The emergence of the global fintech market: economic and technological determinants
The emergence of the global fintech market: economic and technological determinants
Christian Haddad 0 1 3 4 5
Lars Hornuf 0 1 3 4 5
0 C. Haddad SKEMA Business School , Lille , France
1 C. Haddad University of Lille Nord de France , LSMRC, 1 place Déliot-BP381, 59020 Lille Cédex , France
2 Faculty of Business Studies and Economics, University of Bremen , Wilhelm-Herbst-Str. 5, 28359 Bremen , Germany
3 JEL Classification K00 . L26 . O3
4 L. Hornuf CESifo , Poschingerstr. 5, 81679 Munich , Germany
5 L. Hornuf Max Planck Institute for Innovation and Competition , Marstallplatz 1, 80539 Munich , Germany
We investigate the economic and technological determinants inducing entrepreneurs to establish ventures with the purpose of reinventing financial technology (fintech). We find that countries witness more fintech startup formations when the economy is well-developed and venture capital is readily available. Furthermore, the number of secure Internet servers, mobile telephone subscriptions, and the available labor force has a positive impact on the development of this new market segment. Finally, the more difficult it is for companies to access loans, the higher is the number of fintech startups in a country. Overall, the evidence suggests that fintech startup formation need not be left to chance, but active policies can influence the emergence of this new sector.
Fintech; Entrepreneurship; Startups; Financial institutions
Why do some countries have more startups intended to
change the financial industry through innovative services
and digitalization than others? For example, in certain
economies, there has been a large demand for financial
technology (fintech) innovations, while other countries
have made a more benevolent economic and regulatory
environment available. In this article, we investigate
several economic and general technological determinants that
have encouraged fintech startup formations in 55
countries. We find that countries witness more fintech startup
formations when the economy is well-developed and
venture capital is readily available. Furthermore, the
number of secure Internet servers, mobile telephone
subscriptions, and the available labor force has a positive impact
on the development of this new market segment. Finally,
the more difficult it is for companies to access loans, the
higher is the number of fintech startups in a country.
Prior research on fintech mostly focuses on specific
fintech sectors. In the area of crowdlending, scholars
have analyzed the geography of investor
and Viswanathan 2015
), the likelihood of loan defaults
(Serrano-Cinca et al. 2015; Iyer et al. 2016)
investors’ privacy preferences when making an investment
Burtch et al. 2015
). In equity crowdfunding
and reward-based crowdfunding, researchers have
investigated the dynamics of success and failure among
, the determinants
of funding success
(Ahlers et al. 2015; Hornuf and
Schwienbacher 2017a, 2017b; Vulkan et al. 2016)
the regulation of equity crowdfunding
. More generally,
et al. (2016)
investigate the determinants of early-stage
investments on AngelList. They find that the average
investor reacts to information about the founding team,
but not startup traction or existing lead investors.
Recently, scholars have also investigated platform
design principles and risk and regulatory issues related to
virtual currencies such as Bitcoin or Ethereum (
et al. 2015
Gandal and Halaburda 2016
) and the
. Others have analyzed social
(Doering et al. 2015)
), and mobile payment and e-wallet services
(Mjølsnes and Rong 2003; Mallat et al. 2004; Mallat
. To date, only a few studies have investigated the
fintech market in its entirety.
Dushnitsky et al. (2016)
provide a comprehensive overview of the European
crowdfunding market and conclude that legal and
cultural traits affect crowdfunding platform formation.
Cumming and Schwienbacher (2016)
capitalist investments in fintech startups around the world.
They attribute venture capital deals in the fintech sector to
the differential enforcement of financial institution rules
among startups versus large established financial
institutions after the financial crisis.
In this article, we investigate the formation of fintech
startups more generally, rather than focusing on one
particular fintech business model. In line with recent
(Ernst & Young 2016; He et al. 2017;
World Economic Forum 2017)
, we categorize fintechs
into nine different types of startups: those that engage in
financing, payment, asset management, insurance
(insurtechs), loyalty programs, risk management,
exchanges, regulatory technology (regtech), and other
business activities. Table 1 provides a definition for each
fintech category we investigate in this article.
The remainder of the article proceeds as follows:
Section 2 introduces our hypotheses. In Section 3, we
describe the data and introduce the variables used in the
quantitative analysis. Section 4 presents the descriptive
and multivariate results. Finally, Section 5 summarizes
our contribution and derives policy implications.
To derive testable hypotheses regarding the drivers of
fintech startup formations, we regard fintech
innovations and the resulting startups as the outcome of supply
and demand for this particular type of entrepreneurship
in the economy. The demand for fintech startups is the
number of entrepreneurial positions that can be filled by
fintech innovations in an economy
Choi and Phan 2006)
. If the business model and services
provided by the traditional financial industry, for
example, are essentially obsolete, there might be a larger
demand for new and innovative startups. The supply
of fintech startups, in contrast, consists of the
entrepreneurs who are ready to undertake self-employment
(Choi and Phan 2006). Such a supply might be driven
by a large number of investment bankers who lost their
jobs after the financial crises and are now eager to use
their finance skills in a related and promising financial
First, we conjecture that the more developed the
economy and traditional capital market, the higher the
demand for fintech startups. This hypothesis works
through two channels. As in any other startup, fintech
startups need sufficient financing to develop and expand
their business models. If traditional and venture capital
markets are well-developed, entrepreneurs have better
access to the capital required to fund their business.
Although small business financing traditionally does
not take place through regular capital markets, fintech
startups might be eligible to receive funds from
incubators or accelerators established by the traditional
financial sector.1 However, such programs have mostly been
established by large players located in well-developed
economies. Moreover, the more developed the
economy, the more likely it is that individuals need services
such as asset management or financial education tools.
Black and Gilson (1999)
note that active stock
1 See, for example, the Main Incubatur from German Commerzbank
AG (https://www.main-incubator.com), the Barclays Accelerator in the
UK (http://www.barclaysaccelerator.com), or the US-based J.P.
Morg a n I n - H o u s e I n c u b a t o r ( h t t p s : / / w w w . j p m o r g a n .
We classify fintech startups as asset management companies if they offer services such as robo-advice,
social trading, wealth management, personal financial management apps, or software.
We categorize startups as exchanges if they provide financial or stock exchange services, such as securities,
derivatives, and other financial instrument trading.
The category financing entails, for example, startups that provide crowdfunding, crowdlending, microcredit,
and factoring solutions.
