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Review of Managerial Science

https://doi.org/10.1007/s11846-020-00429-6

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ORIGINAL PAPER

Local preferences andtheallocation ofattention

inequity‑based crowdfunding

MarcoBade1· MartinWalther1

Received: 1 April 2020 / Accepted: 2 December 2020

Abstract

This study examines drivers of investment probability in equity-based crowdfunding

using a hand-collected and comprehensive data set from a well-established platform.

The analysis confirms several effects that have been reported in the recent literature

on other crowdfunding markets. Extending recent research, we study moderators of

local preferences of investors. Novel to the literature, we find that (1) local pref-

erences are more pronounced in campaigns of younger ventures, (2) herding-like

behaviour is stronger in local campaigns and (3) local investors are more respon-

sive to updates posted by entrepreneurs, compared to non-locals. Our results suggest

that investors allocate more attention to campaigns for which they have information

advantages, such as local campaigns, due to their limited capacity to process infor-

mation. Such behaviour may eventually amplify information asymmetry and local

preferences. Our findings have practical implications for entrepreneurs, investors

and platforms.

Keywords Individual investor behaviour· Local preferences· Attention allocation·

Limited information processing capacity· Equity-based crowdfunding

JEL Classification D83· G11· L26· M13

1 Introduction

Recently, alternative forms of business financing, such as equity-based crowdfund-

ing, have emerged and are on the rise. In particular, the transaction value of equity-

based crowdfunding in Europe (excluding the UK) has grown from 63.1 million

* Marco Bade

[email protected]

Martin Walther

martin.w[email protected]

1 Chair ofFinance andInvestment, Technische Universitaet Berlin, Sec. H 64, Straße des 17. Juni

135, 10623Berlin, Germany

M.Bade, M.Walther

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EUR in 2012 to 278.1 million EUR in 2018 (Statista 2020). Therefore, the topic

continuously gains attention of researchers. Equity-based crowdfunding is defined as

“[…] a form of financing in which entrepreneurs make an open call to sell a speci-

fied amount of equity or bond-like shares in a company on the Internet, hoping to

attract a large group of investors” (Ahlers etal. 2015, p. 955).

Remarkably, recent research confirms that crowdfunders tend to invest in ven-

tures which are located nearby. The overrepresentation of local assets in a portfolio

is commonly referred to as “home bias” or “local bias”.1 In the following, we refer

to “local preference” as a higher probability of investors to invest in local ventures.

Since French and Poterba (1991), a steadily growing stream of literature has dealt

with the phenomenon of home bias in many different contexts, for example, interna-

tional trade (Wolf 2000; Hillberry and Hummels 2003; Disdier and Head 2008) and

financial investment decisions (Cooper and Kaplanis 1994; Coval and Moskowitz

1999; Stuart and Sorenson 2003; Ahearne etal. 2004; Karlsson and Nordén 2007;

Graham etal. 2009; Dziuda and Mondria 2012).

In the crowdfunding context, Agrawal et al. (2015) examine the prepurchase

platform “SellaBand” that connects musicians with funders. Compared to distant

funders, local funders appear to be less responsive to information about the cumula-

tive investments in an artist. However, this distance-related effect is explained by

funders who fall into the category “friends and family”. According to Hornuf etal.

(2020), the local bias is also present on the German equity-crowdfunding platform

“Innovestment”. Likewise, in the context of equity-based crowdfunding, based on

data from the “ASSOB” equity-based crowdfunding platform, Guenther etal. (2018)

show that geographic distance is negatively correlated with investment probabil-

ity for home country investors. In contrast, overseas investors are not sensitive to

distance. By employing a quasi-experimental design, Lin and Viswanathan (2016)

investigate the mechanisms behind local bias on a virtual peer-to-peer-lending mar-

ketplace called “Prosper”. They find evidence that local bias exists in peer-to-peer

lending. They argue that economic-based explanations cannot fully explain local

bias. Instead, behavioural reasons, such as the familiarity bias, drive this phenom-

enon at least partially.

We aim to contribute to this stream of literature in two ways. First, we test

whether recent findings on drivers of investment decisions in crowdfunding can be

confirmed using a unique hand-collected data set from a well-established platform

and a modified dyadic approach based on Agrawal etal. (2015). Second and new to

the literature, we explore interactions between these drivers and geographic proxim-

ity, in order to examine the explanation of local preferences related to asymmetric

information, in particular the limited information processing capacity and attention

allocation of investors (see, e.g., Sims 2003; van Nieuwerburgh and Veldkamp 2009;

Mondria and Wu 2010).

For this purpose, we investigate several hypotheses that are expected to pro-

vide novel insights. First, we test how the degree of publicly available information

1 Note that the first relates to local preferences across borders and the second refers to local preferences

within countries.

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Local preferences andtheallocation ofattention in…

(proxied by venture age) moderates local preferences. It seems natural to assume

that locals’ information advantages are particularly pronounced in younger ventures.

Therefore, investor’ preference to invest in local campaigns might be stronger the

younger the venture. In our analysis, we aim to substantiate this intuition. Second,

we examine whether locals or non-locals are more responsive to signals (posted

updates) by presumably better-informed entrepreneurs. Third, we test which type of

investor is more responsive to signals from peer investors (recent previous invest-

ments). On the one hand, it seems intuitive that signalling by entrepreneurs or by

peer investors alleviates asymmetric information between locals and non-locals and

thus reduces local preferences. On the other hand, however, if investors pay more

attention to signals concerning local campaigns, as is suggested by the attention-

allocation theory, it is conceivable that information asymmetry and local preferences

get reinforced by updates or recent previous investments. Our study aims to help

clarifying this puzzle.

The results of our study confirm the existence of local preferences in equity-based

crowdfunding. Consistent with recent research, we find indication for L-shaped

investment patterns (Hornuf and Schwienbacher 2018), herding-like behaviour (e.g.,

Hornuf and Schwienbacher 2018; Vismara 2018; Walther and Bade 2020) and a

positive effect of recent updates (Block etal. 2018). Remarkably, the more updates

have already been posted, the weaker the positive effect of updates. Novel to the

literature, we find that, first, local preferences of investors decrease in venture age.

Second, herding-like behaviour is more pronounced among local investors. Third,

compared to non-local investors, locals are more responsive to updates posted by

entrepreneurs. We link these new empirical findings to investors’ limited capacity to

process information and argue that our results are consistent with the related atten-

tion-allocation-based explanation of local preferences.

The remainder of the paper is organized as follows: Sect.2 presents the theory

on local preferences. Section3 develops our hypotheses. In Sect.4, we explain the

empirical setting and the sample construction. Section5 presents our economet-

ric model. Subsequently, in Sect.6, we present the results. Section7 discusses the

results of our analysis. Section8 concludes the paper.

2 Theory onlocal preferences

Table1 provides on overview of explanations of home bias, local bias or local pref-

erences in the non-crowdfunding-related literature. The table is structured as fol-

lows: rows represent reasons that may explain the phenomenon. The first column

lists exemplary studies focusing on the respective explanation from the non-crowd-

funding-related literature. The second column assesses the potential relevance of

each explanation for equity-based crowdfunding. The last column justifies why it

is important to investigate the respective explanation in the context of local prefer-

ences in equity-based crowdfunding. In the following subsections, we present the

theoretical background of our study based on a comprehensive literature review, in

which we refer to this table. Note that for the sake of completeness the table also

includes literature on behavioural explanations, which we do not test in this study.

M.Bade, M.Walther

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Table 1 Explanations of Local Preferences

Studies in the non-crowdfunding-related

literature

Relevance for equity-based crowdfund-

ing

Why should this be investigated in the

context of local preferences in equity-

based crowdfunding?

