Law, trust, and crowdfunding around the world

P. RAGHAVENDRA RAU†

University of Cambridge

March 2019

Abstract

Using a unique hand-collected sample of crowdfunding volume obtained by surveying over 3,000

crowdfunding platforms worldwide, I document the global patterns and determinants of

crowdfunding, an innovative form of financing that has grown faster than any other financial

innovation in the recent past. Crowdfunding volume within a country appears to be driven by

rational economic reasons. The rule of law, quality of regulation, control of corruption, ease of

entry, and financial profitability of extant intermediaries are all significantly positively related to

the volume of crowdfunding. I find little evidence that trust, adventure-seeking, or other social

factors matter.

Keywords: Industrial Organization, Alternative finance, Distributed finance, Crowdsourcing, Crowdfunding, Law and finance, Peer-to-Peer, P2P lending, Equity crowdfunding, Social Finance

JEL Classification: G21; G23

† Cambridge Judge Business School, Trumpington Street, University of Cambridge, Cambridgeshire, CB2 1AG United Kingdom, 415-754-3728; r.rau@jbs.cam.ac.uk. I would like to thank David Chambers, Claudia Custodio, Jens Hilscher, Chris James, Andrew Karolyi, Adair Morse, Tania Ziegler, and seminar participants at Cornell University, the Federal Reserve Bank of Cleveland, the Federal Reserve Bank of Philadelphia, King’s College, Michigan State University, the University of Calgary, the University of California, Berkeley, the University of California, Davis, the University of Florida, and UT San Antonio for helpful comments, and the Cambridge Centre for Alternative Finance, especially John Burton, Kieran James Garvey, Simon Huang, Bob Wardrop, Bryan Zhang, Rui Zhao, and Tania Ziegler, for sharing the data from the 2015-2016 Global Surveys.

Law, trust, and crowdfunding around the world

Abstract

Using a unique hand-collected sample of crowdfunding volume obtained by surveying over 3,000

crowdfunding platforms worldwide, I document the global patterns and determinants of

crowdfunding, an innovative form of financing that has grown faster than any other financial

innovation in the recent past. Crowdfunding volume within a country appears to be driven by

rational economic reasons. The rule of law, quality of regulation, control of corruption, ease of

entry, and financial profitability of extant intermediaries are all significantly positively related to

the volume of crowdfunding. I find little evidence that trust, adventure-seeking, or other social

factors matter.

Keywords: Alternative finance, Distributed finance, Crowdsourcing, Crowdfunding, Law and

finance, Peer-to-Peer, P2P lending, Equity crowdfunding, Social Finance

JEL Classification: G21; G23

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I. Introduction

Crowdfunding, also sometimes referred to as alternative or distributed financing, is not a new

phenomenon. Charities have long relied on donor drives that aggregate small donations to fund

their causes.1 What is new is the global growth of crowdfunding platforms and the volume of

financing they provide. From around $0.5 billion of funding through crowdfunded platforms in

2011, the volume has grown to over $290 billion in 2016, a growth rate of over 250% per annum,

one of the fastest rates of growth of any type of financial innovation documented in recent history.

Crowdfunding is now a global phenomenon with finance available in almost every country in

the world. In several major economies, crowdfunding platforms have overtaken banks to become

the leading source of finance to consumers and small and medium enterprises (SMEs).2 Despite

this, there is no literature that documents the patterns of crowdfunding, the diversity of business

models, or analyzes the determinants of crowdfunding around the world.

In this paper, I aim to fill this gap. Drawing on a unique hand-collected global database on

crowdfunding developed by the Cambridge Centre for Alternative Finance (CCAF), I document

the volume and determinants of crowdfunding in 161 countries across the world, covering a total

of 3,021 unique platforms over the period 2015-2016. The platforms covered by the CCAF are

restricted to online, peer-to-peer, crowd-led marketplaces that are open at least partially to

individual retail investors (the “crowd”). It does not include private crowdfunding platforms that

are only open to corporate employees3, mobile and online payment platforms4, traditional

financing, online or not, not on open platforms (such as invoice factoring), or online loans held by

the intermediary without being open to investment by outsiders.5

1 A frequently cited example is Joseph Pulitzer’s campaign to fund the pedestal of the Statue of Liberty in 1885, described in BBC News Magazine (“The Statue of Liberty and America’s Crowdfunding Pioneer”, April 25, 2013). 2 For example, the Financial Times reports that “Funding Circle (a United Kingdom (UK) based online crowdfunding platform) carried out £114m of net new lending in the three months to September, exceeding for the first time the £95m of net new lending to small businesses by the main high-street banks that make up at least three-quarters of the UK market.” (See Arnold, Martin 2017, “UK fintechs take market share from dominant high-street banks”, Financial

Times, November 1, 2017). 3 Siemens, for example, uses an internal crowdfunding platform, Quickstarter, to finance internal projects without management oversight. This would not be included because it is not open to outside participants. 4 Examples of such platforms include Venmo (US), mPesa (Kenya), WeChat run by Tencent (China), or Alipay run by Alibaba (China). 5 Example of such online platforms for loans and mortgages that would not be included in the survey include Marcus by Goldman Sachs or Quicken loans. Microfinance banks such as Grameen (Bangladesh) would also be excluded.

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These restrictions are imposed because the purpose of this study is to examine the cross-country

determinants of the choice by investors to fund potential strangers without any certification by

traditional financial intermediaries or governments. In most crowdfunding platforms, the funders

are usually geographically distributed and loosely organized, if at all. Almost all communication

occurs in online open communities characterized by high levels of asymmetric information.

Moreover, over the sample period, only a few countries have passed explicit crowdfunding

regulations.6 Lending on a person-to-business (P2B) or a person-to-person (P2P) market is typically

not protected by any form of deposit insurance and leaves the lender with little recourse should the

borrower default. The type of crowdfunding I examine in this paper therefore differs from both

traditional bank or debt market borrowing and venture capital equity funding. Finding that

crowdfunding volume cannot be explained using models driven by standard economic theory has

the potential to call standard models of investor behavior based on information asymmetry into

question.

Understanding the large-scale patterns in crowdfunding is important for several reasons. First,

we know almost nothing about crowdfunding in the aggregate. The general academic impression

is that crowdfunding is a relatively niche area of financing. There is a growing literature on the

micro-determinants of financing by investors on specific online platforms, but to the best of my

knowledge, there is no research on whether the data examined in these studies are, in any way,

representative of the general population. For example, Michels (2012), Zhang, and Liu (2012),

Lin, Prabhala, and Viswanathan (2013), and Iyer, Khwaja, Luttmer, and Shue (2016), all use data

from Prosper.com, a large peer-to-peer (P2P) lending website in the United States (US), Franks,

Serrano-Velarde and Sussman (2018) use data from Funding Circle, a large online marketplace in

the United Kingdom (UK), while Li and Martin (2016), Mollick and Nanda (2016), and Thürridl

and Kamleitner (2016) use data from Kickstarter, a reward-based platform in the US. However, it

is unclear to what extent results from these specialized crowdfunding models can be generalized

to draw broader conclusions on the population, or even to persuade academics that crowdfunding

is significant enough to warrant attention.

6 Countries have either adapted existing regulatory regimes to these new models (predominantly in the US), are developing new regulatory regimes (as in the UK and Singapore) or in most cases, have not explicitly addressed them till after the sample period (as in China). (See Business Intelligence, 2016, “Piecemeal regulation is hindering US fintechs, Oct 17, 2016, available at http://www.businessinsider.com/piecemeal-regulation-is-hindering-us-fintechs-2016-10).

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Second, a large body of theoretical and empirical literature suggests that access to financial

systems is important in affecting economic growth and poverty in developing countries (see, for

example, Levine, 2005, or Burgess and Pande, 2005). Crowdfunding has been suggested as a form

of innovation that is likely to have the same impact on economic development as mobile phone

penetration (Aker and Mbiti, 2010) or microcredit (Johnson, 1998). Bruton et al. (2014) argue that

crowdfunding allows investors and entrepreneurs to connect directly, allowing investors access to

new investment opportunities. However, Stigler (1971) and Rajan and Zingales (2003) argue that

incumbents oppose financial development that increases competition. Hence, it is unclear that

countries with large unbanked populations will have last mover advantages in bypassing the formal

financial system (Arner, Buckley, and Zhou, 2015), making it important to examine if

crowdfunding plays a greater role in emerging markets than in developed markets.

Third, the law and finance literature (beginning with La Porta, Lopez-de-Silanes, Shleifer, and

Vishny (LLSV, 1998) has argued that the extent to which a country’s laws protect investor rights,

and the extent to which those laws are enforced, fundamentally determines how corporate finance

and corporate governance evolves in that country. However, both the financial policies studied in

the prior literature and the regulatory regimes have co-evolved over long periods. For example,

the Sarbanes-Oxley and the Dodd-Frank Acts were enacted in the US in 2002 and 2010

respectively, as a response to several corporate and accounting scandals in the 2000s and the

financial crisis in 2008. Hence, while the prior literature document correlations between legal

regimes and forms of financing, it is difficult to convincingly argue that the legal regime causes

forms of corporate finance and governance to evolve. Rajan and Zingales (2003) show that

countries with Common Law systems were not more developed than countries with Civil Law

systems in 1913 and that the rate of development between 1913 and 1980 cannot be predicted by

the country’s legal regime in 1913. Crowdfunding is a new form of financial innovation, that has

rapidly increased in popularity in a very short period over which the legal systems have not adapted

to these financing types. During the sample period, only a few countries enacted explicit laws to

govern crowdfunding. This lack of regulatory change makes it easier to attribute a causal effect to

extant legal regimes in determining the volume and types of crowdfunding.

I begin by documenting broad global patterns in crowdfunding volume and business models.

Though the popular press often treats crowdfunding platforms as relatively homogenous, the

online crowd-led funding platform models can be classified into four distinct types – debt (lending)

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platforms that specialize in debt financing, equity platforms that allow (typically unlisted) firms to

raise equity financing from investors, reward-based platforms where funders promise backing in

exchange for a non-monetary reward but little in the way of recourse should the reward not arise,

and donation platforms, where funders receive nothing except presumably the satisfaction of

carrying out a good deed, in return for funding. The first two types of platforms are financial return

models while the latter two are non-financial return models. Examples of the four types include

Prosper.com, a P2P lending platform, CircleUp, a US based equity platform, ArtistShare, a reward-

based platform for artists where funders get access to extra material directly from the artists, and

FundMyTravel, a donation platform hosting campaigns by travelers who wish to fund study or

volunteer trips, or simply wish to travel abroad, respectively. Platforms like Kiva where the lenders

simply get their funds back at the end of the project, typically with no interest or financial returns,

are more ambiguous. I classify them as financial return platforms in the paper but the results are

robust to classifying them as non-financial return platforms.

I investigate five major questions in this paper. First, does crowdfunding allow emerging

markets to access financing and investment opportunities (access)? Second, how important an

industry is it (importance)? Third, is it displacing the formal financial sector (displacement)?

Fourth, is the pattern of financing different from firms using more traditional forms of financing

(financing pattern)? Last, platform success is dependent on achieving a deep pool of investors and

fund-raisers, implying that network effects play a significant role in the growth of crowdfunding.

What are the determinants of market share concentration across countries (network effects)?

There is considerable variation in the number of online platforms and the volume of financing

provided in different countries. Of the total global volume of online crowdfunding ($290 billion

in 2016), China, the US, and the United Kingdom (UK) form the three largest markets with around

$243 billion (83%), $35 billion (12%), and $7 billion (2%), respectively, of online crowdfunding

volume. The same pattern holds when I examine the number of platforms. 38% of all crowdfunding

platforms originate in developed countries, while 62% originate in emerging markets. However,

the largest portion of the emerging market volume is in China, which accounts for 35% of all

platforms globally. While the remaining 124 emerging markets account for 31% of all platforms

globally, they account for only around 0.5% of global crowd financed volume. The univariate

evidence suggests that crowdfunding is not yet significant in emerging markets.

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Is alternative finance important? At first glance, it would appear not. Crowdfunding volume

over 2015-2016 is 0.025% of country Gross Domestic Product (GDP) overall, and only 0.034%

and 0.023% of developed and emerging market GDP respectively. By way of comparison, the

entire finance and insurance industry added 7.5% of value to US GDP in 2016.7 Is it displacing

the incumbents in the formal financial sector? Again, the answer appears not. Crowdfunding

volume is 0.042% of the total domestic credit provided by banks overall, and 0.038% and 0.043%

of developed and emerging market bank domestic credit provision respectively. However, the

average annual growth rate of over 250% in platform volume implies that crowdfunding has the

potential to become increasingly important across both dimensions.

There is a considerable degree of heterogeneity across financing patterns globally. In developed

markets, the predominant form of crowdfunding is by simple fixed income instruments, where the

borrower pledges to make interest and principal payments in return for a loan. Specifically, in

developed markets, 55% of total crowdfunding volume is pure debt financing and another 16% is

pure equity financing. In emerging markets, in contrast, 36% is pure debt financing while 4% is

equity financing. The remaining platforms either use innovative mixtures of financing or are

reward and donation-based platforms. Similarly, in developed markets, over 71% of crowdfunding

volume is to obtain a financial return, while in emerging markets, the corresponding figure is 40%.

Non-financial return platforms appear to dominate emerging market crowdfunding.

To the best of my knowledge, there is no accepted framework for modeling crowdfunding. It is

unclear, however, that economic motivations form the sole motivators for individuals to participate

on reward or donation-based platforms. I, therefore, develop a conceptual framework that models

crowdfunding as a function of both economic and social factors. The three major economic factors

I use are barriers to entry, financial profitability of industry incumbents, and financial potential.

Barriers to entry are largely enforced by regulation, and I measure barriers to entry on both indirect

(such as the type of legal regime and the quality of legal enforcement), and direct dimensions (such

as the ease of starting a business). Financial profitability of incumbents is largely measured by

market rents earned by banks and other traditional financial intermediaries. I measure financial

potential on two dimensions – the current financial depth of the market (based on existing markets,

investor protection) and the potential financial depth of the market (including factors such as user

7 Source: Bureau of Economic Analysis (available at https://www.bea.gov/industry/gdpbyind_data.htm).

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sophistication or the ease of access to the Internet). If crowdfunding substitutes for existing types

of financing, I expect a positive relation between current financial depth and platform volume. If

crowdfunding offers new financing opportunities, I expect a positive relation between potential

financial depth and crowdfunding volume. Finally, I measure social characteristics at the country

level using a host of value related survey measures (such as trust or adventure seeking).

