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Economics Of Philanthropy-evidence From Health Crowdfunding Juliane Proelss, Denis Schweizer and Tingyu Zhou

The publisher's version of record is availible at https://doi.org/10.1007/s11187-020-00336-w

Follow this and additional works at DigiNole: FSU's Digital Repository. For more information, please contact [email protected] Economics of Philanthropy— Evidence from Health Crowdfunding**

Small Business Economics, Forthcoming

Juliane Proelss,* Denis Schweizer,† and Tingyu Zhou‡

Abstract

This paper is the first comprehensive empirical study on the economics of health crowdfunding (HCF) campaigns. We develop a new theoretical framework that focuses on the channels Donor-Patient-Psychology and Donor-Donor-Psychology to examine campaign funding speed. Our data highlight that, on average, campaign funding goals are achieved more rapidly if the patient is an infant girl, and if campaign descriptions are more comprehensive but less technical (easier to read). Furthermore, campaigns begun around holidays are funded more quickly, with the highest funding speed found for Christian holidays. We posit that this indicates a “warm-glow” effect. Examining donations controlling for campaign fixed effects, we document strong and economically significantly negative donor-to-donor peer effects, where contributions by donors with public profiles may be crowded out by the previous contributions of their peers.

Keywords: Altruism, Charity, Crowdfunding, Developing Countries, Fundraising, Health Care, Medicine, Philanthropy, Voluntary Contributions JEL Classification: G21, I15, R30

* Concordia University, Assistant Professor of Finance, John Molson School of Business Building, 1450 Guy, Montreal, Quebec, Canada H3H 0A1, Phone: +1 514-848-2424, ext. 2242, Fax: +1-514-848-4500, Email: [email protected]. † Concordia University, Associate Professor of Finance, John Molson School of Business Building, 1450 Rue Guy, Montreal, Quebec, Canada H3G 1M8, Phone: +1 514-848-2424, ext. 2926, Fax: +1-514-848-4500, e-mail: [email protected]. ‡ Florida State University, Assistant Professor of Real Estate, Department of Risk Management/Insurance, Real Estate and Legal Studies, College of Business, 821 Academic Way, Tallahassee, FL 32306-1110, e-mail: [email protected].

**Acknowledgments: We are grateful to guest editors Jörn H. Block, Lars Hornuf, Alexander Groh, Tom Vanacker, and Silvio Vismara, and two anonymous referees for their many helpful comments. We thank Yan Alperovych, Eric Braune, Philipp Geiler, Bianca Grohmann, Christian Koziol, Jean-Michel Sahut, Armin Schwienbacher, and Silvio Vismara, as well as the participants of the conference on Digital Innovation, Entrepreneurship & Financing (Lyon, France) and the Workshop: Developments in Entrepreneurial Finance: Crowdfunding, Blockchain, and ICOs (Lyon, France) for helpful comments and suggestions, and to Isabelle Jolin, Moein Karami, Qiao Nie, Xiao Ma, and Stéphane Sévigny for excellent research assistance. Denis Schweizer gratefully acknowledges the financial support provided through the Manulife Professorship.

ElectronicElectronic copy copy available available atat:: https://ssrn.com/abstract=3524649https://ssrn.com/abstract=3169823

Economics of Philanthropy— Evidence from Health-Crowdfunding

Abstract

This paper is the first comprehensive empirical study on the economics of health crowdfunding (HCF) campaigns. We develop a new theoretical framework that focuses on the channels Donor-Patient-Psychology and Donor-Donor-Psychology to examine campaign funding speed. Our data highlight that, on average, campaign funding goals are achieved more rapidly if the patient is an infant girl, and if campaign descriptions are more comprehensive but less technical (easier to read). Furthermore, campaigns begun around holidays are funded more quickly, with the highest funding speed found for Christian holidays. We posit that this indicates a “warm-glow” effect. Examining donations controlling for campaign fixed effects, we document strong and economically significantly negative donor-to-donor peer effects, where contributions by donors with public profiles may be crowded out by the previous contributions of their peers.

Keywords: Altruism, Charity, Crowdfunding, Developing Countries, Fundraising, Health Care, Medicine, Philanthropy, Voluntary Contributions JEL Classification: G21, I15, R30

ElectronicElectronic copy copy available available atat:: https://ssrn.com/abstract=3524649https://ssrn.com/abstract=3169823 1. Introduction

More than half the world’s population lacks access to essential healthcare services (see World

Bank and WHO, 2017). Even with some form of medical care, out-of-pocket spending1 remains the primary source of funding for healthcare treatments in developing countries (see Wagstaff,

2007). In these countries, such spending pushes a large number of households into poverty,

because they cannot sustain or finance their treatment costs in full. However, even if they could

afford treatment, access to doctors in many developing countries is often lacking. Generally, many

fewer physicians are available, especially in rural areas (see WHO, 2010). This lack of access to,

and inability to finance, medical care has severe consequences and effects, particularly regarding

life expectancy. In fact, there is currently a gap of up to thirty-six years in life expectancy between developing and developed countries (see WHO, 2011).

Some countries have begun thinking more deeply about how to solve the healthcare problem

by introducing (heavily) subsidized or free healthcare insurance for those who cannot afford it (see

Wagstaff, 2007). However, this system is not in place or not effective in most of the poorest

countries, where it is arguably the most needed. Even in the U.S., one of the wealthiest countries,

a significant funding gap in the healthcare system exists.

The intractable problems surrounding healthcare explain at least partially why health

crowdfunding (HCF) platforms, such as GoFundMe and YouCaring, were able to raise $930

million and $240 million, respectively, in the U.S. for medical expenses and treatments over the

one-year period from September 2015 through October 2016 (see Time, 2017). We define HCF as

1 The term out-of-pocket spending refers to medical costs that are not covered or reimbursed by insurance.

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ElectronicElectronic copy copy available available atat:: https://ssrn.com/abstract=3524649https://ssrn.com/abstract=3169823 a method to raise funds for medical expenses or treatment by a group of mostly small donors

through an open call for funding on Internet-based platforms.2

However, the HCF platforms mentioned above are domiciled in the U.S. and concentrate on

raising funds almost exclusively for U.S. citizens. One exception is Watsi.org, 3 which is a

registered U.S. 501(c)(3) non-profit organization that raises funds in the U.S. for patients in developing countries. Watsi.org manages to bridge distance-related barriers quite well (see, for example, Agrawal, Catalini, and Goldfarb, 2010; and Günther, Johan, and Schweizer, 2018). In contrast to other crowdfunding platforms, Watsi.org does not charge any fees, and all donations go to local medical partners. It even covers costs such as credit card fees, and the fundraising campaigns are handled on their website. Watsi.org competes for charitable donations against about

1.5 million other charitable organizations in the U.S. This growing market peaked in 2016 at about

$390 billion in donations, of which approximately 10% was donated to healthcare-related projects

(see Giving USA and the National Center for Charitable Statistics).

Our findings for the Donor-Patient-Psychology channel indicate that donors appear to favor younger over older patients, and are especially attracted to campaigns for infant girls. We also find strong evidence of a high influx of donations around religious holidays, such as Christmas and

Easter, which points to the warm-glow effect. However, and contrary to our hypothesis, we find that campaigns for patients with life-threatening conditions do not always receive preferential donations. Several robustness checks to address, e.g., endogeneity concerns and tax effects, do not alter our findings.

2 Other forms of crowdfunding include equity crowdfunding (see, e.g., Ahlers et al., 2015; Colombo, Franzoni, and Rossi-Lamastra, 2014), reward-based crowdfunding (see, e.g., Mollick, 2014), real estate crowdfunding (see Schweizer and Zhou, 2017), peer-to-peer lending (see, e.g., Lin and Viswanathan, 2015), and Initial Coin Offerings (see Amsden and Schweizer, 2018). For excellent literature overviews, see Moritz and Block (2016), Short et al. (2017), and Wallmeroth, Wirtz, and Groh (2018). 3 The platform takes its name from the town of Watsi in Central America, where one of the cofounders encountered a woman on a bus who was asking passengers for donations to pay for her son’s healthcare (see FAQ on Watsi.org).

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ElectronicElectronic copy copy available available atat:: https://ssrn.com/abstract=3524649https://ssrn.com/abstract=3169823 Our findings related to the Donor-Donor-Psychology channel contribute to research on the limited donor-to-donor peer effects in charitable giving. We find strong evidence of a “crowding- out” effect of donors with public profiles if a previous contribution was made by a donor with a public rather than anonymous profile. We attribute this to the fact that the private benefits of being shown as the first donor with a public profile on the campaign are not shared with other public profile donors. Thus, with each further contribution by a public profile donor, the private benefits are reduced, which reduces the probability of a contribution by another public profile donor.

Similarly, the private benefits of a donor with a public profile are reduced if donations have been made by donors with rich funding histories (referred to as “Shining Knights”). In this case, subsequent donations by public profile donors with fewer previous contributions (“Common

Knights”) are less likely to receive the same attention as “Shining Knights.” Therefore, we posit this will lead to a crowding-out effect of “Common Knights.”

Insights into HCF platforms are especially valuable. By the end of 2016, only 22% of U.S. citizens had made contributions to crowdfunding campaigns, but 41% were aware of the funding channel and were considering becoming backers in the near future. Furthermore, upon completing their first contributions, backers tend to be more loyal to a specific channel, and nearly all contribute again in the future (see Pew Research Center, 2016). This paper is the first empirical study on a worldwide HCF platform4 and our research contributions are twofold: First, we develop

a new theoretical framework for donation-based crowdfunding, which differs substantially from

existing frameworks tailored to “reward-based” crowdfunding (see McKenny et al., 2017, on the

lack of theory and the importance of a cross-disciplinary approach to crowdfunding research). In

the absence of a material reward for donors, our theoretical framework focuses on donor

4 Watsi.org features 4,677 distinct patient campaigns, and has 5,314 registered donors (see Bassani, Marinelli, and Vismara, 2019, for cross-platform evidence on healthcare crowdfunding).

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ElectronicElectronic copy copy available available atat:: https://ssrn.com/abstract=3524649https://ssrn.com/abstract=3169823 psychology by tying together Donor-Patient-Psychology with Donor-Donor-Psychology, which are essentially two different sides of the same coin that explains our dependent variable campaign funding speed. In contrast to other crowdfunding websites, such as Kickstarter, where the funds raised are only transferred if a defined goal amount is reached, Watsi guarantees full financing of all campaigns. To accomplish this, it uses donations from passive donors, who delegate their funds specifically to Watsi’s underfunded campaigns (see section “4. Data” for more details).

Second, we find evidence that donors behave as what we would refer to as “warm-glow” altruists. We aim to understand what motivates them to contribute specifically to healthcare campaigns (see Giudici, Guerini, and Rossi-Lamastra, 2018, who also discuss altruism in a crowdfunding context). Evidence of this dynamic could help optimize healthcare charities’ substantial annual marketing budget of about $3.9 billion (= 10% (average marketing budget) *

10% (healthcare projects) * $390 billion (total charitable donations)) (see Pew Research Center,

2016). Determining how to use marketing dollars more effectively could be the lever needed to increase the volume of total charitable donations and enhance overall social entrepreneurship (see

Lehner, 2013). Social entrepreneurship and non-profit organizations play an important role, not only for those who directly benefit from donations, but also for the organizations’ employees. Non- profits can also be regarded as a type of “venture.” Because they account for about 10% of the total U.S. private workforce, they are considered a “major” industry or sector (see Johns Hopkins

Center for Civil Society Studies, 2019). Thus, we argue that our results are of interest for non- profits at large.

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ElectronicElectronic copy copy available available atat:: https://ssrn.com/abstract=3524649https://ssrn.com/abstract=3169823 2. Theoretical Framework

The vast majority of crowdfunding literature is related to “reward-based” crowdfunding, where

backers expect to receive some kind of future “reward” for their contributions, such as, e.g., a financial return or a product/service. Therefore, the related theoretical framework usually centers around signaling by campaign creators to backers, campaign characteristics (e.g., riskiness, campaign descriptions), campaign creators (e.g., social media, education), distance-related effects

between backers and campaign creators, and herding effects (see, for example, Ahlers et al., 2015;

Giudici, Guerini, and Rossi-Lamastra, 2018; Hornuf and Neuenkirch, 2017; and Vismara, 2016).

However, in health crowdfunding, the funding dynamics are entirely different, due mostly to

the absence of a risk-return relationship. To be specific, 1) donors do not expect any reward from

patients, 2) the Watsi platform is responsible for the campaign websites, meaning patients cannot

actively signal to potential donors as they can on GoFundMe (www.gofundme.com), 3) donors are

all U.S.-based, and therefore not in close proximity to patients, and presumably have never lived

in close proximity to any patient on Watsi, 4) there are no risks involved for donors, because the

treatment costs are known, and Watsi guarantees the transfer of funds to the on-site medical

partners who are responsible for the treatment procedures, and 5) herding should also be less

relevant, or irrelevant, because donors are not expected to gravitate to the same campaigns solely

because others are donating to them.

For these reasons, we cannot rely on established theoretical frameworks. We instead need to

develop a new framework that can capture the unique characteristics of donation-based

crowdfunding. We put forth seven hypotheses for how donor psychology is connected to two

channels, the patient channel (Donor-Patient-Psychology) and their own donor peer group channel

(Donor-Donor-Psychology), and how it relates to campaign funding speed (see Figure 1).

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ElectronicElectronic copy copy available available atat:: https://ssrn.com/abstract=3524649https://ssrn.com/abstract=3169823 For the first channel, Donor-Patient-Psychology, we argue that two mechanisms are mainly

responsible for campaign funding speed on the Watsi platform. The first is based on donors’

Psychological Benefits, meaning that the act of giving can provide individuals with an intangible

psychological benefit that contributes to one’s self-image as an “altruistic,5 empathetic, socially responsible, agreeable, or influential person” (Bekkers and Wiepking, 2010). In other words,

donating can be viewed as a form of emotional “quid pro quo,” where individuals donate with the

implicit expectation of receiving something in return, such as feeling better about themselves.

Neuropsychological studies have found that donations to charity may “elicit neural activity in areas

linked to reward processing” (Harbaugh, Mayr, and Burghart, 2007, p. 1622).

In many cases, donating has been shown to generate an almost automatic emotional response,

producing a “positive mood, alleviating feelings of guilt, reducing aversive arousal, satisfying a

desire to show gratitude, or to be a morally just person” (Bekkers and Wiepking, 2016, p. 70).

Batson and Shaw (1991) label the positive psychological consequences felt by someone helping

another as “empathic joy,” which is also often referred to as a “warm glow,” or the “joy of giving”

(Andreoni, 1989; DellaVigna, List, and Malmendier, 2012). This mood improvement has been

shown to be especially strong when victims are depicted as innocent (Benson and Catt, 1978).

Thus, with children, and especially with female children, who are viewed as more innocent and

more vulnerable, helping them should evoke stronger psychological benefits.

The second mechanism behind Donor-Patient-Psychology, Psychological Altruism, is a driver

of charitable donations because individuals (donors) are acting purely, and out of concern for

others. They are not influenced by patient demographics or their own self-interest, as they are with

Psychological Benefits. Individuals driven by purely altruistic motivations are drawn to those in

5 In the context of this study, we follow Furnham et al. (2016), where altruism is defined as “a selfless exhibition of trading one’s personal resources to benefit another” (p. 359).

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ElectronicElectronic copy copy available available atat:: https://ssrn.com/abstract=3524649https://ssrn.com/abstract=3169823 greater need, and thus may be susceptible to a crowding-out effect. Bekkers and Wiepking (2010,

p. 936) highlight that a “purely altruistic motivation (in the economic sense) would lead individuals

who learn about an increase in contributions by others with US$1 to reduce their own contribution

with US$1.” This would effectively crowd out donations. Consequently, the effects of altruism

can vary with the awareness of need and with the efficacy of donations and other moderating

factors. For example, a campaign page that indicates the severity of a solicitor’s condition through

the use of life-threatening words would highlight the need for contributions, countering the

crowding-out effect.

For the second channel, Donor-Donor-Psychology, we argue it relates more strongly to the

micro-structure of funding speed. This is motivated by the social and observational learning

literature (see, e.g., Bikhchandani et al., 1992, and Acemoglu et al., 2010, for applications to

economics, and Zhang and Liu, 2012, and Kuppuswamy and Bayus, 2014, for applications to crowdfunding). The idea is that a donor’s choice not only affects her own utility, but also that of

subsequent donors. This results in a path dependence that ultimately affects campaign success.

Some literature indicates that people have an increased tendency to give when anonymity is

removed (Soetevent, 2005; Andreoni and Petrie, 2004). Thus, we argue that donors’ funding

Reputation, relative to previous contributors’ reputations, have an impact on donation decisions.

For example, when donating, public profile donors are rewarded privately by having their

donations displayed publicly on the campaign web page, as well as on their personal profiles.

Displaying this information signals the generosity and, potentially, the wealth of donors to anyone

viewing the campaign or profile, thus generating recognition and approval from others. It has been

shown that donors who give to charitable causes are held in high regard by their peers (Muehleman,

Bruker, and Ingram, 1976; Wiepking, 2008). Research has also shown that individuals are willing

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ElectronicElectronic copy copy available available atat:: https://ssrn.com/abstract=3524649https://ssrn.com/abstract=3169823 to incur costs to have generous contributions recognized (Clark, 2002), and, when given a choice, generally prefer that their donations be known by others (Andreoni and Petrie, 2004). Moreover, some donors may be more sensitive to social approval for helping, and may react more strongly to the observability of a donation (Satow, 1975). Consequently, when more individuals donate, it may render subsequent donors less “reputable” than previous donors, which would mean losing out on visibility, social approval, and reputation. This may actually make donations less likely.

