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Entrepreneur’s Facial Trustworthiness, Gender,

and Crowdfunding Success

November 2018

Abstract: This study examines how entrepreneurs’ facial trustworthiness is associated with the

success of their crowdfunding campaigns. We adopt a novel dataset collected from Kickstarter

crowdfunding platform and employ machine learning-based facial detection techniques to

construct a comprehensive facial trustworthiness index for our investigation. Our results show

that entrepreneur’s facial trustworthiness is positively associated with crowdfunding campaign

success. Specifically, trustworthy-looking entrepreneurs receive 22.8% more amount pledged

and attract 9.1% more backers in the crowdfunding campaign compared with untrustworthy-

looking entrepreneurs. We also find that female entrepreneur’s facial trustworthiness plays a

more prominent role in determining project success than that of male entrepreneur’s. Our study

provides the first empirical evidence on how individual facial trustworthiness affects the

outcomes of reward-based crowdfunding.

Keywords: Facial Trustworthiness; Crowdfunding; Gender; Entrepreneurial Finance

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1. Introduction

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

It is widely acknowledged that entrepreneurial activities contribute to the economic growth,

while financial constraint is still the major concern that impedes the action of entrepreneurship

(Chatterji and Seamans 2012). Although professional venture capitalists and angel investors are

only be accessed to a small number of seemingly successful ventures, crowdfunding provides a

new financing solution for a wide range of entrepreneurs who enter into the venture market with

limited product information and tracked records that may help attract commercial funding

sources. In recent years, crowdfunding has grown exponentially in market size and attracted

great attention from both practitioners and scholars. According to Statista (2018), the total dollar

amount of reward-based crowdfunding1 reached USD 919.3 million in the U.S. and USD 6,547.0

million globally in 2017.

Different from traditional commercial funding, such as 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, the information about the early stage ventures on the reward-based

crowdfunding platforms is generally self-reported, with limited or no tracked records to justify

their credentials to help funders make their funding decisions (Calic and Mosakowski 2016). Due

to the opaque information environment in crowdfunding and the lack of monitoring from

financial intermediaries, crowdfunding may suffers from severe information asymmetry issues

(Belleflamme, Omrani, and Peitz 2015; Chemla and Tinn 2017; Kalayci, Ekenel, and Gunes

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

than seeking for equity investments. Statista (2018) acquire the total reward-based crowdfunding statistics by focusing on the

reward-based crowdfunding and pre-financing of products, art, music and films, software or scientific research, while excluding

traditional venture capital investment, equity-based crowdfunding, or lending-based crowdfunding.

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2014; Strausz 2017). Thus, other than the desirability of the rewards, funders need to evaluate the

trustworthiness of the information disclosed by the entrepreneurs to mitigate their concerns of

the asymmetric information (Misztal 2013). Entrepreneur’s facial trustworthiness, which is

defined as a funder’s perceptions of an entrepreneur’s ability, benevolence, and integrity (Mayer,

Davis, and Schoorman 1995) based on the entrepreneur’s facial appearance, can be influential

for funder’s funding decisions. This paper aims to investigate whether an entrepreneur with more

facial trustworthiness are more likely to experience fundraising success in crowdfunding.

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

the trustworthiness of others based on their facial features and also tend to incorporate such

facial trustworthiness judgement into their subsequent social decision-making (Borkenau, Brecke,

Möttig, and Paelecke 2009; Todorov, Loehr, and Oosterhof 2010; Todorov, Olivola, Dotsch, and

Mende-Siedlecki 2015). People could rapidly develop their perceptions of the facial

trustworthiness (Porter, England, Juodis, Ten Brinke, and Wilson 2008), and this appearance-

based trustworthiness perceptions are highly correlated with each other (Blankespoor, Hendricks,

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

research documents the implications of facial trustworthiness in various business settings

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

Chater 2012) with a general impression that 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 market, trust on

entrepreneurs can alleviate funders’ concerns about the credibility of entrepreneurs’ self-reported

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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 stereotype in entrepreneurship advocates masculine over feminine

(García and Welter 2013; Ogbor 2000). Women are often perceived as less competent with their

innovation and business development skills and tend to receive less venture resources compared

with men in entrepreneurship (Lerner and Almor 2002; Mitchelmore and Rowley 2013; Thébaud

2010). Due to such stereotype, 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 female individuals than that of male individuals when browsing the information

on online 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 when being evaluated (Brownmiller 1984; Kaschak 1992). Thus, compared with that of

male entrepreneur’s, female entrepreneur’s facial trustworthiness may play a more important role

to affect funders’ evaluation of the entrepreneurial ventures and their funding decisions. We

therefore hypothesize that the positive association between entrepreneur’s facial trustworthiness

and crowdfunding success is strengthened if the entrepreneur is female.

