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Entrepreneurs’ Facial Trustworthiness, Gender,

and Crowdfunding Success

Abstract

This study examines whether facial trustworthiness mitigates information asymmetry in crowdfunding.

Using photos of entrepreneurs who launched campaigns on Kickstarter, we construct a comprehensive

facial trustworthiness index by employing machine learning-based facial detection techniques. We find

that entrepreneurs who look more trustworthy are more likely to have their campaigns funded, as they

receive more in the amount pledged and attract more funders. We also find that the effect of facial

trustworthiness is more prominent for female entrepreneurs. Moreover, these results are not driven by

facial attractiveness. Overall, our findings suggest that facial trustworthiness alleviates funders’ concerns

about uncertainty in crowdfunding.

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1. Introduction

This study investigates the role of entrepreneurs’ facial trustworthiness in crowdfunding success. While it

is widely acknowledged that entrepreneurial activities contribute to economic growth, financial constraint

remains a major impediment to the action of entrepreneurship and the success of entrepreneurs (Chatterji

and Seamans 2012). Professional venture capital and angel investment can only be accessed by a small

number of ventures. Crowdfunding thus provides a new financing solution for a wide range of

entrepreneurs entering the venture market with limited product information or track records to attract

commercial funding. In recent years, crowdfunding has grown exponentially in market size and garnered

attention from both practitioners and scholars. According to Statista (2018), the total amount of reward-

based crowdfunding1 reached $919.3 million in the U.S. and $6,547.0 million globally in 2017.

Unlike traditional commercial funding or venture capital, crowdfunding allows a large group of

individuals to provide funds directly to the entrepreneurs on various crowdfunding platforms (e.g.,

Kickstarter), without standard financial intermediaries involved (Mollick 2014). However, information

about early stage ventures is generally self-reported by the entrepreneurs, with limited or no track records

to justify their credentials to potential funders (Calic and Mosakowski 2016). Due to the opaque

information environment in crowdfunding and the lack of monitoring from financial intermediaries,

crowdfunding may suffer from severe information asymmetry issues (Belleflamme, Omrani, and Peitz

2015; Chemla and Tinn 2017; Kalayci, Ekenel, and Gunes 2014; Strausz 2017). Other than reward

desirability, potential funders must thus evaluate the trustworthiness of the information that entrepreneurs

disclose to mitigate concerns over asymmetric information (Misztal 2013). Facial trustworthiness, defined

as a funder’s perception of entrepreneurs’ ability, benevolence, and integrity based on facial features, can

be influential in funding decisions. This paper investigates whether entrepreneurs with more trustworthy

facial characteristics are more likely to experience success in crowdfunding.

1 In reward-based crowdfunding, entrepreneurs promise investors a product that the proposed project aims to develop rather than

equity. Statista (2018) acquires the total reward-based crowdfunding statistics by focusing on reward-based crowdfunding and the

pre-financing of products, art, music, films, software, or scientific research, while excluding traditional venture capital

investment, equity-based crowdfunding, or lending-based crowdfunding.

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The psychology and neuroscience literature suggest that people are efficient in judging the

trustworthiness of others based on their facial features and tend to incorporate perceptions of facial

trustworthiness into subsequent social decision-making (Borkenau, Brecke, Möttig, and Paelecke 2009;

Todorov, Loehr, and Oosterhof 2010; Todorov, Olivola, Dotsch, and Mende-Siedlecki 2015). People

rapidly develop perceptions of facial trustworthiness (Porter, England, Juodis, Ten Brinke, and Wilson

2008), and these appearance-based perceptions of trustworthiness are highly correlated (Blankespoor,

Hendricks, and Miller 2017; Oosterhof and Todorov 2008; Rule, Krendl, Ivcevic, and Ambady 2013).

Prior research documents the implications of facial trustworthiness across various business settings

(Blankespoor et al. 2017; Duarte, Siegel, and Young 2012b; Rezlescu, Duchaine, Olivola, and Chater

2012), finding that in general people are more likely to trust an individual who appears more trustworthy

when making business decisions. Trust can mitigate expected moral hazard under asymmetric information

associated with business transactions (Al-Najjar and Casadesus-Masanell 2001) and may serve as a

“lubricant” to facilitate social transactions (Misztal 2013). Given the information asymmetry concerns in

crowdfunding markets, trust in entrepreneurs can alleviate funders’ concerns about the credibility of

entrepreneurs’ self-reported project information. Therefore, we hypothesize that funders are more likely

to fund a project developed by an entrepreneur who appears more trustworthy.

In addition, gender stereotypes in entrepreneurship advocates masculinity over femininity (García

and Welter 2013; Ogbor 2000). Women are often perceived to be less competent in innovation and

business development skills and tend to receive less venture resources when compared to male

entrepreneurs (Lerner and Almor 2002; Mitchelmore and Rowley 2013; Thébaud 2010). Due to such

stereotypes, funders on crowdfunding platforms may exhibit higher uncertainty concerns when evaluating

entrepreneurial ventures developed by women. Moreover, Seidman and Miller (2013) find that

individuals tend to pay more attention to the physical appearance of females than males when browsing

information on social networking sites. One possible explanation is that men are often evaluated based on

the combination of different personal traits while women’s appearance tends to be the focal point for

evaluation (Brownmiller 1984; Kaschak 1992). Thus, compared with their male counterparts, female’s

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facial trustworthiness may play a more important role in funders’ evaluations of entrepreneurial ventures

and their ultimate decision to fund or not. We therefore hypothesize that the positive association between

facial trustworthiness and crowdfunding success is strengthened if the entrepreneur is female.

To examine our hypotheses, we adopt a web crawler to extract detailed crowdfunding information

on 1,770 technology-related projects on Kickstarter, one of the most popular and successful crowdfunding

platforms (Strausz 2017).2

In technology-related entrepreneurial ventures, information asymmetry

problems are more pronounced due to the requisite secrecy of development techniques, especially in early

stages (Kousari 2011). We identify entrepreneurs’ headshot pictures and use machine learning-based

facial detection techniques (Dalal and Triggs 2005; Kazemi and Sullivan 2014; Sagonas, Tzimiropoulos,

Zafeiriou, and Pantic 2013) to measure four distinctive facial features that have been found in

neuroscience and psychology research to be associated with trustworthiness: inner eyebrow ridge, chin

angle, facial roundness, and length of the lip-to-nose distance (Dotsch and Todorov 2012; Enlow and

Hans 1996; Robinson, Blais, Duncan, Forget, and Fiset 2014; Todorov, Baron, and Oosterhof 2008;

Vernon, Sutherland, Young, and Hartley 2014).

Our empirical results show that entrepreneurs’ facial trustworthiness is positively associated with

crowdfunding success, as measured by 1) the likelihood of campaign being fully funded, 2) the total

dollar amount pledged, 3) the percentage of total dollar amount pledged over a campaign goal, and 4) the

total number of backers. We also find that entrepreneurs’ gender moderates the relationship between

entrepreneurs’ facial trustworthiness and crowdfunding success. Specifically, female entrepreneurs’ facial

trustworthiness plays a more important role in determining fundraising success on Kickstarter after

controlling other project-related factors. Our results are robust to both first-time entrepreneurs and

seasoned entrepreneurs on Kickstarter, and also to a subsample of U.S. entrepreneurs. Moreover, our

results hold after controlling for facial features associated with attractiveness rather than trustworthiness

specifically (Graham, Harvey, and Puri 2016; Halford and Hsu 2014).

2 As of October 2018, the number of launched projects on Kickstarter amounted to 419,755, with the success rate among these

projects amounting to 36.4% and 3.92 billion U.S. dollars had been pledged to these projects (Kickstarter 2018).

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Our paper makes several contributions. First, it contributes to the growing literature on

crowdfunding (Belleflamme et al. 2015) by demonstrating that funders evaluate entrepreneurs’

appearances and incorporate their appearance-based perceptions of trustworthiness in their funding

decisions. Our study is most closely related to (Duarte et al. 2012b), who investigate the role that

appearance-based impression plays in peer-to-peer lending. However, our study also differs from Duarte

et al. (2012) in important ways. Funders on Kickstarter must rely on limited and text based information

voluntarily provided by entrepreneurs whereas lenders on peer-to-peer lending platforms have greater

access to hard information of the borrowers, such as credit score range. We also report novel evidence

that female entrepreneurs’ facial trustworthiness plays a more important role than males’ in determining

crowdfunding success. Finally, we construct an appearance-based measure using machine learning-based

facial feature point-detection techniques. The availability and use of these new methods highlight fruitful

directions for future research on facial trustworthiness across business and economic decision settings.

Second, our study adds to the literature on the role of trust in financial decisions. Existing studies

document that trust increases households’ use of trust-intensive contracts rather than cash exchanges

(Guiso, Sapienza, and Zingales 2004) and individuals’ likelihood of participating in the stock market

(Guiso, Sapienza, and Zingales 2008), international trade (Guiso, Sapienza, and Zingales 2009), and

venture capital investment (Bottazzi, Da Rin, and Hellmann 2016). However, this research tends to rely

on measures of generalized societal trust, measured at the national level. Our study examines individual-

level differences in perceived trustworthiness and thus narrows trust measures from the societal to the

individual level, reducing concerns that trust may be heterogeneous within a geographic region.

Third, our paper contributes to finance research on the role of appearance in corporate contexts.

Appearance affects managerial compensation (García and Welter 2013), hedge fund investment (Pareek

and Zuckerman 2014), and shareholder value (Halford and Hsu 2014). We extend this line of research to

the context of entrepreneurial finance to suggest that the appearance of entrepreneurs with early-stage

venture matters in a crowdfunding setting.

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Finally, our results have practical implications for regulation. They suggest that the information

environment in crowdfunding could be improved by adding more transparent and reliable disclosure

policies. This would help funders make decisions based on empirical evidence of trustworthiness, rather

than perceptions based on facial features.