The category insurance entails, for example, startups that broker peer-to-peer insurance, spot insurance,
usage-driven insurance, insurance contract management, and brokerage services as well as claims and
risk management services.
We also consider startups that provide loyalty program services to customers, because they often use big
data analytics and are closely linked to payment transactions. The category loyalty program involves, for
example, startups providing rewards for brand loyalty or giving customers advanced access to new products,
special sales coupons, or free merchandise.
A bulk of fintech startups offer investor education and training, innovative background services (e.g., near-field
communication systems, authorization services), white-label solutions for various business models, or other
technical advancements classified under other business activities of fintech startups.
The category payment entails business models that provide new and innovative payment solutions, such as
mobile payment systems, e-wallets, or crypto currencies.
We classify fintech startups as regulatory technology companies if they offer services based on technology in
the context of regulatory monitoring, reporting, and compliance benefiting the finance industry.
The category risk management contains startups that provide services that help companies better assess the
financial reliability of their counterparties or better manage their own risk.
markets help venture capital and, thus, entrepreneurship
to prosper, because venture capitalists can exit
successful portfolio companies through initial public offerings.
Active stock markets might therefore have a positive
effect on fintech startup formations.
In the case of firms that aim to revolutionize the
financial industry, a well-developed capital market
might also prompt demand for entrepreneurship simply
because a larger financial market also offers greater
potential to change existing business models through
innovative services and digitalization. If the financial
sector is small, not much can be changed through the
introduction of innovative business models. Thus, for a
well-developed but technically obsolescent financial
sector, there are more entrepreneurial positions that can
be filled by fintech innovators. We therefore
hypothesize the following:
Hypothesis 1: Fintech startup formations occur more
frequently in countries with well-developed economies
and capital markets.
A second driver of fintech demand is the extent to
which the latest technology is available in an
economy so that fintech startups can build their
business models on these technologies. Technical
advancements are among the most important drivers
(Dosi 1982; Arend 1999)
because technological revolutions generate
opportunities that may be further developed by entrepreneurial
firms (Stam and Garnsey 2007). Technological
changes enable new practices and business models
to emerge and, in the case of fintech startups, disrupt
the traditional financial services sector. Such
technology-driven changes have in the past occurred
with the move from banking branches to ATM
machines and from ATM machines to telephone and
(Singh and Komal 2009; Puschmann
. Moreover, modern computer-based
technology has widely been used in financial markets
through the implementation of trading algorithms
(Government Office for Science2015). More
generally, many technologies can be accessed through
cloud servers or across multiple vendors or might
even be downloadable as open source software.
Geographic boundaries are increasingly teared
down, and as a result access to supporting
infrastructure such as broadband networks might be of
crucial importance for the emergence of fintech in a
Furthermore, the almost inconceivable growth in
mobile and smartphone usage is placing digital
services in the hands of consumers who previously
could not be reached, delivering richer,
valueadded experiences across the globe. Mobile payment
services differ across regions and countries. Many
users are registered in developing countries where
financial institutions are difficult to access
and Young 2014)
. The prime example of a fintech
that delivers access to essential financial services
through the usage of mobile phones is M-Pesa.
MPesa was launched in 2007 and offers various
financial services such as saving, sending, and receiving
remittances, as well as the direct purchasing of
products and services even when people do not
possess a bank account
(Jack and Suri 2011)
M-Pesa has extended its market across Africa,
Europe, and Asia, reaching 25.3 million active
customers in March 2016
In emerging countries, mobile money has served
as a replacement for formal financial institutions,
and as a result mobile money penetration now
outstrips bank accounts in several emerging countries
(GSMA 2015; PricewaterhouseCoopers 2016)
According to a study conducted among 36,000 online
consumers, the number of Europeans regularly using
a mobile phone device for payments has also tripled
since 2015 (54 vs. 18%) (Visa 2016). The study
found this trend to hold for 19 European countries,
revealing a big shift in customers’ attitudes toward
this new technology. New technology has enabled
fintech startups in developed countries to disrupt
established players and accelerate change.
Technologies such as near-field communication, QR codes,
and Bluetooth Low Energy are being used for retail
point-of-sale and mobile wallet transactions, transit
payments, and retailer loyalty schemes
. Fintech startups largely rely on
advanced new technologies to implement faster
payment services, to offer easy operations to their
customers, to improve the sharing of information, and
generally to cut the costs of banking transactions.
We therefore argue that the better the supporting
infrastructure, the higher is the supply of fintech
startups, as individuals who are seeking
entrepreneurial activity based on these technologies have
more opportunities to succeed.
Hypothesis 2: Fintech startup formations occur more
frequently in countries where the latest technology and
supporting infrastructure are readily available.
A third factor on the demand side of fintech startup
formations concerns the soundness of traditional
financial institutions. The sudden upsurge of fintech startups,
especially in the financing domain, can be partly
attributed to the 2008 global financial crisis (Koetter and
). Moreover, a recent IMF study
(He et al.
shows that market valuations of public fintech
firms have quadrupled since the global financial crisis,
outperforming many other sectors. The financial crisis
may have fostered the demand for fintech startups for
several reasons. There is a widespread lack of trust in
fter the crisis. Guiso et al. (2013
customers’ trust in banks during the financial crisis
and find that the lack of trust also led to strategic defaults
on mortgatges, possibly initiating a vicious circle of
customer distrust, defaults on morgages, even less sound
banks, and again more customer distrust. Fintech
startups, which largely have a clean record, might
benefit from the lack of confidence in traditional banks and
break the vicious circle of distrust and reduced financial
The financial crisis also increased the cost of debt for
many small firms, and in some cases banks stopped
lending money to businesses altogether, forcing them
to contend with refusals on credit lines or bank loans
(Schindele and Szczesny 2016; Lopez de Silanes et al.
. Fintech startups in the area of crowdlending,
crowdfunding, and factoring aim to fill this gap.
) provide convincing evidence
that when bank are stressed, companies are more likely
to use equity crowdfunding as an alternative source of
external finance. The demand for fintech should thus be
particularly high in countries that have extensively
suffered from the financial crises and where the banking
sector is less sound. Finally, some of the fintech business
models are based on exemptions from securities
regulation and would not work under the somewhat more strict
securities regulation that applies to large firms
and Schwienbacher 2017c)
. Stringent financial
regulation was the outcome of the spread of systemic risk to
the financial system (Brunnermeier et al. 2012). Thus,
economies with a more fragile banking sector and
stricter regulation should see more fintech startup
formations that use the existing exemptions from banking
and securities laws.