To examine our hypotheses, we adopt a web crawler to extract the detailed funding

information of 1,770 technology related projects on Kickstarter, one of the most popular and

successful crowdfunding platforms2 (Strausz 2017). We focus on the technology category among

all venture projects on Kickstarter because information asymmetry problems are more prevalent

for technology focused entrepreneurial ventures due to the secrecy of their core techniques

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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especially in early stage (Kousari 2011). We identify founding entrepreneurs’ headshot pictures

and employ machine learning-based facial detection techniques (Dalal and Triggs 2005; Kazemi

and Sullivan 2014; Sagonas, Tzimiropoulos, Zafeiriou, and Pantic 2013) to measure

entrepreneur’s facial trustworthiness based on four distinctive facial features, including inner

eyebrow ridge, angle of the chin, roundness of the face, and length of the lip-to-nose distance, as

suggested by prior neuroscience and psychology literature (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, which is measured by 1) the likelihood of fundraising

success, 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. More specifically, female entrepreneur’s 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 U.S. entrepreneurs’ sample only. Moreover, our results still hold after

controlling for entrepreneurs’ facial attractiveness (Graham, Harvey, and Puri 2016; Halford and

Hsu 2014).

Our paper has a main contribution to the growing literature on crowdfunding

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

crowdfunding platforms, and incorporate their perceptions of entrepreneurs’ facial

trustworthiness in their funding decisions. Our study is most related to Duarte et al. (2012), who

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investigates the role of appearance-based impression plays in peer-to-peer lending, but differs in

1) funders on Kickstarter need to rely on the limited information voluntarily provided by the

entrepreneurs, while funders on peer-to-peer lending platform can easily gain access to hard

information, such as the fundraiser’s credit score range to assist their funding decisions; 2) we

report the novel evidence that female entrepreneur’ facial trustworthiness plays a more important

role than that of male entrepreneur’s in determining crowdfunding success; 3) we construct the

appearance-based measure using machine learning based facial feature point detection

techniques. The availability and use of the new methodology sheds light on fruitful directions for

future studies on facial trustworthiness in various business and economic decision settings. The

results of our study are important for financial regulators to regulate the information environment

in crowdfunding by developing more transparent and reliable disclosure policies. Additionally,

our study adds to the literature on how trust shapes financial decisions. Existing studies

document that trust increases households’ uses of trust-intensive contract instead of using only

cash (Guiso, Sapienza, and Zingales 2004), individual’s participation in stock market (Guiso,

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

venture capital investments (Bottazzi, Da Rin, and Hellmann 2016). These studies rely on

measures of generalized societal trust based at the national level. Our study emphasizes

individual differences in the facial trustworthiness, thus expands societal trust to trust at the

individual level and reduces the concern that individual level trust may be heterogeneous within

a geographic region. Finally, our paper is related to finance literature on appearance in corporate

context. Appearance affects managerial compensation (García and Welter 2013), hedge fund

investment (Pareek and Zuckerman 2014) and shareholder value (Halford and Hsu 2014). We

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extend this line of research to the context of entrepreneurial finance and suggest that the

appearance of entrepreneurs with early-stage venture matters in a crowdfunding setting.

The rest of the paper is organized as follow. Section 2 reviews related literature and

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

trustworthiness, and empirical methodology. Section 4 and 5 present main empirical results and

robustness tests. Section 6 is the concluding remarks.

2. Literature Review and Hypothesis Development

2.1 Crowdfunding and Information Asymmetry

Crowdfunding provides an external financing tool for early stage entrepreneurial ventures to

raise funds on the internet from a large crowd.3 However, the funders (i.e., the crowd) may face

information asymmetry problems in crowdfunding. Entrepreneurs seeking external financing

through crowdfunding platforms tend to present very limited historical performance data and

tracked records of success (Belleflamme et al. 2015; Calic and Mosakowski 2016; Strausz 2017),

and are also reluctant to disclose their innovation secrets to the general public, especially to their

competitors, prior to selling their mature products (Agrawal, Catalini, and Goldfarb (2014).4

Funders, with the limited information being disclosed by entrepreneurs, are not able to exercise

their due diligence to evaluate the crowdfunding projects and therefore are less likely to have

access to the true ability of the entrepreneurs or the true quality of the products. Due to the lack

of necessary information to evaluate the proposed campaigns and an effective monitoring by

3 Compared with traditional financing with financial intermediaries, crowdfunding exhibits obvious strength that it allows

entrepreneurs to learn about potential customers’ demand before the project is launched. da Cruz (2018) argue that crowdfunding

serves as an informational mechanism that provides a valuable information on the crowd’s valuation about the presented idea,

helping entrepreneurs to mitigate the uncertainty regarding the acceptance of a new product or service. Based on the follow-up

survey data, Mollick and Kuppuswamy (2014) find that besides providing funded money, crowdfunding also helps provide

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

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

video campaign description about the projects posted on Kickstarter (Mollick 2014).