The remainder of the paper is organized as follows. Section 2 reviews related literature and

develops testable hypotheses. Section 3 describes the sample construction, the measurement of facial

trustworthiness, and our empirical methodology. Sections 4 and 5 present the main empirical results and

robustness tests. Finally, Section 6 provides concluding remarks.

2. Literature Review and Hypothesis Development

2.1 Crowdfunding and Information Asymmetry

Crowdfunding can be used as an external financing tool for early stage entrepreneurial ventures. Most

crowdfunding ventures take place on internet-based platforms, of which Kickstarter is one of the most

prominent.3 A notable feature across these platforms is that the funders (i.e., the crowd) lack access to

vital information when making a funding decision. Entrepreneurs hoping to secure financing on

crowdfunding platforms tend to present limited historical performance data or track records of success

(Belleflamme et al. 2015; Calic and Mosakowski 2016; Strausz 2017). Many are also reluctant to disclose

secrets related to their innovation to the general public, especially to competitors, prior to completing the

final product (Agrawal, Catalini, and Goldfarb (2014).4 Funders, with only limited information, are

unable to exercise due diligence when evaluating the crowdfunding projects. Often they cannot know the

entrepreneur’s abilities or the final product’s quality before making a funding decision. Due to this lack of

3 Compared with traditional financing with financial intermediaries, crowdfunding allows entrepreneurs to learn about potential

customers’ demand before the project is launched. da Cruz (2018) argues that crowdfunding also has value as information about

how well received an idea is, thus helping entrepreneurs to mitigate uncertainty in the acceptance of a new product or service.

Based on follow-up survey data, Mollick and Kuppuswamy (2014) find that beyond raising funds, crowdfunding also helps

provide access to customers, press, employees, and outside funders. Crowdfunding effectively reduces social and structural

constraints through the “democratization of access to capital” (Greenberg and Mollick 2017). 4 Funders often make funding decisions based on information presented in the “pitch,” a textual, graphical and/or video

description of the project (Mollick 2014).

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information and any monitoring by traditional financial intermediaries, entrepreneurs may thus engage in

short-term opportunistic behavior resulting in a moral hazard problem (Agrawal et al. 2014).

Chemla and Tinn (2017) model moral hazard in crowdfunding, arguing that entrepreneurs may be

more likely to embezzle funds raised on internet-based crowdfunding platforms because they are not

legally responsible for guaranteeing product delivery. Strausz (2017) develops a theoretical model of

profit-maximizing contract taking into account the presence of entrepreneurial moral hazard, consumers’

private information about demand, and entrepreneurs’ private information about production costs, to

provide insights on how to control moral hazard in crowdfunding contexts. Hildebrand, Puri, and Rocholl

(2016) highlight another ethical consequence of information asymmetry in a lending-based crowdfunding

environment based on evidence that group leader5 bids, which may have higher default rates, are often

perceived as positive signals and rewarded with lower interest rates. They argue that unscrupulous loan

originators take advantage of potential lenders due to information asymmetry issues.

2.2 Trust and Its Role to Alleviate Information Asymmetry Problems

Trust refers to the subjective probability of being cheated based on subjective evaluations of an

individual’s characteristics as “trustworthy” (Guiso et al. 2008). Often understood through the lens of

social capital, studies on the effects of trust on economic efficiency have shown that social trust affects

financial decisions at both the household (Guiso et al. 2004) and individual levels (Guiso et al. 2008),

thus affecting economic growth at a macro scale (La Porta, Florencio, Shleifer, and Vishny 1997).

Bilateral trust between nations predicts the trade relations and investments between two countries (Guiso

et al. 2009). Trust has also been found to positively affect venture capital firms’ investment decisions

(Bottazzi et al. 2016). These findings indicate that financial markets function more efficiently when the

degree of generalized trust is higher.

5 On the lending-based crowdfunding platform, members can organize themselves in groups. Every user is eligible to become a

group leader by defining group purpose, nature, and members’ interests. Each user can be the group leader for maximumly one

group. Group leader can act as a lender or borrower. Additionally, the group leader has the right to grant or deny other users’

access to his/her group. It is common that group leaders request additional information from potential borrowers and help them in

writing and designing the borrowing requests.

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One potential mechanism by which trust facilitates economic and inter-organizational efficiency

is by reducing information asymmetries between the contracting parties (Al-Najjar and Casadesus-

Masanell 2001; Chami and Fullenkamp 2002). The “crowdfunding contract” between entrepreneurs and

funders offers few tools for funders to understand the project and to track project information once capital

has been committed. In this sense, funders’ evaluations of entrepreneurs’ trustworthiness can be

understood as alleviating concerns over asymmetric information, as funders may base their decisions on

how much they “trust” an entrepreneur.

In a recent study, Lin and Pursiainen (2017) document a positive relation between social capital

of the entrepreneur’s home county and the success rate of crowdfunding campaigns. The underlying

assumption is that trust is determined by prior beliefs or stereotypes, which are heterogeneous across

different geographical regions but homogeneous within a certain geographical region. However, Bottazzi

et al. (2016) classify trust into two categories, (i) personalized trust, which focuses on a specific trading

partner, and (ii) generalized trust, which concerns regional “institutions,” and call for research to

disentangle the effects of each in business settings. To the best of our knowledge, few studies examine the

relationship between entrepreneur-specific trustworthiness at the individual level in crowdfunding settings.

Our study thus provides a first attempt to investigate how entrepreneurs’ facial trustworthiness affects

funders’ decisions, as measured by crowdfunding campaigns’ performance.

2.3 Facial Trustworthiness and Crowdfunding Success

People generate impressions of others’ personal traits (e.g., trustworthiness) by visually evaluating their

facial appearance (Bar, Neta, and Linz 2006; Todorov et al. 2015). Todorov et al. (2015) find that

impressions of others’ trustworthiness based on facial features can be formed in as little as 34

milliseconds. Further, longer exposures are unlikely to alter initial perceptions of trustworthiness, but

rather reinforce confidence in prior judgments (Todorov et al. 2015; Willis and Todorov 2006).

Research also finds that specific facial traits affect the extent to which individuals are perceived

to be trustworthy (Dotsch and Todorov 2012; Enlow and Hans 1996; Robinson et al. 2014; Todorov et al.

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2008). For example, Enlow and Hans (1996) find that shallow cheeks and a low eyebrow ridge lead to

low perceived trustworthiness and Todorov et al. (2008) observe that upper inner eyebrow ridge,

pronounced cheekbones, wide chin, and a shallow nose sellion are correlated with high facial

trustworthiness. The distance between the mouth and the nose is also an important feature (Todorov et al.

2008), with a longer lip-to-nose distance correlating with lower perceptions of trustworthiness. Dotsch

and Todorov (2012) show that faces perceived as trustworthy are generally smoother and smaller, smiling

with open eyes, while those seen to be untrustworthy generally have a downturned mouth with thick lips,

angry-looking eyes, sagging cheeks, and baldness. Using the “Bubbles” technique, Robinson et al. (2014)

find that facial traits in human eyes and mouths are correlated with trustworthiness judgments.6 People’s

inferences about individuals based on facial properties may achieve a high consensus and tend to be

highly correlated with one another, regardless of culture background (Kim and Rosenberg 1980;

Oosterhof and Todorov 2008; Rosenberg, Nelson, and Vivekananthan 1968; Rule et al. 2013).

Laboratory studies show that people are less willing to trust an individual who has an

untrustworthy-looking face and more likely to trust people who look more trustworthy (Chang, Doll,

van’t Wout, Frank, and Sanfey 2010; Duarte, Siegel, and Young 2012a; Rezlescu et al. 2012; Schlicht,

Shimojo, Camerer, Battaglia, and Nakayama 2010; Stirrat and Perrett 2010; Tingley 2014; Van’t Wout

and Sanfey 2008). This is corroborated with evidence from outside the laboratory. In business settings,

people tend to invest more money with trustworthy-looking business partners (Duarte et al. 2012b;

Rezlescu et al. 2012; Tingley 2014) and firms with CEOs perceived to be more trustworthy tend to

receive higher valuations at all stages of an IPO (Blankespoor et al. 2017).

6 Emotional expressions, especially smiling, can influence trustworthiness impressions (Eckel and Wilson 1998; É thier-Majcher,

Joubert, and Gosselin 2013; Krumhuber et al. 2007; Scharlemann, Eckel, Kacelnik, and Wilson 2001). Additionally, face

typicality is also a critical determinant in facial trustworthiness evaluations (Sofer, Dotsch, Wigboldus, and Todorov 2015). In

our study, we focus on static facial features to measure facial trustworthiness for two reasons. First, entrepreneurs on Kickstarter

are typically smiling in their photos, resulting in little variance in terms of facial expression. Second, facial typicality is by

definition based on the pool of faces that funders have seen and interacted with, which is unobservable from the data.

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Entrepreneurs’ headshots (or photos on the campaign page) on Kickstarter may provide important

facial cues as funders evaluate entrepreneurs’ trustworthiness.7 When potential funders review the

information disclosed on a fundraising site, they may feel uncertain about the true quality of the proposed

project. If entrepreneurs’ pictures are provided on the webpage, funders may naturally interpret the level

of trustworthiness from entrepreneur’s static facial features and incorporate this information into their

funding decision.8 Funders are likely to trust an entrepreneur with trustworthy-looking facial features and

to fund campaigns developed by trustworthy-looking entrepreneurs. This discussion leads to our first

hypothesis, stated as follows:

Hypothesis 1: Entrepreneurs who look more trustworthy are more likely to be funded in

crowdfunding campaigns.