Hypothesis 3: Fintech startup formations occur more
frequently in countries with a more fragile financial
Fourth, on the supply side, we consider the role of
the credit and labor market as well as business
regulation in fintech startup formations. Economies that
aim to promote entrepreneurship and talent generally
adopt a supportive regulatory regime to attract
entrepreneurs. Individuals are more likely to undertake
self-employment if the extent to which credit is
supplied to the private sector is larger and there are no
controls on interest rates that interfere with the credit
market. Moreover, for hiring talented individuals for
fintech startups, a country should allow market forces
to determine wages and establish the conditions that
enable startups to easily hire and fire employees. By
contrast, cumbersome administrative requirements,
large bureaucratic costs, and the high cost of tax
compliance might hamper any entrepreneurial
Armour and Cumming (2008)
highlight the importance of bankruptcy laws to
entrepreneurial activities and evidence that more
favorable bankruptcy laws have a positive impact on
selfemployment. Thus, we conjecture that the quality of
credit and labor market as well as business regulation
should have a significant impact on fintech startup
Moreover, a recent report by
Ernst & Young (2016)
shows that a well-functioning fintech ecosystem is built
on several core ecosystem attributes, in which talent and
entrepreneurial availability are essential factors. We
therefore assume that a rich and varied supply of labor
has a positive influence on fintech startup formations.
Empirical evidence supports the argument that the
population size is a source of entrepreneurial supply, in the
sense that countries experiencing population growth
have a larger portion of entrepreneurs in their workforce
than populations not experiencing growth
Labour Organization 1990)
. To evaluate fintech startup
formations, we thus account for the size of the labor
force and argue that the larger and the more flexible the
labor market, the higher is the potential number of
entrepreneurs who are ready to undertake
Hypothesis 4: Fintech startups are more frequent in
countries with a more benevolent regulation and a larger
3 Data and method
The data source for our dependent variable is the
CrunchBase database, which contains detailed
information on fintech startup formations and their financing.
The database is assembled by more than 200,000
company contributors, 2000 venture partners, and millions of
web data points2 and has recently been used in scholarly
(Bernstein et al. 2016; Cumming et al. 2016)
retrieved the data used in our analysis on September 9,
2017. Because CrunchBase might collect some of the
information with a time lag, the observation period in our
sample ends on December 31, 2015. Overall, we
identified 7353 fintech startups for the relevant sample period.
To analyze the economic and technological determinants
that influence fintech startup formations, we collapsed
the information into a panel dataset that consists of 1177
observations given our 11-year observation period from
2005 to 2015 covering 107 countries (see Appendix
Table 5 for a list of countries in the dataset).3
We restrict our empirical analyses to new firm
formations that focus on the nine business categories
outlined in Section 1. Consequently, established firms
that also provide fintech services (e.g., Amazon or
Facebook providing payment or financing services) are
not part of our analyses. We consider seven dependent
variables: the number of fintech startup formations in a
given year and country and the number of fintech startup
formations in a given year and country for each of the
six categories we identified previously—financing,
payment, asset management, insurance, loyalty programs,
and other business activities.4 Because we measure the
dependent variable as a count variable and because its
unconditional variance suffers from overdispersion, we
decided to estimate a negative binomial regression
model. In particular, we estimate a random effects negative
binomial (RENB) model,5 which allows us to remove
time-invariant heterogeneity from fintech startup
2 See https://about.crunchbase.com.
3 Because of data limitations in our explanatory variables and given
that we use a lag of one year, our sample reduces to the period from
2006 to 2014 covering 55 countries and 5588 fintechs. However, this is
precisely the period when the fintech market emerged in most
4 In the regression analyses, we combine the categories risk
management, exchanges, regtech, and other business activities into others
business activities because we have too little observations to run
separate regression models for each category.
York and Lenox (2014)
Dushnitsky et al. (2016)
appropriateness of using the RENB model in a similar context.
formations, such as the existence of large financial
centers or startup ecosystems for high-tech innovation (e.g.,
Silicon Valley in California). In our baseline
specification, we estimate the following RENB model:
0GDP per capitai;t−1 þ commercial bank branchesi;t−1
þ freedom to tradei;t−1 þ sound moneyi;t−1 þ new startup formationi;t−1
where y is the number of fintech startup formations in
country i and year t and F(.) represents a negative
binomial distribution function as in
For our independent variables, we employ different
databases that provide country-year variables to construct
a panel. To test Hypothesis 1, whether well-developed
economies and capital markets positively affect the
frequency of fintech startup formations, we include the GDP
per capita, the number of commercial bank branches, the
extent of VC financing, and MSCI returns at the
suggests that income level is also
a good proxy of capital market development. We therefore
include the natural logarithm of GDP per capita, which
came from the World Development Indicators database.
To capture the physical presence of banks, which
traditionally allow customers to conduct various types of
transactions, we employ the variable commercial bank
branches per 100,000 adults in the population extracted
from the International Monetary Fund Financial Access
Survey. Furthermore, to measure the development of the
venture capital market, we calculate the variable VC
financing using the data retrieved from the CrunchBase
database. We construct VC financing as the natural
logarithm of the total amount of VC funding of all the firms
available in the CrunchBase database excluding the
fintech startups used in our analysis over the GDP per
capita at the country level.6 Moreover, to control for
changes in market conditions over time, we include MSCI
6 For the calculation, see Félix et al. (2013).
returns. To construct this variable, we extracted the stock
prices from the MSCI website and calculated the
percentage change in the country-specific MSCI returns from the
prior year to the current year.
Next, to test Hypothesis 2, whether the availability of
the latest technology and the respective supporting
infrastructure have a positive impact on fintech startup
formations, we include the variables latest technology, mobile
telephone subscriptions, Internet penetration, secured
Internet servers, and fixed broadband subscriptions. We
retrieved the variable latest technology from the World
Economic Forum Executive Opinion Survey at the
country-year level. It is constructed from responses to the
survey question from the Global Competitiveness Report
Executive Opinion Survey: BIn your country, to what
extent are the latest technologies available?^ (1 = not
available at all, 7 = widely available). Although to our
knowledge this is the only variable measuring the
availability of the latest technology in a country that also covers
a large sample of countries over time, survey respondents
in various countries might not have fully understood
different types of banking technologies to be able to answer
this question adequately. The variable should therefore be
interpreted with caution. Next, we include mobile
telephone subscriptions to assess the extent to which more
people having access to mobile phones affects fintech
startup formations. We retrieved the data from the World
Telecommunication/ICT Development report and database
at the country-year level. The variable measures the
number of mobile telephone subscriptions per 100 adults in the
population. We further account for the Internet penetration
in the countries studied in our analyses. The data is based
on surveys carried out by national statistical offices or
estimates based on the number of Internet subscriptions.
