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; [email protected]. 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
- 1 -
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.
- 2 -
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).
- 3 -
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)
- 4 -
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.
- 5 -
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).
- 6 -
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.
- 7 -
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
- 8 -
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.
- 9 -
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.
- 10 -
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 $7.6 Billion in Ponzi Scheme, China Says”, New York Times, February 2, 2016, page B3.) Ezubao is not in the sample. 12 Other examples include Homestrings.com, bettervest GmbH, Funding Circle, OurCrowd, greenvesting.com, Lendico, Emerging Crowd, Crowdcube, HelpingB, Planeta, Kickstarter, Indiegogo, and Kiva.
- 11 -
the funder country. For loans with a platform guarantee, the legal regime governing the platform
location is likely to be important. However, since the survey data does not distinguish the
proportion of foreign investors from the proportion of domestic investors in the platform’s country,
in a robustness check, I remove multi-country platforms from the sample. The results are
qualitatively similar.
Whenever necessary, the research team validated responses by clarifying ambiguous responses
or by requiring more detailed data breakdowns in various geographies from the platforms. Finally,
the data was anonymized by deleting all platform-identifying information. For all average data
points (e.g. funder sophistication), weightings (by transaction volume) were applied and
significant apparent outliers were removed. To this data, I make a few additional judgment calls
for further classification.13
I construct several dependent variables from this data. I first aggregate individual platform
crowdfunding values to obtain country values. Dollar values are obtained directly from the survey
since most platforms, except for European, British, and Chinese platforms, were asked to convert
their volumes into US$ 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.
- 12 -
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/).
- 13 -
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).
- 14 -
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.
- 15 -
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.
- 16 -
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,
- 17 -
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
- 18 -
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/).
- 19 -
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.
- 20 -
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.
- 21 -
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
- 22 -
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
- 23 -
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.
- 24 -
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
- 25 -
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
- 26 -
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 -
References
Aghion, Philippe, and Patrick Bolton, 1992, An incomplete contracts approach to corporate
bankruptcy, Review of Economic Studies 59, 473-494.
Aker, Jenny C., and Isaac M. Mbiti, 2010, Mobile phones and economic development in Africa,
Journal of Economic Perspectives 24, 207-32.
Arner, Douglas W., Ross P. Buckley, and Weihuan Zhou, 2015, Regulation of Digital Financial
Services in China: Last mover advantage, Tsinghua China Law Review 8(1) 25-62.
Box, G. E. P. and D. R. Cox, 1964, An analysis of transformations, Journal of the Royal Statistics
Society, Series B, 26, 211–234.
Bruton, Garry, Susanna Khavul, Donald Siegel, and Mike Wright, 2015, New financial alternatives
in seeding entrepreneurship: Microfinance, crowdfunding, and peer-to-peer innovations,
Entrepreneurship Theory and Practice 39, 9-26.
Burgess, Robin, and Rohini Pande, 2005, Do rural banks matter? Evidence from the Indian social
banking experiment, American Economic Review, 95, 780-95.
Čihák, Martin, Aslı Demirgüç-Kunt, Erik Feyen, and Ross Levine, 2012, Benchmarking
financial systems around the world, Policy Research working paper WPS 6175, Washington,
DC: World Bank (available at
http://documents.worldbank.org/curated/en/868131468326381955/Benchmarking-financial-
systems-around-the-world).
Claessens, Stijn and Luc Laeven, 2004, What drives bank competition? Some international
evidence, Journal of Money, Credit and Banking, 36, 563-83.
Demirgüç-Kunt, Aslı, and Vojislav Maksimovic, 2002, Funding growth in bank-based and
market-based financial systems: Evidence from firm-level data, Journal of Financial
Economics 65, 337-363.
Demyanyk, Yuliya, Elena Loutskina, and Daniel Kolliner, 2017, The taste of peer-to-peer loans,
working paper, Federal Reserve Bank of Cleveland.
- 31 -
Dittmar, Amy K., Jan Mahrt-Smith, and Henri Servaes, 2003, International corporate governance
and corporate cash holdings, Journal of Financial and Quantitative Analysis 38, 111-134.
Djankov, Simeon, Oliver Hart, Caralee McLiesh, and Andrei Shleifer, 2008, Debt enforcement
around the world, Journal of Political Economy 116, 1105-1149.
Djankov, Simeon, Rafael La Porta, Florencio Lopez-De-Silanes, and Andrei Shleifer, 2002, The
regulation of entry, Quarterly Journal of Economics 117, 1-37.
Dushnitsky, Gary, Massimiliano Guerini, Evila Piva, and Cristina Rossi-Lamastra, 2016,
Crowdfunding in Europe: Determinants of platform creation across countries, California
Management Review 58, 44-71.
Franks, Julian, Nicolas Serrano-Velarde, and Oren Sussman, 2018, Marketplace lending,
information aggregation, and liquidity, Review of Financial Studies, forthcoming.
Gow, Ian D., Gaizka Ormazabal, and Daniel J. Taylor, 2010, Correcting for cross‐sectional and
time‐series dependence in accounting research, Accounting Review 85, 483-512.
Haddad, Christian and Lars Hornuf, 2016, The emergence of the global fintech market:
Economic and technological determinants, CESifo Working Paper Series No. 6131 (available
at https://ssrn.com/abstract=2830124).
Hart, Oliver, and John Moore, 1994, A theory of debt based on the inalienability of human
capital, Quarterly Journal of Economics 109, 841-879.