Economic reasons (related to, e.g.,

asymmetric information and cost of

information acquisition, exchange

rates, transaction costs, riskiness of

investments)

Lewis (1999)

Coval and Moskowitz (1999)

Coval and Moskowitz (2001)

Grinblatt and Keloharju (2001)

Malloy (2005)

Ivković and Weisbenner (2005)

Kimball and Shumway (2006)

Fidora etal. (2007)

Butler (2008)

Hortaçsu etal. (2009)

Baik etal. (2010)

Most relevant: asymmetric information

- Degree of information asymmetry in

equity-based crowdfunding higher than

in other forms of crowdfunding

- Economic/financial objectives of

investors more important than in other

forms of crowdfunding

- Investors are mostly unsophisticated

and have weak skills to acquire private

information, thus might be particularly

receptive to public information signals

Transaction cost, exchange rates, etc.

more relevant in international context

and less relevant in equity-based

crowdfunding, because no product or

service is traded (no shipping cost, no

consumption of services)

Previous research in context of equity-

based crowdfunding provides mixed

evidence on the role of information

asymmetry for local preferences:

- Agrawal etal. (2015): local investors are

less responsive to public information

on cumulative investment than distants;

friends & family explain local bias

largely

- Hornuf etal. (2020): friends & family

and angel-like investors are better at

resolving information asymmetry, thus

have stronger local bias than other inves-

tor types; well-diversified investors care

less about geography

- The role of information asymmetry

between entrepreneurs and different

types of investors and within the crowd

is underexplored

- How differently informed investors

(locals vs. non-locals, non-friends-&-

family) respond to different types of

signals has not yet been studied

Idea: Investors with different levels of

information may behave differently in

terms of investments

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Local preferences andtheallocation ofattention in…

Table 1 (continued)

Studies in the non-crowdfunding-related

literature

Relevance for equity-based crowdfund-

ing

Why should this be investigated in the

context of local preferences in equity-

based crowdfunding?

Limited information processing capacity

(“Attention allocation theory”)

Van Nieuwerburgh and Veldkamp

(2009)

Mondria and Wu (2010)

Prevalence of asymmetric information,

predominance of unsophistication

among investors, weak information

processing skills, limited capital

stock suggest that investors are highly

selective concerning their allocation of

attention to campaigns in equity-based

crowdfunding

- Investors’ allocation of attention has not

yet been studied in the crowdfunding

literature

- Theory is closely connected to

information-asymmetry explanation of

local preferences

- The fact that the literature has over-

looked this theory in the context of

crowdfunding opens research gap

- Understanding how investors’ attention

allocation in the presence of asymmetric

information affects local preferences is

important for entrepreneurs, platforms

and investors themselves

Idea: Locals have superior information;

theory suggests that investors are dif-

ferently attentive to local and remote

campaigns, thus respond differently to

information signals

M.Bade, M.Walther

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Table 1 (continued)

Studies in the non-crowdfunding-related

literature

Relevance for equity-based crowdfund-

ing

Why should this be investigated in the

context of local preferences in equity-

based crowdfunding?

Behavioural reasons (related to, e.g.,

familiarity, trust, warm-glow giving,

altruism, overconfidence, culture,

patriotism, favouritism)

Heath and Tversky (1991)

Kilka and Weber (2000)

Huberman (2001)

Jeske (2001)

Strong and Xu (2003)

Franke etal. (2006)

Massa and Simonov (2006)

Lai and Teo (2008)

Graham etal. (2009)

Morse and Shive (2011)

Pradkhan (2016) Bretschneider and

Leimeister (2017)

Cornaggia etal. (2020)

Familiarity and tangibility issues most

relevant if product or service is traded;

thus rather relevant in, e.g., reward-

based crowdfunding

Warm-glow giving and altruism rather

relevant in donation-based crowdfund-

ing or micro loans

Culture and patriotism rather relevant for

home bias in international context (our

study has rather national context, focus

on local bias/preferences)

Probably most relevant in equity-based

crowdfunding: trust, overconfidence,

favouritism

Only one study focuses on behavioural

explanations of home bias in dept-based

crowdfunding Lin and Viswanathan

(2016): economic explanations not suf-

ficient to explain home bias; behavioural

reasons explain home bias at least partly

In comparison to economic and attention-

allocation-based explanations, relevance

of behavioural explanations appears to

be rather subordinate in the context of

equity-based crowdfunding

Note: This table provides an overview of possible explanations of local preferences, exemplary studies from the non-related crowdfunding literature, the relevance of each

explanation for equity-based crowdfunding and justifications for an investigation in the context in equity-based crowdfunding

1 3

Local preferences andtheallocation ofattention in…

2.1 Economic‑based explanations oflocal preferences

Many economists justify the role of distance with economic arguments. For exam-

ple, Fidora etal. (2007) explain portfolio home bias with real exchange rate vol-

atility. Most of the literature, however, focuses on the importance of information

frictions and transactions costs. Transactions on markets typically cause transac-

tion costs that include costs of, for example, shipping, cultural differences, infor-

mation acquisition, or informational disadvantage because of geographical distance

(e.g., Lewis 1999; Grinblatt and Keloharju 2001). This implies that there are local

advantages related to search and monitoring. Local investors can make use of vari-

ous channels to acquire important information about a company. For example, they

can assess firm quality by visiting a store. They can directly contact the company,

its employees, managers, suppliers and other business partners to obtain first-hand

information. In addition, they may obtain important information from the local

media or from personal contacts to local executives. These channels provide them

with an information advantage over distant investors who do not have access to such

channels (Coval and Moskowitz 1999; 2001; Kimball and Shumway 2006).

Malloy (2005) shows that local analysts provide more accurate forecasts than

remote analysts, suggesting that local analysts have information advantages translat-

ing into better performance. Ivković and Weisbenner (2005) find that local invest-

ments of households yield higher returns than non-local investments. For invest-

ments in smaller companies, where private information advantages should be even

more important, the asymmetry is greater. This suggests that local investors indeed

have information advantages and exploit local knowledge. Baik etal. (2010) show

that local institutional investors, who are considered informed investors, execute

more profitable trades than non-local institutional investors. This suggests that geo-

graphic proximity allows better access to superior information.

Unlike institutional investors or professional analysts, crowdfunders are typically

less sophisticated and invest smaller amounts. Their ability to acquire private infor-

mation are rather weak. Furthermore, crowdfunding involves the investment of pri-

vate assets rather than corporate funds. Therefore, crowd investors typically have

limited capital stocks to invest. Consequently, they need to be selective regarding

their investments and to rely on publicly available online information. This raises

the question whether unsophisticated investors also have local preferences caused

by asymmetric information. The literature on home bias focuses mainly on large and

sophisticated investors, such as US money managers (Coval and Moskowitz 1999),

venture capitalists (Stuart and Sorenson 2003), large custodians and large institu-

tional investors (Ahearne etal. 2004), mutual funds (Karlsson and Nordén 2007)

and fund managers (Dziuda and Mondria 2012).

Note that the studies mentioned above focus on offline investments. It is conceiv-

able that in online markets frictions related to asymmetric information can be over-

come. Remarkably, however, Hortaçsu etal. (2009) show that on the online products

market “eBay”, transactions are still more likely to occur between buyers and sell-

ers from the same area. Geographical distance between buyers and sellers can still

play a role because of shipping charges, localized consumption of the goods (e.g.,

event tickets) and the possibility of direct contract enforcement. In equity-based

M.Bade, M.Walther

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crowdfunding, however, no products or services are traded. Therefore, transaction

costs related to shipping and local consumption should play a minor role. Instead,

financial objectives are more prevalent in equity-based crowdfunding. Thus, inves-

tors in equity-based crowdfunding might behave differently compared to consum-

ers or funders in, for example, reward-based or donation-based crowdfunding

campaigns, as information on venture quality might be more important than, for

example, familiarity with a product or service. In the next subsection, we will dis-

cuss the role of asymmetric information in crowdfunding, especially equity-based

crowdfunding, from a theoretical angle.

2.2 Asymmetric information incrowdfunding

Asymmetric information is a highly relevant problem in crowdfunding, particularly

in equity-based crowdfunding. Hemer (2011) and Ahlers etal. (2015) emphasize that

the degrees of complexity and information asymmetry in equity-based crowdfunding

are higher than in all other forms of crowdfunding. Agrawal etal. (2014) concep-

tually discuss the prevalence of information asymmetry in crowdfunding markets,

especially in the case of equity-based crowdfunding. According to some theoreti-

cal studies, asymmetric information matters for entrepreneurs’ choice of financ-

ing. Belleflamme etal. (2014) demonstrate that asymmetric information changes

financing possibilities in both reward-based and profit-sharing-based (equity-based)

crowdfunding. They argue that observing previous pledges and learning from each

other about campaign quality may help overcome information frictions. While

Belleflamme etal. (2014) find that asymmetric information favours equity-based

crowdfunding, Miglo and Miglo (2019) theoretically show that entrepreneurs with

high-quality projects prefer reward-based crowdfunding. Miglo (2020) considers a

model in which an entrepreneur chooses between different types of crowdfunding.

The model contains elements of asymmetric information and behavioural finance

and predicts that overconfident entrepreneurs prefer equity-based crowdfunding

because they learn from the sales of shares before producing the products or because

crowdfunders strategically anticipate entrepreneurial behaviour. According to Miglo

(2020), reward-based crowdfunding is neither subject to learning nor to strategic

interaction between entrepreneurs and crowdfunders. In contrast, Chakraborty and

Swinney (2020) theoretically examine how an entrepreneur can use the campaign

design in reward-based crowdfunding to signal project quality to funders. They

demonstrate that setting a campaign target that is above the one that would be opti-

mal with full information enables the entrepreneur to signal high quality.