Internet users refer to people using the Internet from any
device, including mobile phones, during the year under
review. In our analyses, we use the percentage of Internet
penetration at the country-year level retrieved from the
World Telecommunication/ICT Development report and
database. We also include the variable secure Internet
servers per one million people to account for the number
of servers using encryption technology in Internet
transactions. We retrieved t he data from the World
Telecommunication/ICT Development report and database
at the country-year level. Finally, we extract the variable
fixed broadband subscriptions, which refers to fixed
subscriptions to high-speed access to the public Internet,
excluding subscriptions that have access to data
communications via mobile-cellular networks. We retrieved the data
from the World Telecommunication/ICT Development
report and database at the country-year level.
Furthermore, to test Hypothesis 3, whether the
soundness of the financial system affects fintech startup
formations, we include the variables soundness of
banks, investment profile, ease of access to loans, and
MSCI crisis period. We retrieved the data measuring
soundness of banks from the World Economic Forum
Executive Opinion Survey at the country-year level. The
variable is constructed from responses to the survey
question from the Global Competitiveness Report
Executive Opinion Survey: BHow do you assess the
soundness of banks?^ (1 = extremely low—banks may require
recapitalization, 7 = extremely high—banks are
generally healthy with sound balance sheets). We retrieved
the data measuring investment profile from the
International Country Risk Guide (ICRG) database at the
country-year level. We calculate the investment profile
variable on the basis of three subcomponents: contract
viability, profits repatriation, and payment delays. Each
subcomponent ranges from 0 to 4 points. A score of 4
points indicates very low country risk and a score of 0
very high country risk. To account for the availability of
financing through bank loans, which might be
determined by the fragility of the financial system, we
retrieve the variable ease of access to loans from the World
Economic Forum Executive Opinion Survey at the
country-year level. It is constructed from responses to
the survey question from the Global Competitiveness
Report Executive Opinion Survey: BDuring the past
year, has it become easier or more difficult to obtain
credit for companies in your country?^ (1 = much more
difficult, 7 = much easier). Furthermore, we control for
the severity of the last financial crisis and include the
variable MSCI crisis period. The variable measures the
equally weighted average of 2008–2009 period MSCI
returns at the country level.
To test Hypothesis 4, which investigates the extent to
which market regulations and the size of the labor force
affects fintech startup formations, we include the two
variables regulation and labor force. We extracted the
variable regulation from the Fraser Institute database,
which assesses the extent to which regulation limits the
freedom of exchange in credit, labor, and product
markets in a specific country. The variable ranges from 0 to
10, with a higher rating indicating that countries have
less control on interest rates, more freedom to market
forces to determine wages and establish the conditions
of hiring and firing, and lower administrative burdens.
To control for differences in bankruptcy laws across
economies, we employ the strength of legal rights
index, which we collected from the World Bank Doing
Business database. The variable measures the degree to
which collateral and bankruptcy laws protect the rights
of borrowers and lenders and thus facilitate lending. The
index ranges from 0 to 12, with higher scores indicating
that laws are better designed to expand access to credit.
We also include the variable labor force, which we
extracted from the World Development Indicators
database. The variable is the natural logarithm of the total
labor force, which comprises people ages 15 and older
who meet the International Labour Organization
definition of the economically active population.
Finally, we include several control variables. To
control for the unemployment rate in an economy, we use
the variable unemployment rate as a percentage of the
total labor force extracted from the World Development
Indicators database. Furthermore, we use the variables
law and order from the ICRG database to capture the
efficiency of the legal system in a country, which might
affect startup formations in general. The index of law
and order runs from 0 to 6, with higher values indicating
better legal systems. We also control for the state of
business cluster development using the data retrieved
from the World Economic Forum Executive Opinion
Survey at the country-year level. The variable is
constructed from responses to the survey question from the
Global Competitiveness Report Executive Opinion
Survey: BIn your country, how widespread are
welldeveloped and deep clusters^ (geographic
concentrations of firms, suppliers, producers of related products
and services, and specialized institutions in a particular
field) (1 = nonexistent, 7 = widespread in many fields).
We also control for economic freedom in an economy
and consider two additional variables: freedom to trade
internationally and sound money. The variable freedom
to trade internationally comes from the Fraser Institute
database and measures a wide variety of restraints that
affect international exchange, including tariffs, quotas,
hidden administrative restraints, control on exchange
rates, and the movement of capital. The variable ranges
from 0 to 10, and higher ratings indicate that countries
have low tariffs, easy clearance and efficient
administration of customs, a freely convertible currency, and
few controls on the movement of physical and human
capital. We also consider the variable sound money,
which contains components such as money growth,
standard deviation of inflation, inflation, and freedom
to own foreign currency bank accounts. The variable
ranges from 0 to 10. To earn a higher rating, a country
must follow policies and adopt institutions that lead to
low rates of inflation and avoid regulations that limit the
ability to use alternative currencies.
To control for the entrepreneurial environment in a
particular economy, we also control for the total number
of new startup formations. This variable comes from the
CrunchBase database and measures the number of new
startups created according to CrunchBase in a given
year and country. Definitions of all variables and their
sources appear in detail in Appendix Table 6.
4.1 Summary statistics
Table 2 presents statistics for the number of fintechs
founded and the rounds and amounts these firms have
raised through venture capital, by year, except panel B,
which provides a summary by country. Panel A
considers the full sample, panel B the top European
countries, panel C the US sample only, and panel D the
EU27 sample only. Panel E reports the number of fintech
startups founded in each year that are still operating, had
an IPO, were closed, or were acquired by another firm
by 2017, considering the total sample, the EU-27
sample, and US sample.
Panel A of Table 2 documents statistics of fintech
startup formations for the period from 2005 to 2015.
