0.6190**

(-3.1084) (-2.4692) (-2.6095) (-2.5636)

Average Age 50 or Over 0.1482 0.0999

(1.1136) (0.5021)

Venture has One Member -0.0463 -0.1414

(-0.4965) (-0.9096)

Member from Northeast 0.0243 0.0973

(0.2923) (0.8923)

Member from Midwest -0.0431 -0.0624

(-0.2473) (-0.1782)

Member from South -0.2014* -0.1312 -0.1361 -0.1236

(-1.6974) (-0.8222) (-1.0584) (-1.0377)

Member with Top 25 Education 0.4344*** 0.3434*** 0.3700*** 0.3183**

(7.4758) (2.7347) (3.6155) (2.5738)

Member with Business Education 0.0932 0.0163

(1.3715) (0.1324)

Intercept 13.2878*** 12.9977*** 13.4835*** 13.3075*** 13.3077*** 13.3217*** 13.3302*** 13.2169*** 13.3216*** 13.3289*** 12.5509***

(57.3696) (49.5795) (59.9115) (55.1463) (68.0968) (50.8365) (47.8469) (57.6936) (38.7970) (41.3809) (32.6349)

N 587 587 587 587 587 587 587 587 587 587 587

Season FE Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes

Industry FE (SIC2) No No No No No No No No No No Yes

Adjusted R2

0.1696 0.2004 0.2079 0.1728 0.1905 0.1685 0.1711 0.1857 0.2672 0.2671 0.2942

OLS Models: Requested Valuation in ln(2015 USD)

42

Table 8: Multivariate Analysis of Investment and Valuation

Table 8 includes multivariate analyses of financial and non-financial factors on the striking of a deal between investors and entrepreneurs (Panel A logit) and the

percentage of received valuation relative to requested valuation (Panel B Tobit) conditional on receiving a deal. Z-statistics calculated with standard errors clustered

by season and/or industry (2-digit SIC code) are presented in parentheses. ***, **, and * indicate statistical significance at the 1%, 5%, and 10% levels respectively.

Panel A

(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11)

Sales/Requested Valuation 0.3523 0.5198 0.3579 0.3570 0.3557 0.3629 0.3199 0.3532 0.4698 0.5137 0.7312

(0.9310) (1.3676) (0.9502) (0.9394) (0.9340) (0.9447) (0.8360) (0.8477) (1.1300) (1.3172) (1.5107)

Margin 1.2681** 0.8611 1.2570** 1.2925** 1.2843** 1.2626** 1.2439* 1.2911* 0.8750 0.8713 0.9659*

(2.0078) (1.5931) (1.9846) (1.9764) (2.0676) (2.0113) (1.9570) (1.9478) (1.4666) (1.5949) (1.7886)

Venture Age -0.0871*** -0.0738*** -0.0753** -0.0824**

(-2.7672) (-2.9517) (-2.5232) (-2.5001)

ln(Prior Investment+1) -0.0000*** -0.0000** -0.0000*** -0.0000**

(-2.6518) (-2.1009) (-2.7061) (-2.3835)

Patent 0.7336*** 0.7992*** 0.7736*** 0.6796***

(3.5768) (3.4747) (3.5982) (3.4953)

Female Member 0.1145 0.0453

(0.8209) (0.2379)

East Asian Member 0.7168*** 0.4528** 0.4884* 0.4816***

(2.8190) (2.0354) (1.8807) (4.2355)

Black Member -0.3652 -0.3316

(-1.1377) (-1.0372)

Latino Member 0.6319 0.8254

(1.1347) (1.2091)

South Asian Member 0.3869 0.2548

(0.8321) (0.6892)

Average Age Under 25 0.4579 0.2961

(1.3779) (0.7936)

Average Age 50 or Over -0.3936*** -0.2871* -0.3391* -0.3339**

(-4.9891) (-1.7084) (-1.7937) (-2.4173)

Venture has One Member -0.4723*** -0.3868 -0.4368** -0.4724**

(-2.6620) (-1.6125) (-2.0856) (-2.4818)

Member from Northeast 0.0753 0.1373

(0.4013) (0.5563)

Member from Midwest -0.0676 -0.0026

(-0.2264) (-0.0101)

Member from South 0.2655 0.3478

(1.1297) (1.4823)

Member with Top 25 Education 0.3916 0.2936

(1.2566) (0.8783)

Member with Business Education 0.4288 0.3753

(1.3622) (0.9395)

Intercept -0.8861*** -0.6411* -0.9333*** -0.8978*** -0.8014*** -0.5687* -0.9532*** -0.9765*** -0.5747** -0.3107 -13.0223***

(-2.8682) (-1.8374) (-3.0474) (-2.9214) (-2.9279) (-1.9030) (-3.2530) (-2.9444) (-2.0009) (-0.9446) (-11.2737)