—Please insert Figure 1 about here—

3. Hypotheses

Childhood is generally viewed as a carefree period of life, with a high dependence on family care and support (see Burman, 1994). The imagery of vulnerability and innocence that characterizes childhood is deeply embedded in societal tenets, and helps foster the nearly universal instinct of adults to protect children. Research has shown that innocence is a determining factor in the decision to donate (Betancourt, 1990; Campbell, Carr, and MacLachlan, 2001; Jackson and

Esses, 1997; Piliavin, Rodin, and Piliavin, 1969; Weiner, 1993). It is generally supported by the belief in a “just-world” principle (Hafer and Begue, 2005; Lerner, 1980; Lerner and Simmons,

1966), where it is thought to be morally right to help those who did not bring about their own plights and those who try to actively help themselves (Seu, 2016). Conversely, self-inflicted plights are judged more negatively, and may deter potential donors.

Bekkers and Wiepking (2010) identify eight mechanisms of donation. One of the driving factors is reputation, which refers to the “social consequences of donations for the donors” (p. 936). A moderating factor of reputation lies in liking the solicitor, which increases the psychological benefits of donating. Luo, Li, and Lee (2011) test the baby schema hypothesis of Lorenz (1943).

They find that adults tend to judge the faces of younger children as more likeable and more

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ElectronicElectronic copy copy available available atat:: https://ssrn.com/abstract=3524649https://ssrn.com/abstract=3169823 attractive than those of older children, and the faces of older children as more likeable and more

attractive than those of adults.

Images of suffering and grieving children provoke emotional reactions (Burt and Strongman,

2005). Research has found that NGOs use such images in more than 50% of their posters to

increase fundraising success (Lamers, 2005). This trend has grown to include an increasing number

of babies on posters (Smillie, 1995), and is described in Hypothesis 1:

Hypothesis 1: Age is negatively correlated with funding speed.

Following this line of argumentation, we note further that women in general are often portrayed

in the media as helpless victims. In psychology, the prescriptive component of a stereotype is how

we think people should behave according to their gender. Gender role stereotypes associate the

feminine role with being passive, and less competent, less independent, and less skilled than the

masculine role (Helgeson, 2016). Stereotypical portrayals of women and men are commonplace

in all types of media (Wood, 1994). The picture of perceived helplessness seems to dominate women’s representation in modern journalism.

For example, a recent study found that the Wall Street Journal’s war coverage paired the term

“women” with “children” or alternatives more than 60% of the time (see New America, 2016).

Hollywood films and children’s programming perpetuate these stereotypes (Wood, 1994). At the

same time, donors and non-donors alike state they are motivated to give because of research

showing that investing in women and girls yields greater social returns (Dale et al., 2018).

The psychology literature also concludes that the feminine role, due to its “desirable” characteristics, is somewhat elevated in society over the male role. For example, Broverman et al.

(1972) suggest that females tend to be gentle, tactful, neat in habits, and more aware than males of others’ feelings. This gendered stereotype has been observed in so-called helping studies, such as

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ElectronicElectronic copy copy available available atat:: https://ssrn.com/abstract=3524649https://ssrn.com/abstract=3169823 Dovido (1982), who found that, when asking for change in a library, females were helped more frequently than males. This is likely also due to social norms, such as the notion of chivalry (Latané and Dabbs, 1975).

Hong, Chen, and Li (2017) explore whether women are more likely to receive “likes” on their

Facebook posts, which is seen as a type of online gift (see also Skågeby, 2010). They find that the likelihood is indeed higher for female than male users, and conclude that gender stereotypes exist.

One of the factors that influences the liking of an individual is beauty (Bekkers and Wiepking,

2010). Various studies have found that people are more likely to give to physically attractive solicitors (West and Brown, 1975; Wiesenthal, Austrom, and Silverman, 1983; Landry et al.,

2006). And, in the literature, when evaluating the physical appearance of children, the terms

“cuteness” and “attractiveness” are often used interchangeably. One study by Hildebrandt and

Fitzgerald (1977) found that female infants and children are rated as cuter and more attractive than

males, which may be attributable to the fact that feminine facial features have a stronger

association with infantile cues than masculine features (Cunningham, 1986). Thus, we posit that

female patient campaigns, similarly to those involving children, will be more appealing to donors.

And we expect to observe a positive correlation with funding speed, particularly when the patient

is a female infant.

Hypothesis 2: Treatments for female (infant) patients are funded more quickly.

Following Hypothesis 1, NGO and charity ads actively use images to evoke donor sympathy,

which results in a higher likelihood of donating and in higher donations. Turning to theories of

emotional contagion and sympathy, we believe a patient’s facial expression can also transmit

emotions. This interaction between sympathy and altruism is well-established in the literature (see

Bagozzi and Moore, 1994; Batson et al., 1997).

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ElectronicElectronic copy copy available available atat:: https://ssrn.com/abstract=3524649https://ssrn.com/abstract=3169823 Consistent with the idea of emotional contagion, Small and Verrochi (2009) find that donors tend to be especially sympathetic and more likely to donate when an ad shows a suffering or sad

facial expression rather than a happy or smiling expression. Thus, garnering potential donors’ sympathy can increase the likelihood of winning contributions to a patient’s campaign.

Hypothesis 3: Smiling patient pictures (lower level of donor sympathy) are negatively

correlated with funding speed.

Peter Buffett, son of the star investor and major donor Warren Buffett, hypothesizes that individuals are more concerned with feeling better about themselves than with tackling the root

causes of problems. Consequently, he has referred to donations as “conscience laundering,” a way

to ease guilt without having to seriously engage (New York Times, 2013).

Bock, Eastman, and Eastman (2016) find support for the argument that the reward charitable

donors are actually seeking is relief from guilt and uncertainty. This effect is especially visible

during the religious holiday season and during times of natural catastrophes, and less so during

summer or vacation periods. It also explains why the Blackbaud Online Giving Index, calculated

by the fundraising software firm Blackbaud, typically begins increasing before Thanksgiving, and

then peaks around Christmas, when about 20% of annual donations are made (MacLaughlin,

2014). Giving at that time is also likely heightened because the year-end represents the last opportunity for a tax-deductible donation. However, Ekström (2018) finds that December is associated with a 14% increase in donation probability that could not be explained by factors such as tax breaks, reciprocity, or pressure from charities.6

Martin and Randal (2009) find a similar “Sunday effect,” where donations tend to be larger and

are made more frequently on Sundays than on other days of the week. This is thought to be because,

6 A potential concern is that donations in December may be driven by the tax effect. In subsection 6.2.5, “December Tax Effect,” we re-run our tests by deleting all the campaigns posted in December. The results remain highly consistent.

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ElectronicElectronic copy copy available available atat:: https://ssrn.com/abstract=3524649https://ssrn.com/abstract=3169823 in Christianity, Sunday is usually the Lord’s Day, the principal day of communal worship, and

when most people are not working. A study of religious involvement by Krause (2009) determined

that individuals who attend religious services tend to have stronger “God-mediated control

beliefs,” which in turn lead them to be more grateful. This increase in gratitude can lead to increased giving, due to the biasing of the “brain’s reward system towards rewards for others

versus oneself” (Karns et al., 2017, p. 9).

Malhotra (2010) also evaluates the impact of religiosity on pro-social behavior (donations), and

finds an increase in the likelihood of that behavior when religious norms are most prominent. A

survey of Protestant pastors by Lifeway Research (2012), for example, found that the highest

worship attendance occurs on Easter, Christmas, and Mother’s Day, respectively. There is thought

to be an increased awareness of need by others at those times, which induces people to reflect more

on their own behavior and situation, to think more about others, and to feel more grateful. Both

factors contribute to the overall higher levels of willingness and time to donate.

Hypothesis 4: Religious holidays and Sundays increase the likelihood of charitable giving

and increase funding speed.

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ElectronicElectronic copy copy available available atat:: https://ssrn.com/abstract=3524649https://ssrn.com/abstract=3169823 Other medical crowdfunding platforms (see, for example, CrowdRise and YouCaring) actively

advertise that donors are more likely to contribute to a campaign if the potential donor sympathizes

with the patient. Presenting the gravity of a patient’s condition in a way that emotionally touches potential donors is believed to create a bond of sympathy (see Snyder, Mathers, and Crooks, 2016).

Patients in the most severe health conditions (presumably closer to death) are expected to evoke the highest levels of sympathy. Therefore, we expect that more sympathetic campaigns will have higher likelihoods of garnering donations.

Hypothesis 5: The severity of a patient’s condition is positively correlated with funding

speed.

Peer effects on donor behavior in fundraising campaigns have been well-documented in the literature on giving. For example, those with a leading role in a group (e.g., a team captain) have

been shown to motivate other team members to give (see Carman, 2004). Social pressure also

plays a part (Gerber, Green, and Larimer, (2008). Higher donation amounts tend to occur when a donor is approached by someone she knows (see Meer, 2011), and the magnitude of average previous donations can positively affect the amount contributed by subsequent donors (see Smith,

Windmeijer, and Wright, 2013).

One explanation for the latter effect is that large donations—e.g., the “Shining Knight” argument—may trigger upward pressure, or set a higher reference point, for donors who wish to show off their wealth. They may also send a positive signal about charity or campaign quality (see

Glazer and Konrad, 1996; Vesterlund, 2003). A private benefit earned by donors who make large donations is that the public announcement of the donation signals their wealth and generosity to others, thereby increasing their marginal benefits (see Vesterlund, 2003). Glazer and Konrad

(1996) propose that the desire to demonstrate wealth is a major motivation of donating.

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ElectronicElectronic copy copy available available atat:: https://ssrn.com/abstract=3524649https://ssrn.com/abstract=3169823 Unfortunately, in our sample, individual donor amounts are unknown. The only information we

have is donor identity (donors with public rather than anonymous profiles), and the sequence of

donations. As soon as a donor with a publicly available profile (PP Donor) makes a contribution,

it will be visible on the campaign web page (see panel A in Figure A1 in the online appendix), and

on the donor’s profile (see panel D in Figure A1 in the online appendix).

Therefore, we adopt, but modify, the previous “Shining Knight” argument for PP Donors. The

private benefit for PP Donors is greatest when they are the first donor with a public profile for a

campaign, because all individuals looking at the campaign will anchor to this PP Donor. As soon

as there is more than one contribution made by a PP Donor, the attention is necessarily shared,

which decreases the perceived private benefits for subsequent PP Donors. This dilution leads to a crowding-out effect of subsequent PP Donors (see Warr, 1982; Roberts, 1984; and Heutel, 2014).

PP Donors are not a homogenous group, and they differ in their number of previous

contributions (our measure for Reputation). PP Donors with richer funding histories and higher funding impacts (higher Reputation) can be seen as “Shining Knights.” The private benefits of a donor with a public profile is reduced if there are donations made by donors with richer funding histories (“Shining Knights”). In this case, subsequent donations by PP Donors with fewer previous contributions (“Common Knights”) are less likely to receive the same attention as those made by “Shining Knights.” We posit this will lead to a crowding-out effect of “Common

Knights.”

Hypothesis 6: Public Profile Donors are less likely to donate if a donation by a Public

Profile Donor has already been made.

Hypothesis 7: Public Profile Donors are less likely to donate if the average reputation of

previous donors is higher.

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ElectronicElectronic copy copy available available atat:: https://ssrn.com/abstract=3524649https://ssrn.com/abstract=3169823 4. Data

The data we use here are based primarily on information from Watsi (https://watsi.org), and

come either from the “Transparency Document v4.xls” tab “Patients,” or from the patient’s

campaign web page. The purpose of Watsi’s Transparency Document is to show that the funds

received for a patient’s treatment were actually transferred to the medical partner. Watsi differs

from other medical crowdfunding platforms, such as CrowdRise or GoFundMe, which usually

charge, e.g., a percentage of the donation, a processing fee, and/or a per donation fee. Watsi transfers each donation to the patient at no charge, and also pays the credit card processing fees.

All operational expenses are covered by a foundation or by donors (when making a donation, donors can choose to add in more funds that go directly to help cover Watsi’s expenses).

Also in contrast to other crowdfunding platforms, Watsi employs a solid vetting methodology to ensure donors are not exposed to fraudulent campaigns (see Snyder, Mathers, and Crooks, 2016;

Cumming et al., 2019). Watsi actively manages a consistent number of campaigns on its website,

so that there is no obvious difference in the choice set over time.7 Watsi’s Transparency Document

also gives the patient’s campaign web page URL (which we use in the second step to collect

Patient Characteristics, Patient Story, and Funding Structure), beginning and end date of the campaign, treatment costs, country, and medical partners. We obtain the patient picture, patient story, and funding structure from the patient’s campaign web page (see Figure A1, panel A, in the online appendix). Our final sample, including complete information for all variables, consists of

4,677 distinct patient campaigns.

7 This differs from other crowdfunding platforms such as Indiegogo, where the number of campaigns launched is determined by demand from campaign creators, and is not managed by the platform. In other words, on Watsi, the choice set donors see during holidays is comparable to that on weekends or weekdays. Thus, there should not be an endogeneity problem driven by fluctuations in campaign supply.

17

ElectronicElectronic copy copy available available atat:: https://ssrn.com/abstract=3524649https://ssrn.com/abstract=3169823 Registered donors on Watsi have the option to reveal their identity in their profile icon (Public

Profile (PP) Donors), by either uploading a picture (Public Profile Picture (PPP) Donors) or using their initials (Public Profile Initials (PPI) Donors). Otherwise, they remain anonymous, and

Watsi’s logo will appear in the profile icon (see Figure A1, panel C, “Funder Types,” in the online appendix).8 All donors also have the option to actively allocate funds to a specific patient (Active

Donors), or to donate a recurring amount (e.g., monthly) and allow Watsi to make the allocation

decision (Passive Donors). All Passive Donors have an “M” in a white circle in the upper right-

hand corner of their profile. We also build a subgroup of PP Donors who actively make donation

decisions and do not chose the “M” option (PPnoM Donors). Overall, our dataset includes 5,314

registered donors who reveal their identities.

The funding structure for all campaigns is the same. The donor who contributes to a campaign

first is always in the top left of the funding structure box. The donor who contributes second is on

the right-hand side of the first donor, and so on, until the sixth donor. The seventh donor appears

below the first donor on the second line, meaning that each line has six donors, then new lines are

added until the campaign is fully funded (see Figure A1, panel C, “Funding Structure,” in the

online appendix).

Based on this systematic approach, we create a sequence where the first donor is number “1,”

the second is number “2,” and so on (see Figure A1, panel C, “Funder Sequence,” in the online

appendix). This allows us to create a panel index for PP Donors that shows the number of

previously funded campaigns for each campaign to which they donated (see Figure A1, panel D,

in the online appendix).

8 We assume these two variables remain donor-specific during the entire sample period. In other words, we do not know whether donors change any of their profile characteristics between their first and last donations.

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ElectronicElectronic copy copy available available atat:: https://ssrn.com/abstract=3524649https://ssrn.com/abstract=3169823 Consider, for example, David Umbaugh. David is marked as the twenty-second donor in

Diana’s case (see panel C, “Funder Sequence,” in Figure A1). David (with yellow icon and initial

“D”) was the twenty-second in the queue. When he made his donation, his information set included the first to twenty-first donations. Of those twenty-one donations made before David’s, nine were by PP Donors. Of those nine, four are PPP Donors and two are Passive Donors.

Since David is a PP Donor, we were able to obtain the most updated information about when he made his last donation for our sample. Panel D of Figure A1 shows that David joined Watsi on

March 12, 2013, and has funded healthcare for eleven patients in eleven countries. Based on his funding history, we construct an index to track David’s impact for his prior donations. We trace all PP Donors backward, and create a time-varying number of donations and number of countries.

For example, when David donated to Diana, he was marked #11 under “all patients funded by

David,” meaning he previously made ten donations to other patients, and therefore his impact was

10 at this point in time. His impact was 9 when he donated to James (marked #10), and so on (see panel D of Figure A1). We posit that when David was deciding whether to donate to Diana, he would take the average impact number of the previous donors as a reference point. In this particular case, the average impact number is 26.37 (Mean Impact Previous Donor) for the twenty-one donors before David. The impact number for anonymous donors (i.e., non-PP Donors) is recorded as zero.9

9 To control for donation dynamics, we also compare each PP Donor’s impact for a particular campaign relative to the PP Donors who donated before him. To do this, we define Public Profile Donor Reputation Higher Than Average as equal to 1 if the donor’s impact number is higher than the average of previous donors to the campaign, and 0 otherwise. In the example described above, David’s impact number is 10, which is lower than the average of the other nine donors, which was 26.37, so this dummy variable takes the value of 0. Similarly, we compare David with the PP Donor who made a donation right before him (using initial “B” (eighteenth in the queue) in panel C, Funder Sequence, in Figure A1). In this particular case, the impact number of the previous donor is 9, so this dummy variable takes the value of 1.