These studies emphasize the importance of considering asymmetric information

in crowdfunding, especially in equity-based crowdfunding. Note that, however, most

of the theoretical studies on crowdfunding consider asymmetric information between

entrepreneurs and investors but not within the crowd. Our study also focuses on the

latter. Therefore, this study aims to contribute to the theoretical literature on crowd-

funding, as our results may provide a basis for future theoretical research.

Additionally, while online markets, such as crowdfunding platforms, enable

entrepreneurs to tap a greater audience, they can exacerbate asymmetric information

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Local preferences andtheallocation ofattention in…

because ventures are typically young and the amount of publicly available informa-

tion is limited (Lin etal. 2013). Some investors may have private information due to

social or geographical proximity. Thus, investors, such as friends and family or local

investors, may have superior information from entrepreneurs, local media or local

communities compared to remote investors who had no contact to the venture before

the crowdfunding campaign. Such heterogeneities among investors are another

particularity of crowdfunding. This stresses the importance to consider asymmet-

ric information between entrepreneurs and investors as well as within the crowd of

investors, when examining local preferences in crowdfunding.

2.3 Limited capacity toprocess information andtheallocation ofattention

Related to asymmetric information, another stream of research dealing with local

preferences in financial markets is based on Sims’ (2003) theory on rational inatten-

tion, which makes use of Kahneman’s (1973) finding that attention is a limited cog-

nitive resource.gabaix and Laibson (2003) as well as Gabaix etal. (2003) show that

this theory plays a role in economic settings by analysing agents’ allocation of think-

ing time when choosing consumption goods from a wide selection of goods. Peng

and Xiong (2006) study the effects of investors’ allocation of attention on asset-price

dynamics. They find evidence for so-called “category learning behaviour” which

means that investors pay more attention to information on markets and industry than

to firm-specific information.

Van Nieuwerburgh and Veldkamp (2009) theoretically demonstrate that investors

choose to allocate more attention to assets for which they already have an informa-

tion advantage. Therefore, they reinforce information asymmetries instead of reduc-

ing their own information disadvantages. The authors further show that small infor-

mation advantages of local investors are sufficient to explain home bias of larger

magnitude. In Mondria and Wu’s (2010) model, investors reduce the uncertainty of

their portfolio by learning information about the economy’s state. However, due to

their limited capacity to process information, they decide to allocate more attention

to domestic assets. As a result, their portfolios consist mostly of domestic assets.

This tendency towards domestic assets is amplified by information advantages of

locals. Moreover, an increasing demand for domestic assets feeds back into inves-

tors’ incentive to learn about these assets.

Regarding the crowdfunding context, this implies that investors might allo-

cate more attention to campaigns of ventures for which they may have informa-

tion advantages, such as local ones. Thus, we expect investment behaviour to differ

between local and non-local investors. The attention-allocation theory represents the

basis for our study and will be used in the further course of this paper to substantiate

the expected effects.

2.4 Prior research onlocal preferences inequity‑based crowdfunding

Agrawal etal. (2015) were the first to find that geography matters for crowdfunding

investments. They attribute this to asymmetric information. However, they further find

M.Bade, M.Walther

1 3

that friends and family, who may have information advantages and live proximate to the

campaign, explain the differences in the behaviour between local and distant funders

to a large extent. Guenther etal. (2018) find that home-country investors in Australia

are sensitive to distance. This may be explained by increasing information asymmetry

with increasing distance. However, they provide no evidence on this explanation. In

contrast, overseas investors are not sensitive to distance. This may be because overseas

investors are unable to directly assess venture quality due to traveling costs anyway.

Consequently, they care less about distance. Recently, Hornuf etal. (2020) argue that

angel-like investors and friends and family have stronger local bias because they are

better at resolving information asymmetry. In contrast, well-diversified investors care

less about geography.

These studies provide varying results regarding the relevance of asymmetric infor-

mation for local preferences in equity-based crowdfunding (see also Table1, first row).

For instance, while Agrawal etal. (2015) find that local bias is explained by friends

and family and social ties between entrepreneurs and investors, Hornuf etal. (2020)

find that local bias also exists among investors who are not friends and family of the

entrepreneur. They attribute this to different levels of transaction cost related to screen-

ing and monitoring incurred by different types of investors, such as angel-like, experi-

enced and friends-and-family investors. However, such investor types are not observ-

able on most crowdfunding platforms and a categorization of investors based on ad-hoc

assumptions could lead to biased results. Therefore, we follow the approach of Agrawal

etal. (2015) and only distinguish between local and non-local investors, which is an

observable characteristic. Moreover, this categorization appears to be most suitable

to proxy for asymmetric information. Additionally, we consider friends and family,

because previous research has shown that this group is most important to consider in

the context of crowdfunding.

Furthermore, how differently informed investors react to relevant information and

signals from local or remote ventures, respectively, is an underexplored field. In par-

ticular, the attention allocation theory has not yet been considered in the crowdfunding

context. We argue that this is an important field of investigation because the resources

of investors in equity-based crowdfunding are likely to be particularly scarce, as they

are usually unsophisticated. The limited capacity to process information in the presence

of information asymmetry makes investors pay even more attention to local campaigns

for which they already have information advantages. This may make them more respon-

sive to information and signals on these campaigns and may reinforce both local pref-

erences and information asymmetry. In a way, this rationale is in contrast to Agrawal

etal.’s (2015) result that distant investors are more responsive to public information. In

the light of the attention allocation theory, geographic proximity may moderate other

drivers of investments, such as venture age, previous investments and updates (see also

Table1, second row). This will be the focus of our hypotheses.

1 3

Local preferences andtheallocation ofattention in…

3 Hypotheses

In view of the studies mentioned above, we need to carefully consider information

asymmetry in the context of our equity-based crowdfunding study. Unfortunately,

however, information asymmetry is not directly observable. This requires us to use

applicable proxies. In line with the knowledge from home bias literature, investors

may have information advantages regarding local ventures. This advantage is likely

to be larger for ventures with less publicly available information, such as younger

ventures (see, e.g. Lu etal., 2010). Nguyen etal. (2019) demonstrate that inves-

tors in fact delay their investments in equity crowdfunding campaigns expecting to

receive more information in the meantime. The idea is that the degree of asymmet-

ric information goes down with venture age as more information becomes naturally

publicly available. Therefore, in line with the attention allocation theory, we expect

investors’ preference for local ventures, for which they have information advantages,

to be particularly strong in the case of younger ventures. This yields our first hypoth-

esis, which to the best of our knowledge has not yet been investigated empirically.

Hypothesis 1 Local preferences are more pronounced in campaigns of younger

ventures.

Note that this hypothesis is not trivial. It is conceivable that older ventures have

had more time to establish themselves in the local community. Consequently, local

preferences might turn out to be larger in older ventures.

An important source of information in equity-based crowdfunding is the publicly

observable investments of other investors. This learning mechanism can be referred

to as observational learning (Bandura 1977). In financial markets, observational

learning is a well-documented phenomenon (e.g., Bikhchandani etal. 1992; Bikh-

chandani and Sharma 2001; Devenow and Welch 1996; Zhang and Liu 2012). It may

give rise to herding-like behaviour, meaning “everyone doing what everyone else is

doing” (Banerjee 1992, p. 798). Recent research shows that herding-like behaviour

is present on crowdfunding platforms (Burtch 2011; Lee and Lee 2012; Zhang and

Liu 2012; Burtch etal. 2013; van de Rijt etal. 2014; Colombo etal. 2015; Kim etal.

2015; Moritz etal. 2015; Vulkan etal. 2016; Hornuf and Neuenkirch 2017; Vismara

2018; Åstebro etal. 2019; Zaggl and Block 2019; Walther and Bade 2020). How-

ever, no attention has been paid to the relation between rational herding-like behav-

iour and local preferences.

The theory on limited information processing capacity and attention allocation

suggests that investors spend more attention to ventures for which they already have

superior information. This implies that investors focus on local ventures when learn-

ing from previous investments of others. This yields Hypothesis 2.

Hypothesis 2 Herding-like behaviour is more pronounced among local investors.