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traditional financial intermediaries, entrepreneurs may engage in short-term opportunistic

behavior that results in moral hazard problem (Agrawal et al. 2014).

Chemla and Tinn (2017) model moral hazard problem in crowdfunding by arguing that

entrepreneurs may embezzle the funds raised on internet-based crowdfunding platforms, e.g.,

Kickstarter, because they are not legally responsible for guaranteeing product delivery. Strausz

(2017) develops a theoretical model of profit-maximizing contract, in the presence of

entrepreneurial moral hazard, consumers’ private information about the demand and

entrepreneurs’ private information about their production cost, to provide insights on how to

control moral hazard in crowdfunding. Hildebrand, Puri, and Rocholl (2016) highlight another

ethical consequence of information asymmetry in a lending-based crowdfunding based on the

evidence that group leader bids, which may have higher default rates, tend to be perceived as a

positive signal and are rewarded with lower interest rates. They argue that potential lenders are

taken advantage of by unscrupulous loan originators due to information asymmetry issues.

2.2 Trust and Its Role to Alleviate Information Asymmetry Problems

Trust refers to the subjective probability that individuals attribute to the possibility of being

cheated by evaluating the subjective characteristics of the person being trusted (Guiso et al.

2008). Align with the social capital literature, studies about the effects of trust on economic

efficiency have shown that social trust affects household’s financial decision (Guiso et al. 2004),

individual’s stock market participation (Guiso et al. 2008) and thus economic growth (La Porta,

Florencio, Shleifer, and Vishny 1997). Bilateral trust between nations also predicts the trade

relations and investments between two countries (Guiso et al. 2009). Trust may also 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.

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One potential mechanism that trust facilitates inter-organizational efficiency is to reduce

information asymmetry between the contracting parties (Al-Najjar and Casadesus-Masanell 2001;

Chami and Fullenkamp 2002). The “crowdfunding contract” between entrepreneurs and funders

offers little tools for funders to fully understand the project and to track project information once

they commit their capital. Trust on entrepreneurs, ignited by potential funders evaluating

entrepreneurs’ facial trustworthiness, can help alleviate funders’ concerns about the asymmetric

information. Thus, funders may condition their funding decisions on how much they “trust” an

entrepreneur based on their observations of entrepreneurs’ facial trustworthiness.

In a recent study, Lin and Pursiainen (2017) document a positive association between

social capital of the entrepreneur’s home county and the success rate of crowdfunding campaigns.

The underlying assumption of this study is that the level of trust is determined by general prior

beliefs or stereotypes, which are heterogeneous across different geographical regions, but

homogeneous within a certain geographical region. Bottazzi et al. (2016) classify trust into two

different categories: (i) personalized trust which focuses on a specific trading partner; and (ii)

generalized trust which concerns regional “institutions” and call for further research that

disentangles the effects of these two types of trust in business settings. To the best of our

knowledge, however, very few prior studies examine the relationship between entrepreneur-

specific trustworthiness at the individual level in crowdfunding setting. Our study aims to

provide the first attempt to investigate how entrepreneur’s facial trustworthiness affects funder’s

funding decisions, as demonstrated by the performance of crowdfunding campaigns.

2.3 Facial Trustworthiness and Crowdfunding Success

People can quickly generate an impression of the personal traits (e.g., trustworthiness) of other

people by visually observing their facial appearance (Bar, Neta, and Linz 2006; Todorov et al.

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2015). Todorov et al. (2015) suggests that people are able to form a trustworthiness impression

based on the facial features of others with an exposure of as little as 34 milliseconds. Longer

exposures to a face would not alter people’s initial perception of trustworthiness, but rather

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

Prior studies find that certain facial traits affect how individuals are perceived as

trustworthy or untrustworthy (Dotsch and Todorov 2012; Enlow and Hans 1996; Robinson et al.

2014; Todorov et al. 2008). For example, Enlow and Hans (1996) find that shallow cheeks and

low eyebrow ridge lead to low perceived trustworthiness. Todorov et al. (2008) suggest that

upper inner eyebrow ridge, pronounced cheekbones, wide chin, and shallow nose sellion are

correlated with high facial trustworthiness. The distance between the mouth and the nose is also

an important feature for trustworthiness judgments (Todorov et al. 2008), with longer lip-to-nose

distance tend to signal less trustworthy looking. Dotsch and Todorov (2012) show that a

trustworthy-looking face is associated with a smooth and small face, a smiling mouth, and open

eyes, while an untrustworthy-looking face is associated with a downturned mouth with thick lips,

angry-looking eyes, sagging cheeks, and a bold spot on top of the head. Using “Bubbles”

technique, Robinson et al. (2014) find that facial traits in human eye and mouth areas are

correlated with trustworthiness judgments.5

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

untrustworthy-looking face, but more likely to trust people who look more trustworthy. For

example, in criminal cases, defendants who have untrustworthy-looking faces are more likely to

5 Emotional expressions, especially smiling, could influence the trustworthiness decisions (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, typical entrepreneurs’

picture from Kickstarter is wearing a smile, resulting in little variance in measuring facial trustworthiness via smiling facial

expression. Second, facial typicality is closely associated with a pool of faces that funders have ever seen and interacted with, so

that it may be impossible for us to define face typicality for every funder from the crowd.