Column (1) in panel A presents statistics on the number
of fintech startup formations in a given year. There is a
notable upsurge of fintech startups following the financial
crisis, as the number of startups founded in 2011 was
more than twice as large as in 2008. In 2014, we observe
for the first time a decrease of fintech startup formations
compared with the previous year. Column (2) shows the
number of financing rounds fintech startup have obtained
in that year, which almost reached 2000 rounds in 2013
and 2014. In column (3), we show the total amount
fintech startups raised each year, which grew until
2011, fluctuated in the following two years, and finally
steadily declined. Together with column (2), this suggests
that the average volume per funding round has recently
dropped. Column (4) shows the number of fintech
startups providing financing services, which constitute
54% from all categories, suggesting that the demand for
innovation in financing activities was substantial.
Column (5) shows statistics of fintech startups providing
payment services, which constitute the second-largest
group with 19% from all categories. Column (6) shows
statistics of fintech startups providing asset management
services, which represents 10% from all categories.
Columns (7)–(11) show statistics of fintech startups
providing insurance, loyalty programs, risk management,
exchanges, and regulatory technology services, which
represent 14% from all categories. Column (12) shows
fintech startups providing other business activities, which
constitutes 3% from all categories. For all categories in
columns (4)–(12), we observe an increase in the number
of fintech startups founded, with a slight decrease in the
last year (2015), except for asset management, insurance,
and regulatory technology startups, the number of which
continued to grow until the end.
To investigate different dynamics in developed
and developing countries, we report descriptive
statistics for the 10 most relevant European countries in
terms of fintech activities, the US sample, and the
total EU-27 sample. Panel B of Table 2 presents
statistics by country for the 10 most relevant
European countries during the period 2005–2015. The
UK is at the top of the list with regard to new
fintech startup formations, followed by Germany
and France (column (1). A recent study conducted
ranked the UK as the number one
place in the European Union to flourish as a fintech
startup and third worldwide after China and the
Panel B: Summary statistics for the 10 most relevant European countries
Columns (1)–(12) are as described in panel A, but calculated for each country separately
Panel D: Summary statistics for the EU-27, by year
Columns (1)–(12) are as described in panel A, but calculated for the EU-27 sample only
USA. With the supposedly most supportive
regulatory regime, effective tax incentives, and London’s
position as global financial center, the country
attracts more talented entrepreneurs willing to engage
in fintech activity. Column (3) shows the total
amount raised by new fintech startups, with firms
located in the UK having raised by far the highest
amount (7.3 billion USD), followed by Germany
and Sweden. According to reports published in the
Computer Business Review (2016
) and by
, the UK also had the highest number of deals
outside the USA and the third-highest total VC
investment after the USA and China. Columns
(4)–(12) again show fintech startup formations for
the nine subcategories, which remain in the same
order of importance as before, except for risk
management fintechs, which slightly outweigh loyalty
As the USA has the overall largest market share in
our sample (see Appendix Table 5 for a ranking), panel
C of Table 2 presents statistics for the US fintech market
only by year. Column (1) shows the number of fintech
startups launched in the USA, which represent almost
53% of the entire sample. Columns (4)–(12) show that
fintech startups reforming financing activities constitute
54% of all fintech startups in the USA, again followed
by payment (17%), asset management (11%), insurance
(5%), other business activities (4%), loyalty program
(3%), risk management (3%), regulatory technology
(2%), and exchanges (1%).
Panel D of Table 2 provides statistics for the EU-27
by year. Columns (1)–(12) are as described previously
but calculated for the EU-27 sample only. Column (1)
shows the number of fintech startups founded by year.
Note that the EU-27 countries constitute only 20% of
the total fintech startups we identified in our sample.
The evidence shows that most financing rounds took
place in the 10 most relevant EU countries, and the
amounts these fintech startups raised there were also
considerable, with the remaining 17 countries
contributing only a tiny fraction. Fintech startups providing
financing services again represent the largest share of
all fintech startups in the EU-27 (54% of all fintechs),
followed by payment services (20%), asset management
(10%), insurance (5%), other business activities (4%),
loyalty programs (3%), risk management (2.5%),
exchanges (1.5%), and regulatory technology (0.3%).
The importance of the fintech subcategories thus
persists for all panels in Table 2.
Panel E of Table 2 reports whether fintech startups
were still operating, had an IPO, were closed, or were
acquired by another firm until 2017 for the total sample,
the EU-27 sample, and the US sample. Columns (1)–(4)
show descriptive statistics of the fintech startups’ status
for our total sample, revealing that the percentage of
fintech startups still operating is substantial (79%),
followed by acquired (14%), closed (4%), and IPO
(3%). Columns (5)–(8) provide descriptive statistics of
the fintech startups’ status for the total EU-27 sample,
and columns (9)–(12) show the descriptive statistics of
the fintech startups’ status for the US sample. As would
be expected, the fintech market in the USA has
experienced a higher percentage of IPOs (1.9 vs. 3.2%) and
acquisitions (11.9 vs. 16.5%); the percentage of firm
failure is higher as well (2.5 vs. 5.6%). Appendix
Tables 7 and 8 show summary statistics and a correlation
table that includes the dependent variables and the main
4.2 Country-level determinants of f intech startup
To analyze which country-level factors drive the
formation of new fintech startups, we use multivariate panel
regressions to predict the annual number of fintech startup
formations in 55 countries between 2006 and 2014. For
the RENB model, we report incident rate ratios, which
can conveniently be interpreted as multiplicative effects
or semi-elasticities. Table 3 reports the estimates from the
RENB models as outlined in Section 3. Column (1)
shows the results on aggregate annual fintech startup
formations, and columns (2)–(7) replicate the analyses
for annual formation of fintech startups providing
financing, payment, asset management, insurance, loyalty
program, and other business activities.
The model in column 1 underscores the role of
country-level factors in shaping the formation of new
fintech startups. We find a significant, positive
relationship between GDP per capita and fintech startup
formations, with a high statistical significance (p < 0.01). An
increase of one unit in Ln (GDP per capita) is associated
with a 59.3% increase in fintech startup formations in
the following year. Furthermore, we find a significant,
positive relationship between VC financing and fintech
startup formations, with a high statistical significance
(p < 0.01). A one-unit increase in the variable VC
financing is associated with a 24.1% increase in fintech
startup formations in the following year. Although we
find no evidence for the impact of the number of bank
branches and MSCI returns on fintech startup
formations, we cannot reject Hypothesis 1 that fintech startup
formations take place in well-developed economies, as
the GDP per capita and VC financing variables are
strong and robust predictors. Moreover, we find positive
relationships between mobile telephone subscriptions
and secure Internet servers and fintech startup
formations, which are both significant at conventional levels.