2.4 Gender, Facial Trustworthiness, and Crowdfunding Campaign Success

The gender disparity in the labor market has been widely documented in both research and practice. For

example, women receive 22% lower wages than men and hold only 6% of the Chief Executive Officer

and other top executive positions in U.S. corporations (Tate and Yang 2015). Koenig, Eagly, Mitchell,

and Ristikari (2011, p.616) argue that “stereotypes often are a potent barrier to women’s advancement to

positions of leadership.” Bird and Brush (2002) argue that entrepreneurship in general is recognized as a

male-dominated profession. Due to gender stereotypes in business ventures, entrepreneurial activities may

be viewed from a gender-biased perspective, prioritizing masculine features over feminine (García and

7 The entrepreneur’s picture is located on the upper left corner of the Kickstarter project webpage adjacent to his or her name,

such that we are confident that the picture is the entrepreneur. Additionally, by clicking on the picture or the “See full profile” tab,

the entrepreneur’s picture is displayed again next to a detailed biography. Such photos are thus part of the information that

funders have available to them when making crowdfunding decisions. The following is a typical example:

https://www.kickstarter.com/profile/966842631/about 8 We argue that funders’ evaluations of an entrepreneur’s trustworthiness based on facial features are made unintentionally. The

neuroscience literature suggests that people’s impressions of others are made unconsciously based on facial features (Evans 2008;

McClure, Laibson, Loewenstein, and Cohen 2004). fMRI studies find that facial trustworthiness evaluations correlate with

activation of the amygdala (Winston, Strange, O'Doherty, and Dolan 2002), which is partially responsible for System 1 thinking

processes, which are rapid, intuitive, and universal, as opposed to System 2 thinking, which is slower, controlled, and more

logical (Evans 2008). McClure et al. (2004) find that limbic (including amygdala) activation may explain impulsive behaviors

associated with the sight, smell, or touch of a desired object. Because the amygdala’s dual function enables people to rapidly

interpret facial trustworthiness (Todorov and Engell 2008; Winston et al. 2002) and to react rapidly and intuitively to such

perceived trustworthiness (Evans 2008; McClure et al. 2004), we argue that on crowdfunding platforms, funders do not

intentionally search for information in entrepreneur’s facial features, but rather are unconsciously influenced by entrepreneur’s

facial trustworthiness.

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Welter 2013; Ogbor 2000). Funders on Kickstarter may be potentially biased by this gender stereotype

when evaluating projects and consider female entrepreneurs as less capable in terms of managing ventures

and achieving successful outcomes. One possible strategy to alleviate funders’ concerns about the

likelihood of success for female entrepreneurs’ venture projects is to gather additional information, such

as trustworthiness indicators from entrepreneurs’ facial appearance. Therefore, funders may put more

weight on the facial trustworthiness of female entrepreneurs than male entrepreneurs when evaluating

Kickstarter campaigns.

Moreover, evidence from Online Social Networking sites suggests that people tend to pay more

attention to females’ profile photos than males’ (Seidman and Miller 2013). For example, Seidman and

Miller (2013) created eight Facebook profiles with eight unique profile pictures, two attractive and two

unattractive for both genders. The profiles’ text was rated as mostly equivalent in terms of personality.

The researchers tracked participants’ eye movements while viewing each profile for 60 seconds and found

that participants spent significantly more time viewing the profile photos of female profiles than males’.9

Such evidence is highly consistent with feminist theory, which argues that physical appearance is more

essential for achieving social status for females than males, as males are more likely to be evaluated on a

broader spectrum of traits (Adolphs, Baron-Cohen, and Tranel 2002; Brownmiller 1984; Kaschak 1992).

Based on these prior studies, we expect that funders on Kickstarter will be more likely to extract and use

information from female entrepreneurs’ photos than from their male counterparts. Therefore, facial

trustworthiness will likely play a more important role in funders’ backing decisions for female

entrepreneurs than males. Hence, we propose our second hypothesis as the following:

Hypothesis 2: The effect of entrepreneur’s facial trustworthiness on crowdfunding success is

more pronounced for female entrepreneurs than for male entrepreneurs.

9 In additional analysis, Seidman and Miller (2013) report insignificant associations between participant gender, Facebook profile

gender, and physical attractiveness, and dependent variables. They thus exclude the alternative explanation that participants’

attention to attractive individuals of the opposite gender is driven by sexual interest.

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3. Research Methodology

3.1 Sample Collection

We collect our sample on Kickstarter, one of the largest and most popular reward-based crowdfunding

platforms.10

We focus on technology-related projects because technology entrepreneurs tend to be most

reluctant to disclose information related to product development and are thus more likely to present

crowdfunding projects with asymmetric information (Kousari 2011). Technology-related projects also

closely resemble entrepreneurial ventures in the conventional venture capital markets. We develop a web

crawling algorithm to scrape data from Kickstarter on project characteristics, including geographic

location, initiation and deadline dates, pledged goals, number of backers, total amount pledged, and

entrepreneurs’ names and profile photos. Our algorithm initially extracts 16,122 Kickstarter projects in

the technology category active from October 2009 to September 2017. Of these, only 1,858 projects

(approximately 11.52%) provide high quality profile headshots of the entrepreneur. We further exclude

56 group photos (multiple entrepreneurs included in one picture), because it is hard to identify the most

influential person in the founding team. Our final sample includes 1,802 high quality individual

entrepreneurs’ headshot photos. We also extract country-specific characteristics that may affect project

development. These include annual gross domestic product per capita and social capital, a country-level

trust index constructed based on the World Values survey. This process further reduced our sample to

1,770 projects from seventeen countries (regions).11

3.2 Facial Trustworthiness Measurements

10 There are four prevalent types of online crowdfunding platforms in the market, including equity-based crowdfunding (investors

gain dividends, e.g., CircleUp, AngelList), donation-based crowdfunding (donors make benevolent contributions, e.g., JustGiving,

GiveForward), lending-based crowdfunding (lenders earn bonuses or interests, e.g., Prosper, Kiva), and reward-based

crowdfunding (funders receive rewards from creators, e.g., Kickstarter, RocketHub). Among the different platforms, finance

researchers have increasingly focused on lending-based and reward-based crowdfunding. Kickstarter is among the most

frequently studied of these platforms. 11 These countries/regions include Australia, Austria, Canada, Switzerland, Germany, Spain, France , the United Kingdom, Hong

Kong, Italy, Mexico, the Netherlands, Norway, New Zealand, Singapore, Sweden, and the United States.

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We use a machine learning-based face detector12

to identify facial features for each entrepreneur based on

their Kickstarter profile picture to measure facial trustworthiness. Following prior studies, we identify

four specific facial features that have been found to contribute to perceptions of trustworthiness.

Specifically, EYEBROW measures the angle of inner eyebrow ridge, which prior studies have found to be

negatively related to trustworthiness (Todorov et al. 2008). FACE captures the roundness of the face,

which is positively associated with perceived trustworthiness (Berry and Zebrowitz-McArthur 1988; Gorn,

Jiang, and Johar 2008; Livingston and Pearce 2009; Todorov et al. 2008). CHIN is defined as the width of

the chin, as wider chins are more likely to be perceived as trustworthy (Todorov et al. 2008). PHILTRUM

measures the lip-to-nose distance scaled by the upper facial height, as faces with high PHILTRUM

measures are less likely to be perceived as trustworthy (Todorov et al. 2008; Vernon et al. 2014).

Appendix A describes the detailed procedures used to measure these four facial features.13

Prior research suggests that when exposed to faces, people build rapid “holistic representations”

to process information from various facial features as an integrated perceptual whole (Taubert, Apthorp,

Aagten-Murphy, and Alais 2011). As such, we construct a composite facial trustworthiness index (TRUST)

by aggregating EYEBROW, FACE, CHIN, and PHILTRUM using the following procedure. First, we

standardize each of the four individual measures by deducting the sample mean and scaling by the sample

standard deviation for ease of comparison across facial features (Beatty, Ke, and Petroni 2002; Beaver,

McNichols, and Nelson 2007; Henry and Leone 2015). We then reverse the signs of EYEBROW and

PHILTRUM, as prior studies suggest that these two measures are negatively associated with facial

trustworthiness. Finally, we average the standardized values of FACE and CHIN, and the reversed

standardized values of EYEBROW and PHILTRUM, to construct the composite measure TRUST. A higher

value of TRUST indicates a more trustworthy facial appearance.

12 The face detector is built on the classic Histogram of Oriented Gradients (HOG) feature, combined with a linear classifier

(Dalal and Triggs 2005; Zhu, Yeh, Cheng, and Avidan 2006). A facial feature point detection, based on an ensemble of

regression trees, is used to estimate the positions of the specific facial features and calculate facial trustworthiness measurements

(Kazemi and Sullivan 2014; Sagonas et al. 2013). 13 All of the facial trustworthiness measurements are 2-D measurements because the photos of entrepreneurs and collected from

Kickstarter are also 2-D.Prior literature indicates that some 3-D measurements, such as pronounced cheekbones (Enlow and Hans

1996; Todorov et al. 2008; Vernon et al. 2014) and shallow nose sellion are associated with trustworthiness. However, as 2-D

pictures do not have depth dimension, we cannot calculate such 3-D trustworthiness measurements.

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3.3 Sample Descriptive Statistics

Table 1 Panel A depicts the number of technology-related Kickstarter projects and the success rate in our

sample by year. The number of technology-related project pages that provide profile photos increases

from 19 projects in 2010 to 270 projects in 2016 and 406 projects for the first nine months of 2017. The

annual success rate was low at around 10% in the first two years and maintained at around 30% - 40%

after and suddenly dropped to 13.79% for the first nine months in 2017. Table 1 Panel B plots our

Kickstarter sample by country. The majority (67.2%) come from the United States. The overall success

rate of crowdfunding is 31.2% throughout the sample period. Projects from certain countries, e.g.,

Norway (57.4%), Sweden (40.0%), the United States (33.5%), and Hong Kong (33.3%), exhibit higher

success rates than other countries.