To answer the first question, on whether crowdfunding allows emerging markets to access

financing and investment opportunities, I regress the country level of crowdfunding (after scaling

by the total population of the country) on the economic and social factors above. Several economic

factors appear consistently significant in explaining the overall level of crowdfunding. Consistent

with the univariate results, developed markets and richer countries have significantly higher

crowdfunding volume than emerging markets or poorer countries. Hence, crowdfunding does not

yet appear to be a strong driver of financial inclusion. After controlling for country characteristics,

regulatory quality is strongly positively related while barriers to entry are strongly negatively

related to crowdfunding volume. Financial system inefficiency, concentration, and profitability all

appear to be positively related to crowdfunding volume. Interestingly, social factors such as trust

and adventure seeking do not appear to be related to crowdfunding volume. The only social factor

that does is dissatisfaction with the current financial situation of the household, which is also likely

to be an economic factor.

What determines the importance of crowdfunding in the economy? Again, country

characteristics matter – more populous developed countries have significantly larger crowdfunding

volume as a proportion of GDP. Controlling for country characteristics, the legal regime matters.

Crowdfunding is more important in common law countries and in countries with a strong rule of

law or regulatory quality. Crowdfunding is also more important in countries that have passed

explicit regulations on crowdfunding though the direction of causality is hard to determine here. It

is plausible that regulators choose to implement explicit regulations when they believe that

crowdfunding is growing in importance. Financial system concentration and profitability also

appear to positively impact crowdfunding importance. Again, the importance of crowdfunding

appears to be strongly related to the dissatisfaction with the current financial situation of the

household.

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Is crowdfunding displacing existing financial institutions in the country? Country

characteristics matter as usual – crowdfunding as a proportion of domestic credit financed by banks

and formal institutions is significantly larger in more populous but poorer countries. Controlling

for country characteristics, the legal regime does not appear to matter – with one exception.

Crowdfunding is more likely to displace financial institutions in countries that have passed explicit

regulations on crowdfunding though as before, the direction of causality is hard to determine.

What determines the pattern of financing? Debt financing as a proportion of total country

crowdfunding is largely driven by country and legal and regulatory characteristics. Debt financing

is significantly larger in more populous but poorer countries. Interestingly the level of protection

for investors in debt markets does not appear to matter, perhaps because investors in crowdfunded

markets do not always have the same protections as investors in formal financial markets. In

contrast to debt financing, I find almost no factors that consistently explain the proportion of equity

financing. The depth of the market, investor protection, and social factors are all insignificant in

explaining equity financing.

Finally, what explains the determinants of network effects within crowdfunding markets? The

answer appears largely related to the ease of starting a business and the lack of competition from

existing banks. Both the number of days to start a business and the concentration of assets within

the five largest banks are significantly negatively related to the Herfindahl market-share index of

crowdfunding volumes in the country.

To the best of my knowledge, this is the only paper that documents the global patterns of

crowdfunding and develops a conceptual framework to model its development. Crowdfunding is

a recent innovation that has demonstrated the fastest growth of any financial innovation over the

past few decades. The limited research on the determinants of aggregate crowdfunding volume is

typically not global nor does it posit a conceptual framework. For example, Dushnitsky et al.

(2016) model the drivers of crowdfunding platform creation in 15 European countries but do not

embed their hypotheses in a framework. Rubanov et al. (2019) use a cluster analysis to examine if

alternative financing models are clustered on a regional basis. Haddad and Hornuf (2016)

investigate the economic determinants of fintech start-ups using 2014 data from Crunchbase.8

Their dependent variable, the number of start-ups, is a count variable, and they aggregate all types

8 Available from crunchbase.com

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of fintech start-ups (including financing, asset management, payment, and other business

activities) into the same econometric model, again without a formal framework.

The remainder of the paper is organized as follows. In Section II, I describe the data used in the

analysis. In Section III, I develop a conceptual framework for crowdfunding and describe my

proxies for each factor affecting crowdfunding. Section IV provides descriptive statistics on the

global patterns of crowdfunding and analyzes the determinants of crowdfunding. Section V

concludes.

II. Data

I obtain my data from the annual surveys conducted by the Cambridge Centre for Alternative

Finance (CCAF) hosted at the University of Cambridge. The data will be published on the CCAF-

World Bank Global Marketplace and Alternative Finance (Market Volume) Data website in 2019.

Since 2014, the CCAF has been conducting a series of annual surveys, initially in the United

Kingdom (UK) alone, expanding to Europe in 2015, and worldwide in 2016-2017. The surveys

collected data on both transaction and model-specific volumes based upon information provided

by individual platforms across Europe, the UK, North America, Latin America, the Caribbean,

Asia-Pacific (including China), the Middle East, and Africa. The surveys were designed to capture

the size and type of crowdfunding activity on each platform between 2013 and 2016. However,

since the volume of activity in prior years is backfilled by the existing platforms in 2015, there is

a potential for survivorship bias in the 2013-2014 data. Hence, in this study, I only analyze cross-

sectional data for 2015 and 2016.

The CCAF database is currently the only source of global crowdfunding data. It has been

extensively cited in academic, industry, and policy documents globally over the past three years.9

There is currently no other comprehensive source of information on the volume of online

marketplace. The closest similar source of information is the Massolution (2015) crowdfunding

report that gathered data from submissions to its own website in 2014. The data collection process

is unclear and the volume of data reported in this report is very small relative to the volumes

documented by the CCAF over the same time horizons.10

9 A list of these citations is available on the CCAF website at www.jbs.cam.ac.uk/faculty-research/centres/alternative-finance/. 10 Massolution has now ceased publishing its annual report on crowdfunding.

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To construct the survey, the CCAF research team first created a list of online crowdfunding

platforms after manually searching news articles describing online platforms, platform websites,

and contacting national crowdfunding associations for platform contacts. The team then

communicated directly with the online platforms, explaining the study’s objectives and providing

a copy of the research proposal and questionnaire. For platforms in countries where the local

business language was not English, the team carried out the survey in collaboration with local

research partners. For example, in China, the surveys were carried out in collaboration with

Tsinghua University and Zhejiang University. In particular, to ensure that the data captured the

overall market volume of crowdfunding, the team specifically targeted the largest platforms in

each country to ensure a survey response, hand-collecting the data directly if necessary, by

webscraping the data.

In cases where the survey team could not obtain primary data (or where there were

discrepancies in reported data), the team obtained secondary data (from public information, annual

reports, and press releases). Finally, as noted above, the CCAF team used Python scripting and

widely used web-scraping methodologies to complement the survey results and confirm reported

data volumes by matching against platforms’ self-published figures for the past six years. The

research team verified all gathered datasets before aggregating.

An obvious concern is whether the survey response rates are different across different regions.

For example, if regions that systematically differ across our explanatory variables also

systematically differ in response rates, the results are likely to be biased. Similarly, it is plausible

that particular types of platforms are more likely to respond to the survey than others. For example,

non-financial crowdfunding platforms might lack both resources and time to respond to the survey.

Hence, significant efforts were made to manually follow up with non-responding platforms, a

labor-intensive process. Average survey response rates for the platforms contacted in the final

sample are 62% (UK), 60% (Americas), 46% (Asia) and 77% (Europe) and do not differ

significantly across the two years, suggesting that survey response bias is not likely to significantly

influence our results. It is still possible that the survey missed the smallest platforms in each

country. However, my unit of analysis is the aggregate volume of crowdfunding in the country.

Because of the additional effort made to collect data for the largest platforms in each country, the

data was close to 100% of the market volume for these platforms.

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A second concern is fraud. The financial press reports cases of a number of platforms,

especially in China, that were fraudulent and operated like Ponzi schemes.11 The CCAF searched

news articles about each firm trying to identify cases of fraud and worked closely with local

partners in the regions to exclude possible cases of fraud from the survey. While it is still possible

that some instances of fraudulent reporting may have entered the sample, the volumes are not likely

to be large.

A final concern is the crowdfunding location. Several platforms in the sample have borrowers

in multiple countries. Examples include Mintos and Twino (both in Latvia) who post lending

opportunities to investors from Germany, France, and other European countries for borrowers from

all over Europe, especially in Eastern Europe and the Baltics. Platforms sometimes offer some

forms of guarantees for these loans. For example, Mintos aggregates loans from other loan

originators across Europe, who report the percentage of the loan they are holding on their own

books, as a form of skin-in-the-game. Twino offers a buyback guarantee on some loans where it

buys back loans if the borrower is late in repaying the loan. It is important to note that there are no

legal requirements that these guarantees must be offered on all loans. If the borrower should default

for a guaranteed loan, then the only recourse the investor has is directly from the platform.

Similarly, platforms such as Kiva raise funds across multiple countries to provide loans in

emerging markets. These loans are usually not protected against default by the borrower and the

lender has little recourse from the platform if the borrower should default.

To handle this concern, for online alternative finance platforms that offered “mixed” or “other”

finance models/products, or operated in more than one country12, the team broke down transaction

volumes further and computed model-specific or country-specific volumes based upon the

information the platform provided. For platforms that operate in more than one country, the team

attributed the volume of crowdfunding based on borrower location, not on lender location. The

reason is that without a platform guarantee, perceptions of the quality of the legal regime in the

borrower’s country are likely to be more important for funding decisions than legal protections in

11 For example, Ezubao, an online P2P platform, was reported to have offered fake investment products to its nearly million investors who invested nearly $7.5 billion on the platform (See Gough, Neil, 2016, “Online lender Ezubao took $7.6 Billion in Ponzi Scheme, China Says”, New York Times, February 2, 2016, page B3.) Ezubao is not in the sample. 12 Other examples include Homestrings.com, bettervest GmbH, Funding Circle, OurCrowd, greenvesting.com, Lendico, Emerging Crowd, Crowdcube, HelpingB, Planeta, Kickstarter, Indiegogo, and Kiva.

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the funder country. For loans with a platform guarantee, the legal regime governing the platform

location is likely to be important. However, since the survey data does not distinguish the

proportion of foreign investors from the proportion of domestic investors in the platform’s country,

in a robustness check, I remove multi-country platforms from the sample. The results are

qualitatively similar.

Whenever necessary, the research team validated responses by clarifying ambiguous responses

or by requiring more detailed data breakdowns in various geographies from the platforms. Finally,

the data was anonymized by deleting all platform-identifying information. For all average data

points (e.g. funder sophistication), weightings (by transaction volume) were applied and

significant apparent outliers were removed. To this data, I make a few additional judgment calls

for further classification.13

I construct several dependent variables from this data. I first aggregate individual platform

crowdfunding values to obtain country values. Dollar values are obtained directly from the survey

since most platforms, except for European, British, and Chinese platforms, were asked to convert

their volumes into US$ based on the exchange rate when they completed their survey. European,

British, and Chinese platforms provided their volume data in Euros, GBP, and RMB, respectively,

and these were converted to US$ based on the exchange rate at the end of 2015 and 2016,

respectively. I then apply a log transformation to minimize the effect of outliers. I further split the

total volume of crowdfunding into financial and non-financial return models. Financial return

models are mainly comprised of debt and equity funding models, while non-financial models are

comprised of reward- and donation-based funding models. Finally, I split the total volume of

financial return models into debt funding models and equity funding models. I classify countries

as developed markets or emerging markets based on both the MSCI market classification

framework and the FTSE Annual Country Classification Review, 2016.14

13 For example, I classify Turkey as an East European country though the Turkish data was collected as part of the Western European survey 2015. Data from some UK firms were collected as part of the European survey. For the purposes of this study, they were reclassified into the UK market. Prodigy Network is a US equity crowdfunding platform that operates in Europe. Data for this firm was collected as part of the European survey. All data will be available on the World Bank website in 2019. 14 The two classifications agree, apart from South Korea which is classified as developed by FTSE and emerging by MSCI. I classify South Korea as a developed market in line with the FTSE framework in my analysis.

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III. Conceptual framework and independent variables

Since I am unaware of an accepted framework for modeling crowdfunding volume, in this

section, I posit an informal conceptual framework that models crowdfunding as a function of both

economic and social factors. I include social factors in the framework because it is unclear that

participants will be driven only by strong economic motivations for participating on reward or

donation-based platforms.

The three economic factors I use are barriers to entry into the industry, financial profitability of

industry incumbents, and financial potential. I posit that these three factors will affect

crowdfunding platforms’ ability and willingness to offer investment opportunities and hence, will

affect the volume of crowdfunding in the market.

Barriers to entry are largely enforced by regulation. There are three models that relate regulatory

barriers to entry to the volume of crowdfunding, all giving rise to roughly the same prediction –

that there will be a positive relationship between the height of barriers to entry into the finance

industry and the volume of crowdfunding.

The first, public choice theory (Stigler, 1971), argues that incumbents in an industry lobby for

regulations to keep out competitors and create rents for themselves. P2P platforms are often not

explicitly covered by these regulations, enabling them to avoid regulatory barriers that would be

faced by a regular banking entrant. For example, in the UK, crowdfunding platforms do not have

banking licenses.15 A banking license would enable deposits to be protected by the Financial

Services Compensation Scheme (FSCS). In contrast, lending on a P2P platform will not receive

compensation from the FSCS if the platform goes bankrupt. In addition, Hornuf and

Schweinbacher (2016) document that in many jurisdictions, security regulations offer explicit

exemptions to prospectus and registration requirements to crowdfunding platforms. This would

imply a positive relation between regulatory barrier height and crowdfunding volume as investors

seek investment opportunities that are not offered by the incumbents.

15 An exception is Zopa which received a banking license (with the restriction that it cannot launch FSCS protected accounts until the end of a further approval process) in December 2018 (see Renton, Peter, 2018, “P2P lender Zopa granted banking license in the UK”, Lend Academy, available at https://www.lendacademy.com/p2p-lender-zopa-granted-a-banking-license-in-the-uk/).

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The second, the politician self-interest view (Shleifer and Vishny, 2002), argues that politicians

and officials create regulations to extract bribes in return for providing permits to operate. This

view also predicts a positive relation between the level (and profitability) of crowdfunding volume

and the height of regulatory barriers, though here the barriers are put in place as a function of

crowdfunding volume instead of the other way around.

Finally, the public interest theory of regulation (Pigou, 1938), argues that regulatory barriers

screen out low-quality or undesirable entrants and consequently also predicts a positive relation

between regulatory barrier height and the level of crowdfunding volume. In the US, for example,

the Securities and Exchange Commission (SEC) mandated in 2008, that all peer-to-peer lending

platforms serving retail investors should register themselves and their individual loan offerings

with the SEC. This was done to protect retail investors who were presumed not to have the ability

to understand or absorb the risks associated with P2P loans (Demyanyk, Loutskina, and Kolliner,

2017). The increased costs of compliance caused many platforms to close but the remaining

platforms increased their market share.16

The difference between the three mechanisms is that in public choice theory, platforms form

because they can bypass the regulations that govern formal financial institutions. In the politician

self-interest view, politicians put in regulations to extract rents from profitable industries. Finally,

in the public interest view, investors trust that regulators have scrutinized the platforms

appropriately and invest greater amounts in approved platforms. These are not necessarily

mutually exclusive hypotheses. It is plausible, for example, that platforms offer innovative

investment opportunities because of the lack of explicit regulations on their offerings while

individual investors, who are not financially sophisticated assume that the platform offerings are

approved by the regulators. Overall though, the empirical predictions are similar across the three

mechanisms, and given the lack of instruments in my short sample period, I do not attempt to

distinguish them.