Not only investors can reduce information asymmetries by observational learn-

ing, but signals can be used to reduce information asymmetries between agents as

M.Bade, M.Walther

1 3

well (Spence 2002; Ahlers etal.2015). In the crowdfunding context, besides the

campaign description and videos, there are updates as a means of communication

and signalling. Xu etal. (2014) find that updates may have an even stronger relation

to campaign success than the campaign description. In general, updates appear to

have a positive effect on campaign success. Kuppuswamy and Bayus (2013) show

that campaigns which have posted an update in the final stage of the campaign are

more likely to succeed. Mollick (2014) provides evidence on the importance of

frequent updates. In particular, he finds that campaigns without early updates are

more likely to fail. By demonstrating a positive relationship between project sup-

port and updates, Kuppuswamy and Bayus (2017) provide an explanation for the

importance of frequent updates. Relatedly, Hornuf and Schwienbacher (2018) show

that the number of investments on a particular day increases after an update has been

posted. Most recently, Block etal. (2018) identify positive effects of recent updates

on the number of investments. The statistical significance of this effect decreases

in the number of updates. Since updates should reduce information asymmetries,

the information asymmetry between locals and non-locals might decrease as more

updates are available. As a result, the local preference should decrease. This yields

the following hypothesis.

Hypothesis 3 Local preferences decrease with the number of updates.

The attention investors pay to a campaign decreases over time, which is reflected

by the L-shaped investment pattern (Hornuf and Schwienbacher 2018) and the

“collective attention effect” introduced by Kuppuswamy and Bayus (2017). There-

fore, it is to be expected that the effect of a recent update on investment probability

decreases in the number of updates. Note that the number of updates may also be a

proxy for the timing of an update: the higher the number of an update in the chrono-

logical order, the later the time in the campaign. However, given that investors allo-

cate more attention to local ventures, we expect that local investors are more respon-

sive to updates. This draws two thoughts. First, the effect of an update on investment

should be stronger for local investors. Second, the decrease in the strength of each

update’s effect on investment should be less pronounced for locals. Therefore, we

hypothesize the following.

Hypothesis 4 The effect of an update on investment probability is stronger for local

investors than for non-local investors.

Hypothesis 5a The effect of an update on investment probability diminishes in the

number of updates.

Hypothesis 5b The effect of an update on investment probability diminishes less

quickly for local investors compared to non-local investors.

Note that Hypothesis 5b is to be captured by a three-way interaction. A possi-

ble interpretation of this is that two moderators (first, the number of updates posted

1 3

Local preferences andtheallocation ofattention in…

before; second, being a local) jointly affect the relationship between investment

probability and a recent update.2 Put differently, we examine how the number of

updates and the appearance of a recent update jointly moderate an investor’s local

preference.

4 Empirical setting anddata

The platform considered in this study is called “Companisto”. It has been founded in

June 2012 in Berlin, Germany. Investors can contribute to a campaign by an amount

of their choice. Investors receive a share in the venture’s profits, which is typical for

equity-based crowdfunding. In addition, investors participate in the proceeds if the

start-up is sold. Each financing round runs until the maximum has been reached.

The campaign duration is limited to a maximum of twelve months. In the first fund-

ing stage (target stage), a campaign has two months to reach the financing threshold

(100,000 EUR). If a start-up does not reach the threshold in full within two months,

investors get a full refund. If this stage is successfully completed, the campaign

shifts to the second stage (limit stage), in which the campaign continues until reach-

ing the funding goal. If it does not reach the financing threshold, the campaign is

abandoned.

For our study, we collected data by hand of all investments that have ever been

made on the platform until January 2019. Seven campaigns were not equity-based

but liability-based. Furthermore, important data, such as the date of foundation

of the venture, was missing for one campaign. We therefore removed eight cam-

paigns from the data set. The reduced data set contains 63,691 investments in 93

campaigns.

All variables used in our model are depicted in italics. The campaign-related

information visible to all investors on Companisto includes the campaign’s name,

location of the venture, goal type of the campaign (i.e. target vs. limit, Goal type),

the amount requested (Goal amount), the amount of Co-financing, the Equity stake

offered to investors, the current overall amount invested as well as the reached per-

centage of the funding goal, the status of the campaign, the number of updates (#

updates) and the date of each update. In addition to this information, we take the

date of foundation of the ventures from the website of the “Bundesanzeiger” and

determine the industry (SIC classification) of each venture. The 93 campaigns com-

prise five different industries: Manufacturing (SIC-code: D), Wholesale trade (F),

Retail trade (G), Finance, insurance and real estate (H) and Services (I).

On the investor basis, we collected data on an investor’s ID, location, amount of

investment and date of investment. In the next step, we pair ventures with investors

on a monthly basis following the approach of Agrawal etal. (2015) and Lin and

Viswanathan (2016). Therefore, we construct two lists for each month in the sample.

One list contains all of the 16,559 unique investors who invested at least once by the

2 In other words, this hypothesis focuses on the moderating impact of being a local investor on the mod-

erating impact of the number of updates on the impact of an update on investment probability.

M.Bade, M.Walther

1 3

month considered, implying an average number of investments of 3.85 per inves-

tor. The other list consists of the campaigns available on the platform in the month

under consideration. If an investor invested in a campaign, investor and campaign

are paired, which means the outcome variable (Investment) is set to 1. Otherwise, it

is set to 0. This procedure leads to a data set of 4,875,752 campaign-investor-month

observations. Note that, given the high number of potential pairs, a day-based or

week-based analysis is computationally intractable.

After that, for each pair of locations in the data set, we determine latitude and

longitude. With this data, we calculate the distance using the reference ellip-

soid specified in the World Geodetic System 1984 (e.g., Kumar 1988). Similar to

Agrawal etal. (2015), we create a dummy variable indicating whether the focal

investor is a local (investor who is located less than 100km from the venture, Local)

or distant/non-local investor. In our sample, about 14% of all investments are local

investments.

In addition, we calculate variables related to the timing of the investment, such as

the number of previous investments in the campaign considered (# previous invest-

ments), the number of investments in the two days before (# investments two days

before) and a dummy for early investments (during the first three days of the cam-

paign, Early). We further calculate the number of potential investors for each month

(# potential investors) to control for the steadily increasing number of potential

investors and the number of months a campaign has been available (Project month

count). Note that these variables are all based on public information, which every

investor can see. A summary of variable definitions is given in Table2. Table3 pro-

vides summary statistics. Table4 contains the correlation matrix.

The mean age of ventures starting a campaign is 3.36years. The negative mini-

mum value for the variable Age belongs to the campaign “Freygeist”. The corre-

sponding venture was founded during the campaign, according to the “Bunde-

sanzeiger”. The maximum age is 17.19years. For each investment, there have been

35.35 investments during the previous two days on average. On average, 11 percent

(46 percent) of investments are made one day (within seven days) after an update

has been posted. 18 percent of the 63,691 investments are classified as friends-and-

family investments.

5 Econometric model

Similar to Agrawal etal. (2015), we use a linear probability model but with fixed

effects on both the investor level and the campaign level. The fixed effects are

included in order to control for unobservable variables, such as taste, wealth, invest-

ment preferences, willingness to participate or willingness to pay (investor level)

and campaign-specific factors, such as industry, venture age, goal type or amount

requested, etc.

In order to test whether geographical proximity matters for investments, the vari-

able Local is included in the model. To address Hypotheses 1 and 2, we insert inter-

action terms between Local and Age and between Local and # investments two days

before, respectively. The interactions between Local and # updates and between

1 3

Local preferences andtheallocation ofattention in…

Local and Update previous day address Hypothesis 3 and Hypothesis 4, respectively.

The interactions between Update previous day and # updates as well as between

Local, Update previous day and # updates are considered to test Hypothesis 5a and

Hypothesis 5b.