[Insert Table 1 about Here]

Table 2 tabulates the descriptive statistics. Of the technology-related projects included in our

sample, 31.2% are successfully funded. A vast majority (89.8%) of entrepreneurs are male, consistent

with Greenberg and Mollick (2017) argument that the Kickstarter technology category is a “male-

dominated” domain. About 4.7% of the projects are led by experienced entrepreneurs, evidenced by

previous crowdfunding application records, regardless of the funding results. Moreover, among the

technology projects in our sample, 76.2% provide video presentations of the product, which is viewed as

an indicator of positive quality and preparedness (Greenberg and Mollick 2017). Table 3 presents the

Pearson correlation coefficient matrix, which shows that our facial trustworthiness index TRUST is

significantly and positively correlated with the total dollar amount successfully pledged (0.063), the

percentage of total dollar amount pledged over project goal (0.063), and the number of backers (0.052).

Variables are defined in Appendix B.

[Insert Tables 2 and 3 about Here]

3.4 Research Design

3.4.1 The effect of entrepreneurs’ facial trustworthiness on crowdfunding success

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Hypothesis 1 predicts that entrepreneurs with trustworthy-looking facial features will be more likely to

successfully crowdfund their project. We estimate the following regression model (1) to test this

hypothesis:

SUCCESS /PLEDGED /PLEDGED_GOAL /BACKER = β0 + β1*TRUST + β2*GENDER + β3*GOAL +

β4*DURATION + β5*PAST_EXPERIENCE + β6*VEDIO + β7*SOCIAL_CAPITAL + β8*GDP +

ΣCountry Fixed Effects + Σ Year Fixed Effects + Σ Category Fixed Effects + ε (1)

We use four measurements to proxy for the performance or success of the Kickstarter projects in

our sample. SUCCESS is a dummy variable that equals 1 if the fundraising is successful, and 0 if the

fundraising is failed, suspended, or canceled. PLEDGED is the natural logarithm of the total amount

pledged, while PLEDGED_GOAL is the percentage of the amount pledged over the goal of a project,

expressed in logarithm form. BACKER represents total number of funders in the project, expressed in

logarithm form. We take logarithms for all three continuous variables because their distributions are

highly skewed. Logit model is performed for the dependent variable SUCCESS and OLS regressions are

used for the other three continuous variables.14

According to H1, we expect that higher TRUST will be

associated with a higher project success rate, a higher total pledged amount, and a higher number of

project backers. We thus expect a positive and significant sign for β1.

In model (1), we control for entrepreneur’s gender (GENDER), as female entrepreneurs are more

likely to successfully crowdfund their projects (Greenberg and Mollick 2017).15

Theoretical models

(Schwienbacher 2017; Strausz 2017) predict that setting higher goal amounts make campaigns less likely

to be funded. Thus, we control for project goal (GOAL) and expect that projects with lower goals will

14 Given a significant number of campaigns with a pledged amount of zero, using OLS regressions may yield biased estimates.

We further use Tobit models to estimate the three continuous variables and find that our results still hold. 15 In crowdfunding platforms, ventures developed by women entrepreneurs tend to be viewed as more trustworthy and are more

likely to be successful than those developed by male entrepreneurs (Eckes 2002; Johnson, Stevenson, and Letwin 2018a).

Johnson, Stevenson, and Letwin (2018b) document that female entrepreneurs on Kickstarter are more likely to be funded than

their male counterparts. Johnson et al. (2018b) attribute female entrepreneurs’ crowdfunding success to their perceptions as

trustworthy. Lin and Pursiainen (2018) argue that male entrepreneurs tend to overestimate the demand for their products and

therefore set a higher goal amount, resulting in a higher failure rate.

- 15 -

have higher success rates. We also include the duration of the projects (DURATION) as a control variable

(Calic and Mosakowski 2016; Lin and Pursiainen 2018). Moreover, we control for the entrepreneur’s

experience in crowdfunding on Kickstarter (PAST_EXPERIENCE) using a binary variable with 1

indicating that an entrepreneur has at least one prior project application on Kickstarter, and 0 otherwise.

We anticipate that PAST_EXPERIENCE is valued by funders and thus positively associated with

crowdfunding success. Greenberg and Mollick (2017) suggest that a video describing project details is

usually a signal of higher quality. We hence include an indicator variable (VEDIO), which equals 1 if the

project webpage provides a video, and 0 otherwise. In addition, we control for country-level social capital

index (SOCIAL_CAPITAL), as Lin and Pursiainen (2017) suggest that crowdfunding projects originating

in counties with higher social capital tend to have higher success rates. Finally, we include country-

specific annual gross domestic product per capita (GDP) and expect that funding decisions may be

influenced by macroeconomic conditions. Country-fixed and year-fixed effects are controlled to capture

any country- and time-variate turbulence affecting crowdfunding outcomes. We also add fixed effects to

control for project sub-categories within the technology category because the information asymmetries

may be heterogeneous across sub-categories.16

All continuous variables are winsorized at the 1% level.

We cluster standard errors by country and by year.

3.4.2 The moderating effect of entrepreneurs’ gender

H2 predicts that the positive relationship between an entrepreneur’s facial trustworthiness and

crowdfunding success will be moderated by the entrepreneur’s gender. We therefore estimate the

following equations to empirically test our prediction:

SUCCESS /PLEDGED /PLEDGED_GOAL /BACKER = γ0 + γ1*TRUST + γ2*TRUST*GENDER +

γ3*GENDER + γ4*GOAL + γ5*DURATION + γ6*PAST_EXPERIENCE + γ7*VEDIO +

γ8*SOCIAL_CAPITAL + γ9*GDP + Σ Country Fixed Effects + Σ Year Fixed Effects +

Σ Category Fixed Effects + ε (2)

16 There are 15 sub-categories of Kickstarter technology projects in our sample: 3D printing, APPs, camera equipment, DIY

electronics, fabrication tool, flight, gadgets, hardware, makerspaces, robots, software, sound, space exploration, wearables, and

web.

- 16 -

Entrepreneur’s gender (GENDER) is a dummy variable equal to 1 if the entrepreneur is a male,

and 0 if the entrepreneur is a female. According to H2, we expect to observe a negative coefficient for the

interaction term (TRUST*GENDER). We include the same set of control variables and fixed effects for

country, year, and technology sub-category. All continuous variables are winsorized at the 1% level. We

cluster standard errors by country and by year.

4. Empirical Results

4.1 Effect of entrepreneur’s facial trustworthiness on crowdfunding success

Table 4 reports the results of hypothesis testing for H1. In column (1), we estimate a logistic regression

model with TRUST as the independent variable and SUCCESS as the dependent variable. Consistent with

H1, TRUST is significantly and positively associated with the likelihood of a project being successfully

funded (0.192, p-value<0.001). In columns (2) to (4), we report the results of the OLS regression models

using PLEDGED, PLEDGED_GOAL, and BACKER as the dependent variables, respectively. Results

indicate that TRUST is positively associated with the total pledged amount (0.254, p-value<0.001), the

total pledged amount over the campaign goal (0.260, p-value<0.001), and the total number of backers

(0.108, p-value=0.04). Therefore, H1 is supported.

In terms of economic significance, we compute changes in the total dollar amount pledged

(PLEDGED) and the total number of backers (BACKER) stemming from variation in facial

trustworthiness from the 25th percentile (-0.397). We find that inter-quartile changes in facial

trustworthiness results in a 22.8% increase in pledged amount and a 9.1% increase in the number of

backers when holding other elements that might influence crowdfunding success constant.17

The results

17

In Table 4 column (2), given the coefficient of 0.254 for TRUST, when an entrepreneur’s facial trustworthiness

increases from the first quartile (at the 25th

percentile) to the third quartile (at the 75th

percentile), the resulting

change in the logged value of the total dollar amount pledged (PLEDGED) amounts to [(0.254*0.413) – (0.254*-

0.397) = 0.20574] and the total dollar amount pledged increases by about 22.84 percent on average [e0.20574

-

1=22.84%]. In Table 4 column (4), given the coefficient of 0.108 for TRUST, when an entrepreneur’s facial

trustworthiness increases from the first quartile (at the 25th

percentile) to the third quartile (at the 75th

percentile), the

- 17 -

for the control variables are generally consistent with the literature. Specifically, female entrepreneurs are

more likely to be successful in crowdfunding on Kickstarter platform. Projects with lower fund-raising

goals and longer durations are also more likely to be successfully funded. Additionally, entrepreneurs

tend to have a higher success rate if they include a video presentation, which could be a signal of both

high quality and preparedness.

[Insert Table 4 about Here]

We further analyze the four individual measures of facial features used to construct TRUST

(EYEBROW, FACE, CHIN and PHILTRUM) to evaluate how our results may be captured when focused

on specific facial features. The regression results are reported in Table 5. Interestingly, we find that

entrepreneurs with a rounder face and a shorter philtrum are more likely to successfully raise funds. This

is consistent with prior literature, which suggests that the roundness of a face (Berry and Zebrowitz-

McArthur 1988; Gorn et al. 2008; Livingston and Pearce 2009; Todorov et al. 2008) can positively, and

the length of a philtrum (Todorov et al. 2008; Vernon et al. 2014) can negatively, predict perceptions of

facial trustworthiness.

[Insert Table 5 about Here]

4.2 Moderation effect of gender

Table 6 tabulates the regression results for H2. We hypothesize that facial trustworthiness will have a

greater impact on backers’ funding decisions for female entrepreneurs than for their male counterparts. As

shown in Table 6, we find that the coefficient of the interaction term (TRUST*GENDER) is significantly

negative across all four measures of crowdfunding success, indicating that relative to male entrepreneurs,

facial trustworthiness is more prominent in determining the success rate of females entrepreneurs’

ventures, pledged amount, pledged amount over total pledged goals, and number of backers. Therefore,

our second hypothesis is also supported.

resulting change in the logged value of the number of backers (BACKER) amounts to [(0.108*0.413) – (0.108*-

0.397) = 0.08748] and the total dollar amount pledged increases by about 9.14 percent on average [e0.08748

-1=9.14%].