I draw upon both indirect and direct measures of empirical proxies for the level of regulatory

barriers. The first indirect measure of regulatory barriers is the underlying legal system in the

16 Fintech groups originated $15 billion of personal loans in the first half of 2017, almost a third of the total US market for new personal loans and a bigger share than banks, credit unions, or other traditional consumer finance companies. (See Gray, Alistair, 2017, “Online lenders shrug off scandals to increase US market share”, Financial Times, November 2, 2017).

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country. I measure the quality of the legal system by its legal regime (civil, common, or Islamic

law) and other proxies. The country’s legal regime has been shown to have direct influence on

various financing and governance policies. For example, common-law countries generally have

the strongest legal protection for investors, and this impacts dividend policy (LLSV, 2000), access

to external finance (Demirgüç-Kunt and Maksimovic, 2002), debt enforcement (Djankov, Hart,

McLiesh, and Shleifer, 2008), the level of cash balances (Dittmar, Mahrt-Smith, and Servaes,

2003) and other financing policies. The data for the legal regime in the country (common-, civil-

and Muslim-law) are taken from the CIA World Factbook.

For the second set of indirect measures of regulatory barriers, I draw on three measures of

regulatory quality and corruption from the Worldwide Governance Indicators, described in

Kaufmann, Kraay, and Mastruzzi (2010): (1) Rule of Law to capture perceptions of the extent to

which agents have confidence in and abide by the rules of society, including the quality of contract

enforcement, property rights, and the courts, (2) Control of Corruption to capture perceptions of

the extent to which public power is exercised for private gain, and (3) Regulatory Quality to

capture perceptions of the ability of the government to formulate and implement sound policies

and regulations that permit and promote private sector development.17

For direct measures of barriers to entry, I hand-collect details on existing regulations on P2P

lending from regulatory websites and news sources and code an indicator variable as one if a

regulation was in force in the year crowdfunding volume is measured (2015 or 2016). I also hand-

count the total number of financial regulatory bodies from Wikipedia and the Bank of International

Settlements (BIS) list of regulatory bodies.18 While this is admittedly a rough and probably

understated measure due to the difficulty of finding public sources for this information, I

hypothesize that in countries with many regulatory bodies, regulatory overlaps and struggles for

oversight control will reduce the volume of crowdfunding. I term this the regulator self-interest

view, similar to the politician self-interest view advanced by Shleifer and Vishny (2002).

I also use a more general type of barrier to entry that is not specific to the financial sector and

hence is expected to affect all start-up platforms negatively. Specifically, I draw on two measures

of the ease of starting a business within a country from the World Bank Doing Business (DB)

17 Table 1 in their paper and the Documentation tab of www.govindicators.org describes the variables in detail. The underlying measures are combined into an aggregate measure using an unobserved components model. 18 As an example, the US has 11 financial regulatory bodies at the federal level.

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website. The number of procedures and days for a small- to medium-sized limited liability

company to start up in the economy’s largest business city measure how easy it is to start a business

in the country. Djankov, La Porta, Lopez-De-Silanes, and Shleifer (2002) describe the variables in

detail.

The financial profitability of incumbents is also likely to attract new competition. Hence, I posit

a positive relation between market rents earned by banks and other traditional financial

intermediaries and the volume of crowdfunding. To measure rents directly, I use three measures

from the World Bank Global Financial Development Index (GFDI) database – the bank cost to

income ratio (a measure of inefficiency), net interest margin (profitability), and asset concentration

at the top five banks (oligopoly and bank market power).19 I use these two measures as proxies for

the economic rents being earned by existing formal financial institutions in the country. I also use

the amount of credit information available through private credit bureaus or public credit registries

as a measure of financial institution efficiency in processing credit information.

Finally, I measure financial potential on two dimensions – the current financial depth of the

market (based on existing markets) and the potential financial depth of the market (including

factors such user sophistication and the ease of access to the Internet). If crowdfunding substitutes

for existing types of financing, I expect a positive relation between current financial depth and

platform volume. If crowdfunding offers new financing opportunities, I expect a positive relation

between potential financial depth and crowdfunding volume.

Extant financial depth has been shown to be important in promoting growth. Rajan and Zingales

(1998) for example, find evidence that industrial sectors that are relatively more in need of external

finance develop disproportionately faster in countries with more developed financial markets.

Because platforms supply additional channels of financing to firms, it seems plausible that the

level of financial depth within a country will be positively related to the level of crowdfunding

volume. However, since my proxies for financial depth are largely dependent on the type of

financing, I use separate financing-specific proxies when I model the levels of debt and equity

financing. I use three measures of debt-market financial depth. I expect debt markets to be deeper

when investor rights are protected. When lenders can more easily force repayment, or gain control

19 See Appendix 1 in Čihák, Demirgüç-Kunt, Feyen, and Levine (2012) for a description of the data sources compiled by the World Bank.

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of collateral, or the firm itself, they are more willing to extend credit (see for example, Aghion and

Bolton, 1992, or Hart and Moore, 1994, 1998). I use the business extent of disclosure (the extent

to which investors are protected through disclosure of ownership and financial information), the

enforcement of contracts, and the strength of insolvency resolution, all taken from the WB World

Development Indicators (WDI) and WB DB databases. For equity-specific measures of depth, I

use measures of market concentration, specifically, the stock market capitalization as a percentage

of GDP, from the GFDI database, and country rankings from the Global Competitiveness Report

from the World Economic Forum (WEF) for the level of financing through the local equity market,

the protection of minority shareholders’ interests, and the strength of investor protection. Finally,

when investors have confidence that the borrowers are ethical, they may not be as concerned about

the lemons problem of financing negative net present value projects, and therefore invest more

(see for example, Stiglitz and Weiss, 1981). I use the WEF country rankings for ethical behavior

by firms (based on survey answers to a question on rating the corporate ethics of companies).

Potential financial depth in contrast, is likely to arise from individuals who are not current

investors but are likely to be future investors in the presence of investment opportunities. The first

measure of potential depth comes from the overall access individuals have to the market,

specifically, the degree to which individuals use financial institutions and markets. To measure

access, I use the overall financial market development rank from the WEF. The WEF measures

this as a composite of two factors, efficiency (incorporating issues such as whether financial

services meet business needs, affordability of financial services) and trustworthiness

(incorporating issues such as the soundness of banks).

I use a set of demographic variables as a second measure of potential financial depth. I measure

potential financial depth by the sophistication of the user base in a country. I use the WB WDI

percentage of individuals using the Internet in that country, and WEF rankings for the quality of

scientific research institutions, quality of the education system, availability of scientists and

engineers, and capacity for innovation.

Finally, I obtain social factors from the World Values Survey (WVS) database to measure non-

economic factors such as trust and adventure seeking. For example, the trust variable is based on

responses to the question: How much do you trust people you meet for the first time? I also use

standard variables from the culture literature such as the level of Individualism, Power-Distance,

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Embeddedness, Harmony, Masculinity, and Trust from the frameworks developed by Hofstede

(1980) and Schwarz (2006).20 However, since this data is available only for a limited subset of

countries in the sample, I report only results using the WVS survey variables.

Appendix A contains the definition of all the independent variables used in the paper along

with details on the construction of these variables.

IV. Results

IV.A. Descriptive statistics

Table 1 describes the major types of business models reported by platforms on a global basis. I

divide the models into financial return models, where investors expect a monetary return in return

for their investments, and non-financial return models, where funders either expect a non-monetary

reward (a T-shirt for example), a product (usually an early or discounted version of a final

commercial product), or invest based on philanthropic or civic motivations with no expectation of

any monetary or material return.

Financial return models are in turn, divided into debt and equity financing models. Debt

financing models are classified into business lending models, where individuals or institutional

funders provide loans to business borrowers, usually an SME, or consumer lending models, where

individuals or institutional funders provide a loan to consumer borrowers, mostly in the form of

unsecured personal loans. Prosper, a platform, whose data is publicly available from its website

and has been extensively studied in the micro-literature on crowdfunding, is a consumer lending

model. As noted in the introduction, some multi-country platforms such as Kiva, offer loans where

investors get back their invested capital but receive no further compensation. Because of the

financial nature of the transaction, I classify these platforms as financial return platforms, but the

results are qualitatively unaffected if I classify them as non-financial return models.

The other types of debt financing models are relatively small in comparison to these two types.

They include online crowd-led invoice trading, or factoring models, where funders purchase

invoices or receivable notes from a business at a discount, mini-bond markets21, where firms issue

20 See Nash and Patel (2018) for a comprehensive survey of this literature. 21 Mini-bond markets exist only in the UK. While mini-bonds are debt instruments and fall under the ‘retail bond’ category, they are exclusively offered on equity-based crowdfunding platforms in the UK. They are not similar to corporate bonds or other debt-instruments that a debt-based crowdfunding platform offers. Mini-bonds typically last around 5 years in duration and offer an interest rate of between 5-8% a year. They are non-transferable, non-readily

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non-recourse bonds with limited disclosure, revenue/profit sharing models, where investors

purchase (usually fixed income) securities but receive a share in profits or royalties, and

microfinancing, where funders lend small sums to entrepreneurs who are often economically

disadvantaged and financially marginalized. While there is a debt obligation incurred in

microfinancing, the amounts lent are typically small. It is important to note that the actual volume

of total invoice trading and microfinance by country is considerably higher than documented in

this paper. The database explicitly surveys the amount of invoice trading or microfinance that is

sourced through an online crowd-led platform, a subset of the overall markets in these countries.

It excludes company-specific platforms that are privately hosted and not available to the general

public.

Equity or profit sharing models are classified into equity funding models and more rarely,

community share models. Equity funding models involve the sale of securities, either registered

(in the US, for example) or unregistered (in the UK, for example), mostly by early–stage firms,

while community share models typically offer shares in social enterprises, serving local

community purposes in selected localities. Community shares are typically purchased by older

investors with strong ties to their communities.

There are two types of non-financial return models - reward-based crowdfunding and donation-

based crowdfunding. In the former, backers provide financing to individuals, projects or

companies in exchange for non-monetary rewards or products. Kickstarter, another platform that

makes its data available, has also been extensively studied in the micro-literature, and is an

example of this type of funding model.22 Donation-based crowdfunding provides funding to

realizable, almost always unsecured, and fall outside of the UK FSCS. If the issuer were to default on its mini-bond, the investor has no recourse, the default being viewed as a loss akin to losing an equity investment. In addition, the bond has little liquidity, with the investor’s funds locked in until maturity in the absence of a secondary market. The issuing company also has limited requirements around disclosure and is unregulated. In contrast, platforms such as UKBondNetwork offer corporate bonds which, unlike mini-bonds, are subject to high levels of due-diligence and disclosure. They also are typically secured and tradeable, if a counter-party exists, and offer risk-adjusted returns. 22 A widely cited example of a project that was successfully funded through Kickstarter was the Veronica Mars movie project. Following the cancellation of the television series on UPN/CW, the director Rob Thomas, sought but failed to obtain financing from Warner Bros. In March 2013, Thomas launched a fundraising campaign to produce the film through Kickstarter, offering incentives to those who donated $10 or more (see Entertainment Weekly, March 13, 2013 at http://ew.com/article/2013/03/13/veronica-mars-movie-kristen-bell-kickstarter/). Funders who pledged $10,000 were promised a part in the film. The campaign reached its $2 million goal in less than eleven hours (Variety 2013, see https://variety.com/2013/more/news/veronica-mars-kickstarter-reaches-1-million-in-funds-1200194274/).

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individuals, projects, or companies based on philanthropic or civic motivations with no expectation

of monetary or material return.

Appendix B lists the unique countries and platforms surveyed by year of first survey. There are

3,021 platforms across all years. The CCAF began surveying platforms in 2014 in the UK,

accounting for the 47 UK platforms with data available in 2013. It expanded to Europe in 2015,

for an additional 194 platforms (including Turkey, which was re-classified as an Eastern European

country in this paper). All the remaining regions were surveyed in 2016 and again in 2017 and data

was obtained for platforms in 2015 and 2016 respectively. Though the platforms were asked to

provide data in the past three years, the data from prior years is subject to a backfilling bias, hence

I focus on the platforms that report data for 2015 and 2016. Eliminating platforms which did not

report sufficient data23 for analysis gives us a final total of 1,604 platforms that form the basis of

the subsequent analyses. As noted above, because of our extensive follow-up hand-collection

procedure, the survey volume data includes all the largest platforms in each country.

Appendix C aggregates crowdfunding volumes by country. It reports the number of platforms

by country, and the aggregate volume of crowdfunding, separated into business financing and

consumer financing respectively. A summarized version of this table is reported in Table 2.

Specifically, Table 2 Panel A reports the number of platforms, total volume, business volume,

financial and non-financial motive volume, debt- and equity-financed volume, by type of market.24

Dushnitsky et al. (2016) quote the Massolution 2015 Crowdfunding Industry Report to note that

Europe forms an extremely large portion of the crowd-financing market. Of the 1,250 platforms

active worldwide, they note that European platforms account for 48%, compared to the 30% share

represented by North American platforms. Table 2 shows that this is inaccurate because it does

not include the volume represented by Chinese platforms. All developed markets represent around

38% of the number of platforms globally. China alone accounts for 35% of the number of platforms

globally.

23 For example, a number of platforms filled up only part of the survey, not the complete survey. For the largest platforms, the CCAF made every effort to fill in the missing survey data using hand-collected data from webscraping and other tools. This procedure was too time-consuming and labor-intensive to follow for the smallest platforms. 24 CCAF also asks platforms for data on the number of funders and fund-raisers. However, since platforms do not track their funders across platforms, in the absence of unique identifiers, it is impossible to eliminate double-counting.

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More important, the number of platforms is less economically important than the volume of

transactions on these platforms. Table 2 shows that the volume of transactions on the platforms is

significantly more concentrated than the number of platforms. Nearly all the crowd-financing

volume in emerging markets arises in China, which accounts for a striking 79% of total

crowdfunding volume, 83% of business volume, and 76% of consumer financing volume. The

remaining volume is almost all in developed markets.

Figures 1 and 2 illustrate the number of platforms and volume of crowdfunding, respectively,

across all models reported by platforms globally for one year, 2015. Both numbers and volumes

are classified using a blue-yellow-red scale with redder hues denoting a greater volume of

crowdfunding. Except for a few countries in central Asia, Africa, and the middle East (the most

notable being Kazakhstan, Libya, Sudan, and Saudi Arabia), crowdfunding platforms are available

in almost every country around the world. However, the contrast between China, the US, and the

rest of the world is also starkly evident in these figures. Because of the extremely high volume of

transactions in China, there is relatively little variation in the rest of the world, apart from the US.