Since the influence of being a local on investments might differ across industries,

we insert interaction terms between Local and industry dummies into the model. To

address the particularities of early-stage investments, the dummy variable Early, that

indicates whether an investment was made in the first three days of a campaign and

the interaction between Early and Local are included in the regression. Note that

this variable is also important to ensure that the results with respect to the impact of

previous investments are not driven by high numbers of investments during the first

Table 2 List of variables

Note: This table provides an overview of variables used in the regressions

Variable name Description

Investment Equals 1 if an investor invests in a campaign; 0 otherwise

Local Equals 1 if investor location is not more than 50km away from the

venture; 0 otherwise

Age Age of the venture at the start of the campaign in years

# investments two days before Number of investments made in the two days before the investment

considered

Update previous day Equals 1 if there has been an update the day before

Update previous week Equals 1 if there has been an update within seven days before

# updates Number of updates posted before the investment considered

Manufacturing Equals 1 if the SIC-code for the venture’s industry is D (manufacturing)

Wholesale trade Equals 1 if the SIC-code for the venture’s industry is F (wholesale trade)

Retail trade Equals 1 if the SIC-code for the venture’s industry is G (retail trade)

Finance Equals 1 if the SIC-code for the venture’s industry is H (finance, insur-

ance and real estate)

Services Equals 1 if the SIC-code for the venture’s industry is I (services)

Early Equals 1 if investment decision is during the first three days of the cam-

paign; 0 otherwise

# previous investments Number of all investments in the venture made before

# potential investors Number of active investors (those who have invested before) on the

platform

Project month count The number of months a campaign has been available on the platform by

the considered month

Campaign Berlin Equals 1 if the venture’s location is Berlin

Goal type Equals 1 if the campaign’s goal type is “target”; 0 if the goal type is

“limit”

Goal amount Requested amount of capital by the venture (in EUR million)

Co-financing Equals 1 if the campaign has been co-financed by an institutional inves-

tor; 0 otherwise

Equity stake offered Percentage of Equity offered

Friends & family Equals 1 if an investor is a friend or family member using the method

described in Agrawal etal. (2015); 0 otherwise

M.Bade, M.Walther

1 3

Table 3 Summary statistics

Notes: This table provides summary statistics for our data set. The upper part of the table includes

friends-and-family investments while the lower part of the table excludes friends-and-family-invest-

ments. The negative minimum value for the variable Age belongs to the campaign “Freygeist”. The cor-

responding venture was founded during the campaign, according to the “Bundesanzeiger”

Variable N Mean SD Min Max

Local 63,691 0.14 0.34 0 1

Age 63,691 3.36 3.95 −0.09 17.19

# investments two days before 63,691 35.35 62.53 0 562

Update previous day 63,691 0.11 0.31 0 1

Update previous week 63,691 0.46 0.50 0 1

# updates 63,691 3.68 4.12 0 26

Manufacturing 63,691 0.30 0.46 0 1

Wholesale trade 63,691 0.01 0.10 0 1

Retail trade 63,691 0.15 0.36 0 1

Finance 63,691 0.02 0.12 0 1

Services 63,691 0.52 0.50 0 1

Early 63,691 0.21 0.41 0 1

# previous investments 63,691 442.64 394.76 0 2,273

# potential investors 63,691 8,901 4,597 39 16,414

Project month count 63,691 2.34 2.83 1 13

Campaign Berlin 63,691 0.48 0.50 0 1

Goal type 63,691 0.68 0.47 0 1

Goal amount 63,691 0.83 0.89 0.05 5.5

Co-financing 63,691 0.07 0.26 0 1

Equity stake offered 63,691 13.01 7.98 2.44 37.50

Friends & family 63,691 0.18 0.38 0 1

Local 52,530 0.14 0.34 0 1

Age 52,530 3.41 3.99 −0.09 17.19

# investments two days before 52,530 35.46 62.33 0 562

Update previous day 52,530 0.11 0.31 0 1

Update previous week 52,530 0.46 0.50 0 1

# updates 52,530 3.53 4.05 0 26

Manufacturing 52,530 0.30 0.46 0 1

Wholesale trade 52,530 0.01 0.10 0 1

Retail trade 52,530 0.15 0.36 0 1

Finance 52,530 0.02 0.12 0 1

Services 52,530 0.52 0.50 0 1

Early 52,530 0.39 0.49 0 1

# previous investments 52,530 420.2 382.04 0 2,273

# potential investors 52,530 9,005 4,575 39 16,414

Project month count 52,530 2.28 2.84 1 13

Campaign Berlin 52,530 0.47 0.50 0 1

Goal type 52,530 0.68 0.47 0 1

Goal amount 52,530 0.82 0.88 0.05 5.5

Co-financing 52,530 0.07 0.26 0 1

Equity stake offered 52,530 12.89 7.89 2.44 37.50

1 3

Local preferences andtheallocation ofattention in…

days. We control for the total number of previous investments (#previous invest-

ments) in the campaign, the number of investments in the two days before (#invest-

ments two days before) and whether there has been an update the day before the

investment considered (Update previous day).

Thus, the main model’s regression formula is specified as follows:

In our main model, we exclude friends-and-family investments. Note that we adopt

the criteria of Agrawal etal. (2015) for friends and family of a venture: (1) they invest

in the focal start-up before investing in any other start-up, (2) that investment is the

largest of their investments on the website and (3) they invest in no more than three

other campaigns. The fact that friends and family behave differently has been shown by

previous research. Their investment decisions may depend less on measurable factors,

such as proximity. Instead, they guide their investments by personal ties, which may

overcome geographical distances. Given that friends and family are typically closer

to the venture, their investments drive local bias (Agrawal etal. 2015). In our sample,

18 percent of investments are identified as friends-and-family investments. This seems

to be a relatively high percentage, which suggests that the criteria of Agrawal etal.

(2015) might not be strict enough for our data set, meaning that too many investors

are identified as friends and family. Consequently, the number of excluded investors

is likely to be higher than the actual number of friends-and-family investors. To check

the robustness of our results, we include the investors identified as friends and family

in an additional regression. Since the qualitative results remain unchanged, it is likely

that these investors do not explain the effects found in our study to a large extent.

In our robustness checks, we consider an alternative definition of the Local variable

(50km). In Germany, distances are much smaller than in the USA, which is the coun-

try Agrawal etal. (2015) examine. Therefore, we argue that only a stricter definition of

being a local needs to be considered. Next, we consider updates within seven days before

the investment (Update previous week). Furthermore, we vary the number of days used

in our variables that capture herding-like behaviour, i.e. we use # investments one day

before and # investments three days before. As explained above, in a further test, we

include friends-and-family investors. In addition, we remove the campaign fixed effects

(1)

Investment

𝛽

1⋅

Local

+𝛽2⋅[Update previous day ⋅#updates

]

+𝛽3⋅[Local ⋅Age

]

+𝛽4⋅[Local ⋅#investments two days before

]

+𝛽5⋅[Local ⋅#updates

]

+𝛽6⋅[Local ⋅Update previous day

]

𝛽7⋅[Local ⋅Update previous day ⋅#updates

]

Controls

Investor fixed effects

Campaign fixed effects

+Error terms

M.Bade, M.Walther

1 3

Table 4 Correlations

Notes: This table provides the correlations between Investment and all other relevant variables. (1) Local, (2) Age, (3) # investments two days before, (4) Update previous

day, (5) # updates, (6) Manufacturing, (7) Wholesale trade, (8) Retail trade, (9) Finance, (10) Services, (11) Early, (12) # previous investments, (13) # potential investors,

(14) Project month count, (15) Campaign Berlin, (16) Goal type, (17) Goal amount, (18) Co-financing, (19) Equity stake offered, (20) Friends and family

(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13) (14) (15) (16) (17) (18) (19) (20)

(1) 1.00 0.01 0.03 0.02 −0.03 −0.04 −0.01 0.02 0.00 0.02 0.06 −0.05 −0.05 −0.03 0.04 0.04 −0.01 −0.02 0.00 0.00

(2) 1.00 0.01 −0.05 0.15 −0.02 −0.02 −0.06 −0.09 0.08 −0.16 0.22 0.48 0.18 −0.18 −0.12 0.47 0.30 −0.43 −0.03

(3) 1.00 0.19 −0.21 −0.04 −0.03 0.10 −0.01 −0.02 0.30 −0.06 −0.15 −0.22 0.03 0.05 0.03 −0.04 0.12 0.00

(4) 1.00 0.02 −0.04 −0.02 0.05 0.06 -0.01 0.08 −0.01 −0.13 −0.04 0.06 0.05 −0.06 −0.05 0.04 −0.01

(5) 1.00 0.00 -0.04 -0.02 0.00 0.02 -0.35 0.57 0.15 0.76 0.12 -0.02 0.01 0.11 -0.15 0.08

(6) 1.00 −0.07 −0.28 −0.08 −0.69 −0.09 0.23 0.18 0.09 0.03 −0.30 0.25 0.11 0.16 0.00

(7) 1.00 −0.04 −0.01 −0.10 0.01 −0.03 −0.02 0.01 −0.10 −0.15 −0.03 −0.03 0.02 −0.02

(8) 1.00 −0.05 −0.44 0.07 −0.05 −0.09 −0.06 0.03 0.12 −0.10 −0.12 −0.06 0.01

(9) 1.00 −0.13 0.02 −0.04 −0.12 −0.04 0.08 0.08 −0.08 −0.03 0.10 −0.01

(10) 1.00 0.03 −0.16 −0.07 −0.03 −0.05 0.20 −0.14 0.00 −0.13 0.00

(11) 1.00 −0.48 −0.29 −0.33 0.08 0.03 −0.04 −0.08 0.11 −0.06

(12) 1.00 0.07 0.60 0.13 −0.17 0.33 0.30 0.03 0.12

(13) 1.00 0.22 −0.26 −0.15 0.31 0.30 −0.25 −0.05

(14) 1.00 0.01 −0.11 0.15 0.30 −0.19 0.07

(15) 1.00 −0.11 0.11 0.17 −0.09 0.04

(16) 1.00 −0.36 −0.10 0.17 −0.01

(17) 1.00 0.46 −0.14 0.03

(18) 1.00 −0.25 0.02

(19) 1.00 0.03

(20) 1.00

1 3

Local preferences andtheallocation ofattention in…

and include campaign-level variables, such as # potential investors, Age, industry types,

Goal type, Goal amount, Co-financing, Campaign Berlin and Equity stake offered.