- 18 -

[Insert Table 6 about Here]

To further explore the role of gender in crowdfunding success, we partition our sample into male

and female groups, respectively, and perform a sub-group analysis to examine the different effects of

facial trustworthiness on funding success. Our sample consists of 1,590 projects initiated by male

entrepreneurs and 180 projects initiated by female entrepreneurs. We rerun the regressions of Eq. (1) to

test the differentiated implications of entrepreneurial facial trustworthiness on crowdfunding success

within each gender group. The results are summarized in Table 7. As shown in Table 7 Panel A, male

entrepreneurs’ facial trustworthiness is positively associated with SUCCESS (0.153, p-value=0.03),

PLEDGED (0.194, p-value=0.04), PLEDGED_GOAL (0.200, p-value=0.04), and BACKER (0.083, p-

value=0.09). In Table 7 Panel B, our results suggest that female entrepreneurs’ facial trustworthiness is

also positively associated with SUCCESS (1.118, p-value=0.00), PLEDGED (1.263, p-value=0.00),

PLEDGED_GOAL (1.255, p-value=0.00), and BACKER (0.654, p-value=0.00) but more prominently and

significantly, as indicated by much larger coefficients and higher significance levels. These results

provide further support for both H1 and H2, suggesting that facial trustworthiness influences backers’

crowdfunding decisions in general, while facial trustworthiness has an even greater impact for female

entrepreneurs.

[Insert Table 7 about Here]

5. Additional Analyses

5.1 Initial Project Applications versus Seasoned Applications

We argue that due to the information asymmetries on crowdfunding sites like Kickstarter, perceptions of

trustworthiness based on entrepreneurs’ facial features can serve as proxy information and facilitate

funders’ decision-making. Perceptions of trust play a vital role given the asymmetric information

environment, especially when funders are exposed to limited information about a proposed project. We

therefore isolate the testing sample to initial project applications, where no prior information is available

about the entrepreneur’s track record and are thus subject to the most severe information asymmetries.

- 19 -

The regression results are reported in Table 8. Consistent with our predictions, entrepreneurs’ facial

trustworthiness is positively associated with the likelihood of success, total dollar amount pledged, and

total number of backers. In Table 8 Panel B, we also find such associations to be more prominent for

female entrepreneurs. In sum, our results are robust using initial project applications in Kickstarter.18

[Insert Table 8 about Here]

5.2 U.S. Sample Only

Because about 63.1% of all Kickstarter projects are pledged in U.S. dollars (Bidaux 2018) and about 67.2%

of the projects included in our sample are developed by entrepreneurs from the U.S., we isolate a sample

of projects by U.S.-based entrepreneurs to further explore the effects of facial trustworthiness on

crowdfunding success. Although we include country fixed effects in our main analyses and our main

results suggest that the role of perceived trustworthiness is not attributable to several well-established

country-level factors, by isolating a U.S. sample, we address concerns that the observed effect of facial

trustworthiness is driven by unobservable country-level factors affecting both backers’ perceptions and

the likelihood of success.

Table 9 reports empirical results for the U.S. sample. Panel A shows that trustworthiness is

associated with SUCCESS (0.206, p-value<0.001), PLEDGED (0.264, p-value=0.03), PLEDGED_GOAL

(0.264, p-value=0.03), and BACKER (0.117, p-value=0.07). Panel B also suggests that facial

trustworthiness plays a more prominent role in determining Kickstarter campaign success for female

entrepreneurs than their male counterparts. Overall, the results are consistent with our hypotheses when

the sample is limited to U.S. projects only.

[Insert Table 9 about Here]

5.3 Controlling for Facial Attractiveness

18 Although we did not find a significantly higher coefficient for the initial project applications, we argue that funders backing the

same entrepreneur’s various projects could be heterogeneous insofar as the entrepreneur’s picture is new to those who only back

a second or third project.

- 20 -

While this study focuses on perceptions of trustworthiness based on facial features, there are a number of

other characteristics that can be gleaned based on a person’s face. One could reasonably argue that it is

the perceived level of attractiveness, rather than trustworthiness, that affects backers’ funding decisions.

To evaluate this alternative explanation, we adapt a proxy for attractiveness based on measures developed

by Fan, Chau, Wan, Zhai, and Lau (2012) and Kalayci et al. (2014). These suggest that facial symmetry is

an informative proxy for predicting perceived attractiveness. We calculate a facial symmetry index19

(ATTRACTIVENESS) for entrepreneurs in our sample and control for facial attractiveness in our

regression models to further test our hypothesis. Overall, the results reported in Table 10 remain

consistent with our hypotheses’ predictions after controlling for attractiveness in the regression model.

Facial attractiveness thus does not significantly impact crowdfunding success.

[Insert Table 10 about Here]

6. Concluding Remarks

Based on prior research on facial trustworthiness in psychology and neuroscience, this study uses

machine learning-based facial feature detection techniques to measure the facial trustworthiness of

entrepreneurs on the Kickstarter crowdfunding platform. Utilizing this measure, we examine the impact

of entrepreneurs’ facial trustworthiness on the success of their crowdfunding projects. We find that

entrepreneurs who look more trustworthy are more likely to succeed in the crowdfunding market.

Additionally, facial trustworthiness plays a more prominent role in affecting project success for female

entrepreneurs than males.

Our study contributes to the crowdfunding literature by providing empirical evidence for how

perceptions of entrepreneurs’ trustworthiness can affect funders’ decision making. We also contribute to

19 Specifically, we calculate the symmetric ratio for three distinct facial features: inner eyebrow ridge, face shape, and chin angle.

The formula for symmetric ratio involves the absolute value of the difference of each facial feature on the right and left sides of

the face, scaled by the lesser values of each facial feature on each side of the face. The symmetric ratio of three separated facial

features are standardized by subtracting the sample mean and then scaled by the sample standard deviation. The facial symmetric

index (ATTRACTIVENESS) is the average of the three symmetric ratios of the facial features. We acknowledge that the facial

symmetry index is at best a proxy for facial attractiveness, as beauty has long been considered a subjective perception influenced

by many other factors, such as race, culture, and era.

- 21 -

the literature on gender differences in finance contexts by demonstrating that facial trustworthiness plays

a more prominent role in determining project success for female entrepreneurs than males. The study’s

methods introduce cutting edge machine learning-based technology to measure facial trustworthiness and

thereby expand what were previously survey-based and societal-level trust measures to the individual

level on a large scale. The availability of this new technology can inform future research into how

individual trustworthiness affects business decision-making. Our results may also prompt crowdfunding

practitioners and regulators to acknowledge that the information environment on crowdfunding sites leads

users to seek out indicators of trustworthiness that may have little to do with the quality of a project.

- 22 -

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- 27 -

Appendix A: Measuring Facial Trustworthiness

A face-detection algorithm is used to identify the required facial feature points from given images. The

face-detection algorithm is built on top of the classic Histogram of Oriented Gradients (HOG) feature,

combined with a linear classifier (Dalal and Triggs 2005; Zhu et al. 2006). Consequently, a facial-feature-

point detector based on an ensemble of regression trees is used and our four facial trustworthiness

measurements (e.g., EYEBROW, FACE, CHIN, and PHILTRUM) are calculated based on the extracted

facial-feature points. We follow numbering rules based on prior literature (Feng, Hu, Kittler, Christmas,

and Wu 2015; Kazemi and Sullivan 2014; Sagonas et al. 2013; Yang, Zou, and Patras 2014).

We extract facial feature points 𝑃18, 𝑃20, 𝑃22, 𝑃23, 𝑃25, 𝑃27 as illustrated in Pictures (A) and (B) to

calculate EYEBROW, which is defined as inner eyebrow ridge. We calculate the inner-eyebrow-ridge

angle on both the right-hand side (μ1) and left-hand side (μ2). Smaller (larger) μ1 and μ2 indicate an

upward-angled (a downward-angled) inner eyebrow ridge. To achieve a more precise measurement, we

average μ1 and μ2 by using the following formula:

EYEBROW = (cos−1 𝑃18𝑃27⃗⃗ ⃗⃗ ⃗⃗ ⃗⃗ ⃗⃗ ⃗⃗ ⃗⃗ ∙𝑃20𝑃22⃗⃗ ⃗⃗ ⃗⃗ ⃗⃗ ⃗⃗ ⃗⃗ ⃗⃗

|𝑃18𝑃27⃗⃗ ⃗⃗ ⃗⃗ ⃗⃗ ⃗⃗ ⃗⃗ ⃗⃗ |∙|𝑃20𝑃22⃗⃗ ⃗⃗ ⃗⃗ ⃗⃗ ⃗⃗ ⃗⃗ ⃗⃗ |+ cos−1 𝑃27𝑃18⃗⃗ ⃗⃗ ⃗⃗ ⃗⃗ ⃗⃗ ⃗⃗ ⃗⃗ ∙𝑃25𝑃23⃗⃗ ⃗⃗ ⃗⃗ ⃗⃗ ⃗⃗ ⃗⃗ ⃗⃗

|𝑃27𝑃18⃗⃗ ⃗⃗ ⃗⃗ ⃗⃗ ⃗⃗ ⃗⃗ ⃗⃗ |∙|𝑃25𝑃23⃗⃗ ⃗⃗ ⃗⃗ ⃗⃗ ⃗⃗ ⃗⃗ ⃗⃗ |)/2

(A) (B)

We extract the facial feature points 𝑃1, 𝑃5, 𝑃9, 𝑃13, 𝑃17 as illustrated in Picture (C) to calculate FACE,

which is defined as the measurement of the roundness of a face. We calculate the face roundness on both

the right-hand (α1) and left-hand (α2) sides. Larger (smaller) α1 and α2 suggest a rounder (narrower) face.