I therefore, scale crowdfunding volume appropriately, depending on my research question, and

then apply a log transform, instead of the actual crowdfunding volume, apply log transformations

to reduce the impact of outliers such as China.25 Figure 3 shows that there is now a considerably

larger degree of dispersion across countries when I scale crowdfunding by capita, for example,

and add a log transform. Classifying total volume into financial and non-financial motives shows

that financial motives appears to dominate crowdfunding across the world (Table 2 Panel A). 97%

of global crowdfunding in developed markets and 99% in emerging markets occurs for financial

motives. However, this is misleading because the proportions are swamped by China, the US, and

to a smaller extent, the UK.

Panel B reports averages of proportions by country across both years. This panel is more

relevant to address our hypotheses. Column 1 examines if alternative finance is a significant

proportion of country GDP. The answer is no. Over the period 2015-2016, average crowdfunding

volume is 0.025% of country GDP, and only 0.034% and 0.023% of developed and emerging

market GDP, respectively. If China is excluded, the proportion in emerging markets drops to

25 A Box-Cox transformation (1964) of the dependent variable shows that the lambda value is close to zero, suggesting that the log transformation is appropriate.

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0.01% of country GDP. Column 2 examines if crowdfunding is displacing the incumbents in the

formal financial sector. Again, the answer is no. Crowdfunding volume is 0.042% of the total

domestic credit provided by banks overall, and 0.038% and 0.043% of developed and emerging

market bank domestic credit provision, respectively. The proportions are even smaller when we

examine the volume of crowdfunding as a proportion of domestic credit provided by all financial

institutions in column 3.

Columns 4-7 show that there is a considerable degree of heterogeneity across financing patterns

globally. In developed markets, the predominant form of crowdfunding is through simple fixed

income instruments, where the borrower pledges to make interest and principal payments in return

for a loan. Specifically, in developed markets, 55% of total crowdfunding volume is pure debt

financing and another 16% is pure equity financing. In emerging markets, in contrast, 36% is pure

debt financing while 4% is equity financing. The remaining platforms either use innovative

mixtures of financing or are reward and donation-based platforms. Interestingly, though Islamic

law prohibits acceptance of specified interest or fees for loans of money (known as riba, or usury),

Islamic law countries also report a greater proportion of debt (27%) than equity financing (12%)

volume. It is noteworthy, however, that the relative proportion of debt financing volume in Islamic

countries is substantially lower than in other areas around the world. Similarly, in developed

markets, over 71% of crowdfunding volume is to obtain a financial return, while in emerging

markets, the corresponding figure is 40%. Non-financial return platforms appear to dominate

emerging market crowdfunding. Finally, column 8 shows that the market appears considerably

more concentrated in emerging markets than in developed markets. This is not entirely surprising

since there are a larger number of competing platforms in developed markets. Excluding China

does not make a significant difference to the measured level of competition in emerging markets.

Table 3 Panels A and B report details on the aggregate number of platforms reporting non-zero

volumes on a geographic basis. Panel A reports broad classifications into debt, equity, and other

(either mixture or non-financial return) platforms, while Panel B reports more granular

classifications. The differences between geographic regions in patterns of platform numbers in

Panel A is striking. In developed regions (Australia, New Zealand, Western Europe, UK, North

America, and the US), debt financing platforms are dominant, ranging from 30%-56% of the

number of platforms. In contrast, in emerging regions (Africa, Eastern Europe, Middle East, and

South America), the corresponding proportions are 16%-37%. Panel B shows that most of the debt

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financing platforms specialize in business or consumer lending. Invoice trading, micro-finance,

debentures/debt-based securities, and mini-bonds account for almost negligible proportions and

are found in relatively few markets. Revenue/profit-sharing models were only observed in 2016;

they did not exist in 2015. Equity platforms mostly offer straight equity-like instruments.

Community share models exist only in the UK. Of the non-financial motive platforms, reward-

based financing platforms are over three times as numerous as donation-based platforms.

Table 4 reports correlations between the volumes of business on crowdfunding platforms.

Given the high proportions of business and consumer finance in total crowdfunding volume, it is

not surprising that business and consumer finance are highly correlated with the total volume of

crowdfunding, at 92% and 88% respectively. Financial-motive volume is relatively uncorrelated

with non-financial motive volume at 51%, suggesting that different economic motivations underlie

the two types of platform models. Similarly, debt and equity financing models are also relatively

uncorrelated at 60%, also suggesting that different economic models drive the two financing

platform volumes.

IV.B. The determinants of crowdfunding

In this section, using a set of multiple regressions, I analyze the five research questions

discussed in the introduction. In each case, the independent variables are drawn as appropriate

from the framework discussed in section III.

IV.B.1 Access: Does crowdfunding allow emerging markets to access financing and investment

opportunities?

Table 5 reports coefficients from an OLS regression of the log of crowdfunding volume by

country. The dependent variable is the log(crowdfunding volume (in US$) per capita+1) by

country. To compute per capita values, I use total population obtained from the WB WDI database.

The overall level of country prosperity, measured by log GDP, is significant in Model 1. This

is consistent with Haddad and Hornuf (2016) who find that GDP is significant in explaining the

number of start-ups founded by country, and in explaining the number of start-ups providing

financing, in particular. The explanatory power of this basic model increases slightly when we

include indicator variables for China, the UK, and the US in Model 2, with adjusted R2 increasing

from 42% to 46%. The explanatory power increases more when we add indicators for all regions

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in Model 3 to 51%. However, including a larger number of fixed effects increases the likelihood

that the variances adjusted for two-way clustering at the country and year levels turn negative,

leaving me unable to compute standard errors for some variables. Hence in the remaining models,

I only include indicators for China, the UK, and the US. My conclusions are largely similar across

the two choices of fixed effects. Interestingly, the developed market indicator is strongly positively

related to crowdfunding volume across almost all the models, suggesting that crowdfunding is not

an emerging market phenomenon.

Models 4-7 examine the impact of the legal system in the country. Model 4 adds two indicator

variables for the legal regime (common or civil law respectively). Model 5 adds the overall rule of

law, while Model 6 breaks up the rule of law variable into control of corruption and the regulatory

quality in the country. While Model 4 shows that common law countries have a higher volume of

crowdfunding, this result largely does not persist in later models, suggesting at best, a weak relation

between the legal regime and crowdfunding. Consistent with public choice theory, the rule of law

ranking is strongly positively related to the level of crowdfunding volume per capita in Model 5.

This is driven by both the control of corruption and the regulatory quality of the country in Model

6. At least one of these two variables is significant in every remaining model.

Model 7 adds two additional variables – an indicator variable for whether the country has

enacted a regulation governing crowdfunding and the number of financial regulatory agencies in

the country. Both are strongly related to the level of crowdfunding. Consistent with public choice

theory, there is a strong positive relation between the presence of explicit regulation and the level

of crowdfunding. I hesitate to attribute causality to this relation because regulators might also

decide to enact crowdfunding regulations in countries where crowdfunding is growing more

important.

More interestingly though, the greater the number of regulatory bodies in the country, the

smaller is the level of crowdfunding. This is consistent with the regulator self-interest hypothesis

that regulators desire increases in their jurisdiction and power. Hence, a larger number of

regulatory bodies implies an increasing likelihood in the number of possibly conflicting hurdles

platforms will have to cross to offer their services. My data on the number of regulators is likely

understated since it is hand-collected using public sources, hence I treat this variable as, at best,

indicative, but not conclusive evidence of regulator self-interest.

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Model 8 adds variables for the ease of setting up a formal business, while continuing to control

for the legal system in the country. Since this is a barrier that affects all start-ups not just entry into

the finance industry, I posit a negative relation between the ease of setting up a business and the

volume of crowdfunding. As expected, the number of procedures to set up a business is strongly

negatively related to the volume of crowdfunding.

Model 9 adds variables to directly measure the level of financial system rents earned by existing

financial intermediaries in the country. Financial system inefficiency (the bank cost to income

ratio), profitability (net income margin), and lack of competition (top five bank asset

concentration) are all significantly positively related to crowdfunding volume, suggesting that the

presence of economic rents significantly influences crowdfunding entry. Model 10 adds proxies

for user sophistication. The quality of scientific research institutions and the quality of the

education system are positively and negatively related to crowdfunding volume respectively,

though these variables do not retain their significance in the overall regression in model 12. Model

11 adds social factors. Neither the level of trust for strangers or the adventure seeking tendency of

the populace is related to the level of crowdfunding. Ironically, the one social factor that is strongly

related to crowdfunding volume appears to be largely economic as well - the level of dissatisfaction

households have with their current financial situations.

Model 12 brings all the variables together. Many of the variables retain their significance and

signs. Richer and less corrupt countries continue to have significantly higher crowdfunding

volume. The difficulty of starting a business continues to be strongly negative. Financial rents and

the level of dissatisfaction households have with their current financial situation continue to be

significantly related to crowdfunding volume. Finally, the explanatory power of the model is

relatively high at 73%.

Instead of using interaction variables, which increase the number of variables and make the

regressions less tractable, Table 6 reports regression models similar to those in Table 5 for

emerging and developed markets separately. Models 1 and 2 are differentiated by the addition of

a China indicator, while Model 6 and 7 are differentiated by the addition of indicators for the US

and UK. In both cases, the explanatory power of the models increases by around 6% on the addition

of these variables. In the remainder of this table, I examine whether the legal system and social

factors drive the volume of crowdfunding after controlling for the ease of starting a platform, the

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efficiency of the financial system, and user sophistication. These latter controls are largely

insignificant, possibly because the level of variation within developed and emerging markets

separately is considerably smaller than when I do not separate the two types of markets in Table

5.

The legal regime has a huge impact on the level of crowdfunding in both emerging and

developed markets separately. For example, adding variables for the rule of law, without

controlling for anything else more than triples the explanatory power for emerging markets from

model 2 to model 4 and nearly doubles it for developed markets from models 7 to 9. Rule of law,

acting either through regulatory quality or control of corruption, strongly positively impacts

crowdfunding volume both in emerging and developed markets. This is consistent with public

choice theory – high regulatory quality appears to promote crowdfunding. As in Table 5 however,

regulator self-interest (proxied by the number of financial regulatory agencies) is strongly

negatively related to the level of crowdfunding.

Models 5 and 10 add social factors and other control variables from Table 5. While the legal

regime factors lose their significance, at least for emerging markets, household financial

dissatisfaction continues to be strongly positively related to crowdfunding volume.

IV.B.2 Importance: What factors increase the relative importance of crowdfunding to the

economy?

Table 7 reports coefficients from an OLS regression of the log of crowdfunding volume as a

proportion of country GDP. Model 1 is the base case model from the prior tables. The developed

market indicator is significant in explaining business financing volume in model 1 but largely

loses its significance once more variables are added. In addition, perhaps not surprisingly,

crowdfunding increases in importance for GDP as the population increases.

As in the previous tables, Models 2 and 3 add a number of proxies for the legal system in the

country. Crowdfunding is significantly more important in common law countries, and in countries

with a strong rule of law. As before, either the control of corruption or regulatory quality is

significant in almost every model. The previous results on the positive impact of extant regulation

and the negative impact on the number of regulatory agencies also continue to hold. Model 4 adds

proxies for the ease of starting a business (largely insignificant) and financial system rents.

Financial system rents (bank profitability and lack of competition) are strongly related to the

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importance of crowdfunding for the economy. Finally, in model 5, the quality of the education

system and the capacity for innovation both affect the level of crowdfunding, albeit in opposite

directions. Finally, the only social factor that affects the importance of crowdfunding continues to

be an economic factor – household financial dissatisfaction.

IV.B.3 Displacement: When is crowdfunding more likely to displace incumbent financial

institutions?

Table 8 reports coefficients from an OLS regression of the log of crowdfunding volume as a

proportion of domestic credit offered by banks (Models 1-3) and by the financial sector, more

generally (Models 4-6). I posit that incumbent financial institutions are more at risk of being

displaced when they earn large profits, are not likely to face much competition, and when they are

inefficient. I also add the quality of the legal infrastructure as explanatory variables. The other

proxies (ease of starting a platform and user sophistication) are used as controls because it is

unclear that they will directly impact the displacement of incumbent financial institutions.

Table 8 shows that country characteristics are largely the only factors that are consistently

related to crowdfunding volume as a proportion of domestic credit. Crowdfunding is a higher

proportion of domestic credit in more populous but poorer countries. Population is consistently

significantly positively, and GDP is negatively related to crowdfunding proportion across all the

model specifications. Beyond country characteristics, few other variables appear to be consistently

significant. The presence of extant crowdfunding legislation does appear to positively impact the

importance of crowdfunding in the financial system, but no other legal or regulatory proxy appears

to matter.

IV.B.4 Financing patterns

In this section, I examine three patterns of financing in crowdfunding at the country level. The

dependent variables are debt financing as a proportion of total crowdfunding volume (aggregate

leverage ratio), equity financing as a proportion of total crowdfunding volume, and the proportion

of crowdfunding undertaken for financial motives. Since the proportion of non-financial motive

crowdfunding is one minus the proportion of financial motive crowdfunding, the conclusions for

the latter are just the opposite of the former. Because of the presence of reward and donation-based

crowdfunding, this is not the case for debt and equity.

- 27 -

Table 9 reports coefficients from logistic regressions of the proportions of debt (Panel A),

equity (Panel B), and financial-motive crowdfunding (Panel C) respectively. I use a different set

of explanatory variables from the variables in previous tables in these regressions. In particular, I

posit that the debt crowdfunding is likely to be driven at least partially by the levels of investor

protection for debt markets. Similarly, I posit that equity crowdfunding is likely to be driven by

the depth of the equity market and the level of shareholder protection. Finally, I include both

investor protection (debt and equity) and market depth proxies in my regressions for financial

motive financing.

As in Table 8, Panel A of Table 9 shows that country characteristics matter when explaining

the leverage ratio. Levered volume is more prevalent in more populous but poorer countries.

Population is consistently significantly positively, and GDP is negatively related to crowdfunding

proportion across almost all the model specifications. Beyond country characteristics, the legal

regime appears to matter. Leverage appears to be more important in both common and civil law

countries than in Islamic countries (model 2), though this is not entirely surprising given the

religious restrictions on payment of interest in Islamic law countries. Regulatory quality is also

significantly positive in four out of five models. However, the level of investor protection does not

appear to matter. None of the variables that serve as plausible proxies for debt investor protection

are significant. One possible explanation for this finding is that these investor protection measures

may be relevant only for investors in formal markets that are regulated. Unregulated P2P markets

may offer very little in the way of recourse should the borrower default.

In contrast, Panel B shows that it is much more difficult to explain the determinants of equity

financing. Civil law countries do appear to raise less equity financing than common or Islamic law

countries, but no other variables are consistently significant in explaining equity financing. In

particular, the depth of the equity market and level of investor protection does not appear to matter.

The number of listed firms, the stock market capitalization of listed firms, a proxy for equity

market depth, or the turnover of the market, a proxy for the presence of active shareholders, are all

insignificant. Similarly, proxies for the protection of minority shareholders, the strength of investor

protection, or the ethical behavior by firms do not appear to be related to equity financing volume.