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receive guilty verdicts (Porter, ten Brinke, and Gustaw 2010). In business settings, for example,

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

Rezlescu et al. 2012; Tingley 2014). Firms with CEOs who are perceived as more trustworthy

also tend to receive higher valuations at all stages of the IPO (Blankespoor et al. 2017). People

also use facial trustworthiness to modify their wagering decisions (Schlicht, Shimojo, Camerer,

Battaglia, and Nakayama 2010). People’s inferences of facial trustworthiness tend to be highly

consistent with each other (Kim and Rosenberg 1980; Oosterhof and Todorov 2008; Rosenberg,

Nelson, and Vivekananthan 1968; Rule et al. 2013).

Entrepreneurs’ headshots on Kickstarter may provide important facial cues to assist

funders evaluate entrepreneurs’ trustworthiness.6

When potential funders are reviewing

information disclosed from fundraising webpage, they may feel uncertain about the true quality

of the proposed project due to high information asymmetry. If entrepreneurs’ pictures are

provided on the webpage, funders may naturally interpret the level of trustworthiness from

entrepreneur’s static facial features and incorporate such facial trustworthiness information into

their subsequent crowdfunding decision-making.7 Funders are likely to trust an entrepreneur with

trustworthy-looking facial appearance and tend to fund the campaign developed by a

6 We identified 16,122 Kickstarter projects in technology category from October 2009 to September 2017, among which about

11.52% (1,858 projects) uploaded entrepreneur pictures on the fund-raising webpage. We further exclude group photos and 1,802

high quality single pictures of entrepreneurs remained. The entrepreneur’s picture is located on the upper left corner of the

Kickstarter project webpage with the name of the entrepreneur well specified, so that we are confidently to argue that the picture

is the entrepreneur herself. Additionally, by single click the picture, the crowd can see the biographical information of the project

entrepreneur. By further clicking “See full profile”, the entrepreneur’s picture is clearly displayed. Therefore, the entrepreneur‘s

picture, if voluntarily provided by the entrepreneur, is part of funders’ information set that facilitates them to make crowdfunding

decisions. One may refer to the following weblink as an example: https://www.kickstarter.com/profile/966842631/about 7 We argue that funders’ trustworthiness perceptions based on entrepreneur’s facial features are made unintentionally. Prior fMRI

studies find that facial trustworthiness evaluation is correlated with the activation of amygdala (Winston, Strange, O'Doherty, and

Dolan 2002). Neuroscience literature suggests that people unconsciously make perceptions based on facial features (Evans 2008;

McClure, Laibson, Loewenstein, and Cohen 2004). Amygdala is partially responsible for System 1 thinking process described as

rapid, intuitive, and universal, compared with System 2 thinking process that is slower, controlled, and logical (Evans 2008).

McClure et al. (2004) find that limbic (including amygdala) activation may explain the impulsive behavior associated with the

sight, smell, or touch of a desired object. Since 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 the information

from entrepreneur’s facial features, but rather are unconsciously influenced by entrepreneur’s facial trustworthiness.

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trustworthy-looking entrepreneur. This discussion leads to our first hypothesis stated in an

alternative form as following:

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 recognized in both academic and

practice. For example, women receive 22% lower wages than man, 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) argue that “stereotypes often are a potent

barrier to women’s advancement to positions of leadership”. Bird and Brush (2002) also indicate

that entrepreneurship has been recognized as a male dominated profession. Due to the gender

stereotypes in business ventures, entrepreneurial activities may be viewed from a gender-biased

perspective, which prioritizes masculine over feminine (García and Welter 2013; Ogbor 2000).

Funders on Kickstarter may be potentially biased by this gender stereotype when evaluating the

proposed projects and consider female entrepreneurs as less capable in terms of managing the

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

about the ability of female entrepreneurs is to gather additional information, such as

trustworthiness indicators from entrepreneurs’ facial appearance, to evaluate the likelihood of

success for female entrepreneurs’ venture projects. Therefore, funders may put more weight on

the facial trustworthiness of female entrepreneurs than those of male entrepreneurs when

evaluating Kickstarter campaigns.