We average α1 and α2 as follows to calculate FACE:

FACE = (cos−1 𝑃1𝑃5⃗⃗ ⃗⃗ ⃗⃗ ⃗⃗ ⃗∙𝑃5𝑃9⃗⃗ ⃗⃗ ⃗⃗ ⃗⃗ ⃗

|𝑃1𝑃5⃗⃗ ⃗⃗ ⃗⃗ ⃗⃗ ⃗|∙|𝑃5𝑃9⃗⃗ ⃗⃗ ⃗⃗ ⃗⃗ ⃗|+ cos−1 𝑃17𝑃13⃗⃗ ⃗⃗ ⃗⃗ ⃗⃗ ⃗⃗ ⃗⃗ ⃗⃗ ∙𝑃13𝑃9⃗⃗ ⃗⃗ ⃗⃗ ⃗⃗ ⃗⃗ ⃗⃗

|𝑃17𝑃13⃗⃗ ⃗⃗ ⃗⃗ ⃗⃗ ⃗⃗ ⃗⃗ ⃗⃗ |∙|𝑃13𝑃9⃗⃗ ⃗⃗ ⃗⃗ ⃗⃗ ⃗⃗ ⃗⃗ |)/2

28

(C) (D)

We extract the facial feature points 𝑃5, 𝑃9, 𝑃13, 𝑃58 as illustrated in Picture (D) to calculate CHIN, which is

defined as the width of the chin. We measure the chin angle on both the right- (β1) and left-hand (β2) sides.

Smaller (larger) β1 and β2 suggest a thinner (wider) chin. We average β1 and β2 according to the following

formula to calculate CHIN:

CHIN = (cos−1 𝑃9𝑃5⃗⃗ ⃗⃗ ⃗⃗ ⃗⃗ ⃗∙𝑃9𝑃58⃗⃗ ⃗⃗ ⃗⃗ ⃗⃗ ⃗⃗ ⃗⃗

|𝑃9𝑃5⃗⃗ ⃗⃗ ⃗⃗ ⃗⃗ ⃗|∙|𝑃9𝑃58⃗⃗ ⃗⃗ ⃗⃗ ⃗⃗ ⃗⃗ ⃗⃗ |+ cos−1 𝑃9𝑃13⃗⃗ ⃗⃗ ⃗⃗ ⃗⃗ ⃗⃗ ⃗⃗ ∙𝑃9𝑃58⃗⃗ ⃗⃗ ⃗⃗ ⃗⃗ ⃗⃗ ⃗⃗

|𝑃9𝑃13⃗⃗ ⃗⃗ ⃗⃗ ⃗⃗ ⃗⃗ ⃗⃗ |∙|𝑃9𝑃58⃗⃗ ⃗⃗ ⃗⃗ ⃗⃗ ⃗⃗ ⃗⃗ |)/2

Finally, we extract the facial feature points 𝑃34, 𝑃52 as illustrated in Picture (E) to calculate PHILTRUM,

which is defined as the lip-to-nose distance, scaled by upper facial height, which is defined as the distance

between P38 and P45 midpoint and P52. The length of the philtrum is measured by using the following

formula:

PHILTRUM = |𝑃34𝑃52⃗⃗ ⃗⃗ ⃗⃗ ⃗⃗ ⃗⃗ ⃗⃗ ⃗⃗ |

|1

2𝑃38𝑃45⃗⃗ ⃗⃗ ⃗⃗ ⃗⃗ ⃗⃗ ⃗⃗ ⃗⃗ −𝑃38𝑃52⃗⃗ ⃗⃗ ⃗⃗ ⃗⃗ ⃗⃗ ⃗⃗ ⃗⃗ |

(E)

Picture Source: Lefevre, Lewis, Perrett, and Penke (2013)

29

Appendix B: Variable Definitions

Variables Definitions

SUCCESS = A dummy variable that equals 1 if the fundraising is successful, and

0 if the fundraising is suspended or canceled;

PLEDGED = Logarithm of amount pledged converted to US dollar on the date

when the project is launched on Kickstarter;

PLEDGED_GOAL = Amount pledged (plus 1) divided by the goal amount translated to

logarithmic form;

BACKER = Number of funders (plus 1) that funded the project on Kickstarter

translated to logarithmic form;

TRUST = Composite measure of facial trustworthiness for Kickstarter

entrepreneur based on the average of the reversed standardized

value for EYEBROW, the standardized value for FACE, the

standardized value for CHIN, and the reversed standardized value

for PHILTRUM;

GENDER = A dummy variable that equals 1 if the entrepreneur is male and 0 if

the entrepreneur is female;

GOAL = Goal amount converted to US dollar on the date when the project is

launched on Kickstarter translated to logarithmic form;

DURATION = Project duration (length) in days set by the entrepreneur translated

to logarithmic form;

PAST_EXPERIENCE = A dummy variable that equals 1 if the same entrepreneur has

launched other project(s) on Kickstarter before the current one and

0 otherwise;

VIDEO = A dummy variable that equals 1 if the project has a video on the

fundraising page on Kickstarter and 0 otherwise;

SOCIAL_CAPITAL = Country-level trust index based on response to the WVS question:

“Generally speaking, would you say that most people can be trusted

or that you need to be very careful in dealing with people?” We

recode the response as 1 if a survey participant reports that most

people can be trusted and 0 otherwise, and calculate the mean

response for each country-year. Higher index values correspond to

higher trust (Data source: World Value Survey Wave 5 & 6);

GDP = Annual Gross Domestic Product per capita in thousands of 2010 US

dollars (Data source: World Bank).

30

Table 1

Sample distribution.

Panel A: Number of Kickstarter technology projects and successful rate by year

Year Project Number Percentage of total projects Success number Success rate

2009 6 0.34% 4 66.67%

2010 19 1.07% 2 10.53%

2011 46 2.60% 17 36.96%

2012 106 5.99% 45 42.45%

2013 192 10.85% 73 38.02%

2014 386 21.81% 121 31.35%

2015 339 19.15% 126 37.17%

2016 270 15.25% 108 40.00%

2017 406 22.94% 56 13.79%

Total 1770 100.00% 552 31.19%

Panel B: Number of Kickstarter projects and successful rate by country

Country Project Number Percentage of total projects Success number Success rate

Australia 69 3.90% 21 30.43%

Austria 12 0.68% 1 8.33%

Canada 105 5.93% 30 28.57%

Switzerland 11 0.62% 3 27.27%

Germany 55 3.11% 14 25.45%

Spain 20 1.13% 1 5.00%

France 27 1.53% 4 14.81%

United Kingdom 178 10.06% 51 28.65%

Hong Kong 6 0.34% 2 33.33%

Italy 25 1.41% 7 28.00%

Mexico 12 0.68% 2 16.67%

Netherlands 33 1.86% 8 24.24%

Norway 7 0.40% 4 57.14%

New Zealand 12 0.68% 3 25.00%

Singapore 3 0.17% 0 0.00%

Sweden 5 0.28% 2 40.00%

United States 1190 67.23% 399 33.53%

Total 1770 100.00% 552 31.19%

31

Table 2

Descriptive statistics.

N. Mean 25% 50% 75% Std. Dev.

SUCCESS 1,770 0.312 0.000 0.000 1.000 0.468

PLEDGED 1,770 6.365 4.535 6.621 8.667 3.174

PLEDGED_GOAL 1,770 -3.011 -5.303 -2.672 0.078 3.531

BACKER 1,770 2.926 1.386 2.708 4.304 1.921

TRUST 1,770 0.000 -0.397 -0.002 0.413 0.607

GENDER 1,770 0.898 1.000 1.000 1.000 0.302

GOAL 1,770 9.124 8.165 9.266 10.461 2.048

DURATION 1,770 4.103 3.611 3.989 4.394 0.725

PAST_EXPERIENCE 1,770 0.047 0.000 0.000 0.000 0.211

VIDEO 1,770 0.762 1.000 1.000 1.000 0.426

SOCIAL_CAPITAL 1,770 0.380 0.378 0.378 0.378 0.073

GDP 1,770 48.690 48.775 49.849 51.486 6.571

Table 2 reports the sample size, mean, percentiles, and standard deviations of our sample variables.

Variable definitions are provided in Appendix B.

32

Table 3:

Correlation matrix.

Variables 1 2 3 4 5 6 7 8 9 10 11

1. SUCCESS

2. PLEDGED 0.638

3. PLEDGED_GOAL 0.730 0.867

4. BACKER 0.695 0.905 0.819

5. TRUST 0.040 0.063 0.052 0.052

6. GENDER -0.014 0.007 -0.009 0.021 0.102

7. GOAL -0.235 0.111 -0.326 0.047 0.024 0.007

8. DURATION 0.049 0.207 0.094 0.189 0.016 0.028 0.185

9. PAST_EXPERIENCE 0.196 0.104 0.179 0.137 0.005 0.047 -0.171 -0.071

10. VIDEO 0.197 0.363 0.261 0.338 0.045 0.016 0.114 0.201 0.026

11. SOCIAL_CAPITAL 0.032 0.046 0.045 0.041 -0.014 0.022 0.133 0.031 -0.005 -0.043

12. GDP 0.050 0.071 0.066 0.068 -0.001 -0.022 0.215 0.014 0.015 -0.020 0.635

Pearson correlations are reported. Boldface indicates significance at the 5% level (two-tailed p-values). Variables are defined in Appendix B.

33

Table 4

Facial trustworthiness and Kickstarter project application outcome.