As in the case of debt financing, perhaps the explanation is that these proxies are only related to

equity investments in regulated firms. Equity crowdfunding, in contrast, typically involves stakes

in small unlisted firms.

- 28 -

Finally, investor protection and the depth of the market do appear to play significant roles in

explaining crowdfunding that is undertaken for financial motives in Panel C. Countries with more

disclosure, stronger property rights, more developed markets, and lower credit information

availability are significantly more likely to be dominated by financial motive crowdfunding.

Oddly, the level of investor protection and the depth of the market are both negatively related to

the proportion of financial motive crowdfunding. This result may be related to the those in Panel

B. The stronger the level of protection for investments in listed firms and the more liquid the listed

markets, the weaker may be the incentive to invest in crowdfunding. Social factors also appear to

matter – as before, the level of household financial dissatisfaction strongly explains the proportion

of financial motive crowdfunding. Trust appears strongly negatively related to the proportion of

financial motive crowdfunding. These two results imply the opposite conclusions for the

proportion of non-financial motive crowdfunding, where the level of trust is strongly positively

correlated, and the level of financial dissatisfaction is negatively correlated with the proportion of

non-financial motive crowdfunding.

IV.B.5 Determinants of crowdfunding market power

In the final part of the paper, I investigate the determinants of market power for the

crowdfunding platforms. This is likely to be a topic of huge importance for the platforms because

of the presence of network effects. Platforms operate by matching investors with fund-raisers. A

deep market with both active investors and fund raisers is thus extremely important for them.

Hence, I next examine the determinants of crowdfunding market power. I measure market power

by computing the Herfindahl index for market share. Table 10 reports coefficients from logistic

regressions on the Herfindahl index.

Table 10 shows that the legal regime, country characteristics, and social factors are largely

irrelevant in determining market power. The only factors that appear to be important are the ease

with which a platform can be started and the degree of competition among incumbent banks. The

time to start a business is strongly negatively related to market power in three out of four

regressions. Similarly, the lack of dominance by incumbent banks (proxied by top five bank asset

concentration and the banks’ net interest margins) encourages market power by the platforms in a

country. It is plausible that platforms will not be able to dominate a market with powerful

- 29 -

competitors who have incentives to develop alternatives to the platforms and also have lobbying

power to hinder entry.

V. Conclusions

In this paper, I document global patterns and analyze the economic determinants of

crowdfunding using a unique hand-collected sample of crowdfunding volume obtained by

surveying 3,021 crowdfunding platforms worldwide. I document that crowdfunding is a global

phenomenon, with financing available across the globe. Financing is significantly higher in

developed than emerging markets, suggesting that, as yet, crowdfunding is not playing a significant

role in increasing financial inclusion.

Controlling for the level of market development, several characteristics of the legal system

appear significant in explaining the level of crowdfunding volume. Specifically, consistent with

public choice theory, the rule of law, proxied by both the control of corruption and the quality of

regulation in the country, appears to be significant in explaining financing volume across a number

of specifications. Other factors that matter are the ease of starting a platform and the financial

profitability of incumbent banks. Social factors do not appear to matter much. The only social

factor that does appears to be important is economic – the household level of dissatisfaction with

its finances.

In its purest state, a crowdfunding transaction consists of an investor agreeing to fund a stranger

with no financial intermediaries to certify value or alleviate information asymmetry.

Understanding how these investors make choices has relevance to models of information

asymmetry. The results in this paper show that the determinants of crowdfunding volume appear

to be consistent with rational economic models.

Studying crowdfunding has the potential to answer difficult econometric questions. A large

number of economic and financial studies use legal or regulatory changes as natural experiments

to demonstrate causality when examining firm financial policy implications. If the legal or

regulatory changes are themselves driven by changes in firm policy, then identifying suitable

instruments to establish causality is much more difficult than using the regulatory change as an

exogenous event. Studying the joint evolution of legal regulations and crowdfunding has the

potential to allow researchers to model the legal system as a dependent variable in a comprehensive

framework. That is however, beyond the scope of the current paper.

- 30 -

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Table 1. This table describes the major types of crowdfunding business models in 2015-16.

Crowd financing business model Description

A. Financial Return Models

A.1 Debt financing Business Lending Individuals or institutional funders provide a loan to a

business borrower, usually a small or medium enterprise (SME)

Consumer Lending Individuals or institutional funders provide a loan to a consumer borrower. Most are unsecured personal loans

Invoice Trading Individuals or institutional funders purchase invoices or receivable notes from a business at a discount.

Microfinance Microfinance refers to the lending of small sums to entrepreneurs who are often economically disadvantaged and financially marginalized. There is a debt obligation incurred, but the amounts lent are very small.

Mini-bonds Non-transferable, usually unsecured retail bonds traded on equity-based crowdfunding platforms, exclusively in the UK

Revenue/ Profit sharing Individuals or institutions purchase securities from a company (usually fixed income) and receive a share in the profits or royalties of the business

A.2 Equity financing

Equity funding Sale of registered security by mostly early–stage usually private firms to investors.

Community shares Community shares refer to the sale of shares in social enterprises serving a community purpose in a particular locality.

B. Non-financial return models

Reward-based Crowdfunding Backers provide finance to individuals, projects or companies in exchange for non-monetary rewards or products.

Donation-based Crowdfunding Donors provide funding to individuals, projects or companies based on philanthropic or civic motivations with no expectation of monetary or material return.

Table 2. This table reports the total number of platforms reporting non-zero volume of crowdfunding volume in 2015-2016. Panel A further classifies the total volume of business into business and consumer platforms, financial and non-financial motive platforms, contractual type (debt or equity), all reported in US$ millions. Proportions are computed as percentages of the total amounts across all countries (row 1) except for financial and non-financial motives and debt and equity volumes which are computed as a percentage of total volume in that market. All data is aggregated by country and averaged across the years of the sample 2015-2016. Panel B reports univariate statistics on the major dependent variables used to test the hypotheses, specifically, crowdfunding volume as a proportion of GDP, domestic credit and total volume, respectively, averaged by country and year. The Herfindahl index is computed from individual crowdfunding platform market shares as a proportion of total crowdfunding volume by country by year.

Panel A. Crowdfunding volumes and proportions across all countries and years

Markets

Number of

platforms

Crowdfunding volume (in $millions)

Business finance

volume (in $millions)

Consumer finance

volume (in $millions)

Financial motives

volume (in $millions)

Non-financial motives volume

(in $millions)

Debt finance volume (in $millions)

Equity finance

volume (in $millions)

Totals All markets 1,604 214,347.49 84,759.80 129,587.69 211,481.06 2,853.74 208,446.30 3,047.27 All developed markets 605 44,304.27 13,875.23 30,429.04 43,132.76 1,158.83 40,918.21 2,227.06

All emerging markets 999 170,043.22 70,884.56 99,158.65 168,348.30 1,694.92 167,528.09 820.21 All emerging markets (excluding China) 495 1,035.68 521.49 514.19 824.24 211.44 727.92 96.32

All common law countries 478 42,436.29 12,740.97 29,695.31 41,455.65 967.95 39,503.57 1,964.77 All civil law countries 1074 171,871.73 71,991.62 99,880.12 169,993.77 1,877.96 168,925.27 1,068.33 All Muslim law countries 52 39.47 27.21 12.26 31.64 7.84 17.47 14.17

Proportions of totals across all countries All markets 100% 100% 100% 100% 99% 1% 99% 1% All developed markets 38% 21% 16% 23% 97% 3% 95% 5%

All emerging markets 62% 79% 84% 77% 99% 1% 100% 0% All emerging markets (excluding China) 31% 0% 1% 0% 80% 20% 88% 12% All common law countries 30% 20% 15% 23% 98% 2% 95% 5% All civil law countries 67% 80% 85% 77% 99% 1% 99% 1% All Muslim law countries 3% 0% 0% 0% 80% 20% 55% 45%

Panel B. Crowdfunding volumes and proportions across all countries and years

Importance Displacing the financial sector Financing patterns

Network effects

Markets

Crowdfunding volume (as a proportion of GDP) (×106)

Crowdfunding volume (as a proportion of

domestic credit by

banks) (in %)

Crowdfunding volume (as a proportion of

domestic credit by financial

sector) (in %)

Debt proportion of

total crowdfunding

volume

Equity proportion of

total crowdfunding

volume

Financial motive

proportion of total

crowdfunding volume

Non-financial motive

proportion of total

crowdfunding volume

Herfindahl index

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

Totals All markets 248.60 0.042 0.030 39.59% 6.07% 45.67% 54.33% 0.680 All developed markets 340.29 0.038 0.019 55.20% 16.13% 71.32% 28.67% 0.350

All emerging markets 229.67 0.043 0.032 36.38% 4.00% 40.38% 59.62% 0.748 All emerging markets (excluding China) 110.37 0.035 0.025 35.90% 4.03% 39.93% 60.07% 0.753

All common law countries 192.93 0.054 0.036 43.25% 5.90% 49.15% 50.84% 0.669 All civil law countries 324.28 0.039 0.031 40.05% 5.00% 45.05% 54.95% 0.666 All Muslim law countries 33.89 0.024 0.002 26.97% 11.88% 38.85% 61.15% 0.779

Table 3. This table lists the number of platforms by type of financing model. Panel A reports a broad classification into debt, equity and other models, while Panel B reports a more detailed classification. Proportions are computed as a percentage of total number of platforms in the region. The models are described in Table 1. All data is first aggregated by country and then averaged by year from the 2015-2016 global surveys of crowdfunding.

Panel A. Broad type of financing model

Type of financing model Type of financing model

Region Debt Equity Other Total Debt Equity Other Africa 39 11 90 139 28% 8% 64%

Asia (except China) 74 16 64 153.5 48% 10% 42% China 464 20 12 495.5 94% 4% 2% Australia and New Zealand 24 11 11 45.5 52% 24% 24%

Europe - Eastern 6 0 33 38.5 16% 0% 84% Europe - Western (except UK) 101 71 130 301 34% 23% 43% United Kingdom (UK) 49 23 14 85.5 57% 27% 16% Middle East 11 6 26 41.5 25% 13% 61%

North America (except USA) 24 8 14 45 52% 18% 30% USA 63 35 15 112.5 56% 31% 13%

South America 32 6 49 86.5 37% 7% 56%

Total 885 206 454 1,544 57% 13% 29%

Panel B. Detailed classifications of financing model

Type of financing model (Detailed definition)

Debt Equity

Non-financial motives Other

Region Business Lending

Consumer Lending

Invoice Trading

Mini bonds

Micro finance

Revenue/ Profit

sharing

Equity Funding

Community Shares

Reward funding

Donation funding Other Total

Africa 6 1 0 0 30 1 10 0 68 22 3 139

Asia (except China) 30 23 2 1 0 2 12 0 50 14 22 154

China 183 230 8 0 0 4 20 0 10 2 40 496

Australia and New Zealand 12 7 3 0 0 0 9 0 8 3 4 46

Europe - Eastern 0 4 2 0 1 0 0 0 31 2 0 39

Europe - West (except UK) 48 27 11 2 0 2 56 0 96 34 28 301

United Kingdom (UK) 21 8 2 1 0 0 17 2 11 3 23 86

Middle East 3 2 1 0 6 0 3 0 19 7 3 42

North America (except USA) 12 9 1 0 0 1 6 0 8 6 5 45

USA 28 23 2 1 0 3 20 0 8 7 23 113

South America 15 11 5 0 0 1 4 0 41 8 4 87

Total 355 343 34 4 36 12 155 2 349 104 152 1,544

Type of financing model (%)

Debt Equity Non-financial motives Other

Region Business Lending

Consumer Lending

Invoice Trading

Micro finance

Mini bonds

Equity Funding

Community Shares

Reward funding

Donation funding Other

Africa 4% 1% 0% 0% 21% 7% 0% 49% 15% 2% Asia (except China) 20% 15% 1% 0% 0% 8% 0% 33% 9% 14%

China 37% 46% 2% 0% 0% 4% 0% 2% 0% 8% Australia and New Zealand 25% 15% 7% 0% 0% 20% 0% 18% 7% 9%

Europe - Eastern 0% 10% 4% 0% 1% 0% 0% 81% 4% 0% Europe - Western (except UK) 16% 9% 3% 0% 0% 19% 0% 32% 11% 9% United Kingdom (UK) 24% 9% 2% 1% 0% 19% 2% 12% 4% 27% Middle East 6% 5% 1% 0% 13% 7% 0% 46% 16% 6% North America (except USA) 26% 20% 1% 0% 0% 12% 0% 18% 12% 10% USA 25% 20% 2% 0% 0% 17% 0% 7% 6% 20% South America 17% 12% 5% 0% 0% 5% 0% 47% 9% 4%

World total 23% 22% 2% 0% 2% 10% 0% 23% 7% 10%

Table 4. This table reports correlations between the volumes of business on crowdfunding platforms. All data is first aggregated by country and then averaged by year from the 2015-2016 global surveys of crowdfunding.

Ln(Crowdfunding volume per capita+1)

Ln(Business finance

volume per capita+1)

Ln(Consumer finance

volume per capita+1)

Ln(Financial motive

volume per capita+1)

Ln(Non-financial motive volume

per capita+1)

Ln(Debt financing volume

per capita+1)

Ln(Equity financing volume

per capita+1)

Ln(Crowdfunding volume per capita+1) 1.00 0.92 0.88 0.87 0.80 0.82 0.69 Ln(Business finance volume per capita+1) 0.92 1.00 0.72 0.81 0.77 0.75 0.74

Ln(Consumer finance volume per capita+1) 0.88 0.72 1.00 0.75 0.81 0.75 0.59 Ln(Financial motive volume per capita+1) 0.87 0.81 0.75 1.00 0.51 0.96 0.72 Ln(Non-financial motive volume per capita+1) 0.80 0.77 0.81 0.51 1.00 0.49 0.57

Ln(Debt financing volume per capita+1) 0.82 0.75 0.75 0.96 0.49 1.00 0.60 Ln(Equity financing volume per capita+1) 0.69 0.74 0.59 0.72 0.57 0.60 1.00

Table 5. This table reports coefficients from an OLS regression of the log of crowdfunding volume per capita by country. The independent variables are described in Appendix A. All data is aggregated by country and year separately from the 2015-2016 global surveys of crowdfunding. Robust standard errors are clustered by country and year, following Thompson (2011). All regressions include year region fixed effects. Major regions are China, the US, and the UK. T-statistics are reported in parentheses. Coefficients significant at at least the 10% level are bolded.