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

more attention to females’ profile photograph than to that of males (Seidman and Miller 2013).

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For example, Seidman and Miller (2013) created eight Facebook profiles with eight photographs

including two attractive and two unattractive for both genders. The text profiles for these eight

profiles are rated as most equivalent on personalities. Participants’ eye movements were tracked

while they viewed each profile for 60 seconds. They found that participants spent significantly

more time viewing profile photos of their female targets than male targets.8 Such evidence is

highly consistent with feminist theory arguing that physical appearance is more essential for

achieving social status for female than for male, whereas males are more likely to be evaluated

on a broader spectrum of other traits (Adolphs, Baron-Cohen, and Tranel 2002; Brownmiller

1984; Kaschak 1992). Based on these prior studies, we expect that funders on Kickstarter are

more likely to extract and utilize the information from female entrepreneurs’ photos than from

male counterparts. Therefore, female entrepreneurs’ facial trustworthiness may play a more

important role in funders’ backing decisions than that of male entrepreneurs. We hence propose

our second hypothesis in the alternative form as following:

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

success is more pronounced for female entrepreneurs than for male entrepreneurs.

3. Research Methodology

3.1 Sample Collection

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

crowdfunding platforms.9

We focus on technology-related projects because entrepreneurs of

technology-related projects tend to be reluctant to disclose information related to their core

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

gender, and physical attractiveness and dependent variables. They hence exclude the alternative explanation that participant’s

attention toward the attractive individuals of the opposite gender is driven by sexual interest. 9 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 different crowdfunding platforms,

finance researchers have increasingly focused on lending-based and reward-based types, and Kickstarter is among the

crowdfunding platforms that are most frequently studied.

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techniques, and thus are more likely to suffer from information asymmetry issues (Kousari 2011).

Technology-related projects also closely resemble entrepreneurial ventures in the conventional

venture capital markets. We adopt a web crawling algorithm to crawl data about the

characteristics of the projects, including the geographic locations, the dates of initiation and

deadline, pledged goals, number of backers and total amount pledged, as well as entrepreneurs’

names and profile photos from Kickstarter. Our algorithm initially extracts 16,122 Kickstarter

projects in the technology category that were active from October 2009 to September 2017.

Among them, only 1,858 projects (approximately 11.52%) provide high quality profile pictures

of the entrepreneurs who developed the projects. We further exclude 56 group photos (multiple

entrepreneurs are included in one picture), because it is hard to identify the most influential

person in the founding team from the picture. Our final picture sample includes 1,802 high

quality individual entrepreneur’s pictures. We also extract country specific characteristics that

may affect the development of the projects in their geographically located countries. These

country specific characteristics include the national annual gross domestic product per capita,

and the national social capital, which is a country level trust index provided by the World Values

survey. This process further reduced our sample to 1,770 projects from seventeen countries

(regions)10

from 2009 to 2017.

3.2 Facial Trustworthiness Measurements

We employ a machine-learning based face detector11

to identify the facial features of

each entrepreneur using their profile picture and measure their facial trustworthiness. Following

prior studies, we identify four specific facial features that are suggested to contribute to the

10 These countries/regions include Australia, Austria, Canada, Switzerland, Germany, Spain, France , United Kingdom, Hong

Kong, Italy, Mexico, Netherlands, Norway, New Zealand, Singapore, Sweden, and United States. 11 The face detector is built on top of 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 the facial trustworthiness measurements

(Kazemi and Sullivan 2014; Sagonas et al. 2013).

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trustworthiness perceptions. Specifically, we calculate the angle of inner eyebrow ridge

(EYEBROW), which is suggested to be negatively associated with facial 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 wideness of the

chin on a face. People with a wider chin is likely to be perceived as more trustworthy (Todorov

et al. 2008). PHILTRUM measures the lip-to-nose distance scaled by the upper facial height.

People with a long PHILTRUM tend to be perceived as less trustworthy (Todorov et al. 2008;

Vernon et al. 2014). Appendix A describes the detailed procedures that we employed to measure

these four facial features.12

Prior literature suggest that when exposed to faces, people quickly build “holistic

representations”, which processes information from various facial feature as an integrated

perceptual whole (Taubert, Apthorp, Aagten-Murphy, and Alais 2011). Consistently, 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 the ease of comparison across different 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 because prior studies suggest that these two measures are negatively associated

with facial trustworthiness. Third, we average the standardized values of FACE and CHIN, and

12 All of these four facial trustworthiness measurements are 2-D measurements, since the CEO’s and CFO’s pictures we collected

from Google Images are 2-D formats. Prior literature also 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 more

trustworthy-looking faces. However, as 2-D pictures do not have depth dimension, we cannot calculate 3-D trustworthiness

measurements using 2-D pictures.

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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.