Variablesa,b

Sign Kickstarter project application outcome

c

SUCCESS PLEDGED PLEDGED_GOAL BACKER

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

TRUST + 0.192***

(0.01)

0.254***

(0.01)

0.260***

(0.01)

0.108**

(0.04)

GENDER - -0.433**

(0.02)

-0.527***

(0.00)

-0.535***

(0.00)

-0.193**

(0.04)

GOAL - -0.549***

(0.00)

0.042

(0.16)

-0.957***

(0.00)

-0.050***

(0.01)

DURATION + 0.155**

(0.02)

0.324***

(0.00)

0.325***

(0.00)

0.184***

(0.00)

PAST_EXPERIENCE + 1.544***

(0.00)

1.163***

(0.00)

1.174***

(0.00)

0.938***

(0.00)

VIDEO + 1.479***

(0.00)

1.843***

(0.00)

1.846***

(0.00)

1.049***

(0.00)

SOCIAL_CAPITAL + 1.830

(0.44)

0.982

(0.47)

0.836

(0.47)

4.308

(0.24)

GDP ? -0.044

(0.45)

-0.092

(0.39)

-0.094

(0.39)

-0.145

(0.19)

Intercept Yes Yes Yes Yes

Country Fixed Effects Yes Yes Yes Yes

Year Fixed Effects Yes Yes Yes Yes

Category Fixed Effects Yes Yes Yes Yes

N 1,770 1,770 1,770 1,770

Adj-R2 0.239 0.303 0.433 0.304

a. Variables are defined in Appendix B.

b. All continuous variables are winsorized at the 1

st and 99

th percentiles.

c. p-values are adjusted based on the predicted sign. ***, **, and * denote significance at the 1%, 5%, and

10% levels, respectively. All of the continuous variables are winsorized at 1%. Country fixed effects, year

fixed effects, and Kickstarter technology project category fixed effects are controlled but not reported for

brevity. Robust standard errors are clustered by country and year.

34

Table 5

Separated facial features and Kickstarter project application outcome.

Panel A: Separated facial features and successful fundraising and amount pledged.

Variablesa,b

Sign SUCCESS

c PLEDGED

c

EYEBROW FACE CHIN PHILTRUM EYEBROW FACE CHIN PHILTRUM

(1) (2) (3) (4) (5) (6) (7) (8)

Separated Measure +/- -0.040

(0.25)

0.054*

(0.09)

0.025

(0.32)

-0.188**

(0.01)

-0.012

(0.42)

0.083*

(0.05)

0.024

(0.34)

-0.286***

(0.00)

GENDER - -0.423**

(0.02)

-0.419**

(0.02)

-0.397**

(0.02)

-0.266*

(0.09)

-0.492***

(0.00)

-0.521***

(0.00)

-0.485***

(0.00)

-0.268*

(0.09)

GOAL - -0.548***

(0.00)

-0.548***

(0.00)

-0.547***

(0.00)

-0.550***

(0.00)

0.043

(0.15)

0.042

(0.15)

0.043

(0.15)

0.049

(0.11)

DURATION + 0.155**

(0.02)

0.158**

(0.02)

0.155**

(0.02)

0.150**

(0.03)

0.324***

(0.00)

0.328***

(0.00)

0.324***

(0.00)

0.313***

(0.00)

PAST_EXPERIENCE + 1.547***

(0.00)

1.541***

(0.00)

1.547***

(0.00)

1.556***

(0.00)

1.166***

(0.00)

1.158***

(0.00)

1.165***

(0.00)

1.170***

(0.00)

VIDEO + 1.482***

(0.00)

1.478***

(0.00)

1.485***

(0.00)

1.504***

(0.00)

1.857***

(0.00)

1.846***

(0.00)

1.858***

(0.00)

1.862***

(0.00)

SOCIAL_CAPITAL + 1.173

(0.46)

2.156

(0.43)

1.762

(0.44)

1.052

(0.46)

0.238

(0.49)

1.169

(0.46)

0.505

(0.48)

-0.384

(0.49)

GDP ? -0.028

(0.47)

-0.052

(0.44)

-0.041

(0.45)

-0.033

(0.46)

-0.071

(0.41)

-0.096

(0.38)

-0.078

(0.40)

-0.069

(0.41)

Intercept Yes Yes Yes Yes Yes Yes Yes Yes

Country Fixed Effects Yes Yes Yes Yes Yes Yes Yes Yes

Year Fixed Effects Yes Yes Yes Yes Yes Yes Yes Yes

Category Fixed Effects Yes Yes Yes Yes Yes Yes Yes Yes

N 1,770 1,770 1,770 1,770 1,770 1,770 1,770 1,770

Adj-R2 0.238 0.238 0.238 0.242 0.301 0.301 0.301 0.308

a. Variables are defined in Appendix B.

b. All continuous variables are winsorized at the 1

st and 99

th percentiles.

c. p-values are adjusted based on the predicted sign. ***, **, and * denote significance at the 1%, 5%, and 10% levels, respectively. All of the

continuous variables are winsorized at 1%. Country fixed effects, year fixed effects, and Kickstarter technology project category fixed effects are

controlled but not reported for brevity. Robust standard errors are clustered by country and year.

35

Panel B: Separate facial features and amount pledged over complain goal amount and number of backers.

Variablesa,b

Sign PLEDGED_GOAL

c BACKER

c

EYEBROW FACE CHIN PHILTRUM EYEBROW FACE CHIN PHILTRUM

(1) (2) (3) (4) (5) (6) (7) (8)

Separated Measure +/- -0.015

(0.40)

0.087*

(0.05)

0.025

(0.34)

-0.288***

(0.00)

-0.011

(0.39)

0.026

(0.22)

-0.002

(0.48)

-0.139**

(0.01)

GENDER - -0.501***

(0.00)

-0.529***

(0.00)

-0.492***

(0.00)

-0.273*

(0.08)

-0.182**

(0.04)

-0.186**

(0.05)

-0.173*

(0.06)

-0.070

(0.28)

GOAL - -0.956***

(0.00)

-0.957***

(0.00)

-0.956***

(0.00)

-0.951***

(0.00)

-0.050***

(0.01)

-0.050***

(0.01)

-0.050***

(0.01)

-0.047***

(0.01)

DURATION + 0.325***

(0.00)

0.329***

(0.00)

0.325***

(0.00)

0.314***

(0.00)

0.184***

(0.00)

0.185***

(0.00)

0.184***

(0.00)

0.178***

(0.00)

PAST_EXPERIENCE + 1.177***

(0.00)

1.169***

(0.00)

1.176***

(0.00)

1.181***

(0.00)

0.939***

(0.00)

0.936***

(0.00)

0.939***

(0.00)

0.941***

(0.00)

VIDEO + 1.861***

(0.00)

1.849***

(0.00)

1.861***

(0.00)

1.865***

(0.00)

1.055***

(0.00)

1.052***

(0.00)

1.056***

(0.00)

1.057***

(0.00)

SOCIAL_CAPITAL + 0.060

(0.50)

1.041

(0.46)

0.351

(0.49)

-0.550

(0.48)

3.959

(0.26)

4.291

(0.24)

4.007

(0.26)

3.685

(0.28)

GDP ? -0.072

(0.41)

-0.098

(0.38)

-0.079

(0.40)

-0.071

(0.41)

-0.135

(0.20)

-0.144

(0.19)

-0.137

(0.20)

-0.135

(0.20)

Intercept Yes Yes Yes Yes Yes Yes Yes Yes

Country Fixed Effects Yes Yes Yes Yes Yes Yes Yes Yes

Year Fixed Effects Yes Yes Yes Yes Yes Yes Yes Yes

Category Fixed Effects Yes Yes Yes Yes Yes Yes Yes Yes

N 1,770 1,770 1,770 1,770 1,770 1,770 1,770 1,770

Adj-R2 0.431 0.432 0.431 0.437 0.302 0.303 0.302 0.307

a. Variables are defined in Appendix B.

b. All continuous variables are winsorized at the 1

st and 99

th percentiles.

c. p-values are adjusted based on the predicted sign. ***, **, and * denote significance at the 1%, 5%, and 10% levels, respectively. All of the

continuous variables are winsorized at 1%. Country fixed effects, year fixed effects, and Kickstarter technology project category fixed effects are

controlled but not reported for brevity. Robust standard errors are clustered by country and year.

36

Table 6

Facial trustworthiness, entrepreneur’s gender, and Kickstarter project application outcome.

Variablesa,b

Sign Kickstarter project application outcome

c

SUCCESS PLEDGED PLEDGED_GOAL BACKER

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

TRUST + 0.614**

(0.01)

0.953***

(0.00)

0.950***

(0.00)

0.412***

(0.01)

TRUST*GENDER - -0.462*

(0.06)

-0.757***

(0.00)

-0.748***

(0.00)

-0.328**

(0.03)

GENDER - -0.496***

(0.00)

-0.639***

(0.00)

-0.646***

(0.00)

-0.242**

(0.01)

GOAL - -0.550***

(0.00)

0.041

(0.16)

-0.958***

(0.00)

-0.051***

(0.01)

DURATION + 0.157**

(0.02)

0.327***

(0.00)

0.328***

(0.00)

0.185***

(0.00)

PAST_EXPERIENCE + 1.537***

(0.00)

1.155***

(0.00)

1.166***

(0.00)

0.934***

(0.00)

VIDEO + 1.484***

(0.00)

1.850***

(0.00)

1.854***

(0.00)

1.052***

(0.00)

SOCIAL_CAPITAL + 1.494

(0.45)

0.756

(0.47)

0.612

(0.48)

4.210

(0.25)

GDP ? -0.036

(0.46)

-0.085

(0.39)

-0.087

(0.39)

-0.142

(0.19)

Intercept Yes Yes Yes Yes

Country Fixed Effects Yes Yes Yes Yes

Year Fixed Effects Yes Yes Yes Yes

Category Fixed Effects Yes Yes Yes Yes

N 1,770 1,770 1,770 1,770

Adj-R2 0.240 0.305 0.434 0.304

a. Variables are defined in Appendix B.

b. All continuous variables are winsorized at the 1

st and 99

th percentiles.

c. p-values are adjusted based on the predicted sign. ***, **, and * denote significance at the 1%, 5%, and

10% levels, respectively. All of the continuous variables are winsorized at 1%. Country fixed effects, year

fixed effects, and Kickstarter technology project category fixed effects are controlled but not reported for

brevity. Robust standard errors are clustered by country and year.