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ElectronicElectronic copy copy available available atat:: https://ssrn.com/abstract=3524649https://ssrn.com/abstract=3169823 Combining donor-level information with donation sequence, we obtain 51,281 case-donation observations from 5,314 unique PP Donors on 5,517 campaign cases. The average number of donations per PP Donor is 9.3. By creating a panel dataset with campaign donation observations, we can estimate the probability of donation made by a PP Donor (relative to the probability of donation by an anonymous donor). We are able to control for campaign fixed effects and estimate within-case variations.

Turning to the composition of donors, 16.3% (867 of 5,314) of PPP Donors choose to upload

a profile picture; the rest are PPI Donors. A high percentage, 22.1% (or 1,175 of 5,314), are PPM

Donors with recurring monthly donations, who allow Watsi to make the allocation decisions. At a

donation level, PPP Donors make up 18% of the total (9,628 of 51,281). Although the proportion

of PPM Donors is relatively small, they are frequent donors, and their donations account for a

significant amount of our sample (31%, or 16,371 of 51,281).

5. Methodology

5.1. Determinants of Funding Time

To examine how rapidly campaigns become fully funded, we conduct the following Poisson regressions:

( ) = + + +

𝑖𝑖 𝑖𝑖 𝑖𝑖 𝑗𝑗 𝑗𝑗 𝑗𝑗 𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶 𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇 𝛼𝛼 ∑ 𝛾𝛾 ∙ 𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃 𝐶𝐶ℎ𝑎𝑎+𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎+𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎+ ∑+ 𝜉𝜉 ∙ 𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷 𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜 𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜 𝑜𝑜 𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃 (1)

𝑘𝑘 𝑘𝑘 𝑘𝑘 𝑠𝑠 𝑡𝑡 𝑢𝑢 The dependent variable∑ 𝜑𝜑 ∙ 𝑃𝑃𝑃𝑃(Time𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃) is𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆 a count𝑆𝑆 𝜓𝜓variable𝜋𝜋 that𝜙𝜙 measures𝜀𝜀 the number of days until fully

funded. Recall that all campaigns in our sample are fully funded, because Watsi allocates a

predetermined amount on behalf of members who choose the “monthly” option. For this reason,

we do not use, e.g., hazard rate regressions.

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ElectronicElectronic copy copy available available atat:: https://ssrn.com/abstract=3524649https://ssrn.com/abstract=3169823 Watsi only adds new campaigns to their site once previous campaigns have been funded.

Therefore, a campaign’s success is not determined by being “fully funded,” but rather by its speed

or time until fully funded.10 The main explanatory variables in the Patient Characteristics block

are Age11 (or Infant, Child, and Adult with reference group Elder), Female, Infant x Female,12

Smile,13 and Treatment Cost. The Date of Campaign Posting block includes First Business Day of

the Month, Religious Holiday, Federal Holiday, and Weekend. The Patient Story block includes

Length, Readability (e.g., the following text indices: Automated Readability Index (ARI),

Coleman-Liau Index (CL), Gunning Fog Index (Gunning Fog), Flesch-Kincaid grade level (FKG),

or Flesch reading ease score (FRE)), and Life-Threatening Words. are country fixed effects,

𝑠𝑠 𝑡𝑡 are year fixed effects, and are medical partner fixed effects.14 See𝜓𝜓 Table 1 for detailed variable𝜋𝜋

𝑢𝑢 descriptions. 𝜙𝜙

We do not control for competing campaigns. This is because, in contrast to crowdfunding

platforms such as Kickstarter, where campaigns are posted based on the crowd’s demand, Watsi

actively controls its new campaigns to ensure a constant supply that will match donor demand.

Watsi also does not “optimize” campaigns based on donor profiles or which campaigns are shown

10 See Ahlers et al. (2015) for different success measures in crowdfunding campaigns, as well as Galak, Small, and Stephen, (2011) and Allison, McKenny, and Short (2013) for the relevance of funding speed as a dependent variable. 11 We classify a patient as a child in accordance with the United Nations Convention on the Rights of the Child as “a human being below the age of 18 years unless under the law applicable to the child” (see United Nations, 1989). Infants (Latin word infans, meaning "unable to speak") are commonly referred to as children up to one year of life, and thereafter usually referred to as toddlers (see, for example, Barker, 2001, for a definition). 12 For some campaigns, it could be very difficult to distinguish infant girls from infant boys from the patient picture. However, certain signs may be gender-revealing, such as ear piercing, or color and style of clothes. Even if it is not possible to guess a child’s sex from a visual inspection of a picture, it is generally revealed in the patient story. 13 We are not able to meaningfully measure patient pictures along dimensions of “sadness.” This is because the pictures are not “randomly” selected, but are instead specifically chosen by Watsi’s medical partners to avoid facial expressions of “anger” or “sadness.” We found no patient pictures with the required confidence score for “sadness” of 0.9 or higher (see Figure A1, panel B, for more details about the confidence scores). This clearly demonstrates how Watsi aims to avoid facial expressions with high levels of “sadness.” Therefore, the reference group for “happiness” is “neutral.” 14 In unreported results, we checked for the influence of natural catastrophes in countries such as Haiti, which was hit by Hurricane Matthew in 2016, or Nepal, which experienced a disastrous earthquake in 2015, on our results. Because these catastrophes increase media attention, they presumably affect potential donors. We find that our results remain qualitatively unchanged.

21

ElectronicElectronic copy copy available available atat:: https://ssrn.com/abstract=3524649https://ssrn.com/abstract=3169823 first. Moreover, it shies away from promoting campaigns with labels such as “Projects we Love,” like Kickstarter does.

—Please insert Table 1 about here—

5.2. Peer Effects

To examine how PP Donors are influenced by the donation(s) of their peers, we run the following regression model:

= + + + + , (2)

𝑖𝑖𝑖𝑖 𝑖𝑖 𝑖𝑖 𝑖𝑖 𝑖𝑖 𝑠𝑠 𝑖𝑖𝑖𝑖 where𝑃𝑃𝑃𝑃 𝛼𝛼 equals∑ 𝛽𝛽 ∙1𝑃𝑃 if𝑃𝑃𝑃𝑃𝑃𝑃 the𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸 s-th donation𝜁𝜁 𝜃𝜃 to case𝜀𝜀 i is made by a PP Donor, and 0 otherwise.

𝑖𝑖𝑖𝑖 𝑃𝑃𝑃𝑃 are a set of test variables for peer effects, including Last Donor Public Profile,

𝑖𝑖 Min𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃 One𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸 Previous Public Profile Donor, and Mean Impact Previous Donor. denotes campaign

𝑖𝑖 fixed effects, and are donation sequence fixed effects. 𝜁𝜁

𝑠𝑠 We are also intere𝜃𝜃 sted in two subgroups of PP Donors: Those who use profile pictures (PPP

Donors), and those who have public profiles and who actively make their own allocation decisions

(PPnoM Donors). In separate specifications, we replace with , which equals 1 for PPP

𝑖𝑖𝑖𝑖 𝑖𝑖𝑖𝑖 Donors, and with , which equals 1 for PPnoM Donors𝑃𝑃𝑃𝑃 , and𝑃𝑃𝑃𝑃𝑃𝑃 0 otherwise.

𝑖𝑖𝑖𝑖 The above fixed𝑃𝑃𝑃𝑃 effects𝑃𝑃𝑃𝑃𝑃𝑃 regression removes the effect of campaign-specific characteristics on donations. Our identification strategy relies on within-case variations in observed past donations arising as a result of donors randomly making their donations. Donation sequence fixed effects control for any systematic heterogeneity and preferences in the donation sequence.15 For example, donors in general prefer to become leaders, and are more likely to donate in the first few positions of a donation sequence.

15 We are unable to control for both donation sequence fixed effects and donor fixed effects because we do not have enough within-case variation in donors. Our donation sequence fixed effects control partially for any systematic preferences among donors.

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ElectronicElectronic copy copy available available atat:: https://ssrn.com/abstract=3524649https://ssrn.com/abstract=3169823 Note that estimating a fixed effects model using within-case specification may lead to a biased estimate of if the error term, , correlates with our test variables. It is likely that is biased

𝑖𝑖𝑖𝑖 upward, because𝛽𝛽 any unobserved𝜀𝜀 factors that affect our test variables are likely to𝛽𝛽 affect our dependent variable in the same direction. For example, an omitted variable that positively correlates with the likelihood of PP Donation in the past (s-1) should also positively correlate with the likelihood of PP Donation in the present (s). To mitigate this concern, we apply a dynamic panel data estimation using two-step differences GMM (Arellano and Bond, 1991) and the two- period lag as instruments for past donations. We expect the GMM estimators to be smaller (i.e., more negative) than the OLS estimators.

There are three commonly used binary discrete choice regression models: the linear probability model, the logit model, and the probit model. We opt here for the linear probability model (LPM), because the simplicity of the linearity assumption has proven robust to alternative functions in a substantial body of empirical work. For example, Greene (2017), among others, has documented the inconsistency of the non-linear fixed effects model.

There are several limitations of our peer effects model, however. We can only examine sequential decisions; simultaneous decisions would be better handled in a formal mathematical game framework. Our models are reduced form, and we do not have data on the donation amount.

We are aware that the choice of donation amount is made alongside the choice of whether to donate.

We model the donation decisions of PP Donors as a way to determine how they respond to observed past donations.

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ElectronicElectronic copy copy available available atat:: https://ssrn.com/abstract=3524649https://ssrn.com/abstract=3169823 6. Empirical Results

6.1. Determinants of Funding Time

Watsi began operations with its first campaigns in June 2012. It was active in only four countries.

In 2013, however, coverage expanded rapidly, and Watsi ultimately financed health treatments in eighteen countries that year. Donation volume (and the number of campaigns) also increased dramatically, from about $40,000 (42) in 2012, to $440,000 (576) in 2013, $970,000 (1,643) in

2014, and $1.3 million (2,082) in 2015. Over our observation period, Watsi received donations

totaling about $2.9 million for 4,677 campaigns in 20 different countries. Watsi currently focuses

heavily on Africa, with 45% of donations made there (e.g., Ethiopia, Kenya, Tanzania, and

Uganda), and Asia with about 30% (e.g., Cambodia, Nepal, and Thailand) (see Table 2 for a more detailed breakdown).

—Please insert Table 2 about here—

Table 3 gives the descriptive statistics for the eighteen explanatory variables. These are available for all campaigns in the dataset. Some explanatory variables cannot be considered simultaneously, such as Age and the development phases Infant, Child, and Adults, because of multicollinearity reasons. Therefore, we only include twelve explanatory variables for our main regression specification (see Table 4 for the correlations). All descriptive campaign information is shown in Table 3.

We find that campaigns are fully funded on average (median) after four (two) days. The average patient age is 27 years, and the majority of campaigns concentrate on children. Moreover, average

24

ElectronicElectronic copy copy available available atat:: https://ssrn.com/abstract=3524649https://ssrn.com/abstract=3169823 treatment cost is $450, with a maximum of about $30,000. We also find that about 22% of campaigns are launched during religious holidays, and 21% during federal holidays.16

Campaign descriptions tend to differ significantly in length, and contain between 60 and 445

words. The severity of patient conditions also varies greatly. Some patients do not appear to be in

a life or death situation (zero life-threatening words in the campaign description); others are in

extremely serious condition (up to 13 life-threatening words in the campaign description).

—Please insert Tables 3 and 4 about here—

Next, we present multivariate evidence by running Poisson regressions to analyze the

correlations between the blocks of explanatory variables (Patient Characteristics, Date of

Campaign Posting, and Patient Story) and the funding period. Table 5 summarizes the main results.

For specification (1), we use Age to proxy for a patient’s state of development; in specification (2),

we group patients into the development states Infant, Child, or Adult, using Elder as the reference

group. Both specifications are otherwise identical. Note that specification (3) builds on

specification (2), with the only difference being that we add the interaction term Infant x Female.

We observe that Age is positively and statistically significantly related to funding duration (see

specification (1)). When we focus instead on development states, we find that all development

states younger than Elder are associated with higher funding speeds. Furthermore, the coefficients

decrease in magnitude, from the development stage Infant over Child to Adult (see specification

(2)). This monotonic decrease reflects quite well what we summarize in Hypothesis 1: Patient age

is negatively correlated with funding speed.

16 Note that Watsi holds the number of campaigns on its website constant at all times. However, campaigns tend to be funded more quickly during the holidays, which results in a higher number being funded at that time. This means there is no obvious difference in the choice set over time.

25

ElectronicElectronic copy copy available available atat:: https://ssrn.com/abstract=3524649https://ssrn.com/abstract=3169823 We also find support for our Hypothesis 2, that female patients, similarly to children, seem to

heighten our protective instincts, which is reflected in faster fundraising (see specifications (1) and

(2)).17 When we include the interaction term Infant x Female in specification (3), we find a higher funding speed for infant girls (in line with our Hypothesis 2). However, the coefficient related to

Female is not only no longer statistically significant, but its magnitude is about zero. This means

that the higher funding speed for female patients is driven by infant girls. As soon as patients enter

later development states, the positive impact on funding speed fades.

Contrary to our Hypothesis 3, we find a statistically insignificant relationship between Smiling

and the speed of funding, despite the negative and expected coefficient. While previous research

largely finds that sympathy is an important trigger, which actually results in higher donations, it is

possible that smiling is a noisy proxy variable. Some may be touched more by a patient who smiles,

showing strength and fortitude despite facing a severe disease. The latter case may be why some

HCF platforms recommend “including photos that show a positive, determined outlook in the face

of adversity” in their “how to run successful campaign” descriptions (see, for example,

www.youcaring.com/medical) (see also Snyder, Mathers, and Crooks, 2016). Finally, in the

Patient Characteristics block, we find that treatment costs, as expected, are “mechanically”

positively correlated to longer time periods until a campaign is fully funded.

In line with our Hypothesis 4, we find strong evidence for the warm-glow effect, which is noticeably higher for most donors during religious holidays. We posit this induces altruistic people to reflect more on their behavior and think more of others (see Date of Campaign Posting block).

We find that religious holidays, federal holidays, and weekends are statistically significantly related to higher funding speed, but the effect is stronger during religious holidays than on federal

17 Related literature in reward-based and equity crowdfunding documented a positive “female effect” on campaign success (see Greenberg and Mollick, 2017 and Cumming, Meoli, and Vismara, 2019).

26

ElectronicElectronic copy copy available available atat:: https://ssrn.com/abstract=3524649https://ssrn.com/abstract=3169823 holidays or weekends. 18 This finding is in contrast to André et al. (2017), who finds that

crowdfunding campaigns relying on altruism are less successful than campaigns in which donors

have a benefit for themselves. This highlights again the need for a theoretical framework for

donation-based crowdfunding.

The coefficient on the control variable, First Business Day of the Month, is also, as expected,

significantly related to faster campaign funding for campaigns posted on the first business day of

the month. This is mechanically induced, however, because Watsi allocates all its passive

donations on this day, resulting in a higher level of donations.

Finally, we find, in contrast to our expectations and to Hypothesis 5, that patients suffering from

more severe medical conditions (as measured by the number of life-threatening words) do not

benefit from faster funding speed. We tried several different measures to proxy for the severity of

a patient’s health condition. We used a dummy variable that equals 1 if from one to five life-

threatening words was mentioned in the campaign description, or if more than five life-threatening

words were mentioned, and 0 otherwise, but the results were almost the same.

One explanation for this puzzling effect is that donors may truly be sympathizing with patients battling very severe medical conditions, but this effect may fade away or be reduced when processing other information, such as reading the patient story (see Small, Loewenstein, and Slovic,

2007).

—Please insert Table 5 about here—

In the next step, we delve into the campaign description details more deeply, because this is one

area that Watsi can actively manage (see Table 6). In the previous analysis, we already documented

that donors are sensitive to the length of the campaign description, but tend to appreciate more

18 The hypothesis Religious Holiday coef. = Federal Holiday coef. and Religious Holiday coef. = Weekend coef. was rejected at the 1% level for all model specifications.

27

ElectronicElectronic copy copy available available atat:: https://ssrn.com/abstract=3524649https://ssrn.com/abstract=3169823 details over shorter descriptions. Allison et al. (2017) and Anglin et al. (2018) show that the

phrasing and wording in campaigns have an impact on crowdfunding performance. Therefore, we build on specification (1) in the previous regression, and add one of five text indices sequentially to see whether donors are also susceptible to the readability of campaign descriptions (see

Cumming et al., 2019).

Despite distinct differences in the readability indices, they all correlate significantly with each other. Therefore, we do not include all indices simultaneously in the regressions. The first four

(ARI, CL, Gunning Fog, and FKG) are designed to approximate how many years of U.S. grade level education would be necessary on average to comprehend the text when reading it for the first time. In other words, the higher the score, the more difficult it is to comprehend a text.

In contrast to the latter two indices, the first two are based on characters instead of number of complex words and syllables per word. Counting characters is presumably a “rawer” measure, but it is more accurate when counted by a computer program. Syllables per word is a more precise measure, but it requires appropriate identification, as well as the identification of complex words, which may require a list. However, in theory, all four measures should return the same score for a given text.

We find that the four readability indices range from 8.12 to 11.31, which corresponds to the difficulty level of a seventh to ninth grade textbook. The Flesch Reading Ease (FRE) score ranges from 0 to 100, and is interpreted differently, meaning that a lower score indicates a more difficult text. An average FRE score of 66.11 for our campaign descriptions is within a similar range of an eighth or ninth grade reading level. For all readability indices, we consistently find that campaign descriptions that are more difficult to read are negatively correlated with funding speed, i.e., a one- unit increase represents a one-grade increase in U.S. higher education level in our first four

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ElectronicElectronic copy copy available available atat:: https://ssrn.com/abstract=3524649https://ssrn.com/abstract=3169823 readability indices (ARI, CL, Gunning Fog, and FKG). This would be expected to increase funding

time by about one day.19 The coefficients on the other variables are qualitatively not affected by

the inclusion of the readability indices.