3.3 Sample Descriptive Statistics

Table 1 Panel A depicts the number of Kickstarter technology projects and the successful

rate in our sample by year. The number of technology projects that provides entrepreneurs’

profile photos on Kickstarter increases from 6 projects in 2009 to 406 projects in 2017.

Interesting, annual success rates, which drops from 66.67% in 2009 to 13.79% in 2017, indicate

that backers are more conservative in funding technology projects13

. Table 1 Panel B plots

Kickstarter projects and success rate by country. The majority (67.2%) of our sample come from

the United States. The overall success rate of crowdfunding is 31.2% throughout the sample

period. Projects from some countries, e.g., Norway (57.4%), Sweden (40.0%), United States

(33.5%), and Hong Kong (33.3%), exhibit higher success rate in crowdfunding, compared with

projects from other countries.

[Insert Table 1 about Here]

Table 2 tabulates the descriptive statistics. 31.2% of sample technology projects are

successfully funded. The great majority (89.8%) of entrepreneurs are male, consistent with

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

dominated” domain. There are about 4.7% of the projects developed by experienced

entrepreneurs on Kickstarter, evidenced by their previous crowdfunding application records,

regardless of the funding results. Moreover, among these technology projects in our sample, 76.2%

of them provide video presentations of their products, which is viewed as a positive quality

13 The project success rate calculated in our sample is also consistent with that generated from Kickstarter project population.

According to Bidaux (January 26, 2018), compared with success rate of all Kickstarter projects in 2017 (36.7%, 19,348

successfully funded projects over a total of 52,741 projects), project success rate in technology category is much lower (20.4%,

1,253 successful projects over total 6,160 projects). Additionally, total number of technology projects in 2017 also declined by

13.1% compared with that in 2016 (6,160 projects in 2017 vs. 7,089 projects in 2016).

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indicator (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 Effect of entrepreneur’s facial trustworthiness on crowdfunding success

Hypothesis 1 predicts that entrepreneurs with trustworthy-looking facial features are more likely

to be successful in crowdfunding. 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 employ four measurements to proxy for the performance of the Kickstarter projects

in our sample. SUCCESS is a dummy variable that equals to 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 a logarithm form. BACKER represents total number of funders

in the project expressed in logarithm form. We take logarithms for all three continuous variables

since the distributions of them are highly skewed. Logit model is performed for dependent

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variable SUCCESS and OLS regressions for the other three continuous variables.14

H1

hypothesizes that entrepreneurs who look more trustworthy are more likely to have their

entrepreneurial venture successfully funded on Kickstarter. Thus, we expect that higher TRUST

is 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 success in crowdfunding platforms (Greenberg and Mollick 2017).15

Theoretical models (Schwienbacher 2017; Strausz 2017) predict that setting higher goal amount

make campaign less likely to be funded. Thus, we control for project goal (GOAL) and expect

that projects with lower goal are related to higher success rate. 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 entrepreneur’s experience in crowdfunding on Kickstarter

(PAST_EXPERIENCE) using a binary variable with 1 indicates 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 that describes project details is

usually a signal for a higher quality and well prepared project. We hence include an indicator

variable video (VEDIO), which equals 1 if the project webpage provides a video. In addition, we

control for country-level social capital index (SOCIAL_CAPITAL), as Lin and Pursiainen (2017)

suggest that crowdfunding projects originated in countries with higher social capital tend to have

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 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 2018). Johnson

et al. (2018) document that female entrepreneurs in Kickstarter are more likely to be funded than their male counterparts. Johnson

et al. (2018) attribute the success of female entrepreneurs in crowdfunding context to trustworthiness perceptions. 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 more failures.

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higher success rate. Finally, we include country specific annual Gross Domestic Product per

capita (GDP) and expect that funders’ funding decisions may be influenced by macroeconomic

conditions. Country-fixed and year-fixed effects are controlled to capture the country variate and

time variate turbulence that may affect crowdfunding outcome. We also add fixed effects to

control for sub-categories of the projects under the technology category because the information

asymmetry issue may be heterogeneous across different sub-categories of technology.16

All

continuous variables are winsorized at 1% level. We cluster standard errors by country and by

year.

3.4.2 Moderating effect of entrepreneurs’ gender

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

crowdfunding success is moderated by 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)

Entrepreneur’s gender (GENDER) is a dummy variable, which equals 1 if the

entrepreneur is a male, and 0 if the entrepreneur is a female. H2 predicts that the impact of

entrepreneurs’ facial trustworthiness on project application outcome is greater for female

entrepreneurs than for male entrepreneurs. Thus, we expect to observe a negative coefficient of

interaction term (TRUST*GENDER). We include the same set of control variables, country fixed

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.