One more secure Internet server per one million people
is associated with a 25.8% increase in fintech startup
formations. We therefore cannot reject Hypothesis 2 that
fintech startup formations occur more frequently in
countries where the supporting infrastructure is readily
available. However, we find no evidence that the latest
technology, as perceived by the survey respondents of
the Global Competitiveness Report Executive Opinion
Survey, Internet penetration, or fixed broadband
subscriptions has an impact on fintech startup formations.
Furthermore, our results show a negative relationship
between ease of access to loans and fintech startup
formations. A one-unit increase in the ease of access
to loans variable is associated with an 18.8% decrease in
the number of fintech startup formations in the
following year. The variable MSCI crisis period is negative
and statistically significant (p < 0.05) as well, indicating
that the demand for fintech is generally higher in
countries that have extensively suffered from the latest
financial crises. While in Table 3, this holds true for the
overall sample and the financing subcategory; in Table
4, which excludes the USA, we find that the effect holds
for all subcategories. Although the variables investment
profile and soundness of banks are not significant, we
cannot reject Hypothesis 3 that fintech startup
formations occur more frequently in countries with a more
fragile financial sector. In line with Hypothesis 4, we
find that our regulation index has a significant, positive
impact on fintech startup formations, with a high
statistical significance (p < 0.05). An increase of one unit in
our regulation variable, which measures the extent to
which regulation limits the freedom of exchange in
credit, labor, and product markets, is associated with
an 18.5% increase in fintech startup formations in the
following year. Furthermore, the strength of legal rights
variable, which measures the degree to which collateral
and bankruptcy laws protect the rights of borrowers and
lenders, indicates a positive relationship and is highly
significant (p < 0.01). We also find that a larger labor
market is associated with an increase in fintech startup
formations, which is in line with Hypothesis 4. An
increase of one unit in Ln (labor force) is associated
with a 79.6% increase in fintech startup formations in
the following year.
Stand-alone analyses of each fintech category reveal
nuanced dynamics. Columns (2)–(7) of Table 3
highlight commonalities among the factors associated with
the formation of fintech startups providing financing,
payment, asset management, insurance, loyalty
program, and other business activities. Consistent with
column (1) of Table 3, the coefficients Ln (labor force)
is positive and statistically significant for all
subcategories, highlighting the importance of human capital for
high-tech services. Moreover, the coefficients Ln (GDP
per capita) and ease of access to loans are positive and
Table 3 Drivers of fintech startup formations, full sample. The
dependent variables in column (1) pertain to the number of new
fintech startups founded in a given country and year. In columns
(2)–(7), we report results for fintech startups providing financing,
payment, asset management, insurance, loyalty program, and
other business activities only. The data take panel structure. We
report random effects negative binomial panel regressions for the
columns (1)–(7) because the dependent variables are count
variables. All variables are defined in Appendix Table 6. Standard
errors are clustered at the country level, and the model allows
dispersion to vary randomly across clusters. Columns (1)–(7)
report incident rate ratios. Significance levels: * < 10%, ** < 5%,
and *** < 1%
significant for all subcategories except for fintechs
providing insurance services. The positive coefficient of
ease of access to loans indicates that fintech and
traditional financial services might be complements in some
market segments. For example, when banks are not able
to extend loans to small and risky firms, fintechs can
reduce transaction costs through digitalization, use big
data analytics, and specialize in high-risk market
segments catered small and high-risk loan projects. We
also find a negative and statistically significant
relationship between the number of bank branches and fintech
startup formations in the realm of payment and
insurance services, which indicates that fintechs might move
in business areas in which traditional banks withdraw.
The coefficients of the VC financing variable are
positive and highly significant for the subcategories that
Table 4 Drivers of fintech startup formations, excluding US
sample. The dependent variables in column (1) pertain to the
number of new fintech startups founded in a given country and
year. In columns (2)–(7), we report results for fintech startups
providing financing, payment, asset management, insurance,
loyalty program, and other business activities only. The data take
panel structure. We report random effects negative binomial panel
regressions for the columns (1)–(7) because the dependent
variables are count variables. All variables are defined in Appendix
Table 6. Standard errors are clustered at the country level, and the
model allows dispersion to vary randomly across clusters.
Columns (1)–(7) report incident rate ratios. Significance levels:
* < 10%, ** < 5%, and *** < 1%
most closely resemble the value chain of a traditional
bank: financing, payment, and asset management.
Moreover, the variable strength of legal rights has a
positive and statistically significant effect on the
formation of fintech startups for all the subcategories except
fintechs providing loyalty program services. Next, we
find that the coefficient of latest technology is positive
and statistically significant for payment and loyalty
program services. We also observe a positive effect of the
variable mobile telephone subscriptions on the formation
of fintech startups providing financing services. Finally,
an increase of one unit in fixed broadband subscriptions
is associated with a 3% increase in fintech startup
formations in the financing domain the following year.
In Table 4, we run the same regression excluding the
US fintech market, because US fintechs constitute almost
53% of the total sample in our analysis. We find the
results largely consistent with Table 3 for our main
variables: Ln (GDP per capita), VC financing, mobile
telephone subscriptions, secure Internet servers, ease of
access to loans, Ln (labor force), and regulation. Moreover,
we find an additional significant effect for the availability
of latest technology variable on fintech startup formations.
In this article, we investigate economic and
technological determinants that have encouraged fintech startup
formations. We find that until 2015, the USA had the
largest fintech market, followed by the UK, India,
Canada, and China at a considerable distance. Categorizing
fintechs in the following subcategories—financing,
asset management, payment, insurance, loyalty programs,
risk management, exchanges, regulatory technology,
and other business activities—we show that financing
is by far the most important segment of the emerging
fintech market, followed by payment, asset
management, insurance, loyalty programs, risk management,
exchanges, and regulatory technology. Furthermore, we
derive the following recommendations for policy and
5.1 Implications for regulators
The insights of this article might guide policymakers in
their decisions on how to promote this new sector. We
find that countries witness more fintech startup
formations when economies are well-developed, the
supporting infrastructure is readily available, and
flexible market regulations are applied. M-Pesa provides an
example of a case in which fintechs can effectively solve
the problems of people living in developing countries.
Nevertheless, many of the new fintech services do not
run on simple mobile phones but require users to
possess a smartphone. However, people living in
developing countries often cannot afford to buy smartphones.
Providing affordable and sustainable technology as well
as the supporting infrastructure is therefore critical to
allow for financial inclusion especially with regard to
fintech services. Moreover, establishing a supporting
infrastructure that allows for secure transactions is
essential for the digitalization of financial services in
developing and developed countries.