(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12)

Intercept -3.328 -0.719 4.081 -2.103 0.870 1.183 -0.698 0.206 -4.765 5.116 -0.939 -15.830

(-1.86) (-0.50) (2.16) (-1.26) (0.68) (0.80) (-0.43) (0.14) (-2.85) (3.10) (-0.25) (-2.34) Country characteristics Ln(GDP) 0.309 0.199 0.166 0.225 0.148 0.118 0.203 0.208 0.231 -0.087 0.132 0.227

(4.09) (3.29) (2.60) (3.67) (2.79) (2.09) (3.24) (3.67) (2.90) (-0.90) (1.08) (1.73)

Developed market indicator 3.713 3.858 2.796 3.700 1.349 1.229 0.826 0.876 0.843 1.560 0.592 -0.222

(10.25) (11.64) (5.80) (10.60) (3.27) (2.65) (1.94) (2.16) (1.72) (4.14) (0.92) (-0.33)

Legal system within country Common Law indicator (1 or 0) 0.972 0.515 0.899 0.625 0.465 0.472 0.869 0.629

(1.97) (1.19) (1.94) (1.49) (0.92) (1.06) (1.58) (0.49)

Civil Law indicator (1 or 0) 0.802 0.308 0.564 0.349 0.621 0.275 0.739 0.931

(0.96) (0.47) (0.81) (0.56) (1.02) (0.56) (0.98) (1.19)

Rule of Law 1.435

(9.48) Control of Corruption 0.322 0.328 0.343 0.399 0.102 1.295 1.165

(1.56) (1.27) (1.21) (1.34) (0.39) (3.79) (2.84)

Regulatory Quality 1.309 1.372 1.219 1.515 1.233 0.794 0.060

(4.25) (4.10) (3.16) (4.74) (2.36) (2.13) (0.07) Existing regulation on P2P lending 1.377 1.134 0.575

(4.84) (4.29) (1.45) Number of regulatory agencies -0.276 -0.207 0.124

(-2.78) (-2.45) (1.11) Ease of starting a platform Number of procedures to start a business -0.166 -0.149

(-2.37) (-2.16)

Time to start a business 0.016 0.013

(1.11) (1.12)

Financial system efficiency Bank cost to income ratio (%) 0.010 -0.001

(1.65) (-0.12) Bank net interest margin (%) 0.195 0.346

(1.74) (4.04)

5-bank asset concentration 0.021 0.051

(3.07) (2.92)

Depth of credit information index -0.016 0.042

(-0.15) (0.25) Credit registry coverage (% of adults) -0.002 -0.003

(-0.31) (-0.52) User sophistication Individuals using the Internet (% of population) -0.002 0.005

(-0.20) (0.16)

Quality of scientific research institutions 0.791 0.594

(2.26) (1.33) Quality of the education system -0.580 -0.427

(-3.09) (-1.67) Availability of scientists and engineers -0.145 0.549

(-0.39) (1.17) Capacity for innovation 0.252 0.742

(0.86) (1.81) Social Factors Trust -0.860 -0.558

(-1.27) (-0.80) Schwartz: Adventure and risk-taking important -0.018 -0.029

(-0.05) (-0.09) Financial situation of household (High=dissatisfied) 0.684 0.863

(4.38) (5.08)

Year fixed effects Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Region fixed effects No Major All Major Major Major Major Major Major Major Major Major

Adj R2 42.4% 46.4% 51.2% 46.9% 58.1% 60.7% 63.7% 65.5% 62.8% 62.2% 66.5% 72.7% N 285 285 285 285 283 283 268 264 224 243 137 124

Table 6. This table reports coefficients from an OLS regression of the log of crowdfunding volume per capita for emerging and developed markets separately. The independent variables are described in Appendix A. Controls are variables reported in specific sections in Table 5. All data is aggregated by country and year separately from the 2015-2016 global surveys of crowdfunding. Robust standard errors are clustered by country and year, following Thompson (2011). All regressions include year and region fixed effects. Major regions are China, the US, and the UK. T-statistics are reported in parentheses. Coefficients significant at at least the 10% level are bolded.

Emerging markets Developed markets

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

Intercept -2.244 0.072 1.687 0.012 -7.082 -6.189 -2.410 -6.403 -8.224 -75.565

(-1.14) (0.05) (1.21) (0.01) (-0.82) (-1.47) (-0.43) (-1.27) (-1.71) (-2.04)

Country prosperity Ln(GDP) 0.265 0.167 0.094 0.185 -0.086 0.552 0.406 0.473 0.561 1.632

(3.18) (2.93) (1.73) (2.88) (-0.39) (3.59) (1.96) (2.69) (2.93) (1.16)

Legal system within country Common Law indicator (1 or 0) . 0.401 0.870 1.335 1.215 1.199 1.849

(0.86) (1.91) (0.81) (2.72) (2.81) (0.79) Civil Law indicator (1 or 0) 0.655 0.516 1.957

(0.81) (0.69) (2.00) Rule of Law 1.456 1.176

(9.00) (3.17) Control of Corruption 0.251 1.777 1.451 -0.126

(0.95) (2.99) (2.19) (-0.06)

Regulatory Quality 1.485 -0.120 -0.474 -1.492

(4.59) (-0.11) (-0.44) (-0.47)

Existing regulation on P2P lending 0.879 1.048

(5.21) (2.83)

Number of regulatory agencies -0.394 -0.225 (-2.97) (-1.73) Social Factors Trust -0.671 -2.014

(-1.07) (-0.38) Schwartz: Adventure and risk-taking important -0.145 -0.256

(-0.27) (-0.28) Financial situation of household (High=dissatisfied) 0.926 1.167

(4.80) (0.55)

Controls

Year fixed effects Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Major region indicator No Yes Yes Yes Yes No Yes Yes Yes Yes

Ease of starting a platform No No No No Yes No No No No Yes

Financial system efficiency No No No No Yes No No No No Yes User sophistication No No No No Yes No No No No Yes

Adj R2 5.9% 12.1% 32.7% 38.9% 54.3% 27.5% 34.4% 52.2% 62.1% 90.1% N 233 233 231 216 93 52 52 52 52 31

Table 7. This table reports coefficients from an OLS regression of the log of crowdfunding volume as a proportion of current country GDP. The independent variables are described in Appendix A. All data is aggregated by country and year separately from the 2015-2016 global surveys of crowdfunding. Robust standard errors are clustered by country and year, following Thompson (2011). All regressions include year and region fixed effects. Major regions are China, the US, and the UK. T-statistics are reported in parentheses. Coefficients significant at at least the 10% level are bolded.

(1) (2) (3) (4) (5)

Intercept 1.505 -0.440 -1.401 -6.006 -6.807

(1.79) (-0.44) (-1.24) (-4.64) (-1.20) Country prosperity Ln(Total population) 0.096 0.188 0.283 0.348 -0.102

(1.84) (3.51) (4.40) (4.51) (-0.65)

Developed market indicator 1.731 0.778 0.275 -0.072 -0.205

(8.00) (2.01) (0.70) (-0.13) (-0.28) Legal system within country Common Law indicator (1 or 0) 0.966 1.294 0.737 0.608

(2.29) (3.23) (1.68) (0.54) Civil Law indicator (1 or 0) 0.448 0.489 0.634 0.776

(0.71) (0.83) (1.38) (1.18) Rule of Law 0.496

(3.23) Control of Corruption 0.075 0.277 0.846

(0.32) (0.96) (2.24)

Regulatory Quality 0.811 1.058 0.173

(2.92) (3.45) (0.25) Existing regulation on P2P lending 1.222 1.276 0.921

(3.87) (3.92) (2.49)

Number of regulatory agencies -0.406 -0.179 0.118

(-4.56) (-1.84) (1.02) Ease of starting a platform Ease of starting a business 0.005 -0.017

(0.64) (-0.98) Financial system efficiency Bank cost to income ratio (%) 0.007 -0.003

(1.08) (-0.36)

Bank net interest margin (%) 0.262 0.234

(2.05) (2.97)

5-bank asset concentration 0.021 0.044

(3.06) (2.55)

Depth of credit information index -0.081 -0.011

(-0.76) (-2.19)

Credit registry coverage (% of adults) -0.005 0.128

(-0.86) (0.81) Financial system development -0.078

(-0.17)

User sophistication Individuals using the Internet (% of population) -0.025

(-0.89)

Quality of scientific research institutions 0.482

(1.28)

Quality of the education system -0.596

(-2.55)

Availability of scientists and engineers 0.425

(0.78) Capacity for innovation 0.990

(2.39)

Social Factors Trust -0.438

(-0.63)

Schwartz: Adventure and risk-taking important -0.041

(-0.17) Financial situation of household (High=dissatisfied) 0.994

(11.81)

Year fixed effects Yes Yes Yes Yes Yes Region fixed effects Major Major Major Major Major

Adj R2 25.0% 28.9% 37.0% 42.5% 56.5% N 285 283 268 224 124

Table 8. This table reports coefficients from an OLS regression of the log of crowdfunding volume as a proportion of domestic credit by banks (columns 1-3) and by the financial sector (columns 4-6). The independent variables are described in Appendix A. Controls are variables reported in specific sections in Table 5. All data is aggregated by country and year separately from the 2015-2016 global surveys of crowdfunding. Robust standard errors are clustered by country and year, following Thompson (2011). All regressions include year and region fixed effects. Major regions are China, the US, and the UK. T-statistics are reported in parentheses. Coefficients significant at at least the 10% level are bolded.

As a proportion of

domestic credit by banks

As a proportion of domestic credit by financial sector

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

Intercept 0.133 0.277 0.214 0.151 0.281 0.237

(2.04) (1.60) (2.17) (1.89) (1.46) (1.86) Financial system efficiency Bank cost to income ratio (%) 0.000 0.000 0.000 0.000 0.000 0.000

(-0.24) (-0.60) (-0.35) (-0.22) (-0.57) (-0.34) Bank net interest margin (%) 0.006 0.008 0.009 0.004 0.006 0.006

(1.40) (1.43) (1.45) (1.15) (1.21) (1.28) 5-bank asset concentration 0.001 0.001 0.001 0.001 0.001 0.001

(1.66) (1.60) (1.74) (1.94) (1.77) (2.00)

Depth of credit information index 0.003 0.002 0.002 0.003 0.003 0.003

(0.74) (0.54) (0.44) (0.91) (0.75) (0.69) Credit registry coverage (% of adults) 0.000 0.000 0.000 0.000 0.000 0.000

(-0.13) (-0.34) (0.03) (-0.27) (-0.41) (-0.08)

Financial system development 0.020 -0.010 -0.009 0.018 -0.008 -0.008

(2.79) (-0.85) (-0.92) (2.36) (-0.95) (-1.02)

Legal regime Common Law indicator (1 or 0) -0.017 -0.012 -0.015 -0.012

(-0.79) (-0.60) (-0.64) (-0.55) Civil Law indicator (1 or 0) -0.004 -0.004 -0.003 -0.003

(-0.38) (-0.33) (-0.26) (-0.28) Control of Corruption 0.023 0.018 0.024 0.020

(1.20) (1.08) (1.26) (1.15) Regulatory Quality 0.060 0.057 0.050 0.049

(1.57) (1.53) (1.49) (1.45)

Existing regulation on P2P lending 0.055 0.048

(2.95) (2.99)

Number of regulatory agencies -0.003 -0.003

(-1.08) (-1.08) Country characteristics Ln(Total population) 0.008 0.039 0.044 0.008 0.037 0.040

(1.70) (1.81) (2.10) (1.73) (1.77) (2.02)

Ln(GDP) -0.015 -0.036 -0.040 -0.016 -0.035 -0.037

(-3.46) (-1.75) (-2.17) (-3.05) (-1.67) (-2.03)

Developed market indicator 0.017 -0.039 -0.031 0.013 -0.037 -0.030

(1.53) (-1.41) (-1.07) (1.18) (-1.61) (-1.26)

Controls for Ease of starting a platform No No Yes No No No

User sophistication No No Yes No No No Year fixed effects Yes Yes Yes Yes Yes Yes

Region fixed effects Major Major Major Major Major Major

Adj R2 46.5% 52.6% 51.5% 37.5% 44.3% 43.0% N 204 202 202 203 201 201

Table 9. This table reports coefficients from a logit regression of the debt (Panel A), equity (Panel B), and financial motive proportion (Panel C) of crowdfunding respectively. The independent variables are described in Appendix A. All data is aggregated by country and year separately from the 2015-2016 global surveys of crowdfunding. Robust standard errors are clustered by country and year, following Gow, Ormazabal, and Taylor (2010). All regressions include year and region fixed effects. Major regions are China, the US, and the UK. Z-statistics are reported in parentheses. The Generalized coefficient of determination is computed as in Nagelkerke (1991). Coefficients significant at at least the 10% level are bolded.

Panel A. Proportion of debt financing

(1) (2) (3) (4) (5)

Investor Protection - Debt Business extent of disclosure 0.090 0.030 0.031 0.112

(0.70) (0.24) (0.24) (0.53)

Property Rights Index 0.497 -0.122 -0.124 -0.521

(0.91) (-0.28) (-0.27) (-0.49)

Credit bureau coverage (% of adults) -0.002 -0.001 -0.001 -0.004

(-0.33) (-0.13) (-0.13) (-0.39) Credit registry coverage (% of adults) -0.002 0.004 0.004 -0.006

(-0.14) (0.28) (0.24) (-0.16)

Financial market development -0.206 -0.305 -0.281 0.004

(-0.29) (-0.52) (-0.51) (0.00)

Enforcement of contracts -0.005 (-0.17) Strength of insolvency resolution 0.012 (0.13) Social Factors Trust -0.663

(-0.62)

Schwartz: Adventure and risk-taking important -0.280

(-0.38) Financial situation of household (High=dissatisfied) 0.046

(0.14) Legal regime Common Law indicator (1 or 0) -0.077 1.496

(-0.10) (1.99) Civil Law indicator (1 or 0) 0.136 1.779

(0.13) (2.20) Control of Corruption 0.112 0.176 0.176 1.242

(0.26) (0.25) (0.23) (1.24)

Regulatory Quality 0.916 1.409 1.407 0.030

(2.34) (2.26) (1.99) (0.03) Country characteristics Ln(Total population) 0.986 0.490 1.045 1.031 0.650

(3.56) (2.25) (2.63) (2.46) (1.16) Ln(GDP) -0.626 -0.228 -0.697 -0.689 -0.439

(-2.89) (-1.08) (-2.59) (-2.22) (-0.83)

Year fixed effects Yes Yes Yes Yes Yes Region fixed effects Major Major Major Major Major

Generalized Coefficient of Determination 18.9% 16.5% 19.4% 19.4% 19.7%

N 283 245 243 243 126

Panel B. Proportion of equity financing

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

Depth of equity market Number of listed companies per 1,000,000 people -0.010 -0.017 -0.016

(-0.50) (-1.01) (-0.40)

Market capitalization of listed domestic firms (% of GDP) -0.001 0.001 0.000

(-0.14) (0.19) (0.08)

Stocks traded, turnover ratio of domestic shares (%) -0.003 -0.004 0.000

(-0.46) (-0.56) (-0.01) Investor Protection - Equity Protection of minority shareholders’ interests 0.352 (0.17) Ethical behavior of firms -1.068

(-0.81) Ease of shareholder suits index -0.328

(-0.62) Extent of shareholder rights index 0.225

(0.90) Social Factors Trust -0.739

(-0.21) Schwartz: Adventure and risk-taking important 0.348

(0.12)