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effects, year fixed effects, and technology sub-categories fixed effects. All continuous variables

are winsorized at 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 the first column, 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 project fundraising success (0.192, p-value<0.001). In columns two to four, 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), 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 the changes in total dollar amount pledged

(PLEDGED) and total number of backers (BACKER) in the crowdfunding campaign arising from

the change in entrepreneur’s facial trustworthiness from the 25th

percentile (-0.397). We find that

such inter-quartile change in entrepreneur’s facial trustworthiness results in 22.8% increase in

pledged amount and 9.1% increase in number of backers, keeping other elements that may

determine crowdfunding campaign success constant.17

The results of control variables are

17

In Table 4 column (2), given the coefficient of 0.254 for TRUST, when 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 logged value of total dollar amount pledged in crowdfunding campaign (PLEDGED) amounts to

[(0.254*0.413) – (0.254*-0.397) = 0.20574]. Therefore, total dollar amount pledged, on average, increase by about

22.84 percent [e0.20574

-1=22.84%]. In Table 4 column (4), given the coefficient of 0.108 for TRUST, when

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 logged value of number of backers in crowdfunding campaign (BACKER)

amounts to [(0.108*0.413) – (0.108*-0.397) = 0.08748]. Therefore, total dollar amount pledged, on average,

increase by about 9.14 percent [e0.08748

-1=9.14%].

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generally consistent with prior 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 higher success rate if they show a video presentation, which could be a signal of high

quality and good preparation of the proposed projects.

[Insert Table 4 about Here]

We further breakdown the composite facial trustworthiness measure TRUST into four

individual measures based on the four facial features that we used to construct TRUST to

evaluate how our results may be captured when we focus on individual facial features. These

four facial features are EYEBROW, FACE, CHIN and PHILTRUM. 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 have a successful fund-raising. This is consistent with prior literature

suggesting that the roundness of a face (Berry and Zebrowitz-McArthur 1988; Gorn et al. 2008;

Livingston and Pearce 2009; Todorov et al. 2008) may positively, and the length of a philtrum

(Todorov et al. 2008; Vernon et al. 2014) may negatively predict people’s perceptions of the

facial trustworthiness.

[Insert Table 5 about Here]

4.2 Moderation effect of gender

Table 6 tabulates the regression results for H2. We hypothesize that the facial trustworthiness of

female entrepreneurs has a greater impact on backers’ funding decisions than that of male

entrepreneurs. As shown in Table 6, we find that the coefficient of the interaction term

(TRUST*GENDER) is significantly negative across all four different measures of crowdfunding

success, indicating that compared with that of male entrepreneur’s, female entrepreneur’s facial

Paper #890363

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trustworthiness is more prominent in determining the success rate of entrepreneurial ventures,

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

our second hypothesis is also supported.

[Insert Table 6 about Here]

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

into male entrepreneur and female entrepreneur groups, respectively, and perform a sub-group

analysis to examine the different effects of facial trustworthiness on crowding success across

these two gender groups. 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 Panel A of Table

7, the facial trustworthiness of male entrepreneurs 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 in a more prominent and significant way as indicated by

much larger coefficients and higher significant levels. These results provide further support for

both H1 and H2, suggesting that both male and female entrepreneurs’ facial trustworthiness

influence backers’ decisions in crowdfunding, while female entrepreneur’s facial trustworthiness

generate more influential impacts.

[Insert Table 7 about Here]

5. Additional Analyses

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5.1 Initial Project Applications versus Seasoned Applications

We argue that information asymmetry exists in crowdfunding, and the level of perceived facial

trustworthiness may alleviate funders’ concerns over information asymmetry and hence facilitate

funders’ decision making. Trust plays a vital role given asymmetric information, especially when

funders are exposed to very limited information about the proposed projects. We therefore isolate

testing sample to initial project applications, where no prior information of entrepreneurs is

available and hence are subject to the most severe information asymmetry. The regression results

are reported in Table 8. Consistent with our predictions, entrepreneurs’ facial trustworthiness is

positively associated with likelihood of success, total dollar amount pledged, and total number of

backers. In Table 8 Panel B, we also find that such association is 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

Since about 63.1% of the Kickstarter projects are pledged in U.S. dollars (Bidaux January 26,

2018) and about 67.2% of projects in our sample is developed by entrepreneurs from the U.S.,

We select the U.S. projects from our sample to further explore the effects of entrepreneurs’ facial

trustworthiness on crowdfunding success. Although we have included the country fixed effect in

our main analyses, and our main results, based on the full sample, suggest that the role of

perceived trustworthiness is not absorbed by some well-established country-level factors, by

restricting the sample into one country, we may address the concern that the observed effect of

entrepreneurs’ facial trustworthiness could be driven by unobservable country-level factors that

are related to both backers’ perceptions and the likelihood of success.