Fintech startup formations in the financing category
might have emerged for multiple reasons, two of which
could be the traditional funding gap that small firms
around the globe face
(Schindele and Szczesny 2016)
and funding constraints potentially due to more stringent
banking regulations in the aftermath of the latest
financial crisis (
Campello et al. 2010
European Central Bank
Banking Authority 2015
Consequently, promoting fintechs from the financing category
through regulatory sandboxes and other policy
measures could be an effective way to close the funding
gap of small firms. Nevertheless, the question of
whether fintech firms provide services that are more efficient
than those of incumbent financial institutions remains
and is worth exploring empirically. Furthermore, an
open question is whether fintechs might ultimately
generate new systemic risks that need to be addressed by
regulation. While market volumes in many fintech
segments are still small, some fintech segments such as
online factoring, marketplace lending, and payment
services might soon become systemically relevant and
should be carefully examined by regulators.
5.2 Implications for incumbent financial organizations
Our empirical analysis shows that the available labor
force has a positive impact on the supply of entrepreneurs
in the fintech industry. Today, entrepreneurial activities
often take place in specific geographic regions, which are
referred to as startup or fintech hubs. Attracting a critical
mass of highly specialized individuals is critical to
establish a new hub or ecosystem. However, in a globalized
world, this requires well-functioning and easily
understandable immigration laws, the possibility to easily
transfer pension claims, affordable housing, and
countable other factors that make moving beyond national
boarders easy. Therefore, the decision where to locate a
fintech firm is crucial despite the progressive
digitalization and flattening of the financial world.
Large financial firms might find it particularly
difficult to hire talented individuals as they are lacking the
innovative appeal and entrepreneurial spirit of fintech
firms. Moreover, incumbent organizations are often
more immobile than fintech startups and cannot easily
relocate to newly emerging fintech hubs. Consequently,
to attract the most talented individuals, incumbent
financial organizations do not only face the challenge to
reinvent their business models, they must also refurbish
their organizational structure and work environment.
Besides fintech startups, established technology firms
and modern ecosystems have recently started to provide
financial services and might quickly become
competitors to incumbent financial organizations. Given their
size and access to customers, the threat from technology
firms and large ecosystems might even be more severe
than the competition that arises from fintech startups.
However, incumbent financial organizations have a
competitive advantage as well. Unlike fintech startups,
large financial institutions often have deep pockets and
can more easily initiate large-scale projects. Given that
many fintech solutions are platforms services, quickly
obtaining a significant market size and establishing a
business standard that locks customers in is often more
important than developing a high-quality product or
. Moreover, while reformed
regulations such as the Payment Service Direction II grant
fintechs access to customer data that was previously
under the sole possession of banks, incumbents de facto
maintain the market power over the standards that
enable fintechs to gain access to customer information
(European Banking Authority 2017)
Finally, not only can ecosystems provide financial
services, incumbent financial organizations can also create
new ecosystems. For example, banks can offer their retail
clients additional services that make deliberate payment
processes superfluous, allow customers to engage with the
bank advisor via Smart TV applications at any time
without having to visit a branch, or bundle services such as the
payment of utility bills and the filing of the tax declaration.
For their professional clients, banks could offer additional
services or software packages. For example, the
investment bank UBS offers small- and medium-sized firms the
accounting software Bbexio^ that connects to clients’ bank
accounts and allows them to manage their customers,
employees, and warehouses.
5.3 Implications for fintech entrepreneurs
Given that many fintech solutions modify or digitize an
existing financial service and do not constitute a genuine
technological innovation, fintech business models can in
some cases easily be copied by incumbent financial
organizations. For example, many banks now offer their
customers personal financial management tools that
integrate checking, savings, and custody accounts from
various institutions. Other fintech innovations like the
notification about bank wire transfers through text messaging
have been adopted by many banks as well. While fintech
entrepreneurs should focus on innovations and their
unique selling point, they also must make sure that their
ideas cannot be easily copied by incumbent financial
organizations. In some cases, it might therefore make
sense for fintechs to cooperate with established financial
organizations, technology firms, and large ecosystems.
Finally, fintech entrepreneurs should closely monitor
upcoming changes in the regulatory environment,
because the core of their business models might be
threatened. For example, the European General Data
Protection Regulation and especially the proposed ePrivacy
Regulation will limit the extent to which firms can
collect data of individuals browsing their websites. Once
the tracking of individuals in the Internet will only be
possible with the individual’s informed consent, fintech
startups that build their services on this data might have
to adapt their business models.
5.4 Implications for investors in fintechs
In this article, we find that access to venture capital is an
important factor to promote fintech startup formations.
Access to venture capital is, however, not equally
available in every region of the world. While the USA and
Asia have recently witnessed large inflows of venture
capital in fintech startups, Europe and the rest of the world
have largely fallen behind
Investment opportunities in fintech therefore strongly differ by
geographic location. The lack of venture capital might
further generate a vicious cycle, as our study also finds
that financing fintechs are the most important categories
in our sample and fintech formations more often take
place if access to loans is more difficult in an economy.
Thus, fintechs might improve financial intermediation
when traditional banks fail to fulfill this task, but are not
founded in the absence of venture capital financing.
Moreover, the case of M-Pesa evidences that
investment opportunities in fintechs are available in developing
and developed countries. Although customers in
developed countries might have higher incomes and are
therefore more likely to benefit from fintech services such as
asset management, more severe problems of financial
intermediation and financial inclusion are potentially
solved by fintechs in developing countries. Some caution
is also warranted when investing in fintechs. While
investments in fintech firms are growing, returns and
profits of fintech startups are in some market segments
such as equity crowdfunding
(Hornuf and Schmitt 2016)
still meager and might remain small for quite some time.
Although many of the fintech innovations appear
revolutionary, convincing mass-market customers about the
quality of the service and implementing innovations on a
large scale can take another decade.
Acknowledgments Open access funding provided by Max Planck
Society. The authors thank two anonymous referees and the
participants of the 4th Crowdinvesting Symposium (Max Planck Institute for
Innovation and Competition), the Risk Forum 2017: Retail Finance
and Insurance (Paris), and the Annual Meeting of the American Law
and Economics Association (Yale University), who provided valuable
comments and suggestions on previous versions of the paper.
Commercial bank branches Is the (Number of institutions + number of bank branches) × 100,000 / adult population in the reporting
country. Source: International Monetary Fund, Financial Access Survey.