6 Results

In this section, we present our results. Table5 depicts our main model’s regression

coefficients with standard errors.

The regression results of our main model (model 1) show that investors tend

to invest in local campaigns. The effect is statistically significant. The coefficient

of Local amounts to 0.6% points. Note that in our final data set, the proportion of

Investment values of 1 is about 4 percent. To get a rough idea on economic sig-

nificances, we consider the partial effects on an average investor. The effect appears

to be economically significant, as it corresponds to an increase in investment

Table 5 Main model

Dependent variable: Investment. Regression type: Linear probability model with investor fixed effects

and campaign fixed effects. Standard errors in parentheses. *p < 0.1; **p < 0.05; ***p < 0.01. Model 1:

Main model excluding Friends and family. Model 2: Main model including Friends and family

Dependent variable:

Investment

(1) (2)

Local 0.0056*** (0.0008) 0.0133*** (0.0009)

# previous investments 0.0001*** (0.000001) 0.0001*** (0.000001)

# investments two days before 0.0006*** (0.00001) 0.0007*** (0.00001)

Project month count 0.0058*** (0.0001) 0.0066*** (0.0001)

Early 0.1326*** (0.0008) 0.1527*** (0.0009)

# updates −0.0017*** (0.00005) −0.0021*** (0.0001)

Update previous day 0.0088*** (0.0006) 0.0095*** (0.0007)

# updates * Update previous day −0.0012*** (0.0001) −0.0015*** (0.0001)

Local * Age −0.0002*** (0.0001) −0.0007*** (0.0001)

Local * # investments two days before 0.0002*** (0.00002) 0.0001*** (0.00002)

Local * # updates 0.0001 (0.0001) 0.00004 (0.0001)

Local * Update previous day 0.0010 (0.0018) −0.0033* (0.0020)

Local * # updates * Update previous day 0.0004** (0.0002) 0.0014*** (0.0002)

Local * Early −0.0053*** (0.0014) −0.0008 (0.0016)

Local * Manufacturing −0.0007 (0.0008) −0.0020** (0.0009)

Local * Wholesale trade −0.0070** (0.0034) −0.0100*** (0.0038)

Local * Finance −0.0185*** (0.0021) −0.0276*** (0.0023)

Local * Services 0.0018** (0.0008) 0.0007 (0.0008)

Observations 4,838,429 4,875,752

R20.2386 0.2189

Adjusted R20.2360 0.2163

Residual Std. Error 0.1690 (df = 4,821,834) 0.1859 (df = 4,859,083)

M.Bade, M.Walther

1 3

probability of about 16%.3 Our result is consistent with previous findings on the

existence of local preferences in crowdfunding (e.g., Agrawal etal. 2015; Lin and

Viswanathan 2016; Guenther etal. 2018; Hornuf etal. 2020).

We can confirm Hypothesis 1, as we find a significantly negative coefficient of

the interaction between Local and Age. The effect strength amounts to 0.02 percent-

age points. Given the standard deviation of Age of 3.99years, this means that an

increase of Age equal to one standard deviation decreases the investment probability

of a local investor by about 2 percent, which may be economically significant.

Regarding herding-like behaviour, we find positive effects of the total number of

previous investments (# previous investments), the number of recent previous invest-

ments (# investments two days before) and a positive coefficient of the interaction

between Local and # investments two days before. The former two results are in

line with recent findings in the literature on herding in equity-based crowdfunding

(e.g., Hornuf and Neuenkirch 2017; Åstebro etal. 2019; Walther and Bade 2020).

The results on the interaction term confirm Hypothesis 2. All effects are statistically

significant. The standard deviation of # investments two days before is 62.33, which

implies an increase of investment probability by about 31 percent when the number

of investments during the two days before increases by one standard deviation. The

effect is therefore likely to be economically significant.

Next, we turn to the impact of updates on investment probability. In line with

recent research, we find a positive effect of a recent update. The coefficient is 0.009

and significant. This means that an update can increase the investment probability of

a non-local by up to 23 percent. In addition, we find that the investment probability

is negatively related to the number of updates.

We cannot confirm Hypothesis 3 because the coefficient of the interaction

between Local and # updates is positive and statistically insignificant. This means

that local preferences do not diminish with the number of updates posted. Our results

do not confirm Hypothesis 4, as the coefficient of the interaction between Local and

Update previous day is not significant. However, we can confirm Hypothesis 5a. The

coefficient of the interaction between Update previous day and # updates is signifi-

cantly negative. It amounts to −0.12 percentage points. This corresponds to a reduc-

tion of the positive effect of an update of up to 23 percent on investment probability

by 14%. This indicates the economic significance of this effect, which to the best of

our knowledge is new to the literature. The three-way interaction between Local,

# updates and Update previous day yields a significant coefficient of 0.04 percent

points. Therefore, the decrease of the effect of an update with each update is 30 per-

cent (= 0.0004/0.0012) less for locals compared to non-locals. Note that excluding

the interactions relevant for Hypothesis 5a and Hypothesis 5b leads to a significantly

positive coefficient of Local * Update previous day (see model 11 in Table9). This

implies that locals are in general more responsive to updates.

3 Note that, due to the structure of our data set, the effect sizes are small. This is mainly because we

have few investments, but many non-investments. When excluding investor and campaign fixed effects,

Cohen’s

(see Cohen 1988) amounts to 0.013. The variable Early (including its interaction with Local)

has the largest contribution (about half) to this. The rest of

is divided among the other variables. For

example, the herding-related variables account for a

of about 0.002.

1 3

Local preferences andtheallocation ofattention in…

In addition to our main results, we find that local preferences are different

across industries. In particular, our results indicate that local preferences are less

pronounced in campaigns of ventures in the finance industry and wholesale trade,

when compared to retail trade. In contrast, local preferences appear to be stronger

in the services industry. This might be due to the fact that finance and wholesale

trade are less regionally bound, which might result in smaller information advan-

tages of locals. On the contrary, services are more often regionally bound and have

to be used locally. Hence, local investors may have an easier time to gain informa-

tion advantages over distant investors. Another interpretation relates to Belleflamme

etal. (2014). In their model, community benefits drive crowdfunding investments.

As such benefits may result from consuming a product or service, locals are more

likely to benefit from the additional utility when the venture provides, for exam-

ple, locally bound services instead of non-local financial products. In this sense, our

results are consistent with the theory developed by Belleflamme etal. (2014). Note

that only a few campaigns are from the finance and wholesale trade industries.

In line with previous research, we can confirm the existence of an L-shaped pat-

tern among investments (Hornuf and Schwienbacher 2018), as the coefficient of

Early is 0.133. Compared to the overall proportion of Investment values of 1 of

about 4 percent, this effect appears to be of high economic significance. In the main

model, the effect of the interaction between Local and Early is negative. However,

the sign of this effect differs among regressions. This is why we do not further dis-

cuss this effect.

Our main qualitative findings are robust to various alternative specifications. The

regression results are provided in Table5 (model 2) and in the tables in the Appen-

dix. Regarding the different effect of updates on locals, there are small differences

between the regression results, which do not change the qualitative conclusion. In

particular, a stricter definition of locals (50km, Table7) yields an additional posi-

tive and significant coefficient of the interaction between Local and Update previous

day. This provides support for Hypothesis 4. Looking at the regression that includes

friends-and-family investors (model 2 in Table 5), there is a negative coefficient

(−0.003) of the interaction between Local and Update previous day. However, as

in the main model, the coefficient of the interaction between Local, Update previ-

ous day and # updates is positive (0.0014). The average number of updates is 3.53,

meaning that the average effect of an update is still stronger for local investors. The

fact that the difference between locals and non-locals is weaker in terms of respon-

siveness to updates is not surprising. Agrawal etal. (2015) have shown that friends-

and-family investors behave differently. These investors are likely to invest in the

early stages, regardless of being a local or whether there have been any updates.