Financial situation of household (High=dissatisfied) 0.620

(0.42)

Legal regime Civil Law indicator (1 or 0) -0.823 -1.218 -1.566 -2.612

(-1.85) (-2.30) (-1.64) (-2.08)

Control of Corruption 0.739 0.378 1.194 0.863

(2.69) (0.41) (0.78) (1.48) Regulatory Quality 0.647 0.305 0.288 -0.011

(1.70) (0.38) (0.24) (-0.01)

Country characteristics Ln(Total population) 0.770 -0.039 -0.085 -0.295

(4.15) (-0.07) (-0.12) (-0.31) Ln(GDP) 0.194 0.989 1.099 1.476

(1.04) (1.37) (1.37) (1.60)

Year fixed effects Yes Yes Yes Yes Region fixed effects Major Major Major Major

Generalized Coefficient of Determination 40.3% 33.0% 33.5% 44.3%

N 283 99 97 77

Panel C. Proportion of crowdfunding undertaken for financial motives

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

Investor Protection - Debt Business extent of disclosure 0.090 0.353

(0.75) (3.33)

Property Rights Index 0.040 1.906

(0.10) (2.34)

Credit bureau coverage (% of adults) -0.003 -0.008

(-0.36) (-1.40)

Credit registry coverage (% of adults) -0.001 -0.025

(-0.08) (-5.80)

Strength of insolvency resolution 0.022 -0.058

(0.32) (-0.29)

Financial market development -0.241 0.275 2.707

(-0.34) (0.32) (2.63)

Investor Protection - Equity Protection of minority shareholders’ interests -0.273 -2.365

(-0.11) (-2.26)

Ethical behavior of firms -0.648 -2.109

(-0.34) (-2.84)

Depth of equity market Number of listed companies per 1,000,000 people -0.009 -0.011

(-0.54) (-0.74) Stocks traded, turnover ratio of domestic shares (%) -0.009 -0.013

(-0.84) (-2.20)

Social Factors Trust -2.418

(-4.40)

Schwartz: Adventure and risk-taking important 0.164

(0.34) Financial situation of household (High=dissatisfied) 0.923

(3.09)

Legal regime Common Law indicator (1 or 0) -0.115 0.019 0.250 -0.517

(-0.22) (0.03) (0.21) (-1.47) Control of Corruption 0.275 0.292 0.890 2.548

(0.63) (0.39) (1.34) (2.93)

Regulatory Quality 0.833 1.050 0.722 -2.706

(2.00) (1.68) (0.39) (-1.83)

Country characteristics Ln(Total population) 0.996 0.958 0.499 -0.641

(4.37) (2.64) (0.46) (-1.52)

Ln(GDP) -0.573 -0.575 0.366 1.579

(-3.04) (-2.30) (0.29) (3.65)

Year fixed effects Yes Yes Yes Yes Region fixed effects Major Major Major Major

Generalized Coefficient of Determination 23.5% 23.7% 38.0% 53.6%

N 283 243 97 75

Table 10. This table reports coefficients from a logit regression of the Herfindahl index of crowdfunding market shares, computed as in Table 2. The independent variables are described in Appendix A. All data is aggregated by country and year separately from the 2015-2016 global surveys of crowdfunding. Robust standard errors are clustered by country and year, following Gow, Ormazabal, and Taylor (2010). All regressions include year and region fixed effects. Major regions are China, the US, and the UK. Z-statistics are reported in parentheses. The Generalized coefficient of determination is computed as in Nagelkerke (1991). Coefficients significant at at least the 10% level are bolded.

(1) (2) (3) (4) (5)

Ease of starting a platform Number of procedures to start a business 0.149 0.128 0.137 0.126

(1.17) (0.71) (0.72) (2.31)

Time to start a business -0.021 -0.022 -0.022 -0.006

(-1.92) (-2.11) (-2.09) (-1.04) Financial system efficiency Bank cost to income ratio (%) -0.019 -0.019 -0.018

(-0.45) (-0.42) (-1.59) Bank net interest margin (%) -0.123 -0.129 -0.287

(-0.85) (-0.88) (-20.02)

5-bank asset concentration -0.026 -0.028 -0.059

(-2.17) (-2.04) (-2.85)

Credit registry coverage (% of adults) -0.118 0.017

(-0.45) (0.16) Number of regulators

Social Factors Trust -0.137

(-0.21) Schwartz: Adventure and risk-taking important 0.040

(0.09) Financial situation of household (High=dissatisfied) -0.812

(-4.84)

Legal regime Common Law indicator (1 or 0) -0.699 -0.478 0.327 0.410 -0.706

(-0.50) (-0.32) (0.08) (0.09) (-0.65) Civil Law indicator (1 or 0) -0.440 -0.289 0.094 0.075 -0.375

(-0.39) (-0.23) (0.03) (0.02) (-0.41)

Control of Corruption -0.466 -0.470 0.154 0.165 -0.067

(-0.65) (-0.68) (0.11) (0.12) (-0.27)

Regulatory Quality -0.793 -0.790 -1.704 -1.678 -1.111

(-0.70) (-0.69) (-0.57) (-0.55) (-1.49) Country characteristics Ln(Total population) -0.677 -0.746 -0.955 -0.957 -0.590

(-1.48) (-2.04) (-1.12) (-1.10) (-0.80) Ln(GDP) -0.267 -0.284 -0.327 -0.296 -1.108

(-1.27) (-1.33) (-1.16) (-0.98) (-4.09)

Year fixed effects Yes Yes Yes Yes Yes Region fixed effects Major Major Major Major Major

Generalized Coefficient of Determination 0.6% 61.9% 63.4% 63.6% 71.4%

N 283 276 224 224 130

Appendix A.

The independent variables used in the paper are described below along with variable names and sources.

Variable Source Variable Name Description Country characteristics

GDP WB WDI NY.GDP.MKTP.CD GDP at purchaser's prices converted using official

exchange rates into current US$ Total population WB WDI SP.POP.TOTL Total population as of mid-year estimates Developed market indicator MSCI and FT

Based on the MSCI and FTSE annual market classification frameworks

Legal system within the country

Common, Civil or Muslim Law indicators CIA

Describes the type of legal system in force within the country

Rule of Law: Estimate ranges from approximately -2.5 (weak) to 2.5 (strong) governance performance

WB Governance RL.EST Rule of law captures perceptions of the extent to which agents have confidence in and abide by the rules of society, and in particular the quality of contract enforcement, property rights, and the courts.

Control of Corruption: Estimate ranges from approximately -2.5 (weak) to 2.5 (strong) governance performance

WB Governance CC.EST Control of corruption captures perceptions of the extent to which public power is exercised for private gain.

Regulatory Quality: Estimate ranges from approximately -2.5 (weak) to 2.5 (strong) governance performance

WB Governance RQ.EST Regulatory quality captures perceptions of the ability of the government to formulate and implement sound policies and regulations that permit and promote private sector development.

Existing regulation on P2P lending

Hand-collected from regulatory websites and news sources

Number of regulatory agencies

Hand-collected from Wikipedia and from the BIS list of regulators at https://www.bis.org/regauth.htm

Ease of starting platform

Ease of starting a business WB DB

Records all procedures officially required, or commonly done in practice, for an entrepreneur to start up and formally operate an industrial or commercial business

Number of days to start a business WB DB

The measure captures the median duration that incorporation lawyers or notaries indicate is necessary in practice to complete a procedure with minimum follow-up with government agencies and no unofficial payments.

Number of procedures to start a business WB DB

A procedure is defined as any interaction of the company founders with external parties.

Financial institutions efficiency Bank cost to income ratio (%) WB GFDI GFDD.EI.07 Raw data are from Bankscope. Bank net interest margin (%) WB GFDI GFDD.EI.01 Raw data are from Bankscope. 5-bank asset concentration WB GFDI GFDD.OI.06 Raw data are from Bankscope. Depth of credit information index WB DB IC.CRD.INFO.XQ Depth of credit information index measures rules

affecting the scope, accessibility, and quality of credit information available through public or private credit registries. The index ranges from 0 to 8, with higher values indicating the availability of more credit information, from either a public registry or a private bureau, to facilitate lending decisions.

Credit bureau coverage (% of adults) WB DB IC.CRD.PRVT.ZS Private credit bureau coverage reports the number of individuals or firms listed by a private credit bureau with current information on repayment history, unpaid debts, or credit outstanding. The number is expressed as a percentage of the adult population.

Credit registry coverage (% of adults) WB DB IC.CRD.PUBL.ZS Public credit registry coverage reports the number of individuals and firms listed in a public credit registry with current information on repayment history, unpaid debts, or credit outstanding. The number is expressed as a percentage of the adult population.

Financial institutions access

Financial market development rank WEF GCI.B.08 Ranks economies on ascending scale

User sophistication

Individuals using Internet (%) rank WB WDI IT.NET.USER.ZS Internet users are individuals who have used the

Internet (from any location) in the last 3 months. The Internet can be used via a computer, mobile phone, personal digital assistant, games machine, digital TV etc.

Quality of scientific research institutions WEF EOSQ071 Ranks economies on a scale of 1-7 (best) Quality of the education system WEF EOSQ128 Ranks economies on a scale of 1-7 (best) Availability of scientists and engineers WEF EOSQ133 Ranks economies on a scale of 1-7 (best) Capacity for innovation WEF EOSQ119 Ranks economies on a scale of 1-7 (best) Social Factors

Trust WVS Wave 6 V105 Based on answers to the question how much you

trust people you meet from the first time (1=Trust completely, 4=Do not trust at all); Inverted for this paper

Schwartz: Adventure and risk-taking important WVS Wave 6 V76 Based on answers to the question: Adventure and taking risks are important to this person; to have an exciting life

Financial situation of household (High=dissatisfied) WVS Wave 6 V59 Based on answers to the question: Satisfaction with

financial situation of household Investor Protection - Debt

Business extent of disclosure WB WDI IC.BUS.DISC.XQ Disclosure index measures the extent to which

investors are protected through disclosure of ownership and financial information. The index ranges from 0 to 10, with higher values indicating more disclosure.

Property Rights Index WEF EOSQ051 Ranks economies on a scale of 1-7 (best) Enforcement of contracts WB DB

The enforcing contracts indicator measures the time and cost for resolving a commercial dispute through a local first-instance court, and the quality of judicial processes index.

Strength of insolvency resolution WB DB

Measures the time, cost and outcome of insolvency proceedings involving domestic legal entities.

Depth of equity market

Number of listed companies per 1,000,000 people WB GFDI GFDD.OM.01 Number of listed domestic companies is the

domestically incorporated companies listed on the country's stock exchanges at the end of the year.

Market capitalization of listed domestic firms (% of GDP) WB WDI CM.MKT.LCAP.GD.ZS Market capitalization is the share price times the number of shares outstanding (including their several classes) for listed domestic companies.

Stocks traded, turnover ratio of domestic shares (%)

CM.MKT.TRNR Turnover ratio is the value of domestic shares traded divided by their market capitalization.

Investor Protection - Equity

Protection of minority shareholders’ interests WB DB

Measures the strength of minority shareholder protections against misuse of corporate assets by directors for their personal gain as well as shareholder rights, governance safeguards and corporate transparency requirements that reduce the risk of abuse.

Ethical behavior by firms WEF EOSQ153 Economies are ranked based on answers to the question: In your country, how would you rate the corporate ethics of companies (ethical behavior in interactions with public officials, politicians, and other firms)?

Ease of shareholder suits index WB DB

Ease of shareholder lawsuits index Extent of shareholder rights index WB DB Shareholder's rights in major corporate decisions WB GFDI: World Bank Global Financial Development Index (2016/12/31) (available at http://data.worldbank.org/data-catalog/global-financial-development) WB WDI: World Bank World Development Indicators (2016/12/31) (available at http://data.worldbank.org/data-catalog/world-development-indicators) WB Governance: World Bank Governance Indicators (available at http://data.worldbank.org/data-catalog/worldwide-governance-indicators) WB DB: World Bank Doing Business Database (available at http://www.doingbusiness.org/data/) WEF: World Economic Forum - The Global Competitiveness Report 2015–2016 (available at http://reports.weforum.org/global-competitiveness-report-2015-2016) CIA: CIA World Factbook (available at https://www.cia.gov/library/publications/the-world-factbook/fields/2100.html) WVS Wave 6: World Values Survey Wave 6 (2010-2014) (Available at http://www.worldvaluessurvey.org)

Appendix B.

This table lists the number of unique countries and platforms surveyed by year of first survey. Platforms reporting more than one business model are treated as independent observations. Multi-country platforms such as Kickstarter, Kiva and Global Giving are classified as separate platforms in different countries.

Year Year

2013 2014 2015 2016 Total 2013 2014 2015 2016 Total

Africa Africa South Africa 0 0 13 7 20 Gambia 0 0 3 1 4 Kenya 0 0 9 10 19 Mauritania 0 0 4 0 4

Nigeria 0 0 9 7 16 Mauritius 0 0 3 1 4

Ghana 0 0 11 3 14 Somalia 0 0 2 2 4

Egypt 0 0 7 5 12 Togo 0 0 2 2 4

Morocco 0 0 5 6 11 Botswana 0 0 2 1 3

Senegal 0 0 5 6 11 Congo Rep. 0 0 2 1 3

Cameroon 0 0 6 3 9 Guinea 0 0 2 1 3

Malawi 0 0 4 5 9 Benin 0 0 1 1 2

Mali 0 0 6 3 9 Cape Verde 0 0 1 1 2

Tanzania 0 0 5 4 9 Central African Republic 0 0 1 1 2

Uganda 0 0 4 5 9 Equatorial Guinea 0 0 2 0 2

Zambia 0 0 5 4 9 Seychelles 0 0 1 1 2

Rwanda 0 0 4 4 8 South Sudan 0 0 1 1 2

Burkina Faso 0 0 4 3 7 Sudan 0 0 2 0 2

Congo Dem. Rep. 0 0 4 3 7 Angola 0 0 1 0 1

Namibia 0 0 4 3 7 Comoros 0 0 1 0 1

Tunisia 0 0 4 3 7 Eritrea 0 0 1 0 1

Zimbabwe 0 0 3 4 7 Gabon 0 0 1 0 1

Ethiopia 0 0 4 2 6 Guinea-Bissau 0 0 1 0 1

Lesotho 0 0 4 2 6 Total 0 0 182 124 306

Liberia 0 0 4 2 6 Asia

Madagascar 0 0 4 2 6 China 0 0 380 491 871

Mozambique 0 0 4 2 6 South Korea 0 0 15 60 75

Cote d'Ivoire 0 0 3 2 5 Singapore 0 0 14 21 35

Niger 0 0 3 2 5 India 0 0 15 16 31

Sierra Leone 0 0 2 3 5 Indonesia 0 0 7 23 30

Swaziland 0 0 3 2 5 Japan 0 0 11 15 26

Algeria 0 0 3 1 4 Malaysia 0 0 10 14 24

Burundi 0 0 2 2 4 Thailand 0 0 8 10 18

Year Year

2013 2014 2015 2016 Total 2013 2014 2015 2016 Total

Asia Europe - Eastern Taiwan 0 0 5 7 12 Bosnia & Herzegovina 0 0 2 1 3 Hong Kong 0 0 5 4 9 Armenia 0 0 1 1 2