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

a same entrepreneur’s different projects could be heterogenous, so the entrepreneur’s picture is still new to those who only

backed the second or third project.

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Table 9 reports the empirical results for hypothesis testing when only the U.S. sample is

selected. Panel A shows that for the U.S. Kickstarter projects, entrepreneurs’ facial

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 female entrepreneurs’ facial trustworthiness plays a more prominent

role in determining Kickstarter campaign success than that of male counterparts. Overall, the

results are consistent with our hypotheses when we limited our sample to U.S. projects, and thus

our results still hold in the U.S. setting.

[Insert Table 9 about Here]

5.3 Controlling for Facial Attractiveness

Our study focuses on the character of trustworthy that can be perceived from human faces.

However, human face may generate perceptions on various characteristics. One may argue that it

is facial attractiveness, instead of facial trustworthiness, that affect backers’ funding decisions. In

order to evaluate such alternative explanation, we create a proxy for founding entrepreneurs’

facial attractive based on the attractiveness measure developed by Fan, Chau, Wan, Zhai, and

Lau (2012) and Kalayci et al. (2014), which suggest that facial symmetry is an informative proxy

for attractiveness prediction. We calculate the facial asymmetry index19

(ATTRACTIVENESS) for

the 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 are still consistent with the

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

angle. The formula for symmetric ratio is absolute value of the difference of separated facial features in right-hand side and in

left-hand side, scaled by the lesser value of the separated facial features in right-hand side and in left-hand side. The symmetric

ratio of three separated facial features are standardized by subtracting sample mean and then scaled by sample standard deviation.

The facial symmetric index (ATTRACTIVENESS) is the average of three symmetric ratios of separated facial features. We

acknowledge that facial symmetric index is at best, a proxy, for facial attractiveness, as beauty is long-time considered as a very

subjective feeling that is influenced by many other factors, such as race, culture, and era.

Paper #890363

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prediction of our hypotheses after we control for entrepreneurs’ facial attractiveness in our

regression model. Facial attractiveness doesn’t show significant impact on crowdfunding success.

[Insert Table 10 about Here]

6. Concluding Remarks

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

employs a machine-learning based facial feature detection techniques to develop a complex

metrics to measure the facial trustworthiness of the entrepreneurs on Kickstarter crowdfunding

platform. Utilizing the facial trustworthiness 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 crowdfunding market. Additionally,

female entrepreneurs’ facial trustworthiness plays a more prominent role in affecting project

success than that of male entrepreneurs.

Our study contributes to the crowdfunding literature by providing the first empirical

evidence on how entrepreneurs’ individual trustworthiness, as expressed by their facial

trustworthiness, affects project funders’ funding decision making. We also contribute to the

gender study literature by demonstrating that female founders’ facial trustworthiness plays a

more prominent role in determining project success than that of male founders. Our study also

provide significant contributes technologically by introducing a cutting-edge machine learning-

based technology to develop the facial trustworthiness measures, which expand the previously

survey-based societal-level trust measure to individual levels in a large scale. The availability of

this new technology shed the light on a fruitful direction for future studies to further explore how

individual trustworthiness affects important business decisions. The results of our study may also

Paper #890363

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help crowdfunding practitioners and regulators to acknowledge the potential effects of facial

trustworthiness in crowdfunding decision-making.

Paper #890363

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Appendix A: Measuring Facial Trustworthiness

A face-detection algorithm is employed to find the required feature points of faces 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 the rules of numbering of the feature points based on the prior literature

(Feng, Hu, Kittler, Christmas, and Wu 2015; Kazemi and Sullivan 2014; Sagonas et al. 2013; Yang, Zou,

and Patras 2014).

We extract the 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 out 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 side (α1) and left-hand side (α2). 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

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(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 wideness of the chin. We measure the chin angle on both the right-hand side (β1) and left-

hand side (β2). 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)

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Appendix B: Variable Definitions

Variables Definitions

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

and 0 if the fundraising is suspended or canceled;

PLEDGED = Logarithm of amount pledged is 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, which equals the average of reversed standardized

value of EYEBROW, standardized value of FACE, standardized

value of CHIN, and reversed standardized value of PHILTRUM;

GENDER = A dummy variable that equals to1 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 to 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 to 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 to this question to 1 if a survey participant

reports that most people can be trusted and 0 otherwise and then

calculate the mean of the response in 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).

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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%

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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.

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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.

Paper #890363

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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 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.

Paper #890363

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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 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.

Paper #890363

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Panel B: Separated 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 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.

Paper #890363

32

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 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.

Paper #890363

33

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 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.

Paper #890363

34

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 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.

Paper #890363

35

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 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.

Paper #890363

36

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 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.

Paper #890363

37

Table 10

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

Panel A: Facial trustworthiness and application outcome controlling 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 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 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.

Paper #890363