This, in fact, may dilute our results, especially regarding updates in the early stages,

in this regression. Substituting campaign fixed effects (Table8) by campaign con-

trol variables does not change any of our main results. Regarding the robustness test

using Update previous week instead of Update previous day (Table6), the signifi-

cantly negative coefficient of Local * Update previous week is surprising. This may

indicate that non-locals take more time to respond to a recent update because they

follow the campaign with less attention and thus notice the update later. Note that

the result concerning the three-way interaction remains unchanged.

M.Bade, M.Walther

1 3

7 Discussion

Our analysis shows that local preferences exist in equity-based crowdfunding. Local

preferences are stronger, the younger the ventures. We argue that information asym-

metries may be larger in young ventures. Therefore, our result is in line with the

theory that information advantages drive local preferences. Furthermore, our find-

ing that updates have a stronger and more persistent positive effect on investment

probability in local campaigns suggests that investors allocate more attention to

local ventures and are thus more responsive to signals on these ventures. This is

also expressed by the fact that investors are more responsive to recent investments

in local campaigns and thus show stronger herding-like behaviour than in non-local

campaigns. Therefore, our results are consistent with the theory that investors need

to allocate their scarce attention resources to selected campaigns, which, in turn,

drives local preferences. Thus, our results provide general support for the attention-

allocation-based theory as an explanation for local preferences introduced by van

Nieuwerburgh and Veldkamp (2009) and Mondria and Wu (2010).

7.1 Implications

Our findings have implications for entrepreneurs, investors and platforms. In addi-

tion, our results can be used to develop new hypotheses that can provide the basis

for future research. First, our results enable investors to better learn from the invest-

ments of others by providing a deeper understanding behind the drivers of invest-

ment probability.

Second, we find that local preferences cannot fully be explained by friends and

family. This is in contrast to Agrawal etal. (2015) but supports the more recent find-

ing of Hornuf etal. (2020). In order to make use of local preferences, entrepreneurs

may want to locate their headquarters in densely populated regions. This is espe-

cially relevant for campaigns with potentially high information asymmetries, such as

young ventures, since local preferences are particularly pronounced in campaigns of

these ventures.

Third, our results suggest that locals are likely to be over-represented not only in

the group of actual investors, but also in the group of potential investors who regu-

larly follow the campaign and its updates. Entrepreneurs should keep this in mind

when creating the campaign description as well as the content and formulation of

updates. For example, regional aspects could be emphasized.

Fourth, we provide evidence on herding-like behaviour, which is particularly

pronounced among local investors. Accordingly, when calculating the marketing

budget, entrepreneurs should consider multiplier effects resulting from herding

behaviour. They should also be aware that marketing targeted to locals is espe-

cially fruitful because their probability of investment is higher. The existence of

herding-like behaviour implies that investors learn from previous investments.

Platforms could support this learning by publicly showing all previous invest-

ments in detailed form (including date, amount, location of investor, etc.) and also

offering cumulative information, such as the number of investments or the amount

1 3

Local preferences andtheallocation ofattention in…

collected to date, in an easily understandable form. Whether this is desirable from

the perspective of the ventures is an interesting research question. This could be

approached as follows: Learning from previous investments should reduce infor-

mation asymmetry and thereby increase the willingness to invest and ultimately

the probability of campaign success. This hypothesis could be checked by compar-

ing the campaign success on platforms that display previous investments in dif-

ferent levels of detail. Empirically, such an investigation should be possible, since

platforms proceed very differently regarding the display of previous investments.

For example, the oldest German crowdfunding platform “Seedmatch” does not

provide information on individual investments, but only cumulative information.

In contrast, all previous individual investments can be tracked on Companisto.

Fifth, we find that locals are more responsive to signals, such as updates or previ-

ous investments. This supports the hypothesis that information asymmetries in con-

nection with attention allocation explain a substantial part of local preferences and

that these are not only caused by social ties, such as friends and family. To further

unravel whether information asymmetries or investor attention allocation is crucial,

it would be interesting for future research to take a closer look at the relationship

between updates and investment probability. Our results suggest that locals react

more strongly (than non-locals) to later updates. Since we are only looking at the

number of updates, we cannot conclusively determine whether the lower information

asymmetry due to the updates or the lower investor attention due to the late point in

time is the decisive factor. By additionally considering the exact timing of an update

(relative to the start of the campaign), future studies could clarify this. Furthermore,

it would be interesting to consider the “size” of an update. It is conceivable that non-

local investors do not notice all small updates but pay attention to major updates.

Accordingly, future studies could test the hypothesis that locals react comparatively

stronger to minor updates, while non-locals react stronger to major updates.

7.2 Limitations

In our analysis, we do not distinguish between different types of updates, such

as minor vs. major updates and ease of language (see, e.g., Block etal., 2018).

Moreover, Companisto does not provide information on investor type (e.g.

sophisticated vs. unsophisticated), wealth, risk tolerance and social connections.

Therefore, we have to include investor-level fixed effects, which do not perfectly

control for investor characteristics. To account for friends and family, we use the

approach of Agrawal etal. (2015), which is only an approximation. Hence, we

cannot fully rule out that these investors may explain local preferences to a larger

extent. For example, the percentage of friend-and-family investors might be

higher for younger ventures, which would also result in local preferences being

more pronounced. However, the founders of older ventures are likely to have

larger personal networks than young entrepreneurs. It is therefore ambiguous

whether friends and family should account for a larger share of investors in cam-

paigns of younger or older ventures. We are confident that our results are not only

driven by the fact that we cannot perfectly control for investor characteristics.

M.Bade, M.Walther

1 3

We also cannot rule out that our results are driven by behavioural explana-

tions or biases, as we can only observe investments but not the motivation of

investors. Since our results are consistent with the predictions based on the

attention allocation theory, it is likely that economic reasons are at least partially

responsible for the observed effects.

8 Concluding remarks

This study investigated drivers of investment probability in equity-based crowdfund-

ing. Novel to the crowdfunding literature, we examined interactions between local

preferences of investors and drivers of investment probability in the light of asym-

metric information and investors’ allocation of attention. Our results provide support

for the rationale that investors prefer to allocate their attention to local campaigns for

which they have information advantages and that this may be explained by their lim-

ited capacity to process information. Our findings do not seem to be largely driven

by social explanations (friends and family).

The main contribution of our study is the following. We show that the explana-

tion of home bias developed by van Nieuwerburgh and Veldkamp (2009) and Mon-

dria and Wu (2010) on country-level international investment behaviour might apply

to equity-based crowdfunding and thus to unsophisticated small investors. Previous

research suggests that less informed investors are more responsive to signals than

well informed ones. For example, Agrawal etal. (2015) find that distant investors

are more responsive to information on cumulative investment than locals. In con-

trast, our study shows that presumably well-informed locals are more responsive to

signals, such as previous investments and updates, compared to non-locals. In a way,

this suggests that equity-based crowdfunding is different in the sense that economic

theories which posit signalling as a mechanism to alleviate asymmetric information

(e.g., Spence 1973) are not applicable to crowdfunding beyond reasonable doubt.

Consistent with van Nieuwerburgh and Veldkamp (2009), our findings suggest that

information signals may amplify information asymmetry and local preferences of

investors in equity-based crowdfunding.

Appendix

See Tables6, 7, 8 and 9.