N 587 587 587 587 587 587 587 587 587 587 565

Season FE Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes

Industry FE (SIC2) No No No No No No No No No No Yes

Pseudo R2

0.0382 0.0715 0.0390 0.0478 0.0449 0.0504 0.0416 0.0470 0.1025 0.0863 0.1192

Logit Models - Venture and Investor(s) Strike Deal

43

Table 8: Multivariate Analysis of Investment and Valuation

Panel B

(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11)

Sales/Requested Valuation 0.0374 0.0619 0.0368 0.0282 0.0383 0.0362 0.0568 0.0319 0.0595 0.0550 0.0867

(0.6131) (1.0085) (0.6070) (0.4919) (0.6024) (0.5731) (1.0021) (0.5383) (1.0686) (1.0663) (1.2828)

Margin -0.0689 -0.0846 -0.0669 -0.0582 -0.0679 -0.0649 -0.0717 -0.0646 -0.0690 -0.0611 -0.0370

(-0.4332) (-0.5213) (-0.4098) (-0.3902) (-0.4279) (-0.3997) (-0.4386) (-0.4088) (-0.4311) (-0.4067) (-0.2030)

Number of Investors in Deal 0.0655 0.0637 0.0649 0.0699 0.0650 0.0659 0.0724 0.0634 0.0726 0.0658 0.0699

(1.1315) (1.1326) (1.1049) (1.1947) (1.1204) (1.1472) (1.2092) (1.0955) (1.2398) (1.2599) (1.5900)

Bidders Remaining at Deal 0.0495*** 0.0487*** 0.0495*** 0.0508*** 0.0495*** 0.0502*** 0.0481*** 0.0527*** 0.0522*** 0.0541*** 0.0576**

(2.7612) (2.8466) (2.7598) (2.7456) (2.8268) (2.7918) (2.6133) (2.9107) (2.9625) (2.7474) (2.4822)

Debt or Royalty Deal 0.2060 0.2061 0.2041 0.2155 0.2075 0.2038 0.2044 0.2161 0.2217 0.2241 0.1737

(1.0955) (1.1881) (1.0548) (1.2084) (1.0950) (1.0644) (1.1346) (1.0799) (1.2723) (1.2701) (1.1560)

Counteroffer Accepted -0.0913** -0.0953** -0.0906** -0.0974** -0.0910** -0.0911** -0.0926** -0.0937** -0.1066** -0.0990** -0.0668

(-2.3325) (-2.2014) (-2.3610) (-2.3041) (-2.2281) (-2.3648) (-2.4763) (-2.2158) (-2.1644) (-2.2291) (-1.2549)

Inverse Mills Ratio (Logit Model 11) -0.0041 -0.0012 -0.0043 -0.0042 -0.0041 -0.0043 -0.0043 -0.0039** -0.0031 -0.0009 0.0734

(-1.4230) (-0.3018) (-1.2560) (-1.2878) (-1.4164) (-1.5570) (-1.2372) (-2.2242) (-0.7334) (-0.2146) (0.6720)

Venture Age -0.0097 -0.0065 -0.0085 -0.0146

(-1.6478) (-0.7963) (-1.2383) (-1.4330)

ln(Prior Investment+1) 0.0051 0.0044 0.0047 0.0008

(1.2168) (1.1822) (0.9934) (0.2856)

Patent 0.1032** 0.0865*** 0.0819** 0.1038

(2.5409) (2.6937) (2.1715) (1.2167)

Female Member -0.0103 0.0015

(-0.2883) (0.0694)

East Asian Member -0.0519 -0.0519

(-0.5857) (-0.5766)

Black Member -0.1689*** -0.1897*** -0.1518*** -0.1150**

(-4.0762) (-3.4191) (-4.3081) (-2.5321)

Latino Member -0.1932** -0.1530* -0.1547 -0.1314

(-2.0104) (-1.7520) (-1.5277) (-1.3485)

South Asian Member 0.0052 -0.0393

(0.0474) (-0.3143)

Average Age Under 25 0.0130 0.0219

(0.1919) (0.3709)

Average Age 50 or Over -0.0165 -0.0508

(-0.2299) (-0.6435)

Venture has One Member 0.0372 0.0540

(0.6239) (0.9735)

Member from Northeast 0.0610 0.0921

(0.8692) (1.4073)

Member from Midwest -0.0661 -0.0542

(-1.0184) (-0.7157)

Member from South -0.0805 -0.0496 -0.0593 -0.0390

(-1.5884) (-0.8999) (-0.9925) (-0.6101)

Member with Top 25 Education 0.0274 0.0302

(0.5212) (0.6126)