Hart, Oliver, and John Moore, 1998, Default and renegotiation: A dynamic model of debt,
Quarterly Journal of Economics 113, 1-42.
Havrylchyk, Olena, Carlotta Mariotto, Talal Rahim, and Marianne Verdier, 2016, What drives
the expansion of the peer-to-peer lending?, working paper, University of Lille (available at
http://dx.doi.org/10.2139/ssrn.2841316).
Hofstede, Geert, 1980, Culture’s consequences: International differences in work-related values,
Thousand Oaks: Sage Publications.
Hornuf, Lars and Schwienbacher, Armin, 2016, Should securities regulation promote equity
crowdfunding?, working paper, University of Trier (available at
http://dx.doi.org/10.2139/ssrn.2412124).
- 32 -
Iyer, Rajkamal, Asim Ijaz Khwaja, Erzo F. P. Luttmer, and Kelly Shue, 2015, Screening peers
softly: Inferring the quality of small borrowers, Management Science 62, 1554-1557.
Johnson, Margaret A., 1998, An overview of basic issues facing microenterprise practices in the
United States, Journal of Developmental Entrepreneurship 3, 5-21.
Kaufmann, Daniel, Aart Kraay, and Massimo Mastruzzi, 2010, The Worldwide Governance
Indicators: Methodology and analytical issues, Policy Research Working Paper No. 5430,
Washington, DC: World Bank (available at: https://ssrn.com/abstract=1682130).
King, Robert G., and Ross Levine, 1993, Finance and growth: Schumpeter might be right,
Quarterly Journal of Economics 108, 717-737.
La Porta, Rafael, Florencio Lopez-de-Silanes, Andrei Shleifer, and Robert W. Vishny, 1998,
Law and finance, Journal of Political Economy 106, 1113-1155.
La Porta, Rafael, Florencio Lopez-de-Silanes, Andrei Shleifer, and Robert W Vishny, 2000,
Agency problems and dividend policies around the world, Journal of Finance 55, 1-33.
Levine, Ross, 2005, Finance and Growth: Theory and Evidence, In Handbook of Economic
Growth, Philippe Aghion and Steven Durlauf (eds), Elsevier Science: The Netherlands.
Li, Emma, and J. Spencer Martin, 2016, Capital formation and financial intermediation: The role
of entrepreneur reputation formation, Journal of Corporate Finance, forthcoming.
Lin, Mingfeng, Nagpurnanand R. Prabhala, and Siva Viswanathan, 2013, Judging borrowers by
the company they keep: Friendship networks and information asymmetry in online peer-to-peer
lending, Management Science 59, 17-35.
Long, J. Scott, 1997, Regression models for categorical and limited dependent variables,
Thousand Oaks, CA: Sage Publications.
Masssolution, 2015, 2015CF: The crowdfunding industry report, 2015.
Michels, Jeremy, 2012, Do unverifiable disclosures matter? Evidence from peer-to-peer lending,
The Accounting Review 87, 1385-1413.
Mollick, Ethan, and Ramana Nanda, 2016, Wisdom or madness? Comparing crowds with expert
evaluation in funding the arts, Management Science 62, 1533-1553.
- 33 -
Nagelkerke, N.J.D., 1991, A note on a general definition of the coefficient of determination,
Biometrika 78, 691-692.
Nash, Bryan and Ajay Patel, 2018, Instrumental variables analysis and the role of national culture
in corporate finance, working paper, Wake-Forest University.
Philippon, Thomas, 2015, Has the US finance industry become less efficient? On the theory and
measurement of financial intermediation, American Economic Review 105, 1408-38.
Pigou, Arthur C., 1938, The Economics of Welfare, 4th ed., London: Macmillan and Co.
Rajan, Raghuram, and Luigi Zingales, 1998, Financial dependence and growth, American
Economic Review 88, 559-586.
Rajan, Raghuram G., and Luigi Zingales, 2003, The great reversals: The politics of financial
development in the twentieth century, Journal of Financial Economics 69, 5-50.
Rubanov, Pavlo, Tetiana Vasylieva, Serhiy Lyeonov, and Svitlana Pokhylko, 2019, Cluster
analysis of development of alternative finance models depending on the regional affiliation of
countries, Business and Economic Horizons 15, 90-106.
Schwartz, Shalom H., 2006, Value Orientations: Measurement, antecedents and consequences
across nations, in R. Jowell, C. Roberts, R. Fitzgerald, and G. Eva (eds.) Measuring attitudes
cross-nationally - Lessons from the European social survey, London: Sage Publications.
Shleifer, Andrei and Robert W. Vishny, 2002, The Grabbing Hand: Government pathologies and
their cures, MA: Harvard University Press.
Stigler, George J., 1971, The theory of economic regulation, Bell Journal of Economics and
Management Science 2, 3-21.
Stiglitz, Joseph E., and Andrew Weiss, 1981, Credit rationing in markets with imperfect
information, American Economic Review 71, 393-410.
Thompson, Samuel B., 2011, Simple formulas for standard errors that cluster by both firm and
time, Journal of Financial Economics 99, 1-10.
Thürridl, Carina, and Bernadette Kamleitner, 2016, What goes around comes around? Rewards as
strategic assets in crowdfunding, California Management Review 58, 88-110.
- 34 -
White, Halbert, 1980, A heteroskedasticity-consistent covariance matrix estimator and a direct test
for heteroskedasticity. Econometrica 48, 817–838.
Zhang, Juanjuan, and Peng Liu, 2012, Rational herding in microloan markets, Management
Science 58, 892-912.
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$