1 3

Local preferences andtheallocation ofattention in…

Table 6 Robustness tests (1/3)

Dependent variable:

Investment

(3) (4) (5)

Local 0.006***

(0.001)

0.006***

(0.001)

0.004***

(0.001)

# previous investments 0.00005***

(0.000001)

0.0001***

(0.000001)

0.0001***

(0.000001)

# investments two days before 0.001***

(0.00001)

– –

# investments one day before – 0.002***

(0.00001)

–

# investments three days before – – 0.0004***

(0.00001)

Project month count 0.006***

(0.0001)

0.006***

(0.0001)

0.006***

(0.0001)

Early 0.137***

(0.001)

0.125***

(0.001)

0.139***

(0.001)

# updates − 0.0003***

(0.0001)

− 0.002***

(0.00005)

− 0.002***

(0.00005)

Update previous week 0.012***

(0.0003)

– –

# updates * Update previous week − 0.002***

(0.00005)

– –

Update previous day – 0.008***

(0.001)

0.009***

(0.001)

# updates * Update previous day – − 0.001***

(0.0001)

– 0.001***

(0.0001)

Local * Age − 0.0003***

(0.0001)

− 0.0002***

(0.0001)

− 0.0002***

(0.0001)

Local * # investments two days before 0.0002***

(0.00002)

– –

Local * # investments one day before – 0.0002***

(0.00003)

–

Local * # investments three days before – – 0.0001***

(0.00001)

Local * # updates − 0.0001

(0.0001)

0.00001

(0.0001)

0.0001**

(0.0001)

Local * Update previous week − 0.002*

(0.001)

– –

Local * # updates * Update previous week 0.0004***

(0.0001)

– –

Local * Update previous day – – 0.001

(0.002)

0.002

(0.002)

Local * # updates * Update previous day – 0.001***

(0.0002)

0.0003*

(0.0002)

Local * Early − 0.006***

(0.001)

– 0.006***

(0.001)

− 0.004***

(0.001)

M.Bade, M.Walther

1 3

Table 6 (continued)

Dependent variable:

Investment

(3) (4) (5)

Local * Manufacturing − 0.0003

(0.001)

– 0.0003

(0.001)

− 0.001

(0.001)

Local * Wholesale trade − 0.007**

(0.003)

– 0.007**

(0.003)

− 0.007*

(0.003)

Local * Finance − 0.018***

(0.002)

– 0.018***

(0.002)

− 0.018***

(0.002)

Local * Services 0.003***

(0.001)

0.002**

(0.001)

0.002**

(0.001)

Observations 4,838,429 4,838,429 4,838,429

R20.239 0.240 0.238

Adjusted R20.236 0.238 0.236

Residual Std. Error (df = 4,821,834) 0.169 0.169 0.169

Dependent variable: Investment. Regression type: Linear probability model with investor fixed effects

and campaign fixed effects. Standard errors in parentheses. *p < 0.1; **p < 0.05; ***p < 0.01. Model 3:

Robustness test with Update previous week. Model 4: Robustness test with # investments one day before.

Model 5: Robustness test with # investments three days before. In this table, some values have been

strongly rounded due to lack of space

1 3

Local preferences andtheallocation ofattention in…

Table 7 Robustness tests (2/3)

Dependent variable: Investment. Regression type: Linear probability

model with investor fixed effects and campaign fixed effects. Stand-

ard errors in parentheses. *p < 0.1; **p < 0.05; ***p < 0.01. Model 6:

Robustness test with Local (50km)

Dependent variable:

Investment

(6)

Local 0.0080*** (0.0010)

# previous investments 0.0001*** (0.000001)

# investments two days before 0.0006*** (0.00001)

Project month count 0.0058*** (0.0001)

Early 0.1281*** (0.0008)

# updates −0.0017*** (0.00005)

Update previous day 0.0083*** (0.0006)

# updates * Update previous day −0.0012*** (0.0001)

Local * Age −0.0008*** (0.0001)

Local * # investments two days before 0.00004** (0.00002)

Local * # updates 0.0003*** (0.0001)

Local * Update previous day 0.0069*** (0.0024)

Local * # updates * Update previous day 0.0005** (0.0002)

Local * Early 0.0483*** (0.0019)

Local * Manufacturing 0.0010 (0.0010)

Local * Wholesale trade −0.0174*** (0.0042)

Local * Finance −0.0421*** (0.0029)

Local * Services 0.0001 (0.0009)

Observations 4,838,429

R20.2388

Adjusted R20.2362

Residual Std. Error 0.1690 (df = 4,821,834)

M.Bade, M.Walther

1 3

Table 8 Robustness tests (3/3)

Dependent variable: Investment. Regression type: Linear probabil-

ity model with investor fixed effects. Standard errors in parentheses.

*p < 0.1; **p < 0.05; ***p < 0.01. Model 7: Robustness test with

campaign variables instead of campaign fixed effects

Dependent variable:

Investment

(7)

Manufacturing −0.0006** (0.0003)

Wholesale trade −0.0044*** (0.0010)

Finance 0.0083*** (0.0007)

Services −0.0019*** (0.0003)

Campaign Berlin 0.0036*** (0.0002)

Goal type 0.0080*** (0.0002)

Goal amount 0.0027*** (0.0002)

Equity stake offered 0.0001*** (0.00002)

Co-financing −0.0159*** (0.0005)

# potential investors −0.00001*** (0.000000)

Age −0.0006*** (0.00003)

Local 0.0059*** (0.0008)

# previous investments 0.0001*** (0.000000)

# investments two days before 0.0007*** (0.00001)

Project month count 0.0038*** (0.0001)

Early 0.0664*** (0.0005)

# updates −0.0006*** (0.00003)

Update previous day 0.0111*** (0.0006)

# updates * Update previous day −0.0012*** (0.0001)

Local * Age −0.0004*** (0.0001)

Local * # investments two days before 0.0002*** (0.00002)

Local * # updates −0.00003 (0.0001)

Local * Update previous day 0.0049*** (0.0018)

Local * # updates * Update previous day −0.0001 (0.0002)

Local * Early 0.0090*** (0.0014)

Local * Manufacturing 0.0006 (0.0008)

Local * Wholesale trade −0.0071** (0.0034)

Local * Finance −0.0198*** (0.0021)

Local * Services 0.0025*** (0.0008)

Observations 4,838,429

R20.2306

Adjusted R20.2280

Residual Std. Error 0.1699 (df = 4,821,915)

1 3

Local preferences andtheallocation ofattention in…

Table 9 Stepwise inclusion of variables

Dependent variable:

Investment

(8) (9) (10) (11) (12) (13) (14)

Local 0.007***

(0.0003)

0.007***

(0.0004)

0.006***

(0.0004)

0.005***

(0.001)

0.005***

(0.001)

0.005***

(0.001)

0.006***

(0.001)

# previous investments 0.0001***

(0.000001)

0.0001***

(0.000001)

0.0001***

(0.000001)

0.0001***

(0.000001)

0.0001***

(0.000001)

0.0001***

(0.000001)

0.0001***

(0.000001)

# investments two days before 0.001***

(0.00001)

0.001***

(0.00001)

0.001***

(0.00001)

0.001***

(0.00001)

0.001***

(0.00001)

0.001***

(0.00001)

0.001***

(0.00001)

Project month count 0.006***

(0.0001)

0.006***

(0.0001)

0.006***

(0.0001)

0.006***

(0.0001)

0.006***

(0.0001)

0.006***

(0.0001)

0.006***

(0.0001)

Early 0.132***

(0.001)

0.132***

(0.001)

0.132***

(0.001)

0.132***

(0.001)

0.132***

(0.001)

0.132***

(0.001)

0.133***

(0.001)

# updates −0.002***

(0.00005)

−0.002***

(0.00005)

−0.002***

(0.00005)

−0.002***

(0.00005)

−0.002***

(0.00005)

−0.002***

(0.00005)

−0.002***

(0.00005)

Update previous day 0.009***

(0.001)

0.009***

(0.001)

0.009***

(0.001)

0.009***

(0.001)

0.008***

(0.001)

0.009***

(0.001)

0.009***

(0.001)

# updates * Update previous day −0.001***

(0.0001)

−0.001***

(0.0001)

−0.001***

(0.0001)

−0.001***

(0.0001)

−0.001***

(0.0001)

−0.001***

(0.0001)

−0.001***

(0.0001)

Local * Age −0.0001

(0.0001)

−0.0001*

(0.0001)

−0.0002***

(0.0001)

−0.0002***

(0.0001)

−0.0002***

(0.0001)

−0.0002***

(0.0001)

Local * # investments two days before 0.0001***

(0.00001)

0.0002***

(0.00002)

0.0001***

(0.00002)

0.0001***

(0.00002)

0.0002***

(0.00002)

Local * # updates 0.0002***

(0.0001)

0.0002***

(0.0001)

0.0001**

(0.0001)

0.0001

(0.0001)

Local * Update previous day 0.005***

(0.001)

0.002

(0.002)

0.001

(0.002)

M.Bade, M.Walther

1 3

Table 9 (continued)

Dependent variable:

Investment

(8) (9) (10) (11) (12) (13) (14)

Local * # updates * Update previous day 0.0004**

(0.0002)

0.0004**

(0.0002)

Local * Early −0.005***

(0.001)

Local * Manufacturing −0.001

(0.001)

Local * Wholesale trade −0.007**

(0.003)

Local * Finance −0.018***

(0.002)

Local * Services 0.002**

(0.001)

Dependent variable: Investment. Regression type: Linear probability model with investor fixed effects and campaign fixed effects. Standard errors in parentheses. *p < 0.1;

**p < 0.05; ***p < 0.01. Model 14 is the main model (model 1) as reported in Table5. In this table, some values have been strongly rounded due to lack of space

1 3

Local preferences andtheallocation ofattention in…

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