Our results are also economically significant. When examining the marginal effects of our main

variables in Table 5, we find infants are funded 36% faster (= (0.64-1) * 100%, or 1.7 days) than

their counterparts. Religious holidays, federal holidays, and weekends result in 28% (= (0.72-1) *

100%, or 1.3 days), 14% (= (0.86-1) * 100%, or 0.6 days), and 6% (= (0.94-1) * 100%, or 0.3 days)

faster funding times, respectively. A 10% increase in treatment costs (length of patient’s campaign

description) is associated with an increase (decrease) in funding time by 17% (8.1%).

The interaction effect in non-linear models cannot be tested by simply checking the coefficients

(Ai and Norton, 2003). To interpret the marginal effect of the interaction term (Infant x Female),

we follow Buis (2010), and interpret the exponentiated coefficient in a Poisson model as a ratio of

ratios by using the log-transformed dependent variable. For example, the time to full funding for

infants is 36% less than for adults. This could be interpreted as the “infant effect.” The interaction

coefficient (Infant x Female) is 0.238645 (= (0.7613155-1) * 100%), suggesting the infant effect

is 24% less for infant girls.

—Please insert Table 6 about here—

19 We find that incident rate ratios, calculated by exponentiating the Poisson regression coefficient for the ARI, CL, Gunning Fog, and FKG indices reported in Table 6, range from 1.05 to 1.14.

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ElectronicElectronic copy copy available available atat:: https://ssrn.com/abstract=3524649https://ssrn.com/abstract=3169823 6.2. Robustness Checks

6.2.1. Different Measurement of the Variable Time

For the first robustness check, we change the methodology from a Poisson to a logistic regression, and replicate the previous main Tables 5 and 6. We make the dependent variable equal to 1 if the number of days until the treatment cost is fully funded is less than or equal to 2 days

(median of variable Time—D_Time_2) or 4 days (average of variable Time— D_Time_4), and 0 otherwise. We find that the results hold for all variables, except for Infant x Female and Weekend, which turn out to be statistically insignificant. But the holiday effect remains robust (see Tables

A1 and A2 in the online appendix).

6.2.2. Selection Bias

Watsi is unlike other crowdfunding platforms, such as Kickstarter, where campaign initiators specify a goal amount that can be used as a reference point to judge whether a campaign was successful. Patient campaigns on Watsi must raise the exact amount necessary for their treatment costs, and donations cannot exceed that amount. All campaigns posted on the website will be financed in full (at some point), at the latest when Watsi allocates the donations on behalf of its

“monthly” members, which can be regarded as a reserve fund. These members, who passively donated (Passive Donors), allow Watsi to decide which campaigns receive their donations.

This channel could allow us to ascertain the “difficulty level” of success. We posit that campaigns with the support of Passive Donors have a higher probability of failure, because there is a chance they may not be funded by regular members. Thus, they may have failed without support from the Passive Donors, which could result in a selection bias.

We use two different approaches to address a potential selection bias: 1) subsampling, and 2) the Heckman approach.

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ElectronicElectronic copy copy available available atat:: https://ssrn.com/abstract=3524649https://ssrn.com/abstract=3169823 Subsampling. We first divide the sample into two subsamples. The first includes no donations

by “monthly” donors (Monthly Donors = 0; see columns (1)-(3) in Table A4 in the online appendix

for robustness of the main Table 5, and columns (1)-(5) in Table A5 in the online appendix for

robustness of the main Table 6), and the second includes at least one donation from a “monthly”

donor (Monthly Donors > 0; see columns (4)-(6) in Table A4, and columns (6)-(10) in Table A5

for robustness of the main tables). The cases where Monthly Donors > 0 are perceived as having

a higher risk of failure.

The idea behind using a subsample test is that, if the selection issue is a serious concern, the

results are expected to be different using two samples with different failure risks. And there are

different ways to slice the sample. For example, we can divide our sample using the presence of a

single Passive Donor (e.g., using a dummy variable for at least one monthly donor), or,

alternatively, using the level of support by Passive Donors (e.g., using an upper quartile

distribution of the number of Passive Donors). We believe using a dummy variable for the

presence of Passive Donors should be considered the strictest test, because even a single and

relatively small allocation by Watsi would be classified as “failed.”20

We find that the results in Table A4 are very similar overall to those in the main Table 5 with

regard to coefficient signs and size. However, there are some noteworthy differences. We find that

smiling reduces funding speed when at least one donation was allocated by Watsi, and it has no

impact when no donation was allocated by Watsi. The coefficient for the variable First Business

20 In an unreported robustness check on the subsampling, we check whether the results are sensitive to the number of allocations by Watsi on behalf of the donors who choose the “monthly” option. Similarly to the previous subsampling, we create a subsample of campaigns with a high level of support by Passive Donors. To define a “high” level, we use the upper quartile (75th percentile) of # of Monthly Donors, which corresponds to more than three contributions from the monthly donors. Therefore, our subsample with a “high” level of Passive Donors consists of campaigns with more than three Passive Donors; the subsample with a “medium-to-low” level of Passive Donors consists of campaigns with three or fewer monthly donors. Instead of using the upper quartile to define a “high” level of Passive Donors, we also tried using the median. Results for the upper quartile and median are available upon request, and are qualitatively similar to those when a single donation from a Passive Donor is classified as failed.

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ElectronicElectronic copy copy available available atat:: https://ssrn.com/abstract=3524649https://ssrn.com/abstract=3169823 Day of the Month is highly significant when at least one donation was allocated by Watsi, which

is logical, because Watsi allocates the “monthly” donations on the first business day of the month.

Furthermore, we find differences in the variables # Life-Threatening Words and Weekend. It seems that Watsi considers the urgency and severity of a patient’s disease when deciding on the allocations.

We find a similar picture when comparing the subsamples. When we focus on the text indices in Table A5, the coefficients are similar in sign and size as in the main Table 6. In summary, we

interpret our subsampling results to mean that highly similar results are found for most variables.

However, for some, such as # Life-Threatening Words and First Business Day of the Month, there

are noteworthy differences that point to a potential sample selection bias. Therefore, we apply the

Heckman procedure next.

Heckman Approach. We apply the Heckman selection model, and use total treatment costs

from country j in year t-1 as an instrument (Lag Sum of Treatment Cost) to predict funding

difficulty. As explained in the subsample tests, funding difficulty is proxied for by a dummy variable that equals 1 if there is at least one donation from a Passive Donor (Monthly Donors > 0).

The expectation is that medical resources in country j in year t may be allocated ex ante depending on the actual costs incurred in year t-1. Therefore, cases in year t in country j that experience an unexpected surge in funding need may face greater difficulty, and, hence, higher risks of failure.

In other words, we expect a negative correlation between total treatment costs in country j year t-

1, and the likelihood of Watsi stepping in with the reserve funds from monthly donors to allocate funds in year t.

Our instrumental variable is created from 2013 onward, because there were only twenty-three

campaigns in 2012 (see Table 2). Therefore, our sample period begins in 2014, because our

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ElectronicElectronic copy copy available available atat:: https://ssrn.com/abstract=3524649https://ssrn.com/abstract=3169823 instrumental variable uses the lagged values of total treatment costs per country. Furthermore, and

as explained above, we construct the instrumental variable by country j year t, which excludes

country and year fixed effects because of collinearity reasons.

We apply a Poisson regression model with endogenous sample selection. The estimated correlation between selection errors and outcome errors should be positive. In this first stage, we run a Probit regression on whether there is at least one allocation made by Watsi on behalf of their

Passive Donors (i.e., Monthly Donors > 0). We include the Lag Sum of Treatment Cost as an

instrument. We should note that identification of the Heckman two-step model (under the

assumption of normal errors) can be achieved without the exclusion restriction.

The first-stage results are summarized in columns (1)-(3) in Table A6 and in columns (1)-(5)

in Table A7 in the online appendix. As expected, the total treatment costs in country j year t-1 (Lag

Sum of Treatment Cost) are negatively correlated with the likelihood of having at least one

allocation made by Watsi on behalf of monthly donors (or, in other words, a “failed” campaign the

next year, in the strictest sense).

The Wald test in the footer indicates that we can reject the null hypothesis of zero correlation

(see again Tables A6 and A7 in the online appendix). This positive and significant correlation

estimate implies that unobservable factors that increase funding speed tend to occur with

unobservable factors that also increase the likelihood of a case being funded.

The second stage is a Poisson regression on Time. Results are summarized in columns (4)-(6)

in Table A6 and columns (6)-(10) in Table A7 in the online appendix. Our main results are

generally robust, except that the coefficients have larger standard errors due to the correction for

selection: Funding speed is negatively correlated with Age and baby girls (Infant x Female), First

Business Day of the Month, and Christian Holiday. As expected, after including the treatment cost

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ElectronicElectronic copy copy available available atat:: https://ssrn.com/abstract=3524649https://ssrn.com/abstract=3169823 on an aggregate level in country j year t-1 (as an instrument), the coefficients of treatment costs

become insignificant. The differences in the variables Smile, # Life-Threatening Words, and

Weekend are consistent with the subsample tests. One noteworthy difference, however, is that the

significance of Federal Holiday and Weekends fades completely.

6.2.3. Infant Effect

For this robustness check, we are interested in determining the robustness of the results related

to the different age groups, or, in other words, which age group is driving the results. Therefore,

we classify infants as those younger than one year (Infant), toddlers as those from one to three years (Toddler), and children as those from four to eighteen years (Child). Next, we interact the three different age groups with our gender dummy variable (Female). In column (2) of Table A8 in the online appendix, we find that the only statistically significant interaction term is Infant x

Female.

We also use the continuous variable Age and the interaction with Female. In column (3) of

Table A8, the signs of Age and Female remain consistent with our previous findings. Interestingly, the interaction between them becomes positive. Taken together with column (2) of Table A8, we conclude that the interaction effect of Age and Female is negative and significant only if the patient is an infant. The interaction effect of Age and Female is statistically not different from zero, or positive in all other age groups. To check this expectation, we plot margin plots in Figure A2, which supports our hypothesis: When the patient is a baby girl, the predicted time to funding is less than it is for baby boys. In other age groups, Female has larger or similar predicted time as

Male.

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ElectronicElectronic copy copy available available atat:: https://ssrn.com/abstract=3524649https://ssrn.com/abstract=3169823 Overall, we find that the gender effect is driven solely by infant girls, which leads to higher funding speed. This effect seems to reverse itself for senior women (see Table A8 and Figure A2

in the online appendix).

6.2.4. Leisure Time Effect

An alternative explanation for higher funding speed during holidays and weekends could be

that altruistic people have more time to worry about others during these periods compared to

regular workdays. If this is true, and assuming that the amount of leisure time is the same for both

Christian and Federal holidays and weekends, we would expect to find (within certain margins) almost the same coefficients. However, we find instead that the coefficient for Christian Holidays is by far the largest (about -0.3). It is half that for Federal Holidays (-0.15) and about one-fourth for weekends (-0.06) (see Table A9 in the online appendix, panel A). We interpret this to mean that donors act differently when they have more free time, because the magnitude of the coefficients varies substantially.

Furthermore, when we apply the Heckman procedure to address selection bias, we find that only the coefficient for Christian holidays remains statistically significant. Its magnitude nearly doubles, from about -0.3 to -0.6. Interestingly, the coefficients for Federal holidays and weekends lose statistical significance with this procedure, and are close to zero. This supports the notion that only Christian holidays, not Federal holidays or weekends, are related to faster funding speed,

despite the fact that donors presumably have similar amounts of leisure time. In summary, we

interpret these results as non-supportive of the alternative explanation that extra free time

motivates altruistic people to donate more.

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ElectronicElectronic copy copy available available atat:: https://ssrn.com/abstract=3524649https://ssrn.com/abstract=3169823 6.2.5. December Tax Effect

Another explanation for the higher funding speed during Christmas could be explained by donation decisions being at least partially driven by tax-related effects, which make donations in

December most attractive. To explore this explanation, we delete all donations posted in December

(790 of the 4,677 observations in our full sample) (see Tables A10 and A11 in the online appendix).

Overall, our main results remain the same. Therefore, we do not find support for the alternative explanation that tax-related effects are driving our main results.21

6.3. Peer Effects

Table 7 presents the peer effects on contributions made by PP Donors. Specifications (1)-(5)

are based on a fixed effects linear probability model (LPM). Specifications (6)-(9) are based on a

dynamic panel data estimation of two-step difference GMM. The dependent variable equals 1 if

donation s is made by a PP Donor, and 0 if anonymous. For test variables, Last Donor Public

Profile equals 1 if the previous donation (s-1) was made by a PP Donor, and 0 if anonymous. Min

One Previous Public Profile Donor equals 1 if at least one prior donation (1 to s-1) was made by

a PP Donor, and 0 otherwise. Mean Impact Previous Donors is the average impact of all previous

donors (1 to s-1). We record the impact of having an anonymous donor as 0. The regressions in

specifications (1)-(5) are controlled for case and sequence fixed effects. Robust standard errors (in

parentheses) are clustered at case level. We suppress the fixed effect coefficients and the constants.

Specifications (1), (2), and (6) test our Hypothesis 6. Our first result shows that the probability

of a contribution by a PP Donor is negatively influenced by whether the previous donor was a PP

Donor (see specification (1) in Table 7). A previous contribution by a PP Donor (s-1) is associated

with a 3.6% reduction in the probability of a subsequent contribution by a PP Donor in s. The

21 We also re-run our analyses by deleting all donations funded in December, and reach the same conclusion. The results are untabulated, but are available from the authors upon request.

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ElectronicElectronic copy copy available available atat:: https://ssrn.com/abstract=3524649https://ssrn.com/abstract=3169823 results in specification (2) suggest that if a PP Donor observes at least one prior donation by a PP

Donor, the probability of contributing is reduced by 21%, which is statistically and economically significant. This suggests a negative peer, or “crowding-out,” effect of future contributions by PP

Donors once a contribution is made by a PP Donor.

Specifications (3)-(5) and (7)-(9) test our Hypothesis 7. The coefficient of Mean Impact

Previous Donors ranges from -0.001 to -0.004, suggesting that a 1-point increase in the average

impact among previous donors is related to a 0.1%-0.4% decrease in the probability of a PP Donor

contribution. In order to control for the dynamics of donation, we add Public Profile Donor

Reputation Higher Than Average (which equals 1 if the donor of donation s has a higher impact

than Mean Impact Previous Donors), and Public Profile Donor Reputation Higher than Last

(which equals 1 if the donor of donation s has a higher impact than previous donor s-1). The

purpose is to control for systematic bias from the donation position. Although we include donation sequence fixed effects, Mean Impact Previous Donors may increase (decrease) as donation position increases if a particular PP Donor favors (disfavors) donating.22

Even after controlling for previous donations, we find that PP Donors are less likely to donate

if the average reputation of previous donors is higher than their own. In other words, there is a

crowding-out effect of “Common Knights” that is created by “Shining Knights.”

In GMM regressions (specifications (6)-(9)), we eliminate the case fixed effect by first differencing, and then control for sequence fixed effects (coefficients suppressed). We use the two-

and three-period lags of the campaign mean impact as instruments for the (change in) mean of past

22 We are unable to add donor fixed effects because of the small number of donations made per donor (i.e., there is no reason to treat all the anonymous donors the same.) Note that these two controls are constructed using the conditional mean. In the example described in the “Data” section, David’s impact number was 10, which was lower than the average of the nine previous PP Donors, which was 71. Therefore, this dummy variable takes the value of 0. Similarly, we compare David with the PP Donors who donated right before him (using initial “B” (eighteenth in the queue) in panel C, Funder Sequence, in Figure A1). In this particular case, the impact number of the previous donor is 9, so this dummy variable equals 1.

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ElectronicElectronic copy copy available available atat:: https://ssrn.com/abstract=3524649https://ssrn.com/abstract=3169823 donations. Specification (1) with a lagged dependent variable is not applicable in the GMM setting.

Specifications (6)-(9) follow the same structure as specifications (2)-(5) in the LPM setting.

Overall, our results are largely consistent with those from the LPM setting. We again document strong negative peer effects (Hypothesis 6), and a crowding-out effect of “Common Knights”

(Hypothesis 7). Relative to anonymous donors, a PP Donor is less likely to contribute if she observes at least one previous contribution by a PP Donor, and if her impact is lower than previous donors. As discussed in the “Methodology” section, the LPM could result in upward-biased coefficients, because any unobservable correlation with the donation decision of earlier donors should also correlate in the same direction with that of current donors. The Arellano-Bond test for serial correlation implies there is no second-order serial correlation. Thus, the two-period lag should be valid as an instrument. And the Hansen test does not imply the instrument set is not valid.

—Please insert Table 7 about here—

As discussed in our “Data” section, the group of PP Donors includes active and passive donors.

The latter choose a “monthly” recurring donation option, which means they donate a certain amount each month, and allow Watsi to allocate accordingly. Therefore, our results may be affected by the unknown allocation mechanism used by Watsi.