Mongolia 0 0 4 5 9 Macedonia 0 0 1 1 2

Philippines 0 0 4 3 7 Moldova 0 0 1 1 2

Pakistan 0 0 3 3 6 Kosovo 0 0 1 0 1

Vietnam 0 0 1 3 4 Total 0 3 34 43 80

Cambodia 0 0 0 3 3 Europe - Western

Nepal 0 0 1 2 3 UK 47 35 55 55 192

Sri Lanka 0 0 2 1 3 France 0 23 47 25 95

Vanuatu 0 0 1 1 2 Germany 0 28 29 29 86

Guam 0 0 1 3 4 Spain 0 28 22 19 69

Kazakhstan 0 0 0 1 1 Netherlands 0 27 22 17 66

Timor-Leste 0 0 1 0 1 Italy 0 4 24 20 48

Total 0 0 488 686 1174 Poland 0 11 8 9 28

Australia and New Zealand Finland 0 4 8 12 24

Australia 0 0 29 29 58 Switzerland 0 4 11 8 23

New Zealand 0 0 11 12 23 Czech Republic 0 2 9 10 21

Total 0 0 40 41 81 Belgium 0 5 6 9 20

Europe - Eastern Estonia 0 4 7 8 19

Georgia 0 1 4 4 9 Austria 0 3 7 8 18

Russia 0 0 4 4 8 Sweden 0 3 6 9 18

Slovakia 0 1 2 5 8 Denmark 0 1 9 5 15

Turkey 0 1 3 4 8 Lithuania 0 0 4 10 14

Belarus 0 0 3 4 7 Norway 0 3 2 9 14

Slovenia 0 0 3 4 7 Romania 0 2 7 4 13

Bulgaria 0 0 2 4 6 Greece 0 1 5 5 11

Croatia 0 0 2 4 6 Latvia 0 0 5 5 10

Serbia 0 0 2 2 4 Hungary 0 1 4 3 8

Ukraine 0 0 2 2 4 Portugal 0 1 3 4 8

Albania 0 0 1 2 3 Iceland 0 1 3 2 6

Year Year

2013 2014 2015 2016 Total 2013 2014 2015 2016 Total

Europe - Western South America Ireland 0 1 3 2 6 Colombia 0 0 5 20 25 Malta 0 0 2 4 6 Argentina 0 0 6 11 17

Cyprus 0 1 2 1 4 Chile 0 0 6 10 16

Monaco 0 0 1 2 3 Peru 0 0 4 7 11

Andorra 0 0 1 1 2 Uruguay 0 0 2 4 6

Montenegro 0 0 1 1 2 Ecuador 0 0 2 3 5

Luxembourg 0 0 0 1 1 Nicaragua 0 0 2 3 5

Total 47 193 313 297 850 Costa Rica 0 0 2 2 4

Middle East Dominican Republic 0 0 2 2 4

Israel 0 0 10 5 15 Haiti 0 0 2 2 4

United Arab Emirates 0 0 7 7 14 Paraguay 0 0 2 2 4

Iran 0 0 6 4 10 Guam 0 0 1 3 4

Lebanon 0 0 6 3 9 Panama 0 0 1 2 3

Jordan 0 0 6 2 8 Puerto Rico 0 0 2 1 3

Palestine 0 0 4 2 6 Anguilla 0 0 1 1 2

Iraq 0 0 4 1 5 Belize 0 0 1 1 2

Kuwait 0 0 3 0 3 Bolivia 0 0 1 1 2

Syria 0 0 3 0 3 Cuba 0 0 1 1 2

Bahrain 0 0 1 1 2 Dominica 0 0 1 1 2

Yemen 0 0 1 1 2 Guatemala 0 0 0 2 2

Qatar 0 0 1 0 1 Honduras 0 0 1 1 2

Saudi Arabia 0 0 0 1 1 Venezuela 0 0 1 1 2

Total 0 0 52 27 79 Virgin Islands (U.S.) 0 0 1 1 2

North America Barbados 0 0 1 0 1

United States 0 1 122 73 196 Curacao 0 0 1 0 1

Mexico 0 0 14 32 46 El Salvador 0 0 0 1 1

Canada 0 0 23 14 37 Jamaica 0 0 1 0 1

Total 0 1 159 119 279 Suriname 0 0 1 0 1

South America Total 0 0 59 87 172

Brazil 0 0 14 24 38 Number of platforms 47 197 1,327 1,424 3,021

Number of countries 1 27 156 145 161

Appendix C.

This table lists the number of unique countries, the number of platforms, and the volume of business averaged across 2015-2016. Platforms are sorted by region, number of platforms and crowdfunding volume.

Country Developed

market Civil Law?

Common Law?

Muslim Law?

Number of

platforms

Crowdfunding

volume (in $millions)

Business finance volume

(in $millions)

Consumer finance

volume (in $millions)

Africa

South Africa N N Y N 11 24.28 20.26 4.02 Kenya N N Y N 10 14.41 1.93 12.48 Nigeria N N Y N 8 21.88 18.52 3.37

Ghana N N Y N 7 3.62 1.18 2.45

Egypt N N N Y 6 3.65 3.08 0.58 Senegal N N Y N 6 2.21 0.02 2.20

Morocco N N N Y 6 0.43 0.06 0.37

Uganda N N Y N 5 4.33 0.32 4.01 Cameroon N N Y N 5 3.73 3.06 0.68

Tanzania N N Y N 5 2.14 0.35 1.79

Mali N N Y N 4 2.23 0.40 1.83 Zambia N N Y N 4 1.33 0.25 1.08

Zimbabwe N N Y N 4 1.01 0.19 0.83

Burkina Faso N Y N N 4 0.98 0.01 0.97 Malawi N N Y N 4 0.89 0.31 0.57

Namibia N N Y N 4 0.26 0.03 0.23 Rwanda N N Y N 3 4.79 0.16 4.62 Madagascar N N Y N 3 0.84 0.03 0.81

Ethiopia N Y N N 3 0.51 0.14 0.37

Lesotho N N Y N 3 0.18 0.01 0.17 Congo Dem. Rep. N Y N N 3 2.62 0.02 2.60 Sierra Leone N N Y N 3 0.92 0.03 0.89

Tunisia N N N Y 3 0.30 0.04 0.26 Swaziland N N Y N 3 0.08 0.00 0.07

Burundi N Y N N 2 0.63 0.00 0.62

Mozambique N Y N N 2 0.62 0.02 0.60 Togo N N Y N 2 0.41 0.00 0.41

Mauritania N N N Y 2 0.18 0.00 0.18 Algeria N N N Y 2 0.15 0.01 0.14 Liberia N N Y N 2 0.13 0.02 0.11 Mauritius N Y N N 2 0.08 0.01 0.08

Somalia N N Y N 2 0.08 0.00 0.08 Cote d'Ivoire N Y N N 2 1.97 1.96 0.01

Congo Rep. N Y N N 2 0.10 0.00 0.10 Niger N N Y N 2 0.01 0.00 0.00 South Sudan N N N Y 1 0.04 - 0.04 Gambia N N N Y 1 0.02 0.01 0.01

Angola N Y N N 1 0.01 0.01 0.01

Benin N Y N N 1 0.01 0.00 0.01

Cape Verde N Y N N 1 0.01 0.00 0.00 Guinea N Y N N 1 0.00 0.00 0.00

Botswana N N Y N 1 0.00 0.00 0.00

Seychelles N N Y N 1 0.00 0.00 0.00 Central African Republic N Y N N 1 0.00 0.00 0.00 Guinea-Bissau N Y N N 1 0.00 0.00 0.00

Gabon N Y N N 1 0.00 0.00 0.00

Australia and New Zealand

Australia Y N Y N 32 476.45 330.48 145.97 New Zealand Y N Y N 15 245.51 17.68 227.83

Asia

China N Y N N 504 169,007.54 70,363.08 98,644.46

South Korea Y Y N N 39 208.74 37.23 171.52 Singapore Y N Y N 19 101.75 95.10 6.65 India N N Y N 16 82.04 43.18 38.86

Japan Y Y N N 16 379.34 366.52 12.82 Indonesia N Y N N 16 18.81 12.87 5.94 Malaysia N N Y N 13 5.82 3.17 2.66

Thailand N N Y N 9 2.38 0.57 1.81

Taiwan N Y N N 7 32.65 2.80 29.86 Hong Kong Y N Y N 5 6.70 4.01 2.70 Mongolia N Y N N 4 0.33 0.06 0.27

Cambodia N Y N N 3 4.51 4.19 0.33 Philippines N N Y N 3 0.16 0.06 0.11

Pakistan N N Y N 3 0.10 0.01 0.09

Vietnam N Y N N 2 0.06 0.02 0.04 Nepal N N Y N 2 0.12 0.04 0.08

Sri Lanka N N Y N 2 0.02 0.01 0.01

Vanuatu N N Y N 1 0.01 0.00 0.00 Kazakhstan N Y N N 1 0.01 0.00 0.00

Europe - Eastern

Georgia N Y N N 4 55.79 0.02 55.77

Russia N Y N N 4 6.71 3.91 2.80

Slovenia N Y N N 4 3.57 2.31 1.26 Slovakia N Y N N 4 3.05 0.22 2.83 Bulgaria N Y N N 4 1.45 0.27 1.18

Turkey N Y N N 4 0.79 0.20 0.59 Belarus N Y N N 4 0.09 0.03 0.07

Croatia N Y N N 3 0.27 0.09 0.18

Serbia N Y N N 3 0.20 0.07 0.13 Ukraine N Y N N 2 0.66 0.23 0.43

Bosnia & Herzegovina N Y N N 2 0.03 0.01 0.02

Albania N Y N N 2 0.02 0.01 0.01

Armenia N Y N N 1 0.38 0.13 0.25 Macedonia N Y N N 1 0.07 0.03 0.05

Moldova N Y N N 1 0.03 0.01 0.02

Kosovo N Y N N 1 0.02 0.01 0.01

Europe - Western

UK Y N Y N 87 5,829.85 4,167.36 1,662.49 France Y Y N N 47 433.56 203.73 229.83

Germany Y Y N N 35 325.15 109.35 215.80

Spain Y Y N N 31 101.28 77.51 23.77 Italy Y Y N N 27 88.11 55.43 32.67

Netherlands Y Y N N 24 172.31 156.79 15.51

Finland Y Y N N 11 115.86 58.53 57.33 Switzerland Y Y N N 11 25.88 11.27 14.61

Czech Republic N Y N N 10 22.55 10.93 11.62 Estonia N Y N N 10 63.85 28.45 35.40 Denmark Y Y N N 9 62.58 33.44 29.14

Poland N Y N N 9 26.91 4.64 22.27 Austria Y Y N N 9 19.14 16.40 2.74 Sweden Y Y N N 9 55.03 45.91 9.12

Belgium Y Y N N 9 52.80 50.58 2.23

Lithuania N Y N N 8 16.12 3.25 12.87 Latvia N Y N N 6 23.93 3.08 20.86 Norway Y Y N N 6 3.47 1.94 1.53

Greece N Y N N 6 2.00 0.54 1.46 Romania N Y N N 6 1.04 0.22 0.82

Portugal Y Y N N 5 3.38 2.45 0.93

Hungary N Y N N 4 0.43 0.11 0.32 Ireland Y N Y N 3 43.73 42.56 1.17

Iceland Y Y N N 3 1.06 0.46 0.60

Malta N N Y N 3 0.12 0.04 0.08 Monaco Y Y N N 2 1.28 1.04 0.24

Cyprus N N Y N 2 0.07 0.02 0.05 Luxembourg N Y N N 1 0.17 0.06 0.11 Montenegro N Y N N 1 0.01 0.01 0.01

Andorra Y Y N N 1 0.00 0.00 0.00

Middle East

Israel Y N Y N 9 131.27 105.19 26.07

United Arab Emirates N N N Y 8 21.71 21.28 0.43 Lebanon N Y N N 5 4.31 0.27 4.03

Iran N N N Y 5 0.16 0.06 0.10

Jordan N N N Y 5 3.67 2.51 1.16 Palestine N N N Y 3 3.87 0.12 3.75

Iraq N N N Y 3 0.03 0.00 0.03 Syria N N N Y 2 0.00 0.00 0.00

Qatar N N N Y 1 5.00 - 5.00 Yemen N N N Y 1 0.22 - 0.22

Saudi Arabia N N N Y 1 0.03 0.03 -

Kuwait N N N Y 1 0.01 0.01 - Bahrain N N N Y 1 0.00 0.00 0.00

North America

United States Y N Y N 117 35,356.36 7,845.67 27,510.68

Mexico N Y N N 24 63.68 38.59 25.10

Canada Y N Y N 22 270.73 132.93 137.80

South America

Brazil N Y N N 20 56.78 21.23 35.54 Colombia N Y N N 12 65.72 52.66 13.06

Argentina N Y N N 10 11.07 1.74 9.32 Chile N Y N N 8 72.67 71.99 0.68 Peru N Y N N 6 5.06 4.64 0.42

Uruguay N Y N N 3 0.59 0.21 0.38 Ecuador N Y N N 3 2.24 2.21 0.03 Nicaragua N Y N N 3 1.08 1.03 0.05

Guam N N Y N 3 0.11 0.04 0.07

Paraguay N Y N N 2 4.78 4.77 0.01 Guatemala N Y N N 2 3.50 3.50 0.00 Haiti N Y N N 2 0.14 0.05 0.09

Costa Rica N Y N N 2 0.11 0.04 0.07 Dominican Republic N Y N N 2 0.08 0.03 0.05

Panama N Y N N 2 0.46 0.02 0.44

Puerto Rico N N Y N 2 0.09 0.06 0.03 Curacao N Y N N 1 14.26 14.26 -

El Salvador N Y N N 1 4.85 4.85 -

Bolivia N Y N N 1 2.54 2.53 0.01 Cuba N Y N N 1 0.18 0.06 0.12

Virgin Islands (U.S.) N N Y N 1 0.10 0.03 0.06 Jamaica N N Y N 1 0.05 0.02 0.03 Barbados N N Y N 1 0.02 0.01 0.01

Venezuela N Y N N 1 0.01 0.00 0.01

Suriname N Y N N 1 0.01 0.00 0.00 Honduras N Y N N 1 0.01 0.00 0.00 Dominica N N Y N 1 0.00 0.00 0.00

Belize N N Y N 1 0.00 0.00 0.00 Anguilla N N Y N 1 0.00 0.00 0.00

Figure 1. The number of platforms reporting crowdfunding business globally

Figure 2. The global volume of crowdfunding (in 2015 US$)

Figure 3. Log(volume of crowdfunding per capita) in 2015 US$