Member with Business Education -0.1071** -0.0855 -0.0972* -0.0813

(-2.1388) (-1.3972) (-1.6943) (-1.2214)

Intercept 0.2990*** 0.2632** 0.3041*** 0.3106*** 0.3022*** 0.2678*** 0.3283*** 0.2956*** 0.2522* 0.2978** 0.3622***

(3.2520) (2.2041) (3.2095) (3.1594) (3.2603) (2.7542) (3.4249) (3.1338) (1.8345) (2.4404) (11.6003)

N 296 296 296 296 296 296 296 296 296 296 296

Season FE Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes

Industry FE (SIC2) No No No No No No No No No No Yes

Pseudo R2

0.0983 0.1191 0.0985 0.1152 0.0985 0.1002 0.1123 0.1076 0.1576 0.1402 0.2755

Tobit Models - Percent of Initially Requested Valuation Received

44

Table 9: Univariate Analysis of Homophily in Investment and Valuation

Table 9 presents homophily for each investor-venture pair. Panel A displays the proportion of entrepreneur

characteristics that match the investor are shown for gender, race, and geography as well as the difference between

the investors and entrepreneur team average education level and age. Panel B presents cross-sectional differences and

associated test statistics for each of the homophily variables. ***, **, and * indicate statistical significance at the 1%,

5%, and 10% levels respectively.

Panel A

Panel B

N Mean Median Stdev

Gender (Female) 2,801 0.0895 0.0000 0.2673

Race (Black) 2,801 0.0145 0.0000 0.1167

BISG Race (Black) 2,801 0.0108 0.0000 0.0987

BISG Race (White) 2,801 0.4212 0.0000 0.4716

Education Level (Numeric Difference) 2,074 0.6922 0.0000 1.0457

Regional Geography (Investor Origin) 2,801 0.1100 0.0000 0.3129

Regional Geography (Investor Residence) 2,801 0.1657 0.0000 0.3718

Shared U.S. State (Investor Origin or Residence) 2,081 0.2695 0.0000 0.4438

Age (Difference in Years) 2,695 -16.3108 -17.0000 11.6539

Homophily Variables (Investor-Venture Pairs)

N Mean Median N Mean Median

Some Gender (Female) Homophily 307 0.3257 0.0000 99 0.6889 0.6061

No Gender (Female) Homophily 2,494 0.2927 0.0000 675 0.7389 0.5600

Difference 0.0330 0.0000 -0.0501 0.0461

Some Race (Black) Homophily 44 0.3864 0.0000 15 0.5415 0.5000

No Race (Black) Homophily 2,757 0.2949 0.0000 759 0.7363 0.5714

Difference 0.0915 0.0000 -0.1948 -0.0714

Some BISG Race (Black) Homophily 36 0.2778 0.0000 9 0.4121 0.4286

No or Indeterminable BISG Race (Black) Homophily 2,765 0.2966 0.0000 776 0.7463 0.5941

Difference -0.0188 0.0000 -0.3342 -0.1655 **

Some BISG Race (White) Homophily 1,300 0.2908 0.0000 357 0.7442 0.6000

No or Indeterminable BISG Race (White) Homophily 1,501 0.3011 0.0000 428 0.7410 0.5670

Difference -0.0104 0.0000 0.0032 0.0330

Entrepreneur(s) has Higher Education Level 1,722 0.2822 0.0000 454 0.7132 0.5000

Entrpreneur(s) has Same or Lower Education Level 1,079 0.3188 0.0000 320 0.7599 0.6000

Difference -0.0366 ** 0.0000 ** -0.0467 -0.1000 *

Some Shared Regional Geography (Origin) 308 0.2987 0.0000 88 0.9210 0.5527

No Shared Regional Geography (Origin) 2,493 0.2960 0.0000 697 0.7536 0.6000

Difference 0.0027 0.0000 0.1674 -0.0473

Some Shared Regional Geography (Residence) 464 0.3147 0.0000 136 0.6927 0.5600

No Shared Regional Geography (Residence) 2,337 0.2927 0.0000 649 0.7529 0.5833

Difference 0.0220 0.0000 -0.0602 -0.0233

Some Shared U.S. State 755 0.2887 0.0000 208 0.6842 0.5714

No Shared U.S. State 2,046 0.2991 0.0000 577 0.7635 0.5882

Difference -0.0104 0.0000 -0.0794 -0.0168

Entrepreneur(s) is Younger 2,443 0.3058 0.0000 694 0.7394 0.5714

Entrepreneur(s) is Same Age or Older 252 0.2222 0.0000 54 0.5982 0.4951

Difference 0.0835 *** 0.0000 *** 0.1412 0.0763 **

(2) Highest Offer vs Requested(1) Offer Made

45

Table 10: Multivariate Analysis of Homophily in Investment and Valuation

Table 10 displays multivariate homophily analysis of both receiving a bid (logit models 1 through 4) and maximum valuation offered relative to requested (Tobit

Models 5-8). Each observation is at the investor-venture level. Homophily variables are calculated as the difference between the investor and the average binary

(gender, race, and geography) or discrete (education, age) variables of the entrepreneurial team. Z-statistics calculated with standard errors clustered by investor

are presented in parentheses. ***, **, and * indicate statistical significance at the 1%, 5%, and 10% levels respectively.