To check on this notion, we focus only on the subgroup of PP Donors who actively make their own allocation decisions, and thus do not choose the monthly option (PPnoM Donors). In Table

8, the dependent variable equals 1 for PPnoM Donors, and 0 otherwise. The negative peer effects are persistent and robust in all regression specifications (supporting Hypothesis 6). The coefficient on Min One Previous Public Profile Picture Donor roughly doubles in magnitude. This suggests that PPnoM Donors are highly sensitive to previous donations from PP Donors, and will hesitate

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ElectronicElectronic copy copy available available atat:: https://ssrn.com/abstract=3524649https://ssrn.com/abstract=3169823 to contribute as soon as a donation by a PP Donor is recorded. The coefficient estimates of Mean

Impact Previous Donors become statistically significant in all specifications. This implies that

PPnoM Donors responds more negatively to a record of earlier donors when the average impact is higher (supporting Hypothesis 7). It seems that the crowding-out argument is weighted even more heavily when the donor is making their own allocation decisions, as we expected. Put

differently, the loss in private benefits is perceived as higher, which results in a lower probability

of contributing. Again, the LPM estimators are larger than the GMM estimators.

—Please insert Table 8 about here—

Finally, we check whether our results are driven by donors with public profiles who voluntarily

decide to show a profile picture (PPP Donors). The dependent variable now equals 1 for active

donors with a public profile that has a profile picture (PPP Donors), and 0 otherwise. We

reconstruct all test variables based on the previous Tables 7 and 8. Qualitatively, we find virtually

the same consistent picture in Table 9 (coefficients remain largely negative), except that the

coefficients in GMM specifications (7)-(9) become statistically insignificant, possibly due to insufficient observations of PPP Donors equals 1.

Overall, our results strongly suggest economically significant negative peer effects. Earlier contributions by donors with public profiles (PP Donors) seem to “crowd out” future contributions from PP Donors. In addition, there is a crowding-out effect of “Common Knight” donors, who have fewer previous donations and lower funding impacts than “Shining Knights.” Our findings are robust to a number of model specifications and robustness checks.

—Please insert Table 9 about here—

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ElectronicElectronic copy copy available available atat:: https://ssrn.com/abstract=3524649https://ssrn.com/abstract=3169823 7. Conclusion

This paper is the first comprehensive empirical study of the economics of HCF campaigns. Our

data highlight the fact that donors exhibit certain patient preferences for their contributions

(Donor-Patient-Psychology), and are also influenced by previous peer investments (Donor-

Donor-Psychology). To summarize, we find that, on average, campaigns for younger patients

(especially infant girls), and those with more comprehensive but easier to read campaign descriptions, achieve funding of the necessary treatment costs faster. The same holds for

campaigns begun around religious holidays, such as Christmas or Easter, when donors seem to

feel more generous (warm-glow effect).

Interestingly, we do not find that donors are particularly touched by campaign descriptions that

indicate a patient is facing a severe or potentially life-threatening illness. We do not find faster

financing of treatment costs in these cases, suggesting that donors may sympathize with these

patients, but the effect may fade or be reduced when processing other information, such as patient

demographics. This means that Psychological Benefits (Hypotheses 1-4), rather than

Psychological Altruism (Hypothesis 5), seems to be the dominant mechanism for donors to

increase funding speed.

Finally, we document significant donor-to-donor peer effects. We find strong support for the

notion that contributions by donors with public profiles are crowding out subsequent contributions

by their peers (donors with public profiles). We explain this effect by the fact that the private

benefits of being shown as the first donor with a public profile on a campaign are not shared with

other public profile donors. Each further contribution by a public profile donor reduces these

private benefits, and thus reduces the probability of a contribution by a later public profile donor.

We also find that donors with public profiles are less likely to donate if the average reputation of

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ElectronicElectronic copy copy available available atat:: https://ssrn.com/abstract=3524649https://ssrn.com/abstract=3169823 previous donors is higher than theirs. Put differently, there is a crowding-out effect of “Common

Knights” that is essentially created by “Shining Knights.” This is in contrast to the literature on

(equity) crowdfunding documenting a herding behavior, i.e., when angel or other sophisticated investors, or reputable investors with public profiles (“Shining Knights” in our context), take the lead and invest during a campaign (Hornuf and Schwienbacher, 2018; Hornuf and Neuenkirch,

2017). These types of investors tend to employ more thorough due diligence, and their experience and solid track records raise the “appeal” of a campaign and serve to “endorse” it. This increases the campaign’s attractiveness, and heightens the subsequent crowding in of other investors

(“Common Knights” in our context) (see Wang et al., 2019; Vismara, 2016). Similar behavior can also be observed in the stock market, where stock liquidity increases after institutional investors take first positions, and others begin “free-riding” on the “smart money’s” due diligence. However, in our setting and mentioned above, we find the opposite, namely, a “crowding-out” effect. We posit this is because contributions by lead investors act as signals of campaign quality, but, in our case, Watsi ensures the quality of each campaign and that donors are not receiving a material reward. This creates fundamentally different funding dynamics, due mostly to the absence of the risk-return relationship. This highlights the importance of the development of a new theoretical framework (see again Figure 1 for a visualization). We are therefore responding to McKenny et al.’s (2017) call to help close the gap in theory, and to develop a cross-disciplinary approach to crowdfunding research.

Our findings have interesting implications. Fundraisers and HCF platform providers alike should pay greater attention to campaign descriptions, because they can be actively managed.

Campaign descriptions should be neither too short nor overly technical. Moreover, our results that donors are seemingly not influenced by the severity of a patient’s condition could also stem from

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ElectronicElectronic copy copy available available atat:: https://ssrn.com/abstract=3524649https://ssrn.com/abstract=3169823 the fact that donors do not easily recognize and may overlook this important fact. It may be worthwhile to include an index about treatment urgency in order to help donors identify which patients need the promptest treatment. Obviously, the higher donation supply around holidays could be actively used by posting more patient campaigns until funding speed equals non-holiday periods.

However, tackling the problem of the crowding-out effect of donors with public profiles by their peers is less straightforward. These investors presumably need an extra incentive to overcome their hesitation to contribute if there are other public profile donors in place. One idea is that HCF platform providers could create virtual collector cards for public profile donors. The card would be completed when the public profile donor completes contributions to, e.g., patient campaigns in ten different countries. The public profile donor would then receive a visible decoration, which would create private benefits. This mechanism may help overcome the loss in private benefits when public profile donors are not the first, and thus do not receive undivided “recognition.”

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ElectronicElectronic copy copy available available atat:: https://ssrn.com/abstract=3524649https://ssrn.com/abstract=3169823 References

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ElectronicElectronic copy copy available available atat:: https://ssrn.com/abstract=3524649https://ssrn.com/abstract=3169823 Zhang, J., and Liu P. (2012). Rational Herding in Microloan Markets, Management Science 58, 892–912.

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ElectronicElectronic copy copy available available atat:: https://ssrn.com/abstract=3524649https://ssrn.com/abstract=3169823 Figure 1: Theoretical Framework

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ElectronicElectronic copy copy available available atat:: https://ssrn.com/abstract=3524649https://ssrn.com/abstract=3169823 Table 1: Variable Definitions This table gives a detailed description of the data-gathering process and calculation methods for all variables. See Table A1 for further details. Readability indices are the same as in Cumming et al. (2019).

Variable Name Description and Calculation Dependent Variables Time Number of days until treatment cost is fully funded. Dummy variable that equals 1 if the number of days until the D_Time_2 treatment cost is fully funded is less than or equal to 2, and 0 otherwise. Dummy variable that equals 1 if the number of days until the D_Time_4 treatment cost is fully funded is less than or equal to 4, and 0 otherwise. Patient Characteristics Age Patient age in years. Dummy variable that equals 1 if the patient is less than 1 year old, and Infant 0 otherwise. Dummy variable that equals 1 if 1 patient’s age < 18, and 0 Child otherwise. ≤ Dummy variable that equals 1 if 18 patient’s age < 60, and 0 Adult otherwise. ≤ Elder Dummy variable that equals 1 if patient’s age 60, and 0 otherwise. Dummy variable that equals 1 if the patient is female, and 0 Female ≥ otherwise. Dummy variable that equals 1 if the patient smiles in the campaign picture, and 0 otherwise. Smile is defined as a confidence score for Smile “Happiness” of above 0.9, based on Microsoft’s cognitive services software Azure (see Figure A1, panel B). Treatment Cost Natural logarithm of the cost of treatment in USD. Date of Campaign Posting Dummy variable that equals 1 if the campaign was posted on the first First Business Day of the Month business day of the month, and 0 otherwise. Dummy variable that equals 1 if the campaign was posted between 7 Religious Holiday days before and 7 days after religious holidays as defined in panel A of Table A1, and 0 otherwise. Dummy variable that equals 1 if the campaign was posted between 4 Federal Holiday days before and 4 days after federal holidays as defined in panel B of Table A1, and 0 otherwise. Dummy variable that equals 1 if the campaign was posted on a Weekend weekend, and 0 otherwise. (continued)

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ElectronicElectronic copy copy available available atat:: https://ssrn.com/abstract=3524649https://ssrn.com/abstract=3169823 Table 1: Variable Definitions—continued

Patient Story Natural logarithm of the number of words in the patient’s descriptive text Length (story). Number of life-threatening words in the patient’s story, as defined in Table # Life-Threatening Words A1. The Automated Readability Index of the patient’s story. ARI equals

4.71 + 0.5 × 21.43 , where is average 𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁 𝑜𝑜𝑜𝑜 𝐶𝐶ℎ𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎 ARI sentence length (i.e., number of words divided by number of sentences). � 𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁 𝑜𝑜𝑜𝑜 𝑤𝑤𝑤𝑤𝑤𝑤𝑤𝑤𝑤𝑤 � 𝐴𝐴𝐴𝐴𝐴𝐴 − 𝐴𝐴𝐴𝐴𝐴𝐴 ARI corresponds to a U.S. grade level; the lower the number, the easier the text is to read. The Coleman-Liau Index of the patient’s story. CL equals

5.88 29.6 × , where is average sentence 𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁 𝑜𝑜𝑜𝑜 𝐶𝐶ℎ𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎 CL length (i.e., number of words divided by number of sentences). CL � 𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁 𝑜𝑜𝑜𝑜 𝑤𝑤𝑤𝑤𝑤𝑤𝑤𝑤𝑤𝑤 � − 𝐴𝐴𝐴𝐴𝐴𝐴 𝐴𝐴𝐴𝐴𝐴𝐴 corresponds to a U.S. grade level; the lower the number, the easier the text is to read. Gunning Fog Index of the patient’s story. The index equals 0.4 [ +

100 ] , where is average sentence length 𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁 𝑜𝑜𝑜𝑜 𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐 𝑤𝑤𝑤𝑤𝑤𝑤𝑤𝑤𝑤𝑤 𝐴𝐴𝐴𝐴𝐴𝐴 (i.e., number of words divided by number of sentences), and Gunning Fog � 𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁 𝑜𝑜𝑜𝑜 𝑤𝑤𝑤𝑤𝑤𝑤𝑤𝑤𝑤𝑤 � 𝐴𝐴𝐴𝐴𝐴𝐴 are words with three or more syllables. The index estimates the years of formal education needed to understand the text on a 𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐first reading;𝑐𝑐𝑐𝑐𝑐𝑐 𝑤𝑤𝑤𝑤𝑤𝑤 the𝑤𝑤𝑤𝑤 lower the number, the easier the text is to read. Flesch-Kincaid grade level of the patient’s story. FKG equals 0.39 × + 11.8 15.59, where is average sentence FKG length (i.e., number of words divided by number of sentences), and is average𝐴𝐴𝐴𝐴 𝐴𝐴number of∗ syllables𝐴𝐴𝐴𝐴𝐴𝐴 − per word. FKG𝐴𝐴𝐴𝐴 corresponds𝐴𝐴 to a U.S. grade level; the lower the number, the easier the text is to read. 𝐴𝐴𝐴𝐴𝐴𝐴 Flesch Reading Ease score of the patient’s story. FRE equals 206.835 (1.015 × ) (86.4 × ), where is average sentence length FRE (i.e., number of words divided by number of sentences), and is − average number𝐴𝐴𝐴𝐴𝐴𝐴 − of syllables𝐴𝐴 𝐴𝐴per𝐴𝐴 word. FRE𝐴𝐴𝐴𝐴 ranges𝐴𝐴 from 0-100; the higher the number, the easier the text is to read. 𝐴𝐴𝐴𝐴𝐴𝐴 Fixed Effects

Year FE Dummy variables for each year.

Country FE Dummy variables for each country as listed in Table 2. Dummy variables for each medical partner. Watsi currently works with a Medical Partner FE total of twenty medical partners. Instrument

Lag Sum of Treatment Cost Total treatment cost in millions USD from country j in year t-1.

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ElectronicElectronic copy copy available available atat:: https://ssrn.com/abstract=3524649https://ssrn.com/abstract=3169823 Table 2: Sample Selection This table shows the number of funded campaigns and amounts raised in USD (Vol.) for each respective year in each country from June 2012 through February 2016.

Total Countries 2012 Vol. 2013 Vol. 2014 Vol. 2015 Vol. 2016 Vol. Total: Vol.: Burma 0 - 27 38,390 64 72,765 98 142,520 19 28,485 208 282,160 Burundi 0 - 0 - 0 - 1 1,260 0 - 1 1,260 Cambodia 4 1,245 128 40,200 546 127,555 687 161,646 116 28,889 1,481 359,535 Ethiopia 4 5,985 35 34,300 21 21,025 25 29,365 8 10,265 93 100,940 Ghana 0 - 1 1,500 1 100 4 6,000 0 - 6 7,600 Guatemala 22 14,000 63 50,030 170 129,550 233 155,708 27 17,311 515 366,599 Haiti 0 - 11 12,520 118 153,020 115 161,229 7 10,500 251 337,269 Kenya 0 - 104 74,655 346 214,230 378 295,515 34 25,400 862 609,800 Malawi 0 - 22 20,045 9 5,519 15 13,283 0 - 46 38,847 Mali 0 - 11 9,760 5 3,475 3 2,286 0 - 19 15,521 Nepal 12 11,530 47 51,220 126 54,720 91 24,353 27 10,195 303 152,018 Nigeria 0 - 3 4,200 1 1,500 7 10,403 0 - 11 16,103 Panama 0 - 1 1,400 0 - 4 4,694 0 - 5 6,094 Philippines 0 - 9 6,465 19 9,606 40 30,293 3 3,988 71 50,352 Somalia 0 - 0 - 0 - 5 3,721 0 - 5 3,721 Somaliland 0 - 1 640 3 1,950 8 8,607 0 - 12 11,197 Tanzania 0 - 79 52,775 169 142,370 208 188,292 27 27,224 483 410,661 Thailand 0 - 11 12,210 15 20,555 26 36,315 1 1,500 53 70,580 Uganda 0 - 2 3,000 26 7,825 134 31,704 65 14,580 227 57,109 Zambia 0 - 21 26,500 4 5,000 0 - 0 - 25 31,500 Total 42 32,760 576 439,810 1,643 970,765 2,082 1,307,194 334 178,337 4,677 2,928,866

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Electronic copy availableElectronic at: https copy:// ssrn.comavailable at:/abstract https://ssrn.com/abstract=3524649=3169823 Table 3: Summary Statistics This table gives the descriptive statistics (mean, standard deviation, min, and max) for the full sample. All variables are considered in subsequent analyses (see Table 1 for variable descriptions and calculation methods).

Variable # Obs. Mean Std. Dev. Min Max Dependent Variables Time 4,677 3.94 4.29 1 69 D_Time_2 4,677 0.52 0.50 0 1 D_Time_4 4,677 0.69 0.46 0 1 Patient Characteristics Age 4,677 26.84 25.00 0 90 Infant 4,677 0.09 0.29 0 1 Child 4,677 0.39 0.49 0 1 Adult 4,677 0.36 0.48 0 1 Elder 4,677 0.16 0.36 0 1 Female 4,677 0.55 0.50 0 1 Smile 4,677 0.53 0.50 0 1 Treatment Cost 4,677 6.13 0.82 4.25 8.01 Date of Campaign Posting First Business Day of the Month 4,677 0.15 0.35 0 1 Religious Holiday 4,677 0.22 0.41 0 1 Federal Holiday 4,677 0.21 0.41 0 1 Weekend 4,677 0.16 0.37 0 1 Patient Story Length 4,677 5.11 0.30 4.09 6.10 # Life-Threatening Words 4,677 0.35 0.95 0 13 ARI 4,677 10.47 1.75 4.6 17.8 CL 4,677 11.31 1.58 5.5 19.66 Gunning Fog 4,677 8.56 1.06 5.2 14.4 FKG 4,677 8.12 1.54 2.9 15.4 FRE 4,677 66.11 8.57 29.86 94.15

55

ElectronicElectronic copy copy available available atat:: https://ssrn.com/abstract=3524649https://ssrn.com/abstract=3169823 Table 4: Correlation Matrix This table shows the Pearson correlation coefficients for all independent variables. All variables are considered in subsequent analyses (see Table 1 for variable descriptions and calculation methods). * indicates statistical significance at a 5% level or below.