The number of fintech startups founded in a given country and year. Source: CrunchBase.
The number of new fintech startups providing asset management services founded in a given country and year.
The number of new fintech startups providing financing services founded in a given country and year. Source:
The number of new fintech startups providing insurance services founded in a given country and year. Source:
The number of new fintech startups providing loyalty program services founded in a given country and year.
The number of new fintech startups providing risk management, exchanges, regtech, and other fintech
services founded in a given country and year. Source: CrunchBase.
The number of new fintech startups providing payment services founded in a given country and year. Source:
Response to the survey question: BIn your country, how widespread are well-developed and deep clusters^
(geographic concentrations of firms, suppliers, producers of related products and services, and specialized
institutions in a particular field). The variable runs from 1 = nonexistent to 7 = widespread in many fields.
Source: World Economic Forum, Global Competitiveness Report, Executive Opinion Survey.
Response to the survey question: BDuring the past year, has it become easier or more difficult to obtain credit
for companies in your country?^ (1 = much more difficult, 7 = much easier). Source: World Economic
Forum, Global Competitiveness Report, Executive Opinion Survey.
Data are collected by national statistics offices through household surveys. Fixed broadband subscriptions
refers to fixed subscriptions to high-speed access to the public Internet, at downstream speeds equal to or
greater than 256 Kbit/s. This include cable modem, DSL, fiber-to-the-home/building, other fixed- (wired-)
broadband subscriptions, satellite broadband, and terrestrial fixed wireless broadband. The variable
measures fixed broadband Internet subscribers per 100 adults in the population. Source: World
Telecommunication/ICT Development report and database.
Data come from third-party sources, such as the International Country Risk guide, the Global Competitiveness
report, and the World Bank’s Doing Business project. The variables include components to measure a wide
variety of restraints that affect international exchange: tariffs, quotas, hidden administrative restraints,
control on exchange rates, and the movement of capital. The variable ranges from 0 to 10. A higher rating
indicates that countries have low tariffs, easy clearance and efficient administration of customs, a freely
convertible currency, and few controls on the movement of physical and human capital. Source: The Fraser
Data are based on surveys carried out by national statistical offices or estimated on the basis of the number of
Internet subscriptions. Internet users refer to people using the Internet from any device (including mobile
phones) during the year under review. We use the percentage of residents using the Internet at the year and
country level. Source: World Telecommunication/ICT Development report and database.
Assessment of factors affecting the risk of investment that are not covered by other political, economic, and
financial risk components. The index is calculated on the basis of three subcomponents as follows: contract
viability, profits repatriation, and payment delays. Each subcomponent ranges from 0 to 4 points; a score of
4 points indicates very low risk, and a score of 0 very high risk. Source: ICRG.
Response to the survey question: BIn your country, to what extent are the latest technologies available?^ (The
variable runs from 1 = not available at all to 7 = widely available.) Source: World Economic Forum, Global
Competitiveness Report, Executive Opinion Survey.
Law and order form a single component, but its two elements are assessed separately, with each element being
scored from 0 to 3 points. The index of law and order runs from 0 to 6, with higher values indicating better
legal systems. Source: ICRG.
GDP per capita is the gross domestic product per capita in USD. Source: World Development Indicators
Total labor force comprises people ages 15 and older who meet the International Labour Organization
definition of the economically active population: all people who supply labor for the production of goods
and services during a specific period. Source: World Development Indicators database.
A mobile telephone subscription refers to a subscription to a public mobile telephone service that provides
access to the public switched telephone network using cellular technology, including the number of
pre-paid SIM cards active during the last three months of the year under review. This includes both analog
and digital cellular systems (IMT-2000, Third Generation, 3G) and 4G subscriptions, but excludes mobile
broadband subscriptions via data cards or USB modems. The variable measures the number of mobile
telephone subscriptions per 100 adults in the population. Source: World Telecommunication/ICT
Development report and database.
The equally weighted average of the percentage change in the country-specific MSCI Stock Market Equity
Index Returns for 2008 and 2009. Source: MSCI website and own calculation
The percentage change in the country-specific MSCI Stock Market Equity Index Returns from the prior year
to the current year. Source: http://www.msci.com/
Annual number of new startups founded in a given year and country. The data were retrieved from the
CrunchBase database and measure the number of new startups created according to CrunchBase in a given
year and country. Source: CrunchBase and own calculations.
Data come from third-party sources, such as the International Country Risk Guide, the Global
Competitiveness Report, and the World Bank’s Doing Business project. The variable measures the extent to which
regulation limits the freedom of exchange in credit, labor, and product markets in a specific country. The
variable ranges from 0 to 10, with higher ratings indicating that countries have less control on interest rates,
have higher freedom to market forces to determine wages and establish the conditions of hiring and firing,
and generally possess lower administrative burdens. Source: The Fraser institute database.
Secure servers per one million people are servers using encryption technology in Internet transactions. Source:
World Bank and https://www.netcraft.com
Data come from third-party sources, such as the International Country Risk guide, the Global Competitiveness
report, and the World Bank’s Doing Business project. The variable includes the components money growth,
standard deviation of inflation, inflation, and freedom to own foreign currency bank accounts. The first
three are designed to measure the consistency of the monetary policy with long-term price stability. The last
component is designed to measure the ease with which other currencies can be used via domestic and
foreign bank accounts. The variable ranges from 0 to 10; to
earn a higher rating, a country must follow policies and adopt institutions that lead to low rates of inflation and
avoid regulations that limit the ability to use alternative currencies. Source: Fraser Institute Database.
Response to the survey question: BIn your country, how do you assess the soundness of banks?^ (The variable
runs from 1 = extremely low—banks may require recapitalization to 7 = extremely high—banks are
generally healthy with sound balance sheets.) World Economic Forum, Global Competitiveness Report,
Executive Opinion Survey.
The index measures the degree to which collateral and bankruptcy laws protect the rights of borrowers and
lenders and thus facilitate lending in a country. The index ranges from 0 to 12, with higher scores indicating
that these laws are better designed to expand access to credit. Source: World Bank, Doing Business
Calculated as the percentage from the total labor force. Source: World Development Indicators database.
The natural logarithm of the total amount of VC funding of all the startups available in the CrunchBase
database excluding the fintech startups used in our analysis over the GDP per capita at the country level.
The variable is constructed using available data in the CrunchBase database. Source: CrunchBase, World
Development Indicators database, and own calculations.
Minimum Maximum 11.84 7.86
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