(1) (2) (3) (4) (5) (6) (7) (8)

Sales/Requested Valuation 0.5745*** 0.5500*** 0.5736*** 0.5565*** 0.1133** 0.0702 0.1237*** -0.0332

(6.1709) (6.3332) (6.3056) (6.4732) (2.0354) (0.6841) (2.7212) (-0.1754)

Margin 0.5845*** 0.7572*** 0.5975*** 0.7409** -0.0017 -0.0554 0.0219 -0.2005

(3.4156) (2.6717) (3.4152) (2.5554) (-0.0183) (-0.5169) (0.2394) (-0.8445)

Venture Age -0.0319*** -0.0192** -0.0260*** -0.0126*** -0.0184** -0.0165** -0.0169** -0.0121*

(-3.6438) (-2.0146) (-5.2898) (-2.8100) (-2.1791) (-2.4081) (-1.9948) (-1.8451)

ln(Prior Investment+1) -0.0037 -0.0054 -0.0030 -0.0048 0.0151** 0.0109 0.0151** 0.0120*

(-1.3322) (-1.4750) (-1.3613) (-1.4524) (2.1637) (1.4891) (2.1484) (1.7117)

Patent 0.6748*** 0.6840*** 0.7029*** 0.7038*** 0.1300*** 0.0469 0.1412*** -0.0898

(4.6347) (6.2595) (5.0168) (6.5621) (5.2560) (0.6638) (6.8016) (-0.5457)

Venture has One Member -0.3464*** -0.3832*** -0.3469*** -0.3738*** 0.1418*** 0.1198*** 0.1257*** 0.1770*

(-4.2013) (-4.0858) (-4.1682) (-3.9822) (3.2594) (2.7488) (2.6837) (1.7843)

Bidders Remaining at Max Offer 0.0530*** 0.0682*** 0.0558*** 0.0693***

(4.0681) (3.1139) (4.5141) (2.9920)

Inverse Mills Ratio (Model 4) -0.0680 -0.1667 -0.0191 -0.4185

(-0.9848) (-1.0215) (-0.4199) (-1.0796)

Gender (Female) Homophily -0.0261 0.0270 0.3403 0.4289 -0.0750*** -0.0173 0.0088 -0.0470

(-0.1886) (0.2637) (1.0632) (1.4853) (-3.6178) (-0.4841) (0.0874) (-0.3082)

Race (Black) Homophily 0.5644*** 0.6505*** 0.3909*** 0.5512*** 0.0118 0.0906 0.0719** 0.0144

(5.9541) (5.4501) (2.8685) (6.1287) (0.2587) (0.9203) (2.3417) (0.0774)

Education Level Relative to Investor -0.0561 -0.0421 -0.1096 -0.0846 -0.0844*** -0.0807*** -0.0907*** -0.0880***

(-1.3515) (-1.1200) (-1.4525) (-1.0201) (-8.2455) (-4.5461) (-4.9951) (-2.8704)

Shared U.S. State -0.0368 -0.0398 0.3933*** 0.3852*** -0.0719** -0.0669* -0.0444 -0.1501*

(-0.3136) (-0.3748) (3.9246) (3.6031) (-2.0211) (-1.7985) (-0.5189) (-1.7811)

Age Relative to Investor -0.0080 -0.0117* -0.0122** -0.0170*** 0.0002 0.0004 -0.0022 0.0024

(-1.3770) (-1.9564) (-2.0019) (-2.7552) (0.0469) (0.0775) (-0.6178) (0.2618)

Intercept -1.2632*** -15.4958*** -1.2213*** -15.2008*** 0.4328* 0.7059 0.3305 1.3503

(-6.9507) (-32.5623) (-9.0763) (-32.5502) (1.8698) (1.1685) (1.5076) (1.3427)

N 2,159 2,139 2,159 2,137 628 628 628 628

Season FE Yes Yes Yes Yes Yes Yes Yes Yes

Industry FE (SIC2) No Yes No Yes No Yes No Yes

Investor FE No No Yes Yes No No Yes Yes

Pseudo R2

0.0406 0.0647 0.0509 0.0747 0.0617 0.1236 0.067 0.133

Logit - Investor Makes Offer to Venture Tobit - Investor Max Offer / Requested Valuation