(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13) (14) (15) (16) (17) (18) (19) Patient Characteristics (1) Age 1 (2) Infant -0.34* 1 (3) Child -0.65* -0.25* 1 (4) Adult 0.30* -0.23* -0.60* 1 (5) Elder 0.75* -0.14* -0.35* -0.32* 1 (6) Female 0.18* -0.08* -0.19* 0.20* 0.06* 1 (7) Smile 0.36* -0.25* -0.22* 0.20* 0.22* 0.10* 1 (8) Treatment Cost -0.46* 0.19* 0.27* -0.07* -0.42* -0.06* -0.29* 1 Date of Campaign Posting First Business Day of (9) -0.02 0.02 0.02 -0.01 -0.02 -0.02 -0.02 0.12* 1 the Month (10) Religious Holiday 0.01 0.02 -0.01 -0.02 0.03 0.04* 0.01 -0.02 -0.05* 1 (11) Federal Holiday -0.02 0.03* 0.00 0.00 -0.03 0.01 -0.04* 0.03* 0.00 0.07* 1 (12) Weekend 0.04* -0.05* -0.02 0.02 0.03* 0.02 0.03* -0.03* -0.18* 0.00 -0.06* 1 Patient Story (13) Length -0.12* 0.08* 0.06* -0.03* -0.10* 0.00 -0.08* 0.23* 0.20* 0.10* 0.09* -0.02 1 # Life-Threatening (14) 0.01 0.00 -0.07* 0.14* -0.09* 0.09* -0.01 0.11* 0.05* -0.02 0.02 -0.01 0.13* 1 Words (15) ARI -0.01 0.03 0.00 0.01 -0.03 -0.03 -0.02 0.09* 0.02 -0.04* -0.02 0.01 0.01 0.02 1 (16) CL -0.01 0.03* -0.01 0.01 -0.02 -0.02 -0.02 0.08* 0.02 -0.04* -0.01 0.00 0.02 0.01 0.78* 1 (17) Gunning Fog -0.01 0.00 0.01 0.00 -0.01 -0.02 0.00 0.04* 0.01 -0.02 -0.02 0.02 -0.01 0.01 0.68* 0.08* 1 (18) FKG -0.01 0.02 0.01 -0.01 -0.01 -0.03* -0.01 0.03* 0.05* -0.01 -0.01 0.00 0.02 0.02 0.88* 0.62* 0.68* 1 (19) FRE 0.01 -0.03 0.00 0.01 0.01 0.03 0.01 -0.02 -0.06* 0.01 0.01 0.02 -0.03 -0.01 -0.76* -0.76* -0.32* -0.92* 1

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Electronic copy availableElectronic at: https copy:// ssrn.comavailable at:/abstract https://ssrn.com/abstract=3524649=3169823 Table 5: Multivariate Analysis of the Funding Period In this table, we apply Poisson regressions to analyze funding period determinants where the dependent variable, Time, equals the number of days (rounded up to the nearest integer) until the treatment cost is fully funded (see Equation (1) for details). The total sample includes 4,677 campaigns (see Table 1 for variable definitions and Table 3 for summary statistics). Numbers in parentheses are t-statistics. ***, **, and * indicate statistical significance at the 1%, 5%, and 10% levels, respectively.

(1) (2) (3) Patient Characteristics Age 0.008*** (20.38) Infant -0.549*** -0.440*** (-15.08) (-10.53) Child -0.481*** -0.476*** (-17.56) (-17.38) Adult -0.197*** -0.197*** (-7.68) (-7.68) Infant x Female -0.273*** (-5.05) Female -0.028* -0.024 0.002 (-1.77) (-1.52) (0.14) Smile -0.022 -0.022 -0.024 (-1.25) (-1.24) (-1.35) Treatment Cost 0.567*** 0.552*** 0.554*** (32.73) (31.49) (31.60) Date of Campaign Posting First Business Day of the Month -0.352*** -0.352*** -0.354*** (-14.86) (-14.86) (-14.91) Religious Holiday -0.330*** -0.326*** -0.325*** (-16.22) (-16.01) (-15.91) Federal Holiday -0.151*** -0.150*** -0.149*** (-7.85) (-7.82) (-7.75) Weekend -0.066*** -0.064*** -0.065*** (-3.18) (-3.12) (-3.15) Patient’s Story Details Length -0.213*** -0.212*** -0.210*** (-6.41) (-6.38) (-6.32) # Life-Threatening Words -0.000 0.002 -0.000 (-0.06) (0.20) (-0.02) Constant -1.368*** -0.762*** -0.815*** (-5.98) (-3.37) (-3.60) Year FE Yes Yes Yes Country FE Yes Yes Yes Medical Partner FE Yes Yes Yes Observations 4,677 4,677 4,677 Pseudo R2 0.113 0.113 0.114

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ElectronicElectronic copy copy available available atat:: https://ssrn.com/abstract=3524649https://ssrn.com/abstract=3169823 Table 6: Multivariate Analysis of Funding Period (Focusing on Text Indices) In this table, we apply Poisson regressions to analyze funding period determinants (with a focus on the readability indices of a patient’s story), where the dependent variable, Time, equals the number of days (rounded up to the nearest integer) until the treatment cost is fully funded (see Equation (1) for details). The total sample includes 4,677 campaigns (see Table 1 for variable definitions and Table 3 for summary statistics). Numbers in parentheses are t- statistics. ***, **, and * indicate statistical significance at the 1%, 5%, and 10% levels, respectively.

(1) (2) (3) (4) (5) Patient Story (Text Indices) ARI 0.103*** (24.67) CL 0.129*** (27.85) Gunning Fog 0.048*** (6.97) FKG 0.057*** (11.96) FRE -0.010*** (-11.68) Patient Characteristics Age 0.008*** 0.008*** 0.008*** 0.008*** 0.008*** (19.86) (19.73) (20.36) (20.50) (20.56) Female -0.018 -0.018 -0.026* -0.023 -0.023 (-1.17) (-1.13) (-1.67) (-1.48) (-1.49) Smile -0.018 -0.021 -0.021 -0.021 -0.022 (-1.02) (-1.20) (-1.17) (-1.20) (-1.28) Treatment Cost 0.542*** 0.544*** 0.562*** 0.563*** 0.566*** (31.23) (31.28) (32.48) (32.51) (32.67) Date of Campaign Posting First Business Day -0.351*** -0.357*** -0.350*** -0.359*** -0.363*** of the Month (-14.84) (-15.08) (-14.79) (-15.16) (-15.30) Religious Holiday -0.314*** -0.314*** -0.328*** -0.328*** -0.329*** (-15.43) (-15.41) (-16.12) (-16.10) (-16.16) Federal Holiday -0.148*** -0.150*** -0.150*** -0.150*** -0.150*** (-7.75) (-7.81) (-7.82) (-7.80) (-7.82) Weekend -0.067*** -0.062*** -0.068*** -0.066*** -0.064*** (-3.25) (-3.02) (-3.28) (-3.19) (-3.10) Patient Story (Other) Length -0.194*** -0.201*** -0.207*** -0.209*** -0.212*** (-5.85) (-6.06) (-6.23) (-6.28) (-6.40) # Life-Threatening -0.003 -0.002 -0.001 -0.002 -0.002 Words (-0.34) (-0.21) (-0.16) (-0.27) (-0.24) Constant -2.460*** -2.818*** -1.792*** -1.849*** -0.710*** (-10.56) (-11.99) (-7.57) (-7.96) (-3.01) Year FE Yes Yes Yes Yes Yes Country FE Yes Yes Yes Yes Yes Medical Partner Yes Yes Yes Yes Yes FE Observations 4,677 4,677 4,677 4,677 4,677 Pseudo R2 0.134 0.140 0.115 0.118 0.118

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ElectronicElectronic copy copy available available atat:: https://ssrn.com/abstract=3524649https://ssrn.com/abstract=3169823 Table 7: Peer Effects on Contributions by PP Donors In this table, we apply a linear probability model (LPM) (specifications (1)-(5)) and a dynamic panel data estimation of two-step difference GMM (specifications (6)-(9)), where the dependent variable, , equals 1 if the s-th donation to campaign i is made by a PP Donor, and 0 otherwise (see Equation (2) for details). The total sample includes 5,314 PP Donors and 51,281 donations. Robust standard errors are clustered at a case level for linear probability models (specifications (1)- 𝑖𝑖𝑖𝑖 (5)). Numbers in parentheses are t-statistics.𝑃𝑃𝑃𝑃 ***, **, and * indicate statistical significance at the 1%, 5%, and 10% levels, respectively.

LPM with FE Dynamic GMM (1) (2) (3) (4) (5) (6) (7) (8) (9) Last Donor Public Profile -0.036*** (-5.64) Min One Previous Public Profile Donor -0.215*** -0.527*** (-14.70) (-19.78) Mean Impact Previous Donors -0.001*** -0.002*** -0.001*** -0.001 -0.004*** -0.001 (-10.68) (-21.69) (-19.04) (-0.71) (-2.70) (-0.95) Public Profile Donor Impact Higher Than Average -0.471*** -0.594*** (-68.75) (-9.93) Public Profile Donor Impact Higher Than Last -0.324*** -0.178*** (-64.03) (-3.11) Campaign FE Yes Yes Yes Yes Yes N.A. N.A. N.A. N.A. Donation Sequence FE Yes Yes Yes Yes Yes Yes Yes Yes Yes Observations 51,281 51,281 51,281 51,281 51,281 37,179 37,179 37,179 37,179 R2 0.275 0.279 0.276 0.440 0.345 Arellano-Bond test for AR (1), p-value 0.000 0.000 0.006 0.000 Arellano-Bond test for AR (2), p-value 0.603 0.736 0.235 0.363 Hansen test, p-value 0.498 0.704 0.744 0.753

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Electronic copy availableElectronic at: https copy:// ssrn.comavailable at:/abstract https://ssrn.com/abstract=3524649=3169823 Table 8: Peer Effects on Contributions by PPnoM Donors In this table, we apply a linear probability model (LPM) (specifications (1)-(5)) and a dynamic panel data estimation of two-step difference GMM (specifications (6)-(9)), where the dependent variable, , equals 1 if the s-th donation to campaign i is made by a PPnoM Donor, and 0 otherwise (see Equation (2) for details). The total sample includes 5,314 PP Donors and 51,281 donations. Robust standard errors are clustered at a case level for linear probability models 𝑖𝑖𝑖𝑖 (specifications (1)-(5)). Numbers in parentheses𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃 are t-statistics. ***, **, and * indicate statistical significance at the 1%, 5%, and 10% levels, respectively.

LPM with FE Dynamic GMM (1) (2) (3) (4) (5) (6) (7) (8) (9) Last Donor Public Profile -0.082*** (0.006) Min One Previous Public Profile Donor -0.187*** -0.391*** (0.014) (-11.94) Mean Impact Previous Donors -0.000** -0.001*** -0.001*** -0.011* -0.014*** -0.016*** (0.000) (0.000) (0.000) (-1.94) (-2.98) (-2.96) Public Profile Donor Impact Higher Than Average -0.427*** -0.535*** (0.008) (-10.90) Public Profile Donor Impact Higher Than Last -0.282*** -0.335*** (0.007) (-4.33) Campaign FE Yes Yes Yes Yes Yes N.A. N.A. N.A. N.A. Donation Sequence FE Yes Yes Yes Yes Yes Yes Yes Yes Yes Observations 51,281 51,281 51,281 51,281 51,281 37,179 37,179 37,179 37,179 R2 0.216 0.215 0.211 0.348 0.264 Arellano-Bond test for AR (1), p-value 0.000 0.000 0.001 0.000 Arellano-Bond test for AR (2), p-value 0.288 0.313 0.13 0.383 Hansen test, p-value 0.592 0.672 0.268 0.034

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Electronic copy availableElectronic at: https copy:// ssrn.comavailable at:/abstract https://ssrn.com/abstract=3524649=3169823 Table 9: Peer Effects on Contributions by PPP Donors In this table, we apply a linear probability model (LPM) (specifications (1)-(5)) and a dynamic panel data estimation of two-step difference GMM (specifications (6)-(9)), where the dependent variable, , equals 1 if the s-th donation to campaign i is made by a PPP Donor, and 0 otherwise (see Equation (2) for details). The total sample includes 5,314 PP Donors and 51,281 donations. Robust standard errors are clustered at a case level for linear probability models (specifications 𝑖𝑖𝑖𝑖 (1)-(5)). Numbers in parentheses are t-statistics.𝑃𝑃𝑃𝑃𝑃𝑃 ***, **, and * indicate statistical significance at the 1%, 5%, and 10% levels, respectively.

LPM with FE Dynamic GMM (1) (2) (3) (4) (5) (6) (7) (8) (9) Last Donor Public Profile Picture -0.003 (-0.68) Min One Previous Public Profile Picture Donor -0.048*** -0.973*** (-4.52) (-73.79) Mean Impact Previous Public Profile Picture Donors -0.001*** -0.001*** -0.001*** 0.000 -0.000 0.000 (-9.57) (-10.11) (-9.88) (0.22) (-0.26) (0.26) Public Profile Picture Donor Impact Higher Than Average -0.022*** -0.068** (-4.48) (-2.03) Public Profile Picture Donor Impact Higher Than Last -0.014*** -0.183** (-2.78) (-2.44) Campaign FE Yes Yes Yes Yes Yes N.A. N.A. N.A. N.A. Donation Sequence FE Yes Yes Yes Yes Yes Yes Yes Yes Yes Observations 51,281 51,281 51,281 51,281 51,281 37,179 37,179 37,179 37,179 R2 0.152 0.152 0.154 0.155 0.155 Arellano-Bond test for AR (1), p-value 0.000 0.000 0.000 0.076 Arellano-Bond test for AR (2), p-value 0.049 0.531 0.63 0.119 Hansen test, p-value 0.437 0.334 0.293 0.748

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Electronic copy availableElectronic at: https copy:// ssrn.comavailable at:/abstract https://ssrn.com/abstract=3524649=3169823

ONLINE APPENDIX

1

ElectronicElectronic copy copy available available atat:: https://ssrn.com/abstract=3524649https://ssrn.com/abstract=3169823 Figure A1: Patient Campaign Web Page This figure shows the details of patients’ campaign web pages (panel A), funding structures (panel B), and each non- anonymous donor’s funding history.

Panel A

(continued)

2

ElectronicElectronic copy copy available available atat:: https://ssrn.com/abstract=3524649https://ssrn.com/abstract=3169823 Figure A1: Patient Campaign Web Page—continued Panel B We use Microsoft’s cognitive services software Azure (see https://azure.microsoft.com/en-ca/) to code our variable Smile as follows: Azure returns eight facial scores for Anger, Contempt, Disgust, Fear, Happiness, Neutral, Sadness, and Surprise. The sum of all scores is 1. Based on these scores, Azure calculates a confidence score for smiling. We classify a person in a picture as smiling when the confidence score is at least 0.9. The allocations to the facial scores can be seen as a “zero sum” allocation, because the scores must sum to 1. Thus, the choice of 0.9 is analogous to rejection of the null hypothesis at 10%. In the following, we illustrate how Azure works for the patient picture from panel A. The facial scores for the Picture 1 are: “Anger”: 0.0, “Contempt”: 0.0, “Disgust”: 0.0, “Fear”: 0.0, “Happiness”: 1.0, “Neutral”: 0.0, “Sadness”: 0.0, and “Surprise”: 0.0, which equals a confidence score for smiling of 1.0, and is consequently classified as “smiling.” If more than one person is in a picture, Azure returns a separate score for each person. We use the maximum of the confidence score for “Happiness” for all persons in the picture to classify the dummy variable Smiling. For Picture 2, the facial scores for the woman are “Anger”: 0.0, “Contempt”: 0.001, “Disgust”: 0.0, “Fear”: 0.0, “Happiness”: 0.733, “Neutral”: 0.266, “Sadness”: 0.0, and “Surprise”: 0.0, with a confidence score for smiling of 0.733. For the infant, the scores are “Anger”: 0.0, “Contempt”: 0.003, “Disgust”: 0.0, “Fear”: 0.0, “Happiness”: 0.052, “Neutral”: 0.941, “Sadness”: 0.004, and “Surprise”: 0.001, which equates to a 0.052 confidence score for smiling. The maximum confidence score for smiling is 0.733, which is below our threshold of 0.9. This means the picture is coded as non-smiling. In an additional robustness check, we manually verify the results generated from Azure, and only classify as smiling based on the patient’s score for “Happiness,” which would be the infant in Picture 2. In untabulated results, we find the results are virtually identical.

Picture 1 Picture 2

(continued)

3

ElectronicElectronic copy copy available available atat:: https://ssrn.com/abstract=3524649https://ssrn.com/abstract=3169823 Figure A1: Patient Campaign Web Page—continued

Panel C

(continued)

4

Electronic copy availableElectronic at: https copy:// ssrn.comavailable at:/abstract https://ssrn.com/abstract=3524649=3169823 Figure A1: Patient Campaign Web Page—continued

Panel D

5

ElectronicElectronic copy copy available available atat:: https://ssrn.com/abstract=3524649https://ssrn.com/abstract=3169823 Figure A2: Margins by Gender and Age Group

6

ElectronicElectronic copy copy available available atat:: https://ssrn.com/abstract=3524649https://ssrn.com/abstract=3169823 Table A1: Variable Definitions This table shows how the variables “Religious Holiday” (panel A), “Federal Holiday” (panel B), and “# Life- Threatening Words” (panel C) are constructed and defined in Table 1. Note that we only consider Christian holidays as religious holidays here. This is because Watsi’s donors are U.S. citizens, and, according to Cox and Ribert (2017), 66% of U.S. Americans identify with Christian religious groups. In comparison, non-Christian religious groups are growing, but represented less than 10% combined.