37

Table 7

Gender group analysis of facial trustworthiness and Kickstarter project application outcome.

Panel A: Male entrepreneurs’ facial trustworthiness and Kickstarter project application outcome.

Variablesa,b

Sign Kickstarter project application outcome

c

SUCCESS PLEDGED PLEDGED_GOAL BACKER

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

TRUST + 0.153**

(0.03)

0.194**

(0.04)

0.200**

(0.04)

0.083*

(0.09)

GOAL - -0.534***

(0.00)

0.039

(0.14)

-0.960***

(0.00)

-0.050***

(0.01)

DURATION + 0.177**

(0.02)

0.293***

(0.00)

0.295***

(0.00)

0.174***

(0.00)

PAST_EXPERIENCE + 1.607***

(0.00)

1.223***

(0.00)

1.236***

(0.00)

0.970***

(0.00)

VIDEO + 1.501***

(0.00)

1.863***

(0.00)

1.865***

(0.00)

1.048***

(0.00)

SOCIAL_CAPITAL + 4.000

(0.36)

6.410

(0.28)

6.656

(0.27)

6.989

(0.13)

GDP ? -0.047

(0.44)

-0.189

(0.27)

-0.201

(0.26)

-0.182

(0.13)

Intercept Yes Yes Yes Yes

Country Fixed Effects Yes Yes Yes Yes

Year Fixed Effects Yes Yes Yes Yes

Category Fixed Effects Yes Yes Yes Yes

N 1,590 1,590 1,590 1,590

Adj-R2 0.243 0.312 0.445 0.310

a. Variables are defined in Appendix B.

b. All continuous variables are winsorized at the 1

st and 99

th percentiles.

c. p-values are adjusted based on the predicted sign. ***, **, and * denote significance at the 1%, 5%, and

10% levels, respectively. All of the continuous variables are winsorized at 1%. Country fixed effects, year

fixed effects, and Kickstarter technology project category fixed effects are controlled but not reported for

brevity. Robust standard errors are clustered by country and year.

38

Panel B: Female entrepreneurs’ facial trustworthiness and Kickstarter project application outcome.

Variablesa,b

Sign Kickstarter project application outcome

c

SUCCESS PLEDGED PLEDGED_GOAL BACKER

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

TRUST + 1.118***

(0.00)

1.263***

(0.00)

1.255***

(0.00)

0.654***

(0.00)

GOAL - -0.819***

(0.00)

0.062

(0.34)

-0.937***

(0.00)

-0.049

(0.23)

DURATION + -0.143

(0.29)

0.772***

(0.00)

0.771***

(0.00)

0.298***

(0.01)

PAST_EXPERIENCE + 0.108

(0.48)

-0.165

(0.46)

-0.182

(0.46)

0.690

(0.28)

VIDEO + 2.286**

(0.02)

1.915***

(0.00)

1.923***

(0.00)

1.212***

(0.00)

SOCIAL_CAPITAL + 284.386

(0.16)

-84.859

(0.19)

-83.801

(0.19)

-51.634

(0.14)

GDP ? 3.622

(0.15)

1.824

(0.26)

1.789

(0.27)

1.075

(0.21)

Intercept Yes Yes Yes Yes

Country Fixed Effects Yes Yes Yes Yes

Year Fixed Effects Yes Yes Yes Yes

Category Fixed Effects Yes Yes Yes Yes

N 180 180 180 180

Adj-R2 0.353 0.358 0.433 0.414

a. Variables are defined in Appendix B.

b. All continuous variables are winsorized at the 1

st and 99

th percentiles.

c. p-values are adjusted based on the predicted sign. ***, **, and * denote significance at the 1%, 5%, and

10% levels, respectively. All of the continuous variables are winsorized at 1%. Country fixed effects, year

fixed effects, and Kickstarter technology project category fixed effects are controlled but not reported for

brevity. Robust standard errors are clustered by country and year.

39

Table 8

Facial trustworthiness and Kickstarter project application outcome in initial applications.

Panel A: Facial trustworthiness and application outcome in initial application.

Variablesa,b

Sign Kickstarter project application outcome

c

SUCCESS PLEDGED PLEDGED_GOAL BACKER

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

TRUST + 0.133*

(0.05)

0.228**

(0.02)

0.234**

(0.02)

0.087*

(0.10)

N 1,687 1,687 1,687 1,687

Adj-R2 0.220 0.301 0.423 0.306

Panel B: Facial trustworthiness, gender, and application outcome in initial applications.

Variablesa,b

Sign Kickstarter project application outcome

c

SUCCESS PLEDGED PLEDGED_GOAL BACKER

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

TRUST + 0.576**

(0.02)

0.948***

(0.00)

0.945***

(0.00)

0.397***

(0.01)

TRUST*GENDER - -0.487**

(0.04)

-0.782***

(0.00)

-0.772***

(0.00)

-0.337**

(0.03)

GENDER - -0.519***

(0.00)

-0.672***

(0.00)

-0.679***

(0.00)

-0.259***

(0.01)

N 1,687 1,687 1,687 1,687

Adj-R2 0.221 0.303 0.425 0.307

a. Variables are defined in Appendix B.

b. All continuous variables are winsorized at the 1

st and 99

th percentiles.

c. p-values are adjusted based on the predicted sign. ***, **, and * denote significance at the 1%, 5%, and

10% levels, respectively. All of the continuous variables are winsorized at 1%. Control variables,

intercept, country fixed effects, year fixed effects, and Kickstarter technology project category fixed

effects are controlled but not reported for brevity. Robust standard errors are clustered by country and

year.

40

Table 9

Facial trustworthiness and Kickstarter project application outcome in U.S. sample.

Panel A: Facial trustworthiness and application outcome in U.S. sample.

Variablesa,b

Sign Kickstarter project application outcome

c

SUCCESS PLEDGED PLEDGED_GOAL BACKER

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

TRUST + 0.206***

(0.00)

0.264**

(0.03)

0.264**

(0.03)

0.117*

(0.07)

N 1,190 1,190 1,190 1,190

Adj-R2 0.219 0.316 0.432 0.304

Panel B: Facial trustworthiness, gender, and application outcome in U.S. sample.

Variablesa,b

Sign Kickstarter project application outcome

c

SUCCESS PLEDGED PLEDGED_GOAL BACKER

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

TRUST + 0.843***

(0.00)

1.235***

(0.00)

1.235***

(0.00)

0.570***

(0.01)

TRUST*GENDER - -0.702***

(0.01)

-1.058***

(0.00)

-1.058***

(0.00)

-0.494**

(0.01)

GENDER - -0.669***

(0.00)

-0.652***

(0.00)

-0.652***

(0.00)

-0.284**

(0.01)

N 1,190 1,190 1,190 1,190

Adj-R2 0.221 0.319 0.434 0.306

a. Variables are defined in Appendix B.

b. All continuous variables are winsorized at the 1

st and 99

th percentiles.

c. p-values are adjusted based on the predicted sign. ***, **, and * denote significance at the 1%, 5%, and

10% levels, respectively. All of the continuous variables are winsorized at 1%. Control variables,

intercept, year fixed effects and Kickstarter technology project category fixed effects are controlled but

not reported for brevity. Robust standard errors are clustered by country and year.

41

Table 10

Facial trustworthiness and project application outcome controlling for entrepreneurs’ facial attractiveness.

Panel A: Facial trustworthiness and application outcome controlling for facial attractiveness.

Variablesa,b

Sign Kickstarter project application outcome

c

SUCCESS PLEDGED PLEDGED_GOAL BACKER

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

TRUST + 0.158**

(0.04)

0.245**

(0.03)

0.253**

(0.02)

0.104*

(0.08)

ATTRACTIVENESS ? 0.183

(0.16)

0.045

(0.82)

0.037

(0.85)

0.023

(0.85)

N 1,770 1,770 1,770 1,770

Adj-R2 0.240 0.303 0.433 0.304

Panel B: Facial trustworthiness, gender, and application outcome controlling for facial attractiveness.

Variablesa,b

Sign Kickstarter project application outcome

c

SUCCESS PLEDGED PLEDGED_GOAL BACKER

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

TRUST + 0.556**

(0.03)

0.942***

(0.00)

0.943***

(0.00)

0.406**

(0.01)

TRUST*GENDER - -0.434*

(0.07)

-0.751***

(0.01)

-0.744***

(0.01)

-0.325**

(0.03)

GENDER - -0.492***

(0.00)

-0.638***

(0.00)

-0.645***

(0.00)

-0.241***

(0.01)

ATTRACTIVENESS ? 0.175*

(0.09)

0.029

(0.44)

0.021

(0.46)

0.016

(0.45)

N 1,770 1,770 1,770 1,770

Adj-R2 0.241 0.305 0.434 0.304

a. Variables are defined in Appendix B.

b. All continuous variables are winsorized at the 1

st and 99

th percentiles.

c. p-values are adjusted based on the predicted sign. ***, **, and * denote significance at the 1%, 5%, and

10% levels, respectively. All of the continuous variables are winsorized at 1%. Control variables,

intercept, country fixed effects, year fixed effects and Kickstarter technology project category fixed

effects are controlled but not reported for brevity. Robust standard errors are clustered by country and

year.