Panel A: Religious Holiday Schedule

Year Christmas Easter Thanksgiving 2012 2012-12-25 2012-04-08 2012-11-22 2013 2013-12-25 2013-03-31 2013-11-28 2014 2014-12-25 2014-04-20 2014-11-27 2015 2015-12-25 2015-04-05 2015-11-26 2016 2016-12-25 2016-03-27 2016-11-24

Panel B: Federal Holiday Schedule (observed dates)

Martin New Luther Presidents Memorial Indepen- Columbus Veterans Year Year’s Labor Day King Jr. Day Day dence Day Day Day Day Day 2012 02.01.2012 16.01.2012 20.02.2012 28.05.2012 04.07.2012 03.09.2015 08.10.2012 11.11.2012 2013 01.01.2013 21.01.2013 18.02.2013 27.05.2013 04.07.2013 02.09.2013 14.10.2013 11.11.2013 2014 01.01.2014 20.01.2014 17.02.2014 26.05.2014 04.07.2014 01.09.2014 13.10.2014 11.11.2014 2015 01.01.2015 19.01.2015 16.02.2015 25.05.2015 03.07.2015 07.09.2015 12.10.2015 11.11.2015 2016 01.01.2016 18.01.2016 15.02.2016 30.05.2016 04.07.2016 05.09.2016 10.10.2016 11.11.2016

Panel C: List of Life-Threatening Words

Die, Death, Kill, Killer, Cancer, Cancerous, Life-Threatening, Survive, Lose, Loss, Loss of Body Part, Disability, Immobility

7

ElectronicElectronic copy copy available available atat:: https://ssrn.com/abstract=3524649https://ssrn.com/abstract=3169823 Table A2: Robustness Check—Multivariate Analysis of Funding Period In this table, we apply logit regressions to analyze funding period determinants. The dependent variable, D_Time_2, equals 1 if the number of days until the treatment cost is fully funded is less than or equal to 2 (median of variable “Time”), and 0 otherwise (specifications (1)-(3)). The dependent variable, D_Time_4, equals 1 if the number of days until the treatment cost is fully funded is less than or equal to 4 (average of variable “Time”), and 0 otherwise (specifications (4)-(6)) (see Equation (1) for details). The total sample includes 4,677 campaigns (see Table 1 for variable definitions and Table 3 for summary statistics). Numbers in parentheses are t-statistics. ***, **, and * indicate statistical significance at the 1%, 5%, and 10% levels, respectively.

(1) (2) (3) (4) (5) (6) Patient Characteristics Age -0.017*** -0.018*** (-9.80) (-9.44) Infant 1.254*** 1.134*** 1.368*** 1.261*** (7.74) (5.99) (7.62) (6.11) Child 1.047*** 1.042*** 1.119*** 1.114*** (8.62) (8.57) (8.17) (8.13) Adult 0.435*** 0.434*** 0.438*** 0.438*** (3.92) (3.91) (3.48) (3.48) Infant x Female 0.279 0.258 (1.23) (1.04) Female 0.086 0.080 0.053 0.071 0.067 0.042 (1.26) (1.18) (0.73) (0.96) (0.91) (0.53) Smile 0.009 0.015 0.017 0.033 0.054 0.056 (0.12) (0.19) (0.22) (0.40) (0.63) (0.65) Treatment Cost -1.235*** -1.194*** -1.196*** -1.428*** -1.386*** -1.388*** (-15.37) (-14.86) (-14.88) (-16.16) (-15.54) (-15.56) Date of Campaign Posting First Business Day of 0.878*** 0.879*** 0.881*** 0.617*** 0.624*** 0.626*** the Month (8.82) (8.82) (8.83) (5.71) (5.77) (5.78) Religious Holiday 0.795*** 0.787*** 0.785*** 0.876*** 0.865*** 0.864*** (9.83) (9.73) (9.70) (9.22) (9.10) (9.08) Federal Holiday 0.338*** 0.336*** 0.335*** 0.275*** 0.273*** 0.272*** (4.20) (4.17) (4.15) (3.10) (3.07) (3.05) Weekend -0.019 -0.020 -0.020 0.148 0.149 0.149 (-0.21) (-0.22) (-0.22) (1.50) (1.51) (1.51) Patient Story Length 0.846*** 0.843*** 0.908*** 0.750*** 0.750*** 0.748*** (5.80) (5.78) (6.27) (4.77) (4.77) (4.75) # Life-Threatening 0.065* 0.060 0.061* 0.042 0.040 0.041 Words (1.77) (1.64) (1.67) (1.11) (1.04) (1.08) Constant 3.877*** 2.485** 2.035** 7.240*** 5.751*** 5.805*** (3.87) (2.54) (2.09) (6.45) (5.22) (5.27) Year FE Yes Yes Yes Yes Yes Yes Country FE Yes Yes Yes Yes Yes Yes Medical Partner FE Yes Yes Yes Yes Yes Yes Observations 4,667 4,667 4,667 4,667 4,667 4,667 Pseudo R2 0.127 0.127 0.127 0.145 0.146 0.146

8

ElectronicElectronic copy copy available available atat:: https://ssrn.com/abstract=3524649https://ssrn.com/abstract=3169823 Table A3: Robustness Check—Multivariate Analysis of Funding Period (Focusing on Text Indices) In this table, we apply logit regressions to analyze the funding period determinants (with a focus on the readability indices of a patient’s story). The dependent variable, D_Time_2, equals 1 if the number of days until the treatment cost is fully funded is less than or equal to 2 (median of variable “Time”), and 0 otherwise (specifications (1)-(5)). The dependent variable, D_Time_4, equals 1 if the number of days until the treatment cost is fully funded is less than or equal to 4 (average of variable “Time”), and 0 otherwise (specifications (6)-(10)) (see Equation (1) for details). The total sample includes 4,677 campaigns (see Table 1 for variable definitions and Table 3 for summary statistics). Numbers in parentheses are t-statistics. ***, **, and * indicate statistical significance at the 1%, 5%, and 10% levels, respectively.

(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) Patient Story Details (Text Indices) ARI -0.198*** -0.246*** (-10.38) (-11.76) CL -0.173*** -0.323*** (-8.31) (-13.69) Gunning Fog -0.217*** -0.089*** (-7.01) (-2.71) FKG -0.026 -0.131*** (-1.24) (-5.71) FRE -0.008** 0.024*** (-2.25) (5.90) Patient Characteristics Age -0.017*** -0.017*** -0.017*** -0.017*** -0.017*** -0.018*** -0.018*** -0.018*** -0.018*** -0.018*** (-9.69) (-9.67) (-9.79) (-9.81) (-9.79) (-9.31) (-9.31) (-9.42) (-9.47) (-9.50) Female 0.070 0.075 0.079 0.084 0.089 0.052 0.049 0.069 0.061 0.061 (1.02) (1.09) (1.15) (1.23) (1.30) (0.69) (0.64) (0.92) (0.82) (0.81) Smile 0.010 0.012 0.005 0.009 0.008 0.025 0.035 0.030 0.031 0.034 (0.12) (0.16) (0.07) (0.12) (0.11) (0.29) (0.41) (0.35) (0.37) (0.40) Treatment Cost -1.213*** -1.220*** -1.226*** -1.233*** -1.238*** -1.411*** -1.430*** -1.421*** -1.425*** -1.431*** (-14.94) (-15.09) (-15.18) (-15.34) (-15.39) (-15.71) (-15.81) (-16.07) (-16.09) (-16.16) Date of Campaign Posting First Business Day 0.903*** 0.895*** 0.889*** 0.883*** 0.868*** 0.641*** 0.662*** 0.615*** 0.642*** 0.652*** of the Month (8.95) (8.90) (8.90) (8.86) (8.70) (5.83) (5.95) (5.70) (5.92) (6.00) Religious Holiday 0.783*** 0.782*** 0.795*** 0.794*** 0.798*** 0.863*** 0.871*** 0.873*** 0.873*** 0.876*** (9.56) (9.60) (9.79) (9.82) (9.86) (8.95) (8.96) (9.18) (9.16) (9.18) Federal Holiday 0.338*** 0.340*** 0.337*** 0.338*** 0.339*** 0.282*** 0.289*** 0.274*** 0.275*** 0.276*** (4.13) (4.18) (4.16) (4.19) (4.21) (3.11) (3.16) (3.08) (3.08) (3.09) Weekend -0.001 -0.017 -0.000 -0.018 -0.019 0.165* 0.151 0.154 0.152 0.147 (-0.02) (-0.19) (-0.00) (-0.20) (-0.21) (1.65) (1.51) (1.56) (1.54) (1.48) Patient Story Details (Other) Length 0.844*** 0.854*** 0.838*** 0.846*** 0.846*** 0.748*** 0.774*** 0.744*** 0.753*** 0.761*** (5.70) (5.79) (5.71) (5.79) (5.80) (4.67) (4.79) (4.73) (4.76) (4.81) # Life-Threatening 0.069* 0.065* 0.069* 0.065* 0.064* 0.046 0.045 0.044 0.045 0.044 Words (1.86) (1.76) (1.89) (1.79) (1.75) (1.20) (1.16) (1.14) (1.18) (1.16) Constant 5.917*** 5.724*** 5.791*** 4.082*** 4.465*** 9.876*** 10.939*** 8.012*** 8.289*** 5.596*** (5.73) (5.53) (5.55) (4.02) (4.31) (8.48) (9.21) (6.92) (7.27) (4.84) Year FE Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Country FE Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Medical Partner FE Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Observations 4,667 4,667 4,667 4,667 4,667 4,667 4,667 4,667 4,667 4,667 Pseudo R2 0.144 0.138 0.135 0.127 0.128 0.170 0.180 0.147 0.151 0.151

9

ElectronicElectronic copy copy available available atat:: https://ssrn.com/abstract=3524649https://ssrn.com/abstract=3169823 Table A4: Multivariate Analysis of the Funding Period – Subsample Monthly Donors (= 0/>0) In this table, we apply Poisson regressions to analyze funding period determinants, where the dependent variable, Time, equals the number of days (rounded up to the nearest integer) until the treatment cost is fully funded (see Equation (1) for details). The subsamples are one with no donations by a “monthly” donor (Monthly Donors = 0), and one with at least one donation by a “monthly” donor (Monthly Donors > 0). The total sample includes 4,677 campaigns (see Table 1 for variable definitions, and Table 3 for summary statistics). Numbers in parentheses are t-statistics. ***, **, and * indicate statistical significance at the 1%, 5%, and 10% levels, respectively.

Monthly Donors = 0 Monthly Donors > 0 (1) (2) (3) (4) (5) (6) Patient Characteristics Age 0.009*** 0.007*** (11.68) (16.32) Infant -0.642*** -0.337*** -0.519*** -0.454*** (-8.08) (-3.81) (-12.28) (-9.37) Child -0.500*** -0.484*** -0.486*** -0.483*** (-10.02) (-9.69) (-14.48) (-14.40) Adult -0.174*** -0.171*** -0.225*** -0.226*** (-3.98) (-3.90) (-7.00) (-7.00) Infant x Female -0.917*** -0.158*** (-6.22) (-2.68) Female 0.056* 0.050 0.105*** -0.058*** -0.051*** -0.034* (1.78) (1.57) (3.17) (-3.21) (-2.81) (-1.76) Smile 0.031 0.020 0.008 -0.038* -0.033 -0.034* (0.83) (0.52) (0.21) (-1.91) (-1.60) (-1.65) Treatment Cost 0.573*** 0.537*** 0.542*** 0.546*** 0.537*** 0.539*** (15.99) (14.94) (15.07) (25.84) (25.07) (25.12) Date of Campaign Posting First Business Day of the Month -0.009 -0.016 -0.010 -0.306*** -0.305*** -0.306*** (-0.11) (-0.19) (-0.12) (-12.08) (-12.03) (-12.05) Christian Holiday -0.578*** -0.575*** -0.572*** -0.251*** -0.249*** -0.248*** (-13.08) (-13.01) (-12.93) (-10.81) (-10.72) (-10.65) Federal Holiday -0.164*** -0.151*** -0.163*** -0.179*** -0.181*** -0.179*** (-4.39) (-4.07) (-4.36) (-7.89) (-7.97) (-7.89) Weekend -0.169*** -0.172*** -0.167*** -0.025 -0.022 -0.023 (-4.13) (-4.19) (-4.07) (-1.05) (-0.93) (-0.96) Patient’s Story Details Length -0.074 -0.052 -0.072 -0.237*** -0.238*** -0.235*** (-1.07) (-0.76) (-1.04) (-6.18) (-6.21) (-6.14) # Life-Threatening Words 0.046*** 0.049*** 0.048*** -0.017* -0.016* -0.017* (3.00) (3.21) (3.13) (-1.94) (-1.74) (-1.88) Constant -3.161*** -2.510*** -2.394*** -0.951*** -0.370 -0.415 (-5.78) (-4.63) (-4.42) (-3.54) (-1.39) (-1.56) Year FE Yes Yes Yes Yes Yes Yes Country FE Yes Yes Yes Yes Yes Yes Medical Partner FE Yes Yes Yes Yes Yes Yes Observations 1,440 1,440 1,440 3,237 3,237 3,237 Pseudo R2 0.134 0.133 0.138 0.121 0.121 0.121

10

ElectronicElectronic copy copy available available atat:: https://ssrn.com/abstract=3524649https://ssrn.com/abstract=3169823 Table A5: Multivariate Analysis of Funding Period (Focusing on Text Indices) – Subsample Monthly Donors (= 0/>0) In this table, we apply Poisson regressions to analyze funding period determinants (with a focus on the readability indices of a patient’s story), where the dependent variable, Time, equals the number of days (rounded up to the nearest integer) until the treatment cost is fully funded (see Equation (1) for details). The subsamples are one with no donations by a “monthly” donor (Monthly Donors = 0), and one with at least one donation by a “monthly” donor (Monthly Donors > 0). The total sample includes 4,677 campaigns (see Table 1 for variable definitions and Table 3 for summary statistics). Numbers in parentheses are t-statistics. ***, **, and * indicate statistical significance at the 1%, 5%, and 10% levels, respectively.

Monthly Donors = 0 Monthly Donors > 0 (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) Patient Story (Text Indices) ARI 0.117*** 0.097*** (14.35) (19.53) CL 0.161*** 0.115*** (18.23) (20.98) Gunning Fog 0.023* 0.055*** (1.74) (6.70) FKG 0.070*** 0.053*** (7.57) (9.24) FRE -0.014*** -0.009*** (-8.78) (-8.34) Patient Characteristics Age 0.007*** 0.007*** 0.008*** 0.008*** 0.008*** 0.007*** 0.007*** 0.008*** 0.008*** 0.008*** (10.24) (10.28) (11.53) (11.25) (11.39) (16.25) (15.94) (16.44) (16.50) (16.45) Female 0.077** 0.087*** 0.057* 0.064** 0.067** -0.052*** -0.053*** -0.057*** -0.055*** -0.056*** (2.41) (2.73) (1.80) (2.02) (2.10) (-2.87) (-2.93) (-3.12) (-3.03) (-3.06) Smile 0.042 0.042 0.032 0.032 0.029 -0.036* -0.038* -0.038* -0.038* -0.039* (1.12) (1.10) (0.86) (0.86) (0.78) (-1.81) (-1.91) (-1.87) (-1.89) (-1.92) Ln (Treatment_Cost) 0.539*** 0.551*** 0.569*** 0.558*** 0.561*** 0.527*** 0.525*** 0.543*** 0.545*** 0.547*** (15.06) (15.33) (15.87) (15.58) (15.66) (24.85) (24.71) (25.71) (25.76) (25.84) Date of Campaign Posting First Business Day_Month 0.090 0.098 -0.001 0.047 0.052 -0.312*** -0.317*** -0.305*** -0.316*** -0.318*** (1.03) (1.13) (-0.02) (0.55) (0.60) (-12.30) (-12.50) (-12.05) (-12.44) (-12.52) Christian Holiday -0.571*** -0.568*** -0.577*** -0.584*** -0.587*** -0.233*** -0.233*** -0.249*** -0.246*** -0.247*** (-12.91) (-12.84) (-13.07) (-13.20) (-13.27) (-10.03) (-10.03) (-10.71) (-10.61) (-10.65) Federal Holiday -0.149*** -0.154*** -0.162*** -0.156*** -0.158*** -0.179*** -0.181*** -0.178*** -0.180*** -0.181*** (-4.02) (-4.13) (-4.35) (-4.19) (-4.23) (-7.86) (-7.95) (-7.85) (-7.91) (-7.95) Weekend -0.145*** -0.137*** -0.169*** -0.156*** -0.151*** -0.033 -0.029 -0.029 -0.028 -0.026 (-3.53) (-3.34) (-4.12) (-3.79) (-3.68) (-1.38) (-1.20) (-1.18) (-1.16) (-1.09) Patient’s Story Details Ln (Length) -0.091 -0.111 -0.074 -0.089 -0.099 -0.207*** -0.216*** -0.227*** -0.227*** -0.232*** (-1.32) (-1.62) (-1.07) (-1.30) (-1.43) (-5.41) (-5.65) (-5.92) (-5.92) (-6.04) # Life-Threatening Words 0.032** 0.032** 0.044*** 0.038** 0.038** -0.017** -0.017* -0.017* -0.018** -0.018** (2.13) (2.07) (2.90) (2.51) (2.51) (-1.97) (-1.93) (-1.96) (-2.02) (-2.03) Constant -4.075*** -4.699*** -3.334*** -3.568*** -2.042*** -2.062*** -2.282*** -1.466*** -1.433*** -0.421 (-7.46) (-8.56) (-6.01) (-6.52) (-3.65) (-7.50) (-8.25) (-5.25) (-5.24) (-1.53) Year FE Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Country FE Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Medical Partner FE Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Observations 1,440 1,440 1,440 1,440 1,440 3,237 3,237 3,237 3,237 3,237 Pseudo R2 0.157 0.170 0.135 0.141 0.143 0.140 0.143 0.123 0.125 0.124 11

Electronic copy availableElectronic at: https copy:// ssrn.comavailable at:/abstract https://ssrn.com/abstract=3524649=3169823 Table A6: Endogenous Treatment Regression – Funding Period This table gives estimates of Poisson regressions with endogenous treatment. In this first stage, we run a Probit regression on Monthly Donors > 0, where we include Lag Sum of Treatment Cost as an instrument. The second stage is a Poisson regression on Time. The total sample includes 4,049 campaigns (see Table 1 for variable definitions, and Table 3 for summary statistics). Numbers in parentheses are t-statistics. ***, **, and * indicate statistical significance at the 1%, 5%, and 10% levels, respectively.

Selection on Monthly Donors > 0 Poisson Regression on Time (1) (2) (3) (1) (2) (3) Patient Characteristics Age -0.001 0.007*** (-1.16) (6.21) Infant 0.062 0.122 -0.499*** -0.294 (0.65) (1.02) (-3.86) (-1.20) Child 0.067 0.056 -0.472*** -0.470*** (0.99) (0.79) (-5.61) (-5.12)

Adult 0.073 0.066 -0.068 -0.066 (1.18) (1.11) (-0.94) (-0.87) Infant x Female -0.196 -0.637** (-1.32) (-2.32) Female 0.064 0.061 0.091 0.081 0.051 0.104 (1.35) (1.23) (1.52) (1.36) (0.85) (1.61) Smile 0.124*** 0.117** 0.117** 0.059 0.020 0.027 (2.68) (2.57) (2.53) (0.88) (0.27) (0.33) Treatment Cost -0.610*** -0.594*** -0.589*** 0.011 -0.001 0.002 (-17.71) (-16.01) (-15.11) (0.21) (-0.02) (0.03) Date of Campaign Posting First Business Day of the Month -1.173*** -1.181*** -1.175*** -0.744*** -0.838*** -0.825*** (-13.21) (-13.75) (-14.08) (-5.38) (-6.47) (-6.45) Christian Holiday -0.137*** -0.129** -0.124** -0.659*** -0.675*** -0.674*** (-2.69) (-2.41) (-2.14) (-9.41) (-9.09) (-8.91) Federal Holiday 0.158*** 0.136*** 0.131** 0.016 -0.004 -0.010 (3.02) (2.63) (2.57) (0.24) (-0.06) (-0.14) Weekend -0.100 -0.085 -0.088 -0.086 -0.073 -0.082 (-1.51) (-1.18) (-1.17) (-1.02) (-0.73) (-0.86) Patient’s Story Details Ln (Length) -0.003 -0.046 -0.046 0.034 -0.076 -0.066 (-0.04) (-0.60) (-0.61) (0.33) (-0.75) (-0.65) # Life-Threatening Words 0.005 0.019 0.018 -0.039 0.014 0.008 (0.19) (1.04) (1.06) (-1.21) (0.52) (0.31) Lag Sum Treatment Cost -1.314*** -1.298*** -1.395*** (-3.51) (-3.47) (-3.76) Constant 3.366*** 3.409*** 3.383*** -0.487 0.662 0.557 (8.53) (9.09) (9.43) (-0.78) (1.12) (0.95) Medical Partner FE Yes Yes Yes Yes Yes Yes athrho 2.356*** 2.427*** 2.515*** (11.42) (10.93) (9.08) lnsigma 0.057** 0.092*** 0.083*** (2.14) (3.67) (3.08) Observations 4,053 4,053 4,053 4,053 4,053 4,053

12

ElectronicElectronic copy copy available available atat:: https://ssrn.com/abstract=3524649https://ssrn.com/abstract=3169823 Table A7: Endogenous Treatment Regression -- Funding Period (Focusing on Text Indices) This table gives estimates of Poisson regressions with endogenous treatment. In this first stage, we run a Probit regression on Monthly Donors > 0, where we include Lag Sum of Treatment Cost as an instrument. The second stage is a Poisson regression on Time. The total sample includes 4,049 campaigns (see Table 1 for variable definitions and Table 3 for summary statistics). Numbers in parentheses are t-statistics. ***, **, and * indicate statistical significance at the 1%, 5%, and 10% levels, respectively.

Selection on Monthly Donors > 0 Poisson Regression on Time (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) Patient Story (Text Indices) ARI -0.009 0.083*** (-0.74) (4.60) CL -0.005 0.107*** (-0.40) (6.64) Gunning Fog -0.018 0.049* (-0.88) (1.77) FKG 0.002 0.039** (0.18) (2.21) FRE -0.002 -0.005* (-0.67) (-1.72) Patient Characteristics Age -0.001 -0.001 -0.001 -0.001 -0.001 0.006*** 0.008*** 0.007*** 0.007*** 0.007*** (-1.29) (-0.71) (-1.11) (-1.26) (-1.23) (5.09) (6.71) (5.95) (5.66) (5.75) Female 0.061 0.091 0.057 0.072 0.070 0.093 0.126** 0.081 0.097 0.094 (1.25) (1.64) (1.20) (1.42) (1.42) (1.44) (2.05) (1.37) (1.60) (1.50) Smile 0.125*** 0.110** 0.118** 0.126*** 0.123*** 0.062 0.008 0.058 0.060 0.056 (2.64) (2.45) (2.50) (2.73) (2.61) (0.89) (0.12) (0.84) (0.88) (0.82) Treatment Cost -0.618*** -0.597*** -0.609*** -0.614*** -0.613*** -0.010 0.081 -0.002 -0.003 0.005 (-17.33) (-17.89) (-17.53) (-17.64) (-17.64) (-0.17) (1.48) (-0.03) (-0.05) (0.10) Date of Campaign Posting First Business Day of the Month -1.193*** -1.178*** -1.181*** -1.170*** -1.170*** -0.691*** -0.719*** -0.727*** -0.712*** -0.722*** (-11.58) (-13.88) (-13.18) (-12.85) (-13.07) (-3.28) (-5.40) (-5.09) (-4.69) (-4.89) Christian Holiday -0.140*** -0.118** -0.139*** -0.134*** -0.136*** -0.659*** -0.640*** -0.654*** -0.655*** -0.657*** (-2.65) (-2.20) (-2.70) (-2.59) (-2.63) (-8.41) (-8.78) (-9.28) (-9.04) (-9.02) Federal Holiday 0.171*** 0.144*** 0.157*** 0.165*** 0.165*** 0.052 -0.012 0.020 0.034 0.032 (3.17) (2.87) (2.95) (3.10) (3.10) (0.71) (-0.18) (0.28) (0.47) (0.44) Weekend -0.097 -0.102 -0.098 -0.100 -0.103 -0.059 -0.074 -0.083 -0.078 -0.082 (-1.53) (-1.51) (-1.48) (-1.55) (-1.60) (-0.73) (-0.84) (-0.95) (-0.94) (-0.99) Patient’s Story Details Length 0.001 -0.058 0.002 -0.002 -0.004 0.012 -0.131 0.029 0.012 0.015 (0.02) (-0.76) (0.02) (-0.03) (-0.06) (0.10) (-1.23) (0.28) (0.11) (0.14) # Life-Threatening Words 0.006 0.028* 0.002 0.004 0.003 -0.038 0.047* -0.048 -0.045 -0.044 (0.22) (1.71) (0.06) (0.14) (0.09) (-1.09) (1.78) (-1.48) (-1.32) (-1.29) Lag Sum Treatment Cost -1.298*** -1.328*** -1.302*** -1.333*** -1.337*** (-3.46) (-3.36) (-3.47) (-3.57) (-3.60) Constant 3.494*** 3.608*** 3.500*** 3.360*** 3.501*** -1.055 -1.300** -0.789 -0.594 0.001 (8.18) (8.96) (7.87) (8.36) (8.26) (-1.52) (-2.15) (-1.26) (-0.97) (0.00) Medical Partner FE Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes athrho 2.324*** 2.490*** 2.301*** 2.381*** 2.377*** (10.96) (9.97) (11.50) (10.73) (11.34) lnsigma 0.024 0.069** 0.059** 0.052* 0.053* (0.72) (2.51) (2.20) (1.91) (1.84) Observations 4,053 4,053 4,053 4,053 4,053 4,053 4,053 4,053 4,053 4,053 13

Electronic copy availableElectronic at: https copy:// ssrn.comavailable at:/abstract https://ssrn.com/abstract=3524649=3169823 Table A8: Multivariate Analysis of the Funding Period – Age Groups for Children In this table, we apply Poisson regressions to analyze funding period determinants when the dependent variable, Time, equals the number of days (rounded up to the nearest integer) until the treatment cost is fully funded (see Equation (1) for details). The total sample includes 4,677 campaigns (see Table 1 for variable definitions and Table 3 for summary statistics). Numbers in parentheses are t-statistics. ***, **, and * indicate statistical significance at the 1%, 5%, and 10% levels, respectively.

(1) (2) (3) Patient Characteristics Infant -0.360*** -0.281*** (-13.85) (-8.54) Toddler -0.316*** -0.337*** (-10.43) (-7.94) Child -0.337*** -0.324*** (-16.18) (-11.33) Infant x Female -0.186*** (-4.08) Toddler Female 0.049 (0.88) Child x Female -0.015 (-0.39) Age 0.007*** (12.92) Female -0.022 0.007 -0.075*** (-1.43) (0.33) (-3.35) Age x Female 0.002*** (2.95) Smile -0.019 -0.023 -0.023 (-1.04) (-1.23) (-1.29) Treatment Cost 0.523*** 0.526*** 0.571*** (30.58) (30.50) (32.86) Date of Campaign Posting First Business Day of the Month -0.352*** -0.350*** -0.352*** (-14.84) (-14.75) (-14.86) Christian Holiday -0.326*** -0.326*** -0.331*** (-15.97) (-15.95) (-16.26) Federal Holiday -0.153*** -0.151*** -0.150*** (-7.98) (-7.84) (-7.80) Weekend -0.061*** -0.061*** -0.065*** (-2.95) (-2.96) (-3.16) Patient’s Story Details Length -0.209*** -0.210*** -0.213*** (-6.30) (-6.33) (-6.41) # Life-Threatening Words -0.002 -0.004 -0.003 (-0.21) (-0.48) (-0.33) Constant -0.740*** -0.782*** -1.386*** (-3.27) (-3.43) (-6.05) Year FE Yes Yes Yes Country FE Yes Yes Yes Medical Partner FE Yes Yes Yes Observations 4,677 4,677 4,677 Pseudo R2 0.111 0.112 0.114

14

ElectronicElectronic copy copy available available atat:: https://ssrn.com/abstract=3524649https://ssrn.com/abstract=3169823 Table A9: Multivariate Analysis of the Funding Period – Excerpt In this table, we apply Poisson regressions to analyze funding period determinants where the dependent variable, Time, equals the number of days (rounded up to the nearest integer) until the treatment cost is fully funded (see Equation (1) for details). Panel A (B) shows the results for the main Table (after applying the Heckman procedure). Result are shown only for the variables Christian Holiday, Federal Holiday, and Weekend. Numbers in parentheses are t-statistics. ***, **, and * indicate statistical significance at the 1%, 5%, and 10% levels, respectively.

Panel A (1) (2) (3) Christian Holiday -0.330*** -0.326*** -0.325*** (-16.22) (-16.01) (-15.91) Federal Holiday -0.151*** -0.150*** -0.149*** (-7.85) (-7.82) (-7.75) Weekend -0.066*** -0.064*** -0.065*** (-3.18) (-3.12) (-3.15)

Panel B

(1) (2) (3) Christian Holiday -0.660*** -0.677*** -0.677*** (-9.43) (-9.14) (-8.88) Federal Holiday 0.016 -0.004 -0.009 (0.24) (-0.05) (-0.13) Weekend -0.090 -0.079 -0.087 (-1.06) (-0.78) (-0.91)

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ElectronicElectronic copy copy available available atat:: https://ssrn.com/abstract=3524649https://ssrn.com/abstract=3169823 Robustness Table A10: Multivariate Analysis of the Funding Period – Deleting December Campaigns This table shows the results of the robustness test on main Table 5. After deleting all campaigns launched in December from the full sample of 4,677 observations, our final sample is comprised of 3,887 observations. Numbers in parentheses are t-statistics. ***, **, and * indicate statistical significance at the 1%, 5%, and 10% levels, respectively.

(1) (2) (3) Patient Characteristics Age 0.007*** (17.63) Infant -0.512*** -0.410*** (-13.20) (-9.32) Child -0.479*** -0.475*** (-16.20) (-16.06) Adult -0.221*** -0.221*** (-7.96) (-7.95) Baby x Female -0.263*** (-4.63) Female -0.042** -0.038** -0.012 (-2.52) (-2.28) (-0.68) Smile -0.019 -0.013 -0.015 (-1.01) (-0.70) (-0.79) Treatment Cost 0.555*** 0.547*** 0.549*** (30.13) (29.24) (29.36) Date of Campaign Posting First Business Day of the Month -0.373*** -0.370*** -0.373*** (-14.91) (-14.81) (-14.92) Religious Holiday -0.186*** -0.185*** -0.182*** (-6.46) (-6.40) (-6.31) Federal Holiday -0.067*** -0.066*** -0.064*** (-3.30) (-3.24) (-3.15) Weekend -0.064*** -0.061*** -0.063*** (-2.94) (-2.82) (-2.87) Patient’s Story Details Length -0.185*** -0.182*** -0.180*** (-5.30) (-5.22) (-5.17) # Life-Threatening Words -0.019** -0.017** -0.019** (-2.30) (-2.07) (-2.28) Constant -1.255*** -0.688*** -0.743*** (-5.19) (-2.87) (-3.10) Year FE Yes Yes Yes Country FE Yes Yes Yes Medical Partner FE Yes Yes Yes Observations 3,887 3,887 3,887 Pseudo R2 0.105 0.106 0.107

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ElectronicElectronic copy copy available available atat:: https://ssrn.com/abstract=3524649https://ssrn.com/abstract=3169823 Robustness Table A11: Multivariate Analysis of Funding Period – Deleting December Campaigns This table shows the results of a robustness test on main Table 6. After deleting all campaigns launched in December from the full sample of 4,677 observations, our final sample is comprised of 3,887 observations. Numbers in parentheses are t-statistics. ***, **, and * indicate statistical significance at the 1%, 5%, and 10% levels, respectively.

(1) (2) (3) (4) (5) Patient Story (Text Indices) ARI 0.100*** (22.28) CL 0.127*** (25.76) Gunning Fog 0.042*** (5.64) FKG 0.054*** (10.41) FRE -0.010*** (-10.44) Patient Characteristics Age 0.007*** 0.007*** 0.007*** 0.007*** 0.007*** (17.25) (17.21) (17.60) (17.83) (17.92) Female -0.033** -0.032* -0.041** -0.038** -0.038** (-1.97) (-1.90) (-2.44) (-2.29) (-2.30) Smile -0.017 -0.020 -0.018 -0.019 -0.020 (-0.92) (-1.08) (-0.97) (-1.04) (-1.10) Treatment Cost 0.524*** 0.527*** 0.550*** 0.549*** 0.552*** (28.37) (28.45) (29.83) (29.79) (29.96) Date of Campaign Posting First Business Day of -0.370*** -0.376*** -0.371*** -0.378*** -0.382*** the Month (-14.80) (-15.07) (-14.83) (-15.14) (-15.28) Religious Holiday -0.185*** -0.182*** -0.188*** -0.188*** -0.187*** (-6.41) (-6.33) (-6.52) (-6.51) (-6.48) Federal Holiday -0.069*** -0.069*** -0.067*** -0.067*** -0.067*** (-3.40) (-3.41) (-3.32) (-3.33) (-3.33) Weekend -0.061*** -0.056** -0.065*** -0.062*** -0.061*** (-2.80) (-2.57) (-2.98) (-2.85) (-2.78) Patient Story (Other) Length -0.168*** -0.175*** -0.180*** -0.182*** -0.185*** (-4.81) (-5.03) (-5.16) (-5.21) (-5.32) # Life-Threatening -0.021** -0.020** -0.020** -0.021** -0.021** Words (-2.52) (-2.42) (-2.37) (-2.48) (-2.47) Constant -2.306*** -2.667*** -1.623*** -1.713*** -0.638** (-9.34) (-10.71) (-6.48) (-6.96) (-2.56) Year FE Yes Yes Yes Yes Yes Country FE Yes Yes Yes Yes Yes Medical Partner FE Yes Yes Yes Yes Yes Observations 3,887 3,887 3,887 3,887 3,887 Pseudo R2 0.126 0.132 0.107 0.110 0.110

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ElectronicElectronic copy copy available available atat:: https://ssrn.com/abstract=3524649https://ssrn.com/abstract=3169823